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===========================================================================================================================================
SOURCE CODE FILE: tokenization_gpt_sw3.py
LINES: 1
SIZE: 12.21 KB
PATH: scripts\freecad_env\Lib\site-packages\transformers\models\gpt_sw3\tokenization_gpt_sw3.py
ENCODING: utf-8
```py
"""The tokenizer used by the GPT-SW3 models."""
import os
import re
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging
if is_torch_available():
import torch
logger = logging.get_logger(__name__)
VOCAB_FILES_NAMES = {"vocab_file": "spiece.model"}
class GPTSw3Tokenizer(PreTrainedTokenizer):
"""
Construct an GPTSw3 tokenizer. Based on [SentencePiece](https://github.com/google/sentencepiece).
This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
this superclass for more information regarding those methods.
Example usage:
```python
>>> from transformers import GPTSw3Tokenizer
>>> tokenizer = GPTSw3Tokenizer.from_pretrained("AI-Sweden-Models/gpt-sw3-126m")
>>> tokenizer("Svenska är kul!")["input_ids"]
[1814, 377, 3617, 63504]
```
Args:
vocab_file (`str`):
[SentencePiece](https://github.com/google/sentencepiece) file (generally has a *.spm* extension) that
contains the vocabulary necessary to instantiate a tokenizer.
do_lower_case (`bool`, *optional*, defaults to `False`):
Whether or not to lowercase the input when tokenizing.
remove_space (`bool`, *optional*, defaults to `False`):
Whether or not to strip the text when tokenizing (removing excess spaces before and after the string).
keep_accents (`bool`, *optional*, defaults to `False`):
Whether or not to keep accents when tokenizing.
pad_token (`str`, *optional*):
The token used for padding, for example when batching sequences of different lengths. If not provided, will
default to '<pad>' or '<unk>' depending on model size.
unk_token (`str`, *optional*):
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead. If not provided, will default to '<unk>'.
eos_token (`str`, *optional*):
The end of sequence token seen during pretraining. If not provided, will default to '<|endoftext|>'
bos_token (`str`, *optional*):
The beginning of sequence token that can be used for downstream task, was not seen during pretraining. If
not provided, will default to '<s>' or '<|endoftext|>', depending on model size.
sp_model_kwargs (`dict`, *optional*):
Will be passed to the `SentencePieceProcessor.__init__()` method. The [Python wrapper for
SentencePiece](https://github.com/google/sentencepiece/tree/master/python) can be used, among other things,
to set:
- `enable_sampling`: Enable subword regularization.
- `nbest_size`: Sampling parameters for unigram. Invalid for BPE-Dropout.
- `nbest_size = {0,1}`: No sampling is performed.
- `nbest_size > 1`: samples from the nbest_size results.
- `nbest_size < 0`: assuming that nbest_size is infinite and samples from the all hypothesis (lattice)
using forward-filtering-and-backward-sampling algorithm.
- `alpha`: Smoothing parameter for unigram sampling, and dropout probability of merge operations for
BPE-dropout.
Attributes:
sp_model (`SentencePieceProcessor`):
The *SentencePiece* processor that is used for every conversion (string, tokens and IDs).
whitespaces (`set`):
The whitespaces that are replaced in the whitespace normalization in preprocessing.
non_printing_characters_re (`Pattern`):
The compiled regular expression to remove non-printing characters in preprocessing.
"""
vocab_files_names = VOCAB_FILES_NAMES
model_input_names = ["input_ids", "attention_mask"]
def __init__(
self,
vocab_file,
do_lower_case=False,
remove_space=False,
keep_accents=False,
pad_token=None,
unk_token=None,
eos_token=None,
bos_token=None,
sp_model_kwargs: Optional[Dict[str, Any]] = None,
**kwargs,
) -> None:
self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
name_or_path = kwargs.get("name_or_path")
if name_or_path is None:
logger.warning(
"name_or_path not provided, will work for all GPTSw3 models except gpt-sw3-7b,"
" you are testing the model, this can safely be ignored"
)
name_or_path = "None"
# Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
eos_token = "<|endoftext|>" if eos_token is None else eos_token
unk_token = "<unk>" if unk_token is None else unk_token
if "gpt-sw3-7b" in name_or_path:
pad_token = unk_token if pad_token is None else pad_token
bos_token = eos_token if bos_token is None else bos_token
else:
pad_token = "<pad>" if pad_token is None else pad_token
bos_token = "<s>" if bos_token is None else bos_token
self.do_lower_case = do_lower_case
self.remove_space = remove_space
self.keep_accents = keep_accents
self.vocab_file = vocab_file
self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
self.sp_model.Load(vocab_file)
# Used for whitespace normalization in input texts
# fmt : off
self.whitespaces = {" ", " ", " ", " ", " ", " ", " ", " ", " ", " ", "", ""}
# fmt : on
# Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
self.non_printing_characters_re = re.compile(
f"[{''.join(map(chr, list(range(0, 9)) + list(range(11, 32)) + list(range(127, 160)) + [160, 173, 8203]))}]"
)
super().__init__(
do_lower_case=do_lower_case,
remove_space=remove_space,
keep_accents=keep_accents,
bos_token=bos_token,
eos_token=eos_token,
unk_token=unk_token,
pad_token=pad_token,
sp_model_kwargs=self.sp_model_kwargs,
**kwargs,
)
# Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.__getstate__
def __getstate__(self):
state = self.__dict__.copy()
state["sp_model"] = None
return state
# Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.__setstate__
def __setstate__(self, d):
self.__dict__ = d
# for backward compatibility
if not hasattr(self, "sp_model_kwargs"):
self.sp_model_kwargs = {}
self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
self.sp_model.Load(self.vocab_file)
@property
# Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.vocab_size
def vocab_size(self) -> int:
return len(self.sp_model)
def preprocess_text(self, text: str) -> str:
"""
Returns the preprocessed text. This procedure is identical to what was used when training the tokenizer.
"""
# Remove non-printing characters
text = self.non_printing_characters_re.sub("", text)
# Normalize whitespaces
text = "".join([char if char not in self.whitespaces else " " for char in text])
# NFC Unicode normalization
text = unicodedata.normalize("NFC", text)
return text
def _tokenize(self, text: str, **kwargs) -> List[str]:
text = self.preprocess_text(text)
return self.sp_model.encode(text, out_type=str)
def _convert_token_to_id(self, token: str) -> int:
"""Converts a token (str) to an id (int) using the vocab."""
return self.sp_model.PieceToId(token)
def _convert_id_to_token(self, index: int) -> str:
"""Converts an index (int) to a token (str) using the vocab."""
return self.sp_model.IdToPiece(index)
@staticmethod
def clean_up_tokenization(out_string: str) -> str:
"""Returns the input string, this function is overridden to remove the default clean up."""
return out_string
def convert_tokens_to_string(self, tokens: List[str]) -> str:
"""Converts a sequence of tokens (strings) to a single string. Special tokens remain intact."""
current_sub_tokens = []
out_string = ""
prev_is_special = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
# TODO: Check if this is needed, as it ensures that decode(encode(doc)) != doc by adding extra whitespace in the decoded document
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(current_sub_tokens) + token
prev_is_special = True
current_sub_tokens = []
else:
current_sub_tokens.append(token)
prev_is_special = False
out_string += self.sp_model.decode(current_sub_tokens)
return out_string
# Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.get_vocab
def get_vocab(self) -> Dict[str, int]:
vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
vocab.update(self.added_tokens_encoder)
return vocab
# Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.save_vocabulary
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
if not os.path.isdir(save_directory):
logger.error(f"Vocabulary path ({save_directory}) should be a directory")
return
out_vocab_file = os.path.join(
save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
)
if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file):
copyfile(self.vocab_file, out_vocab_file)
elif not os.path.isfile(self.vocab_file):
with open(out_vocab_file, "wb") as fi:
content_spiece_model = self.sp_model.serialized_model_proto()
fi.write(content_spiece_model)
return (out_vocab_file,)
def encode_fast(
self, text: Union[str, List[str]], return_tensors: Union[str, bool] = False
) -> Union[List[int], List[List[int]], "torch.Tensor"]:
"""
Encodes a text or batch of texts to token ids using preprocessing and the raw SP tokenizer. This has reduced
functionality but is often much faster.
Does NOT handle special tokens correctly, these can manually be added as ids afterwards.
Does NOT support padding, these can manually be added as ids afterwards.
Use default HuggingFace tokenization methods for full functionality.
Args:
text (`str` or `List[str]`): One or several text(s) to convert to token ids.
return_tensors (`str` or `bool`): Returns PyTorch tensors if set to True or "pt"
Returns:
`List[int]`, `List[List[int]]`, or `torch.Tensor`: The encoded text(s) as token ids.
"""
if isinstance(text, str):
text = self.preprocess_text(text)
token_ids = self.sp_model.encode(text)
else:
text = [self.preprocess_text(t) for t in text]
token_ids = self.sp_model.encode(text)
if return_tensors is True or return_tensors == "pt":
token_ids = torch.tensor(token_ids)
return token_ids
def decode_fast(self, token_ids: Union[int, List[int]]) -> str:
"""
Encodes a text or batch of texts to token ids using preprocessing and the raw SP tokenizer. This has reduced
functionality but is often much faster.
Args:
token_ids (`int` or `List[int]`): Encoded token or text as token id(s).
Returns:
`str`: Decoded text
"""
return self.sp_model.decode(token_ids)
__all__ = ["GPTSw3Tokenizer"]
```
|
============================================================================================================================
SOURCE CODE FILE: __init__.py
LINES: 1
SIZE: 1.04 KB
PATH: scripts\freecad_env\Lib\site-packages\transformers\models\gptj\__init__.py
ENCODING: utf-8
```py
# Copyright 2024 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import _LazyModule
from ...utils.import_utils import define_import_structure
if TYPE_CHECKING:
from .configuration_gptj import *
from .modeling_flax_gptj import *
from .modeling_gptj import *
from .modeling_tf_gptj import *
else:
import sys
_file = globals()["__file__"]
sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)
```
|
======================================================================================================================================
SOURCE CODE FILE: configuration_gptj.py
LINES: 1
SIZE: 8.62 KB
PATH: scripts\freecad_env\Lib\site-packages\transformers\models\gptj\configuration_gptj.py
ENCODING: utf-8
```py
# coding=utf-8
# Copyright 2021 The EleutherAI and HuggingFace Teams. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""GPT-J model configuration"""
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
logger = logging.get_logger(__name__)
class GPTJConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`GPTJModel`]. It is used to instantiate a GPT-J
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar configuration to that of the GPT-J
[EleutherAI/gpt-j-6B](https://huggingface.co/EleutherAI/gpt-j-6B) architecture. Configuration objects inherit from
[`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`]
for more information.
Args:
vocab_size (`int`, *optional*, defaults to 50400):
Vocabulary size of the GPT-J model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`GPTJModel`].
n_positions (`int`, *optional*, defaults to 2048):
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).
n_embd (`int`, *optional*, defaults to 4096):
Dimensionality of the embeddings and hidden states.
n_layer (`int`, *optional*, defaults to 28):
Number of hidden layers in the Transformer encoder.
n_head (`int`, *optional*, defaults to 16):
Number of attention heads for each attention layer in the Transformer encoder.
rotary_dim (`int`, *optional*, defaults to 64):
Number of dimensions in the embedding that Rotary Position Embedding is applied to.
n_inner (`int`, *optional*, defaults to None):
Dimensionality of the inner feed-forward layers. `None` will set it to 4 times n_embd
activation_function (`str`, *optional*, defaults to `"gelu_new"`):
Activation function, to be selected in the list `["relu", "silu", "gelu", "tanh", "gelu_new"]`.
resid_pdrop (`float`, *optional*, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
embd_pdrop (`int`, *optional*, defaults to 0.1):
The dropout ratio for the embeddings.
attn_pdrop (`float`, *optional*, defaults to 0.1):
The dropout ratio for the attention.
layer_norm_epsilon (`float`, *optional*, defaults to 1e-5):
The epsilon to use in the layer normalization layers.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models).
Example:
```python
>>> from transformers import GPTJModel, GPTJConfig
>>> # Initializing a GPT-J 6B configuration
>>> configuration = GPTJConfig()
>>> # Initializing a model from the configuration
>>> model = GPTJModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "gptj"
attribute_map = {
"max_position_embeddings": "n_positions",
"hidden_size": "n_embd",
"num_attention_heads": "n_head",
"num_hidden_layers": "n_layer",
}
def __init__(
self,
vocab_size=50400,
n_positions=2048,
n_embd=4096,
n_layer=28,
n_head=16,
rotary_dim=64,
n_inner=None,
activation_function="gelu_new",
resid_pdrop=0.0,
embd_pdrop=0.0,
attn_pdrop=0.0,
layer_norm_epsilon=1e-5,
initializer_range=0.02,
use_cache=True,
bos_token_id=50256,
eos_token_id=50256,
tie_word_embeddings=False,
**kwargs,
):
self.vocab_size = vocab_size
self.n_positions = n_positions
self.n_embd = n_embd
self.n_layer = n_layer
self.n_head = n_head
self.n_inner = n_inner
self.rotary_dim = rotary_dim
self.activation_function = activation_function
self.resid_pdrop = resid_pdrop
self.embd_pdrop = embd_pdrop
self.attn_pdrop = attn_pdrop
self.layer_norm_epsilon = layer_norm_epsilon
self.initializer_range = initializer_range
self.use_cache = use_cache
self.bos_token_id = bos_token_id
self.eos_token_id = eos_token_id
super().__init__(
bos_token_id=bos_token_id, eos_token_id=eos_token_id, tie_word_embeddings=tie_word_embeddings, **kwargs
)
# Copied from transformers.models.gpt2.configuration_gpt2.GPT2OnnxConfig
class GPTJOnnxConfig(OnnxConfigWithPast):
def __init__(
self,
config: PretrainedConfig,
task: str = "default",
patching_specs: List[PatchingSpec] = None,
use_past: bool = False,
):
super().__init__(config, task=task, patching_specs=patching_specs, use_past=use_past)
if not getattr(self._config, "pad_token_id", None):
# TODO: how to do that better?
self._config.pad_token_id = 0
@property
def inputs(self) -> Mapping[str, Mapping[int, str]]:
common_inputs = OrderedDict({"input_ids": {0: "batch", 1: "sequence"}})
if self.use_past:
self.fill_with_past_key_values_(common_inputs, direction="inputs")
common_inputs["attention_mask"] = {0: "batch", 1: "past_sequence + sequence"}
else:
common_inputs["attention_mask"] = {0: "batch", 1: "sequence"}
return common_inputs
@property
def num_layers(self) -> int:
return self._config.n_layer
@property
def num_attention_heads(self) -> int:
return self._config.n_head
def generate_dummy_inputs(
self,
tokenizer: PreTrainedTokenizer,
batch_size: int = -1,
seq_length: int = -1,
is_pair: bool = False,
framework: Optional[TensorType] = None,
) -> Mapping[str, Any]:
common_inputs = super(OnnxConfigWithPast, self).generate_dummy_inputs(
tokenizer, batch_size=batch_size, seq_length=seq_length, is_pair=is_pair, framework=framework
)
# We need to order the input in the way they appears in the forward()
ordered_inputs = OrderedDict({"input_ids": common_inputs["input_ids"]})
# Need to add the past_keys
if self.use_past:
if not is_torch_available():
raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed.")
else:
import torch
batch, seqlen = common_inputs["input_ids"].shape
# Not using the same length for past_key_values
past_key_values_length = seqlen + 2
past_shape = (
batch,
self.num_attention_heads,
past_key_values_length,
self._config.hidden_size // self.num_attention_heads,
)
ordered_inputs["past_key_values"] = [
(torch.zeros(past_shape), torch.zeros(past_shape)) for _ in range(self.num_layers)
]
ordered_inputs["attention_mask"] = common_inputs["attention_mask"]
if self.use_past:
mask_dtype = ordered_inputs["attention_mask"].dtype
ordered_inputs["attention_mask"] = torch.cat(
[ordered_inputs["attention_mask"], torch.ones(batch, past_key_values_length, dtype=mask_dtype)], dim=1
)
return ordered_inputs
@property
def default_onnx_opset(self) -> int:
return 13
__all__ = ["GPTJConfig", "GPTJOnnxConfig"]
```
|
======================================================================================================================================
SOURCE CODE FILE: modeling_flax_gptj.py
LINES: 1
SIZE: 27.94 KB
PATH: scripts\freecad_env\Lib\site-packages\transformers\models\gptj\modeling_flax_gptj.py
ENCODING: utf-8
```py
# coding=utf-8
# Copyright 2021 The EleutherAI and The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from functools import partial
from typing import Optional, Tuple
import flax.linen as nn
import jax
import jax.numpy as jnp
import numpy as np
from flax.core.frozen_dict import FrozenDict, freeze, unfreeze
from flax.linen import combine_masks, make_causal_mask
from flax.linen.attention import dot_product_attention_weights
from flax.traverse_util import flatten_dict, unflatten_dict
from jax import lax
from ...modeling_flax_outputs import FlaxBaseModelOutput, FlaxCausalLMOutput
from ...modeling_flax_utils import ACT2FN, FlaxPreTrainedModel, append_call_sample_docstring
from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_gptj import GPTJConfig
logger = logging.get_logger(__name__)
_CHECKPOINT_FOR_DOC = "gptj"
_CONFIG_FOR_DOC = "GPTJConfig"
GPTJ_START_DOCSTRING = r"""
This model inherits from [`FlaxPreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a Flax Linen
[flax.nn.Module](https://flax.readthedocs.io/en/latest/_autosummary/flax.nn.module.html) subclass. Use it as a
regular Flax Module and refer to the Flax documentation for all matter related to general usage and behavior.
Finally, this model supports inherent JAX features such as:
- [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit)
- [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation)
- [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap)
- [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap)
Parameters:
config ([`GPTJConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~FlaxPreTrainedModel.from_pretrained`] method to load the model weights.
dtype (`jax.numpy.dtype`, *optional*, defaults to `jax.numpy.float32`):
The data type of the computation. Can be one of `jax.numpy.float32`, `jax.numpy.float16` (on GPUs) and
`jax.numpy.bfloat16` (on TPUs).
This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If
specified all the computation will be performed with the given `dtype`.
**Note that this only specifies the dtype of the computation and does not influence the dtype of model
parameters.**
If you wish to change the dtype of the model parameters, see [`~FlaxPreTrainedModel.to_fp16`] and
[`~FlaxPreTrainedModel.to_bf16`].
"""
GPTJ_INPUTS_DOCSTRING = r"""
Args:
input_ids (`numpy.ndarray` of shape `(batch_size, input_ids_length)`):
`input_ids_length` = `sequence_length`. Indices of input sequence tokens in the vocabulary.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
position_ids (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*):
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
config.max_position_embeddings - 1]`.
past_key_values (`Dict[str, np.ndarray]`, *optional*, returned by `init_cache` or when passing previous `past_key_values`):
Dictionary of pre-computed hidden-states (key and values in the attention blocks) that can be used for fast
auto-regressive decoding. Pre-computed key and value hidden-states are of shape *[batch_size, max_length]*.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
def create_sinusoidal_positions(num_pos, dim):
inv_freq = 1.0 / (10000 ** (np.arange(0, dim, 2) / dim))
sinusoid_inp = np.einsum("i , j -> i j", np.arange(num_pos), inv_freq).astype("float32")
sin, cos = np.sin(sinusoid_inp), np.cos(sinusoid_inp)
sentinel = dim // 2 + dim % 2
out = np.zeros((num_pos, dim))
out[:, 0:sentinel] = sin
out[:, sentinel:] = cos
return jnp.array(out)
def rotate_every_two(tensor):
rotate_half_tensor = jnp.stack((-tensor[:, :, :, 1::2], tensor[:, :, :, ::2]), axis=-1)
rotate_half_tensor = rotate_half_tensor.reshape(rotate_half_tensor.shape[:-2] + (-1,))
return rotate_half_tensor
def apply_rotary_pos_emb(tensor, sincos):
sin_pos, cos_pos = sincos
sin_pos = sin_pos[:, :, None, :].repeat(2, 3)
cos_pos = cos_pos[:, :, None, :].repeat(2, 3)
return (tensor * cos_pos) + (rotate_every_two(tensor) * sin_pos)
class FlaxGPTJAttention(nn.Module):
config: GPTJConfig
dtype: jnp.dtype = jnp.float32
causal: bool = True
is_cross_attention: bool = False
def setup(self):
config = self.config
self.embed_dim = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
self.rotary_dim = config.rotary_dim
dense = partial(
nn.Dense,
self.embed_dim,
use_bias=False,
dtype=self.dtype,
kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
)
self.q_proj, self.k_proj, self.v_proj = dense(), dense(), dense()
self.out_proj = dense()
self.resid_dropout = nn.Dropout(rate=config.resid_pdrop)
self.causal_mask = make_causal_mask(jnp.ones((1, config.max_position_embeddings), dtype="bool"), dtype="bool")
pos_embd_dim = self.rotary_dim or self.embed_dim
self.embed_positions = create_sinusoidal_positions(config.max_position_embeddings, pos_embd_dim)
def _split_heads(self, hidden_states):
return hidden_states.reshape(hidden_states.shape[:2] + (self.num_heads, self.head_dim))
def _merge_heads(self, hidden_states):
return hidden_states.reshape(hidden_states.shape[:2] + (self.embed_dim,))
@nn.compact
def _concatenate_to_cache(self, key, value, query, attention_mask):
"""
This function takes projected key, value states from a single input token and concatenates the states to cached
states from previous steps. This function is slightly adapted from the official Flax repository:
https://github.com/google/flax/blob/491ce18759622506588784b4fca0e4bf05f8c8cd/flax/linen/attention.py#L252
"""
# detect if we're initializing by absence of existing cache data.
is_initialized = self.has_variable("cache", "cached_key")
cached_key = self.variable("cache", "cached_key", jnp.zeros, key.shape, key.dtype)
cached_value = self.variable("cache", "cached_value", jnp.zeros, value.shape, value.dtype)
cache_index = self.variable("cache", "cache_index", lambda: jnp.array(0, dtype=jnp.int32))
if is_initialized:
*batch_dims, max_length, num_heads, depth_per_head = cached_key.value.shape
# update key, value caches with our new 1d spatial slices
cur_index = cache_index.value
indices = (0,) * len(batch_dims) + (cur_index, 0, 0)
key = lax.dynamic_update_slice(cached_key.value, key, indices)
value = lax.dynamic_update_slice(cached_value.value, value, indices)
cached_key.value = key
cached_value.value = value
num_updated_cache_vectors = query.shape[1]
cache_index.value = cache_index.value + num_updated_cache_vectors
# causal mask for cached decoder self-attention: our single query position should only attend to those key
# positions that have already been generated and cached, not the remaining zero elements.
pad_mask = jnp.broadcast_to(
jnp.arange(max_length) < cur_index + num_updated_cache_vectors,
tuple(batch_dims) + (1, num_updated_cache_vectors, max_length),
)
attention_mask = combine_masks(pad_mask, attention_mask)
return key, value, attention_mask
def __call__(
self,
hidden_states,
attention_mask,
position_ids,
deterministic: bool = True,
init_cache: bool = False,
output_attentions: bool = False,
):
query = self.q_proj(hidden_states)
key = self.k_proj(hidden_states)
value = self.v_proj(hidden_states)
query = self._split_heads(query)
key = self._split_heads(key)
value = self._split_heads(value)
sincos = jnp.take(self.embed_positions, position_ids, axis=0)
sincos = jnp.split(sincos, 2, axis=-1)
if self.rotary_dim is not None:
k_rot = key[:, :, :, : self.rotary_dim]
k_pass = key[:, :, :, self.rotary_dim :]
q_rot = query[:, :, :, : self.rotary_dim]
q_pass = query[:, :, :, self.rotary_dim :]
k_rot = apply_rotary_pos_emb(k_rot, sincos)
q_rot = apply_rotary_pos_emb(q_rot, sincos)
key = jnp.concatenate([k_rot, k_pass], axis=-1)
query = jnp.concatenate([q_rot, q_pass], axis=-1)
else:
key = apply_rotary_pos_emb(key, sincos)
query = apply_rotary_pos_emb(query, sincos)
query_length, key_length = query.shape[1], key.shape[1]
if self.has_variable("cache", "cached_key"):
mask_shift = self.variables["cache"]["cache_index"]
max_decoder_length = self.variables["cache"]["cached_key"].shape[1]
causal_mask = lax.dynamic_slice(
self.causal_mask, (0, 0, mask_shift, 0), (1, 1, query_length, max_decoder_length)
)
else:
causal_mask = self.causal_mask[:, :, :query_length, :key_length]
batch_size = hidden_states.shape[0]
causal_mask = jnp.broadcast_to(causal_mask, (batch_size,) + causal_mask.shape[1:])
attention_mask = jnp.broadcast_to(jnp.expand_dims(attention_mask, axis=(-3, -2)), causal_mask.shape)
attention_mask = combine_masks(attention_mask, causal_mask)
dropout_rng = None
if not deterministic and self.config.attn_pdrop > 0.0:
dropout_rng = self.make_rng("dropout")
# During fast autoregressive decoding, we feed one position at a time,
# and cache the keys and values step by step.
if self.has_variable("cache", "cached_key") or init_cache:
key, value, attention_mask = self._concatenate_to_cache(key, value, query, attention_mask)
# transform boolean mask into float mask
attention_bias = lax.select(
attention_mask > 0,
jnp.full(attention_mask.shape, 0.0).astype(self.dtype),
jnp.full(attention_mask.shape, jnp.finfo(self.dtype).min).astype(self.dtype),
)
# usual dot product attention
attn_weights = dot_product_attention_weights(
query,
key,
bias=attention_bias,
dropout_rng=dropout_rng,
dropout_rate=self.config.attn_pdrop,
deterministic=deterministic,
dtype=self.dtype,
precision=None,
)
attn_output = jnp.einsum("...hqk,...khd->...qhd", attn_weights, value)
attn_output = self._merge_heads(attn_output)
attn_output = self.out_proj(attn_output)
attn_output = self.resid_dropout(attn_output, deterministic=deterministic)
outputs = (attn_output, attn_weights) if output_attentions else (attn_output,)
return outputs
class FlaxGPTJMLP(nn.Module):
config: GPTJConfig
intermediate_size: int
dtype: jnp.dtype = jnp.float32
def setup(self):
embed_dim = self.config.hidden_size
kernel_init = jax.nn.initializers.normal(self.config.initializer_range)
self.fc_in = nn.Dense(self.intermediate_size, dtype=self.dtype, kernel_init=kernel_init)
self.fc_out = nn.Dense(embed_dim, dtype=self.dtype, kernel_init=kernel_init)
self.act = ACT2FN[self.config.activation_function]
self.dropout = nn.Dropout(rate=self.config.resid_pdrop)
def __call__(self, hidden_states, deterministic: bool = True):
hidden_states = self.fc_in(hidden_states)
hidden_states = self.act(hidden_states)
hidden_states = self.fc_out(hidden_states)
hidden_states = self.dropout(hidden_states, deterministic=deterministic)
return hidden_states
class FlaxGPTJBlock(nn.Module):
config: GPTJConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
hidden_size = self.config.hidden_size
inner_dim = self.config.n_inner if self.config.n_inner is not None else 4 * hidden_size
self.ln_1 = nn.LayerNorm(epsilon=self.config.layer_norm_epsilon, dtype=self.dtype)
self.attn = FlaxGPTJAttention(self.config, dtype=self.dtype)
self.mlp = FlaxGPTJMLP(self.config, inner_dim, dtype=self.dtype)
def __call__(
self,
hidden_states,
attention_mask=None,
position_ids=None,
deterministic: bool = True,
init_cache: bool = False,
output_attentions: bool = False,
):
residual = hidden_states
hidden_states = self.ln_1(hidden_states)
attn_outputs = self.attn(
hidden_states,
attention_mask=attention_mask,
position_ids=position_ids,
deterministic=deterministic,
init_cache=init_cache,
output_attentions=output_attentions,
)
attn_output = attn_outputs[0]
feed_forward_hidden_states = self.mlp(hidden_states, deterministic=deterministic)
# residual connection
hidden_states = attn_output + feed_forward_hidden_states + residual
return (hidden_states,) + attn_outputs[1:]
class FlaxGPTJPreTrainedModel(FlaxPreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = GPTJConfig
base_model_prefix = "transformer"
module_class: nn.Module = None
def __init__(
self,
config: GPTJConfig,
input_shape: Tuple = (1, 1),
seed: int = 0,
dtype: jnp.dtype = jnp.float32,
_do_init: bool = True,
**kwargs,
):
module = self.module_class(config=config, dtype=dtype, **kwargs)
super().__init__(config, module, input_shape=input_shape, seed=seed, dtype=dtype, _do_init=_do_init)
def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple, params: FrozenDict = None) -> FrozenDict:
# init input tensors
input_ids = jnp.zeros(input_shape, dtype="i4")
attention_mask = jnp.ones_like(input_ids)
position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_shape)
params_rng, dropout_rng = jax.random.split(rng)
rngs = {"params": params_rng, "dropout": dropout_rng}
if self.config.add_cross_attention:
encoder_hidden_states = jnp.zeros(input_shape + (self.config.n_embd,))
encoder_attention_mask = attention_mask
module_init_outputs = self.module.init(
rngs,
input_ids,
attention_mask,
position_ids,
encoder_hidden_states,
encoder_attention_mask,
return_dict=False,
)
else:
module_init_outputs = self.module.init(rngs, input_ids, attention_mask, position_ids, return_dict=False)
random_params = module_init_outputs["params"]
if params is not None:
random_params = flatten_dict(unfreeze(random_params))
params = flatten_dict(unfreeze(params))
for missing_key in self._missing_keys:
params[missing_key] = random_params[missing_key]
self._missing_keys = set()
return freeze(unflatten_dict(params))
else:
return random_params
def init_cache(self, batch_size, max_length):
r"""
Args:
batch_size (`int`):
batch_size used for fast auto-regressive decoding. Defines the batch size of the initialized cache.
max_length (`int`):
maximum possible length for auto-regressive decoding. Defines the sequence length of the initialized
cache.
"""
# init input variables to retrieve cache
input_ids = jnp.ones((batch_size, max_length))
attention_mask = jnp.ones_like(input_ids)
position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_ids.shape)
init_variables = self.module.init(
jax.random.PRNGKey(0), input_ids, attention_mask, position_ids, return_dict=False, init_cache=True
)
return init_variables["cache"]
@add_start_docstrings_to_model_forward(GPTJ_INPUTS_DOCSTRING)
def __call__(
self,
input_ids,
attention_mask=None,
position_ids=None,
params: dict = None,
past_key_values: dict = None,
dropout_rng: jax.random.PRNGKey = None,
train: bool = False,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
):
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.return_dict
batch_size, sequence_length = input_ids.shape
if position_ids is None:
if past_key_values is not None:
raise ValueError("Make sure to provide `position_ids` when passing `past_key_values`.")
position_ids = jnp.broadcast_to(jnp.arange(sequence_length)[None, :], (batch_size, sequence_length))
if attention_mask is None:
attention_mask = jnp.ones((batch_size, sequence_length))
# Handle any PRNG if needed
rngs = {}
if dropout_rng is not None:
rngs["dropout"] = dropout_rng
inputs = {"params": params or self.params}
# if past_key_values are passed then cache is already initialized a private flag init_cache has to be passed down to ensure cache is used. It has to be made sure that cache is marked as mutable so that it can be changed by FlaxGPTJAttention module
if past_key_values:
inputs["cache"] = past_key_values
mutable = ["cache"]
else:
mutable = False
outputs = self.module.apply(
inputs,
jnp.array(input_ids, dtype="i4"),
jnp.array(attention_mask, dtype="i4"),
jnp.array(position_ids, dtype="i4"),
not train,
False,
output_attentions,
output_hidden_states,
return_dict,
rngs=rngs,
mutable=mutable,
)
# add updated cache to model output
if past_key_values is not None and return_dict:
outputs, past_key_values = outputs
outputs["past_key_values"] = unfreeze(past_key_values["cache"])
return outputs
elif past_key_values is not None and not return_dict:
outputs, past_key_values = outputs
outputs = outputs[:1] + (unfreeze(past_key_values["cache"]),) + outputs[1:]
return outputs
class FlaxGPTJBlockCollection(nn.Module):
config: GPTJConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
self.blocks = [
FlaxGPTJBlock(self.config, name=str(i), dtype=self.dtype) for i in range(self.config.num_hidden_layers)
]
def __call__(
self,
hidden_states,
attention_mask=None,
position_ids=None,
deterministic: bool = True,
init_cache: bool = False,
output_attentions: bool = False,
output_hidden_states: bool = False,
return_dict: bool = True,
):
all_attentions = () if output_attentions else None
all_hidden_states = () if output_hidden_states else None
for block in self.blocks:
if output_hidden_states:
all_hidden_states += (hidden_states,)
layer_outputs = block(
hidden_states,
attention_mask,
position_ids=position_ids,
deterministic=deterministic,
init_cache=init_cache,
output_attentions=output_attentions,
)
hidden_states = layer_outputs[0]
if output_attentions:
all_attentions += (layer_outputs[1],)
# this contains possible `None` values - `FlaxGPTJModule` will filter them out
outputs = (hidden_states, all_hidden_states, all_attentions)
return outputs
class FlaxGPTJModule(nn.Module):
config: GPTJConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
self.embed_dim = self.config.hidden_size
self.wte = nn.Embed(
self.config.vocab_size,
self.config.hidden_size,
embedding_init=jax.nn.initializers.normal(stddev=self.config.initializer_range),
)
self.dropout = nn.Dropout(rate=self.config.embd_pdrop)
self.h = FlaxGPTJBlockCollection(self.config, dtype=self.dtype)
self.ln_f = nn.LayerNorm(epsilon=self.config.layer_norm_epsilon, dtype=self.dtype)
def __call__(
self,
input_ids,
attention_mask,
position_ids,
deterministic=True,
init_cache: bool = False,
output_attentions: bool = False,
output_hidden_states: bool = False,
return_dict: bool = True,
):
input_embeds = self.wte(input_ids.astype("i4"))
hidden_states = self.dropout(input_embeds, deterministic=deterministic)
outputs = self.h(
hidden_states,
attention_mask,
position_ids=position_ids,
deterministic=deterministic,
init_cache=init_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
hidden_states = outputs[0]
hidden_states = self.ln_f(hidden_states)
if output_hidden_states:
all_hidden_states = outputs[1] + (hidden_states,)
outputs = (hidden_states, all_hidden_states) + outputs[2:]
else:
outputs = (hidden_states,) + outputs[1:]
if not return_dict:
return tuple(v for v in outputs if v is not None)
return FlaxBaseModelOutput(
last_hidden_state=hidden_states,
hidden_states=outputs[1],
attentions=outputs[-1],
)
@add_start_docstrings(
"The bare GPTJ Model transformer outputting raw hidden-states without any specific head on top.",
GPTJ_START_DOCSTRING,
)
class FlaxGPTJModel(FlaxGPTJPreTrainedModel):
module_class = FlaxGPTJModule
append_call_sample_docstring(
FlaxGPTJModel,
_CHECKPOINT_FOR_DOC,
FlaxCausalLMOutput,
_CONFIG_FOR_DOC,
)
class FlaxGPTJForCausalLMModule(nn.Module):
config: GPTJConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
self.transformer = FlaxGPTJModule(self.config, dtype=self.dtype)
self.lm_head = nn.Dense(
self.config.vocab_size,
dtype=self.dtype,
kernel_init=jax.nn.initializers.normal(stddev=self.config.initializer_range),
)
def __call__(
self,
input_ids,
attention_mask,
position_ids,
deterministic: bool = True,
init_cache: bool = False,
output_attentions: bool = False,
output_hidden_states: bool = False,
return_dict: bool = True,
):
outputs = self.transformer(
input_ids,
attention_mask,
position_ids,
deterministic=deterministic,
init_cache=init_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
hidden_states = outputs[0]
if self.config.tie_word_embeddings:
shared_kernel = self.transformer.variables["params"]["wte"]["embedding"].T
lm_logits = self.lm_head.apply({"params": {"kernel": shared_kernel}}, hidden_states)
else:
lm_logits = self.lm_head(hidden_states)
if not return_dict:
return (lm_logits,) + outputs[1:]
return FlaxCausalLMOutput(logits=lm_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions)
@add_start_docstrings(
"""
The GPTJ Model transformer with a language modeling head on top.
""",
GPTJ_START_DOCSTRING,
)
class FlaxGPTJForCausalLM(FlaxGPTJPreTrainedModel):
module_class = FlaxGPTJForCausalLMModule
def prepare_inputs_for_generation(self, input_ids, max_length, attention_mask: Optional[jax.Array] = None):
# initializing the cache
batch_size, seq_length = input_ids.shape
past_key_values = self.init_cache(batch_size, max_length)
# Note that usually one would have to put 0's in the attention_mask for x > input_ids.shape[-1] and x < cache_length.
# But since GPTJ uses a causal mask, those positions are masked anyways.
# Thus we can create a single static attention_mask here, which is more efficient for compilation
extended_attention_mask = jnp.ones((batch_size, max_length), dtype="i4")
if attention_mask is not None:
position_ids = attention_mask.cumsum(axis=-1) - 1
extended_attention_mask = lax.dynamic_update_slice(extended_attention_mask, attention_mask, (0, 0))
else:
position_ids = jnp.broadcast_to(jnp.arange(seq_length, dtype="i4")[None, :], (batch_size, seq_length))
return {
"past_key_values": past_key_values,
"attention_mask": extended_attention_mask,
"position_ids": position_ids,
}
def update_inputs_for_generation(self, model_outputs, model_kwargs):
model_kwargs["past_key_values"] = model_outputs.past_key_values
model_kwargs["position_ids"] = model_kwargs["position_ids"][:, -1:] + 1
return model_kwargs
append_call_sample_docstring(
FlaxGPTJForCausalLM,
_CHECKPOINT_FOR_DOC,
FlaxCausalLMOutput,
_CONFIG_FOR_DOC,
)
__all__ = ["FlaxGPTJForCausalLM", "FlaxGPTJModel", "FlaxGPTJPreTrainedModel"]
```
|
=================================================================================================================================
SOURCE CODE FILE: modeling_gptj.py
LINES: 1
SIZE: 61.51 KB
PATH: scripts\freecad_env\Lib\site-packages\transformers\models\gptj\modeling_gptj.py
ENCODING: utf-8
```py
# coding=utf-8
# Copyright 2021 The EleutherAI and HuggingFace Teams. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""PyTorch GPT-J model."""
import warnings
from typing import Optional, Tuple, Union
import torch
import torch.fx
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACT2FN
from ...cache_utils import Cache, DynamicCache, StaticCache
from ...generation import GenerationMixin
from ...modeling_attn_mask_utils import AttentionMaskConverter
from ...modeling_flash_attention_utils import flash_attn_supports_top_left_mask, is_flash_attn_available
from ...modeling_outputs import (
BaseModelOutputWithPast,
CausalLMOutputWithPast,
QuestionAnsweringModelOutput,
SequenceClassifierOutputWithPast,
)
from ...modeling_utils import PreTrainedModel
from ...utils import (
add_code_sample_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
is_torch_flex_attn_available,
is_torch_fx_proxy,
logging,
)
from ...utils.model_parallel_utils import assert_device_map, get_device_map
from .configuration_gptj import GPTJConfig
if is_torch_flex_attn_available():
from torch.nn.attention.flex_attention import BlockMask
from ...integrations.flex_attention import make_flex_block_causal_mask
if is_flash_attn_available():
from ...modeling_flash_attention_utils import _flash_attention_forward
logger = logging.get_logger(__name__)
_CHECKPOINT_FOR_DOC = "hf-internal-testing/tiny-random-gptj"
_REAL_CHECKPOINT_FOR_DOC = "EleutherAI/gpt-j-6B"
_CONFIG_FOR_DOC = "GPTJConfig"
def create_sinusoidal_positions(num_pos: int, dim: int) -> torch.Tensor:
inv_freq = 1.0 / (10000 ** (torch.arange(0, dim, 2, dtype=torch.int64) / dim))
sinusoid_inp = torch.einsum("i , j -> i j", torch.arange(num_pos, dtype=torch.int64).float(), inv_freq).float()
return torch.cat((torch.sin(sinusoid_inp), torch.cos(sinusoid_inp)), dim=1)
@torch.fx.wrap
def get_embed_positions(embed_positions, position_ids):
return embed_positions.to(position_ids.device).repeat(position_ids.shape[0], 1, 1)
def rotate_every_two(x: torch.Tensor) -> torch.Tensor:
x1 = x[:, :, :, ::2]
x2 = x[:, :, :, 1::2]
x = torch.stack((-x2, x1), dim=-1)
return x.flatten(-2) # in einsum notation: rearrange(x, '... d j -> ... (d j)')
def apply_rotary_pos_emb(tensor: torch.Tensor, sin: torch.Tensor, cos: torch.Tensor) -> torch.Tensor:
sin = torch.repeat_interleave(sin[:, :, None, :], 2, 3)
cos = torch.repeat_interleave(cos[:, :, None, :], 2, 3)
return (tensor * cos) + (rotate_every_two(tensor) * sin)
class GPTJAttention(nn.Module):
def __init__(self, config, layer_idx=None):
super().__init__()
self.config = config
max_positions = config.max_position_embeddings
self.attn_dropout = nn.Dropout(config.attn_pdrop)
self.resid_dropout = nn.Dropout(config.resid_pdrop)
self.is_causal = True
self.layer_idx = layer_idx
if layer_idx is None:
logger.warning_once(
f"Instantiating {self.__class__.__name__} without passing a `layer_idx` is not recommended and will "
"lead to errors during the forward call if caching is used. Please make sure to provide a `layer_idx` "
"when creating this class."
)
self.embed_dim = config.hidden_size
self.num_attention_heads = config.num_attention_heads
self.head_dim = self.embed_dim // self.num_attention_heads
if self.head_dim * self.num_attention_heads != self.embed_dim:
raise ValueError(
f"embed_dim must be divisible by num_attention_heads (got `embed_dim`: {self.embed_dim} and"
f" `num_attention_heads`: {self.num_attention_heads})."
)
self.scale_attn = torch.sqrt(torch.tensor(self.head_dim, dtype=torch.float32)).to(torch.get_default_dtype())
self.k_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=False)
self.v_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=False)
self.q_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=False)
self.out_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=False)
self.rotary_dim = config.rotary_dim
pos_embd_dim = self.rotary_dim or self.embed_dim
self.embed_positions = create_sinusoidal_positions(max_positions, pos_embd_dim)
def _split_heads(self, tensor, num_attention_heads, attn_head_size, rotary):
"""
Splits hidden dim into attn_head_size and num_attention_heads
"""
new_shape = tensor.size()[:-1] + (num_attention_heads, attn_head_size)
tensor = tensor.view(new_shape)
if rotary:
return tensor
if len(tensor.shape) == 5:
return tensor.permute(0, 1, 3, 2, 4) # (batch, blocks, head, block_length, head_features)
elif len(tensor.shape) == 4:
return tensor.permute(0, 2, 1, 3) # (batch, head, seq_length, head_features)
else:
raise ValueError(f"Input tensor rank should be one of [4, 5], but is: {len(tensor.shape)}")
def _merge_heads(self, tensor, num_attention_heads, attn_head_size):
"""
Merges attn_head_size dim and num_attn_heads dim into hidden dim
"""
if len(tensor.shape) == 5:
tensor = tensor.permute(0, 1, 3, 2, 4).contiguous()
elif len(tensor.shape) == 4:
tensor = tensor.permute(0, 2, 1, 3).contiguous()
else:
raise ValueError(f"Input tensor rank should be one of [4, 5], but is: {len(tensor.shape)}")
new_shape = tensor.size()[:-2] + (num_attention_heads * attn_head_size,)
return tensor.view(new_shape)
def _attn(
self,
query,
key,
value,
attention_mask=None,
head_mask=None,
):
# Keep the attention weights computation in fp32 to avoid overflow issues
query = query.to(torch.float32)
key = key.to(torch.float32)
attn_weights = torch.matmul(query, key.transpose(-1, -2))
attn_weights = attn_weights / self.scale_attn
if attention_mask is not None: # no matter the length, we just slice it
causal_mask = attention_mask[:, :, :, : key.shape[-2]]
attn_weights = attn_weights + causal_mask
attn_weights = nn.functional.softmax(attn_weights, dim=-1)
attn_weights = attn_weights.to(value.dtype)
attn_weights = self.attn_dropout(attn_weights)
# Mask heads if we want to
if head_mask is not None:
attn_weights = attn_weights * head_mask
attn_output = torch.matmul(attn_weights, value)
return attn_output, attn_weights
def _get_embed_positions(self, position_ids):
embed_positions = self.embed_positions
if embed_positions.device != position_ids.device:
embed_positions = embed_positions.to(position_ids.device)
self.embed_positions = embed_positions
return embed_positions.repeat(position_ids.shape[0], 1, 1)
def forward(
self,
hidden_states: torch.FloatTensor,
layer_past: Optional[Cache] = None,
attention_mask: Optional[torch.FloatTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
use_cache: Optional[bool] = False,
output_attentions: Optional[bool] = False,
cache_position: Optional[torch.LongTensor] = None,
) -> Union[
Tuple[torch.Tensor, Tuple[torch.Tensor]],
Optional[Tuple[torch.Tensor, Tuple[torch.Tensor], Tuple[torch.Tensor, ...]]],
]:
query = self.q_proj(hidden_states)
key = self.k_proj(hidden_states)
value = self.v_proj(hidden_states)
query = self._split_heads(query, self.num_attention_heads, self.head_dim, True)
key = self._split_heads(key, self.num_attention_heads, self.head_dim, True)
value = self._split_heads(value, self.num_attention_heads, self.head_dim, False)
if is_torch_fx_proxy(position_ids) or torch.jit.is_tracing():
# The logic to conditionally copy to GPU could not be traced, so we do this
# every time in the torch.fx case
embed_positions = get_embed_positions(self.embed_positions, position_ids)
else:
embed_positions = self._get_embed_positions(position_ids)
repeated_position_ids = position_ids.unsqueeze(-1).repeat(1, 1, embed_positions.shape[-1])
sincos = torch.gather(embed_positions, 1, repeated_position_ids)
sin, cos = torch.split(sincos, sincos.shape[-1] // 2, dim=-1)
if self.rotary_dim is not None:
k_rot = key[:, :, :, : self.rotary_dim]
k_pass = key[:, :, :, self.rotary_dim :]
q_rot = query[:, :, :, : self.rotary_dim]
q_pass = query[:, :, :, self.rotary_dim :]
k_rot = apply_rotary_pos_emb(k_rot, sin, cos)
q_rot = apply_rotary_pos_emb(q_rot, sin, cos)
key = torch.cat([k_rot, k_pass], dim=-1)
query = torch.cat([q_rot, q_pass], dim=-1)
else:
key = apply_rotary_pos_emb(key, sin, cos)
query = apply_rotary_pos_emb(query, sin, cos)
key = key.permute(0, 2, 1, 3)
query = query.permute(0, 2, 1, 3)
if layer_past is not None:
cache_kwargs = {
"sin": sin,
"cos": cos,
"partial_rotation_size": self.rotary_dim,
"cache_position": cache_position,
}
key, value = layer_past.update(key, value, self.layer_idx, cache_kwargs)
# compute self-attention: V x Softmax(QK^T)
attn_output, attn_weights = self._attn(query, key, value, attention_mask, head_mask)
attn_output = self._merge_heads(attn_output, self.num_attention_heads, self.head_dim)
attn_output = self.out_proj(attn_output)
attn_output = self.resid_dropout(attn_output)
outputs = (attn_output, layer_past)
if output_attentions:
outputs += (attn_weights,)
return outputs # a, present, (attentions)
class GPTJFlashAttention2(GPTJAttention):
"""
GPTJ flash attention module. This module inherits from `GPTJAttention` as the weights of the module stays
untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
flash attention and deal with padding tokens in case the input contains any of them.
"""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
# TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
# flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignment, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
# Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left).
self._flash_attn_uses_top_left_mask = flash_attn_supports_top_left_mask()
def forward(
self,
hidden_states: torch.FloatTensor,
layer_past: Optional[Cache] = None,
attention_mask: Optional[torch.FloatTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
use_cache: Optional[bool] = False,
output_attentions: Optional[bool] = False,
cache_position: Optional[torch.LongTensor] = None,
) -> Union[
Tuple[torch.Tensor, Tuple[torch.Tensor]],
Optional[Tuple[torch.Tensor, Tuple[torch.Tensor], Tuple[torch.Tensor, ...]]],
]:
query = self.q_proj(hidden_states)
key = self.k_proj(hidden_states)
value = self.v_proj(hidden_states)
query = self._split_heads(query, self.num_attention_heads, self.head_dim, True)
key = self._split_heads(key, self.num_attention_heads, self.head_dim, True)
value = self._split_heads(value, self.num_attention_heads, self.head_dim, False)
if is_torch_fx_proxy(position_ids) or torch.jit.is_tracing():
# The logic to conditionally copy to GPU could not be traced, so we do this
# every time in the torch.fx case
embed_positions = get_embed_positions(self.embed_positions, position_ids)
else:
embed_positions = self._get_embed_positions(position_ids)
repeated_position_ids = position_ids.unsqueeze(-1).repeat(1, 1, embed_positions.shape[-1])
sincos = torch.gather(embed_positions, 1, repeated_position_ids)
sin, cos = torch.split(sincos, sincos.shape[-1] // 2, dim=-1)
if self.rotary_dim is not None:
k_rot = key[:, :, :, : self.rotary_dim]
k_pass = key[:, :, :, self.rotary_dim :]
q_rot = query[:, :, :, : self.rotary_dim]
q_pass = query[:, :, :, self.rotary_dim :]
k_rot = apply_rotary_pos_emb(k_rot, sin, cos)
q_rot = apply_rotary_pos_emb(q_rot, sin, cos)
key = torch.cat([k_rot, k_pass], dim=-1)
query = torch.cat([q_rot, q_pass], dim=-1)
else:
key = apply_rotary_pos_emb(key, sin, cos)
query = apply_rotary_pos_emb(query, sin, cos)
# tanspose to have the desired shape
# before transpose: batch_size x seq_length x num_attention_heads x head_dim
# after transpose: batch_size x num_attention_heads x seq_length x head_dim
key = key.permute(0, 2, 1, 3)
query = query.permute(0, 2, 1, 3)
# value: batch_size x num_attention_heads x seq_length x head_dim
if layer_past is not None:
cache_kwargs = {
"sin": sin,
"cos": cos,
"partial_rotation_size": self.rotary_dim,
"cache_position": cache_position,
}
key, value = layer_past.update(key, value, self.layer_idx, cache_kwargs)
# The Flash attention requires the input to have the shape
# batch_size x seq_length x head_dim x hidden_dim
# therefore we need to keep the original shape for query and key, and reshape value
# to have the correct shape.
key = key.permute(0, 2, 1, 3).contiguous()
query = query.permute(0, 2, 1, 3).contiguous()
value = value.permute(0, 2, 1, 3).contiguous()
# In PEFT, usually we cast the layer norms in float32 for training stability reasons
# therefore the input hidden states gets silently casted in float32. Hence, we need
# cast them back in the correct dtype just to be sure everything works as expected.
# This might slowdown training & inference so it is recommended to not cast the LayerNorms
# in fp32. (LlamaRMSNorm handles it correctly)
input_dtype = query.dtype
if input_dtype == torch.float32:
if torch.is_autocast_enabled():
target_dtype = torch.get_autocast_gpu_dtype()
# Handle the case where the model is quantized
elif hasattr(self.config, "_pre_quantization_dtype"):
target_dtype = self.config._pre_quantization_dtype
else:
target_dtype = self.q_proj.weight.dtype
logger.warning_once(
f"The input hidden states seems to be silently casted in float32, this might be related to"
f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
f" {target_dtype}."
)
query = query.to(target_dtype)
key = key.to(target_dtype)
value = value.to(target_dtype)
attention_dropout = self.config.attn_pdrop if self.training else 0.0 # attn_pdrop in gptj
query_length = query.shape[1]
# Compute attention
attn_weights = _flash_attention_forward(
query,
key,
value,
attention_mask,
query_length,
dropout=attention_dropout,
is_causal=self.is_causal,
use_top_left_mask=self._flash_attn_uses_top_left_mask,
)
# Reshape outputs
attn_output = attn_weights.reshape(
attn_weights.shape[0], attn_weights.shape[1], attn_weights.shape[2] * attn_weights.shape[3]
)
attn_output = self.out_proj(attn_output)
attn_output = self.resid_dropout(attn_output)
outputs = (attn_output, layer_past)
if output_attentions:
outputs += (attn_weights,)
return outputs
GPTJ_ATTENTION_CLASSES = {
"eager": GPTJAttention,
"flash_attention_2": GPTJFlashAttention2,
}
class GPTJMLP(nn.Module):
def __init__(self, intermediate_size, config): # in MLP: intermediate_size= 4 * embed_dim
super().__init__()
embed_dim = config.n_embd
self.fc_in = nn.Linear(embed_dim, intermediate_size)
self.fc_out = nn.Linear(intermediate_size, embed_dim)
self.act = ACT2FN[config.activation_function]
self.dropout = nn.Dropout(config.resid_pdrop)
def forward(self, hidden_states: Optional[torch.FloatTensor]) -> torch.FloatTensor:
hidden_states = self.fc_in(hidden_states)
hidden_states = self.act(hidden_states)
hidden_states = self.fc_out(hidden_states)
hidden_states = self.dropout(hidden_states)
return hidden_states
class GPTJBlock(nn.Module):
def __init__(self, config, layer_idx=None):
super().__init__()
inner_dim = config.n_inner if config.n_inner is not None else 4 * config.n_embd
self.ln_1 = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)
self.attn = GPTJ_ATTENTION_CLASSES[config._attn_implementation](config, layer_idx)
self.mlp = GPTJMLP(inner_dim, config)
def forward(
self,
hidden_states: Optional[torch.FloatTensor],
layer_past: Optional[Cache] = None,
attention_mask: Optional[torch.FloatTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
use_cache: Optional[bool] = False,
output_attentions: Optional[bool] = False,
cache_position: Optional[torch.LongTensor] = None,
) -> Union[Tuple[torch.Tensor], Optional[Tuple[torch.Tensor, Tuple[torch.FloatTensor, ...]]]]:
residual = hidden_states
hidden_states = self.ln_1(hidden_states)
attn_outputs = self.attn(
hidden_states=hidden_states,
layer_past=layer_past,
attention_mask=attention_mask,
position_ids=position_ids,
head_mask=head_mask,
use_cache=use_cache,
output_attentions=output_attentions,
cache_position=cache_position,
)
attn_output = attn_outputs[0] # output_attn: a, present, (attentions)
outputs = attn_outputs[1:]
feed_forward_hidden_states = self.mlp(hidden_states)
hidden_states = attn_output + feed_forward_hidden_states + residual
if use_cache:
outputs = (hidden_states,) + outputs
else:
outputs = (hidden_states,) + outputs[1:]
return outputs # hidden_states, present, (attentions)
class GPTJPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = GPTJConfig
base_model_prefix = "transformer"
is_parallelizable = True
supports_gradient_checkpointing = True
_no_split_modules = ["GPTJBlock"]
_skip_keys_device_placement = "past_key_values"
_supports_flash_attn_2 = True
_supports_cache_class = True
_supports_quantized_cache = True
_supports_static_cache = True
_supports_param_buffer_assignment = False
def __init__(self, *inputs, **kwargs):
super().__init__(*inputs, **kwargs)
def _init_weights(self, module):
"""Initialize the weights."""
if isinstance(module, (nn.Linear,)):
# Slightly different from Mesh Transformer JAX which uses truncated_normal for initialization
# cf https://github.com/pytorch/pytorch/pull/5617
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.Embedding):
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
elif isinstance(module, nn.LayerNorm):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
GPTJ_START_DOCSTRING = r"""
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`GPTJConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
GPTJ_INPUTS_DOCSTRING = r"""
Args:
input_ids (`torch.LongTensor` of shape `({0})`):
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*):
Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
1]`:
- 0 corresponds to a *sentence A* token,
- 1 corresponds to a *sentence B* token.
[What are token type IDs?](../glossary#token-type-ids)
position_ids (`torch.LongTensor` of shape `({0})`, *optional*):
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
config.n_positions - 1]`.
[What are position IDs?](../glossary#position-ids)
head_mask (`torch.FloatTensor` of shape `(num_attention_heads,)` or `(n_layer, num_attention_heads)`, *optional*):
Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_dim)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert *input_ids* indices into associated vectors than the
model's internal embedding lookup matrix.
past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*):
Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values`
returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.
Two formats are allowed:
- a [`~cache_utils.Cache`] instance, see our
[kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache);
- Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy
cache format.
The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the
legacy cache format will be returned.
If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't
have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids`
of shape `(batch_size, sequence_length)`.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
Indices depicting the position of the input sequence tokens in the sequence. Contrarily to `position_ids`,
this tensor is not affected by padding. It is used to update the cache in the correct position and to infer
the complete sequence length.
"""
PARALLELIZE_DOCSTRING = r"""
This is an experimental feature and is a subject to change at a moment's notice. Uses a device map to distribute
attention modules of the model across several devices. If no device map is given, it will evenly distribute blocks
across all devices.
Args:
device_map (`Dict[int, list]`, *optional*):
A dictionary that maps attention modules to devices. Note that the embedding module and LMHead are always
automatically mapped to the first device (for esoteric reasons). That means that the first device should
have fewer attention modules mapped to it than other devices. For reference, the GPT-J models have the
following number of attention modules:
- gpt-j-6B: 28
Example:
```python
# Here is an example of a device map on a machine with 4 GPUs using gpt-j-6B, which has a total of 28 attention modules:
model = GPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B")
device_map = {
0: [0, 1, 2, 3, 4, 5, 6],
1: [7, 8, 9, 10, 11, 12, 13],
2: [14, 15, 16, 17, 18, 19, 20],
3: [21, 22, 23, 24, 25, 26, 27],
}
model.parallelize(device_map)
```
"""
DEPARALLELIZE_DOCSTRING = r"""
Moves the model to CPU from a model parallel state.
Example:
```python
# On a 4 GPU machine with gpt-j-6B:
model = GPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B")
device_map = {
0: [0, 1, 2, 3, 4, 5, 6],
1: [7, 8, 9, 10, 11, 12, 13],
2: [14, 15, 16, 17, 18, 19, 20],
3: [21, 22, 23, 24, 25, 26, 27],
}
model.parallelize(device_map) # Splits the model across several devices
model.deparallelize() # Put the model back on cpu and cleans memory by calling torch.cuda.empty_cache()
```
"""
@add_start_docstrings(
"The bare GPT-J Model transformer outputting raw hidden-states without any specific head on top.",
GPTJ_START_DOCSTRING,
)
class GPTJModel(GPTJPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.embed_dim = config.n_embd
self.vocab_size = config.vocab_size
self.wte = nn.Embedding(config.vocab_size, self.embed_dim)
self.drop = nn.Dropout(config.embd_pdrop)
self.h = nn.ModuleList([GPTJBlock(config, layer_idx=i) for i in range(config.n_layer)])
self.ln_f = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_epsilon)
# Model parallel
self.model_parallel = False
self.device_map = None
self.gradient_checkpointing = False
# Initialize weights and apply final processing
self.post_init()
self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2"
@add_start_docstrings(PARALLELIZE_DOCSTRING)
def parallelize(self, device_map=None):
warnings.warn(
"`GPTJModel.parallelize` is deprecated and will be removed in v5 of Transformers, you should load your"
" model with `device_map='balanced'` in the call to `from_pretrained`. You can also provide your own"
" `device_map` but it needs to be a dictionary module_name to device, so for instance {'h.0': 0, 'h.1': 1,"
" ...}",
FutureWarning,
)
# Check validity of device_map
self.device_map = (
get_device_map(len(self.h), range(torch.cuda.device_count())) if device_map is None else device_map
)
assert_device_map(self.device_map, len(self.h))
self.model_parallel = True
self.first_device = "cpu" if "cpu" in self.device_map.keys() else "cuda:" + str(min(self.device_map.keys()))
self.last_device = "cuda:" + str(max(self.device_map.keys()))
self.wte = self.wte.to(self.first_device)
# Load onto devices
for k, v in self.device_map.items():
for block in v:
cuda_device = "cuda:" + str(k)
self.h[block] = self.h[block].to(cuda_device)
# ln_f to last
self.ln_f = self.ln_f.to(self.last_device)
@add_start_docstrings(DEPARALLELIZE_DOCSTRING)
def deparallelize(self):
warnings.warn(
"Like `parallelize`, `deparallelize` is deprecated and will be removed in v5 of Transformers.",
FutureWarning,
)
self.model_parallel = False
self.device_map = None
self.first_device = "cpu"
self.last_device = "cpu"
self.wte = self.wte.to("cpu")
for index in range(len(self.h)):
self.h[index] = self.h[index].to("cpu")
self.ln_f = self.ln_f.to("cpu")
torch.cuda.empty_cache()
def get_input_embeddings(self):
return self.wte
def set_input_embeddings(self, new_embeddings):
self.wte = new_embeddings
@add_start_docstrings_to_model_forward(GPTJ_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=BaseModelOutputWithPast,
config_class=_CONFIG_FOR_DOC,
real_checkpoint=_REAL_CHECKPOINT_FOR_DOC,
)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Union[Cache, Tuple[Tuple[torch.Tensor]]]] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
cache_position: Optional[torch.LongTensor] = None,
) -> Union[Tuple, BaseModelOutputWithPast]:
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
use_cache = use_cache if use_cache is not None else self.config.use_cache
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if (input_ids is None) ^ (inputs_embeds is not None):
raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
if self.gradient_checkpointing and self.training:
if use_cache:
logger.warning_once(
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
)
use_cache = False
if inputs_embeds is None:
inputs_embeds = self.wte(input_ids)
# kept for BC (non `Cache` `past_key_values` inputs)
return_legacy_cache = False
if use_cache and not isinstance(past_key_values, Cache):
return_legacy_cache = True
if past_key_values is None:
past_key_values = DynamicCache()
else:
past_key_values = DynamicCache.from_legacy_cache(past_key_values)
logger.warning_once(
"We detected that you are passing `past_key_values` as a tuple of tuples. This is deprecated and "
"will be removed in v4.47. Please convert your cache or use an appropriate `Cache` class "
"(https://huggingface.co/docs/transformers/kv_cache#legacy-cache-format)"
)
seq_length = inputs_embeds.shape[1]
if cache_position is None:
past_key_values_length = past_key_values.get_seq_length() if past_key_values is not None else 0
cache_position = torch.arange(
past_key_values_length, past_key_values_length + seq_length, device=inputs_embeds.device
)
if position_ids is None:
position_ids = cache_position.unsqueeze(0)
causal_mask = self._update_causal_mask(
attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions
)
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x num_attention_heads x N x N
# head_mask has shape n_layer x batch x num_attention_heads x N x N
head_mask = self.get_head_mask(head_mask, self.config.n_layer)
hidden_states = inputs_embeds
if token_type_ids is not None:
token_type_ids = token_type_ids.view(-1, seq_length)
token_type_embeds = self.wte(token_type_ids)
hidden_states = hidden_states + token_type_embeds
hidden_states = self.drop(hidden_states)
output_shape = (-1, seq_length, hidden_states.size(-1))
next_decoder_cache = None
all_self_attentions = () if output_attentions else None
all_hidden_states = () if output_hidden_states else None
for i, block in enumerate(self.h):
# Model parallel
if self.model_parallel:
torch.cuda.set_device(hidden_states.device)
# Ensure layer_past is on same device as hidden_states (might not be correct)
if past_key_values is not None:
past_key_values.key_cache = past_key_values.key_cache.to(hidden_states.device)
past_key_values.value_cache = past_key_values.value_cache.to(hidden_states.device)
# Ensure that attention_mask is always on the same device as hidden_states
if causal_mask is not None:
causal_mask = causal_mask.to(hidden_states.device)
if isinstance(head_mask, torch.Tensor):
head_mask = head_mask.to(hidden_states.device)
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if self.gradient_checkpointing and self.training:
outputs = self._gradient_checkpointing_func(
block.__call__,
hidden_states,
None,
causal_mask,
position_ids,
head_mask[i],
use_cache,
output_attentions,
cache_position,
)
else:
outputs = block(
hidden_states=hidden_states,
layer_past=past_key_values,
attention_mask=causal_mask,
position_ids=position_ids,
head_mask=head_mask[i],
use_cache=use_cache,
output_attentions=output_attentions,
cache_position=cache_position,
)
hidden_states = outputs[0]
if use_cache is True:
next_decoder_cache = outputs[1]
if output_attentions:
all_self_attentions = all_self_attentions + (outputs[2 if use_cache else 1],)
# Model Parallel: If it's the last layer for that device, put things on the next device
if self.model_parallel:
for k, v in self.device_map.items():
if i == v[-1] and "cuda:" + str(k) != self.last_device:
hidden_states = hidden_states.to("cuda:" + str(k + 1))
hidden_states = self.ln_f(hidden_states)
hidden_states = hidden_states.view(output_shape)
# Add last hidden state
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
next_cache = next_decoder_cache if use_cache else None
if return_legacy_cache:
next_cache = next_cache.to_legacy_cache()
if not return_dict:
return tuple(
v for v in [hidden_states, next_cache, all_hidden_states, all_self_attentions] if v is not None
)
return BaseModelOutputWithPast(
last_hidden_state=hidden_states,
past_key_values=next_cache,
hidden_states=all_hidden_states,
attentions=all_self_attentions,
)
# Copied from transformers.models.llama.modeling_llama.LlamaModel._update_causal_mask
def _update_causal_mask(
self,
attention_mask: torch.Tensor,
input_tensor: torch.Tensor,
cache_position: torch.Tensor,
past_key_values: Cache,
output_attentions: bool = False,
):
if self.config._attn_implementation == "flash_attention_2":
if attention_mask is not None and (attention_mask == 0.0).any():
return attention_mask
return None
if self.config._attn_implementation == "flex_attention":
if isinstance(attention_mask, torch.Tensor):
attention_mask = make_flex_block_causal_mask(attention_mask)
if isinstance(attention_mask, BlockMask):
return attention_mask
# For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in
# order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail
# to infer the attention mask.
past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
using_static_cache = isinstance(past_key_values, StaticCache)
# When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward
if self.config._attn_implementation == "sdpa" and not using_static_cache and not output_attentions:
if AttentionMaskConverter._ignore_causal_mask_sdpa(
attention_mask,
inputs_embeds=input_tensor,
past_key_values_length=past_seen_tokens,
is_training=self.training,
):
return None
dtype, device = input_tensor.dtype, input_tensor.device
sequence_length = input_tensor.shape[1]
if using_static_cache:
target_length = past_key_values.get_max_cache_shape()
else:
target_length = (
attention_mask.shape[-1]
if isinstance(attention_mask, torch.Tensor)
else past_seen_tokens + sequence_length + 1
)
# In case the provided `attention` mask is 2D, we generate a causal mask here (4D).
causal_mask = self._prepare_4d_causal_attention_mask_with_cache_position(
attention_mask,
sequence_length=sequence_length,
target_length=target_length,
dtype=dtype,
device=device,
cache_position=cache_position,
batch_size=input_tensor.shape[0],
)
if (
self.config._attn_implementation == "sdpa"
and attention_mask is not None
and attention_mask.device.type in ["cuda", "xpu"]
and not output_attentions
):
# Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when
# using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path.
# Details: https://github.com/pytorch/pytorch/issues/110213
min_dtype = torch.finfo(dtype).min
causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype)
return causal_mask
@staticmethod
# Copied from transformers.models.llama.modeling_llama.LlamaModel._prepare_4d_causal_attention_mask_with_cache_position
def _prepare_4d_causal_attention_mask_with_cache_position(
attention_mask: torch.Tensor,
sequence_length: int,
target_length: int,
dtype: torch.dtype,
device: torch.device,
cache_position: torch.Tensor,
batch_size: int,
**kwargs,
):
"""
Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape
`(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing.
Args:
attention_mask (`torch.Tensor`):
A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape
`(batch_size, 1, query_length, key_value_length)`.
sequence_length (`int`):
The sequence length being processed.
target_length (`int`):
The target length: when generating with static cache, the mask should be as long as the static cache,
to account for the 0 padding, the part of the cache that is not filled yet.
dtype (`torch.dtype`):
The dtype to use for the 4D attention mask.
device (`torch.device`):
The device to place the 4D attention mask on.
cache_position (`torch.Tensor`):
Indices depicting the position of the input sequence tokens in the sequence.
batch_size (`torch.Tensor`):
Batch size.
"""
if attention_mask is not None and attention_mask.dim() == 4:
# In this case we assume that the mask comes already in inverted form and requires no inversion or slicing.
causal_mask = attention_mask
else:
min_dtype = torch.finfo(dtype).min
causal_mask = torch.full(
(sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=device
)
if sequence_length != 1:
causal_mask = torch.triu(causal_mask, diagonal=1)
causal_mask *= torch.arange(target_length, device=device) > cache_position.reshape(-1, 1)
causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1)
if attention_mask is not None:
causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit
mask_length = attention_mask.shape[-1]
padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :].to(
causal_mask.device
)
padding_mask = padding_mask == 0
causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
padding_mask, min_dtype
)
return causal_mask
@add_start_docstrings(
"""
The GPT-J Model transformer with a language modeling head on top.
""",
GPTJ_START_DOCSTRING,
)
class GPTJForCausalLM(GPTJPreTrainedModel, GenerationMixin):
_tied_weights_keys = ["lm_head.weight"]
def __init__(self, config):
super().__init__(config)
self.transformer = GPTJModel(config)
self.lm_head = nn.Linear(config.n_embd, config.vocab_size)
# Model parallel
self.model_parallel = False
self.device_map = None
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings(PARALLELIZE_DOCSTRING)
def parallelize(self, device_map=None):
warnings.warn(
"`GPTJForCausalLM.parallelize` is deprecated and will be removed in v5 of Transformers, you should load"
" your model with `device_map='balanced'` in the call to `from_pretrained`. You can also provide your own"
" `device_map` but it needs to be a dictionary module_name to device, so for instance {'transformer.h.0':"
" 0, 'transformer.h.1': 1, ...}",
FutureWarning,
)
self.device_map = (
get_device_map(len(self.transformer.h), range(torch.cuda.device_count()))
if device_map is None
else device_map
)
assert_device_map(self.device_map, len(self.transformer.h))
self.transformer.parallelize(self.device_map)
self.lm_head = self.lm_head.to(self.transformer.first_device)
self.model_parallel = True
@add_start_docstrings(DEPARALLELIZE_DOCSTRING)
def deparallelize(self):
warnings.warn(
"Like `parallelize`, `deparallelize` is deprecated and will be removed in v5 of Transformers.",
FutureWarning,
)
self.transformer.deparallelize()
self.transformer = self.transformer.to("cpu")
self.lm_head = self.lm_head.to("cpu")
self.model_parallel = False
torch.cuda.empty_cache()
def get_output_embeddings(self):
return self.lm_head
def set_output_embeddings(self, new_embeddings):
self.lm_head = new_embeddings
@add_start_docstrings_to_model_forward(GPTJ_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=CausalLMOutputWithPast,
config_class=_CONFIG_FOR_DOC,
real_checkpoint=_REAL_CHECKPOINT_FOR_DOC,
)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Union[Cache, Tuple[Tuple[torch.Tensor]]]] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
cache_position: Optional[torch.LongTensor] = None,
**kwargs,
) -> Union[Tuple, CausalLMOutputWithPast]:
r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set
`labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100`
are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]`
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
transformer_outputs = self.transformer(
input_ids,
past_key_values=past_key_values,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
cache_position=cache_position,
)
hidden_states = transformer_outputs[0]
# Set device for model parallelism
if self.model_parallel:
torch.cuda.set_device(self.transformer.first_device)
hidden_states = hidden_states.to(self.lm_head.weight.device)
# make sure sampling in fp16 works correctly and
# compute loss in fp32 to match with mesh-tf version
# https://github.com/EleutherAI/gpt-neo/blob/89ce74164da2fb16179106f54e2269b5da8db333/models/gpt2/gpt2.py#L179
lm_logits = self.lm_head(hidden_states).to(torch.float32)
loss = None
if labels is not None:
# move labels to correct device to enable model parallelism
labels = labels.to(lm_logits.device)
# Flatten the tokens
loss = self.loss_function(
lm_logits,
labels,
vocab_size=self.config.vocab_size,
**kwargs,
)
loss = loss.to(hidden_states.dtype)
if not return_dict:
output = (lm_logits,) + transformer_outputs[1:]
return ((loss,) + output) if loss is not None else output
return CausalLMOutputWithPast(
loss=loss,
logits=lm_logits,
past_key_values=transformer_outputs.past_key_values,
hidden_states=transformer_outputs.hidden_states,
attentions=transformer_outputs.attentions,
)
@staticmethod
def _reorder_cache(
past_key_values: Tuple[Tuple[torch.Tensor]], beam_idx: torch.Tensor
) -> Tuple[Tuple[torch.Tensor]]:
"""
This function is used to re-order the `past_key_values` cache if [`~PretrainedModel.beam_search`] or
[`~PretrainedModel.beam_sample`] is called. This is required to match `past_key_values` with the correct
beam_idx at every generation step.
"""
return tuple(
tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past)
for layer_past in past_key_values
)
@add_start_docstrings(
"""
The GPT-J Model transformer with a sequence classification head on top (linear layer).
[`GPTJForSequenceClassification`] uses the last token in order to do the classification, as other causal models
(e.g. GPT, GPT-2, GPT-Neo) do.
Since it does classification on the last token, it requires to know the position of the last token. If a
`pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
each row of the batch).
""",
GPTJ_START_DOCSTRING,
)
class GPTJForSequenceClassification(GPTJPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.transformer = GPTJModel(config)
self.score = nn.Linear(config.n_embd, self.num_labels, bias=False)
# Model parallel
self.model_parallel = False
self.device_map = None
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(GPTJ_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint="ydshieh/tiny-random-gptj-for-sequence-classification",
output_type=SequenceClassifierOutputWithPast,
config_class=_CONFIG_FOR_DOC,
real_checkpoint=_REAL_CHECKPOINT_FOR_DOC,
)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, SequenceClassifierOutputWithPast]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
transformer_outputs = self.transformer(
input_ids,
past_key_values=past_key_values,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
hidden_states = transformer_outputs[0]
logits = self.score(hidden_states)
if input_ids is not None:
batch_size = input_ids.shape[0]
else:
batch_size = inputs_embeds.shape[0]
if self.config.pad_token_id is None and batch_size != 1:
raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
if self.config.pad_token_id is None:
last_non_pad_token = -1
elif input_ids is not None:
# To handle both left- and right- padding, we take the rightmost token that is not equal to pad_token_id
non_pad_mask = (input_ids != self.config.pad_token_id).to(logits.device, torch.int32)
token_indices = torch.arange(input_ids.shape[-1], device=logits.device, dtype=torch.int32)
last_non_pad_token = (token_indices * non_pad_mask).argmax(-1)
else:
last_non_pad_token = -1
logger.warning_once(
f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be "
"unexpected if using padding tokens in conjunction with `inputs_embeds.`"
)
pooled_logits = logits[torch.arange(batch_size, device=logits.device), last_non_pad_token]
loss = None
if labels is not None:
labels = labels.to(pooled_logits.device)
if self.config.problem_type is None:
if self.num_labels == 1:
self.config.problem_type = "regression"
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
self.config.problem_type = "single_label_classification"
else:
self.config.problem_type = "multi_label_classification"
if self.config.problem_type == "regression":
loss_fct = MSELoss()
if self.num_labels == 1:
loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
else:
loss = loss_fct(pooled_logits, labels)
elif self.config.problem_type == "single_label_classification":
loss_fct = CrossEntropyLoss()
loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
elif self.config.problem_type == "multi_label_classification":
loss_fct = BCEWithLogitsLoss()
loss = loss_fct(pooled_logits, labels)
if not return_dict:
output = (pooled_logits,) + transformer_outputs[1:]
return ((loss,) + output) if loss is not None else output
return SequenceClassifierOutputWithPast(
loss=loss,
logits=pooled_logits,
past_key_values=transformer_outputs.past_key_values,
hidden_states=transformer_outputs.hidden_states,
attentions=transformer_outputs.attentions,
)
@add_start_docstrings(
"""
The GPT-J Model transformer with a span classification head on top for extractive question-answering tasks like
SQuAD (a linear layers on top of the hidden-states output to compute `span start logits` and `span end logits`).
""",
GPTJ_START_DOCSTRING,
)
class GPTJForQuestionAnswering(GPTJPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.transformer = GPTJModel(config)
self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
# Model parallel
self.model_parallel = False
self.device_map = None
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(GPTJ_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=QuestionAnsweringModelOutput,
config_class=_CONFIG_FOR_DOC,
real_checkpoint=_REAL_CHECKPOINT_FOR_DOC,
)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
start_positions: Optional[torch.LongTensor] = None,
end_positions: Optional[torch.LongTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, QuestionAnsweringModelOutput]:
r"""
start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for position (index) of the start of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
are not taken into account for computing the loss.
end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for position (index) of the end of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
are not taken into account for computing the loss.
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.transformer(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = outputs[0]
logits = self.qa_outputs(sequence_output)
start_logits, end_logits = logits.split(1, dim=-1)
start_logits = start_logits.squeeze(-1).contiguous()
end_logits = end_logits.squeeze(-1).contiguous()
total_loss = None
if start_positions is not None and end_positions is not None:
# If we are on multi-GPU, split add a dimension
if len(start_positions.size()) > 1:
start_positions = start_positions.squeeze(-1).to(start_logits.device)
if len(end_positions.size()) > 1:
end_positions = end_positions.squeeze(-1).to(end_logits.device)
# sometimes the start/end positions are outside our model inputs, we ignore these terms
ignored_index = start_logits.size(1)
start_positions = start_positions.clamp(0, ignored_index)
end_positions = end_positions.clamp(0, ignored_index)
loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
start_loss = loss_fct(start_logits, start_positions)
end_loss = loss_fct(end_logits, end_positions)
total_loss = (start_loss + end_loss) / 2
if not return_dict:
output = (start_logits, end_logits) + outputs[2:]
return ((total_loss,) + output) if total_loss is not None else output
return QuestionAnsweringModelOutput(
loss=total_loss,
start_logits=start_logits,
end_logits=end_logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
__all__ = [
"GPTJForCausalLM",
"GPTJForQuestionAnswering",
"GPTJForSequenceClassification",
"GPTJModel",
"GPTJPreTrainedModel",
]
```
|
====================================================================================================================================
SOURCE CODE FILE: modeling_tf_gptj.py
LINES: 1
SIZE: 47.04 KB
PATH: scripts\freecad_env\Lib\site-packages\transformers\models\gptj\modeling_tf_gptj.py
ENCODING: utf-8
```py
# coding=utf-8
# Copyright 2022 The EleutherAI and HuggingFace Teams. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""TF 2.0 GPT-J model."""
from __future__ import annotations
from typing import Optional, Tuple, Union
import numpy as np
import tensorflow as tf
from ...activations_tf import get_tf_activation
from ...file_utils import (
add_code_sample_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
)
from ...modeling_tf_outputs import (
TFBaseModelOutputWithPast,
TFCausalLMOutputWithPast,
TFQuestionAnsweringModelOutput,
TFSequenceClassifierOutputWithPast,
)
from ...modeling_tf_utils import (
TFCausalLanguageModelingLoss,
TFModelInputType,
TFPreTrainedModel,
TFQuestionAnsweringLoss,
TFSequenceClassificationLoss,
TFSharedEmbeddings,
get_initializer,
keras,
keras_serializable,
unpack_inputs,
)
from ...tf_utils import check_embeddings_within_bounds, shape_list, stable_softmax
from ...utils import logging
from .configuration_gptj import GPTJConfig
logger = logging.get_logger(__name__)
_CHECKPOINT_FOR_DOC = "EleutherAI/gpt-j-6B"
_CONFIG_FOR_DOC = "GPTJConfig"
def create_sinusoidal_positions(num_pos: int, dim: int) -> tf.Tensor:
inv_freq = tf.cast(1.0 / (10000 ** (tf.range(0, dim, 2) / dim)), tf.float32)
sinusoid_inp = tf.cast(tf.einsum("i , j -> i j", tf.range(num_pos, dtype=tf.float32), inv_freq), tf.float32)
sin, cos = tf.sin(sinusoid_inp), tf.cos(sinusoid_inp)
out = tf.concat((sin, cos), axis=1)
return out
def rotate_every_two(x: tf.Tensor) -> tf.Tensor:
rotate_half_tensor = tf.stack((-x[:, :, :, 1::2], x[:, :, :, ::2]), axis=-1)
new_shape = shape_list(rotate_half_tensor)[:-2] + [tf.math.reduce_prod(shape_list(rotate_half_tensor)[-2:])]
rotate_half_tensor = tf.reshape(rotate_half_tensor, new_shape)
return rotate_half_tensor
def apply_rotary_pos_emb(tensor: tf.Tensor, sincos: tf.Tensor) -> tf.Tensor:
sin_pos, cos_pos = sincos
sin_pos = tf.repeat(sin_pos[:, :, None, :], 2, 3)
cos_pos = tf.repeat(cos_pos[:, :, None, :], 2, 3)
return (tensor * cos_pos) + (rotate_every_two(tensor) * sin_pos)
class TFGPTJAttention(keras.layers.Layer):
def __init__(self, config: GPTJConfig, **kwargs):
super().__init__(**kwargs)
self.embed_dim = config.hidden_size
self.num_attention_heads = config.num_attention_heads
self.head_dim = self.embed_dim // self.num_attention_heads
if self.head_dim * self.num_attention_heads != self.embed_dim:
raise ValueError(
f"embed_dim must be divisible by num_attention_heads (got `embed_dim`: {self.embed_dim} and"
f" `num_attention_heads`: {self.num_attention_heads})."
)
self.scale_attn = self.head_dim**0.5
self.rotary_dim = config.rotary_dim
self.attn_dropout = keras.layers.Dropout(config.attn_pdrop)
self.resid_dropout = keras.layers.Dropout(config.resid_pdrop)
self.q_proj = keras.layers.Dense(
self.embed_dim,
use_bias=False,
kernel_initializer=get_initializer(config.initializer_range),
name="q_proj",
)
self.k_proj = keras.layers.Dense(
self.embed_dim,
use_bias=False,
kernel_initializer=get_initializer(config.initializer_range),
name="k_proj",
)
self.v_proj = keras.layers.Dense(
self.embed_dim,
use_bias=False,
kernel_initializer=get_initializer(config.initializer_range),
name="v_proj",
)
self.out_proj = keras.layers.Dense(
self.embed_dim,
use_bias=False,
kernel_initializer=get_initializer(config.initializer_range),
name="out_proj",
)
self.max_positions = config.max_position_embeddings
self.lower_triangle_mask = tf.reshape(
tf.cast(tf.experimental.numpy.tril(tf.ones((self.max_positions, self.max_positions))), tf.int8),
(1, 1, self.max_positions, self.max_positions),
)
pos_embd_dim = self.rotary_dim or self.embed_dim
self.embed_positions = create_sinusoidal_positions(self.max_positions, pos_embd_dim)
def get_causal_mask(self, key_length, query_length) -> tf.Tensor:
return tf.cast(self.lower_triangle_mask[:, :, key_length - query_length : key_length, :key_length], tf.bool)
@staticmethod
def get_masked_bias(dtype: tf.DType) -> tf.Tensor:
return tf.cast(tf.constant(-1e9), dtype)
def _split_heads(self, hidden_states: tf.Tensor, rotary: bool) -> tf.Tensor:
"""
Splits hidden dim into attn_head_size and num_attention_heads
"""
new_shape = shape_list(hidden_states)[:-1] + [self.num_attention_heads, self.head_dim]
hidden_states = tf.reshape(hidden_states, new_shape)
if rotary:
return hidden_states
if len(shape_list(hidden_states)) == 4:
return tf.transpose(hidden_states, (0, 2, 1, 3)) # (batch, head, seq_length, head_features)
if len(shape_list(hidden_states)) == 5:
return tf.transpose(hidden_states, (0, 1, 3, 2, 4)) # (batch, blocks, head, block_length, head_features)
raise ValueError(f"Input tensor rank should be one of [4, 5], but is: {len(shape_list(hidden_states))}")
def _merge_heads(self, hidden_states: tf.Tensor) -> tf.Tensor:
"""
Merges attn_head_size dim and num_attn_heads dim into hidden dim
"""
if len(shape_list(hidden_states)) == 4:
hidden_states = tf.transpose(hidden_states, (0, 2, 1, 3))
elif len(shape_list(hidden_states)) == 5:
hidden_states = tf.transpose(hidden_states, (0, 1, 3, 2, 4))
else:
raise ValueError(f"Input tensor rank should be one of [4, 5], but is: {len(shape_list(hidden_states))}")
new_shape = shape_list(hidden_states)[:-2] + [self.num_attention_heads * self.head_dim]
return tf.reshape(hidden_states, new_shape)
def _attn(
self,
query: tf.Tensor,
key: tf.Tensor,
value: tf.Tensor,
attention_mask: tf.Tensor | None = None,
head_mask: tf.Tensor | None = None,
) -> Tuple[tf.Tensor, tf.Tensor]:
# compute causal mask from causal mask buffer
query_length, key_length = shape_list(query)[-2], shape_list(key)[-2]
causal_mask = self.get_causal_mask(key_length, query_length)
# Keep the attention weights computation in fp32 to avoid overflow issues
query = tf.cast(query, tf.float32)
key = tf.cast(key, tf.float32)
attn_weights = tf.matmul(query, key, transpose_b=True)
attn_weights = tf.where(causal_mask, attn_weights, self.get_masked_bias(attn_weights.dtype))
attn_weights = attn_weights / self.scale_attn
if attention_mask is not None:
# Apply the attention mask
attn_weights = attn_weights + attention_mask
attn_weights = stable_softmax(attn_weights, axis=-1)
attn_weights = tf.cast(attn_weights, value.dtype)
attn_weights = self.attn_dropout(attn_weights)
# Mask heads if we want to
if head_mask is not None:
attn_weights = attn_weights * head_mask
attn_output = tf.matmul(attn_weights, value)
return attn_output, attn_weights
def call(
self,
hidden_states: tf.Tensor,
layer_past: Optional[Tuple[tf.Tensor, tf.Tensor]] = None,
attention_mask: tf.Tensor | None = None,
position_ids: tf.Tensor | None = None,
head_mask: tf.Tensor | None = None,
use_cache: bool = False,
output_attentions: bool = False,
):
query = self.q_proj(hidden_states)
key = self.k_proj(hidden_states)
value = self.v_proj(hidden_states)
query = self._split_heads(query, True)
key = self._split_heads(key, True)
value = self._split_heads(value, False)
sincos = tf.cast(tf.gather(self.embed_positions, position_ids, axis=0), hidden_states.dtype)
sincos = tf.split(sincos, 2, axis=-1)
if self.rotary_dim is not None:
k_rot = key[:, :, :, : self.rotary_dim]
k_pass = key[:, :, :, self.rotary_dim :]
q_rot = query[:, :, :, : self.rotary_dim]
q_pass = query[:, :, :, self.rotary_dim :]
k_rot = apply_rotary_pos_emb(k_rot, sincos)
q_rot = apply_rotary_pos_emb(q_rot, sincos)
key = tf.concat((k_rot, k_pass), axis=-1)
query = tf.concat((q_rot, q_pass), axis=-1)
else:
key = apply_rotary_pos_emb(key, sincos)
query = apply_rotary_pos_emb(query, sincos)
key = tf.transpose(key, (0, 2, 1, 3))
query = tf.transpose(query, (0, 2, 1, 3))
if layer_past is not None:
past_key = layer_past[0]
past_value = layer_past[1]
key = tf.concat((past_key, key), axis=-2)
value = tf.concat((past_value, value), axis=-2)
if use_cache is True:
present = (key, value)
else:
present = None
# compute self-attention: V x Softmax(QK^T)
attn_output, attn_weights = self._attn(query, key, value, attention_mask, head_mask)
attn_output = self._merge_heads(attn_output)
attn_output = self.out_proj(attn_output)
attn_output = self.resid_dropout(attn_output)
outputs = (attn_output, present)
if output_attentions:
outputs += (attn_weights,)
return outputs # a, present, (attentions)
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "q_proj", None) is not None:
with tf.name_scope(self.q_proj.name):
self.q_proj.build([None, None, self.embed_dim])
if getattr(self, "k_proj", None) is not None:
with tf.name_scope(self.k_proj.name):
self.k_proj.build([None, None, self.embed_dim])
if getattr(self, "v_proj", None) is not None:
with tf.name_scope(self.v_proj.name):
self.v_proj.build([None, None, self.embed_dim])
if getattr(self, "out_proj", None) is not None:
with tf.name_scope(self.out_proj.name):
self.out_proj.build([None, None, self.embed_dim])
class TFGPTJMLP(keras.layers.Layer):
def __init__(self, intermediate_size: int, config: GPTJConfig, **kwargs):
super().__init__(**kwargs)
embed_dim = config.n_embd
self.fc_in = keras.layers.Dense(
intermediate_size, kernel_initializer=get_initializer(config.initializer_range), name="fc_in"
)
self.fc_out = keras.layers.Dense(
embed_dim, kernel_initializer=get_initializer(config.initializer_range), name="fc_out"
)
self.act = get_tf_activation(config.activation_function)
self.dropout = keras.layers.Dropout(config.embd_pdrop)
self.embed_dim = config.n_embd
self.intermediate_size = intermediate_size
def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
hidden_states = self.fc_in(hidden_states)
hidden_states = self.act(hidden_states)
hidden_states = self.fc_out(hidden_states)
hidden_states = self.dropout(hidden_states)
return hidden_states
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "fc_in", None) is not None:
with tf.name_scope(self.fc_in.name):
self.fc_in.build([None, None, self.embed_dim])
if getattr(self, "fc_out", None) is not None:
with tf.name_scope(self.fc_out.name):
self.fc_out.build([None, None, self.intermediate_size])
class TFGPTJBlock(keras.layers.Layer):
def __init__(self, config: GPTJConfig, **kwargs):
super().__init__(**kwargs)
inner_dim = config.n_inner if config.n_inner is not None else 4 * config.n_embd
self.ln_1 = keras.layers.LayerNormalization(epsilon=config.layer_norm_epsilon, name="ln_1")
self.attn = TFGPTJAttention(config, name="attn")
self.mlp = TFGPTJMLP(inner_dim, config, name="mlp")
self.config = config
def call(
self,
hidden_states: tf.Tensor,
layer_past: tf.Tensor | None = None,
attention_mask: tf.Tensor | None = None,
position_ids: tf.Tensor | None = None,
head_mask: tf.Tensor | None = None,
use_cache: bool = False,
output_attentions: bool = False,
):
residual = hidden_states
hidden_states = self.ln_1(hidden_states)
attn_outputs = self.attn(
hidden_states=hidden_states,
layer_past=layer_past,
attention_mask=attention_mask,
position_ids=position_ids,
head_mask=head_mask,
use_cache=use_cache,
output_attentions=output_attentions,
) # attn_outputs: attn_output, present, (attentions)
attn_output = attn_outputs[0]
outputs = attn_outputs[1:]
feed_forward_hidden_states = self.mlp(hidden_states)
hidden_states = attn_output + feed_forward_hidden_states + residual
if use_cache:
outputs = (hidden_states,) + outputs
else:
outputs = (hidden_states,) + outputs[1:]
return outputs # hidden_states, present, (attentions)
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "ln_1", None) is not None:
with tf.name_scope(self.ln_1.name):
self.ln_1.build([None, None, self.config.n_embd])
if getattr(self, "attn", None) is not None:
with tf.name_scope(self.attn.name):
self.attn.build(None)
if getattr(self, "mlp", None) is not None:
with tf.name_scope(self.mlp.name):
self.mlp.build(None)
@keras_serializable
class TFGPTJMainLayer(keras.layers.Layer):
config_class = GPTJConfig
def __init__(self, config: GPTJConfig, *inputs, **kwargs):
super().__init__(*inputs, **kwargs)
self.config = config
self.output_attentions = config.output_attentions
self.output_hidden_states = config.output_hidden_states
self.use_cache = config.use_cache
self.return_dict = config.use_return_dict
self.num_hidden_layers = config.n_layer
self.n_embd = config.n_embd
self.n_positions = config.n_positions
self.initializer_range = config.initializer_range
self.wte = TFSharedEmbeddings(
config.vocab_size, config.hidden_size, initializer_range=config.initializer_range, name="wte"
)
self.drop = keras.layers.Dropout(config.embd_pdrop)
self.h = [TFGPTJBlock(config, name=f"h_._{i}") for i in range(config.n_layer)]
self.ln_f = keras.layers.LayerNormalization(epsilon=config.layer_norm_epsilon, name="ln_f")
self.embed_dim = config.n_embd
def get_input_embeddings(self):
return self.wte
def set_input_embeddings(self, value: tf.Tensor):
self.wte.weight = value
self.wte.vocab_size = shape_list(value)[0]
def _prune_heads(self, heads_to_prune):
"""
Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer}
"""
raise NotImplementedError
@unpack_inputs
def call(
self,
input_ids=None,
past_key_values=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
use_cache=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
training=False,
) -> Union[TFBaseModelOutputWithPast, Tuple[tf.Tensor]]:
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
elif input_ids is not None:
input_shape = shape_list(input_ids)
input_ids = tf.reshape(input_ids, [-1, input_shape[-1]])
elif inputs_embeds is not None:
input_shape = shape_list(inputs_embeds)[:-1]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
if past_key_values is None:
past_length = 0
past_key_values = [None] * len(self.h)
else:
past_length = shape_list(past_key_values[0][0])[-2]
if position_ids is None:
position_ids = tf.expand_dims(tf.range(past_length, input_shape[-1] + past_length), axis=0)
if attention_mask is not None:
# We create a 3D attention mask from a 2D tensor mask.
# Sizes are [batch_size, 1, 1, to_seq_length]
# So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
# this attention mask is more simple than the triangular masking of causal attention
# used in OpenAI GPT, we just need to prepare the broadcast dimension here.
attention_mask_shape = shape_list(attention_mask)
attention_mask = tf.reshape(attention_mask, (attention_mask_shape[0], 1, 1, attention_mask_shape[1]))
# Since attention_mask is 1.0 for positions we want to attend and 0.0 for
# masked positions, this operation will create a tensor which is 0.0 for
# positions we want to attend and -10000.0 for masked positions.
# Since we are adding it to the raw scores before the softmax, this is
# effectively the same as removing these entirely.
one_cst = tf.constant(1.0)
attention_mask = tf.cast(attention_mask, dtype=one_cst.dtype)
attention_mask = tf.multiply(tf.subtract(one_cst, attention_mask), tf.constant(-10000.0))
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x n_heads x N x N
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
if head_mask is not None:
raise NotImplementedError
else:
head_mask = [None] * self.num_hidden_layers
# head_mask = tf.constant([0] * self.num_hidden_layers)
position_ids = tf.reshape(position_ids, [-1, shape_list(position_ids)[-1]])
if inputs_embeds is None:
check_embeddings_within_bounds(input_ids, self.wte.vocab_size)
inputs_embeds = self.wte(input_ids, mode="embedding")
if token_type_ids is not None:
token_type_ids = tf.reshape(token_type_ids, [-1, shape_list(token_type_ids)[-1]])
token_type_embeds = self.wte(token_type_ids, mode="embedding")
else:
token_type_embeds = tf.constant(0.0)
token_type_embeds = tf.cast(token_type_embeds, dtype=inputs_embeds.dtype)
hidden_states = inputs_embeds + token_type_embeds
hidden_states = self.drop(hidden_states, training=training)
output_shape = input_shape + [shape_list(hidden_states)[-1]]
presents = () if use_cache else None
all_attentions = () if output_attentions else None
all_hidden_states = () if output_hidden_states else None
for i, (block, layer_past) in enumerate(zip(self.h, past_key_values)):
if output_hidden_states:
all_hidden_states = all_hidden_states + (tf.reshape(hidden_states, output_shape),)
outputs = block(
hidden_states=hidden_states,
layer_past=layer_past,
attention_mask=attention_mask,
position_ids=position_ids,
head_mask=head_mask[i],
use_cache=use_cache,
output_attentions=output_attentions,
training=training,
)
hidden_states = outputs[0]
if use_cache:
presents = presents + (outputs[1],)
if output_attentions:
all_attentions = all_attentions + (outputs[2 if use_cache else 1],)
hidden_states = self.ln_f(hidden_states)
hidden_states = tf.reshape(hidden_states, output_shape)
# Add last hidden state
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if output_attentions:
# let the number of heads free (-1) so we can extract attention even after head pruning
attention_output_shape = input_shape[:-1] + [-1] + shape_list(all_attentions[0])[-2:]
all_attentions = tuple(tf.reshape(t, attention_output_shape) for t in all_attentions)
if not return_dict:
return tuple(v for v in [hidden_states, presents, all_hidden_states, all_attentions] if v is not None)
return TFBaseModelOutputWithPast(
last_hidden_state=hidden_states,
past_key_values=presents,
hidden_states=all_hidden_states,
attentions=all_attentions,
)
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "wte", None) is not None:
with tf.name_scope(self.wte.name):
self.wte.build(None)
if getattr(self, "ln_f", None) is not None:
with tf.name_scope(self.ln_f.name):
self.ln_f.build([None, None, self.embed_dim])
if getattr(self, "h", None) is not None:
for layer in self.h:
with tf.name_scope(layer.name):
layer.build(None)
class TFGPTJPreTrainedModel(TFPreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = GPTJConfig
base_model_prefix = "transformer"
# names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model
_keys_to_ignore_on_load_unexpected = [r"h.\d+.attn.bias"]
GPTJ_START_DOCSTRING = r"""
This model inherits from [`TFPreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a [keras.Model](https://www.tensorflow.org/api_docs/python/tf/keras/Model) subclass. Use it
as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and
behavior.
<Tip>
TensorFlow models and layers in `transformers` accept two formats as input:
- having all inputs as keyword arguments (like PyTorch models), or
- having all inputs as a list, tuple or dict in the first positional argument.
The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
and layers. Because of this support, when using methods like `model.fit()` things should "just work" for you - just
pass your inputs and labels in any format that `model.fit()` supports! If, however, you want to use the second
format outside of Keras methods like `fit()` and `predict()`, such as when creating your own layers or models with
the Keras `Functional` API, there are three possibilities you can use to gather all the input Tensors in the first
positional argument:
- a single Tensor with `input_ids` only and nothing else: `model(input_ids)`
- a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
`model([input_ids, attention_mask])` or `model([input_ids, attention_mask, token_type_ids])`
- a dictionary with one or several input Tensors associated to the input names given in the docstring:
`model({"input_ids": input_ids, "token_type_ids": token_type_ids})`
Note that when creating models and layers with
[subclassing](https://keras.io/guides/making_new_layers_and_models_via_subclassing/) then you don't need to worry
about any of this, as you can just pass inputs like you would to any other Python function!
</Tip>
Parameters:
config ([`GPTJConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.
"""
GPTJ_INPUTS_DOCSTRING = r"""
Args:
input_ids (`Numpy array` or `tf.Tensor` of shape `(batch_size, input_ids_length)`):
`input_ids_length` = `sequence_length` if `past` is `None` else `past[0].shape[-2]` (`sequence_length` of
input past key value states). Indices of input sequence tokens in the vocabulary.
If `past` is used, only input IDs that do not have their past calculated should be passed as `input_ids`.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.__call__`] and
[`PreTrainedTokenizer.encode`] for details.
[What are input IDs?](../glossary#input-ids)
past_key_values (`List[tf.Tensor]` of length `config.n_layers`):
Contains pre-computed hidden-states (key and values in the attention blocks) as computed by the model (see
`past` output below). Can be used to speed up sequential decoding. The token ids which have their past
given to this model should not be passed as input ids as they have already been computed.
attention_mask (`tf.Tensor` or `Numpy array` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
token_type_ids (`tf.Tensor` or `Numpy array` of shape `(batch_size, sequence_length)`, *optional*):
Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
1]`:
- 0 corresponds to a *sentence A* token,
- 1 corresponds to a *sentence B* token.
[What are token type IDs?](../glossary#token-type-ids)
position_ids (`tf.Tensor` or `Numpy array` of shape `(batch_size, sequence_length)`, *optional*):
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
config.max_position_embeddings - 1]`.
[What are position IDs?](../glossary#position-ids)
head_mask (`Numpy array` or `tf.Tensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
inputs_embeds (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
model's internal embedding lookup matrix.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the
config will be used instead.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
used instead.
return_dict (`bool`, *optional*):
Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple. This argument can be used
in eager mode, in graph mode the value will always be set to True.
training (`bool`, *optional*, defaults to `False`):
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).
"""
@add_start_docstrings(
"The bare GPT-J Model transformer outputting raw hidden-states without any specific head on top.",
GPTJ_START_DOCSTRING,
)
class TFGPTJModel(TFGPTJPreTrainedModel):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.transformer = TFGPTJMainLayer(config, name="transformer")
@unpack_inputs
@add_start_docstrings_to_model_forward(GPTJ_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TFBaseModelOutputWithPast,
config_class=_CONFIG_FOR_DOC,
)
def call(
self,
input_ids: TFModelInputType | None = None,
past_key_values: Optional[Tuple[Tuple[Union[np.ndarray, tf.Tensor]]]] = None,
attention_mask: np.ndarray | tf.Tensor | None = None,
token_type_ids: np.ndarray | tf.Tensor | None = None,
position_ids: np.ndarray | tf.Tensor | None = None,
head_mask: np.ndarray | tf.Tensor | None = None,
inputs_embeds: np.ndarray | tf.Tensor | None = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
training: Optional[bool] = False,
) -> Union[TFBaseModelOutputWithPast, Tuple[tf.Tensor]]:
r"""
use_cache (`bool`, *optional*, defaults to `True`):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
`past`). Set to `False` during training, `True` during generation
"""
outputs = self.transformer(
input_ids=input_ids,
past_key_values=past_key_values,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
return outputs
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "transformer", None) is not None:
with tf.name_scope(self.transformer.name):
self.transformer.build(None)
@add_start_docstrings(
"""
The GPT-J Model transformer with a language modeling head on top.
""",
GPTJ_START_DOCSTRING,
)
class TFGPTJForCausalLM(TFGPTJPreTrainedModel, TFCausalLanguageModelingLoss):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.transformer = TFGPTJMainLayer(config, name="transformer")
self.lm_head = keras.layers.Dense(
config.vocab_size, kernel_initializer=get_initializer(config.initializer_range), name="lm_head"
)
self.config = config
def get_output_embeddings(self):
return self.lm_head
def set_output_embeddings(self, new_embeddings):
self.lm_head = new_embeddings
def prepare_inputs_for_generation(self, inputs, past_key_values=None, use_cache=None, **kwargs):
token_type_ids = kwargs.get("token_type_ids", None)
# only last token for inputs_ids if past is defined in kwargs
if past_key_values:
inputs = tf.expand_dims(inputs[:, -1], -1)
if token_type_ids is not None:
token_type_ids = tf.expand_dims(token_type_ids[:, -1], -1)
position_ids = kwargs.get("position_ids", None)
attention_mask = kwargs.get("attention_mask", None)
if attention_mask is not None and position_ids is None:
position_ids = tf.math.cumsum(attention_mask, axis=-1, exclusive=True)
if past_key_values:
position_ids = tf.expand_dims(position_ids[:, -1], -1)
return {
"input_ids": inputs,
"attention_mask": attention_mask,
"position_ids": position_ids,
"past_key_values": past_key_values,
"use_cache": use_cache,
"token_type_ids": token_type_ids,
}
@unpack_inputs
@add_start_docstrings_to_model_forward(GPTJ_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TFCausalLMOutputWithPast,
config_class=_CONFIG_FOR_DOC,
)
def call(
self,
input_ids: TFModelInputType | None = None,
past_key_values: Optional[Tuple[Tuple[Union[np.ndarray, tf.Tensor]]]] = None,
attention_mask: np.ndarray | tf.Tensor | None = None,
token_type_ids: np.ndarray | tf.Tensor | None = None,
position_ids: np.ndarray | tf.Tensor | None = None,
head_mask: np.ndarray | tf.Tensor | None = None,
inputs_embeds: np.ndarray | tf.Tensor | None = None,
labels: np.ndarray | tf.Tensor | None = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
training: Optional[bool] = False,
) -> Union[TFCausalLMOutputWithPast, Tuple[tf.Tensor]]:
r"""
labels (`np.ndarray` or `tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set
`labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100`
are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]`
"""
transformer_outputs = self.transformer(
input_ids=input_ids,
past_key_values=past_key_values,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
hidden_states = transformer_outputs[0]
lm_logits = self.lm_head(hidden_states)
loss = None
if labels is not None:
# shift labels to the left and cut last logit token
shifted_logits = lm_logits[:, :-1]
labels = labels[:, 1:]
loss = self.hf_compute_loss(labels, shifted_logits)
if not return_dict:
output = (lm_logits,) + transformer_outputs[1:]
return ((loss,) + output) if loss is not None else output
return TFCausalLMOutputWithPast(
loss=loss,
logits=lm_logits,
past_key_values=transformer_outputs.past_key_values,
hidden_states=transformer_outputs.hidden_states,
attentions=transformer_outputs.attentions,
)
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "transformer", None) is not None:
with tf.name_scope(self.transformer.name):
self.transformer.build(None)
if getattr(self, "lm_head", None) is not None:
with tf.name_scope(self.lm_head.name):
self.lm_head.build([None, None, self.config.n_embd])
@add_start_docstrings(
"""
The GPT-J Model transformer with a sequence classification head on top (linear layer).
[`GPTJForSequenceClassification`] uses the last token in order to do the classification, as other causal models
(e.g. GPT, GPT-2, GPT-Neo) do.
Since it does classification on the last token, it requires to know the position of the last token. If a
`pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
each row of the batch).
""",
GPTJ_START_DOCSTRING,
)
class TFGPTJForSequenceClassification(TFGPTJPreTrainedModel, TFSequenceClassificationLoss):
_keys_to_ignore_on_load_missing = [r"h.\d+.attn.masked_bias", r"h.\d+.attn.bias", r"lm_head.weight"]
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.num_labels = config.num_labels
self.transformer = TFGPTJMainLayer(config, name="transformer")
self.score = keras.layers.Dense(
self.num_labels,
use_bias=False,
kernel_initializer=get_initializer(config.initializer_range),
name="score",
)
self.config = config
@unpack_inputs
@add_start_docstrings_to_model_forward(GPTJ_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TFSequenceClassifierOutputWithPast,
config_class=_CONFIG_FOR_DOC,
)
def call(
self,
input_ids: TFModelInputType | None = None,
past_key_values: Optional[Tuple[Tuple[Union[np.ndarray, tf.Tensor]]]] = None,
attention_mask: np.ndarray | tf.Tensor | None = None,
token_type_ids: np.ndarray | tf.Tensor | None = None,
position_ids: np.ndarray | tf.Tensor | None = None,
head_mask: np.ndarray | tf.Tensor | None = None,
inputs_embeds: np.ndarray | tf.Tensor | None = None,
labels: np.ndarray | tf.Tensor | None = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
training: Optional[bool] = False,
) -> Union[TFSequenceClassifierOutputWithPast, Tuple[tf.Tensor]]:
r"""
labels (`np.ndarray` or `tf.Tensor` of shape `(batch_size,)`, *optional*):
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
"""
if labels is not None and self.config.pad_token_id is None and input_ids.shape[0] != 1:
raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
transformer_outputs = self.transformer(
input_ids=input_ids,
past_key_values=past_key_values,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
hidden_states = transformer_outputs[0]
logits = self.score(hidden_states)
logits_shape = shape_list(logits)
batch_size = logits_shape[0]
if self.config.pad_token_id is None:
last_non_pad_token = tf.fill((batch_size,), value=logits_shape[1] - 1)
else:
if input_ids is not None:
token_indices = tf.range(shape_list(input_ids)[-1])
non_pad_mask = tf.cast(input_ids != self.config.pad_token_id, token_indices.dtype)
last_non_pad_token = tf.reduce_max(token_indices * non_pad_mask, axis=-1)
else:
last_non_pad_token = tf.fill((batch_size,), value=logits_shape[1] - 1)
logger.warning_once(
f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be "
"unexpected if using padding tokens in conjunction with `inputs_embeds.`"
)
loss = None
pooled_logits = tf.gather(logits, last_non_pad_token, batch_dims=1, axis=1)
if labels is not None:
if self.config.pad_token_id is None and logits_shape[0] != 1:
raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
loss = self.hf_compute_loss(tf.reshape(labels, [-1]), tf.reshape(pooled_logits, [-1, self.num_labels]))
if not return_dict:
output = (pooled_logits,) + transformer_outputs[1:]
return ((loss,) + output) if loss is not None else output
return TFSequenceClassifierOutputWithPast(
loss=loss,
logits=pooled_logits,
past_key_values=transformer_outputs.past_key_values,
hidden_states=transformer_outputs.hidden_states,
attentions=transformer_outputs.attentions,
)
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "transformer", None) is not None:
with tf.name_scope(self.transformer.name):
self.transformer.build(None)
if getattr(self, "score", None) is not None:
with tf.name_scope(self.score.name):
self.score.build([None, None, self.config.n_embd])
@add_start_docstrings(
"""
The GPT-J Model transformer with a span classification head on top for extractive question-answering tasks like
SQuAD (a linear layers on top of the hidden-states output to compute `span start logits` and `span end logits`).
""",
GPTJ_START_DOCSTRING,
)
class TFGPTJForQuestionAnswering(TFGPTJPreTrainedModel, TFQuestionAnsweringLoss):
_keys_to_ignore_on_load_missing = [r"h.\d+.attn.masked_bias", r"h.\d+.attn.bias", r"lm_head.weight"]
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.num_labels = config.num_labels
self.transformer = TFGPTJMainLayer(config, name="transformer")
self.qa_outputs = keras.layers.Dense(
self.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="qa_outputs"
)
self.config = config
@unpack_inputs
@add_start_docstrings_to_model_forward(GPTJ_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TFQuestionAnsweringModelOutput,
config_class=_CONFIG_FOR_DOC,
)
def call(
self,
input_ids: TFModelInputType | None = None,
past_key_values: Optional[Tuple[Tuple[Union[np.ndarray, tf.Tensor]]]] = None,
attention_mask: np.ndarray | tf.Tensor | None = None,
token_type_ids: np.ndarray | tf.Tensor | None = None,
position_ids: np.ndarray | tf.Tensor | None = None,
head_mask: np.ndarray | tf.Tensor | None = None,
inputs_embeds: np.ndarray | tf.Tensor | None = None,
start_positions: np.ndarray | tf.Tensor | None = None,
end_positions: np.ndarray | tf.Tensor | None = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
training: Optional[bool] = False,
) -> Union[TFQuestionAnsweringModelOutput, Tuple[tf.Tensor]]:
r"""
start_positions (`np.ndarray` or `tf.Tensor` of shape `(batch_size,)`, *optional*):
Labels for position (index) of the start of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
are not taken into account for computing the loss.
end_positions (`np.ndarray` or `tf.Tensor` of shape `(batch_size,)`, *optional*):
Labels for position (index) of the end of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
are not taken into account for computing the loss.
"""
transformer_outputs = self.transformer(
input_ids=input_ids,
past_key_values=past_key_values,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
sequence_output = transformer_outputs[0]
logits = self.qa_outputs(sequence_output)
start_logits, end_logits = tf.split(logits, 2, axis=-1)
start_logits = tf.squeeze(start_logits, axis=-1)
end_logits = tf.squeeze(end_logits, axis=-1)
loss = None
if start_positions is not None and end_positions is not None:
labels = {"start_position": start_positions}
labels["end_position"] = end_positions
loss = self.hf_compute_loss(labels, (start_logits, end_logits))
if not return_dict:
output = (start_logits, end_logits) + transformer_outputs[2:]
return ((loss,) + output) if loss is not None else output
return TFQuestionAnsweringModelOutput(
loss=loss,
start_logits=start_logits,
end_logits=end_logits,
hidden_states=transformer_outputs.hidden_states,
attentions=transformer_outputs.attentions,
)
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "transformer", None) is not None:
with tf.name_scope(self.transformer.name):
self.transformer.build(None)
if getattr(self, "qa_outputs", None) is not None:
with tf.name_scope(self.qa_outputs.name):
self.qa_outputs.build([None, None, self.config.hidden_size])
__all__ = [
"TFGPTJForCausalLM",
"TFGPTJForQuestionAnswering",
"TFGPTJForSequenceClassification",
"TFGPTJModel",
"TFGPTJPreTrainedModel",
]
```
|
===============================================================================================================================
SOURCE CODE FILE: __init__.py
LINES: 1
SIZE: 1.59 KB
PATH: scripts\freecad_env\Lib\site-packages\transformers\models\granite\__init__.py
ENCODING: utf-8
```py
# Copyright 2024 EleutherAI and The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
_import_structure = {
"configuration_granite": ["GraniteConfig"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_granite"] = [
"GraniteForCausalLM",
"GraniteModel",
"GranitePreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_granite import GraniteConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_granite import (
GraniteForCausalLM,
GraniteModel,
GranitePreTrainedModel,
)
else:
import sys
sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
```
|
============================================================================================================================================
SOURCE CODE FILE: configuration_granite.py
LINES: 1
SIZE: 9.09 KB
PATH: scripts\freecad_env\Lib\site-packages\transformers\models\granite\configuration_granite.py
ENCODING: utf-8
```py
# coding=utf-8
# Copyright 2024 EleutherAI and the HuggingFace Inc. team. All rights reserved.
#
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
# and OPT implementations in this library. It has been modified from its
# original forms to accommodate minor architectural differences compared
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Granite model configuration"""
from ...configuration_utils import PretrainedConfig
from ...modeling_rope_utils import rope_config_validation
from ...utils import logging
logger = logging.get_logger(__name__)
class GraniteConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`GraniteModel`]. It is used to instantiate an Granite
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar configuration to that of the Granite-3B.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 32000):
Vocabulary size of the Granite model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`GraniteModel`]
hidden_size (`int`, *optional*, defaults to 4096):
Dimension of the hidden representations.
intermediate_size (`int`, *optional*, defaults to 11008):
Dimension of the MLP representations.
num_hidden_layers (`int`, *optional*, defaults to 32):
Number of hidden layers in the Transformer decoder.
num_attention_heads (`int`, *optional*, defaults to 32):
Number of attention heads for each attention layer in the Transformer decoder.
num_key_value_heads (`int`, *optional*):
This is the number of key_value heads that should be used to implement Grouped Query Attention. If
`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
`num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
by meanpooling all the original heads within that group. For more details checkout [this
paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
`num_attention_heads`.
hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
The non-linear activation function (function or string) in the decoder.
max_position_embeddings (`int`, *optional*, defaults to 2048):
The maximum sequence length that this model might ever be used with.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
rms_norm_eps (`float`, *optional*, defaults to 1e-06):
The epsilon used by the rms normalization layers.
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models). Only
relevant if `config.is_decoder=True`.
pad_token_id (`int`, *optional*):
Padding token id.
bos_token_id (`int`, *optional*, defaults to 1):
Beginning of stream token id.
eos_token_id (`int`, *optional*, defaults to 2):
End of stream token id.
tie_word_embeddings (`bool`, *optional*, defaults to `False`):
Whether to tie weight embeddings
rope_theta (`float`, *optional*, defaults to 10000.0):
The base period of the RoPE embeddings.
rope_scaling (`Dict`, *optional*):
Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is
`{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
`max_position_embeddings` to the expected new maximum. See the following thread for more information on how
these scaling strategies behave:
https://www.reddit.com/r/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an
experimental feature, subject to breaking API changes in future versions.
attention_bias (`bool`, *optional*, defaults to `False`):
Whether to use a bias in the query, key, value and output projection layers during self-attention.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
mlp_bias (`bool`, *optional*, defaults to `False`):
Whether to use a bias in up_proj, down_proj and gate_proj layers in the MLP layers.
embedding_multiplier (`float`, *optional*, defaults to 1.0): embedding multiplier
logits_scaling (`float`, *optional*, defaults to 1.0): divisor for output logits
residual_multiplier (`float`, *optional*, defaults to 1.0): residual multiplier
attention_multiplier (`float`, *optional*, defaults to 1.0): attention multiplier
```python
>>> from transformers import GraniteModel, GraniteConfig
>>> # Initializing a Granite granite-3b style configuration
>>> configuration = GraniteConfig()
>>> # Initializing a model from the granite-7b style configuration
>>> model = GraniteModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "granite"
keys_to_ignore_at_inference = ["past_key_values"]
# Default tensor parallel plan for base model `GraniteModel`
base_model_tp_plan = {
"layers.*.self_attn.q_proj": "colwise",
"layers.*.self_attn.k_proj": "colwise",
"layers.*.self_attn.v_proj": "colwise",
"layers.*.self_attn.o_proj": "rowwise",
"layers.*.mlp.gate_proj": "colwise",
"layers.*.mlp.up_proj": "colwise",
"layers.*.mlp.down_proj": "rowwise",
}
base_model_pp_plan = {
"embed_tokens": (["input_ids"], ["inputs_embeds"]),
"layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
"norm": (["hidden_states"], ["hidden_states"]),
}
def __init__(
self,
vocab_size=32000,
hidden_size=4096,
intermediate_size=11008,
num_hidden_layers=32,
num_attention_heads=32,
num_key_value_heads=None,
hidden_act="silu",
max_position_embeddings=2048,
initializer_range=0.02,
rms_norm_eps=1e-6,
use_cache=True,
pad_token_id=None,
bos_token_id=1,
eos_token_id=2,
tie_word_embeddings=False,
rope_theta=10000.0,
rope_scaling=None,
attention_bias=False,
attention_dropout=0.0,
mlp_bias=False,
embedding_multiplier=1.0,
logits_scaling=1.0,
residual_multiplier=1.0,
attention_multiplier=1.0,
**kwargs,
):
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
# for backward compatibility
if num_key_value_heads is None:
num_key_value_heads = num_attention_heads
self.num_key_value_heads = num_key_value_heads
self.hidden_act = hidden_act
self.initializer_range = initializer_range
self.rms_norm_eps = rms_norm_eps
self.use_cache = use_cache
self.rope_theta = rope_theta
self.rope_scaling = rope_scaling
self.attention_bias = attention_bias
self.attention_dropout = attention_dropout
self.mlp_bias = mlp_bias
self.embedding_multiplier = embedding_multiplier
self.logits_scaling = logits_scaling
self.residual_multiplier = residual_multiplier
self.attention_multiplier = attention_multiplier
super().__init__(
pad_token_id=pad_token_id,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
tie_word_embeddings=tie_word_embeddings,
**kwargs,
)
rope_config_validation(self)
```
|
=======================================================================================================================================
SOURCE CODE FILE: modeling_granite.py
LINES: 2
SIZE: 38.97 KB
PATH: scripts\freecad_env\Lib\site-packages\transformers\models\granite\modeling_granite.py
ENCODING: utf-8
```py
# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
# This file was automatically generated from src/transformers/models/granite/modular_granite.py.
# Do NOT edit this file manually as any edits will be overwritten by the generation of
# the file from the modular. If any change should be done, please apply the change to the
# modular_granite.py file directly. One of our CI enforces this.
# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
# coding=utf-8
# Copyright 2024 IBM and the HuggingFace Inc. team. All rights reserved.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from functools import partial
from typing import Callable, List, Optional, Tuple, Union
import torch
from torch import nn
from ...activations import ACT2FN
from ...cache_utils import Cache, DynamicCache, StaticCache
from ...generation import GenerationMixin
from ...modeling_attn_mask_utils import AttentionMaskConverter
from ...modeling_flash_attention_utils import FlashAttentionKwargs
from ...modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
from ...processing_utils import Unpack
from ...utils import (
LossKwargs,
add_start_docstrings,
add_start_docstrings_to_model_forward,
can_return_tuple,
is_torch_flex_attn_available,
logging,
replace_return_docstrings,
)
from ...utils.deprecation import deprecate_kwarg
from .configuration_granite import GraniteConfig
if is_torch_flex_attn_available():
from torch.nn.attention.flex_attention import BlockMask
from ...integrations.flex_attention import make_flex_block_causal_mask
logger = logging.get_logger(__name__)
_CONFIG_FOR_DOC = "GraniteConfig"
def rotate_half(x):
"""Rotates half the hidden dims of the input."""
x1 = x[..., : x.shape[-1] // 2]
x2 = x[..., x.shape[-1] // 2 :]
return torch.cat((-x2, x1), dim=-1)
def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
"""Applies Rotary Position Embedding to the query and key tensors.
Args:
q (`torch.Tensor`): The query tensor.
k (`torch.Tensor`): The key tensor.
cos (`torch.Tensor`): The cosine part of the rotary embedding.
sin (`torch.Tensor`): The sine part of the rotary embedding.
position_ids (`torch.Tensor`, *optional*):
Deprecated and unused.
unsqueeze_dim (`int`, *optional*, defaults to 1):
The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
Returns:
`tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
"""
cos = cos.unsqueeze(unsqueeze_dim)
sin = sin.unsqueeze(unsqueeze_dim)
q_embed = (q * cos) + (rotate_half(q) * sin)
k_embed = (k * cos) + (rotate_half(k) * sin)
return q_embed, k_embed
def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
"""
This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
"""
batch, num_key_value_heads, slen, head_dim = hidden_states.shape
if n_rep == 1:
return hidden_states
hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
def eager_attention_forward(
module: nn.Module,
query: torch.Tensor,
key: torch.Tensor,
value: torch.Tensor,
attention_mask: Optional[torch.Tensor],
scaling: float,
dropout: float = 0.0,
**kwargs,
):
key_states = repeat_kv(key, module.num_key_value_groups)
value_states = repeat_kv(value, module.num_key_value_groups)
attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling
if attention_mask is not None:
causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
attn_weights = attn_weights + causal_mask
attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
attn_output = torch.matmul(attn_weights, value_states)
attn_output = attn_output.transpose(1, 2).contiguous()
return attn_output, attn_weights
class GraniteAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(self, config: GraniteConfig, layer_idx: Optional[int] = None):
super().__init__()
self.config = config
self.layer_idx = layer_idx
self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads
self.scaling = config.attention_multiplier
self.attention_dropout = config.attention_dropout
self.is_causal = True
self.q_proj = nn.Linear(
config.hidden_size, config.num_attention_heads * self.head_dim, bias=config.attention_bias
)
self.k_proj = nn.Linear(
config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
)
self.v_proj = nn.Linear(
config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
)
self.o_proj = nn.Linear(
config.num_attention_heads * self.head_dim, config.hidden_size, bias=config.attention_bias
)
def forward(
self,
hidden_states: torch.Tensor,
position_embeddings: Tuple[torch.Tensor, torch.Tensor],
attention_mask: Optional[torch.Tensor],
past_key_value: Optional[Cache] = None,
cache_position: Optional[torch.LongTensor] = None,
**kwargs: Unpack[FlashAttentionKwargs],
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
input_shape = hidden_states.shape[:-1]
hidden_shape = (*input_shape, -1, self.head_dim)
query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2)
key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2)
value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2)
cos, sin = position_embeddings
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
if past_key_value is not None:
# sin and cos are specific to RoPE models; cache_position needed for the static cache
cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
attention_interface: Callable = eager_attention_forward
if self.config._attn_implementation != "eager":
if self.config._attn_implementation == "sdpa" and kwargs.get("output_attentions", False):
logger.warning_once(
"`torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to "
'eager attention. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
)
else:
attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
attn_output, attn_weights = attention_interface(
self,
query_states,
key_states,
value_states,
attention_mask,
dropout=0.0 if not self.training else self.attention_dropout,
scaling=self.scaling,
**kwargs,
)
attn_output = attn_output.reshape(*input_shape, -1).contiguous()
attn_output = self.o_proj(attn_output)
return attn_output, attn_weights
class GraniteRMSNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-6):
"""
GraniteRMSNorm is equivalent to T5LayerNorm
"""
super().__init__()
self.weight = nn.Parameter(torch.ones(hidden_size))
self.variance_epsilon = eps
def forward(self, hidden_states):
input_dtype = hidden_states.dtype
hidden_states = hidden_states.to(torch.float32)
variance = hidden_states.pow(2).mean(-1, keepdim=True)
hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
return self.weight * hidden_states.to(input_dtype)
def extra_repr(self):
return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}"
class GraniteMLP(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.hidden_size = config.hidden_size
self.intermediate_size = config.intermediate_size
self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=config.mlp_bias)
self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=config.mlp_bias)
self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=config.mlp_bias)
self.act_fn = ACT2FN[config.hidden_act]
def forward(self, x):
down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
return down_proj
class GraniteDecoderLayer(nn.Module):
def __init__(self, config: GraniteConfig, layer_idx: int):
super().__init__()
self.hidden_size = config.hidden_size
self.self_attn = GraniteAttention(config=config, layer_idx=layer_idx)
self.mlp = GraniteMLP(config)
self.input_layernorm = GraniteRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.post_attention_layernorm = GraniteRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.residual_multiplier = config.residual_multiplier
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Cache] = None,
output_attentions: Optional[bool] = False,
use_cache: Optional[bool] = False,
cache_position: Optional[torch.LongTensor] = None,
position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, # necessary, but kept here for BC
**kwargs,
) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
"""
Args:
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
attention_mask (`torch.FloatTensor`, *optional*):
attention mask of size `(batch_size, sequence_length)` if flash attention is used or `(batch_size, 1,
query_sequence_length, key_sequence_length)` if default attention is used.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
(see `past_key_values`).
past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
Indices depicting the position of the input sequence tokens in the sequence
position_embeddings (`Tuple[torch.FloatTensor, torch.FloatTensor]`, *optional*):
Tuple containing the cosine and sine positional embeddings of shape `(batch_size, seq_len, head_dim)`,
with `head_dim` being the embedding dimension of each attention head.
kwargs (`dict`, *optional*):
Arbitrary kwargs to be ignored, used for FSDP and other methods that injects code
into the model
"""
residual = hidden_states
hidden_states = self.input_layernorm(hidden_states)
# Self Attention
hidden_states, self_attn_weights = self.self_attn(
hidden_states=hidden_states,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_value=past_key_value,
output_attentions=output_attentions,
use_cache=use_cache,
cache_position=cache_position,
position_embeddings=position_embeddings,
**kwargs,
)
hidden_states = residual + hidden_states * self.residual_multiplier
# Fully Connected
residual = hidden_states
hidden_states = self.post_attention_layernorm(hidden_states)
hidden_states = self.mlp(hidden_states)
hidden_states = residual + hidden_states * self.residual_multiplier # main diff with Llama
outputs = (hidden_states,)
if output_attentions:
outputs += (self_attn_weights,)
return outputs
class GraniteRotaryEmbedding(nn.Module):
def __init__(self, config: GraniteConfig, device=None):
super().__init__()
# BC: "rope_type" was originally "type"
if hasattr(config, "rope_scaling") and config.rope_scaling is not None:
self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type"))
else:
self.rope_type = "default"
self.max_seq_len_cached = config.max_position_embeddings
self.original_max_seq_len = config.max_position_embeddings
self.config = config
self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device)
self.register_buffer("inv_freq", inv_freq, persistent=False)
self.original_inv_freq = self.inv_freq
@torch.no_grad()
@dynamic_rope_update # power user: used with advanced RoPE types (e.g. dynamic rope)
def forward(self, x, position_ids):
inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device)
position_ids_expanded = position_ids[:, None, :].float()
device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
with torch.autocast(device_type=device_type, enabled=False): # Force float32
freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
emb = torch.cat((freqs, freqs), dim=-1)
cos = emb.cos() * self.attention_scaling
sin = emb.sin() * self.attention_scaling
return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
GRANITE_START_DOCSTRING = r"""
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.
Parameters:
config ([`GraniteConfig`]):
Model configuration class with all the parameters of the model. Initializing with a config file does not
load the weights associated with the model, only the configuration. Check out the
[`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
@add_start_docstrings(
"The bare Granite Model outputting raw hidden-states without any specific head on top.",
GRANITE_START_DOCSTRING,
)
class GranitePreTrainedModel(PreTrainedModel):
config_class = GraniteConfig
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = ["GraniteDecoderLayer"]
_skip_keys_device_placement = ["past_key_values"]
_supports_flash_attn_2 = True
_supports_sdpa = True
_supports_flex_attn = True
_supports_cache_class = True
_supports_quantized_cache = True
_supports_static_cache = True
_supports_attention_backend = True
def _init_weights(self, module):
std = self.config.initializer_range
if isinstance(module, nn.Linear):
module.weight.data.normal_(mean=0.0, std=std)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.Embedding):
module.weight.data.normal_(mean=0.0, std=std)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
GRANITE_INPUTS_DOCSTRING = r"""
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
it.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
If `past_key_values` is used, optionally only the last `input_ids` have to be input (see
`past_key_values`).
If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
information on the default strategy.
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
config.n_positions - 1]`.
[What are position IDs?](../glossary#position-ids)
past_key_values (`Cache`, *optional*):
Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values`
returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.
It is a [`~cache_utils.Cache`] instance. For more details, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache).
If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't
have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids`
of shape `(batch_size, sequence_length)`.
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
model's internal embedding lookup matrix.
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
`past_key_values`).
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
Indices depicting the position of the input sequence tokens in the sequence. Contrarily to `position_ids`,
this tensor is not affected by padding. It is used to update the cache in the correct position and to infer
the complete sequence length.
"""
@add_start_docstrings(
"The bare Granite Model outputting raw hidden-states without any specific head on top.",
GRANITE_START_DOCSTRING,
)
class GraniteModel(GranitePreTrainedModel):
"""
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`GraniteDecoderLayer`]
Args:
config: GraniteConfig
"""
def __init__(self, config: GraniteConfig):
super().__init__(config)
self.padding_idx = config.pad_token_id
self.vocab_size = config.vocab_size
self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
self.layers = nn.ModuleList(
[GraniteDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
)
self.norm = GraniteRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.rotary_emb = GraniteRotaryEmbedding(config=config)
self.gradient_checkpointing = False
self.embedding_multiplier = config.embedding_multiplier
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.embed_tokens
def set_input_embeddings(self, value):
self.embed_tokens = value
@can_return_tuple
@add_start_docstrings_to_model_forward(GRANITE_INPUTS_DOCSTRING)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Cache] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
cache_position: Optional[torch.LongTensor] = None,
**flash_attn_kwargs: Unpack[FlashAttentionKwargs],
) -> BaseModelOutputWithPast:
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
use_cache = use_cache if use_cache is not None else self.config.use_cache
if (input_ids is None) ^ (inputs_embeds is not None):
raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
if self.gradient_checkpointing and self.training and use_cache:
logger.warning_once(
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`."
)
use_cache = False
if inputs_embeds is None:
inputs_embeds = self.embed_tokens(input_ids)
inputs_embeds = inputs_embeds * self.embedding_multiplier # main diff with Llama
if use_cache and past_key_values is None:
past_key_values = DynamicCache()
if cache_position is None:
past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
cache_position = torch.arange(
past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
)
if position_ids is None:
position_ids = cache_position.unsqueeze(0)
causal_mask = self._update_causal_mask(
attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions
)
hidden_states = inputs_embeds
# create position embeddings to be shared across the decoder layers
position_embeddings = self.rotary_emb(hidden_states, position_ids)
# decoder layers
all_hidden_states = () if output_hidden_states else None
all_self_attns = () if output_attentions else None
for decoder_layer in self.layers[: self.config.num_hidden_layers]:
if output_hidden_states:
all_hidden_states += (hidden_states,)
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
partial(decoder_layer.__call__, **flash_attn_kwargs),
hidden_states,
causal_mask,
position_ids,
past_key_values,
output_attentions,
use_cache,
cache_position,
position_embeddings,
)
else:
layer_outputs = decoder_layer(
hidden_states,
attention_mask=causal_mask,
position_ids=position_ids,
past_key_value=past_key_values,
output_attentions=output_attentions,
use_cache=use_cache,
cache_position=cache_position,
position_embeddings=position_embeddings,
**flash_attn_kwargs,
)
hidden_states = layer_outputs[0]
if output_attentions:
all_self_attns += (layer_outputs[1],)
hidden_states = self.norm(hidden_states)
# add hidden states from the last decoder layer
if output_hidden_states:
all_hidden_states += (hidden_states,)
return BaseModelOutputWithPast(
last_hidden_state=hidden_states,
past_key_values=past_key_values if use_cache else None,
hidden_states=all_hidden_states,
attentions=all_self_attns,
)
def _update_causal_mask(
self,
attention_mask: torch.Tensor,
input_tensor: torch.Tensor,
cache_position: torch.Tensor,
past_key_values: Cache,
output_attentions: bool = False,
):
if self.config._attn_implementation == "flash_attention_2":
if attention_mask is not None and (attention_mask == 0.0).any():
return attention_mask
return None
if self.config._attn_implementation == "flex_attention":
if isinstance(attention_mask, torch.Tensor):
attention_mask = make_flex_block_causal_mask(attention_mask)
if isinstance(attention_mask, BlockMask):
return attention_mask
# For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in
# order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail
# to infer the attention mask.
past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
using_static_cache = isinstance(past_key_values, StaticCache)
# When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward
if self.config._attn_implementation == "sdpa" and not using_static_cache and not output_attentions:
if AttentionMaskConverter._ignore_causal_mask_sdpa(
attention_mask,
inputs_embeds=input_tensor,
past_key_values_length=past_seen_tokens,
is_training=self.training,
):
return None
dtype, device = input_tensor.dtype, input_tensor.device
sequence_length = input_tensor.shape[1]
if using_static_cache:
target_length = past_key_values.get_max_cache_shape()
else:
target_length = (
attention_mask.shape[-1]
if isinstance(attention_mask, torch.Tensor)
else past_seen_tokens + sequence_length + 1
)
# In case the provided `attention` mask is 2D, we generate a causal mask here (4D).
causal_mask = self._prepare_4d_causal_attention_mask_with_cache_position(
attention_mask,
sequence_length=sequence_length,
target_length=target_length,
dtype=dtype,
device=device,
cache_position=cache_position,
batch_size=input_tensor.shape[0],
)
if (
self.config._attn_implementation == "sdpa"
and attention_mask is not None
and attention_mask.device.type in ["cuda", "xpu"]
and not output_attentions
):
# Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when
# using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path.
# Details: https://github.com/pytorch/pytorch/issues/110213
min_dtype = torch.finfo(dtype).min
causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype)
return causal_mask
@staticmethod
def _prepare_4d_causal_attention_mask_with_cache_position(
attention_mask: torch.Tensor,
sequence_length: int,
target_length: int,
dtype: torch.dtype,
device: torch.device,
cache_position: torch.Tensor,
batch_size: int,
**kwargs,
):
"""
Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape
`(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing.
Args:
attention_mask (`torch.Tensor`):
A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape
`(batch_size, 1, query_length, key_value_length)`.
sequence_length (`int`):
The sequence length being processed.
target_length (`int`):
The target length: when generating with static cache, the mask should be as long as the static cache,
to account for the 0 padding, the part of the cache that is not filled yet.
dtype (`torch.dtype`):
The dtype to use for the 4D attention mask.
device (`torch.device`):
The device to place the 4D attention mask on.
cache_position (`torch.Tensor`):
Indices depicting the position of the input sequence tokens in the sequence.
batch_size (`torch.Tensor`):
Batch size.
"""
if attention_mask is not None and attention_mask.dim() == 4:
# In this case we assume that the mask comes already in inverted form and requires no inversion or slicing.
causal_mask = attention_mask
else:
min_dtype = torch.finfo(dtype).min
causal_mask = torch.full(
(sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=device
)
if sequence_length != 1:
causal_mask = torch.triu(causal_mask, diagonal=1)
causal_mask *= torch.arange(target_length, device=device) > cache_position.reshape(-1, 1)
causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1)
if attention_mask is not None:
causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit
mask_length = attention_mask.shape[-1]
padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :].to(
causal_mask.device
)
padding_mask = padding_mask == 0
causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
padding_mask, min_dtype
)
return causal_mask
class KwargsForCausalLM(FlashAttentionKwargs, LossKwargs): ...
class GraniteForCausalLM(GranitePreTrainedModel, GenerationMixin):
_tied_weights_keys = ["lm_head.weight"]
_tp_plan = {"lm_head": "colwise_rep"}
_pp_plan = {"lm_head": (["hidden_states"], ["logits"])}
def __init__(self, config):
super().__init__(config)
self.model = GraniteModel(config)
self.vocab_size = config.vocab_size
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.model.embed_tokens
def set_input_embeddings(self, value):
self.model.embed_tokens = value
def get_output_embeddings(self):
return self.lm_head
def set_output_embeddings(self, new_embeddings):
self.lm_head = new_embeddings
def set_decoder(self, decoder):
self.model = decoder
def get_decoder(self):
return self.model
@can_return_tuple
@deprecate_kwarg("num_logits_to_keep", version="4.50", new_name="logits_to_keep")
@add_start_docstrings_to_model_forward(GRANITE_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
cache_position: Optional[torch.LongTensor] = None,
logits_to_keep: Union[int, torch.Tensor] = 0,
**kwargs: Unpack[KwargsForCausalLM],
) -> CausalLMOutputWithPast:
r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
logits_to_keep (`int` or `torch.Tensor`, *optional*):
If an `int`, compute logits for the last `logits_to_keep` tokens. If `0`, calculate logits for all
`input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that
token can save memory, which becomes pretty significant for long sequences or large vocabulary size.
If a `torch.Tensor`, must be 1D corresponding to the indices to keep in the sequence length dimension.
This is useful when using packed tensor format (single dimension for batch and sequence length).
Returns:
Example:
```python
>>> from transformers import AutoTokenizer, GraniteForCausalLM
>>> model = GraniteForCausalLM.from_pretrained("meta-granite/Granite-2-7b-hf")
>>> tokenizer = AutoTokenizer.from_pretrained("meta-granite/Granite-2-7b-hf")
>>> prompt = "Hey, are you conscious? Can you talk to me?"
>>> inputs = tokenizer(prompt, return_tensors="pt")
>>> # Generate
>>> generate_ids = model.generate(inputs.input_ids, max_length=30)
>>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
"Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
```"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
outputs: BaseModelOutputWithPast = self.model(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
cache_position=cache_position,
**kwargs,
)
hidden_states = outputs.last_hidden_state
# Only compute necessary logits, and do not upcast them to float if we are not computing the loss
slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
logits = self.lm_head(hidden_states[:, slice_indices, :])
logits = logits / self.config.logits_scaling # main diff with Llama
loss = None
if labels is not None:
loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.config.vocab_size, **kwargs)
return CausalLMOutputWithPast(
loss=loss,
logits=logits,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
```
|
======================================================================================================================================
SOURCE CODE FILE: modular_granite.py
LINES: 1
SIZE: 11.88 KB
PATH: scripts\freecad_env\Lib\site-packages\transformers\models\granite\modular_granite.py
ENCODING: utf-8
```py
# coding=utf-8
# Copyright 2024 IBM and the HuggingFace Inc. team. All rights reserved.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from functools import partial
from typing import List, Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from ...cache_utils import Cache, DynamicCache
from ...modeling_flash_attention_utils import FlashAttentionKwargs
from ...modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
from ...processing_utils import Unpack
from ...utils import LossKwargs, logging
from ..llama.modeling_llama import LlamaAttention, LlamaDecoderLayer, LlamaForCausalLM, LlamaModel
from .configuration_granite import GraniteConfig
logger = logging.get_logger(__name__)
class GraniteAttention(LlamaAttention):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(self, config: GraniteConfig, layer_idx: Optional[int] = None):
super().__init__(config, layer_idx)
self.scaling = config.attention_multiplier
class GraniteDecoderLayer(LlamaDecoderLayer):
def __init__(self, config: GraniteConfig, layer_idx: int):
super().__init__(config, layer_idx)
self.residual_multiplier = config.residual_multiplier
self.self_attn = GraniteAttention(config=config, layer_idx=layer_idx)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Cache] = None,
output_attentions: Optional[bool] = False,
use_cache: Optional[bool] = False,
cache_position: Optional[torch.LongTensor] = None,
position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, # necessary, but kept here for BC
**kwargs,
) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
"""
Args:
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
attention_mask (`torch.FloatTensor`, *optional*):
attention mask of size `(batch_size, sequence_length)` if flash attention is used or `(batch_size, 1,
query_sequence_length, key_sequence_length)` if default attention is used.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
(see `past_key_values`).
past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
Indices depicting the position of the input sequence tokens in the sequence
position_embeddings (`Tuple[torch.FloatTensor, torch.FloatTensor]`, *optional*):
Tuple containing the cosine and sine positional embeddings of shape `(batch_size, seq_len, head_dim)`,
with `head_dim` being the embedding dimension of each attention head.
kwargs (`dict`, *optional*):
Arbitrary kwargs to be ignored, used for FSDP and other methods that injects code
into the model
"""
residual = hidden_states
hidden_states = self.input_layernorm(hidden_states)
# Self Attention
hidden_states, self_attn_weights = self.self_attn(
hidden_states=hidden_states,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_value=past_key_value,
output_attentions=output_attentions,
use_cache=use_cache,
cache_position=cache_position,
position_embeddings=position_embeddings,
**kwargs,
)
hidden_states = residual + hidden_states * self.residual_multiplier
# Fully Connected
residual = hidden_states
hidden_states = self.post_attention_layernorm(hidden_states)
hidden_states = self.mlp(hidden_states)
hidden_states = residual + hidden_states * self.residual_multiplier # main diff with Llama
outputs = (hidden_states,)
if output_attentions:
outputs += (self_attn_weights,)
return outputs
class GraniteModel(LlamaModel):
def __init__(self, config: GraniteConfig):
super().__init__(config)
self.embedding_multiplier = config.embedding_multiplier
self.layers = nn.ModuleList(
[GraniteDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Cache] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
cache_position: Optional[torch.LongTensor] = None,
**flash_attn_kwargs: Unpack[FlashAttentionKwargs],
) -> BaseModelOutputWithPast:
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
use_cache = use_cache if use_cache is not None else self.config.use_cache
if (input_ids is None) ^ (inputs_embeds is not None):
raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
if self.gradient_checkpointing and self.training and use_cache:
logger.warning_once(
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`."
)
use_cache = False
if inputs_embeds is None:
inputs_embeds = self.embed_tokens(input_ids)
inputs_embeds = inputs_embeds * self.embedding_multiplier # main diff with Llama
if use_cache and past_key_values is None:
past_key_values = DynamicCache()
if cache_position is None:
past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
cache_position = torch.arange(
past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
)
if position_ids is None:
position_ids = cache_position.unsqueeze(0)
causal_mask = self._update_causal_mask(
attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions
)
hidden_states = inputs_embeds
# create position embeddings to be shared across the decoder layers
position_embeddings = self.rotary_emb(hidden_states, position_ids)
# decoder layers
all_hidden_states = () if output_hidden_states else None
all_self_attns = () if output_attentions else None
for decoder_layer in self.layers[: self.config.num_hidden_layers]:
if output_hidden_states:
all_hidden_states += (hidden_states,)
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
partial(decoder_layer.__call__, **flash_attn_kwargs),
hidden_states,
causal_mask,
position_ids,
past_key_values,
output_attentions,
use_cache,
cache_position,
position_embeddings,
)
else:
layer_outputs = decoder_layer(
hidden_states,
attention_mask=causal_mask,
position_ids=position_ids,
past_key_value=past_key_values,
output_attentions=output_attentions,
use_cache=use_cache,
cache_position=cache_position,
position_embeddings=position_embeddings,
**flash_attn_kwargs,
)
hidden_states = layer_outputs[0]
if output_attentions:
all_self_attns += (layer_outputs[1],)
hidden_states = self.norm(hidden_states)
# add hidden states from the last decoder layer
if output_hidden_states:
all_hidden_states += (hidden_states,)
return BaseModelOutputWithPast(
last_hidden_state=hidden_states,
past_key_values=past_key_values if use_cache else None,
hidden_states=all_hidden_states,
attentions=all_self_attns,
)
class KwargsForCausalLM(FlashAttentionKwargs, LossKwargs): ...
class GraniteForCausalLM(LlamaForCausalLM):
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
cache_position: Optional[torch.LongTensor] = None,
logits_to_keep: Union[int, torch.Tensor] = 0,
**kwargs: Unpack[KwargsForCausalLM],
) -> CausalLMOutputWithPast:
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
outputs: BaseModelOutputWithPast = self.model(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
cache_position=cache_position,
**kwargs,
)
hidden_states = outputs.last_hidden_state
# Only compute necessary logits, and do not upcast them to float if we are not computing the loss
slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
logits = self.lm_head(hidden_states[:, slice_indices, :])
logits = logits / self.config.logits_scaling # main diff with Llama
loss = None
if labels is not None:
loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.config.vocab_size, **kwargs)
return CausalLMOutputWithPast(
loss=loss,
logits=logits,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
```
|
==================================================================================================================================
SOURCE CODE FILE: __init__.py
LINES: 1
SIZE: 0.98 KB
PATH: scripts\freecad_env\Lib\site-packages\transformers\models\granitemoe\__init__.py
ENCODING: utf-8
```py
# Copyright 2024 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import _LazyModule
from ...utils.import_utils import define_import_structure
if TYPE_CHECKING:
from .configuration_granitemoe import *
from .modeling_granitemoe import *
else:
import sys
_file = globals()["__file__"]
sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)
```
|
==================================================================================================================================================
SOURCE CODE FILE: configuration_granitemoe.py
LINES: 1
SIZE: 9.18 KB
PATH: scripts\freecad_env\Lib\site-packages\transformers\models\granitemoe\configuration_granitemoe.py
ENCODING: utf-8
```py
# coding=utf-8
# Copyright 2024 EleutherAI and the HuggingFace Inc. team. All rights reserved.
#
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
# and OPT implementations in this library. It has been modified from its
# original forms to accommodate minor architectural differences compared
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""GraniteMoe model configuration"""
from ...configuration_utils import PretrainedConfig
from ...modeling_rope_utils import rope_config_validation
from ...utils import logging
logger = logging.get_logger(__name__)
class GraniteMoeConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`GraniteMoeModel`]. It is used to instantiate an GraniteMoe
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar configuration to that of the GraniteMoe-3B.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 32000):
Vocabulary size of the GraniteMoe model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`GraniteMoeModel`]
hidden_size (`int`, *optional*, defaults to 4096):
Dimension of the hidden representations.
intermediate_size (`int`, *optional*, defaults to 11008):
Dimension of the MLP representations.
num_hidden_layers (`int`, *optional*, defaults to 32):
Number of hidden layers in the Transformer decoder.
num_attention_heads (`int`, *optional*, defaults to 32):
Number of attention heads for each attention layer in the Transformer decoder.
num_key_value_heads (`int`, *optional*):
This is the number of key_value heads that should be used to implement Grouped Query Attention. If
`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
`num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
by meanpooling all the original heads within that group. For more details checkout [this
paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
`num_attention_heads`.
hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
The non-linear activation function (function or string) in the decoder.
max_position_embeddings (`int`, *optional*, defaults to 2048):
The maximum sequence length that this model might ever be used with.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
rms_norm_eps (`float`, *optional*, defaults to 1e-06):
The epsilon used by the rms normalization layers.
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models). Only
relevant if `config.is_decoder=True`.
pad_token_id (`int`, *optional*):
Padding token id.
bos_token_id (`int`, *optional*, defaults to 1):
Beginning of stream token id.
eos_token_id (`int`, *optional*, defaults to 2):
End of stream token id.
tie_word_embeddings (`bool`, *optional*, defaults to `False`):
Whether to tie weight embeddings
rope_theta (`float`, *optional*, defaults to 10000.0):
The base period of the RoPE embeddings.
rope_scaling (`Dict`, *optional*):
Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is
`{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
`max_position_embeddings` to the expected new maximum. See the following thread for more information on how
these scaling strategies behave:
https://www.reddit.com/r/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an
experimental feature, subject to breaking API changes in future versions.
attention_bias (`bool`, *optional*, defaults to `False`):
Whether to use a bias in the query, key, value and output projection layers during self-attention.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
embedding_multiplier (`float`, *optional*, defaults to 1.0): embedding multiplier
logits_scaling (`float`, *optional*, defaults to 1.0): divisor for output logits
residual_multiplier (`float`, *optional*, defaults to 1.0): residual multiplier
attention_multiplier (`float`, *optional*, defaults to 1.0): attention multiplier
num_local_experts (`int`, *optional*, defaults to 8): total number of experts
num_experts_per_tok (`int`, *optional*, defaults to 2): number of experts per token
output_router_logits (`bool`, *optional*, defaults to `False`):
Whether or not the router logits should be returned by the model. Enabeling this will also
allow the model to output the auxiliary loss.
router_aux_loss_coef (`float`, *optional*, defaults to 0.001): router auxialiary loss coefficient
```python
>>> from transformers import GraniteMoeModel, GraniteMoeConfig
>>> # Initializing a GraniteMoe granitemoe-3b style configuration
>>> configuration = GraniteMoeConfig()
>>> # Initializing a model from the granitemoe-7b style configuration
>>> model = GraniteMoeModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "granitemoe"
keys_to_ignore_at_inference = ["past_key_values"]
def __init__(
self,
vocab_size=32000,
hidden_size=4096,
intermediate_size=11008,
num_hidden_layers=32,
num_attention_heads=32,
num_key_value_heads=None,
hidden_act="silu",
max_position_embeddings=2048,
initializer_range=0.02,
rms_norm_eps=1e-6,
use_cache=True,
pad_token_id=None,
bos_token_id=1,
eos_token_id=2,
tie_word_embeddings=False,
rope_theta=10000.0,
rope_scaling=None,
attention_bias=False,
attention_dropout=0.0,
embedding_multiplier=1.0,
logits_scaling=1.0,
residual_multiplier=1.0,
attention_multiplier=1.0,
num_local_experts=8,
num_experts_per_tok=2,
output_router_logits=False,
router_aux_loss_coef=0.001,
**kwargs,
):
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
# for backward compatibility
if num_key_value_heads is None:
num_key_value_heads = num_attention_heads
self.num_key_value_heads = num_key_value_heads
self.hidden_act = hidden_act
self.initializer_range = initializer_range
self.rms_norm_eps = rms_norm_eps
self.use_cache = use_cache
self.rope_theta = rope_theta
self.rope_scaling = rope_scaling
self.attention_bias = attention_bias
self.attention_dropout = attention_dropout
self.embedding_multiplier = embedding_multiplier
self.logits_scaling = logits_scaling
self.residual_multiplier = residual_multiplier
self.attention_multiplier = attention_multiplier
self.num_local_experts = num_local_experts
self.num_experts_per_tok = num_experts_per_tok
self.output_router_logits = output_router_logits
self.router_aux_loss_coef = router_aux_loss_coef
super().__init__(
pad_token_id=pad_token_id,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
tie_word_embeddings=tie_word_embeddings,
**kwargs,
)
rope_config_validation(self)
__all__ = ["GraniteMoeConfig"]
```
|
=============================================================================================================================================
SOURCE CODE FILE: modeling_granitemoe.py
LINES: 2
SIZE: 61.82 KB
PATH: scripts\freecad_env\Lib\site-packages\transformers\models\granitemoe\modeling_granitemoe.py
ENCODING: utf-8
```py
# coding=utf-8
# Copyright 2024 IBM and the HuggingFace Inc. team. All rights reserved.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import List, Optional, Tuple, Union
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
from torch import nn
from ...activations import ACT2FN
from ...cache_utils import Cache, DynamicCache, StaticCache
from ...generation import GenerationMixin
from ...modeling_attn_mask_utils import AttentionMaskConverter
from ...modeling_flash_attention_utils import _flash_attention_forward, flash_attn_supports_top_left_mask
from ...modeling_outputs import (
BaseModelOutputWithPast,
MoeCausalLMOutputWithPast,
MoeModelOutputWithPast,
)
from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
from ...modeling_utils import PreTrainedModel
from ...pytorch_utils import ALL_LAYERNORM_LAYERS
from ...utils import (
add_start_docstrings,
add_start_docstrings_to_model_forward,
is_torch_flex_attn_available,
logging,
replace_return_docstrings,
)
from .configuration_granitemoe import GraniteMoeConfig
if is_torch_flex_attn_available():
from torch.nn.attention.flex_attention import BlockMask
from ...integrations.flex_attention import make_flex_block_causal_mask
logger = logging.get_logger(__name__)
_CONFIG_FOR_DOC = "GraniteMoeConfig"
# Copied from transformers.models.jetmoe.modeling_jetmoe.load_balancing_loss_func
def load_balancing_loss_func(
gate_logits: Union[torch.Tensor, Tuple[torch.Tensor], None],
num_experts: Optional[int] = None,
top_k=2,
attention_mask: Optional[torch.Tensor] = None,
) -> Union[torch.Tensor, int]:
r"""
Computes auxiliary load balancing loss as in Switch Transformer - implemented in Pytorch.
See Switch Transformer (https://arxiv.org/abs/2101.03961) for more details. This function implements the loss
function presented in equations (4) - (6) of the paper. It aims at penalizing cases where the routing between
experts is too unbalanced.
Args:
gate_logits:
Logits from the `gate`, should be a tuple of model.config.num_hidden_layers tensors of
shape [batch_size X sequence_length, num_experts].
num_experts:
Number of experts
top_k:
The number of experts to route per-token, can be also interpreted as the `top-k` routing
parameter.
attention_mask (`torch.Tensor`, *optional*):
The attention_mask used in forward function
shape [batch_size X sequence_length] if not None.
Returns:
The auxiliary loss.
"""
if gate_logits is None or not isinstance(gate_logits, tuple):
return 0
if isinstance(gate_logits, tuple):
compute_device = gate_logits[0].device
concatenated_gate_logits = torch.cat([layer_gate.to(compute_device) for layer_gate in gate_logits], dim=0)
routing_weights = torch.nn.functional.softmax(concatenated_gate_logits, dim=-1)
_, selected_experts = torch.topk(routing_weights, top_k, dim=-1)
expert_mask = torch.nn.functional.one_hot(selected_experts, num_experts)
if attention_mask is None:
# Compute the percentage of tokens routed to each experts
tokens_per_expert = torch.mean(expert_mask.float(), dim=0)
# Compute the average probability of routing to these experts
router_prob_per_expert = torch.mean(routing_weights, dim=0)
else:
batch_size, sequence_length = attention_mask.shape
num_hidden_layers = concatenated_gate_logits.shape[0] // (batch_size * sequence_length)
# Compute the mask that masks all padding tokens as 0 with the same shape of expert_mask
expert_attention_mask = (
attention_mask[None, :, :, None, None]
.expand((num_hidden_layers, batch_size, sequence_length, top_k, num_experts))
.reshape(-1, top_k, num_experts)
.to(compute_device)
)
# Compute the percentage of tokens routed to each experts
tokens_per_expert = torch.sum(expert_mask.float() * expert_attention_mask, dim=0) / torch.sum(
expert_attention_mask, dim=0
)
# Compute the mask that masks all padding tokens as 0 with the same shape of tokens_per_expert
router_per_expert_attention_mask = (
attention_mask[None, :, :, None]
.expand((num_hidden_layers, batch_size, sequence_length, num_experts))
.reshape(-1, num_experts)
.to(compute_device)
)
# Compute the average probability of routing to these experts
router_prob_per_expert = torch.sum(routing_weights * router_per_expert_attention_mask, dim=0) / torch.sum(
router_per_expert_attention_mask, dim=0
)
overall_loss = torch.sum(tokens_per_expert * router_prob_per_expert.unsqueeze(0))
return overall_loss * num_experts
# Copied from transformers.models.granite.modeling_granite.GraniteRMSNorm with Granite->GraniteMoe
class GraniteMoeRMSNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-6):
"""
GraniteMoeRMSNorm is equivalent to T5LayerNorm
"""
super().__init__()
self.weight = nn.Parameter(torch.ones(hidden_size))
self.variance_epsilon = eps
def forward(self, hidden_states):
input_dtype = hidden_states.dtype
hidden_states = hidden_states.to(torch.float32)
variance = hidden_states.pow(2).mean(-1, keepdim=True)
hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
return self.weight * hidden_states.to(input_dtype)
def extra_repr(self):
return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}"
ALL_LAYERNORM_LAYERS.append(GraniteMoeRMSNorm)
# Copied from transformers.models.granite.modeling_granite.GraniteRotaryEmbedding with Granite->GraniteMoe
class GraniteMoeRotaryEmbedding(nn.Module):
def __init__(self, config: GraniteMoeConfig, device=None):
super().__init__()
# BC: "rope_type" was originally "type"
if hasattr(config, "rope_scaling") and config.rope_scaling is not None:
self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type"))
else:
self.rope_type = "default"
self.max_seq_len_cached = config.max_position_embeddings
self.original_max_seq_len = config.max_position_embeddings
self.config = config
self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device)
self.register_buffer("inv_freq", inv_freq, persistent=False)
self.original_inv_freq = self.inv_freq
@torch.no_grad()
@dynamic_rope_update # power user: used with advanced RoPE types (e.g. dynamic rope)
def forward(self, x, position_ids):
inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device)
position_ids_expanded = position_ids[:, None, :].float()
device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
with torch.autocast(device_type=device_type, enabled=False): # Force float32
freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
emb = torch.cat((freqs, freqs), dim=-1)
cos = emb.cos() * self.attention_scaling
sin = emb.sin() * self.attention_scaling
return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
# Copied from transformers.models.granite.modeling_granite.rotate_half with Granite->GraniteMoe
def rotate_half(x):
"""Rotates half the hidden dims of the input."""
x1 = x[..., : x.shape[-1] // 2]
x2 = x[..., x.shape[-1] // 2 :]
return torch.cat((-x2, x1), dim=-1)
# Copied from transformers.models.granite.modeling_granite.apply_rotary_pos_emb with Granite->GraniteMoe
def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
"""Applies Rotary Position Embedding to the query and key tensors.
Args:
q (`torch.Tensor`): The query tensor.
k (`torch.Tensor`): The key tensor.
cos (`torch.Tensor`): The cosine part of the rotary embedding.
sin (`torch.Tensor`): The sine part of the rotary embedding.
position_ids (`torch.Tensor`, *optional*):
Deprecated and unused.
unsqueeze_dim (`int`, *optional*, defaults to 1):
The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
Returns:
`tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
"""
cos = cos.unsqueeze(unsqueeze_dim)
sin = sin.unsqueeze(unsqueeze_dim)
q_embed = (q * cos) + (rotate_half(q) * sin)
k_embed = (k * cos) + (rotate_half(k) * sin)
return q_embed, k_embed
# Copied from transformers.models.jetmoe.modeling_jetmoe.JetMoeParallelExperts with JetMoe->GraniteMoe
class GraniteMoeParallelExperts(nn.Module):
def __init__(self, num_experts: int, input_size: int, output_size: int) -> None:
"""
Initialize the GraniteMoeParallelExperts module.
The experts weights are stored in [num_experts, output_size, input_size] format. Such that it's comptible with
many MoE libraries, such as [Megablock](https://github.com/databricks/megablocks) and
[ScatterMoE](https://github.com/shawntan/scattermoe), as well as the
[MoE kernel](https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/layers/fused_moe/fused_moe.py)
used in vllm.
Args:
num_experts (int):
Number of experts.
input_size (int):
Size of the input.
output_size (int):
Size of the output.
"""
super().__init__()
self.weight = nn.Parameter(torch.empty(num_experts, output_size, input_size))
self.num_experts = num_experts
self.input_size = input_size
self.output_size = output_size
def forward(self, inputs, expert_size):
"""
Forward pass of the GraniteMoeParallelExperts module.
Args:
inputs (Tensor):
Input tensor.
expert_size:
Expert size information.
Returns:
Tensor: Output tensor.
"""
input_list = inputs.split(expert_size, dim=0)
output_list = []
for i in range(self.num_experts):
output_list.append(F.linear(input_list[i], self.weight[i]))
results = torch.cat(output_list, dim=0)
return results
# Copied from transformers.models.jetmoe.modeling_jetmoe.JetMoeTopKGating with JetMoe->GraniteMoe
class GraniteMoeTopKGating(nn.Module):
def __init__(self, input_size: int, num_experts: int, top_k: int):
"""
Initialize the top-k gating mechanism.
Args:
input_size (`int`):
Size of the input.
num_experts (`int`):
Number of experts.
top_k (`int`):
Number of top experts to select.
"""
super().__init__()
self.num_experts = num_experts
self.input_size = input_size
self.top_k = top_k
self.layer = nn.Linear(input_size, num_experts, bias=False)
def forward(self, hidden_states):
# compute the top_k routing decision
logits = self.layer(hidden_states).float() # [batch_size x seq_len, num_experts]
top_k_logits, top_k_indices = logits.topk(self.top_k, dim=1) # [num_tokens, top_k]
top_k_gates = torch.softmax(top_k_logits, dim=1).type_as(hidden_states) # [num_tokens, top_k]
# compute number of input given to each expert
zeros = torch.zeros(
[top_k_gates.size(0), self.num_experts], dtype=top_k_gates.dtype, device=top_k_gates.device
) # [num_tokens, num_experts]
gates = zeros.scatter(1, top_k_indices, 1) # [num_tokens, num_experts]
expert_size = gates.long().sum(0) # [num_experts,]
# (This cause torch.compile to fail with `torch._dynamo.exc.Unsupported: Backend compiler failed with a fake tensor exception at`)
# (and `DataDependentOutputException`)
expert_size = expert_size.tolist()
# sort and group input tokens according to expert assignment
top_k_experts = top_k_indices.flatten() # [num_tokens * top_k]
_, index_sorted_experts = top_k_experts.sort(0) # [num_tokens * top_k]
batch_index = index_sorted_experts.div(self.top_k, rounding_mode="trunc") # [num_tokens * top_k]
# gather the gate values for grouped input tokens
top_k_gates = top_k_gates.flatten() # [num_tokens * top_k]
batch_gates = top_k_gates[index_sorted_experts] # [num_tokens * top_k]
return index_sorted_experts, batch_index, batch_gates, expert_size, logits
class GraniteMoeMoE(nn.Module):
"""
A Sparsely gated mixture of experts layer with 1-layer Feed-Forward networks as experts.
Args:
config:
Configuration object with model hyperparameters.
"""
def __init__(self, config: GraniteMoeConfig):
super(GraniteMoeMoE, self).__init__()
self.input_size = config.hidden_size
self.hidden_size = config.intermediate_size
self.activation = ACT2FN[config.hidden_act]
self.input_linear = GraniteMoeParallelExperts(config.num_local_experts, self.input_size, self.hidden_size * 2)
self.output_linear = GraniteMoeParallelExperts(config.num_local_experts, self.hidden_size, self.input_size)
self.router = GraniteMoeTopKGating(
input_size=self.input_size,
num_experts=config.num_local_experts,
top_k=config.num_experts_per_tok,
)
def forward(self, layer_input):
"""
Forward pass of the mixture of experts layer.
Args:
layer_input (Tensor):
Input tensor.
Returns:
Tensor:
Output tensor.
Tensor:
Router logits.
"""
bsz, length, emb_size = layer_input.size()
layer_input = layer_input.reshape(-1, emb_size)
_, batch_index, batch_gates, expert_size, router_logits = self.router(layer_input)
expert_inputs = layer_input[batch_index]
hidden_states = self.input_linear(expert_inputs, expert_size)
chunked_hidden_states = hidden_states.chunk(2, dim=-1)
hidden_states = self.activation(chunked_hidden_states[0]) * chunked_hidden_states[1]
expert_outputs = self.output_linear(hidden_states, expert_size)
expert_outputs = expert_outputs * batch_gates[:, None]
zeros = torch.zeros((bsz * length, self.input_size), dtype=expert_outputs.dtype, device=expert_outputs.device)
layer_output = zeros.index_add(0, batch_index, expert_outputs)
layer_output = layer_output.view(bsz, length, self.input_size)
return layer_output, router_logits
# Copied from transformers.models.granite.modeling_granite.repeat_kv with Granite->GraniteMoe
def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
"""
This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
"""
batch, num_key_value_heads, slen, head_dim = hidden_states.shape
if n_rep == 1:
return hidden_states
hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
# copied from transformers.models.granite.modeling_granite.GraniteAttention with Granite->GraniteMoe
# no longer copied after attention refactors
class GraniteMoeAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(self, config: GraniteMoeConfig, layer_idx: Optional[int] = None):
super().__init__()
self.config = config
self.layer_idx = layer_idx
if layer_idx is None:
logger.warning_once(
f"Instantiating {self.__class__.__name__} without passing a `layer_idx` is not recommended and will "
"lead to errors during the forward call if caching is used. Please make sure to provide a `layer_idx` "
"when creating this class."
)
self.attention_dropout = config.attention_dropout
self.hidden_size = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = self.hidden_size // self.num_heads
self.num_key_value_heads = config.num_key_value_heads
self.num_key_value_groups = self.num_heads // self.num_key_value_heads
self.is_causal = True
self.scaling = config.attention_multiplier
if (self.head_dim * self.num_heads) != self.hidden_size:
raise ValueError(
f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
f" and `num_heads`: {self.num_heads})."
)
self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=config.attention_bias)
self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
self.o_proj = nn.Linear(self.hidden_size, self.hidden_size, bias=config.attention_bias)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Cache] = None,
output_attentions: bool = False,
use_cache: bool = False,
cache_position: Optional[torch.LongTensor] = None,
position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, # necessary, but kept here for BC
**kwargs,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
bsz, q_len, _ = hidden_states.size()
query_states = self.q_proj(hidden_states)
key_states = self.k_proj(hidden_states)
value_states = self.v_proj(hidden_states)
query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
cos, sin = position_embeddings
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
if past_key_value is not None:
# sin and cos are specific to RoPE models; cache_position needed for the static cache
cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
key_states = repeat_kv(key_states, self.num_key_value_groups)
value_states = repeat_kv(value_states, self.num_key_value_groups)
attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) * self.scaling
if attention_mask is not None: # no matter the length, we just slice it
causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
attn_weights = attn_weights + causal_mask
# upcast attention to fp32
attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training)
attn_output = torch.matmul(attn_weights, value_states)
if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
raise ValueError(
f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
f" {attn_output.size()}"
)
attn_output = attn_output.transpose(1, 2).contiguous()
attn_output = attn_output.view(bsz, q_len, -1)
attn_output = self.o_proj(attn_output)
if not output_attentions:
attn_weights = None
return attn_output, attn_weights, past_key_value
# NO LONGER EXIST Copied from transformers.models.granite.modeling_granite.GraniteFlashAttention2 with Granite->GraniteMoe
# TODO cyril: modular
class GraniteMoeFlashAttention2(GraniteMoeAttention):
"""
GraniteMoe flash attention module. This module inherits from `GraniteMoeAttention` as the weights of the module stays
untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
flash attention and deal with padding tokens in case the input contains any of them.
"""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
# TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
# flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignment, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
# Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left).
self._flash_attn_uses_top_left_mask = flash_attn_supports_top_left_mask()
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Cache] = None,
output_attentions: bool = False,
use_cache: bool = False,
cache_position: Optional[torch.LongTensor] = None,
position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, # necessary, but kept here for BC
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
output_attentions = False
bsz, q_len, _ = hidden_states.size()
query_states = self.q_proj(hidden_states)
key_states = self.k_proj(hidden_states)
value_states = self.v_proj(hidden_states)
# Flash attention requires the input to have the shape
# batch_size x seq_length x head_dim x hidden_dim
# therefore we just need to keep the original shape
query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
cos, sin = position_embeddings
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
if past_key_value is not None:
# sin and cos are specific to RoPE models; cache_position needed for the static cache
cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
# TODO: These transpose are quite inefficient but Flash Attention requires the layout [batch_size, sequence_length, num_heads, head_dim]. We would need to refactor the KV cache
# to be able to avoid many of these transpose/reshape/view.
query_states = query_states.transpose(1, 2)
key_states = key_states.transpose(1, 2)
value_states = value_states.transpose(1, 2)
dropout_rate = self.attention_dropout if self.training else 0.0
# In PEFT, usually we cast the layer norms in float32 for training stability reasons
# therefore the input hidden states gets silently casted in float32. Hence, we need
# cast them back in the correct dtype just to be sure everything works as expected.
# This might slowdown training & inference so it is recommended to not cast the LayerNorms
# in fp32. (GraniteMoeRMSNorm handles it correctly)
input_dtype = query_states.dtype
if input_dtype == torch.float32:
if torch.is_autocast_enabled():
target_dtype = torch.get_autocast_gpu_dtype()
# Handle the case where the model is quantized
elif hasattr(self.config, "_pre_quantization_dtype"):
target_dtype = self.config._pre_quantization_dtype
else:
target_dtype = self.q_proj.weight.dtype
logger.warning_once(
f"The input hidden states seems to be silently casted in float32, this might be related to"
f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
f" {target_dtype}."
)
query_states = query_states.to(target_dtype)
key_states = key_states.to(target_dtype)
value_states = value_states.to(target_dtype)
attn_output = _flash_attention_forward(
query_states,
key_states,
value_states,
attention_mask,
q_len,
position_ids=position_ids,
dropout=dropout_rate,
softmax_scale=self.scaling,
sliding_window=getattr(self, "sliding_window", None),
use_top_left_mask=self._flash_attn_uses_top_left_mask,
is_causal=self.is_causal,
)
attn_output = attn_output.reshape(bsz, q_len, -1).contiguous()
attn_output = self.o_proj(attn_output)
if not output_attentions:
attn_weights = None
return attn_output, attn_weights, past_key_value
# NO LONGER EXIST Copied from transformers.models.granite.modeling_granite.GraniteSdpaAttention with Granite->GraniteMoe
# TODO cyril: modular
class GraniteMoeSdpaAttention(GraniteMoeAttention):
"""
GraniteMoe attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
`GraniteMoeAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to
SDPA API.
"""
# Adapted from GraniteMoeAttention.forward
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Cache] = None,
output_attentions: bool = False,
use_cache: bool = False,
cache_position: Optional[torch.LongTensor] = None,
position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, # necessary, but kept here for BC
**kwargs,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
if output_attentions:
# TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"` once this is implemented.
logger.warning_once(
"GraniteMoeModel is using GraniteMoeSdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to the manual attention implementation, "
'but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
)
return super().forward(
hidden_states=hidden_states,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_value=past_key_value,
output_attentions=output_attentions,
use_cache=use_cache,
cache_position=cache_position,
position_embeddings=position_embeddings,
)
bsz, q_len, _ = hidden_states.size()
query_states = self.q_proj(hidden_states)
key_states = self.k_proj(hidden_states)
value_states = self.v_proj(hidden_states)
query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
cos, sin = position_embeddings
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
if past_key_value is not None:
# sin and cos are specific to RoPE models; cache_position needed for the static cache
cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
key_states = repeat_kv(key_states, self.num_key_value_groups)
value_states = repeat_kv(value_states, self.num_key_value_groups)
causal_mask = attention_mask
if attention_mask is not None:
causal_mask = causal_mask[:, :, :, : key_states.shape[-2]]
# SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with custom attn_mask,
# Reference: https://github.com/pytorch/pytorch/issues/112577.
if query_states.device.type == "cuda" and causal_mask is not None:
query_states = query_states.contiguous()
key_states = key_states.contiguous()
value_states = value_states.contiguous()
# We dispatch to SDPA's Flash Attention or Efficient kernels via this `is_causal` if statement instead of an inline conditional assignment
# in SDPA to support both torch.compile's dynamic shapes and full graph options. An inline conditional prevents dynamic shapes from compiling.
is_causal = True if causal_mask is None and q_len > 1 else False
attn_output = torch.nn.functional.scaled_dot_product_attention(
query_states,
key_states,
value_states,
attn_mask=causal_mask,
dropout_p=self.attention_dropout if self.training else 0.0,
is_causal=is_causal,
scale=self.scaling,
)
attn_output = attn_output.transpose(1, 2).contiguous()
attn_output = attn_output.view(bsz, q_len, -1)
attn_output = self.o_proj(attn_output)
return attn_output, None, past_key_value
GRANITEMOE_ATTENTION_CLASSES = {
"eager": GraniteMoeAttention,
"flash_attention_2": GraniteMoeFlashAttention2,
"sdpa": GraniteMoeSdpaAttention,
}
class GraniteMoeDecoderLayer(nn.Module):
def __init__(self, config: GraniteMoeConfig, layer_idx: int):
super().__init__()
self.hidden_size = config.hidden_size
self.self_attn = GRANITEMOE_ATTENTION_CLASSES[config._attn_implementation](config=config, layer_idx=layer_idx)
self.block_sparse_moe = GraniteMoeMoE(config)
self.input_layernorm = GraniteMoeRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.post_attention_layernorm = GraniteMoeRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.residual_multiplier = config.residual_multiplier
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Cache] = None,
output_attentions: Optional[bool] = False,
use_cache: Optional[bool] = False,
cache_position: Optional[torch.LongTensor] = None,
output_router_logits: Optional[bool] = False,
position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, # necessary, but kept here for BC
**kwargs,
) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
"""
Args:
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
attention_mask (`torch.FloatTensor`, *optional*):
attention mask of size `(batch_size, sequence_length)` if flash attention is used or `(batch_size, 1,
query_sequence_length, key_sequence_length)` if default attention is used.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
(see `past_key_values`).
past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
Indices depicting the position of the input sequence tokens in the sequence
output_router_logits (`bool`, *optional*):
Whether or not to return the logits of all the routers. They are useful for computing the router loss, and
should not be returned during inference.
position_embeddings (`Tuple[torch.FloatTensor, torch.FloatTensor]`, *optional*):
Tuple containing the cosine and sine positional embeddings of shape `(batch_size, seq_len, head_dim)`,
with `head_dim` being the embedding dimension of each attention head.
kwargs (`dict`, *optional*):
Arbitrary kwargs to be ignored, used for FSDP and other methods that injects code
into the model
"""
residual = hidden_states
hidden_states = self.input_layernorm(hidden_states)
# Self Attention
hidden_states, self_attn_weights, present_key_value = self.self_attn(
hidden_states=hidden_states,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_value=past_key_value,
output_attentions=output_attentions,
use_cache=use_cache,
cache_position=cache_position,
position_embeddings=position_embeddings,
**kwargs,
)
hidden_states = residual + hidden_states * self.residual_multiplier
# Fully Connected
residual = hidden_states
hidden_states = self.post_attention_layernorm(hidden_states)
hidden_states, router_logits = self.block_sparse_moe(hidden_states)
hidden_states = residual + hidden_states * self.residual_multiplier
outputs = (hidden_states,)
if output_attentions:
outputs += (self_attn_weights,)
if use_cache:
outputs += (present_key_value,)
if output_router_logits:
outputs += (router_logits,)
return outputs
GRANITEMOE_START_DOCSTRING = r"""
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.
Parameters:
config ([`GraniteMoeConfig`]):
Model configuration class with all the parameters of the model. Initializing with a config file does not
load the weights associated with the model, only the configuration. Check out the
[`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
@add_start_docstrings(
"The bare GraniteMoe Model outputting raw hidden-states without any specific head on top.",
GRANITEMOE_START_DOCSTRING,
)
class GraniteMoePreTrainedModel(PreTrainedModel):
config_class = GraniteMoeConfig
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = ["GraniteMoeDecoderLayer"]
_skip_keys_device_placement = ["past_key_values"]
_supports_flash_attn_2 = True
_supports_sdpa = True
_supports_cache_class = True
_supports_quantized_cache = True
_supports_static_cache = False # MoE models don't work with torch.compile (`torch.where(condition)` not supported)
def _init_weights(self, module):
std = self.config.initializer_range
if isinstance(module, nn.Linear):
module.weight.data.normal_(mean=0.0, std=std)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.Embedding):
module.weight.data.normal_(mean=0.0, std=std)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
elif isinstance(module, nn.LayerNorm):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
elif isinstance(module, GraniteMoeParallelExperts):
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
GRANITEMOE_INPUTS_DOCSTRING = r"""
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
it.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
If `past_key_values` is used, optionally only the last `input_ids` have to be input (see
`past_key_values`).
If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
information on the default strategy.
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
config.n_positions - 1]`.
[What are position IDs?](../glossary#position-ids)
past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*):
Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values`
returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.
Two formats are allowed:
- a [`~cache_utils.Cache`] instance;
- Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy
cache format.
The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the
legacy cache format will be returned.
If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't
have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids`
of shape `(batch_size, sequence_length)`.
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
model's internal embedding lookup matrix.
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
`past_key_values`).
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
Indices depicting the position of the input sequence tokens in the sequence. Contrarily to `position_ids`,
this tensor is not affected by padding. It is used to update the cache in the correct position and to infer
the complete sequence length.
"""
@add_start_docstrings(
"The bare GraniteMoe Model outputting raw hidden-states without any specific head on top.",
GRANITEMOE_START_DOCSTRING,
)
class GraniteMoeModel(GraniteMoePreTrainedModel):
"""
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`GraniteMoeDecoderLayer`]
Args:
config: GraniteMoeConfig
"""
def __init__(self, config: GraniteMoeConfig):
super().__init__(config)
self.padding_idx = config.pad_token_id
self.vocab_size = config.vocab_size
self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
self.layers = nn.ModuleList(
[GraniteMoeDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
)
self.norm = GraniteMoeRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.gradient_checkpointing = False
self.embedding_multiplier = config.embedding_multiplier
self.hidden_size = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = self.hidden_size // self.num_heads
self.max_position_embeddings = config.max_position_embeddings
self.rope_theta = config.rope_theta
# rope
self.rotary_emb = GraniteMoeRotaryEmbedding(config)
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.embed_tokens
def set_input_embeddings(self, value):
self.embed_tokens = value
@add_start_docstrings_to_model_forward(GRANITEMOE_INPUTS_DOCSTRING)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
output_router_logits: Optional[bool] = None,
return_dict: Optional[bool] = None,
cache_position: Optional[torch.LongTensor] = None,
) -> Union[Tuple, BaseModelOutputWithPast]:
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
use_cache = use_cache if use_cache is not None else self.config.use_cache
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if (input_ids is None) ^ (inputs_embeds is not None):
raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
if self.gradient_checkpointing and self.training and use_cache:
logger.warning_once(
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`."
)
use_cache = False
if inputs_embeds is None:
inputs_embeds = self.embed_tokens(input_ids)
inputs_embeds = inputs_embeds * self.embedding_multiplier
return_legacy_cache = False
if use_cache and not isinstance(past_key_values, Cache): # kept for BC (non `Cache` `past_key_values` inputs)
return_legacy_cache = True
past_key_values = DynamicCache.from_legacy_cache(past_key_values)
logger.warning_once(
"We detected that you are passing `past_key_values` as a tuple and this is deprecated and will be removed in v4.43. "
"Please use an appropriate `Cache` class (https://huggingface.co/docs/transformers/v4.41.3/en/internal/generation_utils#transformers.Cache)"
)
if cache_position is None:
past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
cache_position = torch.arange(
past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
)
if position_ids is None:
position_ids = cache_position.unsqueeze(0)
causal_mask = self._update_causal_mask(
attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions
)
# embed positions
hidden_states = inputs_embeds
# create position embeddings to be shared across the decoder layers
position_embeddings = self.rotary_emb(hidden_states, position_ids)
# decoder layers
all_hidden_states = () if output_hidden_states else None
all_self_attns = () if output_attentions else None
all_router_logits = () if output_router_logits else None
next_decoder_cache = None
for decoder_layer in self.layers:
if output_hidden_states:
all_hidden_states += (hidden_states,)
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
decoder_layer.__call__,
hidden_states,
causal_mask,
position_ids,
past_key_values,
output_attentions,
use_cache,
cache_position,
output_router_logits,
position_embeddings,
)
else:
layer_outputs = decoder_layer(
hidden_states,
attention_mask=causal_mask,
position_ids=position_ids,
past_key_value=past_key_values,
output_attentions=output_attentions,
use_cache=use_cache,
cache_position=cache_position,
output_router_logits=output_router_logits,
position_embeddings=position_embeddings,
)
hidden_states = layer_outputs[0]
if use_cache:
next_decoder_cache = layer_outputs[2 if output_attentions else 1]
if output_attentions:
all_self_attns += (layer_outputs[1],)
if output_router_logits:
all_router_logits += (layer_outputs[-1],)
hidden_states = self.norm(hidden_states)
# add hidden states from the last decoder layer
if output_hidden_states:
all_hidden_states += (hidden_states,)
next_cache = next_decoder_cache if use_cache else None
if return_legacy_cache:
next_cache = next_cache.to_legacy_cache()
if not return_dict:
return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
return MoeModelOutputWithPast(
last_hidden_state=hidden_states,
past_key_values=next_cache,
hidden_states=all_hidden_states,
attentions=all_self_attns,
router_logits=all_router_logits,
)
# Copied from transformers.models.llama.modeling_llama.LlamaModel._update_causal_mask
def _update_causal_mask(
self,
attention_mask: torch.Tensor,
input_tensor: torch.Tensor,
cache_position: torch.Tensor,
past_key_values: Cache,
output_attentions: bool = False,
):
if self.config._attn_implementation == "flash_attention_2":
if attention_mask is not None and (attention_mask == 0.0).any():
return attention_mask
return None
if self.config._attn_implementation == "flex_attention":
if isinstance(attention_mask, torch.Tensor):
attention_mask = make_flex_block_causal_mask(attention_mask)
if isinstance(attention_mask, BlockMask):
return attention_mask
# For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in
# order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail
# to infer the attention mask.
past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
using_static_cache = isinstance(past_key_values, StaticCache)
# When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward
if self.config._attn_implementation == "sdpa" and not using_static_cache and not output_attentions:
if AttentionMaskConverter._ignore_causal_mask_sdpa(
attention_mask,
inputs_embeds=input_tensor,
past_key_values_length=past_seen_tokens,
is_training=self.training,
):
return None
dtype, device = input_tensor.dtype, input_tensor.device
sequence_length = input_tensor.shape[1]
if using_static_cache:
target_length = past_key_values.get_max_cache_shape()
else:
target_length = (
attention_mask.shape[-1]
if isinstance(attention_mask, torch.Tensor)
else past_seen_tokens + sequence_length + 1
)
# In case the provided `attention` mask is 2D, we generate a causal mask here (4D).
causal_mask = self._prepare_4d_causal_attention_mask_with_cache_position(
attention_mask,
sequence_length=sequence_length,
target_length=target_length,
dtype=dtype,
device=device,
cache_position=cache_position,
batch_size=input_tensor.shape[0],
)
if (
self.config._attn_implementation == "sdpa"
and attention_mask is not None
and attention_mask.device.type in ["cuda", "xpu"]
and not output_attentions
):
# Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when
# using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path.
# Details: https://github.com/pytorch/pytorch/issues/110213
min_dtype = torch.finfo(dtype).min
causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype)
return causal_mask
@staticmethod
# Copied from transformers.models.llama.modeling_llama.LlamaModel._prepare_4d_causal_attention_mask_with_cache_position
def _prepare_4d_causal_attention_mask_with_cache_position(
attention_mask: torch.Tensor,
sequence_length: int,
target_length: int,
dtype: torch.dtype,
device: torch.device,
cache_position: torch.Tensor,
batch_size: int,
**kwargs,
):
"""
Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape
`(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing.
Args:
attention_mask (`torch.Tensor`):
A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape
`(batch_size, 1, query_length, key_value_length)`.
sequence_length (`int`):
The sequence length being processed.
target_length (`int`):
The target length: when generating with static cache, the mask should be as long as the static cache,
to account for the 0 padding, the part of the cache that is not filled yet.
dtype (`torch.dtype`):
The dtype to use for the 4D attention mask.
device (`torch.device`):
The device to place the 4D attention mask on.
cache_position (`torch.Tensor`):
Indices depicting the position of the input sequence tokens in the sequence.
batch_size (`torch.Tensor`):
Batch size.
"""
if attention_mask is not None and attention_mask.dim() == 4:
# In this case we assume that the mask comes already in inverted form and requires no inversion or slicing.
causal_mask = attention_mask
else:
min_dtype = torch.finfo(dtype).min
causal_mask = torch.full(
(sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=device
)
if sequence_length != 1:
causal_mask = torch.triu(causal_mask, diagonal=1)
causal_mask *= torch.arange(target_length, device=device) > cache_position.reshape(-1, 1)
causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1)
if attention_mask is not None:
causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit
mask_length = attention_mask.shape[-1]
padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :].to(
causal_mask.device
)
padding_mask = padding_mask == 0
causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
padding_mask, min_dtype
)
return causal_mask
class GraniteMoeForCausalLM(GraniteMoePreTrainedModel, GenerationMixin):
_tied_weights_keys = ["lm_head.weight"]
def __init__(self, config: GraniteMoeConfig):
super().__init__(config)
self.model = GraniteMoeModel(config)
self.vocab_size = config.vocab_size
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
self.router_aux_loss_coef = config.router_aux_loss_coef
self.num_experts = config.num_local_experts
self.num_experts_per_tok = config.num_experts_per_tok
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.model.embed_tokens
def set_input_embeddings(self, value):
self.model.embed_tokens = value
def get_output_embeddings(self):
return self.lm_head
def set_output_embeddings(self, new_embeddings):
self.lm_head = new_embeddings
def set_decoder(self, decoder):
self.model = decoder
def get_decoder(self):
return self.model
@add_start_docstrings_to_model_forward(GRANITEMOE_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=MoeCausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
output_router_logits: Optional[bool] = None,
return_dict: Optional[bool] = None,
cache_position: Optional[torch.LongTensor] = None,
**kwargs,
) -> Union[Tuple, MoeCausalLMOutputWithPast]:
r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
Returns:
Example:
```python
>>> from transformers import AutoTokenizer, GraniteMoeForCausalLM
>>> model = GraniteMoeForCausalLM.from_pretrained("ibm/PowerMoE-3b")
>>> tokenizer = AutoTokenizer.from_pretrained("ibm/PowerMoE-3b")
>>> prompt = "Hey, are you conscious? Can you talk to me?"
>>> inputs = tokenizer(prompt, return_tensors="pt")
>>> # Generate
>>> generate_ids = model.generate(inputs.input_ids, max_length=30)
>>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
"Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
```"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_router_logits = (
output_router_logits if output_router_logits is not None else self.config.output_router_logits
)
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
outputs = self.model(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
output_router_logits=output_router_logits,
return_dict=return_dict,
cache_position=cache_position,
)
hidden_states = outputs[0]
logits = self.lm_head(hidden_states)
logits = logits / self.config.logits_scaling
loss = None
if labels is not None:
# Upcast to float if we need to compute the loss to avoid potential precision issues
logits = logits.float()
# Flatten the tokens
loss = self.loss_function(
logits,
labels,
vocab_size=self.config.vocab_size,
**kwargs,
)
aux_loss = None
if output_router_logits:
aux_loss = load_balancing_loss_func(
outputs.router_logits if return_dict else outputs[-1],
self.num_experts,
self.num_experts_per_tok,
attention_mask,
)
if labels is not None:
loss += self.router_aux_loss_coef * aux_loss.to(loss.device) # make sure to reside in the same device
if not return_dict:
output = (logits,) + outputs[1:]
if output_router_logits:
output = (aux_loss,) + output
return (loss,) + output if loss is not None else output
return MoeCausalLMOutputWithPast(
loss=loss,
aux_loss=aux_loss,
logits=logits,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
router_logits=outputs.router_logits,
)
@staticmethod
def _reorder_cache(past_key_values, beam_idx):
reordered_past = ()
for layer_past in past_key_values:
reordered_past += (
tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
)
return reordered_past
__all__ = ["GraniteMoeForCausalLM", "GraniteMoeModel", "GraniteMoePreTrainedModel"]
```
|
========================================================================================================================================
SOURCE CODE FILE: __init__.py
LINES: 1
SIZE: 0.99 KB
PATH: scripts\freecad_env\Lib\site-packages\transformers\models\granitemoeshared\__init__.py
ENCODING: utf-8
```py
# Copyright 2024 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import _LazyModule
from ...utils.import_utils import define_import_structure
if TYPE_CHECKING:
from .configuration_granitemoeshared import *
from .modeling_granitemoeshared import *
else:
import sys
_file = globals()["__file__"]
sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)
```
|
==============================================================================================================================================================
SOURCE CODE FILE: configuration_granitemoeshared.py
LINES: 1
SIZE: 9.60 KB
PATH: scripts\freecad_env\Lib\site-packages\transformers\models\granitemoeshared\configuration_granitemoeshared.py
ENCODING: utf-8
```py
# coding=utf-8
# Copyright 2024 EleutherAI and the HuggingFace Inc. team. All rights reserved.
#
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
# and OPT implementations in this library. It has been modified from its
# original forms to accommodate minor architectural differences compared
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""GraniteMoeShared model configuration"""
from ...configuration_utils import PretrainedConfig
from ...modeling_rope_utils import rope_config_validation
from ...utils import logging
logger = logging.get_logger(__name__)
class GraniteMoeSharedConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`GraniteMoeSharedModel`]. It is used to instantiate an GraniteMoeShared
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar configuration to that of the [ibm-research/moe-7b-1b-active-shared-experts](https://huggingface.co/ibm-research/moe-7b-1b-active-shared-experts).
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 32000):
Vocabulary size of the GraniteMoeShared model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`GraniteMoeSharedModel`]
hidden_size (`int`, *optional*, defaults to 4096):
Dimension of the hidden representations.
intermediate_size (`int`, *optional*, defaults to 11008):
Dimension of the MLP representations.
num_hidden_layers (`int`, *optional*, defaults to 32):
Number of hidden layers in the Transformer decoder.
num_attention_heads (`int`, *optional*, defaults to 32):
Number of attention heads for each attention layer in the Transformer decoder.
num_key_value_heads (`int`, *optional*):
This is the number of key_value heads that should be used to implement Grouped Query Attention. If
`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
`num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
by meanpooling all the original heads within that group. For more details checkout [this
paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
`num_attention_heads`.
hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
The non-linear activation function (function or string) in the decoder.
max_position_embeddings (`int`, *optional*, defaults to 2048):
The maximum sequence length that this model might ever be used with.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
rms_norm_eps (`float`, *optional*, defaults to 1e-06):
The epsilon used by the rms normalization layers.
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models). Only
relevant if `config.is_decoder=True`.
pad_token_id (`int`, *optional*):
Padding token id.
bos_token_id (`int`, *optional*, defaults to 1):
Beginning of stream token id.
eos_token_id (`int`, *optional*, defaults to 2):
End of stream token id.
tie_word_embeddings (`bool`, *optional*, defaults to `False`):
Whether to tie weight embeddings
rope_theta (`float`, *optional*, defaults to 10000.0):
The base period of the RoPE embeddings.
rope_scaling (`Dict`, *optional*):
Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is
`{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
`max_position_embeddings` to the expected new maximum. See the following thread for more information on how
these scaling strategies behave:
https://www.reddit.com/r/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an
experimental feature, subject to breaking API changes in future versions.
attention_bias (`bool`, *optional*, defaults to `False`):
Whether to use a bias in the query, key, value and output projection layers during self-attention.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
embedding_multiplier (`float`, *optional*, defaults to 1.0): embedding multiplier
logits_scaling (`float`, *optional*, defaults to 1.0): divisor for output logits
residual_multiplier (`float`, *optional*, defaults to 1.0): residual multiplier
attention_multiplier (`float`, *optional*, defaults to 1.0): attention multiplier
num_local_experts (`int`, *optional*, defaults to 8): total number of experts
num_experts_per_tok (`int`, *optional*, defaults to 2): number of experts per token
output_router_logits (`bool`, *optional*, defaults to `False`):
Whether or not the router logits should be returned by the model. Enabeling this will also
allow the model to output the auxiliary loss.
router_aux_loss_coef (`float`, *optional*, defaults to 0.001): router auxialiary loss coefficient
shared_intermediate_size (`int`, *optional*, defaults to 0): intermediate size for shared experts. 0 implies
no shared experts.
```python
>>> from transformers import GraniteMoeSharedModel, GraniteMoeSharedConfig
>>> # Initializing a GraniteMoeShared granitemoe-3b style configuration
>>> configuration = GraniteMoeSharedConfig()
>>> # Initializing a model from the granitemoe-7b style configuration
>>> model = GraniteMoeSharedModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "granitemoeshared"
keys_to_ignore_at_inference = ["past_key_values"]
def __init__(
self,
vocab_size=32000,
hidden_size=4096,
intermediate_size=11008,
num_hidden_layers=32,
num_attention_heads=32,
num_key_value_heads=None,
hidden_act="silu",
max_position_embeddings=2048,
initializer_range=0.02,
rms_norm_eps=1e-6,
use_cache=True,
pad_token_id=None,
bos_token_id=1,
eos_token_id=2,
tie_word_embeddings=False,
rope_theta=10000.0,
rope_scaling=None,
attention_bias=False,
attention_dropout=0.0,
embedding_multiplier=1.0,
logits_scaling=1.0,
residual_multiplier=1.0,
attention_multiplier=1.0,
num_local_experts=8,
num_experts_per_tok=2,
output_router_logits=False,
router_aux_loss_coef=0.001,
shared_intermediate_size=0,
**kwargs,
):
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
# for backward compatibility
if num_key_value_heads is None:
num_key_value_heads = num_attention_heads
self.num_key_value_heads = num_key_value_heads
self.hidden_act = hidden_act
self.initializer_range = initializer_range
self.rms_norm_eps = rms_norm_eps
self.use_cache = use_cache
self.rope_theta = rope_theta
self.rope_scaling = rope_scaling
self.attention_bias = attention_bias
self.attention_dropout = attention_dropout
self.embedding_multiplier = embedding_multiplier
self.logits_scaling = logits_scaling
self.residual_multiplier = residual_multiplier
self.attention_multiplier = attention_multiplier
self.num_local_experts = num_local_experts
self.num_experts_per_tok = num_experts_per_tok
self.output_router_logits = output_router_logits
self.router_aux_loss_coef = router_aux_loss_coef
self.shared_intermediate_size = shared_intermediate_size
super().__init__(
pad_token_id=pad_token_id,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
tie_word_embeddings=tie_word_embeddings,
**kwargs,
)
rope_config_validation(self)
__all__ = ["GraniteMoeSharedConfig"]
```
|
=========================================================================================================================================================
SOURCE CODE FILE: modeling_granitemoeshared.py
LINES: 2
SIZE: 63.26 KB
PATH: scripts\freecad_env\Lib\site-packages\transformers\models\granitemoeshared\modeling_granitemoeshared.py
ENCODING: utf-8
```py
# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
# This file was automatically generated from src/transformers/models/granitemoeshared/modular_granitemoeshared.py.
# Do NOT edit this file manually as any edits will be overwritten by the generation of
# the file from the modular. If any change should be done, please apply the change to the
# modular_granitemoeshared.py file directly. One of our CI enforces this.
# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
# coding=utf-8
# Copyright 2024 IBM and the HuggingFace Inc. team. All rights reserved.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import List, Optional, Tuple, Union
import torch
import torch.nn.functional as F
from torch import nn
from ...activations import ACT2FN
from ...cache_utils import Cache, DynamicCache, StaticCache
from ...generation import GenerationMixin
from ...modeling_attn_mask_utils import AttentionMaskConverter
from ...modeling_flash_attention_utils import _flash_attention_forward, flash_attn_supports_top_left_mask
from ...modeling_outputs import BaseModelOutputWithPast, MoeCausalLMOutputWithPast, MoeModelOutputWithPast
from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
from ...modeling_utils import PreTrainedModel
from ...utils import (
add_start_docstrings,
add_start_docstrings_to_model_forward,
is_torch_flex_attn_available,
logging,
replace_return_docstrings,
)
from .configuration_granitemoeshared import GraniteMoeSharedConfig
if is_torch_flex_attn_available():
from torch.nn.attention.flex_attention import BlockMask
from ...integrations.flex_attention import make_flex_block_causal_mask
logger = logging.get_logger(__name__)
_CONFIG_FOR_DOC = "GraniteMoeSharedConfig"
class GraniteMoeSharedMLP(nn.Module):
"""
MLP layer for shared experts
Args:
config:
Configuration object with model hyperparameters.
"""
def __init__(self, config: GraniteMoeSharedConfig):
super(GraniteMoeSharedMLP, self).__init__()
self.input_size = config.hidden_size
self.hidden_size = config.shared_intermediate_size
self.activation = ACT2FN[config.hidden_act]
self.input_linear = nn.Linear(self.input_size, self.hidden_size * 2, bias=False)
self.output_linear = nn.Linear(self.hidden_size, self.input_size, bias=False)
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidden_states = self.input_linear(hidden_states)
chunked_hidden_states = hidden_states.chunk(2, dim=-1)
hidden_states = self.activation(chunked_hidden_states[0]) * chunked_hidden_states[1]
hidden_states = self.output_linear(hidden_states)
return hidden_states
class GraniteMoeSharedRMSNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-6):
"""
GraniteMoeSharedRMSNorm is equivalent to T5LayerNorm
"""
super().__init__()
self.weight = nn.Parameter(torch.ones(hidden_size))
self.variance_epsilon = eps
def forward(self, hidden_states):
input_dtype = hidden_states.dtype
hidden_states = hidden_states.to(torch.float32)
variance = hidden_states.pow(2).mean(-1, keepdim=True)
hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
return self.weight * hidden_states.to(input_dtype)
def extra_repr(self):
return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}"
class GraniteMoeSharedParallelExperts(nn.Module):
def __init__(self, num_experts: int, input_size: int, output_size: int) -> None:
"""
Initialize the GraniteMoeSharedParallelExperts module.
The experts weights are stored in [num_experts, output_size, input_size] format. Such that it's comptible with
many MoE libraries, such as [Megablock](https://github.com/databricks/megablocks) and
[ScatterMoE](https://github.com/shawntan/scattermoe), as well as the
[MoE kernel](https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/layers/fused_moe/fused_moe.py)
used in vllm.
Args:
num_experts (int):
Number of experts.
input_size (int):
Size of the input.
output_size (int):
Size of the output.
"""
super().__init__()
self.weight = nn.Parameter(torch.empty(num_experts, output_size, input_size))
self.num_experts = num_experts
self.input_size = input_size
self.output_size = output_size
def forward(self, inputs, expert_size):
"""
Forward pass of the GraniteMoeSharedParallelExperts module.
Args:
inputs (Tensor):
Input tensor.
expert_size:
Expert size information.
Returns:
Tensor: Output tensor.
"""
input_list = inputs.split(expert_size, dim=0)
output_list = []
for i in range(self.num_experts):
output_list.append(F.linear(input_list[i], self.weight[i]))
results = torch.cat(output_list, dim=0)
return results
class GraniteMoeSharedTopKGating(nn.Module):
def __init__(self, input_size: int, num_experts: int, top_k: int):
"""
Initialize the top-k gating mechanism.
Args:
input_size (`int`):
Size of the input.
num_experts (`int`):
Number of experts.
top_k (`int`):
Number of top experts to select.
"""
super().__init__()
self.num_experts = num_experts
self.input_size = input_size
self.top_k = top_k
self.layer = nn.Linear(input_size, num_experts, bias=False)
def forward(self, hidden_states):
# compute the top_k routing decision
logits = self.layer(hidden_states).float() # [batch_size x seq_len, num_experts]
top_k_logits, top_k_indices = logits.topk(self.top_k, dim=1) # [num_tokens, top_k]
top_k_gates = torch.softmax(top_k_logits, dim=1).type_as(hidden_states) # [num_tokens, top_k]
# compute number of input given to each expert
zeros = torch.zeros(
[top_k_gates.size(0), self.num_experts], dtype=top_k_gates.dtype, device=top_k_gates.device
) # [num_tokens, num_experts]
gates = zeros.scatter(1, top_k_indices, 1) # [num_tokens, num_experts]
expert_size = gates.long().sum(0) # [num_experts,]
# (This cause torch.compile to fail with `torch._dynamo.exc.Unsupported: Backend compiler failed with a fake tensor exception at`)
# (and `DataDependentOutputException`)
expert_size = expert_size.tolist()
# sort and group input tokens according to expert assignment
top_k_experts = top_k_indices.flatten() # [num_tokens * top_k]
_, index_sorted_experts = top_k_experts.sort(0) # [num_tokens * top_k]
batch_index = index_sorted_experts.div(self.top_k, rounding_mode="trunc") # [num_tokens * top_k]
# gather the gate values for grouped input tokens
top_k_gates = top_k_gates.flatten() # [num_tokens * top_k]
batch_gates = top_k_gates[index_sorted_experts] # [num_tokens * top_k]
return index_sorted_experts, batch_index, batch_gates, expert_size, logits
class GraniteMoeSharedMoE(nn.Module):
"""
A Sparsely gated mixture of experts layer with 1-layer Feed-Forward networks as experts.
Args:
config:
Configuration object with model hyperparameters.
"""
def __init__(self, config: GraniteMoeSharedConfig):
super(GraniteMoeSharedMoE, self).__init__()
self.input_size = config.hidden_size
self.hidden_size = config.intermediate_size
self.activation = ACT2FN[config.hidden_act]
self.input_linear = GraniteMoeSharedParallelExperts(
config.num_local_experts, self.input_size, self.hidden_size * 2
)
self.output_linear = GraniteMoeSharedParallelExperts(
config.num_local_experts, self.hidden_size, self.input_size
)
self.router = GraniteMoeSharedTopKGating(
input_size=self.input_size,
num_experts=config.num_local_experts,
top_k=config.num_experts_per_tok,
)
def forward(self, layer_input):
"""
Forward pass of the mixture of experts layer.
Args:
layer_input (Tensor):
Input tensor.
Returns:
Tensor:
Output tensor.
Tensor:
Router logits.
"""
bsz, length, emb_size = layer_input.size()
layer_input = layer_input.reshape(-1, emb_size)
_, batch_index, batch_gates, expert_size, router_logits = self.router(layer_input)
expert_inputs = layer_input[batch_index]
hidden_states = self.input_linear(expert_inputs, expert_size)
chunked_hidden_states = hidden_states.chunk(2, dim=-1)
hidden_states = self.activation(chunked_hidden_states[0]) * chunked_hidden_states[1]
expert_outputs = self.output_linear(hidden_states, expert_size)
expert_outputs = expert_outputs * batch_gates[:, None]
zeros = torch.zeros((bsz * length, self.input_size), dtype=expert_outputs.dtype, device=expert_outputs.device)
layer_output = zeros.index_add(0, batch_index, expert_outputs)
layer_output = layer_output.view(bsz, length, self.input_size)
return layer_output, router_logits
def rotate_half(x):
"""Rotates half the hidden dims of the input."""
x1 = x[..., : x.shape[-1] // 2]
x2 = x[..., x.shape[-1] // 2 :]
return torch.cat((-x2, x1), dim=-1)
def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
"""Applies Rotary Position Embedding to the query and key tensors.
Args:
q (`torch.Tensor`): The query tensor.
k (`torch.Tensor`): The key tensor.
cos (`torch.Tensor`): The cosine part of the rotary embedding.
sin (`torch.Tensor`): The sine part of the rotary embedding.
position_ids (`torch.Tensor`, *optional*):
Deprecated and unused.
unsqueeze_dim (`int`, *optional*, defaults to 1):
The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
Returns:
`tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
"""
cos = cos.unsqueeze(unsqueeze_dim)
sin = sin.unsqueeze(unsqueeze_dim)
q_embed = (q * cos) + (rotate_half(q) * sin)
k_embed = (k * cos) + (rotate_half(k) * sin)
return q_embed, k_embed
def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
"""
This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
"""
batch, num_key_value_heads, slen, head_dim = hidden_states.shape
if n_rep == 1:
return hidden_states
hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
# copied from transformers.models.granite.modeling_granite.GraniteAttention with Granite->GraniteMoeShared
# no longer copied after attention refactors
class GraniteMoeSharedAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(self, config: GraniteMoeSharedConfig, layer_idx: Optional[int] = None):
super().__init__()
self.config = config
self.layer_idx = layer_idx
if layer_idx is None:
logger.warning_once(
f"Instantiating {self.__class__.__name__} without passing a `layer_idx` is not recommended and will "
"lead to errors during the forward call if caching is used. Please make sure to provide a `layer_idx` "
"when creating this class."
)
self.attention_dropout = config.attention_dropout
self.hidden_size = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = self.hidden_size // self.num_heads
self.num_key_value_heads = config.num_key_value_heads
self.num_key_value_groups = self.num_heads // self.num_key_value_heads
self.is_causal = True
self.scaling = config.attention_multiplier
if (self.head_dim * self.num_heads) != self.hidden_size:
raise ValueError(
f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
f" and `num_heads`: {self.num_heads})."
)
self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=config.attention_bias)
self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
self.o_proj = nn.Linear(self.hidden_size, self.hidden_size, bias=config.attention_bias)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Cache] = None,
output_attentions: bool = False,
use_cache: bool = False,
cache_position: Optional[torch.LongTensor] = None,
position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, # necessary, but kept here for BC
**kwargs,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
bsz, q_len, _ = hidden_states.size()
query_states = self.q_proj(hidden_states)
key_states = self.k_proj(hidden_states)
value_states = self.v_proj(hidden_states)
query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
cos, sin = position_embeddings
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
if past_key_value is not None:
# sin and cos are specific to RoPE models; cache_position needed for the static cache
cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
key_states = repeat_kv(key_states, self.num_key_value_groups)
value_states = repeat_kv(value_states, self.num_key_value_groups)
attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) * self.scaling
if attention_mask is not None: # no matter the length, we just slice it
causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
attn_weights = attn_weights + causal_mask
# upcast attention to fp32
attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training)
attn_output = torch.matmul(attn_weights, value_states)
if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
raise ValueError(
f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
f" {attn_output.size()}"
)
attn_output = attn_output.transpose(1, 2).contiguous()
attn_output = attn_output.view(bsz, q_len, -1)
attn_output = self.o_proj(attn_output)
if not output_attentions:
attn_weights = None
return attn_output, attn_weights, past_key_value
# NO LONGER EXIST Copied from transformers.models.granite.modeling_granite.GraniteFlashAttention2 with Granite->GraniteMoeShared
# TODO cyril: modular
class GraniteMoeSharedFlashAttention2(GraniteMoeSharedAttention):
"""
GraniteMoeShared flash attention module. This module inherits from `GraniteMoeSharedAttention` as the weights of the module stays
untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
flash attention and deal with padding tokens in case the input contains any of them.
"""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
# TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
# flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignment, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
# Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left).
self._flash_attn_uses_top_left_mask = flash_attn_supports_top_left_mask()
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Cache] = None,
output_attentions: bool = False,
use_cache: bool = False,
cache_position: Optional[torch.LongTensor] = None,
position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, # necessary, but kept here for BC
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
output_attentions = False
bsz, q_len, _ = hidden_states.size()
query_states = self.q_proj(hidden_states)
key_states = self.k_proj(hidden_states)
value_states = self.v_proj(hidden_states)
# Flash attention requires the input to have the shape
# batch_size x seq_length x head_dim x hidden_dim
# therefore we just need to keep the original shape
query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
cos, sin = position_embeddings
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
if past_key_value is not None:
# sin and cos are specific to RoPE models; cache_position needed for the static cache
cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
# TODO: These transpose are quite inefficient but Flash Attention requires the layout [batch_size, sequence_length, num_heads, head_dim]. We would need to refactor the KV cache
# to be able to avoid many of these transpose/reshape/view.
query_states = query_states.transpose(1, 2)
key_states = key_states.transpose(1, 2)
value_states = value_states.transpose(1, 2)
dropout_rate = self.attention_dropout if self.training else 0.0
# In PEFT, usually we cast the layer norms in float32 for training stability reasons
# therefore the input hidden states gets silently casted in float32. Hence, we need
# cast them back in the correct dtype just to be sure everything works as expected.
# This might slowdown training & inference so it is recommended to not cast the LayerNorms
# in fp32. (GraniteMoeSharedRMSNorm handles it correctly)
input_dtype = query_states.dtype
if input_dtype == torch.float32:
if torch.is_autocast_enabled():
target_dtype = torch.get_autocast_gpu_dtype()
# Handle the case where the model is quantized
elif hasattr(self.config, "_pre_quantization_dtype"):
target_dtype = self.config._pre_quantization_dtype
else:
target_dtype = self.q_proj.weight.dtype
logger.warning_once(
f"The input hidden states seems to be silently casted in float32, this might be related to"
f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
f" {target_dtype}."
)
query_states = query_states.to(target_dtype)
key_states = key_states.to(target_dtype)
value_states = value_states.to(target_dtype)
attn_output = _flash_attention_forward(
query_states,
key_states,
value_states,
attention_mask,
q_len,
position_ids=position_ids,
dropout=dropout_rate,
softmax_scale=self.scaling,
sliding_window=getattr(self, "sliding_window", None),
use_top_left_mask=self._flash_attn_uses_top_left_mask,
is_causal=self.is_causal,
)
attn_output = attn_output.reshape(bsz, q_len, -1).contiguous()
attn_output = self.o_proj(attn_output)
if not output_attentions:
attn_weights = None
return attn_output, attn_weights, past_key_value
# NO LONGER EXIST Copied from transformers.models.granite.modeling_granite.GraniteSdpaAttention with Granite->GraniteMoeShared
# TODO cyril: modular
class GraniteMoeSharedSdpaAttention(GraniteMoeSharedAttention):
"""
GraniteMoeShared attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
`GraniteMoeSharedAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to
SDPA API.
"""
# Adapted from GraniteMoeSharedAttention.forward
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Cache] = None,
output_attentions: bool = False,
use_cache: bool = False,
cache_position: Optional[torch.LongTensor] = None,
position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, # necessary, but kept here for BC
**kwargs,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
if output_attentions:
# TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"` once this is implemented.
logger.warning_once(
"GraniteMoeSharedModel is using GraniteMoeSharedSdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to the manual attention implementation, "
'but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
)
return super().forward(
hidden_states=hidden_states,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_value=past_key_value,
output_attentions=output_attentions,
use_cache=use_cache,
cache_position=cache_position,
position_embeddings=position_embeddings,
)
bsz, q_len, _ = hidden_states.size()
query_states = self.q_proj(hidden_states)
key_states = self.k_proj(hidden_states)
value_states = self.v_proj(hidden_states)
query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
cos, sin = position_embeddings
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
if past_key_value is not None:
# sin and cos are specific to RoPE models; cache_position needed for the static cache
cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
key_states = repeat_kv(key_states, self.num_key_value_groups)
value_states = repeat_kv(value_states, self.num_key_value_groups)
causal_mask = attention_mask
if attention_mask is not None:
causal_mask = causal_mask[:, :, :, : key_states.shape[-2]]
# SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with custom attn_mask,
# Reference: https://github.com/pytorch/pytorch/issues/112577.
if query_states.device.type == "cuda" and causal_mask is not None:
query_states = query_states.contiguous()
key_states = key_states.contiguous()
value_states = value_states.contiguous()
# We dispatch to SDPA's Flash Attention or Efficient kernels via this `is_causal` if statement instead of an inline conditional assignment
# in SDPA to support both torch.compile's dynamic shapes and full graph options. An inline conditional prevents dynamic shapes from compiling.
is_causal = True if causal_mask is None and q_len > 1 else False
attn_output = torch.nn.functional.scaled_dot_product_attention(
query_states,
key_states,
value_states,
attn_mask=causal_mask,
dropout_p=self.attention_dropout if self.training else 0.0,
is_causal=is_causal,
scale=self.scaling,
)
attn_output = attn_output.transpose(1, 2).contiguous()
attn_output = attn_output.view(bsz, q_len, -1)
attn_output = self.o_proj(attn_output)
return attn_output, None, past_key_value
GRANITEMOESHARED_ATTENTION_CLASSES = {
"eager": GraniteMoeSharedAttention,
"flash_attention_2": GraniteMoeSharedFlashAttention2,
"sdpa": GraniteMoeSharedSdpaAttention,
}
class GraniteMoeSharedDecoderLayer(nn.Module):
def __init__(self, config: GraniteMoeSharedConfig, layer_idx: int):
super().__init__()
self.hidden_size = config.hidden_size
self.self_attn = GRANITEMOESHARED_ATTENTION_CLASSES[config._attn_implementation](
config=config, layer_idx=layer_idx
)
self.block_sparse_moe = GraniteMoeSharedMoE(config)
self.input_layernorm = GraniteMoeSharedRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.post_attention_layernorm = GraniteMoeSharedRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.residual_multiplier = config.residual_multiplier
self.shared_mlp = None if config.shared_intermediate_size == 0 else GraniteMoeSharedMLP(config)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Cache] = None,
output_attentions: Optional[bool] = False,
use_cache: Optional[bool] = False,
cache_position: Optional[torch.LongTensor] = None,
output_router_logits: Optional[bool] = False,
position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, # necessary, but kept here for BC
**kwargs,
) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
"""
Args:
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
attention_mask (`torch.FloatTensor`, *optional*):
attention mask of size `(batch_size, sequence_length)` if flash attention is used or `(batch_size, 1,
query_sequence_length, key_sequence_length)` if default attention is used.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
(see `past_key_values`).
past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
Indices depicting the position of the input sequence tokens in the sequence
output_router_logits (`bool`, *optional*):
Whether or not to return the logits of all the routers. They are useful for computing the router loss, and
should not be returned during inference.
position_embeddings (`Tuple[torch.FloatTensor, torch.FloatTensor]`, *optional*):
Tuple containing the cosine and sine positional embeddings of shape `(batch_size, seq_len, head_dim)`,
with `head_dim` being the embedding dimension of each attention head.
kwargs (`dict`, *optional*):
Arbitrary kwargs to be ignored, used for FSDP and other methods that injects code
into the model
"""
residual = hidden_states
hidden_states = self.input_layernorm(hidden_states)
# Self Attention
hidden_states, self_attn_weights, present_key_value = self.self_attn(
hidden_states=hidden_states,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_value=past_key_value,
output_attentions=output_attentions,
use_cache=use_cache,
cache_position=cache_position,
position_embeddings=position_embeddings,
**kwargs,
)
hidden_states = residual + hidden_states * self.residual_multiplier
# Fully Connected
residual = hidden_states
hidden_states = self.post_attention_layernorm(hidden_states)
moe_hidden_states, router_logits = self.block_sparse_moe(hidden_states)
if self.shared_mlp is None:
hidden_states = moe_hidden_states
else:
hidden_states = moe_hidden_states + self.shared_mlp(hidden_states)
hidden_states = residual + hidden_states * self.residual_multiplier
outputs = (hidden_states,)
if output_attentions:
outputs += (self_attn_weights,)
if use_cache:
outputs += (present_key_value,)
if output_router_logits:
outputs += (router_logits,)
return outputs
GRANITEMOESHARED_START_DOCSTRING = r"""
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.
Parameters:
config ([`GraniteMoeSharedConfig`]):
Model configuration class with all the parameters of the model. Initializing with a config file does not
load the weights associated with the model, only the configuration. Check out the
[`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
@add_start_docstrings(
"The bare GraniteMoeShared Model outputting raw hidden-states without any specific head on top.",
GRANITEMOESHARED_START_DOCSTRING,
)
class GraniteMoeSharedPreTrainedModel(PreTrainedModel):
config_class = GraniteMoeSharedConfig
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = ["GraniteMoeSharedDecoderLayer"]
_skip_keys_device_placement = ["past_key_values"]
_supports_flash_attn_2 = True
_supports_sdpa = True
_supports_cache_class = True
_supports_quantized_cache = True
_supports_static_cache = False # MoE models don't work with torch.compile (`torch.where(condition)` not supported)
def _init_weights(self, module):
std = self.config.initializer_range
if isinstance(module, nn.Linear):
module.weight.data.normal_(mean=0.0, std=std)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.Embedding):
module.weight.data.normal_(mean=0.0, std=std)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
elif isinstance(module, nn.LayerNorm):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
elif isinstance(module, GraniteMoeSharedParallelExperts):
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
class GraniteMoeSharedRotaryEmbedding(nn.Module):
def __init__(self, config: GraniteMoeSharedConfig, device=None):
super().__init__()
# BC: "rope_type" was originally "type"
if hasattr(config, "rope_scaling") and config.rope_scaling is not None:
self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type"))
else:
self.rope_type = "default"
self.max_seq_len_cached = config.max_position_embeddings
self.original_max_seq_len = config.max_position_embeddings
self.config = config
self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device)
self.register_buffer("inv_freq", inv_freq, persistent=False)
self.original_inv_freq = self.inv_freq
@torch.no_grad()
@dynamic_rope_update # power user: used with advanced RoPE types (e.g. dynamic rope)
def forward(self, x, position_ids):
inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device)
position_ids_expanded = position_ids[:, None, :].float()
device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
with torch.autocast(device_type=device_type, enabled=False): # Force float32
freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
emb = torch.cat((freqs, freqs), dim=-1)
cos = emb.cos() * self.attention_scaling
sin = emb.sin() * self.attention_scaling
return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
GRANITEMOESHARED_INPUTS_DOCSTRING = r"""
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
it.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
If `past_key_values` is used, optionally only the last `input_ids` have to be input (see
`past_key_values`).
If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
information on the default strategy.
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
config.n_positions - 1]`.
[What are position IDs?](../glossary#position-ids)
past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*):
Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values`
returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.
Two formats are allowed:
- a [`~cache_utils.Cache`] instance;
- Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy
cache format.
The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the
legacy cache format will be returned.
If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't
have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids`
of shape `(batch_size, sequence_length)`.
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
model's internal embedding lookup matrix.
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
`past_key_values`).
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
Indices depicting the position of the input sequence tokens in the sequence. Contrarily to `position_ids`,
this tensor is not affected by padding. It is used to update the cache in the correct position and to infer
the complete sequence length.
"""
@add_start_docstrings(
"The bare GraniteMoeShared Model outputting raw hidden-states without any specific head on top.",
GRANITEMOESHARED_START_DOCSTRING,
)
class GraniteMoeSharedModel(GraniteMoeSharedPreTrainedModel):
"""
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`GraniteMoeDecoderLayer`]
Args:
config: GraniteMoeSharedConfig
"""
def __init__(self, config: GraniteMoeSharedConfig):
super().__init__(config)
self.padding_idx = config.pad_token_id
self.vocab_size = config.vocab_size
self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
self.layers = nn.ModuleList(
[GraniteMoeSharedDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
)
self.norm = GraniteMoeSharedRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.gradient_checkpointing = False
self.embedding_multiplier = config.embedding_multiplier
self.hidden_size = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = self.hidden_size // self.num_heads
self.max_position_embeddings = config.max_position_embeddings
self.rope_theta = config.rope_theta
# rope
self.rotary_emb = GraniteMoeSharedRotaryEmbedding(config)
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.embed_tokens
def set_input_embeddings(self, value):
self.embed_tokens = value
@add_start_docstrings_to_model_forward(GRANITEMOESHARED_INPUTS_DOCSTRING)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
output_router_logits: Optional[bool] = None,
return_dict: Optional[bool] = None,
cache_position: Optional[torch.LongTensor] = None,
) -> Union[Tuple, BaseModelOutputWithPast]:
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
use_cache = use_cache if use_cache is not None else self.config.use_cache
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if (input_ids is None) ^ (inputs_embeds is not None):
raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
if self.gradient_checkpointing and self.training and use_cache:
logger.warning_once(
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`."
)
use_cache = False
if inputs_embeds is None:
inputs_embeds = self.embed_tokens(input_ids)
inputs_embeds = inputs_embeds * self.embedding_multiplier
return_legacy_cache = False
if use_cache and not isinstance(past_key_values, Cache): # kept for BC (non `Cache` `past_key_values` inputs)
return_legacy_cache = True
past_key_values = DynamicCache.from_legacy_cache(past_key_values)
logger.warning_once(
"We detected that you are passing `past_key_values` as a tuple and this is deprecated and will be removed in v4.43. "
"Please use an appropriate `Cache` class (https://huggingface.co/docs/transformers/v4.41.3/en/internal/generation_utils#transformers.Cache)"
)
if cache_position is None:
past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
cache_position = torch.arange(
past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
)
if position_ids is None:
position_ids = cache_position.unsqueeze(0)
causal_mask = self._update_causal_mask(
attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions
)
# embed positions
hidden_states = inputs_embeds
# create position embeddings to be shared across the decoder layers
position_embeddings = self.rotary_emb(hidden_states, position_ids)
# decoder layers
all_hidden_states = () if output_hidden_states else None
all_self_attns = () if output_attentions else None
all_router_logits = () if output_router_logits else None
next_decoder_cache = None
for decoder_layer in self.layers:
if output_hidden_states:
all_hidden_states += (hidden_states,)
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
decoder_layer.__call__,
hidden_states,
causal_mask,
position_ids,
past_key_values,
output_attentions,
use_cache,
cache_position,
output_router_logits,
position_embeddings,
)
else:
layer_outputs = decoder_layer(
hidden_states,
attention_mask=causal_mask,
position_ids=position_ids,
past_key_value=past_key_values,
output_attentions=output_attentions,
use_cache=use_cache,
cache_position=cache_position,
output_router_logits=output_router_logits,
position_embeddings=position_embeddings,
)
hidden_states = layer_outputs[0]
if use_cache:
next_decoder_cache = layer_outputs[2 if output_attentions else 1]
if output_attentions:
all_self_attns += (layer_outputs[1],)
if output_router_logits:
all_router_logits += (layer_outputs[-1],)
hidden_states = self.norm(hidden_states)
# add hidden states from the last decoder layer
if output_hidden_states:
all_hidden_states += (hidden_states,)
next_cache = next_decoder_cache if use_cache else None
if return_legacy_cache:
next_cache = next_cache.to_legacy_cache()
if not return_dict:
return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
return MoeModelOutputWithPast(
last_hidden_state=hidden_states,
past_key_values=next_cache,
hidden_states=all_hidden_states,
attentions=all_self_attns,
router_logits=all_router_logits,
)
def _update_causal_mask(
self,
attention_mask: torch.Tensor,
input_tensor: torch.Tensor,
cache_position: torch.Tensor,
past_key_values: Cache,
output_attentions: bool = False,
):
if self.config._attn_implementation == "flash_attention_2":
if attention_mask is not None and (attention_mask == 0.0).any():
return attention_mask
return None
if self.config._attn_implementation == "flex_attention":
if isinstance(attention_mask, torch.Tensor):
attention_mask = make_flex_block_causal_mask(attention_mask)
if isinstance(attention_mask, BlockMask):
return attention_mask
# For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in
# order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail
# to infer the attention mask.
past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
using_static_cache = isinstance(past_key_values, StaticCache)
# When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward
if self.config._attn_implementation == "sdpa" and not using_static_cache and not output_attentions:
if AttentionMaskConverter._ignore_causal_mask_sdpa(
attention_mask,
inputs_embeds=input_tensor,
past_key_values_length=past_seen_tokens,
is_training=self.training,
):
return None
dtype, device = input_tensor.dtype, input_tensor.device
sequence_length = input_tensor.shape[1]
if using_static_cache:
target_length = past_key_values.get_max_cache_shape()
else:
target_length = (
attention_mask.shape[-1]
if isinstance(attention_mask, torch.Tensor)
else past_seen_tokens + sequence_length + 1
)
# In case the provided `attention` mask is 2D, we generate a causal mask here (4D).
causal_mask = self._prepare_4d_causal_attention_mask_with_cache_position(
attention_mask,
sequence_length=sequence_length,
target_length=target_length,
dtype=dtype,
device=device,
cache_position=cache_position,
batch_size=input_tensor.shape[0],
)
if (
self.config._attn_implementation == "sdpa"
and attention_mask is not None
and attention_mask.device.type in ["cuda", "xpu"]
and not output_attentions
):
# Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when
# using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path.
# Details: https://github.com/pytorch/pytorch/issues/110213
min_dtype = torch.finfo(dtype).min
causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype)
return causal_mask
@staticmethod
def _prepare_4d_causal_attention_mask_with_cache_position(
attention_mask: torch.Tensor,
sequence_length: int,
target_length: int,
dtype: torch.dtype,
device: torch.device,
cache_position: torch.Tensor,
batch_size: int,
**kwargs,
):
"""
Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape
`(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing.
Args:
attention_mask (`torch.Tensor`):
A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape
`(batch_size, 1, query_length, key_value_length)`.
sequence_length (`int`):
The sequence length being processed.
target_length (`int`):
The target length: when generating with static cache, the mask should be as long as the static cache,
to account for the 0 padding, the part of the cache that is not filled yet.
dtype (`torch.dtype`):
The dtype to use for the 4D attention mask.
device (`torch.device`):
The device to place the 4D attention mask on.
cache_position (`torch.Tensor`):
Indices depicting the position of the input sequence tokens in the sequence.
batch_size (`torch.Tensor`):
Batch size.
"""
if attention_mask is not None and attention_mask.dim() == 4:
# In this case we assume that the mask comes already in inverted form and requires no inversion or slicing.
causal_mask = attention_mask
else:
min_dtype = torch.finfo(dtype).min
causal_mask = torch.full(
(sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=device
)
if sequence_length != 1:
causal_mask = torch.triu(causal_mask, diagonal=1)
causal_mask *= torch.arange(target_length, device=device) > cache_position.reshape(-1, 1)
causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1)
if attention_mask is not None:
causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit
mask_length = attention_mask.shape[-1]
padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :].to(
causal_mask.device
)
padding_mask = padding_mask == 0
causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
padding_mask, min_dtype
)
return causal_mask
def load_balancing_loss_func(
gate_logits: Union[torch.Tensor, Tuple[torch.Tensor], None],
num_experts: Optional[int] = None,
top_k=2,
attention_mask: Optional[torch.Tensor] = None,
) -> Union[torch.Tensor, int]:
r"""
Computes auxiliary load balancing loss as in Switch Transformer - implemented in Pytorch.
See Switch Transformer (https://arxiv.org/abs/2101.03961) for more details. This function implements the loss
function presented in equations (4) - (6) of the paper. It aims at penalizing cases where the routing between
experts is too unbalanced.
Args:
gate_logits:
Logits from the `gate`, should be a tuple of model.config.num_hidden_layers tensors of
shape [batch_size X sequence_length, num_experts].
num_experts:
Number of experts
top_k:
The number of experts to route per-token, can be also interpreted as the `top-k` routing
parameter.
attention_mask (`torch.Tensor`, *optional*):
The attention_mask used in forward function
shape [batch_size X sequence_length] if not None.
Returns:
The auxiliary loss.
"""
if gate_logits is None or not isinstance(gate_logits, tuple):
return 0
if isinstance(gate_logits, tuple):
compute_device = gate_logits[0].device
concatenated_gate_logits = torch.cat([layer_gate.to(compute_device) for layer_gate in gate_logits], dim=0)
routing_weights = torch.nn.functional.softmax(concatenated_gate_logits, dim=-1)
_, selected_experts = torch.topk(routing_weights, top_k, dim=-1)
expert_mask = torch.nn.functional.one_hot(selected_experts, num_experts)
if attention_mask is None:
# Compute the percentage of tokens routed to each experts
tokens_per_expert = torch.mean(expert_mask.float(), dim=0)
# Compute the average probability of routing to these experts
router_prob_per_expert = torch.mean(routing_weights, dim=0)
else:
batch_size, sequence_length = attention_mask.shape
num_hidden_layers = concatenated_gate_logits.shape[0] // (batch_size * sequence_length)
# Compute the mask that masks all padding tokens as 0 with the same shape of expert_mask
expert_attention_mask = (
attention_mask[None, :, :, None, None]
.expand((num_hidden_layers, batch_size, sequence_length, top_k, num_experts))
.reshape(-1, top_k, num_experts)
.to(compute_device)
)
# Compute the percentage of tokens routed to each experts
tokens_per_expert = torch.sum(expert_mask.float() * expert_attention_mask, dim=0) / torch.sum(
expert_attention_mask, dim=0
)
# Compute the mask that masks all padding tokens as 0 with the same shape of tokens_per_expert
router_per_expert_attention_mask = (
attention_mask[None, :, :, None]
.expand((num_hidden_layers, batch_size, sequence_length, num_experts))
.reshape(-1, num_experts)
.to(compute_device)
)
# Compute the average probability of routing to these experts
router_prob_per_expert = torch.sum(routing_weights * router_per_expert_attention_mask, dim=0) / torch.sum(
router_per_expert_attention_mask, dim=0
)
overall_loss = torch.sum(tokens_per_expert * router_prob_per_expert.unsqueeze(0))
return overall_loss * num_experts
class GraniteMoeSharedForCausalLM(GraniteMoeSharedPreTrainedModel, GenerationMixin):
_tied_weights_keys = ["lm_head.weight"]
def __init__(self, config: GraniteMoeSharedConfig):
super().__init__(config)
self.model = GraniteMoeSharedModel(config)
self.vocab_size = config.vocab_size
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
self.router_aux_loss_coef = config.router_aux_loss_coef
self.num_experts = config.num_local_experts
self.num_experts_per_tok = config.num_experts_per_tok
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.model.embed_tokens
def set_input_embeddings(self, value):
self.model.embed_tokens = value
def get_output_embeddings(self):
return self.lm_head
def set_output_embeddings(self, new_embeddings):
self.lm_head = new_embeddings
def set_decoder(self, decoder):
self.model = decoder
def get_decoder(self):
return self.model
@add_start_docstrings_to_model_forward(GRANITEMOESHARED_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=MoeCausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
output_router_logits: Optional[bool] = None,
return_dict: Optional[bool] = None,
cache_position: Optional[torch.LongTensor] = None,
**kwargs,
) -> Union[Tuple, MoeCausalLMOutputWithPast]:
r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
Returns:
Example:
```python
>>> from transformers import AutoTokenizer, GraniteMoeSharedForCausalLM
>>> model = GraniteMoeSharedForCausalLM.from_pretrained("ibm/PowerMoE-3b")
>>> tokenizer = AutoTokenizer.from_pretrained("ibm/PowerMoE-3b")
>>> prompt = "Hey, are you conscious? Can you talk to me?"
>>> inputs = tokenizer(prompt, return_tensors="pt")
>>> # Generate
>>> generate_ids = model.generate(inputs.input_ids, max_length=30)
>>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
"Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
```"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_router_logits = (
output_router_logits if output_router_logits is not None else self.config.output_router_logits
)
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
outputs = self.model(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
output_router_logits=output_router_logits,
return_dict=return_dict,
cache_position=cache_position,
)
hidden_states = outputs[0]
logits = self.lm_head(hidden_states)
logits = logits / self.config.logits_scaling
loss = None
if labels is not None:
# Upcast to float if we need to compute the loss to avoid potential precision issues
logits = logits.float()
# Flatten the tokens
loss = self.loss_function(
logits,
labels,
vocab_size=self.config.vocab_size,
**kwargs,
)
aux_loss = None
if output_router_logits:
aux_loss = load_balancing_loss_func(
outputs.router_logits if return_dict else outputs[-1],
self.num_experts,
self.num_experts_per_tok,
attention_mask,
)
if labels is not None:
loss += self.router_aux_loss_coef * aux_loss.to(loss.device) # make sure to reside in the same device
if not return_dict:
output = (logits,) + outputs[1:]
if output_router_logits:
output = (aux_loss,) + output
return (loss,) + output if loss is not None else output
return MoeCausalLMOutputWithPast(
loss=loss,
aux_loss=aux_loss,
logits=logits,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
router_logits=outputs.router_logits,
)
@staticmethod
def _reorder_cache(past_key_values, beam_idx):
reordered_past = ()
for layer_past in past_key_values:
reordered_past += (
tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
)
return reordered_past
__all__ = ["GraniteMoeSharedForCausalLM", "GraniteMoeSharedModel", "GraniteMoeSharedPreTrainedModel"]
```
|
========================================================================================================================================================
SOURCE CODE FILE: modular_granitemoeshared.py
LINES: 1
SIZE: 12.91 KB
PATH: scripts\freecad_env\Lib\site-packages\transformers\models\granitemoeshared\modular_granitemoeshared.py
ENCODING: utf-8
```py
# coding=utf-8
# Copyright 2024 IBM and the HuggingFace Inc. team. All rights reserved.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Optional, Tuple
import torch
import torch.utils.checkpoint
from torch import nn
from ...activations import ACT2FN
from ...cache_utils import Cache
from ...utils import add_start_docstrings, logging
from ..granitemoe.modeling_granitemoe import (
GraniteMoeDecoderLayer,
GraniteMoeForCausalLM,
GraniteMoeModel,
GraniteMoePreTrainedModel,
)
from .configuration_granitemoeshared import GraniteMoeSharedConfig
logger = logging.get_logger(__name__)
_CONFIG_FOR_DOC = "GraniteMoeSharedConfig"
class GraniteMoeSharedMLP(nn.Module):
"""
MLP layer for shared experts
Args:
config:
Configuration object with model hyperparameters.
"""
def __init__(self, config: GraniteMoeSharedConfig):
super(GraniteMoeSharedMLP, self).__init__()
self.input_size = config.hidden_size
self.hidden_size = config.shared_intermediate_size
self.activation = ACT2FN[config.hidden_act]
self.input_linear = nn.Linear(self.input_size, self.hidden_size * 2, bias=False)
self.output_linear = nn.Linear(self.hidden_size, self.input_size, bias=False)
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidden_states = self.input_linear(hidden_states)
chunked_hidden_states = hidden_states.chunk(2, dim=-1)
hidden_states = self.activation(chunked_hidden_states[0]) * chunked_hidden_states[1]
hidden_states = self.output_linear(hidden_states)
return hidden_states
class GraniteMoeSharedDecoderLayer(GraniteMoeDecoderLayer):
def __init__(self, config: GraniteMoeSharedConfig, layer_idx: int):
super().__init__(config, layer_idx)
self.shared_mlp = None if config.shared_intermediate_size == 0 else GraniteMoeSharedMLP(config)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Cache] = None,
output_attentions: Optional[bool] = False,
use_cache: Optional[bool] = False,
cache_position: Optional[torch.LongTensor] = None,
output_router_logits: Optional[bool] = False,
position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, # necessary, but kept here for BC
**kwargs,
) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
"""
Args:
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
attention_mask (`torch.FloatTensor`, *optional*):
attention mask of size `(batch_size, sequence_length)` if flash attention is used or `(batch_size, 1,
query_sequence_length, key_sequence_length)` if default attention is used.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
(see `past_key_values`).
past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
Indices depicting the position of the input sequence tokens in the sequence
output_router_logits (`bool`, *optional*):
Whether or not to return the logits of all the routers. They are useful for computing the router loss, and
should not be returned during inference.
position_embeddings (`Tuple[torch.FloatTensor, torch.FloatTensor]`, *optional*):
Tuple containing the cosine and sine positional embeddings of shape `(batch_size, seq_len, head_dim)`,
with `head_dim` being the embedding dimension of each attention head.
kwargs (`dict`, *optional*):
Arbitrary kwargs to be ignored, used for FSDP and other methods that injects code
into the model
"""
residual = hidden_states
hidden_states = self.input_layernorm(hidden_states)
# Self Attention
hidden_states, self_attn_weights, present_key_value = self.self_attn(
hidden_states=hidden_states,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_value=past_key_value,
output_attentions=output_attentions,
use_cache=use_cache,
cache_position=cache_position,
position_embeddings=position_embeddings,
**kwargs,
)
hidden_states = residual + hidden_states * self.residual_multiplier
# Fully Connected
residual = hidden_states
hidden_states = self.post_attention_layernorm(hidden_states)
moe_hidden_states, router_logits = self.block_sparse_moe(hidden_states)
if self.shared_mlp is None:
hidden_states = moe_hidden_states
else:
hidden_states = moe_hidden_states + self.shared_mlp(hidden_states)
del moe_hidden_states
hidden_states = residual + hidden_states * self.residual_multiplier
outputs = (hidden_states,)
if output_attentions:
outputs += (self_attn_weights,)
if use_cache:
outputs += (present_key_value,)
if output_router_logits:
outputs += (router_logits,)
return outputs
GRANITEMOESHARED_START_DOCSTRING = r"""
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.
Parameters:
config ([`GraniteMoeSharedConfig`]):
Model configuration class with all the parameters of the model. Initializing with a config file does not
load the weights associated with the model, only the configuration. Check out the
[`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
@add_start_docstrings(
"The bare GraniteMoeShared Model outputting raw hidden-states without any specific head on top.",
GRANITEMOESHARED_START_DOCSTRING,
)
class GraniteMoeSharedPreTrainedModel(GraniteMoePreTrainedModel):
config_class = GraniteMoeSharedConfig
_no_split_modules = ["GraniteMoeSharedDecoderLayer"]
GRANITEMOESHARED_INPUTS_DOCSTRING = r"""
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
it.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
If `past_key_values` is used, optionally only the last `input_ids` have to be input (see
`past_key_values`).
If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
information on the default strategy.
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
config.n_positions - 1]`.
[What are position IDs?](../glossary#position-ids)
past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*):
Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values`
returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.
Two formats are allowed:
- a [`~cache_utils.Cache`] instance;
- Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy
cache format.
The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the
legacy cache format will be returned.
If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't
have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids`
of shape `(batch_size, sequence_length)`.
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
model's internal embedding lookup matrix.
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
`past_key_values`).
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
Indices depicting the position of the input sequence tokens in the sequence. Contrarily to `position_ids`,
this tensor is not affected by padding. It is used to update the cache in the correct position and to infer
the complete sequence length.
"""
@add_start_docstrings(
"The bare GraniteMoeShared Model outputting raw hidden-states without any specific head on top.",
GRANITEMOESHARED_START_DOCSTRING,
)
class GraniteMoeSharedModel(GraniteMoeModel):
"""
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`GraniteMoeDecoderLayer`]
Args:
config: GraniteMoeSharedConfig
"""
def __init__(self, config: GraniteMoeSharedConfig):
super().__init__(config)
self.layers = nn.ModuleList(
[GraniteMoeSharedDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
)
class GraniteMoeSharedForCausalLM(GraniteMoeForCausalLM):
_tied_weights_keys = ["lm_head.weight"]
def __init__(self, config: GraniteMoeSharedConfig):
super().__init__(config)
self.model = GraniteMoeSharedModel(config)
# Initialize weights and apply final processing
self.post_init()
__all__ = ["GraniteMoeSharedForCausalLM", "GraniteMoeSharedModel", "GraniteMoeSharedPreTrainedModel"]
```
|
======================================================================================================================================
SOURCE CODE FILE: __init__.py
LINES: 1
SIZE: 1.08 KB
PATH: scripts\freecad_env\Lib\site-packages\transformers\models\grounding_dino\__init__.py
ENCODING: utf-8
```py
# Copyright 2024 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import _LazyModule
from ...utils.import_utils import define_import_structure
if TYPE_CHECKING:
from .configuration_grounding_dino import *
from .image_processing_grounding_dino import *
from .modeling_grounding_dino import *
from .processing_grounding_dino import *
else:
import sys
_file = globals()["__file__"]
sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)
```
|
==========================================================================================================================================================
SOURCE CODE FILE: configuration_grounding_dino.py
LINES: 1
SIZE: 14.47 KB
PATH: scripts\freecad_env\Lib\site-packages\transformers\models\grounding_dino\configuration_grounding_dino.py
ENCODING: utf-8
```py
# coding=utf-8
# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Grounding DINO model configuration"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import verify_backbone_config_arguments
from ..auto import CONFIG_MAPPING
logger = logging.get_logger(__name__)
class GroundingDinoConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`GroundingDinoModel`]. It is used to instantiate a
Grounding DINO model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will yield a similar configuration to that of the Grounding DINO
[IDEA-Research/grounding-dino-tiny](https://huggingface.co/IDEA-Research/grounding-dino-tiny) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
backbone_config (`PretrainedConfig` or `dict`, *optional*, defaults to `ResNetConfig()`):
The configuration of the backbone model.
backbone (`str`, *optional*):
Name of backbone to use when `backbone_config` is `None`. If `use_pretrained_backbone` is `True`, this
will load the corresponding pretrained weights from the timm or transformers library. If `use_pretrained_backbone`
is `False`, this loads the backbone's config and uses that to initialize the backbone with random weights.
use_pretrained_backbone (`bool`, *optional*, defaults to `False`):
Whether to use pretrained weights for the backbone.
use_timm_backbone (`bool`, *optional*, defaults to `False`):
Whether to load `backbone` from the timm library. If `False`, the backbone is loaded from the transformers
library.
backbone_kwargs (`dict`, *optional*):
Keyword arguments to be passed to AutoBackbone when loading from a checkpoint
e.g. `{'out_indices': (0, 1, 2, 3)}`. Cannot be specified if `backbone_config` is set.
text_config (`Union[AutoConfig, dict]`, *optional*, defaults to `BertConfig`):
The config object or dictionary of the text backbone.
num_queries (`int`, *optional*, defaults to 900):
Number of object queries, i.e. detection slots. This is the maximal number of objects
[`GroundingDinoModel`] can detect in a single image.
encoder_layers (`int`, *optional*, defaults to 6):
Number of encoder layers.
encoder_ffn_dim (`int`, *optional*, defaults to 2048):
Dimension of the "intermediate" (often named feed-forward) layer in decoder.
encoder_attention_heads (`int`, *optional*, defaults to 8):
Number of attention heads for each attention layer in the Transformer encoder.
decoder_layers (`int`, *optional*, defaults to 6):
Number of decoder layers.
decoder_ffn_dim (`int`, *optional*, defaults to 2048):
Dimension of the "intermediate" (often named feed-forward) layer in decoder.
decoder_attention_heads (`int`, *optional*, defaults to 8):
Number of attention heads for each attention layer in the Transformer decoder.
is_encoder_decoder (`bool`, *optional*, defaults to `True`):
Whether the model is used as an encoder/decoder or not.
activation_function (`str` or `function`, *optional*, defaults to `"relu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"silu"` and `"gelu_new"` are supported.
d_model (`int`, *optional*, defaults to 256):
Dimension of the layers.
dropout (`float`, *optional*, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
activation_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for activations inside the fully connected layer.
auxiliary_loss (`bool`, *optional*, defaults to `False`):
Whether auxiliary decoding losses (loss at each decoder layer) are to be used.
position_embedding_type (`str`, *optional*, defaults to `"sine"`):
Type of position embeddings to be used on top of the image features. One of `"sine"` or `"learned"`.
num_feature_levels (`int`, *optional*, defaults to 4):
The number of input feature levels.
encoder_n_points (`int`, *optional*, defaults to 4):
The number of sampled keys in each feature level for each attention head in the encoder.
decoder_n_points (`int`, *optional*, defaults to 4):
The number of sampled keys in each feature level for each attention head in the decoder.
two_stage (`bool`, *optional*, defaults to `True`):
Whether to apply a two-stage deformable DETR, where the region proposals are also generated by a variant of
Grounding DINO, which are further fed into the decoder for iterative bounding box refinement.
class_cost (`float`, *optional*, defaults to 1.0):
Relative weight of the classification error in the Hungarian matching cost.
bbox_cost (`float`, *optional*, defaults to 5.0):
Relative weight of the L1 error of the bounding box coordinates in the Hungarian matching cost.
giou_cost (`float`, *optional*, defaults to 2.0):
Relative weight of the generalized IoU loss of the bounding box in the Hungarian matching cost.
bbox_loss_coefficient (`float`, *optional*, defaults to 5.0):
Relative weight of the L1 bounding box loss in the object detection loss.
giou_loss_coefficient (`float`, *optional*, defaults to 2.0):
Relative weight of the generalized IoU loss in the object detection loss.
focal_alpha (`float`, *optional*, defaults to 0.25):
Alpha parameter in the focal loss.
disable_custom_kernels (`bool`, *optional*, defaults to `False`):
Disable the use of custom CUDA and CPU kernels. This option is necessary for the ONNX export, as custom
kernels are not supported by PyTorch ONNX export.
max_text_len (`int`, *optional*, defaults to 256):
The maximum length of the text input.
text_enhancer_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the text enhancer.
fusion_droppath (`float`, *optional*, defaults to 0.1):
The droppath ratio for the fusion module.
fusion_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the fusion module.
embedding_init_target (`bool`, *optional*, defaults to `True`):
Whether to initialize the target with Embedding weights.
query_dim (`int`, *optional*, defaults to 4):
The dimension of the query vector.
decoder_bbox_embed_share (`bool`, *optional*, defaults to `True`):
Whether to share the bbox regression head for all decoder layers.
two_stage_bbox_embed_share (`bool`, *optional*, defaults to `False`):
Whether to share the bbox embedding between the two-stage bbox generator and the region proposal
generation.
positional_embedding_temperature (`float`, *optional*, defaults to 20):
The temperature for Sine Positional Embedding that is used together with vision backbone.
init_std (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (`float`, *optional*, defaults to 1e-05):
The epsilon used by the layer normalization layers.
Examples:
```python
>>> from transformers import GroundingDinoConfig, GroundingDinoModel
>>> # Initializing a Grounding DINO IDEA-Research/grounding-dino-tiny style configuration
>>> configuration = GroundingDinoConfig()
>>> # Initializing a model (with random weights) from the IDEA-Research/grounding-dino-tiny style configuration
>>> model = GroundingDinoModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "grounding-dino"
attribute_map = {
"hidden_size": "d_model",
"num_attention_heads": "encoder_attention_heads",
}
def __init__(
self,
backbone_config=None,
backbone=None,
use_pretrained_backbone=False,
use_timm_backbone=False,
backbone_kwargs=None,
text_config=None,
num_queries=900,
encoder_layers=6,
encoder_ffn_dim=2048,
encoder_attention_heads=8,
decoder_layers=6,
decoder_ffn_dim=2048,
decoder_attention_heads=8,
is_encoder_decoder=True,
activation_function="relu",
d_model=256,
dropout=0.1,
attention_dropout=0.0,
activation_dropout=0.0,
auxiliary_loss=False,
position_embedding_type="sine",
num_feature_levels=4,
encoder_n_points=4,
decoder_n_points=4,
two_stage=True,
class_cost=1.0,
bbox_cost=5.0,
giou_cost=2.0,
bbox_loss_coefficient=5.0,
giou_loss_coefficient=2.0,
focal_alpha=0.25,
disable_custom_kernels=False,
# other parameters
max_text_len=256,
text_enhancer_dropout=0.0,
fusion_droppath=0.1,
fusion_dropout=0.0,
embedding_init_target=True,
query_dim=4,
decoder_bbox_embed_share=True,
two_stage_bbox_embed_share=False,
positional_embedding_temperature=20,
init_std=0.02,
layer_norm_eps=1e-5,
**kwargs,
):
if backbone_config is None and backbone is None:
logger.info("`backbone_config` is `None`. Initializing the config with the default `Swin` backbone.")
backbone_config = CONFIG_MAPPING["swin"](
window_size=7,
image_size=224,
embed_dim=96,
depths=[2, 2, 6, 2],
num_heads=[3, 6, 12, 24],
out_indices=[2, 3, 4],
)
elif isinstance(backbone_config, dict):
backbone_model_type = backbone_config.pop("model_type")
config_class = CONFIG_MAPPING[backbone_model_type]
backbone_config = config_class.from_dict(backbone_config)
verify_backbone_config_arguments(
use_timm_backbone=use_timm_backbone,
use_pretrained_backbone=use_pretrained_backbone,
backbone=backbone,
backbone_config=backbone_config,
backbone_kwargs=backbone_kwargs,
)
if text_config is None:
text_config = {}
logger.info("text_config is None. Initializing the text config with default values (`BertConfig`).")
self.backbone_config = backbone_config
self.backbone = backbone
self.use_pretrained_backbone = use_pretrained_backbone
self.use_timm_backbone = use_timm_backbone
self.backbone_kwargs = backbone_kwargs
self.num_queries = num_queries
self.d_model = d_model
self.encoder_ffn_dim = encoder_ffn_dim
self.encoder_layers = encoder_layers
self.encoder_attention_heads = encoder_attention_heads
self.decoder_ffn_dim = decoder_ffn_dim
self.decoder_layers = decoder_layers
self.decoder_attention_heads = decoder_attention_heads
self.dropout = dropout
self.attention_dropout = attention_dropout
self.activation_dropout = activation_dropout
self.activation_function = activation_function
self.auxiliary_loss = auxiliary_loss
self.position_embedding_type = position_embedding_type
# deformable attributes
self.num_feature_levels = num_feature_levels
self.encoder_n_points = encoder_n_points
self.decoder_n_points = decoder_n_points
self.two_stage = two_stage
# Hungarian matcher
self.class_cost = class_cost
self.bbox_cost = bbox_cost
self.giou_cost = giou_cost
# Loss coefficients
self.bbox_loss_coefficient = bbox_loss_coefficient
self.giou_loss_coefficient = giou_loss_coefficient
self.focal_alpha = focal_alpha
self.disable_custom_kernels = disable_custom_kernels
# Text backbone
if isinstance(text_config, dict):
text_config["model_type"] = text_config["model_type"] if "model_type" in text_config else "bert"
text_config = CONFIG_MAPPING[text_config["model_type"]](**text_config)
elif text_config is None:
text_config = CONFIG_MAPPING["bert"]()
self.text_config = text_config
self.max_text_len = max_text_len
# Text Enhancer
self.text_enhancer_dropout = text_enhancer_dropout
# Fusion
self.fusion_droppath = fusion_droppath
self.fusion_dropout = fusion_dropout
# Others
self.embedding_init_target = embedding_init_target
self.query_dim = query_dim
self.decoder_bbox_embed_share = decoder_bbox_embed_share
self.two_stage_bbox_embed_share = two_stage_bbox_embed_share
if two_stage_bbox_embed_share and not decoder_bbox_embed_share:
raise ValueError("If two_stage_bbox_embed_share is True, decoder_bbox_embed_share must be True.")
self.positional_embedding_temperature = positional_embedding_temperature
self.init_std = init_std
self.layer_norm_eps = layer_norm_eps
super().__init__(is_encoder_decoder=is_encoder_decoder, **kwargs)
@property
def num_attention_heads(self) -> int:
return self.encoder_attention_heads
@property
def hidden_size(self) -> int:
return self.d_model
__all__ = ["GroundingDinoConfig"]
```
|
=============================================================================================================================================================
SOURCE CODE FILE: image_processing_grounding_dino.py
LINES: 1
SIZE: 70.55 KB
PATH: scripts\freecad_env\Lib\site-packages\transformers\models\grounding_dino\image_processing_grounding_dino.py
ENCODING: utf-8
```py
# coding=utf-8
# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Image processor class for Deformable DETR."""
import io
import pathlib
from collections import defaultdict
from typing import TYPE_CHECKING, Any, Callable, Dict, Iterable, List, Optional, Set, Tuple, Union
import numpy as np
from ...feature_extraction_utils import BatchFeature
from ...image_processing_utils import BaseImageProcessor, get_size_dict
from ...image_transforms import (
PaddingMode,
center_to_corners_format,
corners_to_center_format,
id_to_rgb,
pad,
rescale,
resize,
rgb_to_id,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_DEFAULT_MEAN,
IMAGENET_DEFAULT_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
get_image_size,
infer_channel_dimension_format,
is_scaled_image,
make_list_of_images,
to_numpy_array,
valid_images,
validate_annotations,
validate_kwargs,
validate_preprocess_arguments,
)
from ...utils import (
ExplicitEnum,
TensorType,
is_flax_available,
is_jax_tensor,
is_scipy_available,
is_tf_available,
is_tf_tensor,
is_torch_available,
is_torch_tensor,
is_vision_available,
logging,
)
if is_torch_available():
import torch
from torch import nn
if is_vision_available():
import PIL
if is_scipy_available():
import scipy.special
import scipy.stats
if TYPE_CHECKING:
from .modeling_grounding_dino import GroundingDinoObjectDetectionOutput
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
AnnotationType = Dict[str, Union[int, str, List[Dict]]]
class AnnotationFormat(ExplicitEnum):
COCO_DETECTION = "coco_detection"
COCO_PANOPTIC = "coco_panoptic"
SUPPORTED_ANNOTATION_FORMATS = (AnnotationFormat.COCO_DETECTION, AnnotationFormat.COCO_PANOPTIC)
# Copied from transformers.models.detr.image_processing_detr.get_size_with_aspect_ratio
def get_size_with_aspect_ratio(image_size, size, max_size=None) -> Tuple[int, int]:
"""
Computes the output image size given the input image size and the desired output size.
Args:
image_size (`Tuple[int, int]`):
The input image size.
size (`int`):
The desired output size.
max_size (`int`, *optional*):
The maximum allowed output size.
"""
height, width = image_size
raw_size = None
if max_size is not None:
min_original_size = float(min((height, width)))
max_original_size = float(max((height, width)))
if max_original_size / min_original_size * size > max_size:
raw_size = max_size * min_original_size / max_original_size
size = int(round(raw_size))
if (height <= width and height == size) or (width <= height and width == size):
oh, ow = height, width
elif width < height:
ow = size
if max_size is not None and raw_size is not None:
oh = int(raw_size * height / width)
else:
oh = int(size * height / width)
else:
oh = size
if max_size is not None and raw_size is not None:
ow = int(raw_size * width / height)
else:
ow = int(size * width / height)
return (oh, ow)
# Copied from transformers.models.detr.image_processing_detr.get_resize_output_image_size
def get_resize_output_image_size(
input_image: np.ndarray,
size: Union[int, Tuple[int, int], List[int]],
max_size: Optional[int] = None,
input_data_format: Optional[Union[str, ChannelDimension]] = None,
) -> Tuple[int, int]:
"""
Computes the output image size given the input image size and the desired output size. If the desired output size
is a tuple or list, the output image size is returned as is. If the desired output size is an integer, the output
image size is computed by keeping the aspect ratio of the input image size.
Args:
input_image (`np.ndarray`):
The image to resize.
size (`int` or `Tuple[int, int]` or `List[int]`):
The desired output size.
max_size (`int`, *optional*):
The maximum allowed output size.
input_data_format (`ChannelDimension` or `str`, *optional*):
The channel dimension format of the input image. If not provided, it will be inferred from the input image.
"""
image_size = get_image_size(input_image, input_data_format)
if isinstance(size, (list, tuple)):
return size
return get_size_with_aspect_ratio(image_size, size, max_size)
# Copied from transformers.models.detr.image_processing_detr.get_image_size_for_max_height_width
def get_image_size_for_max_height_width(
input_image: np.ndarray,
max_height: int,
max_width: int,
input_data_format: Optional[Union[str, ChannelDimension]] = None,
) -> Tuple[int, int]:
"""
Computes the output image size given the input image and the maximum allowed height and width. Keep aspect ratio.
Important, even if image_height < max_height and image_width < max_width, the image will be resized
to at least one of the edges be equal to max_height or max_width.
For example:
- input_size: (100, 200), max_height: 50, max_width: 50 -> output_size: (25, 50)
- input_size: (100, 200), max_height: 200, max_width: 500 -> output_size: (200, 400)
Args:
input_image (`np.ndarray`):
The image to resize.
max_height (`int`):
The maximum allowed height.
max_width (`int`):
The maximum allowed width.
input_data_format (`ChannelDimension` or `str`, *optional*):
The channel dimension format of the input image. If not provided, it will be inferred from the input image.
"""
image_size = get_image_size(input_image, input_data_format)
height, width = image_size
height_scale = max_height / height
width_scale = max_width / width
min_scale = min(height_scale, width_scale)
new_height = int(height * min_scale)
new_width = int(width * min_scale)
return new_height, new_width
# Copied from transformers.models.detr.image_processing_detr.get_numpy_to_framework_fn
def get_numpy_to_framework_fn(arr) -> Callable:
"""
Returns a function that converts a numpy array to the framework of the input array.
Args:
arr (`np.ndarray`): The array to convert.
"""
if isinstance(arr, np.ndarray):
return np.array
if is_tf_available() and is_tf_tensor(arr):
import tensorflow as tf
return tf.convert_to_tensor
if is_torch_available() and is_torch_tensor(arr):
import torch
return torch.tensor
if is_flax_available() and is_jax_tensor(arr):
import jax.numpy as jnp
return jnp.array
raise ValueError(f"Cannot convert arrays of type {type(arr)}")
# Copied from transformers.models.detr.image_processing_detr.safe_squeeze
def safe_squeeze(arr: np.ndarray, axis: Optional[int] = None) -> np.ndarray:
"""
Squeezes an array, but only if the axis specified has dim 1.
"""
if axis is None:
return arr.squeeze()
try:
return arr.squeeze(axis=axis)
except ValueError:
return arr
# Copied from transformers.models.detr.image_processing_detr.normalize_annotation
def normalize_annotation(annotation: Dict, image_size: Tuple[int, int]) -> Dict:
image_height, image_width = image_size
norm_annotation = {}
for key, value in annotation.items():
if key == "boxes":
boxes = value
boxes = corners_to_center_format(boxes)
boxes /= np.asarray([image_width, image_height, image_width, image_height], dtype=np.float32)
norm_annotation[key] = boxes
else:
norm_annotation[key] = value
return norm_annotation
# Copied from transformers.models.detr.image_processing_detr.max_across_indices
def max_across_indices(values: Iterable[Any]) -> List[Any]:
"""
Return the maximum value across all indices of an iterable of values.
"""
return [max(values_i) for values_i in zip(*values)]
# Copied from transformers.models.detr.image_processing_detr.get_max_height_width
def get_max_height_width(
images: List[np.ndarray], input_data_format: Optional[Union[str, ChannelDimension]] = None
) -> List[int]:
"""
Get the maximum height and width across all images in a batch.
"""
if input_data_format is None:
input_data_format = infer_channel_dimension_format(images[0])
if input_data_format == ChannelDimension.FIRST:
_, max_height, max_width = max_across_indices([img.shape for img in images])
elif input_data_format == ChannelDimension.LAST:
max_height, max_width, _ = max_across_indices([img.shape for img in images])
else:
raise ValueError(f"Invalid channel dimension format: {input_data_format}")
return (max_height, max_width)
# Copied from transformers.models.detr.image_processing_detr.make_pixel_mask
def make_pixel_mask(
image: np.ndarray, output_size: Tuple[int, int], input_data_format: Optional[Union[str, ChannelDimension]] = None
) -> np.ndarray:
"""
Make a pixel mask for the image, where 1 indicates a valid pixel and 0 indicates padding.
Args:
image (`np.ndarray`):
Image to make the pixel mask for.
output_size (`Tuple[int, int]`):
Output size of the mask.
"""
input_height, input_width = get_image_size(image, channel_dim=input_data_format)
mask = np.zeros(output_size, dtype=np.int64)
mask[:input_height, :input_width] = 1
return mask
# Copied from transformers.models.detr.image_processing_detr.convert_coco_poly_to_mask
def convert_coco_poly_to_mask(segmentations, height: int, width: int) -> np.ndarray:
"""
Convert a COCO polygon annotation to a mask.
Args:
segmentations (`List[List[float]]`):
List of polygons, each polygon represented by a list of x-y coordinates.
height (`int`):
Height of the mask.
width (`int`):
Width of the mask.
"""
try:
from pycocotools import mask as coco_mask
except ImportError:
raise ImportError("Pycocotools is not installed in your environment.")
masks = []
for polygons in segmentations:
rles = coco_mask.frPyObjects(polygons, height, width)
mask = coco_mask.decode(rles)
if len(mask.shape) < 3:
mask = mask[..., None]
mask = np.asarray(mask, dtype=np.uint8)
mask = np.any(mask, axis=2)
masks.append(mask)
if masks:
masks = np.stack(masks, axis=0)
else:
masks = np.zeros((0, height, width), dtype=np.uint8)
return masks
# Copied from transformers.models.detr.image_processing_detr.prepare_coco_detection_annotation with DETR->GroundingDino
def prepare_coco_detection_annotation(
image,
target,
return_segmentation_masks: bool = False,
input_data_format: Optional[Union[ChannelDimension, str]] = None,
):
"""
Convert the target in COCO format into the format expected by GroundingDino.
"""
image_height, image_width = get_image_size(image, channel_dim=input_data_format)
image_id = target["image_id"]
image_id = np.asarray([image_id], dtype=np.int64)
# Get all COCO annotations for the given image.
annotations = target["annotations"]
annotations = [obj for obj in annotations if "iscrowd" not in obj or obj["iscrowd"] == 0]
classes = [obj["category_id"] for obj in annotations]
classes = np.asarray(classes, dtype=np.int64)
# for conversion to coco api
area = np.asarray([obj["area"] for obj in annotations], dtype=np.float32)
iscrowd = np.asarray([obj["iscrowd"] if "iscrowd" in obj else 0 for obj in annotations], dtype=np.int64)
boxes = [obj["bbox"] for obj in annotations]
# guard against no boxes via resizing
boxes = np.asarray(boxes, dtype=np.float32).reshape(-1, 4)
boxes[:, 2:] += boxes[:, :2]
boxes[:, 0::2] = boxes[:, 0::2].clip(min=0, max=image_width)
boxes[:, 1::2] = boxes[:, 1::2].clip(min=0, max=image_height)
keep = (boxes[:, 3] > boxes[:, 1]) & (boxes[:, 2] > boxes[:, 0])
new_target = {}
new_target["image_id"] = image_id
new_target["class_labels"] = classes[keep]
new_target["boxes"] = boxes[keep]
new_target["area"] = area[keep]
new_target["iscrowd"] = iscrowd[keep]
new_target["orig_size"] = np.asarray([int(image_height), int(image_width)], dtype=np.int64)
if annotations and "keypoints" in annotations[0]:
keypoints = [obj["keypoints"] for obj in annotations]
# Converting the filtered keypoints list to a numpy array
keypoints = np.asarray(keypoints, dtype=np.float32)
# Apply the keep mask here to filter the relevant annotations
keypoints = keypoints[keep]
num_keypoints = keypoints.shape[0]
keypoints = keypoints.reshape((-1, 3)) if num_keypoints else keypoints
new_target["keypoints"] = keypoints
if return_segmentation_masks:
segmentation_masks = [obj["segmentation"] for obj in annotations]
masks = convert_coco_poly_to_mask(segmentation_masks, image_height, image_width)
new_target["masks"] = masks[keep]
return new_target
# Copied from transformers.models.detr.image_processing_detr.masks_to_boxes
def masks_to_boxes(masks: np.ndarray) -> np.ndarray:
"""
Compute the bounding boxes around the provided panoptic segmentation masks.
Args:
masks: masks in format `[number_masks, height, width]` where N is the number of masks
Returns:
boxes: bounding boxes in format `[number_masks, 4]` in xyxy format
"""
if masks.size == 0:
return np.zeros((0, 4))
h, w = masks.shape[-2:]
y = np.arange(0, h, dtype=np.float32)
x = np.arange(0, w, dtype=np.float32)
# see https://github.com/pytorch/pytorch/issues/50276
y, x = np.meshgrid(y, x, indexing="ij")
x_mask = masks * np.expand_dims(x, axis=0)
x_max = x_mask.reshape(x_mask.shape[0], -1).max(-1)
x = np.ma.array(x_mask, mask=~(np.array(masks, dtype=bool)))
x_min = x.filled(fill_value=1e8)
x_min = x_min.reshape(x_min.shape[0], -1).min(-1)
y_mask = masks * np.expand_dims(y, axis=0)
y_max = y_mask.reshape(x_mask.shape[0], -1).max(-1)
y = np.ma.array(y_mask, mask=~(np.array(masks, dtype=bool)))
y_min = y.filled(fill_value=1e8)
y_min = y_min.reshape(y_min.shape[0], -1).min(-1)
return np.stack([x_min, y_min, x_max, y_max], 1)
# Copied from transformers.models.detr.image_processing_detr.prepare_coco_panoptic_annotation with DETR->GroundingDino
def prepare_coco_panoptic_annotation(
image: np.ndarray,
target: Dict,
masks_path: Union[str, pathlib.Path],
return_masks: bool = True,
input_data_format: Union[ChannelDimension, str] = None,
) -> Dict:
"""
Prepare a coco panoptic annotation for GroundingDino.
"""
image_height, image_width = get_image_size(image, channel_dim=input_data_format)
annotation_path = pathlib.Path(masks_path) / target["file_name"]
new_target = {}
new_target["image_id"] = np.asarray([target["image_id"] if "image_id" in target else target["id"]], dtype=np.int64)
new_target["size"] = np.asarray([image_height, image_width], dtype=np.int64)
new_target["orig_size"] = np.asarray([image_height, image_width], dtype=np.int64)
if "segments_info" in target:
masks = np.asarray(PIL.Image.open(annotation_path), dtype=np.uint32)
masks = rgb_to_id(masks)
ids = np.array([segment_info["id"] for segment_info in target["segments_info"]])
masks = masks == ids[:, None, None]
masks = masks.astype(np.uint8)
if return_masks:
new_target["masks"] = masks
new_target["boxes"] = masks_to_boxes(masks)
new_target["class_labels"] = np.array(
[segment_info["category_id"] for segment_info in target["segments_info"]], dtype=np.int64
)
new_target["iscrowd"] = np.asarray(
[segment_info["iscrowd"] for segment_info in target["segments_info"]], dtype=np.int64
)
new_target["area"] = np.asarray(
[segment_info["area"] for segment_info in target["segments_info"]], dtype=np.float32
)
return new_target
# Copied from transformers.models.detr.image_processing_detr.get_segmentation_image
def get_segmentation_image(
masks: np.ndarray, input_size: Tuple, target_size: Tuple, stuff_equiv_classes, deduplicate=False
):
h, w = input_size
final_h, final_w = target_size
m_id = scipy.special.softmax(masks.transpose(0, 1), -1)
if m_id.shape[-1] == 0:
# We didn't detect any mask :(
m_id = np.zeros((h, w), dtype=np.int64)
else:
m_id = m_id.argmax(-1).reshape(h, w)
if deduplicate:
# Merge the masks corresponding to the same stuff class
for equiv in stuff_equiv_classes.values():
for eq_id in equiv:
m_id[m_id == eq_id] = equiv[0]
seg_img = id_to_rgb(m_id)
seg_img = resize(seg_img, (final_w, final_h), resample=PILImageResampling.NEAREST)
return seg_img
# Copied from transformers.models.detr.image_processing_detr.get_mask_area
def get_mask_area(seg_img: np.ndarray, target_size: Tuple[int, int], n_classes: int) -> np.ndarray:
final_h, final_w = target_size
np_seg_img = seg_img.astype(np.uint8)
np_seg_img = np_seg_img.reshape(final_h, final_w, 3)
m_id = rgb_to_id(np_seg_img)
area = [(m_id == i).sum() for i in range(n_classes)]
return area
# Copied from transformers.models.detr.image_processing_detr.score_labels_from_class_probabilities
def score_labels_from_class_probabilities(logits: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
probs = scipy.special.softmax(logits, axis=-1)
labels = probs.argmax(-1, keepdims=True)
scores = np.take_along_axis(probs, labels, axis=-1)
scores, labels = scores.squeeze(-1), labels.squeeze(-1)
return scores, labels
# Copied from transformers.models.detr.image_processing_detr.post_process_panoptic_sample
def post_process_panoptic_sample(
out_logits: np.ndarray,
masks: np.ndarray,
boxes: np.ndarray,
processed_size: Tuple[int, int],
target_size: Tuple[int, int],
is_thing_map: Dict,
threshold=0.85,
) -> Dict:
"""
Converts the output of [`DetrForSegmentation`] into panoptic segmentation predictions for a single sample.
Args:
out_logits (`torch.Tensor`):
The logits for this sample.
masks (`torch.Tensor`):
The predicted segmentation masks for this sample.
boxes (`torch.Tensor`):
The prediced bounding boxes for this sample. The boxes are in the normalized format `(center_x, center_y,
width, height)` and values between `[0, 1]`, relative to the size the image (disregarding padding).
processed_size (`Tuple[int, int]`):
The processed size of the image `(height, width)`, as returned by the preprocessing step i.e. the size
after data augmentation but before batching.
target_size (`Tuple[int, int]`):
The target size of the image, `(height, width)` corresponding to the requested final size of the
prediction.
is_thing_map (`Dict`):
A dictionary mapping class indices to a boolean value indicating whether the class is a thing or not.
threshold (`float`, *optional*, defaults to 0.85):
The threshold used to binarize the segmentation masks.
"""
# we filter empty queries and detection below threshold
scores, labels = score_labels_from_class_probabilities(out_logits)
keep = (labels != out_logits.shape[-1] - 1) & (scores > threshold)
cur_scores = scores[keep]
cur_classes = labels[keep]
cur_boxes = center_to_corners_format(boxes[keep])
if len(cur_boxes) != len(cur_classes):
raise ValueError("Not as many boxes as there are classes")
cur_masks = masks[keep]
cur_masks = resize(cur_masks[:, None], processed_size, resample=PILImageResampling.BILINEAR)
cur_masks = safe_squeeze(cur_masks, 1)
b, h, w = cur_masks.shape
# It may be that we have several predicted masks for the same stuff class.
# In the following, we track the list of masks ids for each stuff class (they are merged later on)
cur_masks = cur_masks.reshape(b, -1)
stuff_equiv_classes = defaultdict(list)
for k, label in enumerate(cur_classes):
if not is_thing_map[label]:
stuff_equiv_classes[label].append(k)
seg_img = get_segmentation_image(cur_masks, processed_size, target_size, stuff_equiv_classes, deduplicate=True)
area = get_mask_area(cur_masks, processed_size, n_classes=len(cur_scores))
# We filter out any mask that is too small
if cur_classes.size() > 0:
# We know filter empty masks as long as we find some
filtered_small = np.array([a <= 4 for a in area], dtype=bool)
while filtered_small.any():
cur_masks = cur_masks[~filtered_small]
cur_scores = cur_scores[~filtered_small]
cur_classes = cur_classes[~filtered_small]
seg_img = get_segmentation_image(cur_masks, (h, w), target_size, stuff_equiv_classes, deduplicate=True)
area = get_mask_area(seg_img, target_size, n_classes=len(cur_scores))
filtered_small = np.array([a <= 4 for a in area], dtype=bool)
else:
cur_classes = np.ones((1, 1), dtype=np.int64)
segments_info = [
{"id": i, "isthing": is_thing_map[cat], "category_id": int(cat), "area": a}
for i, (cat, a) in enumerate(zip(cur_classes, area))
]
del cur_classes
with io.BytesIO() as out:
PIL.Image.fromarray(seg_img).save(out, format="PNG")
predictions = {"png_string": out.getvalue(), "segments_info": segments_info}
return predictions
# Copied from transformers.models.detr.image_processing_detr.resize_annotation
def resize_annotation(
annotation: Dict[str, Any],
orig_size: Tuple[int, int],
target_size: Tuple[int, int],
threshold: float = 0.5,
resample: PILImageResampling = PILImageResampling.NEAREST,
):
"""
Resizes an annotation to a target size.
Args:
annotation (`Dict[str, Any]`):
The annotation dictionary.
orig_size (`Tuple[int, int]`):
The original size of the input image.
target_size (`Tuple[int, int]`):
The target size of the image, as returned by the preprocessing `resize` step.
threshold (`float`, *optional*, defaults to 0.5):
The threshold used to binarize the segmentation masks.
resample (`PILImageResampling`, defaults to `PILImageResampling.NEAREST`):
The resampling filter to use when resizing the masks.
"""
ratios = tuple(float(s) / float(s_orig) for s, s_orig in zip(target_size, orig_size))
ratio_height, ratio_width = ratios
new_annotation = {}
new_annotation["size"] = target_size
for key, value in annotation.items():
if key == "boxes":
boxes = value
scaled_boxes = boxes * np.asarray([ratio_width, ratio_height, ratio_width, ratio_height], dtype=np.float32)
new_annotation["boxes"] = scaled_boxes
elif key == "area":
area = value
scaled_area = area * (ratio_width * ratio_height)
new_annotation["area"] = scaled_area
elif key == "masks":
masks = value[:, None]
masks = np.array([resize(mask, target_size, resample=resample) for mask in masks])
masks = masks.astype(np.float32)
masks = masks[:, 0] > threshold
new_annotation["masks"] = masks
elif key == "size":
new_annotation["size"] = target_size
else:
new_annotation[key] = value
return new_annotation
# Copied from transformers.models.detr.image_processing_detr.binary_mask_to_rle
def binary_mask_to_rle(mask):
"""
Converts given binary mask of shape `(height, width)` to the run-length encoding (RLE) format.
Args:
mask (`torch.Tensor` or `numpy.array`):
A binary mask tensor of shape `(height, width)` where 0 denotes background and 1 denotes the target
segment_id or class_id.
Returns:
`List`: Run-length encoded list of the binary mask. Refer to COCO API for more information about the RLE
format.
"""
if is_torch_tensor(mask):
mask = mask.numpy()
pixels = mask.flatten()
pixels = np.concatenate([[0], pixels, [0]])
runs = np.where(pixels[1:] != pixels[:-1])[0] + 1
runs[1::2] -= runs[::2]
return list(runs)
# Copied from transformers.models.detr.image_processing_detr.convert_segmentation_to_rle
def convert_segmentation_to_rle(segmentation):
"""
Converts given segmentation map of shape `(height, width)` to the run-length encoding (RLE) format.
Args:
segmentation (`torch.Tensor` or `numpy.array`):
A segmentation map of shape `(height, width)` where each value denotes a segment or class id.
Returns:
`List[List]`: A list of lists, where each list is the run-length encoding of a segment / class id.
"""
segment_ids = torch.unique(segmentation)
run_length_encodings = []
for idx in segment_ids:
mask = torch.where(segmentation == idx, 1, 0)
rle = binary_mask_to_rle(mask)
run_length_encodings.append(rle)
return run_length_encodings
# Copied from transformers.models.detr.image_processing_detr.remove_low_and_no_objects
def remove_low_and_no_objects(masks, scores, labels, object_mask_threshold, num_labels):
"""
Binarize the given masks using `object_mask_threshold`, it returns the associated values of `masks`, `scores` and
`labels`.
Args:
masks (`torch.Tensor`):
A tensor of shape `(num_queries, height, width)`.
scores (`torch.Tensor`):
A tensor of shape `(num_queries)`.
labels (`torch.Tensor`):
A tensor of shape `(num_queries)`.
object_mask_threshold (`float`):
A number between 0 and 1 used to binarize the masks.
Raises:
`ValueError`: Raised when the first dimension doesn't match in all input tensors.
Returns:
`Tuple[`torch.Tensor`, `torch.Tensor`, `torch.Tensor`]`: The `masks`, `scores` and `labels` without the region
< `object_mask_threshold`.
"""
if not (masks.shape[0] == scores.shape[0] == labels.shape[0]):
raise ValueError("mask, scores and labels must have the same shape!")
to_keep = labels.ne(num_labels) & (scores > object_mask_threshold)
return masks[to_keep], scores[to_keep], labels[to_keep]
# Copied from transformers.models.detr.image_processing_detr.check_segment_validity
def check_segment_validity(mask_labels, mask_probs, k, mask_threshold=0.5, overlap_mask_area_threshold=0.8):
# Get the mask associated with the k class
mask_k = mask_labels == k
mask_k_area = mask_k.sum()
# Compute the area of all the stuff in query k
original_area = (mask_probs[k] >= mask_threshold).sum()
mask_exists = mask_k_area > 0 and original_area > 0
# Eliminate disconnected tiny segments
if mask_exists:
area_ratio = mask_k_area / original_area
if not area_ratio.item() > overlap_mask_area_threshold:
mask_exists = False
return mask_exists, mask_k
# Copied from transformers.models.detr.image_processing_detr.compute_segments
def compute_segments(
mask_probs,
pred_scores,
pred_labels,
mask_threshold: float = 0.5,
overlap_mask_area_threshold: float = 0.8,
label_ids_to_fuse: Optional[Set[int]] = None,
target_size: Tuple[int, int] = None,
):
height = mask_probs.shape[1] if target_size is None else target_size[0]
width = mask_probs.shape[2] if target_size is None else target_size[1]
segmentation = torch.zeros((height, width), dtype=torch.int32, device=mask_probs.device)
segments: List[Dict] = []
if target_size is not None:
mask_probs = nn.functional.interpolate(
mask_probs.unsqueeze(0), size=target_size, mode="bilinear", align_corners=False
)[0]
current_segment_id = 0
# Weigh each mask by its prediction score
mask_probs *= pred_scores.view(-1, 1, 1)
mask_labels = mask_probs.argmax(0) # [height, width]
# Keep track of instances of each class
stuff_memory_list: Dict[str, int] = {}
for k in range(pred_labels.shape[0]):
pred_class = pred_labels[k].item()
should_fuse = pred_class in label_ids_to_fuse
# Check if mask exists and large enough to be a segment
mask_exists, mask_k = check_segment_validity(
mask_labels, mask_probs, k, mask_threshold, overlap_mask_area_threshold
)
if mask_exists:
if pred_class in stuff_memory_list:
current_segment_id = stuff_memory_list[pred_class]
else:
current_segment_id += 1
# Add current object segment to final segmentation map
segmentation[mask_k] = current_segment_id
segment_score = round(pred_scores[k].item(), 6)
segments.append(
{
"id": current_segment_id,
"label_id": pred_class,
"was_fused": should_fuse,
"score": segment_score,
}
)
if should_fuse:
stuff_memory_list[pred_class] = current_segment_id
return segmentation, segments
# Copied from transformers.models.owlvit.image_processing_owlvit._scale_boxes
def _scale_boxes(boxes, target_sizes):
"""
Scale batch of bounding boxes to the target sizes.
Args:
boxes (`torch.Tensor` of shape `(batch_size, num_boxes, 4)`):
Bounding boxes to scale. Each box is expected to be in (x1, y1, x2, y2) format.
target_sizes (`List[Tuple[int, int]]` or `torch.Tensor` of shape `(batch_size, 2)`):
Target sizes to scale the boxes to. Each target size is expected to be in (height, width) format.
Returns:
`torch.Tensor` of shape `(batch_size, num_boxes, 4)`: Scaled bounding boxes.
"""
if isinstance(target_sizes, (list, tuple)):
image_height = torch.tensor([i[0] for i in target_sizes])
image_width = torch.tensor([i[1] for i in target_sizes])
elif isinstance(target_sizes, torch.Tensor):
image_height, image_width = target_sizes.unbind(1)
else:
raise ValueError("`target_sizes` must be a list, tuple or torch.Tensor")
scale_factor = torch.stack([image_width, image_height, image_width, image_height], dim=1)
scale_factor = scale_factor.unsqueeze(1).to(boxes.device)
boxes = boxes * scale_factor
return boxes
class GroundingDinoImageProcessor(BaseImageProcessor):
r"""
Constructs a Grounding DINO image processor.
Args:
format (`str`, *optional*, defaults to `AnnotationFormat.COCO_DETECTION`):
Data format of the annotations. One of "coco_detection" or "coco_panoptic".
do_resize (`bool`, *optional*, defaults to `True`):
Controls whether to resize the image's (height, width) dimensions to the specified `size`. Can be
overridden by the `do_resize` parameter in the `preprocess` method.
size (`Dict[str, int]` *optional*, defaults to `{"shortest_edge": 800, "longest_edge": 1333}`):
Size of the image's `(height, width)` dimensions after resizing. Can be overridden by the `size` parameter
in the `preprocess` method. Available options are:
- `{"height": int, "width": int}`: The image will be resized to the exact size `(height, width)`.
Do NOT keep the aspect ratio.
- `{"shortest_edge": int, "longest_edge": int}`: The image will be resized to a maximum size respecting
the aspect ratio and keeping the shortest edge less or equal to `shortest_edge` and the longest edge
less or equal to `longest_edge`.
- `{"max_height": int, "max_width": int}`: The image will be resized to the maximum size respecting the
aspect ratio and keeping the height less or equal to `max_height` and the width less or equal to
`max_width`.
resample (`PILImageResampling`, *optional*, defaults to `Resampling.BILINEAR`):
Resampling filter to use if resizing the image.
do_rescale (`bool`, *optional*, defaults to `True`):
Controls whether to rescale the image by the specified scale `rescale_factor`. Can be overridden by the
`do_rescale` parameter in the `preprocess` method.
rescale_factor (`int` or `float`, *optional*, defaults to `1/255`):
Scale factor to use if rescaling the image. Can be overridden by the `rescale_factor` parameter in the
`preprocess` method. Controls whether to normalize the image. Can be overridden by the `do_normalize`
parameter in the `preprocess` method.
do_normalize (`bool`, *optional*, defaults to `True`):
Whether to normalize the image. Can be overridden by the `do_normalize` parameter in the `preprocess`
method.
image_mean (`float` or `List[float]`, *optional*, defaults to `IMAGENET_DEFAULT_MEAN`):
Mean values to use when normalizing the image. Can be a single value or a list of values, one for each
channel. Can be overridden by the `image_mean` parameter in the `preprocess` method.
image_std (`float` or `List[float]`, *optional*, defaults to `IMAGENET_DEFAULT_STD`):
Standard deviation values to use when normalizing the image. Can be a single value or a list of values, one
for each channel. Can be overridden by the `image_std` parameter in the `preprocess` method.
do_convert_annotations (`bool`, *optional*, defaults to `True`):
Controls whether to convert the annotations to the format expected by the DETR model. Converts the
bounding boxes to the format `(center_x, center_y, width, height)` and in the range `[0, 1]`.
Can be overridden by the `do_convert_annotations` parameter in the `preprocess` method.
do_pad (`bool`, *optional*, defaults to `True`):
Controls whether to pad the image. Can be overridden by the `do_pad` parameter in the `preprocess`
method. If `True`, padding will be applied to the bottom and right of the image with zeros.
If `pad_size` is provided, the image will be padded to the specified dimensions.
Otherwise, the image will be padded to the maximum height and width of the batch.
pad_size (`Dict[str, int]`, *optional*):
The size `{"height": int, "width" int}` to pad the images to. Must be larger than any image size
provided for preprocessing. If `pad_size` is not provided, images will be padded to the largest
height and width in the batch.
"""
model_input_names = ["pixel_values", "pixel_mask"]
# Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.__init__
def __init__(
self,
format: Union[str, AnnotationFormat] = AnnotationFormat.COCO_DETECTION,
do_resize: bool = True,
size: Dict[str, int] = None,
resample: PILImageResampling = PILImageResampling.BILINEAR,
do_rescale: bool = True,
rescale_factor: Union[int, float] = 1 / 255,
do_normalize: bool = True,
image_mean: Union[float, List[float]] = None,
image_std: Union[float, List[float]] = None,
do_convert_annotations: Optional[bool] = None,
do_pad: bool = True,
pad_size: Optional[Dict[str, int]] = None,
**kwargs,
) -> None:
if "pad_and_return_pixel_mask" in kwargs:
do_pad = kwargs.pop("pad_and_return_pixel_mask")
if "max_size" in kwargs:
logger.warning_once(
"The `max_size` parameter is deprecated and will be removed in v4.26. "
"Please specify in `size['longest_edge'] instead`.",
)
max_size = kwargs.pop("max_size")
else:
max_size = None if size is None else 1333
size = size if size is not None else {"shortest_edge": 800, "longest_edge": 1333}
size = get_size_dict(size, max_size=max_size, default_to_square=False)
# Backwards compatibility
if do_convert_annotations is None:
do_convert_annotations = do_normalize
super().__init__(**kwargs)
self.format = format
self.do_resize = do_resize
self.size = size
self.resample = resample
self.do_rescale = do_rescale
self.rescale_factor = rescale_factor
self.do_normalize = do_normalize
self.do_convert_annotations = do_convert_annotations
self.image_mean = image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
self.image_std = image_std if image_std is not None else IMAGENET_DEFAULT_STD
self.do_pad = do_pad
self.pad_size = pad_size
self._valid_processor_keys = [
"images",
"annotations",
"return_segmentation_masks",
"masks_path",
"do_resize",
"size",
"resample",
"do_rescale",
"rescale_factor",
"do_normalize",
"do_convert_annotations",
"image_mean",
"image_std",
"do_pad",
"pad_size",
"format",
"return_tensors",
"data_format",
"input_data_format",
]
@classmethod
# Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.from_dict with Detr->GroundingDino
def from_dict(cls, image_processor_dict: Dict[str, Any], **kwargs):
"""
Overrides the `from_dict` method from the base class to make sure parameters are updated if image processor is
created using from_dict and kwargs e.g. `GroundingDinoImageProcessor.from_pretrained(checkpoint, size=600,
max_size=800)`
"""
image_processor_dict = image_processor_dict.copy()
if "max_size" in kwargs:
image_processor_dict["max_size"] = kwargs.pop("max_size")
if "pad_and_return_pixel_mask" in kwargs:
image_processor_dict["pad_and_return_pixel_mask"] = kwargs.pop("pad_and_return_pixel_mask")
return super().from_dict(image_processor_dict, **kwargs)
# Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.prepare_annotation with DETR->GroundingDino
def prepare_annotation(
self,
image: np.ndarray,
target: Dict,
format: Optional[AnnotationFormat] = None,
return_segmentation_masks: Optional[bool] = None,
masks_path: Optional[Union[str, pathlib.Path]] = None,
input_data_format: Optional[Union[str, ChannelDimension]] = None,
) -> Dict:
"""
Prepare an annotation for feeding into GroundingDino model.
"""
format = format if format is not None else self.format
if format == AnnotationFormat.COCO_DETECTION:
return_segmentation_masks = False if return_segmentation_masks is None else return_segmentation_masks
target = prepare_coco_detection_annotation(
image, target, return_segmentation_masks, input_data_format=input_data_format
)
elif format == AnnotationFormat.COCO_PANOPTIC:
return_segmentation_masks = True if return_segmentation_masks is None else return_segmentation_masks
target = prepare_coco_panoptic_annotation(
image,
target,
masks_path=masks_path,
return_masks=return_segmentation_masks,
input_data_format=input_data_format,
)
else:
raise ValueError(f"Format {format} is not supported.")
return target
# Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.resize
def resize(
self,
image: np.ndarray,
size: Dict[str, int],
resample: PILImageResampling = PILImageResampling.BILINEAR,
data_format: Optional[ChannelDimension] = None,
input_data_format: Optional[Union[str, ChannelDimension]] = None,
**kwargs,
) -> np.ndarray:
"""
Resize the image to the given size. Size can be `min_size` (scalar) or `(height, width)` tuple. If size is an
int, smaller edge of the image will be matched to this number.
Args:
image (`np.ndarray`):
Image to resize.
size (`Dict[str, int]`):
Size of the image's `(height, width)` dimensions after resizing. Available options are:
- `{"height": int, "width": int}`: The image will be resized to the exact size `(height, width)`.
Do NOT keep the aspect ratio.
- `{"shortest_edge": int, "longest_edge": int}`: The image will be resized to a maximum size respecting
the aspect ratio and keeping the shortest edge less or equal to `shortest_edge` and the longest edge
less or equal to `longest_edge`.
- `{"max_height": int, "max_width": int}`: The image will be resized to the maximum size respecting the
aspect ratio and keeping the height less or equal to `max_height` and the width less or equal to
`max_width`.
resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BILINEAR`):
Resampling filter to use if resizing the image.
data_format (`str` or `ChannelDimension`, *optional*):
The channel dimension format for the output image. If unset, the channel dimension format of the input
image is used.
input_data_format (`ChannelDimension` or `str`, *optional*):
The channel dimension format of the input image. If not provided, it will be inferred.
"""
if "max_size" in kwargs:
logger.warning_once(
"The `max_size` parameter is deprecated and will be removed in v4.26. "
"Please specify in `size['longest_edge'] instead`.",
)
max_size = kwargs.pop("max_size")
else:
max_size = None
size = get_size_dict(size, max_size=max_size, default_to_square=False)
if "shortest_edge" in size and "longest_edge" in size:
new_size = get_resize_output_image_size(
image, size["shortest_edge"], size["longest_edge"], input_data_format=input_data_format
)
elif "max_height" in size and "max_width" in size:
new_size = get_image_size_for_max_height_width(
image, size["max_height"], size["max_width"], input_data_format=input_data_format
)
elif "height" in size and "width" in size:
new_size = (size["height"], size["width"])
else:
raise ValueError(
"Size must contain 'height' and 'width' keys or 'shortest_edge' and 'longest_edge' keys. Got"
f" {size.keys()}."
)
image = resize(
image,
size=new_size,
resample=resample,
data_format=data_format,
input_data_format=input_data_format,
**kwargs,
)
return image
# Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.resize_annotation
def resize_annotation(
self,
annotation,
orig_size,
size,
resample: PILImageResampling = PILImageResampling.NEAREST,
) -> Dict:
"""
Resize the annotation to match the resized image. If size is an int, smaller edge of the mask will be matched
to this number.
"""
return resize_annotation(annotation, orig_size=orig_size, target_size=size, resample=resample)
# Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.rescale
def rescale(
self,
image: np.ndarray,
rescale_factor: float,
data_format: Optional[Union[str, ChannelDimension]] = None,
input_data_format: Optional[Union[str, ChannelDimension]] = None,
) -> np.ndarray:
"""
Rescale the image by the given factor. image = image * rescale_factor.
Args:
image (`np.ndarray`):
Image to rescale.
rescale_factor (`float`):
The value to use for rescaling.
data_format (`str` or `ChannelDimension`, *optional*):
The channel dimension format for the output image. If unset, the channel dimension format of the input
image is used. Can be one of:
- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
input_data_format (`str` or `ChannelDimension`, *optional*):
The channel dimension format for the input image. If unset, is inferred from the input image. Can be
one of:
- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
"""
return rescale(image, rescale_factor, data_format=data_format, input_data_format=input_data_format)
# Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.normalize_annotation
def normalize_annotation(self, annotation: Dict, image_size: Tuple[int, int]) -> Dict:
"""
Normalize the boxes in the annotation from `[top_left_x, top_left_y, bottom_right_x, bottom_right_y]` to
`[center_x, center_y, width, height]` format and from absolute to relative pixel values.
"""
return normalize_annotation(annotation, image_size=image_size)
# Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor._update_annotation_for_padded_image
def _update_annotation_for_padded_image(
self,
annotation: Dict,
input_image_size: Tuple[int, int],
output_image_size: Tuple[int, int],
padding,
update_bboxes,
) -> Dict:
"""
Update the annotation for a padded image.
"""
new_annotation = {}
new_annotation["size"] = output_image_size
for key, value in annotation.items():
if key == "masks":
masks = value
masks = pad(
masks,
padding,
mode=PaddingMode.CONSTANT,
constant_values=0,
input_data_format=ChannelDimension.FIRST,
)
masks = safe_squeeze(masks, 1)
new_annotation["masks"] = masks
elif key == "boxes" and update_bboxes:
boxes = value
boxes *= np.asarray(
[
input_image_size[1] / output_image_size[1],
input_image_size[0] / output_image_size[0],
input_image_size[1] / output_image_size[1],
input_image_size[0] / output_image_size[0],
]
)
new_annotation["boxes"] = boxes
elif key == "size":
new_annotation["size"] = output_image_size
else:
new_annotation[key] = value
return new_annotation
# Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor._pad_image
def _pad_image(
self,
image: np.ndarray,
output_size: Tuple[int, int],
annotation: Optional[Dict[str, Any]] = None,
constant_values: Union[float, Iterable[float]] = 0,
data_format: Optional[ChannelDimension] = None,
input_data_format: Optional[Union[str, ChannelDimension]] = None,
update_bboxes: bool = True,
) -> np.ndarray:
"""
Pad an image with zeros to the given size.
"""
input_height, input_width = get_image_size(image, channel_dim=input_data_format)
output_height, output_width = output_size
pad_bottom = output_height - input_height
pad_right = output_width - input_width
padding = ((0, pad_bottom), (0, pad_right))
padded_image = pad(
image,
padding,
mode=PaddingMode.CONSTANT,
constant_values=constant_values,
data_format=data_format,
input_data_format=input_data_format,
)
if annotation is not None:
annotation = self._update_annotation_for_padded_image(
annotation, (input_height, input_width), (output_height, output_width), padding, update_bboxes
)
return padded_image, annotation
# Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.pad
def pad(
self,
images: List[np.ndarray],
annotations: Optional[Union[AnnotationType, List[AnnotationType]]] = None,
constant_values: Union[float, Iterable[float]] = 0,
return_pixel_mask: bool = True,
return_tensors: Optional[Union[str, TensorType]] = None,
data_format: Optional[ChannelDimension] = None,
input_data_format: Optional[Union[str, ChannelDimension]] = None,
update_bboxes: bool = True,
pad_size: Optional[Dict[str, int]] = None,
) -> BatchFeature:
"""
Pads a batch of images to the bottom and right of the image with zeros to the size of largest height and width
in the batch and optionally returns their corresponding pixel mask.
Args:
images (List[`np.ndarray`]):
Images to pad.
annotations (`AnnotationType` or `List[AnnotationType]`, *optional*):
Annotations to transform according to the padding that is applied to the images.
constant_values (`float` or `Iterable[float]`, *optional*):
The value to use for the padding if `mode` is `"constant"`.
return_pixel_mask (`bool`, *optional*, defaults to `True`):
Whether to return a pixel mask.
return_tensors (`str` or `TensorType`, *optional*):
The type of tensors to return. Can be one of:
- Unset: Return a list of `np.ndarray`.
- `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`.
- `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
- `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`.
- `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`.
data_format (`str` or `ChannelDimension`, *optional*):
The channel dimension format of the image. If not provided, it will be the same as the input image.
input_data_format (`ChannelDimension` or `str`, *optional*):
The channel dimension format of the input image. If not provided, it will be inferred.
update_bboxes (`bool`, *optional*, defaults to `True`):
Whether to update the bounding boxes in the annotations to match the padded images. If the
bounding boxes have not been converted to relative coordinates and `(centre_x, centre_y, width, height)`
format, the bounding boxes will not be updated.
pad_size (`Dict[str, int]`, *optional*):
The size `{"height": int, "width" int}` to pad the images to. Must be larger than any image size
provided for preprocessing. If `pad_size` is not provided, images will be padded to the largest
height and width in the batch.
"""
pad_size = pad_size if pad_size is not None else self.pad_size
if pad_size is not None:
padded_size = (pad_size["height"], pad_size["width"])
else:
padded_size = get_max_height_width(images, input_data_format=input_data_format)
annotation_list = annotations if annotations is not None else [None] * len(images)
padded_images = []
padded_annotations = []
for image, annotation in zip(images, annotation_list):
padded_image, padded_annotation = self._pad_image(
image,
padded_size,
annotation,
constant_values=constant_values,
data_format=data_format,
input_data_format=input_data_format,
update_bboxes=update_bboxes,
)
padded_images.append(padded_image)
padded_annotations.append(padded_annotation)
data = {"pixel_values": padded_images}
if return_pixel_mask:
masks = [
make_pixel_mask(image=image, output_size=padded_size, input_data_format=input_data_format)
for image in images
]
data["pixel_mask"] = masks
encoded_inputs = BatchFeature(data=data, tensor_type=return_tensors)
if annotations is not None:
encoded_inputs["labels"] = [
BatchFeature(annotation, tensor_type=return_tensors) for annotation in padded_annotations
]
return encoded_inputs
# Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.preprocess
def preprocess(
self,
images: ImageInput,
annotations: Optional[Union[AnnotationType, List[AnnotationType]]] = None,
return_segmentation_masks: Optional[bool] = None,
masks_path: Optional[Union[str, pathlib.Path]] = None,
do_resize: Optional[bool] = None,
size: Optional[Dict[str, int]] = None,
resample=None, # PILImageResampling
do_rescale: Optional[bool] = None,
rescale_factor: Optional[Union[int, float]] = None,
do_normalize: Optional[bool] = None,
do_convert_annotations: Optional[bool] = None,
image_mean: Optional[Union[float, List[float]]] = None,
image_std: Optional[Union[float, List[float]]] = None,
do_pad: Optional[bool] = None,
format: Optional[Union[str, AnnotationFormat]] = None,
return_tensors: Optional[Union[TensorType, str]] = None,
data_format: Union[str, ChannelDimension] = ChannelDimension.FIRST,
input_data_format: Optional[Union[str, ChannelDimension]] = None,
pad_size: Optional[Dict[str, int]] = None,
**kwargs,
) -> BatchFeature:
"""
Preprocess an image or a batch of images so that it can be used by the model.
Args:
images (`ImageInput`):
Image or batch of images to preprocess. Expects a single or batch of images with pixel values ranging
from 0 to 255. If passing in images with pixel values between 0 and 1, set `do_rescale=False`.
annotations (`AnnotationType` or `List[AnnotationType]`, *optional*):
List of annotations associated with the image or batch of images. If annotation is for object
detection, the annotations should be a dictionary with the following keys:
- "image_id" (`int`): The image id.
- "annotations" (`List[Dict]`): List of annotations for an image. Each annotation should be a
dictionary. An image can have no annotations, in which case the list should be empty.
If annotation is for segmentation, the annotations should be a dictionary with the following keys:
- "image_id" (`int`): The image id.
- "segments_info" (`List[Dict]`): List of segments for an image. Each segment should be a dictionary.
An image can have no segments, in which case the list should be empty.
- "file_name" (`str`): The file name of the image.
return_segmentation_masks (`bool`, *optional*, defaults to self.return_segmentation_masks):
Whether to return segmentation masks.
masks_path (`str` or `pathlib.Path`, *optional*):
Path to the directory containing the segmentation masks.
do_resize (`bool`, *optional*, defaults to self.do_resize):
Whether to resize the image.
size (`Dict[str, int]`, *optional*, defaults to self.size):
Size of the image's `(height, width)` dimensions after resizing. Available options are:
- `{"height": int, "width": int}`: The image will be resized to the exact size `(height, width)`.
Do NOT keep the aspect ratio.
- `{"shortest_edge": int, "longest_edge": int}`: The image will be resized to a maximum size respecting
the aspect ratio and keeping the shortest edge less or equal to `shortest_edge` and the longest edge
less or equal to `longest_edge`.
- `{"max_height": int, "max_width": int}`: The image will be resized to the maximum size respecting the
aspect ratio and keeping the height less or equal to `max_height` and the width less or equal to
`max_width`.
resample (`PILImageResampling`, *optional*, defaults to self.resample):
Resampling filter to use when resizing the image.
do_rescale (`bool`, *optional*, defaults to self.do_rescale):
Whether to rescale the image.
rescale_factor (`float`, *optional*, defaults to self.rescale_factor):
Rescale factor to use when rescaling the image.
do_normalize (`bool`, *optional*, defaults to self.do_normalize):
Whether to normalize the image.
do_convert_annotations (`bool`, *optional*, defaults to self.do_convert_annotations):
Whether to convert the annotations to the format expected by the model. Converts the bounding
boxes from the format `(top_left_x, top_left_y, width, height)` to `(center_x, center_y, width, height)`
and in relative coordinates.
image_mean (`float` or `List[float]`, *optional*, defaults to self.image_mean):
Mean to use when normalizing the image.
image_std (`float` or `List[float]`, *optional*, defaults to self.image_std):
Standard deviation to use when normalizing the image.
do_pad (`bool`, *optional*, defaults to self.do_pad):
Whether to pad the image. If `True`, padding will be applied to the bottom and right of
the image with zeros. If `pad_size` is provided, the image will be padded to the specified
dimensions. Otherwise, the image will be padded to the maximum height and width of the batch.
format (`str` or `AnnotationFormat`, *optional*, defaults to self.format):
Format of the annotations.
return_tensors (`str` or `TensorType`, *optional*, defaults to self.return_tensors):
Type of tensors to return. If `None`, will return the list of images.
data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`):
The channel dimension format for the output image. Can be one of:
- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
- Unset: Use the channel dimension format of the input image.
input_data_format (`ChannelDimension` or `str`, *optional*):
The channel dimension format for the input image. If unset, the channel dimension format is inferred
from the input image. Can be one of:
- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
- `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
pad_size (`Dict[str, int]`, *optional*):
The size `{"height": int, "width" int}` to pad the images to. Must be larger than any image size
provided for preprocessing. If `pad_size` is not provided, images will be padded to the largest
height and width in the batch.
"""
if "pad_and_return_pixel_mask" in kwargs:
logger.warning_once(
"The `pad_and_return_pixel_mask` argument is deprecated and will be removed in a future version, "
"use `do_pad` instead."
)
do_pad = kwargs.pop("pad_and_return_pixel_mask")
if "max_size" in kwargs:
logger.warning_once(
"The `max_size` argument is deprecated and will be removed in a future version, use"
" `size['longest_edge']` instead."
)
size = kwargs.pop("max_size")
do_resize = self.do_resize if do_resize is None else do_resize
size = self.size if size is None else size
size = get_size_dict(size=size, default_to_square=False)
resample = self.resample if resample is None else resample
do_rescale = self.do_rescale if do_rescale is None else do_rescale
rescale_factor = self.rescale_factor if rescale_factor is None else rescale_factor
do_normalize = self.do_normalize if do_normalize is None else do_normalize
image_mean = self.image_mean if image_mean is None else image_mean
image_std = self.image_std if image_std is None else image_std
do_convert_annotations = (
self.do_convert_annotations if do_convert_annotations is None else do_convert_annotations
)
do_pad = self.do_pad if do_pad is None else do_pad
pad_size = self.pad_size if pad_size is None else pad_size
format = self.format if format is None else format
images = make_list_of_images(images)
if not valid_images(images):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
"torch.Tensor, tf.Tensor or jax.ndarray."
)
validate_kwargs(captured_kwargs=kwargs.keys(), valid_processor_keys=self._valid_processor_keys)
# Here, the pad() method pads to the maximum of (width, height). It does not need to be validated.
validate_preprocess_arguments(
do_rescale=do_rescale,
rescale_factor=rescale_factor,
do_normalize=do_normalize,
image_mean=image_mean,
image_std=image_std,
do_resize=do_resize,
size=size,
resample=resample,
)
if annotations is not None and isinstance(annotations, dict):
annotations = [annotations]
if annotations is not None and len(images) != len(annotations):
raise ValueError(
f"The number of images ({len(images)}) and annotations ({len(annotations)}) do not match."
)
format = AnnotationFormat(format)
if annotations is not None:
validate_annotations(format, SUPPORTED_ANNOTATION_FORMATS, annotations)
if (
masks_path is not None
and format == AnnotationFormat.COCO_PANOPTIC
and not isinstance(masks_path, (pathlib.Path, str))
):
raise ValueError(
"The path to the directory containing the mask PNG files should be provided as a"
f" `pathlib.Path` or string object, but is {type(masks_path)} instead."
)
# All transformations expect numpy arrays
images = [to_numpy_array(image) for image in images]
if do_rescale and is_scaled_image(images[0]):
logger.warning_once(
"It looks like you are trying to rescale already rescaled images. If the input"
" images have pixel values between 0 and 1, set `do_rescale=False` to avoid rescaling them again."
)
if input_data_format is None:
# We assume that all images have the same channel dimension format.
input_data_format = infer_channel_dimension_format(images[0])
# prepare (COCO annotations as a list of Dict -> DETR target as a single Dict per image)
if annotations is not None:
prepared_images = []
prepared_annotations = []
for image, target in zip(images, annotations):
target = self.prepare_annotation(
image,
target,
format,
return_segmentation_masks=return_segmentation_masks,
masks_path=masks_path,
input_data_format=input_data_format,
)
prepared_images.append(image)
prepared_annotations.append(target)
images = prepared_images
annotations = prepared_annotations
del prepared_images, prepared_annotations
# transformations
if do_resize:
if annotations is not None:
resized_images, resized_annotations = [], []
for image, target in zip(images, annotations):
orig_size = get_image_size(image, input_data_format)
resized_image = self.resize(
image, size=size, resample=resample, input_data_format=input_data_format
)
resized_annotation = self.resize_annotation(
target, orig_size, get_image_size(resized_image, input_data_format)
)
resized_images.append(resized_image)
resized_annotations.append(resized_annotation)
images = resized_images
annotations = resized_annotations
del resized_images, resized_annotations
else:
images = [
self.resize(image, size=size, resample=resample, input_data_format=input_data_format)
for image in images
]
if do_rescale:
images = [self.rescale(image, rescale_factor, input_data_format=input_data_format) for image in images]
if do_normalize:
images = [
self.normalize(image, image_mean, image_std, input_data_format=input_data_format) for image in images
]
if do_convert_annotations and annotations is not None:
annotations = [
self.normalize_annotation(annotation, get_image_size(image, input_data_format))
for annotation, image in zip(annotations, images)
]
if do_pad:
# Pads images and returns their mask: {'pixel_values': ..., 'pixel_mask': ...}
encoded_inputs = self.pad(
images,
annotations=annotations,
return_pixel_mask=True,
data_format=data_format,
input_data_format=input_data_format,
update_bboxes=do_convert_annotations,
return_tensors=return_tensors,
pad_size=pad_size,
)
else:
images = [
to_channel_dimension_format(image, data_format, input_channel_dim=input_data_format)
for image in images
]
encoded_inputs = BatchFeature(data={"pixel_values": images}, tensor_type=return_tensors)
if annotations is not None:
encoded_inputs["labels"] = [
BatchFeature(annotation, tensor_type=return_tensors) for annotation in annotations
]
return encoded_inputs
# Copied from transformers.models.owlvit.image_processing_owlvit.OwlViTImageProcessor.post_process_object_detection with OwlViT->GroundingDino
def post_process_object_detection(
self,
outputs: "GroundingDinoObjectDetectionOutput",
threshold: float = 0.1,
target_sizes: Optional[Union[TensorType, List[Tuple]]] = None,
):
"""
Converts the raw output of [`GroundingDinoForObjectDetection`] into final bounding boxes in (top_left_x, top_left_y,
bottom_right_x, bottom_right_y) format.
Args:
outputs ([`GroundingDinoObjectDetectionOutput`]):
Raw outputs of the model.
threshold (`float`, *optional*, defaults to 0.1):
Score threshold to keep object detection predictions.
target_sizes (`torch.Tensor` or `List[Tuple[int, int]]`, *optional*):
Tensor of shape `(batch_size, 2)` or list of tuples (`Tuple[int, int]`) containing the target size
`(height, width)` of each image in the batch. If unset, predictions will not be resized.
Returns:
`List[Dict]`: A list of dictionaries, each dictionary containing the following keys:
- "scores": The confidence scores for each predicted box on the image.
- "labels": Indexes of the classes predicted by the model on the image.
- "boxes": Image bounding boxes in (top_left_x, top_left_y, bottom_right_x, bottom_right_y) format.
"""
batch_logits, batch_boxes = outputs.logits, outputs.pred_boxes
batch_size = len(batch_logits)
if target_sizes is not None and len(target_sizes) != batch_size:
raise ValueError("Make sure that you pass in as many target sizes as images")
# batch_logits of shape (batch_size, num_queries, num_classes)
batch_class_logits = torch.max(batch_logits, dim=-1)
batch_scores = torch.sigmoid(batch_class_logits.values)
batch_labels = batch_class_logits.indices
# Convert to [x0, y0, x1, y1] format
batch_boxes = center_to_corners_format(batch_boxes)
# Convert from relative [0, 1] to absolute [0, height] coordinates
if target_sizes is not None:
batch_boxes = _scale_boxes(batch_boxes, target_sizes)
results = []
for scores, labels, boxes in zip(batch_scores, batch_labels, batch_boxes):
keep = scores > threshold
scores = scores[keep]
labels = labels[keep]
boxes = boxes[keep]
results.append({"scores": scores, "labels": labels, "boxes": boxes})
return results
__all__ = ["GroundingDinoImageProcessor"]
```
|
=====================================================================================================================================================
SOURCE CODE FILE: modeling_grounding_dino.py
LINES: 1
SIZE: 134.66 KB
PATH: scripts\freecad_env\Lib\site-packages\transformers\models\grounding_dino\modeling_grounding_dino.py
ENCODING: utf-8
```py
# coding=utf-8
# Copyright 2024 IDEA Research and The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""PyTorch Grounding DINO model."""
import math
import warnings
from dataclasses import dataclass
from typing import Dict, List, Optional, Tuple, Union
import torch
import torch.nn.functional as F
from torch import Tensor, nn
from ...activations import ACT2FN
from ...file_utils import (
ModelOutput,
add_start_docstrings,
add_start_docstrings_to_model_forward,
is_timm_available,
replace_return_docstrings,
requires_backends,
)
from ...integrations import use_kernel_forward_from_hub
from ...modeling_utils import PreTrainedModel
from ...pytorch_utils import meshgrid
from ...utils import logging
from ...utils.backbone_utils import load_backbone
from ..auto import AutoModel
from .configuration_grounding_dino import GroundingDinoConfig
if is_timm_available():
from timm import create_model
logger = logging.get_logger(__name__)
_CONFIG_FOR_DOC = "GroundingDinoConfig"
_CHECKPOINT_FOR_DOC = "IDEA-Research/grounding-dino-tiny"
@use_kernel_forward_from_hub("MultiScaleDeformableAttention")
# Copied from transformers.models.deformable_detr.modeling_deformable_detr.MultiScaleDeformableAttention
class MultiScaleDeformableAttention(nn.Module):
def forward(
self,
value: Tensor,
value_spatial_shapes: Tensor,
value_spatial_shapes_list: List[Tuple],
level_start_index: Tensor,
sampling_locations: Tensor,
attention_weights: Tensor,
im2col_step: int,
):
batch_size, _, num_heads, hidden_dim = value.shape
_, num_queries, num_heads, num_levels, num_points, _ = sampling_locations.shape
value_list = value.split([height * width for height, width in value_spatial_shapes_list], dim=1)
sampling_grids = 2 * sampling_locations - 1
sampling_value_list = []
for level_id, (height, width) in enumerate(value_spatial_shapes_list):
# batch_size, height*width, num_heads, hidden_dim
# -> batch_size, height*width, num_heads*hidden_dim
# -> batch_size, num_heads*hidden_dim, height*width
# -> batch_size*num_heads, hidden_dim, height, width
value_l_ = (
value_list[level_id]
.flatten(2)
.transpose(1, 2)
.reshape(batch_size * num_heads, hidden_dim, height, width)
)
# batch_size, num_queries, num_heads, num_points, 2
# -> batch_size, num_heads, num_queries, num_points, 2
# -> batch_size*num_heads, num_queries, num_points, 2
sampling_grid_l_ = sampling_grids[:, :, :, level_id].transpose(1, 2).flatten(0, 1)
# batch_size*num_heads, hidden_dim, num_queries, num_points
sampling_value_l_ = nn.functional.grid_sample(
value_l_,
sampling_grid_l_,
mode="bilinear",
padding_mode="zeros",
align_corners=False,
)
sampling_value_list.append(sampling_value_l_)
# (batch_size, num_queries, num_heads, num_levels, num_points)
# -> (batch_size, num_heads, num_queries, num_levels, num_points)
# -> (batch_size, num_heads, 1, num_queries, num_levels*num_points)
attention_weights = attention_weights.transpose(1, 2).reshape(
batch_size * num_heads, 1, num_queries, num_levels * num_points
)
output = (
(torch.stack(sampling_value_list, dim=-2).flatten(-2) * attention_weights)
.sum(-1)
.view(batch_size, num_heads * hidden_dim, num_queries)
)
return output.transpose(1, 2).contiguous()
@dataclass
class GroundingDinoDecoderOutput(ModelOutput):
"""
Base class for outputs of the GroundingDinoDecoder. This class adds two attributes to
BaseModelOutputWithCrossAttentions, namely:
- a stacked tensor of intermediate decoder hidden states (i.e. the output of each decoder layer)
- a stacked tensor of intermediate reference points.
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the model.
intermediate_hidden_states (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, hidden_size)`):
Stacked intermediate hidden states (output of each layer of the decoder).
intermediate_reference_points (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, sequence_length, hidden_size)`):
Stacked intermediate reference points (reference points of each layer of the decoder).
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer
plus the initial embedding outputs.
attentions (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of tuples of `torch.FloatTensor` (one for attention for each layer) of shape `(batch_size, num_heads,
sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the
weighted average in the self-attention, cross-attention and multi-scale deformable attention heads.
"""
last_hidden_state: Optional[torch.FloatTensor] = None
intermediate_hidden_states: Optional[torch.FloatTensor] = None
intermediate_reference_points: Optional[torch.FloatTensor] = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
@dataclass
class GroundingDinoEncoderOutput(ModelOutput):
"""
Base class for outputs of the GroundingDinoEncoder. This class extends BaseModelOutput, due to:
- vision and text last hidden states
- vision and text intermediate hidden states
Args:
last_hidden_state_vision (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the vision encoder.
last_hidden_state_text (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the text encoder.
vision_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the vision embeddings + one for the output of each
layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the vision encoder at the
output of each layer plus the initial embedding outputs.
text_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the text embeddings + one for the output of each layer)
of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the text encoder at the output of
each layer plus the initial embedding outputs.
attentions (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of tuples of `torch.FloatTensor` (one for attention for each layer) of shape `(batch_size, num_heads,
sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the
weighted average in the text-vision attention, vision-text attention, text-enhancer (self-attention) and
multi-scale deformable attention heads.
"""
last_hidden_state_vision: Optional[torch.FloatTensor] = None
last_hidden_state_text: Optional[torch.FloatTensor] = None
vision_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
text_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
@dataclass
class GroundingDinoModelOutput(ModelOutput):
"""
Base class for outputs of the Grounding DINO encoder-decoder model.
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_queries, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the decoder of the model.
init_reference_points (`torch.FloatTensor` of shape `(batch_size, num_queries, 4)`):
Initial reference points sent through the Transformer decoder.
intermediate_hidden_states (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, hidden_size)`):
Stacked intermediate hidden states (output of each layer of the decoder).
intermediate_reference_points (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, 4)`):
Stacked intermediate reference points (reference points of each layer of the decoder).
decoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
shape `(batch_size, num_queries, hidden_size)`. Hidden-states of the decoder at the output of each layer
plus the initial embedding outputs.
decoder_attentions (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of tuples of `torch.FloatTensor` (one for attention for each layer) of shape `(batch_size, num_heads,
sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the
weighted average in the self-attention, cross-attention and multi-scale deformable attention heads.
encoder_last_hidden_state_vision (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Sequence of hidden-states at the output of the last layer of the encoder of the model.
encoder_last_hidden_state_text (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Sequence of hidden-states at the output of the last layer of the encoder of the model.
encoder_vision_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the vision embeddings + one for the output of each
layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the vision encoder at the
output of each layer plus the initial embedding outputs.
encoder_text_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the text embeddings + one for the output of each layer)
of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the text encoder at the output of
each layer plus the initial embedding outputs.
encoder_attentions (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of tuples of `torch.FloatTensor` (one for attention for each layer) of shape `(batch_size, num_heads,
sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the
weighted average in the text-vision attention, vision-text attention, text-enhancer (self-attention) and
multi-scale deformable attention heads. attention softmax, used to compute the weighted average in the
bi-attention heads.
enc_outputs_class (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.num_labels)`, *optional*, returned when `config.two_stage=True`):
Predicted bounding boxes scores where the top `config.num_queries` scoring bounding boxes are picked as
region proposals in the first stage. Output of bounding box binary classification (i.e. foreground and
background).
enc_outputs_coord_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, 4)`, *optional*, returned when `config.two_stage=True`):
Logits of predicted bounding boxes coordinates in the first stage.
encoder_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.num_labels)`, *optional*, returned when `config.two_stage=True`):
Logits of top `config.num_queries` scoring bounding boxes in the first stage.
encoder_pred_boxes (`torch.FloatTensor` of shape `(batch_size, sequence_length, 4)`, *optional*, returned when `config.two_stage=True`):
Coordinates of top `config.num_queries` scoring bounding boxes in the first stage.
"""
last_hidden_state: Optional[torch.FloatTensor] = None
init_reference_points: Optional[torch.FloatTensor] = None
intermediate_hidden_states: Optional[torch.FloatTensor] = None
intermediate_reference_points: Optional[torch.FloatTensor] = None
decoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
decoder_attentions: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
encoder_last_hidden_state_vision: Optional[torch.FloatTensor] = None
encoder_last_hidden_state_text: Optional[torch.FloatTensor] = None
encoder_vision_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
encoder_text_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
encoder_attentions: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
enc_outputs_class: Optional[torch.FloatTensor] = None
enc_outputs_coord_logits: Optional[torch.FloatTensor] = None
encoder_logits: Optional[torch.FloatTensor] = None
encoder_pred_boxes: Optional[torch.FloatTensor] = None
@dataclass
class GroundingDinoObjectDetectionOutput(ModelOutput):
"""
Output type of [`GroundingDinoForObjectDetection`].
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` are provided)):
Total loss as a linear combination of a negative log-likehood (cross-entropy) for class prediction and a
bounding box loss. The latter is defined as a linear combination of the L1 loss and the generalized
scale-invariant IoU loss.
loss_dict (`Dict`, *optional*):
A dictionary containing the individual losses. Useful for logging.
logits (`torch.FloatTensor` of shape `(batch_size, num_queries, num_classes + 1)`):
Classification logits (including no-object) for all queries.
pred_boxes (`torch.FloatTensor` of shape `(batch_size, num_queries, 4)`):
Normalized boxes coordinates for all queries, represented as (center_x, center_y, width, height). These
values are normalized in [0, 1], relative to the size of each individual image in the batch (disregarding
possible padding). You can use [`~GroundingDinoProcessor.post_process_grounded_object_detection`] to retrieve the
unnormalized bounding boxes.
auxiliary_outputs (`List[Dict]`, *optional*):
Optional, only returned when auxilary losses are activated (i.e. `config.auxiliary_loss` is set to `True`)
and labels are provided. It is a list of dictionaries containing the two above keys (`logits` and
`pred_boxes`) for each decoder layer.
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_queries, hidden_size)`, *optional*):
Sequence of hidden-states at the output of the last layer of the decoder of the model.
decoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
shape `(batch_size, num_queries, hidden_size)`. Hidden-states of the decoder at the output of each layer
plus the initial embedding outputs.
decoder_attentions (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of tuples of `torch.FloatTensor` (one for attention for each layer) of shape `(batch_size, num_heads,
sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the
weighted average in the self-attention, cross-attention and multi-scale deformable attention heads.
encoder_last_hidden_state_vision (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Sequence of hidden-states at the output of the last layer of the encoder of the model.
encoder_last_hidden_state_text (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Sequence of hidden-states at the output of the last layer of the encoder of the model.
encoder_vision_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the vision embeddings + one for the output of each
layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the vision encoder at the
output of each layer plus the initial embedding outputs.
encoder_text_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the text embeddings + one for the output of each layer)
of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the text encoder at the output of
each layer plus the initial embedding outputs.
encoder_attentions (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of tuples of `torch.FloatTensor` (one for attention for each layer) of shape `(batch_size, num_heads,
sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the
weighted average in the text-vision attention, vision-text attention, text-enhancer (self-attention) and
multi-scale deformable attention heads.
intermediate_hidden_states (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, hidden_size)`):
Stacked intermediate hidden states (output of each layer of the decoder).
intermediate_reference_points (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, 4)`):
Stacked intermediate reference points (reference points of each layer of the decoder).
init_reference_points (`torch.FloatTensor` of shape `(batch_size, num_queries, 4)`):
Initial reference points sent through the Transformer decoder.
enc_outputs_class (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.num_labels)`, *optional*, returned when `config.two_stage=True`):
Predicted bounding boxes scores where the top `config.num_queries` scoring bounding boxes are picked as
region proposals in the first stage. Output of bounding box binary classification (i.e. foreground and
background).
enc_outputs_coord_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, 4)`, *optional*, returned when `config.two_stage=True`):
Logits of predicted bounding boxes coordinates in the first stage.
encoder_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.num_labels)`, *optional*, returned when `config.two_stage=True`):
Logits of top `config.num_queries` scoring bounding boxes in the first stage.
encoder_pred_boxes (`torch.FloatTensor` of shape `(batch_size, sequence_length, 4)`, *optional*, returned when `config.two_stage=True`):
Coordinates of top `config.num_queries` scoring bounding boxes in the first stage.
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Encoded candidate labels sequence. Used in processor to post process object detection result.
"""
loss: Optional[torch.FloatTensor] = None
loss_dict: Optional[Dict] = None
logits: Optional[torch.FloatTensor] = None
pred_boxes: Optional[torch.FloatTensor] = None
auxiliary_outputs: Optional[List[Dict]] = None
last_hidden_state: Optional[torch.FloatTensor] = None
init_reference_points: Optional[torch.FloatTensor] = None
intermediate_hidden_states: Optional[torch.FloatTensor] = None
intermediate_reference_points: Optional[torch.FloatTensor] = None
decoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
decoder_attentions: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
encoder_last_hidden_state_vision: Optional[torch.FloatTensor] = None
encoder_last_hidden_state_text: Optional[torch.FloatTensor] = None
encoder_vision_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
encoder_text_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
encoder_attentions: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
enc_outputs_class: Optional[torch.FloatTensor] = None
enc_outputs_coord_logits: Optional[torch.FloatTensor] = None
encoder_logits: Optional[torch.FloatTensor] = None
encoder_pred_boxes: Optional[torch.FloatTensor] = None
input_ids: Optional[torch.LongTensor] = None
# Copied from transformers.models.detr.modeling_detr.DetrFrozenBatchNorm2d with Detr->GroundingDino
class GroundingDinoFrozenBatchNorm2d(nn.Module):
"""
BatchNorm2d where the batch statistics and the affine parameters are fixed.
Copy-paste from torchvision.misc.ops with added eps before rqsrt, without which any other models than
torchvision.models.resnet[18,34,50,101] produce nans.
"""
def __init__(self, n):
super().__init__()
self.register_buffer("weight", torch.ones(n))
self.register_buffer("bias", torch.zeros(n))
self.register_buffer("running_mean", torch.zeros(n))
self.register_buffer("running_var", torch.ones(n))
def _load_from_state_dict(
self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs
):
num_batches_tracked_key = prefix + "num_batches_tracked"
if num_batches_tracked_key in state_dict:
del state_dict[num_batches_tracked_key]
super()._load_from_state_dict(
state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs
)
def forward(self, x):
# move reshapes to the beginning
# to make it user-friendly
weight = self.weight.reshape(1, -1, 1, 1)
bias = self.bias.reshape(1, -1, 1, 1)
running_var = self.running_var.reshape(1, -1, 1, 1)
running_mean = self.running_mean.reshape(1, -1, 1, 1)
epsilon = 1e-5
scale = weight * (running_var + epsilon).rsqrt()
bias = bias - running_mean * scale
return x * scale + bias
# Copied from transformers.models.detr.modeling_detr.replace_batch_norm with Detr->GroundingDino
def replace_batch_norm(model):
r"""
Recursively replace all `torch.nn.BatchNorm2d` with `GroundingDinoFrozenBatchNorm2d`.
Args:
model (torch.nn.Module):
input model
"""
for name, module in model.named_children():
if isinstance(module, nn.BatchNorm2d):
new_module = GroundingDinoFrozenBatchNorm2d(module.num_features)
if not module.weight.device == torch.device("meta"):
new_module.weight.data.copy_(module.weight)
new_module.bias.data.copy_(module.bias)
new_module.running_mean.data.copy_(module.running_mean)
new_module.running_var.data.copy_(module.running_var)
model._modules[name] = new_module
if len(list(module.children())) > 0:
replace_batch_norm(module)
class GroundingDinoConvEncoder(nn.Module):
"""
Convolutional backbone, using either the AutoBackbone API or one from the timm library.
nn.BatchNorm2d layers are replaced by GroundingDinoFrozenBatchNorm2d as defined above.
"""
def __init__(self, config):
super().__init__()
self.config = config
if config.use_timm_backbone:
requires_backends(self, ["timm"])
backbone = create_model(
config.backbone,
pretrained=config.use_pretrained_backbone,
features_only=True,
**config.backbone_kwargs,
)
else:
backbone = load_backbone(config)
# replace batch norm by frozen batch norm
with torch.no_grad():
replace_batch_norm(backbone)
self.model = backbone
self.intermediate_channel_sizes = (
self.model.feature_info.channels() if config.use_timm_backbone else self.model.channels
)
backbone_model_type = None
if config.backbone is not None:
backbone_model_type = config.backbone
elif config.backbone_config is not None:
backbone_model_type = config.backbone_config.model_type
else:
raise ValueError("Either `backbone` or `backbone_config` should be provided in the config")
if "resnet" in backbone_model_type:
for name, parameter in self.model.named_parameters():
if config.use_timm_backbone:
if "layer2" not in name and "layer3" not in name and "layer4" not in name:
parameter.requires_grad_(False)
else:
if "stage.1" not in name and "stage.2" not in name and "stage.3" not in name:
parameter.requires_grad_(False)
# Copied from transformers.models.detr.modeling_detr.DetrConvEncoder.forward with Detr->GroundingDino
def forward(self, pixel_values: torch.Tensor, pixel_mask: torch.Tensor):
# send pixel_values through the model to get list of feature maps
features = self.model(pixel_values) if self.config.use_timm_backbone else self.model(pixel_values).feature_maps
out = []
for feature_map in features:
# downsample pixel_mask to match shape of corresponding feature_map
mask = nn.functional.interpolate(pixel_mask[None].float(), size=feature_map.shape[-2:]).to(torch.bool)[0]
out.append((feature_map, mask))
return out
# Copied from transformers.models.detr.modeling_detr.DetrConvModel with Detr->GroundingDino
class GroundingDinoConvModel(nn.Module):
"""
This module adds 2D position embeddings to all intermediate feature maps of the convolutional encoder.
"""
def __init__(self, conv_encoder, position_embedding):
super().__init__()
self.conv_encoder = conv_encoder
self.position_embedding = position_embedding
def forward(self, pixel_values, pixel_mask):
# send pixel_values and pixel_mask through backbone to get list of (feature_map, pixel_mask) tuples
out = self.conv_encoder(pixel_values, pixel_mask)
pos = []
for feature_map, mask in out:
# position encoding
pos.append(self.position_embedding(feature_map, mask).to(feature_map.dtype))
return out, pos
class GroundingDinoSinePositionEmbedding(nn.Module):
"""
This is a more standard version of the position embedding, very similar to the one used by the Attention is all you
need paper, generalized to work on images.
"""
def __init__(self, config):
super().__init__()
self.embedding_dim = config.d_model // 2
self.temperature = config.positional_embedding_temperature
self.scale = 2 * math.pi
def forward(self, pixel_values, pixel_mask):
y_embed = pixel_mask.cumsum(1, dtype=torch.float32)
x_embed = pixel_mask.cumsum(2, dtype=torch.float32)
eps = 1e-6
y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale
x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale
dim_t = torch.arange(self.embedding_dim, dtype=torch.float32, device=pixel_values.device)
dim_t = self.temperature ** (2 * torch.div(dim_t, 2, rounding_mode="floor") / self.embedding_dim)
pos_x = x_embed[:, :, :, None] / dim_t
pos_y = y_embed[:, :, :, None] / dim_t
pos_x = torch.stack((pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4).flatten(3)
pos_y = torch.stack((pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4).flatten(3)
pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)
return pos
class GroundingDinoLearnedPositionEmbedding(nn.Module):
"""
This module learns positional embeddings up to a fixed maximum size.
"""
def __init__(self, config):
super().__init__()
embedding_dim = config.d_model // 2
self.row_embeddings = nn.Embedding(50, embedding_dim)
self.column_embeddings = nn.Embedding(50, embedding_dim)
def forward(self, pixel_values, pixel_mask=None):
height, width = pixel_values.shape[-2:]
width_values = torch.arange(width, device=pixel_values.device)
height_values = torch.arange(height, device=pixel_values.device)
x_emb = self.column_embeddings(width_values)
y_emb = self.row_embeddings(height_values)
pos = torch.cat([x_emb.unsqueeze(0).repeat(height, 1, 1), y_emb.unsqueeze(1).repeat(1, width, 1)], dim=-1)
pos = pos.permute(2, 0, 1)
pos = pos.unsqueeze(0)
pos = pos.repeat(pixel_values.shape[0], 1, 1, 1)
return pos
def build_position_encoding(config):
if config.position_embedding_type == "sine":
position_embedding = GroundingDinoSinePositionEmbedding(config)
elif config.position_embedding_type == "learned":
position_embedding = GroundingDinoLearnedPositionEmbedding(config)
else:
raise ValueError(f"Not supported {config.position_embedding_type}")
return position_embedding
# Copied from transformers.models.deformable_detr.modeling_deformable_detr.DeformableDetrMultiscaleDeformableAttention with DeformableDetr->GroundingDino, Deformable DETR->Grounding DINO
class GroundingDinoMultiscaleDeformableAttention(nn.Module):
"""
Multiscale deformable attention as proposed in Deformable DETR.
"""
def __init__(self, config: GroundingDinoConfig, num_heads: int, n_points: int):
super().__init__()
self.attn = MultiScaleDeformableAttention()
if config.d_model % num_heads != 0:
raise ValueError(
f"embed_dim (d_model) must be divisible by num_heads, but got {config.d_model} and {num_heads}"
)
dim_per_head = config.d_model // num_heads
# check if dim_per_head is power of 2
if not ((dim_per_head & (dim_per_head - 1) == 0) and dim_per_head != 0):
warnings.warn(
"You'd better set embed_dim (d_model) in GroundingDinoMultiscaleDeformableAttention to make the"
" dimension of each attention head a power of 2 which is more efficient in the authors' CUDA"
" implementation."
)
self.im2col_step = 64
self.d_model = config.d_model
self.n_levels = config.num_feature_levels
self.n_heads = num_heads
self.n_points = n_points
self.sampling_offsets = nn.Linear(config.d_model, num_heads * self.n_levels * n_points * 2)
self.attention_weights = nn.Linear(config.d_model, num_heads * self.n_levels * n_points)
self.value_proj = nn.Linear(config.d_model, config.d_model)
self.output_proj = nn.Linear(config.d_model, config.d_model)
self.disable_custom_kernels = config.disable_custom_kernels
def with_pos_embed(self, tensor: torch.Tensor, position_embeddings: Optional[Tensor]):
return tensor if position_embeddings is None else tensor + position_embeddings
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
encoder_hidden_states=None,
encoder_attention_mask=None,
position_embeddings: Optional[torch.Tensor] = None,
reference_points=None,
spatial_shapes=None,
spatial_shapes_list=None,
level_start_index=None,
output_attentions: bool = False,
):
# add position embeddings to the hidden states before projecting to queries and keys
if position_embeddings is not None:
hidden_states = self.with_pos_embed(hidden_states, position_embeddings)
batch_size, num_queries, _ = hidden_states.shape
batch_size, sequence_length, _ = encoder_hidden_states.shape
# Ignore copy
if (spatial_shapes[:, 0] * spatial_shapes[:, 1]).sum() != sequence_length:
raise ValueError(
"Make sure to align the spatial shapes with the sequence length of the encoder hidden states"
)
value = self.value_proj(encoder_hidden_states)
if attention_mask is not None:
# we invert the attention_mask
value = value.masked_fill(~attention_mask[..., None], float(0))
value = value.view(batch_size, sequence_length, self.n_heads, self.d_model // self.n_heads)
sampling_offsets = self.sampling_offsets(hidden_states).view(
batch_size, num_queries, self.n_heads, self.n_levels, self.n_points, 2
)
attention_weights = self.attention_weights(hidden_states).view(
batch_size, num_queries, self.n_heads, self.n_levels * self.n_points
)
attention_weights = F.softmax(attention_weights, -1).view(
batch_size, num_queries, self.n_heads, self.n_levels, self.n_points
)
# batch_size, num_queries, n_heads, n_levels, n_points, 2
num_coordinates = reference_points.shape[-1]
if num_coordinates == 2:
offset_normalizer = torch.stack([spatial_shapes[..., 1], spatial_shapes[..., 0]], -1)
sampling_locations = (
reference_points[:, :, None, :, None, :]
+ sampling_offsets / offset_normalizer[None, None, None, :, None, :]
)
elif num_coordinates == 4:
sampling_locations = (
reference_points[:, :, None, :, None, :2]
+ sampling_offsets / self.n_points * reference_points[:, :, None, :, None, 2:] * 0.5
)
else:
raise ValueError(f"Last dim of reference_points must be 2 or 4, but got {reference_points.shape[-1]}")
output = self.attn(
value,
spatial_shapes,
spatial_shapes_list,
level_start_index,
sampling_locations,
attention_weights,
self.im2col_step,
)
output = self.output_proj(output)
return output, attention_weights
class GroundingDinoTextEnhancerLayer(nn.Module):
"""Vanilla Transformer with text embeddings as input"""
def __init__(self, config):
super().__init__()
self.self_attn = GroundingDinoMultiheadAttention(
config, num_attention_heads=config.encoder_attention_heads // 2
)
# Implementation of Feedforward model
self.fc1 = nn.Linear(config.d_model, config.encoder_ffn_dim // 2)
self.fc2 = nn.Linear(config.encoder_ffn_dim // 2, config.d_model)
self.layer_norm_before = nn.LayerNorm(config.d_model, config.layer_norm_eps)
self.layer_norm_after = nn.LayerNorm(config.d_model, config.layer_norm_eps)
self.activation = ACT2FN[config.activation_function]
self.num_heads = config.encoder_attention_heads // 2
self.dropout = config.text_enhancer_dropout
def with_pos_embed(self, hidden_state: Tensor, position_embeddings: Optional[Tensor]):
return hidden_state if position_embeddings is None else hidden_state + position_embeddings
def forward(
self,
hidden_states: torch.FloatTensor,
attention_masks: Optional[torch.BoolTensor] = None,
position_embeddings: Optional[torch.FloatTensor] = None,
) -> Tuple[torch.FloatTensor, torch.FloatTensor]:
"""Text self-attention to enhance projection of text features generated by
the text encoder (AutoModel based on text_config) within GroundingDinoEncoderLayer
Args:
hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_dim)`):
Text features generated by the text encoder.
attention_masks (`torch.BoolTensor`, *optional*):
Attention mask for text self-attention. False for real tokens and True for padding tokens.
position_embeddings (`torch.FloatTensor`, *optional*):
Position embeddings to be added to the hidden states.
Returns:
`tuple(torch.FloatTensor)` comprising two elements:
- **hidden_states** (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`) --
Output of the text self-attention layer.
- **attention_weights** (`torch.FloatTensor` of shape `(batch_size, num_heads, sequence_length,
sequence_length)`) --
Attention weights of the text self-attention layer.
"""
# repeat attn mask
if attention_masks.dim() == 3 and attention_masks.shape[0] == hidden_states.shape[0]:
# batch_size, num_queries, num_keys
attention_masks = attention_masks[:, None, :, :]
attention_masks = attention_masks.repeat(1, self.num_heads, 1, 1)
dtype = hidden_states.dtype
attention_masks = attention_masks.to(dtype=dtype) # fp16 compatibility
attention_masks = (1.0 - attention_masks) * torch.finfo(dtype).min
queries = keys = self.with_pos_embed(hidden_states, position_embeddings)
attention_output, attention_weights = self.self_attn(
queries=queries,
keys=keys,
values=hidden_states,
attention_mask=attention_masks,
output_attentions=True,
)
attention_output = nn.functional.dropout(attention_output, p=self.dropout, training=self.training)
hidden_states = hidden_states + attention_output
hidden_states = self.layer_norm_before(hidden_states)
residual = hidden_states
hidden_states = self.activation(self.fc1(hidden_states))
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = self.fc2(hidden_states)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = hidden_states + residual
hidden_states = self.layer_norm_after(hidden_states)
return hidden_states, attention_weights
class GroundingDinoBiMultiHeadAttention(nn.Module):
def __init__(self, config):
super().__init__()
vision_dim = text_dim = config.d_model
embed_dim = config.encoder_ffn_dim // 2
num_heads = config.encoder_attention_heads // 2
dropout = config.fusion_dropout
self.embed_dim = embed_dim
self.num_heads = num_heads
self.head_dim = embed_dim // num_heads
self.vision_dim = vision_dim
self.text_dim = text_dim
if self.head_dim * self.num_heads != self.embed_dim:
raise ValueError(
f"`embed_dim` must be divisible by `num_heads` (got `embed_dim`: {self.embed_dim} and `num_heads`: {self.num_heads})."
)
self.scale = self.head_dim ** (-0.5)
self.dropout = dropout
self.vision_proj = nn.Linear(self.vision_dim, self.embed_dim)
self.text_proj = nn.Linear(self.text_dim, self.embed_dim)
self.values_vision_proj = nn.Linear(self.vision_dim, self.embed_dim)
self.values_text_proj = nn.Linear(self.text_dim, self.embed_dim)
self.out_vision_proj = nn.Linear(self.embed_dim, self.vision_dim)
self.out_text_proj = nn.Linear(self.embed_dim, self.text_dim)
def _reshape(self, tensor: torch.Tensor, seq_len: int, batch_size: int):
return tensor.view(batch_size, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
def forward(
self,
vision_features: torch.FloatTensor,
text_features: torch.FloatTensor,
vision_attention_mask: Optional[torch.BoolTensor] = None,
text_attention_mask: Optional[torch.BoolTensor] = None,
) -> Tuple[Tuple[torch.FloatTensor, torch.FloatTensor], Tuple[torch.FloatTensor, torch.FloatTensor]]:
"""Image-to-text and text-to-image cross-attention
Args:
vision_features (`torch.FloatTensor` of shape `(batch_size, vision_sequence_length, hidden_dim)`):
Projected flattened image features generated by the vision backbone.
text_features (`torch.FloatTensor` of shape `(batch_size, text_sequence_length, hidden_dim)`):
Projected text features generated by the text encoder.
vision_attention_mask (`torch.BoolTensor`, **optional**):
Attention mask for image-to-text cross-attention. False for real tokens and True for padding tokens.
text_attention_mask (`torch.BoolTensor`, **optional**):
Attention mask for text-to-image cross-attention. False for real tokens and True for padding tokens.
Returns:
`tuple(tuple(torch.FloatTensor), tuple(torch.FloatTensor))` where each inner tuple comprises an attention
output and weights:
- **vision_attn_output** (`torch.FloatTensor` of shape `(batch_size, vision_sequence_length, hidden_din)`)
--
Output of the image-to-text cross-attention layer.
- **vision_attn_weights** (`torch.FloatTensor` of shape `(batch_size, num_heads, vision_sequence_length,
vision_sequence_length)`) --
Attention weights of the image-to-text cross-attention layer.
- **text_attn_output** (`torch.FloatTensor` of shape `(batch_size, text_sequence_length, hidden_dim)`) --
Output of the text-to-image cross-attention layer.
- **text_attn_weights** (`torch.FloatTensor` of shape `(batch_size, num_heads, text_sequence_length,
text_sequence_length)`) --
Attention weights of the text-to-image cross-attention layer.
"""
batch_size, tgt_len, _ = vision_features.size()
vision_query_states = self.vision_proj(vision_features) * self.scale
vision_query_states = self._reshape(vision_query_states, tgt_len, batch_size)
text_key_states = self.text_proj(text_features)
text_key_states = self._reshape(text_key_states, -1, batch_size)
vision_value_states = self.values_vision_proj(vision_features)
vision_value_states = self._reshape(vision_value_states, -1, batch_size)
text_value_states = self.values_text_proj(text_features)
text_value_states = self._reshape(text_value_states, -1, batch_size)
proj_shape = (batch_size * self.num_heads, -1, self.head_dim)
vision_query_states = vision_query_states.view(*proj_shape)
text_key_states = text_key_states.view(*proj_shape)
vision_value_states = vision_value_states.view(*proj_shape)
text_value_states = text_value_states.view(*proj_shape)
src_len = text_key_states.size(1)
attn_weights = torch.bmm(vision_query_states, text_key_states.transpose(1, 2)) # bs*nhead, nimg, ntxt
if attn_weights.size() != (batch_size * self.num_heads, tgt_len, src_len):
raise ValueError(
f"Attention weights should be of size {(batch_size * self.num_heads, tgt_len, src_len)}, but is {attn_weights.size()}"
)
attn_weights = attn_weights - attn_weights.max()
# Do not increase -50000/50000, data type half has quite limited range
attn_weights = torch.clamp(attn_weights, min=-50000, max=50000)
attn_weights_transposed = attn_weights.transpose(1, 2)
text_attn_weights = attn_weights_transposed - torch.max(attn_weights_transposed, dim=-1, keepdim=True)[0]
# Do not increase -50000/50000, data type half has quite limited range
text_attn_weights = torch.clamp(text_attn_weights, min=-50000, max=50000)
# mask vision for language
if vision_attention_mask is not None:
vision_attention_mask = (
vision_attention_mask[:, None, None, :].repeat(1, self.num_heads, 1, 1).flatten(0, 1)
)
text_attn_weights.masked_fill_(vision_attention_mask, float("-inf"))
text_attn_weights = text_attn_weights.softmax(dim=-1)
# mask language for vision
if text_attention_mask is not None:
text_attention_mask = text_attention_mask[:, None, None, :].repeat(1, self.num_heads, 1, 1).flatten(0, 1)
attn_weights.masked_fill_(text_attention_mask, float("-inf"))
vision_attn_weights = attn_weights.softmax(dim=-1)
vision_attn_probs = F.dropout(vision_attn_weights, p=self.dropout, training=self.training)
text_attn_probs = F.dropout(text_attn_weights, p=self.dropout, training=self.training)
vision_attn_output = torch.bmm(vision_attn_probs, text_value_states)
text_attn_output = torch.bmm(text_attn_probs, vision_value_states)
if vision_attn_output.size() != (batch_size * self.num_heads, tgt_len, self.head_dim):
raise ValueError(
f"`vision_attn_output` should be of size {(batch_size, self.num_heads, tgt_len, self.head_dim)}, but is {vision_attn_output.size()}"
)
if text_attn_output.size() != (batch_size * self.num_heads, src_len, self.head_dim):
raise ValueError(
f"`text_attn_output` should be of size {(batch_size, self.num_heads, src_len, self.head_dim)}, but is {text_attn_output.size()}"
)
vision_attn_output = vision_attn_output.view(batch_size, self.num_heads, tgt_len, self.head_dim)
vision_attn_output = vision_attn_output.transpose(1, 2)
vision_attn_output = vision_attn_output.reshape(batch_size, tgt_len, self.embed_dim)
text_attn_output = text_attn_output.view(batch_size, self.num_heads, src_len, self.head_dim)
text_attn_output = text_attn_output.transpose(1, 2)
text_attn_output = text_attn_output.reshape(batch_size, src_len, self.embed_dim)
vision_attn_output = self.out_vision_proj(vision_attn_output)
text_attn_output = self.out_text_proj(text_attn_output)
return (vision_attn_output, vision_attn_weights), (text_attn_output, text_attn_weights)
# Copied from transformers.models.beit.modeling_beit.drop_path
def drop_path(input: torch.Tensor, drop_prob: float = 0.0, training: bool = False) -> torch.Tensor:
"""
Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
Comment by Ross Wightman: This is the same as the DropConnect impl I created for EfficientNet, etc networks,
however, the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper...
See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for changing the
layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use 'survival rate' as the
argument.
"""
if drop_prob == 0.0 or not training:
return input
keep_prob = 1 - drop_prob
shape = (input.shape[0],) + (1,) * (input.ndim - 1) # work with diff dim tensors, not just 2D ConvNets
random_tensor = keep_prob + torch.rand(shape, dtype=input.dtype, device=input.device)
random_tensor.floor_() # binarize
output = input.div(keep_prob) * random_tensor
return output
# Copied from transformers.models.beit.modeling_beit.BeitDropPath with Beit->GroundingDino
class GroundingDinoDropPath(nn.Module):
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks)."""
def __init__(self, drop_prob: Optional[float] = None) -> None:
super().__init__()
self.drop_prob = drop_prob
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
return drop_path(hidden_states, self.drop_prob, self.training)
def extra_repr(self) -> str:
return "p={}".format(self.drop_prob)
class GroundingDinoFusionLayer(nn.Module):
def __init__(self, config):
super().__init__()
drop_path = config.fusion_droppath
# pre layer norm
self.layer_norm_vision = nn.LayerNorm(config.d_model, config.layer_norm_eps)
self.layer_norm_text = nn.LayerNorm(config.d_model, config.layer_norm_eps)
self.attn = GroundingDinoBiMultiHeadAttention(config)
# add layer scale for training stability
self.drop_path = GroundingDinoDropPath(drop_path) if drop_path > 0.0 else nn.Identity()
init_values = 1e-4
self.vision_param = nn.Parameter(init_values * torch.ones((config.d_model)), requires_grad=True)
self.text_param = nn.Parameter(init_values * torch.ones((config.d_model)), requires_grad=True)
def forward(
self,
vision_features: torch.FloatTensor,
text_features: torch.FloatTensor,
attention_mask_vision: Optional[torch.BoolTensor] = None,
attention_mask_text: Optional[torch.BoolTensor] = None,
) -> Tuple[Tuple[torch.FloatTensor, torch.FloatTensor], Tuple[torch.FloatTensor, torch.FloatTensor]]:
"""Image and text features fusion
Args:
vision_features (`torch.FloatTensor` of shape `(batch_size, vision_sequence_length, hidden_dim)`):
Projected flattened image features generated by the vision backbone.
text_features (`torch.FloatTensor` of shape `(batch_size, text_sequence_length, hidden_dim)`):
Projected text features generated by the text encoder.
attention_mask_vision (`torch.BoolTensor`, **optional**):
Attention mask for image-to-text cross-attention. False for real tokens and True for padding tokens.
attention_mask_text (`torch.BoolTensor`, **optional**):
Attention mask for text-to-image cross-attention. False for real tokens and True for padding tokens.
Returns:
`tuple(tuple(torch.FloatTensor), tuple(torch.FloatTensor))` where each inner tuple comprises an enhanced
feature and attention output and weights:
- **vision_features** (`torch.FloatTensor` of shape `(batch_size, vision_sequence_length, vision_dim)`) --
Updated vision features with attention output from image-to-text cross-attention layer.
- **vision_attn_weights** (`torch.FloatTensor` of shape `(batch_size, num_heads, vision_sequence_length,
vision_sequence_length)`) --
Attention weights of the image-to-text cross-attention layer.
- **text_features** (`torch.FloatTensor` of shape `(batch_size, text_sequence_length, text_dim)`) --
Updated text features with attention output from text-to-image cross-attention layer.
- **text_attn_weights** (`torch.FloatTensor` of shape `(batch_size, num_heads, text_sequence_length,
text_sequence_length)`) --
Attention weights of the text-to-image cross-attention layer.
"""
vision_features = self.layer_norm_vision(vision_features)
text_features = self.layer_norm_text(text_features)
(delta_v, vision_attn), (delta_t, text_attn) = self.attn(
vision_features,
text_features,
vision_attention_mask=attention_mask_vision,
text_attention_mask=attention_mask_text,
)
vision_features = vision_features + self.drop_path(self.vision_param * delta_v)
text_features = text_features + self.drop_path(self.text_param * delta_t)
return (vision_features, vision_attn), (text_features, text_attn)
class GroundingDinoDeformableLayer(nn.Module):
def __init__(self, config: GroundingDinoConfig):
super().__init__()
self.embed_dim = config.d_model
self.self_attn = GroundingDinoMultiscaleDeformableAttention(
config, num_heads=config.encoder_attention_heads, n_points=config.encoder_n_points
)
self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim, config.layer_norm_eps)
self.dropout = config.dropout
self.activation_fn = ACT2FN[config.activation_function]
self.activation_dropout = config.activation_dropout
self.fc1 = nn.Linear(self.embed_dim, config.encoder_ffn_dim)
self.fc2 = nn.Linear(config.encoder_ffn_dim, self.embed_dim)
self.final_layer_norm = nn.LayerNorm(self.embed_dim, config.layer_norm_eps)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: torch.Tensor,
position_embeddings: Optional[torch.Tensor] = None,
reference_points=None,
spatial_shapes=None,
spatial_shapes_list=None,
level_start_index=None,
output_attentions: bool = False,
):
"""
Args:
hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Input to the layer.
attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`):
Attention mask.
position_embeddings (`torch.FloatTensor`, *optional*):
Position embeddings, to be added to `hidden_states`.
reference_points (`torch.FloatTensor`, *optional*):
Reference points.
spatial_shapes (`torch.LongTensor`, *optional*):
Spatial shapes of the backbone feature maps.
spatial_shapes_list (`List[Tuple[int, int]]`, *optional*):
Spatial shapes of the backbone feature maps (but as list for export compatibility).
level_start_index (`torch.LongTensor`, *optional*):
Level start index.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
"""
residual = hidden_states
# Apply Multi-scale Deformable Attention Module on the multi-scale feature maps.
hidden_states, attn_weights = self.self_attn(
hidden_states=hidden_states,
attention_mask=attention_mask,
encoder_hidden_states=hidden_states,
encoder_attention_mask=attention_mask,
position_embeddings=position_embeddings,
reference_points=reference_points,
spatial_shapes=spatial_shapes,
spatial_shapes_list=spatial_shapes_list,
level_start_index=level_start_index,
output_attentions=output_attentions,
)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = residual + hidden_states
hidden_states = self.self_attn_layer_norm(hidden_states)
residual = hidden_states
hidden_states = self.activation_fn(self.fc1(hidden_states))
hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)
hidden_states = self.fc2(hidden_states)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = residual + hidden_states
hidden_states = self.final_layer_norm(hidden_states)
if self.training:
if torch.isinf(hidden_states).any() or torch.isnan(hidden_states).any():
clamp_value = torch.finfo(hidden_states.dtype).max - 1000
hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)
return hidden_states, attn_weights
# Based on https://github.com/IDEA-Research/GroundingDINO/blob/2b62f419c292ca9c518daae55512fabc3fead4a4/groundingdino/models/GroundingDINO/utils.py#L24
def get_sine_pos_embed(
pos_tensor: torch.Tensor, num_pos_feats: int = 128, temperature: int = 10000, exchange_xy: bool = True
) -> Tensor:
"""
Generate sine position embeddings from a position tensor.
Args:
pos_tensor (torch.Tensor):
Tensor containing positions. Shape: [..., n].
num_pos_feats (`int`, *optional*, defaults to 128):
Projected shape for each float in the tensor.
temperature (`int`, *optional*, defaults to 10000):
Temperature in the sine/cosine function.
exchange_xy (`bool`, *optional*, defaults to `True`):
Exchange pos x and pos y. For example, input tensor is [x,y], the results will be [pos(y), pos(x)].
Returns:
position_embeddings (torch.Tensor): shape: [..., n * hidden_size].
"""
scale = 2 * math.pi
dim_t = torch.arange(num_pos_feats, dtype=torch.float32, device=pos_tensor.device)
dim_t = temperature ** (2 * torch.div(dim_t, 2, rounding_mode="floor") / num_pos_feats)
def sine_func(x: torch.Tensor):
sin_x = x * scale / dim_t
sin_x = torch.stack((sin_x[..., 0::2].sin(), sin_x[..., 1::2].cos()), dim=3).flatten(2)
return sin_x
pos_tensor = pos_tensor.split([1] * pos_tensor.shape[-1], dim=-1)
position_embeddings = [sine_func(x) for x in pos_tensor]
if exchange_xy:
position_embeddings[0], position_embeddings[1] = position_embeddings[1], position_embeddings[0]
position_embeddings = torch.cat(position_embeddings, dim=-1)
return position_embeddings
class GroundingDinoEncoderLayer(nn.Module):
def __init__(self, config) -> None:
super().__init__()
self.d_model = config.d_model
self.text_enhancer_layer = GroundingDinoTextEnhancerLayer(config)
self.fusion_layer = GroundingDinoFusionLayer(config)
self.deformable_layer = GroundingDinoDeformableLayer(config)
def get_text_position_embeddings(
self,
text_features: Tensor,
text_position_embedding: Optional[torch.Tensor],
text_position_ids: Optional[torch.Tensor],
) -> Tensor:
batch_size, seq_length, _ = text_features.shape
if text_position_embedding is None and text_position_ids is None:
text_position_embedding = torch.arange(seq_length, device=text_features.device)
text_position_embedding = text_position_embedding.float()
text_position_embedding = text_position_embedding.unsqueeze(0).unsqueeze(-1)
text_position_embedding = text_position_embedding.repeat(batch_size, 1, 1)
text_position_embedding = get_sine_pos_embed(
text_position_embedding, num_pos_feats=self.d_model, exchange_xy=False
)
if text_position_ids is not None:
text_position_embedding = get_sine_pos_embed(
text_position_ids[..., None], num_pos_feats=self.d_model, exchange_xy=False
)
return text_position_embedding
def forward(
self,
vision_features: Tensor,
vision_position_embedding: Tensor,
spatial_shapes: Tensor,
spatial_shapes_list: List[Tuple[int, int]],
level_start_index: Tensor,
key_padding_mask: Tensor,
reference_points: Tensor,
text_features: Optional[Tensor] = None,
text_attention_mask: Optional[Tensor] = None,
text_position_embedding: Optional[Tensor] = None,
text_self_attention_masks: Optional[Tensor] = None,
text_position_ids: Optional[Tensor] = None,
):
text_position_embedding = self.get_text_position_embeddings(
text_features, text_position_embedding, text_position_ids
)
(vision_features, vision_fused_attn), (text_features, text_fused_attn) = self.fusion_layer(
vision_features=vision_features,
text_features=text_features,
attention_mask_vision=key_padding_mask,
attention_mask_text=text_attention_mask,
)
(text_features, text_enhanced_attn) = self.text_enhancer_layer(
hidden_states=text_features,
attention_masks=~text_self_attention_masks, # note we use ~ for mask here
position_embeddings=(text_position_embedding if text_position_embedding is not None else None),
)
(vision_features, vision_deformable_attn) = self.deformable_layer(
hidden_states=vision_features,
attention_mask=~key_padding_mask,
position_embeddings=vision_position_embedding,
reference_points=reference_points,
spatial_shapes=spatial_shapes,
spatial_shapes_list=spatial_shapes_list,
level_start_index=level_start_index,
)
return (
(vision_features, text_features),
(vision_fused_attn, text_fused_attn, text_enhanced_attn, vision_deformable_attn),
)
class GroundingDinoMultiheadAttention(nn.Module):
"""Equivalent implementation of nn.MultiheadAttention with `batch_first=True`."""
def __init__(self, config, num_attention_heads=None):
super().__init__()
if config.hidden_size % num_attention_heads != 0 and not hasattr(config, "embedding_size"):
raise ValueError(
f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention "
f"heads ({num_attention_heads})"
)
self.num_attention_heads = num_attention_heads
self.attention_head_size = int(config.hidden_size / num_attention_heads)
self.all_head_size = self.num_attention_heads * self.attention_head_size
self.query = nn.Linear(config.hidden_size, self.all_head_size)
self.key = nn.Linear(config.hidden_size, self.all_head_size)
self.value = nn.Linear(config.hidden_size, self.all_head_size)
self.out_proj = nn.Linear(config.hidden_size, config.hidden_size)
self.dropout = nn.Dropout(config.attention_dropout)
def transpose_for_scores(self, x: torch.Tensor) -> torch.Tensor:
new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
x = x.view(new_x_shape)
return x.permute(0, 2, 1, 3)
def forward(
self,
queries: torch.Tensor,
keys: torch.Tensor,
values: torch.Tensor,
attention_mask: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = False,
) -> Tuple[torch.Tensor]:
query_layer = self.transpose_for_scores(self.query(queries))
key_layer = self.transpose_for_scores(self.key(keys))
value_layer = self.transpose_for_scores(self.value(values))
# Take the dot product between "query" and "key" to get the raw attention scores.
attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
attention_scores = attention_scores / math.sqrt(self.attention_head_size)
if attention_mask is not None:
# Apply the attention mask is (precomputed for all layers in GroundingDinoModel forward() function)
attention_scores = attention_scores + attention_mask
# Normalize the attention scores to probabilities.
attention_probs = nn.functional.softmax(attention_scores, dim=-1)
# This is actually dropping out entire tokens to attend to, which might
# seem a bit unusual, but is taken from the original Transformer paper.
attention_probs = self.dropout(attention_probs)
context_layer = torch.matmul(attention_probs, value_layer)
context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
context_layer = context_layer.view(new_context_layer_shape)
context_layer = self.out_proj(context_layer)
outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
return outputs
class GroundingDinoDecoderLayer(nn.Module):
def __init__(self, config: GroundingDinoConfig):
super().__init__()
self.embed_dim = config.d_model
# self-attention
self.self_attn = GroundingDinoMultiheadAttention(config, num_attention_heads=config.decoder_attention_heads)
self.dropout = config.dropout
self.activation_fn = ACT2FN[config.activation_function]
self.activation_dropout = config.activation_dropout
self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim, config.layer_norm_eps)
# cross-attention text
self.encoder_attn_text = GroundingDinoMultiheadAttention(
config, num_attention_heads=config.decoder_attention_heads
)
self.encoder_attn_text_layer_norm = nn.LayerNorm(self.embed_dim, config.layer_norm_eps)
# cross-attention
self.encoder_attn = GroundingDinoMultiscaleDeformableAttention(
config,
num_heads=config.decoder_attention_heads,
n_points=config.decoder_n_points,
)
self.encoder_attn_layer_norm = nn.LayerNorm(self.embed_dim, config.layer_norm_eps)
# feedforward neural networks
self.fc1 = nn.Linear(self.embed_dim, config.decoder_ffn_dim)
self.fc2 = nn.Linear(config.decoder_ffn_dim, self.embed_dim)
self.final_layer_norm = nn.LayerNorm(self.embed_dim, config.layer_norm_eps)
def with_pos_embed(self, tensor: torch.Tensor, position_embeddings: Optional[Tensor]):
return tensor if position_embeddings is None else tensor + position_embeddings
def forward(
self,
hidden_states: torch.Tensor,
position_embeddings: Optional[torch.Tensor] = None,
reference_points=None,
spatial_shapes=None,
spatial_shapes_list=None,
level_start_index=None,
vision_encoder_hidden_states: Optional[torch.Tensor] = None,
vision_encoder_attention_mask: Optional[torch.Tensor] = None,
text_encoder_hidden_states: Optional[torch.Tensor] = None,
text_encoder_attention_mask: Optional[torch.Tensor] = None,
self_attn_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = False,
):
residual = hidden_states
# Self Attention
queries = keys = self.with_pos_embed(hidden_states, position_embeddings)
hidden_states, self_attn_weights = self.self_attn(
queries=queries,
keys=keys,
values=hidden_states,
attention_mask=self_attn_mask,
output_attentions=True,
)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = residual + hidden_states
hidden_states = self.self_attn_layer_norm(hidden_states)
second_residual = hidden_states
# Cross-Attention Text
queries = self.with_pos_embed(hidden_states, position_embeddings)
hidden_states, text_cross_attn_weights = self.encoder_attn_text(
queries=queries,
keys=text_encoder_hidden_states,
values=text_encoder_hidden_states,
attention_mask=text_encoder_attention_mask,
output_attentions=True,
)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = second_residual + hidden_states
hidden_states = self.encoder_attn_text_layer_norm(hidden_states)
third_residual = hidden_states
# Cross-Attention
cross_attn_weights = None
hidden_states, cross_attn_weights = self.encoder_attn(
hidden_states=hidden_states,
attention_mask=vision_encoder_attention_mask,
encoder_hidden_states=vision_encoder_hidden_states,
encoder_attention_mask=vision_encoder_attention_mask,
position_embeddings=position_embeddings,
reference_points=reference_points,
spatial_shapes=spatial_shapes,
spatial_shapes_list=spatial_shapes_list,
level_start_index=level_start_index,
output_attentions=output_attentions,
)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = third_residual + hidden_states
hidden_states = self.encoder_attn_layer_norm(hidden_states)
# Fully Connected
residual = hidden_states
hidden_states = self.activation_fn(self.fc1(hidden_states))
hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)
hidden_states = self.fc2(hidden_states)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = residual + hidden_states
hidden_states = self.final_layer_norm(hidden_states)
outputs = (hidden_states,)
if output_attentions:
outputs += (self_attn_weights, text_cross_attn_weights, cross_attn_weights)
return outputs
class GroundingDinoContrastiveEmbedding(nn.Module):
def __init__(self, config):
super().__init__()
self.max_text_len = config.max_text_len
def forward(
self,
vision_hidden_state: torch.FloatTensor,
text_hidden_state: torch.FloatTensor,
text_token_mask: torch.BoolTensor,
) -> torch.FloatTensor:
output = vision_hidden_state @ text_hidden_state.transpose(-1, -2)
output = output.masked_fill(~text_token_mask[:, None, :], float("-inf"))
# padding to max_text_len
new_output = torch.full((*output.shape[:-1], self.max_text_len), float("-inf"), device=output.device)
new_output[..., : output.shape[-1]] = output
return new_output
class GroundingDinoPreTrainedModel(PreTrainedModel):
config_class = GroundingDinoConfig
base_model_prefix = "model"
main_input_name = "pixel_values"
def _init_weights(self, module):
std = self.config.init_std
if isinstance(module, GroundingDinoLearnedPositionEmbedding):
nn.init.uniform_(module.row_embeddings.weight)
nn.init.uniform_(module.column_embeddings.weight)
elif isinstance(module, GroundingDinoMultiscaleDeformableAttention):
nn.init.constant_(module.sampling_offsets.weight.data, 0.0)
default_dtype = torch.get_default_dtype()
thetas = torch.arange(module.n_heads, dtype=torch.int64).to(default_dtype) * (
2.0 * math.pi / module.n_heads
)
grid_init = torch.stack([thetas.cos(), thetas.sin()], -1)
grid_init = (
(grid_init / grid_init.abs().max(-1, keepdim=True)[0])
.view(module.n_heads, 1, 1, 2)
.repeat(1, module.n_levels, module.n_points, 1)
)
for i in range(module.n_points):
grid_init[:, :, i, :] *= i + 1
with torch.no_grad():
module.sampling_offsets.bias = nn.Parameter(grid_init.view(-1))
nn.init.constant_(module.attention_weights.weight.data, 0.0)
nn.init.constant_(module.attention_weights.bias.data, 0.0)
nn.init.xavier_uniform_(module.value_proj.weight.data)
nn.init.constant_(module.value_proj.bias.data, 0.0)
nn.init.xavier_uniform_(module.output_proj.weight.data)
nn.init.constant_(module.output_proj.bias.data, 0.0)
elif isinstance(module, GroundingDinoBiMultiHeadAttention):
nn.init.xavier_uniform_(module.vision_proj.weight)
module.vision_proj.bias.data.fill_(0)
nn.init.xavier_uniform_(module.text_proj.weight)
module.text_proj.bias.data.fill_(0)
nn.init.xavier_uniform_(module.values_vision_proj.weight)
module.values_vision_proj.bias.data.fill_(0)
nn.init.xavier_uniform_(module.values_text_proj.weight)
module.values_text_proj.bias.data.fill_(0)
nn.init.xavier_uniform_(module.out_vision_proj.weight)
module.out_vision_proj.bias.data.fill_(0)
nn.init.xavier_uniform_(module.out_text_proj.weight)
module.out_text_proj.bias.data.fill_(0)
elif isinstance(module, (GroundingDinoEncoderLayer, GroundingDinoDecoderLayer)):
for p in module.parameters():
if p.dim() > 1:
nn.init.normal_(p, mean=0.0, std=std)
elif isinstance(module, (nn.Linear, nn.Conv2d, nn.BatchNorm2d)):
# Slightly different from the TF version which uses truncated_normal for initialization
# cf https://github.com/pytorch/pytorch/pull/5617
module.weight.data.normal_(mean=0.0, std=std)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.Embedding):
module.weight.data.normal_(mean=0.0, std=std)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
elif isinstance(module, GroundingDinoMLPPredictionHead):
nn.init.constant_(module.layers[-1].weight.data, 0)
nn.init.constant_(module.layers[-1].bias.data, 0)
if hasattr(module, "reference_points") and not self.config.two_stage:
nn.init.xavier_uniform_(module.reference_points.weight.data, gain=1.0)
nn.init.constant_(module.reference_points.bias.data, 0.0)
if hasattr(module, "level_embed"):
nn.init.normal_(module.level_embed)
def _set_gradient_checkpointing(self, module, value=False):
if isinstance(module, GroundingDinoDecoder):
module.gradient_checkpointing = value
GROUNDING_DINO_START_DOCSTRING = r"""
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.
Parameters:
config ([`GroundingDinoConfig`]):
Model configuration class with all the parameters of the model. Initializing with a config file does not
load the weights associated with the model, only the configuration. Check out the
[`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
GROUNDING_DINO_INPUTS_DOCSTRING = r"""
Args:
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Padding will be ignored by default should you provide it.
Pixel values can be obtained using [`AutoImageProcessor`]. See [`GroundingDinoImageProcessor.__call__`] for
details.
input_ids (`torch.LongTensor` of shape `(batch_size, text_sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
it.
Indices can be obtained using [`AutoTokenizer`]. See [`BertTokenizer.__call__`] for details.
token_type_ids (`torch.LongTensor` of shape `(batch_size, text_sequence_length)`, *optional*):
Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
1]`: 0 corresponds to a `sentence A` token, 1 corresponds to a `sentence B` token
[What are token type IDs?](../glossary#token-type-ids)
attention_mask (`torch.LongTensor` of shape `(batch_size, text_sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are real (i.e. **not masked**),
- 0 for tokens that are padding (i.e. **masked**).
[What are attention masks?](../glossary#attention-mask)
pixel_mask (`torch.LongTensor` of shape `(batch_size, height, width)`, *optional*):
Mask to avoid performing attention on padding pixel values. Mask values selected in `[0, 1]`:
- 1 for pixels that are real (i.e. **not masked**),
- 0 for pixels that are padding (i.e. **masked**).
[What are attention masks?](../glossary#attention-mask)
encoder_outputs (`tuple(tuple(torch.FloatTensor)`, *optional*):
Tuple consists of (`last_hidden_state_vision`, *optional*: `last_hidden_state_text`, *optional*:
`vision_hidden_states`, *optional*: `text_hidden_states`, *optional*: `attentions`)
`last_hidden_state_vision` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) is a sequence
of hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the
decoder.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
"""
class GroundingDinoEncoder(GroundingDinoPreTrainedModel):
"""
Transformer encoder consisting of *config.encoder_layers* deformable attention layers. Each layer is a
[`GroundingDinoEncoderLayer`].
The encoder updates the flattened multi-scale feature maps through multiple deformable attention layers.
Args:
config: GroundingDinoConfig
"""
def __init__(self, config: GroundingDinoConfig):
super().__init__(config)
self.dropout = config.dropout
self.layers = nn.ModuleList([GroundingDinoEncoderLayer(config) for _ in range(config.encoder_layers)])
# Initialize weights and apply final processing
self.post_init()
@staticmethod
def get_reference_points(spatial_shapes, valid_ratios, device):
"""
Get reference points for each feature map.
Args:
spatial_shapes (`torch.LongTensor` of shape `(num_feature_levels, 2)`):
Spatial shapes of each feature map.
valid_ratios (`torch.FloatTensor` of shape `(batch_size, num_feature_levels, 2)`):
Valid ratios of each feature map.
device (`torch.device`):
Device on which to create the tensors.
Returns:
`torch.FloatTensor` of shape `(batch_size, num_queries, num_feature_levels, 2)`
"""
reference_points_list = []
for level, (height, width) in enumerate(spatial_shapes):
ref_y, ref_x = meshgrid(
torch.linspace(0.5, height - 0.5, height, dtype=torch.float32, device=device),
torch.linspace(0.5, width - 0.5, width, dtype=torch.float32, device=device),
indexing="ij",
)
# TODO: valid_ratios could be useless here. check https://github.com/fundamentalvision/Deformable-DETR/issues/36
ref_y = ref_y.reshape(-1)[None] / (valid_ratios[:, None, level, 1] * height)
ref_x = ref_x.reshape(-1)[None] / (valid_ratios[:, None, level, 0] * width)
ref = torch.stack((ref_x, ref_y), -1)
reference_points_list.append(ref)
reference_points = torch.cat(reference_points_list, 1)
reference_points = reference_points[:, :, None] * valid_ratios[:, None]
return reference_points
def forward(
self,
vision_features: Tensor,
vision_attention_mask: Tensor,
vision_position_embedding: Tensor,
spatial_shapes: Tensor,
spatial_shapes_list: List[Tuple[int, int]],
level_start_index: Tensor,
valid_ratios=None,
text_features: Optional[Tensor] = None,
text_attention_mask: Optional[Tensor] = None,
text_position_embedding: Optional[Tensor] = None,
text_self_attention_masks: Optional[Tensor] = None,
text_position_ids: Optional[Tensor] = None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
r"""
Args:
vision_features (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Flattened feature map (output of the backbone + projection layer) that is passed to the encoder.
vision_attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding pixel features. Mask values selected in `[0, 1]`:
- 0 for pixel features that are real (i.e. **not masked**),
- 1 for pixel features that are padding (i.e. **masked**).
[What are attention masks?](../glossary#attention-mask)
vision_position_embedding (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Position embeddings that are added to the queries and keys in each self-attention layer.
spatial_shapes (`torch.LongTensor` of shape `(num_feature_levels, 2)`):
Spatial shapes of each feature map.
spatial_shapes_list (`List[Tuple[int, int]]`):
Spatial shapes of each feature map (but as list for export compatibility).
level_start_index (`torch.LongTensor` of shape `(num_feature_levels)`):
Starting index of each feature map.
valid_ratios (`torch.FloatTensor` of shape `(batch_size, num_feature_levels, 2)`):
Ratio of valid area in each feature level.
text_features (`torch.FloatTensor` of shape `(batch_size, text_seq_len, hidden_size)`):
Flattened text features that are passed to the encoder.
text_attention_mask (`torch.Tensor` of shape `(batch_size, text_seq_len)`, *optional*):
Mask to avoid performing attention on padding text features. Mask values selected in `[0, 1]`:
- 0 for text features that are real (i.e. **not masked**),
- 1 for text features that are padding (i.e. **masked**).
[What are attention masks?](../glossary#attention-mask)
text_position_embedding (`torch.FloatTensor` of shape `(batch_size, text_seq_len)`):
Position embeddings that are added to the queries and keys in each self-attention layer.
text_self_attention_masks (`torch.BoolTensor` of shape `(batch_size, text_seq_len, text_seq_len)`):
Masks to avoid performing attention between padding text features. Mask values selected in `[0, 1]`:
- 1 for text features that are real (i.e. **not masked**),
- 0 for text features that are padding (i.e. **masked**).
text_position_ids (`torch.LongTensor` of shape `(batch_size, num_queries)`):
Position ids for text features.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
for more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
reference_points = self.get_reference_points(spatial_shapes, valid_ratios, device=vision_features.device)
encoder_vision_states = () if output_hidden_states else None
encoder_text_states = () if output_hidden_states else None
all_attns = () if output_attentions else None
all_attn_fused_text = () if output_attentions else None
all_attn_fused_vision = () if output_attentions else None
all_attn_enhanced_text = () if output_attentions else None
all_attn_deformable = () if output_attentions else None
for i, encoder_layer in enumerate(self.layers):
if output_hidden_states:
encoder_vision_states += (vision_features,)
encoder_text_states += (text_features,)
(vision_features, text_features), attentions = encoder_layer(
vision_features=vision_features,
vision_position_embedding=vision_position_embedding,
spatial_shapes=spatial_shapes,
spatial_shapes_list=spatial_shapes_list,
level_start_index=level_start_index,
key_padding_mask=vision_attention_mask,
reference_points=reference_points,
text_features=text_features,
text_attention_mask=text_attention_mask,
text_position_embedding=text_position_embedding,
text_self_attention_masks=text_self_attention_masks,
text_position_ids=text_position_ids,
)
if output_attentions:
all_attn_fused_vision += (attentions[0],)
all_attn_fused_text += (attentions[1],)
all_attn_enhanced_text += (attentions[2],)
all_attn_deformable += (attentions[3],)
if output_hidden_states:
encoder_vision_states += (vision_features,)
encoder_text_states += (text_features,)
if output_attentions:
all_attns = (all_attn_fused_vision, all_attn_fused_text, all_attn_enhanced_text, all_attn_deformable)
if not return_dict:
enc_outputs = [vision_features, text_features, encoder_vision_states, encoder_text_states, all_attns]
return tuple(v for v in enc_outputs if v is not None)
return GroundingDinoEncoderOutput(
last_hidden_state_vision=vision_features,
last_hidden_state_text=text_features,
vision_hidden_states=encoder_vision_states,
text_hidden_states=encoder_text_states,
attentions=all_attns,
)
class GroundingDinoDecoder(GroundingDinoPreTrainedModel):
"""
Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`GroundingDinoDecoderLayer`].
The decoder updates the query embeddings through multiple self-attention and cross-attention layers.
Some tweaks for Grounding DINO:
- `position_embeddings`, `reference_points`, `spatial_shapes` and `valid_ratios` are added to the forward pass.
- it also returns a stack of intermediate outputs and reference points from all decoding layers.
Args:
config: GroundingDinoConfig
"""
def __init__(self, config: GroundingDinoConfig):
super().__init__(config)
self.dropout = config.dropout
self.layer_norm = nn.LayerNorm(config.d_model, config.layer_norm_eps)
self.layers = nn.ModuleList([GroundingDinoDecoderLayer(config) for _ in range(config.decoder_layers)])
self.reference_points_head = GroundingDinoMLPPredictionHead(
config.query_dim // 2 * config.d_model, config.d_model, config.d_model, 2
)
self.gradient_checkpointing = False
# hack implementation for iterative bounding box refinement as in two-stage Deformable DETR
self.bbox_embed = None
self.class_embed = None
self.query_scale = None
# Initialize weights and apply final processing
self.post_init()
def forward(
self,
inputs_embeds,
vision_encoder_hidden_states,
vision_encoder_attention_mask=None,
text_encoder_hidden_states=None,
text_encoder_attention_mask=None,
reference_points=None,
spatial_shapes=None,
spatial_shapes_list=None,
level_start_index=None,
valid_ratios=None,
self_attn_mask=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
r"""
Args:
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, num_queries, hidden_size)`):
The query embeddings that are passed into the decoder.
vision_encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Last hidden state from encoder related to vision feature map.
vision_encoder_attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding pixel features. Mask values selected in `[0, 1]`:
- 1 for pixel features that are real (i.e. **not masked**),
- 0 for pixel features that are padding (i.e. **masked**).
text_encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, text_seq_len, hidden_size)`):
Last hidden state from encoder related to text features.
text_encoder_attention_mask (`torch.Tensor` of shape `(batch_size, text_seq_len)`, *optional*):
Mask to avoid performing attention on padding text features. Mask values selected in `[0, 1]`:
- 0 for text features that are real (i.e. **not masked**),
- 1 for text features that are padding (i.e. **masked**).
reference_points (`torch.FloatTensor` of shape `(batch_size, num_queries, 4)` is `as_two_stage` else `(batch_size, num_queries, 2)` or , *optional*):
Reference point in range `[0, 1]`, top-left (0,0), bottom-right (1, 1), including padding area.
spatial_shapes (`torch.FloatTensor` of shape `(num_feature_levels, 2)`):
Spatial shapes of the feature maps.
spatial_shapes_list (`List[Tuple[int, int]]`):
Spatial shapes of the feature maps (but as list for export compatibility).
level_start_index (`torch.LongTensor` of shape `(num_feature_levels)`, *optional*):
Indexes for the start of each feature level. In range `[0, sequence_length]`.
valid_ratios (`torch.FloatTensor` of shape `(batch_size, num_feature_levels, 2)`, *optional*):
Ratio of valid area in each feature level.
self_attn_mask (`torch.BoolTensor` of shape `(batch_size, text_seq_len)`):
Masks to avoid performing self-attention between vision hidden state. Mask values selected in `[0, 1]`:
- 1 for queries that are real (i.e. **not masked**),
- 0 for queries that are padding (i.e. **masked**).
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
for more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if inputs_embeds is not None:
hidden_states = inputs_embeds
# decoder layers
all_hidden_states = () if output_hidden_states else None
all_self_attns = () if output_attentions else None
all_attns = () if output_attentions else None
all_cross_attns_vision = () if (output_attentions and vision_encoder_hidden_states is not None) else None
all_cross_attns_text = () if (output_attentions and text_encoder_hidden_states is not None) else None
intermediate = ()
intermediate_reference_points = ()
if text_encoder_attention_mask is not None:
dtype = text_encoder_hidden_states.dtype
text_encoder_attention_mask = text_encoder_attention_mask[:, None, None, :]
text_encoder_attention_mask = text_encoder_attention_mask.repeat(
1, self.config.decoder_attention_heads, self.config.num_queries, 1
)
text_encoder_attention_mask = text_encoder_attention_mask.to(dtype=dtype)
text_encoder_attention_mask = text_encoder_attention_mask * torch.finfo(dtype).min
for idx, decoder_layer in enumerate(self.layers):
num_coordinates = reference_points.shape[-1]
if num_coordinates == 4:
reference_points_input = (
reference_points[:, :, None] * torch.cat([valid_ratios, valid_ratios], -1)[:, None]
)
elif num_coordinates == 2:
reference_points_input = reference_points[:, :, None] * valid_ratios[:, None]
else:
raise ValueError("Last dim of reference_points must be 2 or 4, but got {reference_points.shape[-1]}")
query_pos = get_sine_pos_embed(reference_points_input[:, :, 0, :], num_pos_feats=self.config.d_model // 2)
query_pos = self.reference_points_head(query_pos)
# In original implementation they apply layer norm before outputting intermediate hidden states
# Though that's not through between layers so the layers use as input the output of the previous layer
# withtout layer norm
if output_hidden_states:
all_hidden_states += (self.layer_norm(hidden_states),)
if self.gradient_checkpointing and self.training:
def create_custom_forward(module):
def custom_forward(*inputs):
return module(*inputs, output_attentions)
return custom_forward
layer_outputs = torch.utils.checkpoint.checkpoint(
create_custom_forward(decoder_layer),
hidden_states,
query_pos,
reference_points_input,
spatial_shapes,
level_start_index,
vision_encoder_hidden_states,
vision_encoder_attention_mask,
text_encoder_hidden_states,
text_encoder_attention_mask,
self_attn_mask,
None,
)
else:
layer_outputs = decoder_layer(
hidden_states=hidden_states,
position_embeddings=query_pos,
reference_points=reference_points_input,
spatial_shapes=spatial_shapes,
spatial_shapes_list=spatial_shapes_list,
level_start_index=level_start_index,
vision_encoder_hidden_states=vision_encoder_hidden_states,
vision_encoder_attention_mask=vision_encoder_attention_mask,
text_encoder_hidden_states=text_encoder_hidden_states,
text_encoder_attention_mask=text_encoder_attention_mask,
self_attn_mask=self_attn_mask,
output_attentions=output_attentions,
)
hidden_states = layer_outputs[0]
# hack implementation for iterative bounding box refinement
if self.bbox_embed is not None:
tmp = self.bbox_embed[idx](hidden_states)
num_coordinates = reference_points.shape[-1]
if num_coordinates == 4:
new_reference_points = tmp + torch.special.logit(reference_points, eps=1e-5)
new_reference_points = new_reference_points.sigmoid()
elif num_coordinates == 2:
new_reference_points = tmp
new_reference_points[..., :2] = tmp[..., :2] + torch.special.logit(reference_points, eps=1e-5)
new_reference_points = new_reference_points.sigmoid()
else:
raise ValueError(
f"Last dim of reference_points must be 2 or 4, but got {reference_points.shape[-1]}"
)
reference_points = new_reference_points.detach()
intermediate += (self.layer_norm(hidden_states),)
intermediate_reference_points += (reference_points,)
if output_attentions:
all_self_attns += (layer_outputs[1],)
if text_encoder_hidden_states is not None:
all_cross_attns_text += (layer_outputs[2],)
if vision_encoder_hidden_states is not None:
all_cross_attns_vision += (layer_outputs[3],)
# Keep batch_size as first dimension
intermediate = torch.stack(intermediate, dim=1)
intermediate_reference_points = torch.stack(intermediate_reference_points, dim=1)
hidden_states = self.layer_norm(hidden_states)
# add hidden states from the last decoder layer
if output_hidden_states:
all_hidden_states += (hidden_states,)
if output_attentions:
all_attns += (all_self_attns, all_cross_attns_text, all_cross_attns_vision)
if not return_dict:
return tuple(
v
for v in [
hidden_states,
intermediate,
intermediate_reference_points,
all_hidden_states,
all_attns,
]
if v is not None
)
return GroundingDinoDecoderOutput(
last_hidden_state=hidden_states,
intermediate_hidden_states=intermediate,
intermediate_reference_points=intermediate_reference_points,
hidden_states=all_hidden_states,
attentions=all_attns,
)
# these correspond to [CLS], [SEP], . and ?
SPECIAL_TOKENS = [101, 102, 1012, 1029]
def generate_masks_with_special_tokens_and_transfer_map(input_ids: torch.LongTensor) -> Tuple[Tensor, Tensor]:
"""Generate attention mask between each pair of special tokens and positional ids.
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary.
Returns:
`tuple(torch.Tensor)` comprising attention mask between each special tokens and position_ids:
- **attention_mask** (`torch.BoolTensor` of shape `(batch_size, sequence_length, sequence_length)`)
- **position_ids** (`torch.LongTensor` of shape `(batch_size, sequence_length)`)
"""
batch_size, num_token = input_ids.shape
# special_tokens_mask: batch_size, num_token. 1 for special tokens. 0 for normal tokens
special_tokens_mask = torch.zeros((batch_size, num_token), device=input_ids.device).bool()
for special_token in SPECIAL_TOKENS:
special_tokens_mask |= input_ids == special_token
# idxs: each row is a list of indices of special tokens
idxs = torch.nonzero(special_tokens_mask)
# generate attention mask and positional ids
attention_mask = torch.eye(num_token, device=input_ids.device).bool().unsqueeze(0).repeat(batch_size, 1, 1)
position_ids = torch.zeros((batch_size, num_token), device=input_ids.device)
previous_col = 0
for i in range(idxs.shape[0]):
row, col = idxs[i]
if (col == 0) or (col == num_token - 1):
attention_mask[row, col, col] = True
position_ids[row, col] = 0
else:
attention_mask[row, previous_col + 1 : col + 1, previous_col + 1 : col + 1] = True
position_ids[row, previous_col + 1 : col + 1] = torch.arange(
0, col - previous_col, device=input_ids.device
)
previous_col = col
return attention_mask, position_ids.to(torch.long)
@add_start_docstrings(
"""
The bare Grounding DINO Model (consisting of a backbone and encoder-decoder Transformer) outputting raw
hidden-states without any specific head on top.
""",
GROUNDING_DINO_START_DOCSTRING,
)
class GroundingDinoModel(GroundingDinoPreTrainedModel):
def __init__(self, config: GroundingDinoConfig):
super().__init__(config)
# Create backbone + positional encoding
backbone = GroundingDinoConvEncoder(config)
position_embeddings = build_position_encoding(config)
self.backbone = GroundingDinoConvModel(backbone, position_embeddings)
# Create input projection layers
if config.num_feature_levels > 1:
num_backbone_outs = len(backbone.intermediate_channel_sizes)
input_proj_list = []
for i in range(num_backbone_outs):
in_channels = backbone.intermediate_channel_sizes[i]
input_proj_list.append(
nn.Sequential(
nn.Conv2d(in_channels, config.d_model, kernel_size=1),
nn.GroupNorm(32, config.d_model),
)
)
for _ in range(config.num_feature_levels - num_backbone_outs):
input_proj_list.append(
nn.Sequential(
nn.Conv2d(in_channels, config.d_model, kernel_size=3, stride=2, padding=1),
nn.GroupNorm(32, config.d_model),
)
)
in_channels = config.d_model
self.input_proj_vision = nn.ModuleList(input_proj_list)
else:
self.input_proj_vision = nn.ModuleList(
[
nn.Sequential(
nn.Conv2d(backbone.intermediate_channel_sizes[-1], config.d_model, kernel_size=1),
nn.GroupNorm(32, config.d_model),
)
]
)
# Create text backbone
self.text_backbone = AutoModel.from_config(config.text_config, add_pooling_layer=False)
self.text_projection = nn.Linear(config.text_config.hidden_size, config.d_model)
if config.embedding_init_target or not config.two_stage:
self.query_position_embeddings = nn.Embedding(config.num_queries, config.d_model)
self.encoder = GroundingDinoEncoder(config)
self.decoder = GroundingDinoDecoder(config)
self.level_embed = nn.Parameter(torch.Tensor(config.num_feature_levels, config.d_model))
if config.two_stage:
self.enc_output = nn.Linear(config.d_model, config.d_model)
self.enc_output_norm = nn.LayerNorm(config.d_model, config.layer_norm_eps)
if (
config.two_stage_bbox_embed_share
and config.decoder_bbox_embed_share
and self.decoder.bbox_embed is not None
):
self.encoder_output_bbox_embed = self.decoder.bbox_embed
else:
self.encoder_output_bbox_embed = GroundingDinoMLPPredictionHead(
input_dim=config.d_model, hidden_dim=config.d_model, output_dim=4, num_layers=3
)
self.encoder_output_class_embed = GroundingDinoContrastiveEmbedding(config)
else:
self.reference_points = nn.Embedding(config.num_queries, 4)
self.post_init()
def get_encoder(self):
return self.encoder
def get_decoder(self):
return self.decoder
def freeze_backbone(self):
for name, param in self.backbone.conv_encoder.model.named_parameters():
param.requires_grad_(False)
def unfreeze_backbone(self):
for name, param in self.backbone.conv_encoder.model.named_parameters():
param.requires_grad_(True)
def get_valid_ratio(self, mask):
"""Get the valid ratio of all feature maps."""
_, height, width = mask.shape
valid_height = torch.sum(mask[:, :, 0], 1)
valid_width = torch.sum(mask[:, 0, :], 1)
valid_ratio_heigth = valid_height.float() / height
valid_ratio_width = valid_width.float() / width
valid_ratio = torch.stack([valid_ratio_width, valid_ratio_heigth], -1)
return valid_ratio
def generate_encoder_output_proposals(self, enc_output, padding_mask, spatial_shapes):
"""Generate the encoder output proposals from encoded enc_output.
Args:
enc_output (`torch.Tensor[batch_size, sequence_length, hidden_size]`): Output of the encoder.
padding_mask (`torch.Tensor[batch_size, sequence_length]`): Padding mask for `enc_output`.
spatial_shapes (`torch.Tensor[num_feature_levels, 2]`): Spatial shapes of the feature maps.
Returns:
`tuple(torch.FloatTensor)`: A tuple of feature map and bbox prediction.
- object_query (Tensor[batch_size, sequence_length, hidden_size]): Object query features. Later used to
directly predict a bounding box. (without the need of a decoder)
- output_proposals (Tensor[batch_size, sequence_length, 4]): Normalized proposals, after an inverse
sigmoid.
"""
batch_size = enc_output.shape[0]
proposals = []
current_position = 0
for level, (height, width) in enumerate(spatial_shapes):
mask_flatten_ = padding_mask[:, current_position : (current_position + height * width)]
mask_flatten_ = mask_flatten_.view(batch_size, height, width, 1)
valid_height = torch.sum(~mask_flatten_[:, :, 0, 0], 1)
valid_width = torch.sum(~mask_flatten_[:, 0, :, 0], 1)
grid_y, grid_x = meshgrid(
torch.linspace(0, height - 1, height, dtype=torch.float32, device=enc_output.device),
torch.linspace(0, width - 1, width, dtype=torch.float32, device=enc_output.device),
indexing="ij",
)
grid = torch.cat([grid_x.unsqueeze(-1), grid_y.unsqueeze(-1)], -1)
scale = torch.cat([valid_width.unsqueeze(-1), valid_height.unsqueeze(-1)], 1).view(batch_size, 1, 1, 2)
grid = (grid.unsqueeze(0).expand(batch_size, -1, -1, -1) + 0.5) / scale
width_heigth = torch.ones_like(grid) * 0.05 * (2.0**level)
proposal = torch.cat((grid, width_heigth), -1).view(batch_size, -1, 4)
proposals.append(proposal)
current_position += height * width
output_proposals = torch.cat(proposals, 1)
output_proposals_valid = ((output_proposals > 0.01) & (output_proposals < 0.99)).all(-1, keepdim=True)
output_proposals = torch.log(output_proposals / (1 - output_proposals)) # inverse sigmoid
output_proposals = output_proposals.masked_fill(padding_mask.unsqueeze(-1), float("inf"))
output_proposals = output_proposals.masked_fill(~output_proposals_valid, float("inf"))
# assign each pixel as an object query
object_query = enc_output
object_query = object_query.masked_fill(padding_mask.unsqueeze(-1), float(0))
object_query = object_query.masked_fill(~output_proposals_valid, float(0))
object_query = self.enc_output_norm(self.enc_output(object_query))
return object_query, output_proposals
@add_start_docstrings_to_model_forward(GROUNDING_DINO_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=GroundingDinoModelOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
pixel_values: Tensor,
input_ids: Tensor,
token_type_ids: Optional[Tensor] = None,
attention_mask: Optional[Tensor] = None,
pixel_mask: Optional[Tensor] = None,
encoder_outputs=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
r"""
Returns:
Examples:
```python
>>> from transformers import AutoProcessor, AutoModel
>>> from PIL import Image
>>> import requests
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> text = "a cat."
>>> processor = AutoProcessor.from_pretrained("IDEA-Research/grounding-dino-tiny")
>>> model = AutoModel.from_pretrained("IDEA-Research/grounding-dino-tiny")
>>> inputs = processor(images=image, text=text, return_tensors="pt")
>>> outputs = model(**inputs)
>>> last_hidden_states = outputs.last_hidden_state
>>> list(last_hidden_states.shape)
[1, 900, 256]
```"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
text_self_attention_masks, position_ids = generate_masks_with_special_tokens_and_transfer_map(input_ids)
if attention_mask is None:
attention_mask = torch.ones_like(input_ids)
if token_type_ids is None:
token_type_ids = torch.zeros_like(input_ids)
text_token_mask = attention_mask.bool() # just to avoid renaming everywhere
max_text_len = self.config.max_text_len
if text_self_attention_masks.shape[1] > max_text_len:
text_self_attention_masks = text_self_attention_masks[:, :max_text_len, :max_text_len]
position_ids = position_ids[:, :max_text_len]
input_ids = input_ids[:, :max_text_len]
token_type_ids = token_type_ids[:, :max_text_len]
text_token_mask = text_token_mask[:, :max_text_len]
# Extract text features from text backbone
text_outputs = self.text_backbone(
input_ids, text_self_attention_masks, token_type_ids, position_ids, return_dict=return_dict
)
text_features = text_outputs.last_hidden_state if return_dict else text_outputs[0]
text_features = self.text_projection(text_features)
batch_size, num_channels, height, width = pixel_values.shape
device = pixel_values.device
if pixel_mask is None:
pixel_mask = torch.ones(((batch_size, height, width)), dtype=torch.long, device=device)
# Extract multi-scale feature maps of same resolution `config.d_model` (cf Figure 4 in paper)
# First, sent pixel_values + pixel_mask through Backbone to obtain the features
# which is a list of tuples
vision_features, position_embeddings_list = self.backbone(pixel_values, pixel_mask)
# Then, apply 1x1 convolution to reduce the channel dimension to d_model (256 by default)
feature_maps = []
masks = []
for level, (source, mask) in enumerate(vision_features):
feature_maps.append(self.input_proj_vision[level](source))
masks.append(mask)
# Lowest resolution feature maps are obtained via 3x3 stride 2 convolutions on the final stage
if self.config.num_feature_levels > len(feature_maps):
_len_sources = len(feature_maps)
for level in range(_len_sources, self.config.num_feature_levels):
if level == _len_sources:
source = self.input_proj_vision[level](vision_features[-1][0])
else:
source = self.input_proj_vision[level](feature_maps[-1])
mask = nn.functional.interpolate(pixel_mask[None].float(), size=source.shape[-2:]).to(torch.bool)[0]
pos_l = self.backbone.position_embedding(source, mask).to(source.dtype)
feature_maps.append(source)
masks.append(mask)
position_embeddings_list.append(pos_l)
# Create queries
query_embeds = None
if self.config.embedding_init_target or self.config.two_stage:
query_embeds = self.query_position_embeddings.weight
# Prepare encoder inputs (by flattening)
source_flatten = []
mask_flatten = []
lvl_pos_embed_flatten = []
spatial_shapes_list = []
for level, (source, mask, pos_embed) in enumerate(zip(feature_maps, masks, position_embeddings_list)):
batch_size, num_channels, height, width = source.shape
spatial_shape = (height, width)
spatial_shapes_list.append(spatial_shape)
source = source.flatten(2).transpose(1, 2)
mask = mask.flatten(1)
pos_embed = pos_embed.flatten(2).transpose(1, 2)
lvl_pos_embed = pos_embed + self.level_embed[level].view(1, 1, -1)
lvl_pos_embed_flatten.append(lvl_pos_embed)
source_flatten.append(source)
mask_flatten.append(mask)
source_flatten = torch.cat(source_flatten, 1)
mask_flatten = torch.cat(mask_flatten, 1)
lvl_pos_embed_flatten = torch.cat(lvl_pos_embed_flatten, 1)
spatial_shapes = torch.as_tensor(spatial_shapes_list, dtype=torch.long, device=source_flatten.device)
level_start_index = torch.cat((spatial_shapes.new_zeros((1,)), spatial_shapes.prod(1).cumsum(0)[:-1]))
valid_ratios = torch.stack([self.get_valid_ratio(m) for m in masks], 1)
valid_ratios = valid_ratios.float()
# Fourth, sent source_flatten + mask_flatten + lvl_pos_embed_flatten (backbone + proj layer output) through encoder
# Also provide spatial_shapes, level_start_index and valid_ratios
if encoder_outputs is None:
encoder_outputs = self.encoder(
vision_features=source_flatten,
vision_attention_mask=~mask_flatten,
vision_position_embedding=lvl_pos_embed_flatten,
spatial_shapes=spatial_shapes,
spatial_shapes_list=spatial_shapes_list,
level_start_index=level_start_index,
valid_ratios=valid_ratios,
text_features=text_features,
text_attention_mask=~text_token_mask,
text_position_embedding=None,
text_self_attention_masks=~text_self_attention_masks,
text_position_ids=position_ids,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
# If the user passed a tuple for encoder_outputs, we wrap it in a GroundingDinoEncoderOutput when return_dict=True
elif return_dict and not isinstance(encoder_outputs, GroundingDinoEncoderOutput):
encoder_outputs = GroundingDinoEncoderOutput(
last_hidden_state_vision=encoder_outputs[0],
last_hidden_state_text=encoder_outputs[1],
vision_hidden_states=encoder_outputs[2] if output_hidden_states else None,
text_hidden_states=encoder_outputs[3] if output_hidden_states else None,
attentions=encoder_outputs[-1] if output_attentions else None,
)
# Fifth, prepare decoder inputs
topk_proposals = None
enc_outputs_class = None
enc_outputs_coord_logits = None
encoder_logits = None
encoder_pred_boxes = None
if self.config.two_stage:
object_query_embedding, output_proposals = self.generate_encoder_output_proposals(
encoder_outputs[0], ~mask_flatten, spatial_shapes
)
# hack implementation as in two-stage Deformable DETR
# apply a detection head to each pixel (A.4 in paper)
# linear projection for bounding box binary classification (i.e. foreground and background)
enc_outputs_class = self.encoder_output_class_embed(
object_query_embedding, encoder_outputs[1], text_token_mask
)
# 3-layer FFN to predict bounding boxes coordinates (bbox regression branch)
delta_bbox = self.encoder_output_bbox_embed(object_query_embedding)
enc_outputs_coord_logits = delta_bbox + output_proposals
# only keep top scoring `config.num_queries` proposals
topk = self.config.num_queries
topk_logits = enc_outputs_class.max(-1)[0]
topk_proposals = torch.topk(topk_logits, topk, dim=1)[1]
topk_coords_logits = torch.gather(
enc_outputs_coord_logits, 1, topk_proposals.unsqueeze(-1).repeat(1, 1, 4)
)
topk_coords_logits = topk_coords_logits.detach()
reference_points = topk_coords_logits.sigmoid()
init_reference_points = reference_points
if query_embeds is not None:
target = query_embeds.unsqueeze(0).repeat(batch_size, 1, 1)
else:
target = torch.gather(
object_query_embedding, 1, topk_proposals.unsqueeze(-1).repeat(1, 1, self.d_model)
).detach()
# Set intermediate topk proposals (coords and class) for loss computation
encoder_pred_boxes = reference_points
encoder_logits = self.encoder_output_class_embed(target, text_features, text_token_mask)
else:
target = query_embeds.unsqueeze(0).repeat(batch_size, 1, 1)
reference_points = self.reference_points.weight.unsqueeze(0).repeat(batch_size, 1, 1).sigmoid()
init_reference_points = reference_points
decoder_outputs = self.decoder(
inputs_embeds=target,
vision_encoder_hidden_states=encoder_outputs[0],
vision_encoder_attention_mask=mask_flatten,
text_encoder_hidden_states=encoder_outputs[1],
text_encoder_attention_mask=~text_token_mask,
reference_points=reference_points,
spatial_shapes=spatial_shapes,
spatial_shapes_list=spatial_shapes_list,
level_start_index=level_start_index,
valid_ratios=valid_ratios,
self_attn_mask=None,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
if not return_dict:
enc_outputs = tuple(
value
for value in [
enc_outputs_class,
enc_outputs_coord_logits,
encoder_logits,
encoder_pred_boxes,
]
if value is not None
)
tuple_outputs = (
(decoder_outputs[0], init_reference_points) + decoder_outputs[1:] + encoder_outputs + enc_outputs
)
return tuple_outputs
return GroundingDinoModelOutput(
last_hidden_state=decoder_outputs.last_hidden_state,
init_reference_points=init_reference_points,
intermediate_hidden_states=decoder_outputs.intermediate_hidden_states,
intermediate_reference_points=decoder_outputs.intermediate_reference_points,
decoder_hidden_states=decoder_outputs.hidden_states,
decoder_attentions=decoder_outputs.attentions,
encoder_last_hidden_state_vision=encoder_outputs.last_hidden_state_vision,
encoder_last_hidden_state_text=encoder_outputs.last_hidden_state_text,
encoder_vision_hidden_states=encoder_outputs.vision_hidden_states,
encoder_text_hidden_states=encoder_outputs.text_hidden_states,
encoder_attentions=encoder_outputs.attentions,
enc_outputs_class=enc_outputs_class,
enc_outputs_coord_logits=enc_outputs_coord_logits,
encoder_logits=encoder_logits,
encoder_pred_boxes=encoder_pred_boxes,
)
# Copied from transformers.models.detr.modeling_detr.DetrMLPPredictionHead
class GroundingDinoMLPPredictionHead(nn.Module):
"""
Very simple multi-layer perceptron (MLP, also called FFN), used to predict the normalized center coordinates,
height and width of a bounding box w.r.t. an image.
Copied from https://github.com/facebookresearch/detr/blob/master/models/detr.py
"""
def __init__(self, input_dim, hidden_dim, output_dim, num_layers):
super().__init__()
self.num_layers = num_layers
h = [hidden_dim] * (num_layers - 1)
self.layers = nn.ModuleList(nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim]))
def forward(self, x):
for i, layer in enumerate(self.layers):
x = nn.functional.relu(layer(x)) if i < self.num_layers - 1 else layer(x)
return x
def build_label_maps(logits: torch.FloatTensor, input_ids: torch.LongTensor) -> Tuple[torch.FloatTensor]:
"""
Computes a mapping between tokens and their corresponding labels, where `num_labels` is determined by the number of classes in the input prompt.
The function identifies segments of tokens between specific delimiter tokens and generates label maps for those segments.
Args:
logits (`torch.Tensor` of shape `(batch_size, seq_length, hidden_size)`):
The output logits from the model, where `hidden_size` corresponds to the dimension of the model's output features.
input_ids (`torch.Tensor` of shape `(batch_size, seq_length)`):
The input token IDs corresponding to the input prompt. For example, given the prompt "fish. shark.",
`input_ids` might look like `[101, 3869, 1012, 11420, 1012, 102]` where each number corresponds to a token including special tokens.
Returns:
tuple: A tuple containing label maps for each instance in the batch.
- label_maps (tuple of `torch.Tensor`):
A tuple of tensors, where each tensor in the tuple corresponds to an instance in the batch. Each tensor
has shape `(num_labels, hidden_size)` and contains binary values (0 or 1), where `1` indicates the tokens
that are associated with a specific label (class) between delimiter tokens, and `0` elsewhere.
Example:
Given an input prompt "fish. shark." and corresponding `input_ids` as `[101, 3869, 1012, 11420, 1012, 102]`:
- The function identifies the tokens for "fish" (IDs `[3869]`) and "shark" (IDs `[11420]`).
- The function then constructs label maps for these tokens, where each label map indicates which tokens
correspond to which label between the delimiter tokens (e.g., between the period `.`).
- The output is a tuple of label maps, one for each instance in the batch.
Note:
- `SPECIAL_TOKENS` should be a predefined list of tokens that are considered special (e.g., `[CLS]`, `[SEP]`, etc.).
"""
max_seq_len = logits.shape[-1]
# Add [PAD] token to the list of special tokens
delimiter_tokens = torch.tensor(SPECIAL_TOKENS + [0], device=input_ids.device)
delimiter_token_masks = torch.isin(input_ids, delimiter_tokens)
label_groups = torch.cumsum(delimiter_token_masks, dim=1) * (~delimiter_token_masks).to(torch.int32)
label_maps = ()
# Iterate over batch dimension as we can have different number of labels
for label_group in label_groups:
# `label_group` is a tensor of shape `(seq_len,)` with zeros for non-label tokens and integers for label tokens
# label tokens with same integer value are part of the same label group
# Get unique labels and exclude 0 (i.e. non-label tokens)
unique_labels = torch.unique(label_group)[1:, None]
num_labels = unique_labels.shape[0]
# Create one-hot encoding for each label group
label_map = label_group.unsqueeze(0).repeat(num_labels, 1)
label_map = torch.where(label_map == unique_labels, 1, 0)
# Pad label_map to match `max_seq_len`
label_map = F.pad(label_map, (0, max_seq_len - label_map.shape[1]), value=0)
label_maps += (label_map,)
return label_maps
def build_text_mask(logits, attention_mask):
"""
Create text_mask based on the matching indices
"""
seq_len = attention_mask.shape[1]
text_mask = torch.zeros_like(logits, device=logits.device, dtype=attention_mask.dtype)
text_mask[:, :, :seq_len] = attention_mask[:, None, :]
return text_mask.bool()
@add_start_docstrings(
"""
Grounding DINO Model (consisting of a backbone and encoder-decoder Transformer) with object detection heads on top,
for tasks such as COCO detection.
""",
GROUNDING_DINO_START_DOCSTRING,
)
class GroundingDinoForObjectDetection(GroundingDinoPreTrainedModel):
# When using clones, all layers > 0 will be clones, but layer 0 *is* required
# the bbox_embed in the decoder are all clones though
_tied_weights_keys = [r"bbox_embed\.[1-9]\d*", r"model\.decoder\.bbox_embed\.[0-9]\d*"]
def __init__(self, config: GroundingDinoConfig):
super().__init__(config)
self.model = GroundingDinoModel(config)
_class_embed = GroundingDinoContrastiveEmbedding(config)
if config.decoder_bbox_embed_share:
_bbox_embed = GroundingDinoMLPPredictionHead(
input_dim=config.d_model, hidden_dim=config.d_model, output_dim=4, num_layers=3
)
self.bbox_embed = nn.ModuleList([_bbox_embed for _ in range(config.decoder_layers)])
else:
for _ in range(config.decoder_layers):
_bbox_embed = GroundingDinoMLPPredictionHead(
input_dim=config.d_model, hidden_dim=config.d_model, output_dim=4, num_layers=3
)
self.bbox_embed = nn.ModuleList([_bbox_embed for _ in range(config.decoder_layers)])
self.class_embed = nn.ModuleList([_class_embed for _ in range(config.decoder_layers)])
# hack for box-refinement
self.model.decoder.bbox_embed = self.bbox_embed
# hack implementation for two-stage
self.model.decoder.class_embed = self.class_embed
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(GROUNDING_DINO_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=GroundingDinoObjectDetectionOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
pixel_values: torch.FloatTensor,
input_ids: torch.LongTensor,
token_type_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.LongTensor] = None,
pixel_mask: Optional[torch.BoolTensor] = None,
encoder_outputs: Optional[Union[GroundingDinoEncoderOutput, Tuple]] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
labels: List[Dict[str, Union[torch.LongTensor, torch.FloatTensor]]] = None,
):
r"""
labels (`List[Dict]` of len `(batch_size,)`, *optional*):
Labels for computing the bipartite matching loss. List of dicts, each dictionary containing at least the
following 2 keys: 'class_labels' and 'boxes' (the class labels and bounding boxes of an image in the batch
respectively). The class labels themselves should be a `torch.LongTensor` of len `(number of bounding boxes
in the image,)` and the boxes a `torch.FloatTensor` of shape `(number of bounding boxes in the image, 4)`.
Returns:
Examples:
```python
>>> import requests
>>> import torch
>>> from PIL import Image
>>> from transformers import AutoProcessor, AutoModelForZeroShotObjectDetection
>>> model_id = "IDEA-Research/grounding-dino-tiny"
>>> device = "cuda"
>>> processor = AutoProcessor.from_pretrained(model_id)
>>> model = AutoModelForZeroShotObjectDetection.from_pretrained(model_id).to(device)
>>> image_url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(image_url, stream=True).raw)
>>> # Check for cats and remote controls
>>> text_labels = [["a cat", "a remote control"]]
>>> inputs = processor(images=image, text=text_labels, return_tensors="pt").to(device)
>>> with torch.no_grad():
... outputs = model(**inputs)
>>> results = processor.post_process_grounded_object_detection(
... outputs,
... threshold=0.4,
... text_threshold=0.3,
... target_sizes=[(image.height, image.width)]
... )
>>> # Retrieve the first image result
>>> result = results[0]
>>> for box, score, text_label in zip(result["boxes"], result["scores"], result["text_labels"]):
... box = [round(x, 2) for x in box.tolist()]
... print(f"Detected {text_label} with confidence {round(score.item(), 3)} at location {box}")
Detected a cat with confidence 0.479 at location [344.7, 23.11, 637.18, 374.28]
Detected a cat with confidence 0.438 at location [12.27, 51.91, 316.86, 472.44]
Detected a remote control with confidence 0.478 at location [38.57, 70.0, 176.78, 118.18]
```"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if attention_mask is None:
attention_mask = torch.ones_like(input_ids)
# First, sent images through Grounding DINO base model to obtain encoder + decoder outputs
outputs = self.model(
pixel_values=pixel_values,
input_ids=input_ids,
token_type_ids=token_type_ids,
attention_mask=attention_mask,
pixel_mask=pixel_mask,
encoder_outputs=encoder_outputs,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
idx = 5 + (1 if output_attentions else 0) + (1 if output_hidden_states else 0)
enc_text_hidden_state = outputs.encoder_last_hidden_state_text if return_dict else outputs[idx]
hidden_states = outputs.intermediate_hidden_states if return_dict else outputs[2]
init_reference_points = outputs.init_reference_points if return_dict else outputs[1]
inter_references_points = outputs.intermediate_reference_points if return_dict else outputs[3]
# class logits + predicted bounding boxes
outputs_classes = []
outputs_coords = []
# hidden_states are of shape (batch_size, num_stages, height, width)
# predict class and bounding box deltas for each stage
num_levels = hidden_states.shape[1]
for level in range(num_levels):
if level == 0:
reference = init_reference_points
else:
reference = inter_references_points[:, level - 1]
reference = torch.special.logit(reference, eps=1e-5)
outputs_class = self.class_embed[level](
vision_hidden_state=hidden_states[:, level],
text_hidden_state=enc_text_hidden_state,
text_token_mask=attention_mask.bool(),
)
delta_bbox = self.bbox_embed[level](hidden_states[:, level])
reference_coordinates = reference.shape[-1]
if reference_coordinates == 4:
outputs_coord_logits = delta_bbox + reference
elif reference_coordinates == 2:
delta_bbox[..., :2] += reference
outputs_coord_logits = delta_bbox
else:
raise ValueError(f"reference.shape[-1] should be 4 or 2, but got {reference.shape[-1]}")
outputs_coord = outputs_coord_logits.sigmoid()
outputs_classes.append(outputs_class)
outputs_coords.append(outputs_coord)
outputs_class = torch.stack(outputs_classes)
outputs_coord = torch.stack(outputs_coords)
logits = outputs_class[-1]
pred_boxes = outputs_coord[-1]
loss, loss_dict, auxiliary_outputs = None, None, None
if labels is not None:
label_maps = build_label_maps(logits, input_ids)
text_mask = build_text_mask(logits, attention_mask)
loss, loss_dict, auxiliary_outputs = self.loss_function(
logits,
labels,
self.device,
pred_boxes,
self.config,
label_maps,
text_mask,
outputs_class=outputs_class,
outputs_coord=outputs_coord,
encoder_logits=outputs[-2],
encoder_pred_boxes=outputs[-1],
)
if not return_dict:
auxiliary_outputs = auxiliary_outputs if auxiliary_outputs is not None else []
output = [loss, loss_dict, logits, pred_boxes, *auxiliary_outputs, *outputs, input_ids]
output = tuple(out for out in output if out is not None)
return output
dict_outputs = GroundingDinoObjectDetectionOutput(
loss=loss,
loss_dict=loss_dict,
logits=logits,
pred_boxes=pred_boxes,
last_hidden_state=outputs.last_hidden_state,
auxiliary_outputs=auxiliary_outputs,
decoder_hidden_states=outputs.decoder_hidden_states,
decoder_attentions=outputs.decoder_attentions,
encoder_last_hidden_state_vision=outputs.encoder_last_hidden_state_vision,
encoder_last_hidden_state_text=outputs.encoder_last_hidden_state_text,
encoder_vision_hidden_states=outputs.encoder_vision_hidden_states,
encoder_text_hidden_states=outputs.encoder_text_hidden_states,
encoder_attentions=outputs.encoder_attentions,
intermediate_hidden_states=outputs.intermediate_hidden_states,
intermediate_reference_points=outputs.intermediate_reference_points,
init_reference_points=outputs.init_reference_points,
enc_outputs_class=outputs.enc_outputs_class,
enc_outputs_coord_logits=outputs.enc_outputs_coord_logits,
encoder_logits=outputs.encoder_logits,
encoder_pred_boxes=outputs.encoder_pred_boxes,
input_ids=input_ids,
)
return dict_outputs
__all__ = ["GroundingDinoForObjectDetection", "GroundingDinoModel", "GroundingDinoPreTrainedModel"]
```
|
=======================================================================================================================================================
SOURCE CODE FILE: processing_grounding_dino.py
LINES: 1
SIZE: 13.87 KB
PATH: scripts\freecad_env\Lib\site-packages\transformers\models\grounding_dino\processing_grounding_dino.py
ENCODING: utf-8
```py
# coding=utf-8
# Copyright 2024 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Processor class for Grounding DINO.
"""
import pathlib
import warnings
from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, Union
from ...image_processing_utils import BatchFeature
from ...image_transforms import center_to_corners_format
from ...image_utils import AnnotationFormat, ImageInput
from ...processing_utils import ImagesKwargs, ProcessingKwargs, ProcessorMixin, Unpack
from ...tokenization_utils_base import BatchEncoding, PreTokenizedInput, TextInput
from ...utils import TensorType, is_torch_available
from ...utils.deprecation import deprecate_kwarg
if is_torch_available():
import torch
if TYPE_CHECKING:
from .modeling_grounding_dino import GroundingDinoObjectDetectionOutput
AnnotationType = Dict[str, Union[int, str, List[Dict]]]
def get_phrases_from_posmap(posmaps, input_ids):
"""Get token ids of phrases from posmaps and input_ids.
Args:
posmaps (`torch.BoolTensor` of shape `(num_boxes, hidden_size)`):
A boolean tensor of text-thresholded logits related to the detected bounding boxes.
input_ids (`torch.LongTensor`) of shape `(sequence_length, )`):
A tensor of token ids.
"""
left_idx = 0
right_idx = posmaps.shape[-1] - 1
# Avoiding altering the input tensor
posmaps = posmaps.clone()
posmaps[:, 0 : left_idx + 1] = False
posmaps[:, right_idx:] = False
token_ids = []
for posmap in posmaps:
non_zero_idx = posmap.nonzero(as_tuple=True)[0].tolist()
token_ids.append([input_ids[i] for i in non_zero_idx])
return token_ids
def _is_list_of_candidate_labels(text) -> bool:
"""Check that text is list/tuple of strings and each string is a candidate label and not merged candidate labels text.
Merged candidate labels text is a string with candidate labels separated by a dot.
"""
if isinstance(text, (list, tuple)):
return all(isinstance(t, str) and "." not in t for t in text)
return False
def _merge_candidate_labels_text(text: List[str]) -> str:
"""
Merge candidate labels text into a single string. Ensure all labels are lowercase.
For example, ["A cat", "a dog"] -> "a cat. a dog."
"""
labels = [t.strip().lower() for t in text] # ensure lowercase
merged_labels_str = ". ".join(labels) + "." # join with dot and add a dot at the end
return merged_labels_str
class DictWithDeprecationWarning(dict):
message = (
"The key `labels` is will return integer ids in `GroundingDinoProcessor.post_process_grounded_object_detection` "
"output since v4.51.0. Use `text_labels` instead to retrieve string object names."
)
def __getitem__(self, key):
if key == "labels":
warnings.warn(self.message, FutureWarning)
return super().__getitem__(key)
def get(self, key, *args, **kwargs):
if key == "labels":
warnings.warn(self.message, FutureWarning)
return super().get(key, *args, **kwargs)
class GroundingDinoImagesKwargs(ImagesKwargs, total=False):
annotations: Optional[Union[AnnotationType, List[AnnotationType]]]
return_segmentation_masks: Optional[bool]
masks_path: Optional[Union[str, pathlib.Path]]
do_convert_annotations: Optional[bool]
format: Optional[Union[str, AnnotationFormat]]
class GroundingDinoProcessorKwargs(ProcessingKwargs, total=False):
images_kwargs: GroundingDinoImagesKwargs
_defaults = {
"text_kwargs": {
"add_special_tokens": True,
"padding": False,
"stride": 0,
"return_overflowing_tokens": False,
"return_special_tokens_mask": False,
"return_offsets_mapping": False,
"return_token_type_ids": True,
"return_length": False,
"verbose": True,
}
}
class GroundingDinoProcessor(ProcessorMixin):
r"""
Constructs a Grounding DINO processor which wraps a Deformable DETR image processor and a BERT tokenizer into a
single processor.
[`GroundingDinoProcessor`] offers all the functionalities of [`GroundingDinoImageProcessor`] and
[`AutoTokenizer`]. See the docstring of [`~GroundingDinoProcessor.__call__`] and [`~GroundingDinoProcessor.decode`]
for more information.
Args:
image_processor (`GroundingDinoImageProcessor`):
An instance of [`GroundingDinoImageProcessor`]. The image processor is a required input.
tokenizer (`AutoTokenizer`):
An instance of ['PreTrainedTokenizer`]. The tokenizer is a required input.
"""
attributes = ["image_processor", "tokenizer"]
image_processor_class = "GroundingDinoImageProcessor"
tokenizer_class = "AutoTokenizer"
def __init__(self, image_processor, tokenizer):
super().__init__(image_processor, tokenizer)
def __call__(
self,
images: ImageInput = None,
text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,
audio=None,
videos=None,
**kwargs: Unpack[GroundingDinoProcessorKwargs],
) -> BatchEncoding:
"""
This method uses [`GroundingDinoImageProcessor.__call__`] method to prepare image(s) for the model, and
[`BertTokenizerFast.__call__`] to prepare text for the model.
Args:
images (`ImageInput`, `List[ImageInput]`, *optional*):
The image or batch of images to be processed. The image might be either PIL image, numpy array or a torch tensor.
text (`TextInput`, `PreTokenizedInput`, `List[TextInput]`, `List[PreTokenizedInput]`, *optional*):
Candidate labels to be detected on the image. The text might be one of the following:
- A list of candidate labels (strings) to be detected on the image (e.g. ["a cat", "a dog"]).
- A batch of candidate labels to be detected on the batch of images (e.g. [["a cat", "a dog"], ["a car", "a person"]]).
- A merged candidate labels string to be detected on the image, separated by "." (e.g. "a cat. a dog.").
- A batch of merged candidate labels text to be detected on the batch of images (e.g. ["a cat. a dog.", "a car. a person."]).
"""
if images is None and text is None:
raise ValueError("You must specify either text or images.")
output_kwargs = self._merge_kwargs(
GroundingDinoProcessorKwargs,
tokenizer_init_kwargs=self.tokenizer.init_kwargs,
**kwargs,
)
# Get only text
if images is not None:
encoding_image_processor = self.image_processor(images, **output_kwargs["images_kwargs"])
else:
encoding_image_processor = BatchFeature()
if text is not None:
text = self._preprocess_input_text(text)
text_encoding = self.tokenizer(
text=text,
**output_kwargs["text_kwargs"],
)
else:
text_encoding = BatchEncoding()
text_encoding.update(encoding_image_processor)
return text_encoding
def _preprocess_input_text(self, text):
"""
Preprocess input text to ensure that labels are in the correct format for the model.
If the text is a list of candidate labels, merge the candidate labels into a single string,
for example, ["a cat", "a dog"] -> "a cat. a dog.". In case candidate labels are already in a form of
"a cat. a dog.", the text is returned as is.
"""
if _is_list_of_candidate_labels(text):
text = _merge_candidate_labels_text(text)
# for batched input
elif isinstance(text, (list, tuple)) and all(_is_list_of_candidate_labels(t) for t in text):
text = [_merge_candidate_labels_text(sample) for sample in text]
return text
# Copied from transformers.models.blip.processing_blip.BlipProcessor.batch_decode with BertTokenizerFast->PreTrainedTokenizer
def batch_decode(self, *args, **kwargs):
"""
This method forwards all its arguments to PreTrainedTokenizer's [`~PreTrainedTokenizer.batch_decode`]. Please
refer to the docstring of this method for more information.
"""
return self.tokenizer.batch_decode(*args, **kwargs)
# Copied from transformers.models.blip.processing_blip.BlipProcessor.decode with BertTokenizerFast->PreTrainedTokenizer
def decode(self, *args, **kwargs):
"""
This method forwards all its arguments to PreTrainedTokenizer's [`~PreTrainedTokenizer.decode`]. Please refer to
the docstring of this method for more information.
"""
return self.tokenizer.decode(*args, **kwargs)
@property
# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
def model_input_names(self):
tokenizer_input_names = self.tokenizer.model_input_names
image_processor_input_names = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))
@deprecate_kwarg("box_threshold", new_name="threshold", version="4.51.0")
def post_process_grounded_object_detection(
self,
outputs: "GroundingDinoObjectDetectionOutput",
input_ids: Optional[TensorType] = None,
threshold: float = 0.25,
text_threshold: float = 0.25,
target_sizes: Optional[Union[TensorType, List[Tuple]]] = None,
text_labels: Optional[List[List[str]]] = None,
):
"""
Converts the raw output of [`GroundingDinoForObjectDetection`] into final bounding boxes in (top_left_x, top_left_y,
bottom_right_x, bottom_right_y) format and get the associated text label.
Args:
outputs ([`GroundingDinoObjectDetectionOutput`]):
Raw outputs of the model.
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
The token ids of the input text. If not provided will be taken from the model output.
threshold (`float`, *optional*, defaults to 0.25):
Threshold to keep object detection predictions based on confidence score.
text_threshold (`float`, *optional*, defaults to 0.25):
Score threshold to keep text detection predictions.
target_sizes (`torch.Tensor` or `List[Tuple[int, int]]`, *optional*):
Tensor of shape `(batch_size, 2)` or list of tuples (`Tuple[int, int]`) containing the target size
`(height, width)` of each image in the batch. If unset, predictions will not be resized.
text_labels (`List[List[str]]`, *optional*):
List of candidate labels to be detected on each image. At the moment it's *NOT used*, but required
to be in signature for the zero-shot object detection pipeline. Text labels are instead extracted
from the `input_ids` tensor provided in `outputs`.
Returns:
`List[Dict]`: A list of dictionaries, each dictionary containing the
- **scores**: tensor of confidence scores for detected objects
- **boxes**: tensor of bounding boxes in [x0, y0, x1, y1] format
- **labels**: list of text labels for each detected object (will be replaced with integer ids in v4.51.0)
- **text_labels**: list of text labels for detected objects
"""
batch_logits, batch_boxes = outputs.logits, outputs.pred_boxes
input_ids = input_ids if input_ids is not None else outputs.input_ids
if target_sizes is not None and len(target_sizes) != len(batch_logits):
raise ValueError("Make sure that you pass in as many target sizes as the batch dimension of the logits")
batch_probs = torch.sigmoid(batch_logits) # (batch_size, num_queries, 256)
batch_scores = torch.max(batch_probs, dim=-1)[0] # (batch_size, num_queries)
# Convert to [x0, y0, x1, y1] format
batch_boxes = center_to_corners_format(batch_boxes)
# Convert from relative [0, 1] to absolute [0, height] coordinates
if target_sizes is not None:
if isinstance(target_sizes, List):
img_h = torch.Tensor([i[0] for i in target_sizes])
img_w = torch.Tensor([i[1] for i in target_sizes])
else:
img_h, img_w = target_sizes.unbind(1)
scale_fct = torch.stack([img_w, img_h, img_w, img_h], dim=1).to(batch_boxes.device)
batch_boxes = batch_boxes * scale_fct[:, None, :]
results = []
for idx, (scores, boxes, probs) in enumerate(zip(batch_scores, batch_boxes, batch_probs)):
keep = scores > threshold
scores = scores[keep]
boxes = boxes[keep]
# extract text labels
prob = probs[keep]
label_ids = get_phrases_from_posmap(prob > text_threshold, input_ids[idx])
objects_text_labels = self.batch_decode(label_ids)
result = DictWithDeprecationWarning(
{
"scores": scores,
"boxes": boxes,
"text_labels": objects_text_labels,
# TODO: @pavel, set labels to None since v4.51.0 or find a way to extract ids
"labels": objects_text_labels,
}
)
results.append(result)
return results
__all__ = ["GroundingDinoProcessor"]
```
|
================================================================================================================================
SOURCE CODE FILE: __init__.py
LINES: 1
SIZE: 1.01 KB
PATH: scripts\freecad_env\Lib\site-packages\transformers\models\groupvit\__init__.py
ENCODING: utf-8
```py
# Copyright 2024 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import _LazyModule
from ...utils.import_utils import define_import_structure
if TYPE_CHECKING:
from .configuration_groupvit import *
from .modeling_groupvit import *
from .modeling_tf_groupvit import *
else:
import sys
_file = globals()["__file__"]
sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)
```
|
==============================================================================================================================================
SOURCE CODE FILE: configuration_groupvit.py
LINES: 1
SIZE: 18.71 KB
PATH: scripts\freecad_env\Lib\site-packages\transformers\models\groupvit\configuration_groupvit.py
ENCODING: utf-8
```py
# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""GroupViT model configuration"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ...processing_utils import ProcessorMixin
from ...utils import TensorType
logger = logging.get_logger(__name__)
class GroupViTTextConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`GroupViTTextModel`]. It is used to instantiate an
GroupViT model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a similar configuration to that of the GroupViT
[nvidia/groupvit-gcc-yfcc](https://huggingface.co/nvidia/groupvit-gcc-yfcc) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 49408):
Vocabulary size of the GroupViT text model. Defines the number of different tokens that can be represented
by the `inputs_ids` passed when calling [`GroupViTModel`].
hidden_size (`int`, *optional*, defaults to 256):
Dimensionality of the encoder layers and the pooler layer.
intermediate_size (`int`, *optional*, defaults to 1024):
Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
num_hidden_layers (`int`, *optional*, defaults to 12):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 4):
Number of attention heads for each attention layer in the Transformer encoder.
max_position_embeddings (`int`, *optional*, defaults to 77):
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).
hidden_act (`str` or `function`, *optional*, defaults to `"quick_gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"selu"` and `"gelu_new"` `"quick_gelu"` are supported.
layer_norm_eps (`float`, *optional*, defaults to 1e-5):
The epsilon used by the layer normalization layers.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
dropout (`float`, *optional*, defaults to 0.0):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
initializer_factor (`float`, *optional*, defaults to 1.0):
A factor for initializing all weight matrices (should be kept to 1, used internally for initialization
testing).
Example:
```python
>>> from transformers import GroupViTTextConfig, GroupViTTextModel
>>> # Initializing a GroupViTTextModel with nvidia/groupvit-gcc-yfcc style configuration
>>> configuration = GroupViTTextConfig()
>>> model = GroupViTTextModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "groupvit_text_model"
base_config_key = "text_config"
def __init__(
self,
vocab_size=49408,
hidden_size=256,
intermediate_size=1024,
num_hidden_layers=12,
num_attention_heads=4,
max_position_embeddings=77,
hidden_act="quick_gelu",
layer_norm_eps=1e-5,
dropout=0.0,
attention_dropout=0.0,
initializer_range=0.02,
initializer_factor=1.0,
pad_token_id=1,
bos_token_id=49406,
eos_token_id=49407,
**kwargs,
):
super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.dropout = dropout
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.max_position_embeddings = max_position_embeddings
self.layer_norm_eps = layer_norm_eps
self.hidden_act = hidden_act
self.initializer_range = initializer_range
self.initializer_factor = initializer_factor
self.attention_dropout = attention_dropout
class GroupViTVisionConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`GroupViTVisionModel`]. It is used to instantiate
an GroupViT model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will yield a similar configuration to that of the GroupViT
[nvidia/groupvit-gcc-yfcc](https://huggingface.co/nvidia/groupvit-gcc-yfcc) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
hidden_size (`int`, *optional*, defaults to 384):
Dimensionality of the encoder layers and the pooler layer.
intermediate_size (`int`, *optional*, defaults to 1536):
Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
depths (`List[int]`, *optional*, defaults to [6, 3, 3]):
The number of layers in each encoder block.
num_group_tokens (`List[int]`, *optional*, defaults to [64, 8, 0]):
The number of group tokens for each stage.
num_output_groups (`List[int]`, *optional*, defaults to [64, 8, 8]):
The number of output groups for each stage, 0 means no group.
num_attention_heads (`int`, *optional*, defaults to 6):
Number of attention heads for each attention layer in the Transformer encoder.
image_size (`int`, *optional*, defaults to 224):
The size (resolution) of each image.
patch_size (`int`, *optional*, defaults to 16):
The size (resolution) of each patch.
hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"selu"` and `"gelu_new"` `"quick_gelu"` are supported.
layer_norm_eps (`float`, *optional*, defaults to 1e-5):
The epsilon used by the layer normalization layers.
dropout (`float`, *optional*, defaults to 0.0):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
initializer_factor (`float`, *optional*, defaults to 1.0):
A factor for initializing all weight matrices (should be kept to 1, used internally for initialization
testing).
Example:
```python
>>> from transformers import GroupViTVisionConfig, GroupViTVisionModel
>>> # Initializing a GroupViTVisionModel with nvidia/groupvit-gcc-yfcc style configuration
>>> configuration = GroupViTVisionConfig()
>>> model = GroupViTVisionModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "groupvit_vision_model"
base_config_key = "vision_config"
def __init__(
self,
hidden_size=384,
intermediate_size=1536,
depths=[6, 3, 3],
num_hidden_layers=12,
num_group_tokens=[64, 8, 0],
num_output_groups=[64, 8, 8],
num_attention_heads=6,
image_size=224,
patch_size=16,
num_channels=3,
hidden_act="gelu",
layer_norm_eps=1e-5,
dropout=0.0,
attention_dropout=0.0,
initializer_range=0.02,
initializer_factor=1.0,
assign_eps=1.0,
assign_mlp_ratio=[0.5, 4],
**kwargs,
):
super().__init__(**kwargs)
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.depths = depths
if num_hidden_layers != sum(depths):
logger.warning(
f"Manually setting num_hidden_layers to {num_hidden_layers}, but we expect num_hidden_layers ="
f" sum(depth) = {sum(depths)}"
)
self.num_hidden_layers = num_hidden_layers
self.num_group_tokens = num_group_tokens
self.num_output_groups = num_output_groups
self.num_attention_heads = num_attention_heads
self.image_size = image_size
self.patch_size = patch_size
self.num_channels = num_channels
self.hidden_act = hidden_act
self.layer_norm_eps = layer_norm_eps
self.dropout = dropout
self.attention_dropout = attention_dropout
self.initializer_range = initializer_range
self.initializer_factor = initializer_factor
self.assign_eps = assign_eps
self.assign_mlp_ratio = assign_mlp_ratio
class GroupViTConfig(PretrainedConfig):
r"""
[`GroupViTConfig`] is the configuration class to store the configuration of a [`GroupViTModel`]. It is used to
instantiate a GroupViT model according to the specified arguments, defining the text model and vision model
configs. Instantiating a configuration with the defaults will yield a similar configuration to that of the GroupViT
[nvidia/groupvit-gcc-yfcc](https://huggingface.co/nvidia/groupvit-gcc-yfcc) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
text_config (`dict`, *optional*):
Dictionary of configuration options used to initialize [`GroupViTTextConfig`].
vision_config (`dict`, *optional*):
Dictionary of configuration options used to initialize [`GroupViTVisionConfig`].
projection_dim (`int`, *optional*, defaults to 256):
Dimensionality of text and vision projection layers.
projection_intermediate_dim (`int`, *optional*, defaults to 4096):
Dimensionality of intermediate layer of text and vision projection layers.
logit_scale_init_value (`float`, *optional*, defaults to 2.6592):
The initial value of the *logit_scale* parameter. Default is used as per the original GroupViT
implementation.
kwargs (*optional*):
Dictionary of keyword arguments.
"""
model_type = "groupvit"
sub_configs = {"text_config": GroupViTTextConfig, "vision_config": GroupViTVisionConfig}
def __init__(
self,
text_config=None,
vision_config=None,
projection_dim=256,
projection_intermediate_dim=4096,
logit_scale_init_value=2.6592,
**kwargs,
):
# If `_config_dict` exist, we use them for the backward compatibility.
# We pop out these 2 attributes before calling `super().__init__` to avoid them being saved (which causes a lot
# of confusion!).
text_config_dict = kwargs.pop("text_config_dict", None)
vision_config_dict = kwargs.pop("vision_config_dict", None)
super().__init__(**kwargs)
# Instead of simply assigning `[text|vision]_config_dict` to `[text|vision]_config`, we use the values in
# `[text|vision]_config_dict` to update the values in `[text|vision]_config`. The values should be same in most
# cases, but we don't want to break anything regarding `_config_dict` that existed before commit `8827e1b2`.
if text_config_dict is not None:
if text_config is None:
text_config = {}
# This is the complete result when using `text_config_dict`.
_text_config_dict = GroupViTTextConfig(**text_config_dict).to_dict()
# Give a warning if the values exist in both `_text_config_dict` and `text_config` but being different.
for key, value in _text_config_dict.items():
if key in text_config and value != text_config[key] and key not in ["transformers_version"]:
# If specified in `text_config_dict`
if key in text_config_dict:
message = (
f"`{key}` is found in both `text_config_dict` and `text_config` but with different values. "
f'The value `text_config_dict["{key}"]` will be used instead.'
)
# If inferred from default argument values (just to be super careful)
else:
message = (
f"`text_config_dict` is provided which will be used to initialize `GroupViTTextConfig`. "
f'The value `text_config["{key}"]` will be overridden.'
)
logger.info(message)
# Update all values in `text_config` with the ones in `_text_config_dict`.
text_config.update(_text_config_dict)
if vision_config_dict is not None:
if vision_config is None:
vision_config = {}
# This is the complete result when using `vision_config_dict`.
_vision_config_dict = GroupViTVisionConfig(**vision_config_dict).to_dict()
# convert keys to string instead of integer
if "id2label" in _vision_config_dict:
_vision_config_dict["id2label"] = {
str(key): value for key, value in _vision_config_dict["id2label"].items()
}
# Give a warning if the values exist in both `_vision_config_dict` and `vision_config` but being different.
for key, value in _vision_config_dict.items():
if key in vision_config and value != vision_config[key] and key not in ["transformers_version"]:
# If specified in `vision_config_dict`
if key in vision_config_dict:
message = (
f"`{key}` is found in both `vision_config_dict` and `vision_config` but with different "
f'values. The value `vision_config_dict["{key}"]` will be used instead.'
)
# If inferred from default argument values (just to be super careful)
else:
message = (
f"`vision_config_dict` is provided which will be used to initialize `GroupViTVisionConfig`."
f' The value `vision_config["{key}"]` will be overridden.'
)
logger.info(message)
# Update all values in `vision_config` with the ones in `_vision_config_dict`.
vision_config.update(_vision_config_dict)
if text_config is None:
text_config = {}
logger.info("`text_config` is `None`. Initializing the `GroupViTTextConfig` with default values.")
if vision_config is None:
vision_config = {}
logger.info("`vision_config` is `None`. initializing the `GroupViTVisionConfig` with default values.")
self.text_config = GroupViTTextConfig(**text_config)
self.vision_config = GroupViTVisionConfig(**vision_config)
self.projection_dim = projection_dim
self.projection_intermediate_dim = projection_intermediate_dim
self.logit_scale_init_value = logit_scale_init_value
self.initializer_range = 0.02
self.initializer_factor = 1.0
self.output_segmentation = False
@classmethod
def from_text_vision_configs(cls, text_config: GroupViTTextConfig, vision_config: GroupViTVisionConfig, **kwargs):
r"""
Instantiate a [`GroupViTConfig`] (or a derived class) from groupvit text model configuration and groupvit
vision model configuration.
Returns:
[`GroupViTConfig`]: An instance of a configuration object
"""
return cls(text_config=text_config.to_dict(), vision_config=vision_config.to_dict(), **kwargs)
class GroupViTOnnxConfig(OnnxConfig):
@property
def inputs(self) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("input_ids", {0: "batch", 1: "sequence"}),
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
("attention_mask", {0: "batch", 1: "sequence"}),
]
)
@property
def outputs(self) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("logits_per_image", {0: "batch"}),
("logits_per_text", {0: "batch"}),
("text_embeds", {0: "batch"}),
("image_embeds", {0: "batch"}),
]
)
@property
def atol_for_validation(self) -> float:
return 1e-4
def generate_dummy_inputs(
self,
processor: "ProcessorMixin",
batch_size: int = -1,
seq_length: int = -1,
framework: Optional["TensorType"] = None,
) -> Mapping[str, Any]:
text_input_dict = super().generate_dummy_inputs(
processor.tokenizer, batch_size=batch_size, seq_length=seq_length, framework=framework
)
image_input_dict = super().generate_dummy_inputs(
processor.image_processor, batch_size=batch_size, framework=framework
)
return {**text_input_dict, **image_input_dict}
@property
def default_onnx_opset(self) -> int:
return 14
__all__ = ["GroupViTConfig", "GroupViTOnnxConfig", "GroupViTTextConfig", "GroupViTVisionConfig"]
```
|
=========================================================================================================================================
SOURCE CODE FILE: modeling_groupvit.py
LINES: 1
SIZE: 66.74 KB
PATH: scripts\freecad_env\Lib\site-packages\transformers\models\groupvit\modeling_groupvit.py
ENCODING: utf-8
```py
# coding=utf-8
# Copyright 2022 NVIDIA and The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""PyTorch GroupViT model."""
import collections.abc
from dataclasses import dataclass
from typing import Any, Optional, Tuple, Union
import numpy as np
import torch
import torch.utils.checkpoint
from torch import nn
from ...activations import ACT2FN
from ...modeling_attn_mask_utils import _create_4d_causal_attention_mask, _prepare_4d_attention_mask
from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling
from ...modeling_utils import PreTrainedModel
from ...utils import (
ModelOutput,
add_start_docstrings,
add_start_docstrings_to_model_forward,
logging,
replace_return_docstrings,
torch_int,
)
from .configuration_groupvit import GroupViTConfig, GroupViTTextConfig, GroupViTVisionConfig
logger = logging.get_logger(__name__)
_CHECKPOINT_FOR_DOC = "nvidia/groupvit-gcc-yfcc"
# contrastive loss function, adapted from
# https://sachinruk.github.io/blog/pytorch/pytorch%20lightning/loss%20function/gpu/2021/03/07/CLIP.html
def contrastive_loss(logits: torch.Tensor) -> torch.Tensor:
return nn.functional.cross_entropy(logits, torch.arange(len(logits), device=logits.device))
# Copied from transformers.models.clip.modeling_clip.clip_loss with clip->groupvit
def groupvit_loss(similarity: torch.Tensor) -> torch.Tensor:
caption_loss = contrastive_loss(similarity)
image_loss = contrastive_loss(similarity.t())
return (caption_loss + image_loss) / 2.0
def hard_softmax(logits: torch.Tensor, dim: int):
y_soft = logits.softmax(dim)
# Straight through.
index = y_soft.max(dim, keepdim=True)[1]
y_hard = torch.zeros_like(logits, memory_format=torch.legacy_contiguous_format).scatter_(dim, index, 1.0)
ret = y_hard - y_soft.detach() + y_soft
return ret
def gumbel_softmax(logits: torch.Tensor, tau: float = 1, hard: bool = False, dim: int = -1) -> torch.Tensor:
# more stable https://github.com/pytorch/pytorch/issues/41663
gumbel_dist = torch.distributions.gumbel.Gumbel(
torch.tensor(0.0, device=logits.device, dtype=logits.dtype),
torch.tensor(1.0, device=logits.device, dtype=logits.dtype),
)
gumbels = gumbel_dist.sample(logits.shape)
gumbels = (logits + gumbels) / tau # ~Gumbel(logits,tau)
y_soft = gumbels.softmax(dim)
if hard:
# Straight through.
index = y_soft.max(dim, keepdim=True)[1]
y_hard = torch.zeros_like(logits, memory_format=torch.legacy_contiguous_format).scatter_(dim, index, 1.0)
ret = y_hard - y_soft.detach() + y_soft
else:
# Reparametrization trick.
ret = y_soft
return ret
def resize_attention_map(attentions, height, width, align_corners=False):
"""
Args:
attentions (`torch.Tensor`): attention map of shape [batch_size, groups, feat_height*feat_width]
height (`int`): height of the output attention map
width (`int`): width of the output attention map
align_corners (`bool`, *optional*): the `align_corner` argument for `nn.functional.interpolate`.
Returns:
`torch.Tensor`: resized attention map of shape [batch_size, groups, height, width]
"""
scale = (height * width // attentions.shape[2]) ** 0.5
if height > width:
feat_width = int(np.round(width / scale))
feat_height = attentions.shape[2] // feat_width
else:
feat_height = int(np.round(height / scale))
feat_width = attentions.shape[2] // feat_height
batch_size = attentions.shape[0]
groups = attentions.shape[1] # number of group token
# [batch_size, groups, height*width, groups] -> [batch_size, groups, height, width]
attentions = attentions.reshape(batch_size, groups, feat_height, feat_width)
attentions = nn.functional.interpolate(
attentions, size=(height, width), mode="bilinear", align_corners=align_corners
)
return attentions
def get_grouping_from_attentions(attentions, hw_shape):
"""
Args:
attentions (`tuple(torch.FloatTensor)`: tuple of attention maps returned by `GroupViTVisionTransformer`
hw_shape (`tuple(int)`): height and width of the output attention map
Returns:
`torch.Tensor`: the attention map of shape [batch_size, groups, height, width]
"""
attn_maps = []
with torch.no_grad():
prev_attn_masks = None
for attn_masks in attentions:
# [batch_size, num_groups, height x width] -> [batch_size, height x width, num_groups]
attn_masks = attn_masks.permute(0, 2, 1).contiguous()
if prev_attn_masks is None:
prev_attn_masks = attn_masks
else:
prev_attn_masks = prev_attn_masks @ attn_masks
# [batch_size, heightxwidth, num_groups] -> [batch_size, num_groups, heightxwidth] -> [batch_size, num_groups, height, width]
cur_attn_map = resize_attention_map(prev_attn_masks.permute(0, 2, 1).contiguous(), *hw_shape)
attn_maps.append(cur_attn_map)
# [batch_size, num_groups, height, width]
final_grouping = attn_maps[-1]
return final_grouping
class GroupViTCrossAttentionLayer(nn.Module):
def __init__(self, config: GroupViTVisionConfig):
super().__init__()
self.attn = GroupViTAttention(config)
self.norm2 = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.mlp = GroupViTMLP(config)
self.norm_post = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
def forward(self, query, key):
x = query
x = x + self.attn(query, encoder_hidden_states=key)[0]
x = x + self.mlp(self.norm2(x))
x = self.norm_post(x)
return x
class GroupViTAssignAttention(nn.Module):
def __init__(self, config: GroupViTVisionConfig):
super().__init__()
self.scale = config.hidden_size**-0.5
self.q_proj = nn.Linear(config.hidden_size, config.hidden_size)
self.k_proj = nn.Linear(config.hidden_size, config.hidden_size)
self.v_proj = nn.Linear(config.hidden_size, config.hidden_size)
self.proj = nn.Linear(config.hidden_size, config.hidden_size)
self.assign_eps = config.assign_eps
def get_attn(self, attn, gumbel=True, hard=True):
if gumbel and self.training:
attn = gumbel_softmax(attn, dim=-2, hard=hard)
else:
if hard:
attn = hard_softmax(attn, dim=-2)
else:
attn = nn.functional.softmax(attn, dim=-2)
return attn
def forward(self, query, key):
value = key
# [batch_size, query_length, channels]
query = self.q_proj(query)
# [batch_size, key_length, channels]
key = self.k_proj(key)
# [batch_size, key_length, channels]
value = self.v_proj(value)
# [batch_size, query_length, key_length]
raw_attn = (query @ key.transpose(-2, -1)) * self.scale
attn = self.get_attn(raw_attn)
soft_attn = self.get_attn(raw_attn, gumbel=False, hard=False)
attn = attn / (attn.sum(dim=-1, keepdim=True) + self.assign_eps)
out = attn @ value
out = self.proj(out)
return out, soft_attn
class GroupViTTokenAssign(nn.Module):
def __init__(self, config: GroupViTVisionConfig, num_group_token, num_output_group):
super().__init__()
self.num_output_group = num_output_group
# norm on group_tokens
self.norm_tokens = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
assign_mlp_ratio = (
config.assign_mlp_ratio
if isinstance(config.assign_mlp_ratio, collections.abc.Iterable)
else (config.assign_mlp_ratio, config.assign_mlp_ratio)
)
tokens_dim, channels_dim = [int(x * config.hidden_size) for x in assign_mlp_ratio]
self.mlp_inter = GroupViTMixerMLP(config, num_group_token, tokens_dim, num_output_group)
self.norm_post_tokens = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
# norm on x
self.norm_x = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.pre_assign_attn = GroupViTCrossAttentionLayer(config)
self.assign = GroupViTAssignAttention(config)
self.norm_new_x = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.mlp_channels = GroupViTMLP(config, config.hidden_size, channels_dim, config.hidden_size)
def project_group_token(self, group_tokens):
"""
Args:
group_tokens (torch.Tensor): group tokens, [batch_size, num_group_tokens, channels]
Returns:
projected_group_tokens (torch.Tensor): [batch_size, num_output_groups, channels]
"""
# [B, num_output_groups, C] <- [B, num_group_tokens, C]
projected_group_tokens = self.mlp_inter(group_tokens)
projected_group_tokens = self.norm_post_tokens(projected_group_tokens)
return projected_group_tokens
def forward(self, image_tokens, group_tokens):
"""
Args:
image_tokens (`torch.Tensor`): image tokens, of shape [batch_size, input_length, channels]
group_tokens (`torch.Tensor`): group tokens, [batch_size, num_group_tokens, channels]
"""
group_tokens = self.norm_tokens(group_tokens)
image_tokens = self.norm_x(image_tokens)
# [batch_size, num_output_groups, channels]
projected_group_tokens = self.project_group_token(group_tokens)
projected_group_tokens = self.pre_assign_attn(projected_group_tokens, image_tokens)
new_image_tokens, attention = self.assign(projected_group_tokens, image_tokens)
new_image_tokens += projected_group_tokens
new_image_tokens = new_image_tokens + self.mlp_channels(self.norm_new_x(new_image_tokens))
return new_image_tokens, attention
@dataclass
class GroupViTModelOutput(ModelOutput):
"""
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `return_loss` is `True`):
Contrastive loss for image-text similarity.
logits_per_image (`torch.FloatTensor` of shape `(image_batch_size, text_batch_size)`):
The scaled dot product scores between `image_embeds` and `text_embeds`. This represents the image-text
similarity scores.
logits_per_text (`torch.FloatTensor` of shape `(text_batch_size, image_batch_size)`):
The scaled dot product scores between `text_embeds` and `image_embeds`. This represents the text-image
similarity scores.
segmentation_logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels, logits_height, logits_width)`):
Classification scores for each pixel.
<Tip warning={true}>
The logits returned do not necessarily have the same size as the `pixel_values` passed as inputs. This is
to avoid doing two interpolations and lose some quality when a user needs to resize the logits to the
original image size as post-processing. You should always check your logits shape and resize as needed.
</Tip>
text_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim`):
The text embeddings obtained by applying the projection layer to the pooled output of
[`GroupViTTextModel`].
image_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim`):
The image embeddings obtained by applying the projection layer to the pooled output of
[`GroupViTVisionModel`].
text_model_output (`BaseModelOutputWithPooling`):
The output of the [`GroupViTTextModel`].
vision_model_output (`BaseModelOutputWithPooling`):
The output of the [`GroupViTVisionModel`].
"""
loss: Optional[torch.FloatTensor] = None
logits_per_image: Optional[torch.FloatTensor] = None
logits_per_text: Optional[torch.FloatTensor] = None
segmentation_logits: Optional[torch.FloatTensor] = None
text_embeds: Optional[torch.FloatTensor] = None
image_embeds: Optional[torch.FloatTensor] = None
text_model_output: BaseModelOutputWithPooling = None
vision_model_output: BaseModelOutputWithPooling = None
def to_tuple(self) -> Tuple[Any]:
return tuple(
self[k] if k not in ["text_model_output", "vision_model_output"] else getattr(self, k).to_tuple()
for k in self.keys()
)
class GroupViTPatchEmbeddings(nn.Module):
"""
Image to Patch Embedding.
"""
def __init__(
self,
image_size: int = 224,
patch_size: Union[int, Tuple[int, int]] = 16,
num_channels: int = 3,
embed_dim: int = 768,
):
super().__init__()
image_size = image_size if isinstance(image_size, collections.abc.Iterable) else (image_size, image_size)
patch_size = patch_size if isinstance(patch_size, collections.abc.Iterable) else (patch_size, patch_size)
num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
self.image_size = image_size
self.patch_size = patch_size
self.num_patches = num_patches
self.projection = nn.Conv2d(num_channels, embed_dim, kernel_size=patch_size, stride=patch_size)
def forward(self, pixel_values: torch.Tensor, interpolate_pos_encoding: bool = False) -> torch.Tensor:
batch_size, num_channels, height, width = pixel_values.shape
if not interpolate_pos_encoding:
if height != self.image_size[0] or width != self.image_size[1]:
raise ValueError(
f"Input image size ({height}*{width}) doesn't match model"
f" ({self.image_size[0]}*{self.image_size[1]})."
)
x = self.projection(pixel_values).flatten(2).transpose(1, 2)
return x
class GroupViTVisionEmbeddings(nn.Module):
def __init__(self, config: GroupViTVisionConfig):
super().__init__()
self.patch_embeddings = GroupViTPatchEmbeddings(
image_size=config.image_size,
patch_size=config.patch_size,
num_channels=config.num_channels,
embed_dim=config.hidden_size,
)
num_patches = self.patch_embeddings.num_patches
self.position_embeddings = nn.Parameter(torch.zeros(1, num_patches, config.hidden_size))
self.dropout = nn.Dropout(config.dropout)
self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.patch_size = config.patch_size
self.config = config
def interpolate_pos_encoding(self, embeddings: torch.Tensor, height: int, width: int) -> torch.Tensor:
"""
This method allows to interpolate the pre-trained position encodings, to be able to use the model on higher resolution
images. This method is also adapted to support torch.jit tracing and no class embeddings.
Adapted from:
- https://github.com/facebookresearch/dino/blob/de9ee3df6cf39fac952ab558447af1fa1365362a/vision_transformer.py#L174-L194, and
- https://github.com/facebookresearch/dinov2/blob/e1277af2ba9496fbadf7aec6eba56e8d882d1e35/dinov2/models/vision_transformer.py#L179-L211
"""
num_patches = embeddings.shape[1]
num_positions = self.position_embeddings.shape[1]
# always interpolate when tracing to ensure the exported model works for dynamic input shapes
if not torch.jit.is_tracing() and num_patches == num_positions and height == width:
return self.position_embeddings
patch_pos_embed = self.position_embeddings
dim = embeddings.shape[-1]
new_height = height // self.patch_size
new_width = width // self.patch_size
sqrt_num_positions = torch_int(num_positions**0.5)
patch_pos_embed = patch_pos_embed.reshape(1, sqrt_num_positions, sqrt_num_positions, dim)
patch_pos_embed = patch_pos_embed.permute(0, 3, 1, 2)
patch_pos_embed = nn.functional.interpolate(
patch_pos_embed,
size=(new_height, new_width),
mode="bicubic",
align_corners=False,
)
patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim)
return patch_pos_embed
def forward(self, pixel_values: torch.Tensor, interpolate_pos_encoding: bool = False) -> torch.Tensor:
batch_size, num_channels, height, width = pixel_values.shape
embeddings = self.patch_embeddings(pixel_values, interpolate_pos_encoding=interpolate_pos_encoding)
embeddings = self.layernorm(embeddings)
batch_size, seq_len, _ = embeddings.size()
# add positional encoding to each token
if interpolate_pos_encoding:
embeddings = embeddings + self.interpolate_pos_encoding(embeddings, height, width)
else:
embeddings = embeddings + self.position_embeddings
embeddings = self.dropout(embeddings)
return embeddings
# Copied from transformers.models.clip.modeling_clip.CLIPTextEmbeddings with CLIP->GroupViT
class GroupViTTextEmbeddings(nn.Module):
def __init__(self, config: GroupViTTextConfig):
super().__init__()
embed_dim = config.hidden_size
self.token_embedding = nn.Embedding(config.vocab_size, embed_dim)
self.position_embedding = nn.Embedding(config.max_position_embeddings, embed_dim)
# position_ids (1, len position emb) is contiguous in memory and exported when serialized
self.register_buffer(
"position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False
)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
) -> torch.Tensor:
seq_length = input_ids.shape[-1] if input_ids is not None else inputs_embeds.shape[-2]
max_position_embedding = self.position_embedding.weight.shape[0]
if seq_length > max_position_embedding:
raise ValueError(
f"Sequence length must be less than max_position_embeddings (got `sequence length`: "
f"{seq_length} and max_position_embeddings: {max_position_embedding}"
)
if position_ids is None:
position_ids = self.position_ids[:, :seq_length]
if inputs_embeds is None:
inputs_embeds = self.token_embedding(input_ids)
position_embeddings = self.position_embedding(position_ids)
embeddings = inputs_embeds + position_embeddings
return embeddings
class GroupViTStage(nn.Module):
"""This corresponds to the `GroupingLayer` class in the GroupViT implementation."""
def __init__(
self,
config: GroupViTVisionConfig,
depth: int,
num_prev_group_token: int,
num_group_token: int,
num_output_group: int,
):
super().__init__()
self.depth = depth
self.num_group_token = num_group_token
if num_group_token > 0:
self.group_token = nn.Parameter(torch.zeros(1, num_group_token, config.hidden_size))
else:
self.group_token = None
self.layers = nn.ModuleList([GroupViTEncoderLayer(config) for _ in range(depth)])
if num_group_token > 0:
self.downsample = GroupViTTokenAssign(
config=config,
num_group_token=num_group_token,
num_output_group=num_output_group,
)
else:
self.downsample = None
if num_prev_group_token > 0 and num_group_token > 0:
self.group_projector = nn.Sequential(
nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps),
GroupViTMixerMLP(config, num_prev_group_token, config.hidden_size // 2, num_group_token),
)
else:
self.group_projector = None
@property
def with_group_token(self):
return self.group_token is not None
def split_x(self, x):
if self.with_group_token:
return x[:, : -self.num_group_token], x[:, -self.num_group_token :]
else:
return x, None
def concat_x(self, x: torch.Tensor, group_token: Optional[torch.Tensor] = None) -> torch.Tensor:
if group_token is None:
return x
return torch.cat([x, group_token], dim=1)
def forward(
self,
hidden_states: torch.Tensor,
prev_group_token: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = False,
) -> Tuple[torch.FloatTensor]:
"""
Args:
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
attention_mask (`torch.FloatTensor`): attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
`(config.encoder_attention_heads,)`.
output_attentions (`bool`, *optional*):
Whether or not to return the grouping tensors of Grouping block.
"""
if self.with_group_token:
group_token = self.group_token.expand(hidden_states.size(0), -1, -1)
if self.group_projector is not None:
group_token = group_token + self.group_projector(prev_group_token)
else:
group_token = None
x = hidden_states
cat_x = self.concat_x(x, group_token)
for layer in self.layers:
layer_out = layer(cat_x, attention_mask=None, causal_attention_mask=None)
cat_x = layer_out[0]
x, group_token = self.split_x(cat_x)
attention = None
if self.downsample is not None:
x, attention = self.downsample(x, group_token)
outputs = (x, group_token)
if output_attentions:
outputs = outputs + (attention,)
return outputs
class GroupViTMLP(nn.Module):
def __init__(
self,
config: GroupViTVisionConfig,
hidden_size: Optional[int] = None,
intermediate_size: Optional[int] = None,
output_size: Optional[int] = None,
):
super().__init__()
self.config = config
self.activation_fn = ACT2FN[config.hidden_act]
hidden_size = hidden_size if hidden_size is not None else config.hidden_size
intermediate_size = intermediate_size if intermediate_size is not None else config.intermediate_size
output_size = output_size if output_size is not None else hidden_size
self.fc1 = nn.Linear(hidden_size, intermediate_size)
self.fc2 = nn.Linear(intermediate_size, output_size)
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidden_states = self.fc1(hidden_states)
hidden_states = self.activation_fn(hidden_states)
hidden_states = self.fc2(hidden_states)
return hidden_states
class GroupViTMixerMLP(GroupViTMLP):
def forward(self, x):
x = super().forward(x.transpose(1, 2))
return x.transpose(1, 2)
class GroupViTAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(self, config):
super().__init__()
self.config = config
self.embed_dim = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_heads != self.embed_dim:
raise ValueError(
f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:"
f" {self.num_heads})."
)
self.scale = self.head_dim**-0.5
self.dropout = config.attention_dropout
self.k_proj = nn.Linear(self.embed_dim, self.embed_dim)
self.v_proj = nn.Linear(self.embed_dim, self.embed_dim)
self.q_proj = nn.Linear(self.embed_dim, self.embed_dim)
self.out_proj = nn.Linear(self.embed_dim, self.embed_dim)
def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
causal_attention_mask: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = False,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
"""Input shape: Batch x Time x Channel"""
bsz, tgt_len, embed_dim = hidden_states.size()
is_cross_attention = encoder_hidden_states is not None
# get query proj
query_states = self.q_proj(hidden_states) * self.scale
if is_cross_attention:
key_states = self._shape(self.k_proj(encoder_hidden_states), -1, bsz)
value_states = self._shape(self.v_proj(encoder_hidden_states), -1, bsz)
else:
key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
proj_shape = (bsz * self.num_heads, -1, self.head_dim)
query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape)
key_states = key_states.view(*proj_shape)
value_states = value_states.view(*proj_shape)
src_len = key_states.size(1)
attn_weights = torch.bmm(query_states, key_states.transpose(1, 2))
if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len):
raise ValueError(
f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is"
f" {attn_weights.size()}"
)
# apply the causal_attention_mask first
if causal_attention_mask is not None:
if causal_attention_mask.size() != (bsz, 1, tgt_len, src_len):
raise ValueError(
f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is"
f" {causal_attention_mask.size()}"
)
attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + causal_attention_mask
attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
if attention_mask is not None:
if attention_mask.size() != (bsz, 1, tgt_len, src_len):
raise ValueError(
f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}"
)
attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask
attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
attn_weights = nn.functional.softmax(attn_weights, dim=-1)
if output_attentions:
# this operation is a bit akward, but it's required to
# make sure that attn_weights keeps its gradient.
# In order to do so, attn_weights have to reshaped
# twice and have to be reused in the following
attn_weights_reshaped = attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
attn_weights = attn_weights_reshaped.view(bsz * self.num_heads, tgt_len, src_len)
else:
attn_weights_reshaped = None
attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)
attn_output = torch.bmm(attn_probs, value_states)
if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim):
raise ValueError(
f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is"
f" {attn_output.size()}"
)
attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim)
attn_output = attn_output.transpose(1, 2)
attn_output = attn_output.reshape(bsz, tgt_len, embed_dim)
attn_output = self.out_proj(attn_output)
return attn_output, attn_weights_reshaped
# Copied from transformers.models.altclip.modeling_altclip.AltCLIPEncoderLayer with AltCLIP->GroupViT
class GroupViTEncoderLayer(nn.Module):
def __init__(self, config: GroupViTConfig):
super().__init__()
self.embed_dim = config.hidden_size
self.self_attn = GroupViTAttention(config)
self.layer_norm1 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
self.mlp = GroupViTMLP(config)
self.layer_norm2 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: torch.Tensor,
causal_attention_mask: torch.Tensor,
output_attentions: Optional[bool] = False,
) -> Tuple[torch.FloatTensor]:
"""
Args:
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
attention_mask (`torch.FloatTensor`): attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
`(config.encoder_attention_heads,)`.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
"""
residual = hidden_states
hidden_states = self.layer_norm1(hidden_states)
hidden_states, attn_weights = self.self_attn(
hidden_states=hidden_states,
attention_mask=attention_mask,
causal_attention_mask=causal_attention_mask,
output_attentions=output_attentions,
)
hidden_states = residual + hidden_states
residual = hidden_states
hidden_states = self.layer_norm2(hidden_states)
hidden_states = self.mlp(hidden_states)
hidden_states = residual + hidden_states
outputs = (hidden_states,)
if output_attentions:
outputs += (attn_weights,)
return outputs
class GroupViTPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = GroupViTConfig
base_model_prefix = "groupvit"
supports_gradient_checkpointing = True
def _init_weights(self, module):
"""Initialize the weights"""
init_range = self.config.initializer_range
if isinstance(module, (nn.Linear, nn.Conv2d)):
# Slightly different from the TF version which uses truncated_normal for initialization
# cf https://github.com/pytorch/pytorch/pull/5617
module.weight.data.normal_(mean=0.0, std=init_range)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.LayerNorm):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
factor = self.config.initializer_factor
if isinstance(module, GroupViTTextEmbeddings):
module.token_embedding.weight.data.normal_(mean=0.0, std=factor * 0.02)
module.position_embedding.weight.data.normal_(mean=0.0, std=factor * 0.02)
elif isinstance(module, GroupViTAttention):
factor = self.config.initializer_factor
in_proj_std = (module.embed_dim**-0.5) * ((2 * module.config.num_hidden_layers) ** -0.5) * factor
out_proj_std = (module.embed_dim**-0.5) * factor
nn.init.normal_(module.q_proj.weight, std=in_proj_std)
nn.init.normal_(module.k_proj.weight, std=in_proj_std)
nn.init.normal_(module.v_proj.weight, std=in_proj_std)
nn.init.normal_(module.out_proj.weight, std=out_proj_std)
elif isinstance(module, GroupViTMLP):
factor = self.config.initializer_factor
in_proj_std = (module.config.hidden_size**-0.5) * ((2 * module.config.num_hidden_layers) ** -0.5) * factor
fc_std = (2 * module.config.hidden_size) ** -0.5 * factor
nn.init.normal_(module.fc1.weight, std=fc_std)
nn.init.normal_(module.fc2.weight, std=in_proj_std)
GROUPVIT_START_DOCSTRING = r"""
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`GroupViTConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
GROUPVIT_TEXT_INPUTS_DOCSTRING = r"""
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
it.
Indices can be obtained using [`CLIPTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
config.max_position_embeddings - 1]`.
[What are position IDs?](../glossary#position-ids)
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
GROUPVIT_VISION_INPUTS_DOCSTRING = r"""
Args:
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using
[`AutoImageProcessor`]. See [`CLIPImageProcessor.__call__`] for details.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
GROUPVIT_INPUTS_DOCSTRING = r"""
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
it.
Indices can be obtained using [`CLIPTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
config.max_position_embeddings - 1]`.
[What are position IDs?](../glossary#position-ids)
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
[`CLIPImageProcessor.__call__`] for details.
return_loss (`bool`, *optional*):
Whether or not to return the contrastive loss.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
class GroupViTVisionEncoder(nn.Module):
def __init__(self, config: GroupViTVisionConfig) -> None:
super().__init__()
self.config = config
self.stages = nn.ModuleList(
[
GroupViTStage(
config=config,
depth=config.depths[i],
num_group_token=config.num_group_tokens[i],
num_output_group=config.num_output_groups[i],
num_prev_group_token=config.num_output_groups[i - 1] if i > 0 else 0,
)
for i in range(len(config.depths))
]
)
self.gradient_checkpointing = False
def forward(
self,
hidden_states: torch.Tensor,
output_hidden_states: Optional[bool] = None,
output_attentions: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[tuple, BaseModelOutput]:
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
all_hidden_states = () if output_hidden_states else None
all_groupings = () if output_attentions else None
group_tokens = None
for i, stage in enumerate(self.stages):
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
layer_outputs = stage(hidden_states, group_tokens, output_attentions)
hidden_states = layer_outputs[0]
group_tokens = layer_outputs[1]
if output_attentions and layer_outputs[2] is not None:
all_groupings = all_groupings + (layer_outputs[2],)
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if not return_dict:
return tuple(v for v in [hidden_states, all_hidden_states, all_groupings] if v is not None)
return BaseModelOutput(
last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_groupings
)
class GroupViTTextEncoder(nn.Module):
"""
Transformer encoder consisting of `config.num_hidden_layers` self-attention layers. Each layer is a
[`GroupViTEncoderLayer`].
Args:
config: GroupViTTextConfig
"""
def __init__(self, config: GroupViTTextConfig):
super().__init__()
self.config = config
self.layers = nn.ModuleList([GroupViTEncoderLayer(config) for _ in range(config.num_hidden_layers)])
self.gradient_checkpointing = False
def forward(
self,
inputs_embeds,
attention_mask: Optional[torch.Tensor] = None,
causal_attention_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, BaseModelOutput]:
r"""
Args:
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
This is useful if you want more control over how to convert `input_ids` indices into associated vectors
than the model's internal embedding lookup matrix.
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
causal_attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Causal mask for the text model. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
for more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
encoder_states = () if output_hidden_states else None
all_attentions = () if output_attentions else None
hidden_states = inputs_embeds
for idx, encoder_layer in enumerate(self.layers):
if output_hidden_states:
encoder_states = encoder_states + (hidden_states,)
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
encoder_layer.__call__,
hidden_states,
attention_mask,
causal_attention_mask,
output_attentions,
)
else:
layer_outputs = encoder_layer(
hidden_states,
attention_mask,
causal_attention_mask,
output_attentions=output_attentions,
)
hidden_states = layer_outputs[0]
if output_attentions:
all_attentions = all_attentions + (layer_outputs[1],)
if output_hidden_states:
encoder_states = encoder_states + (hidden_states,)
if not return_dict:
return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None)
return BaseModelOutput(
last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions
)
class GroupViTTextTransformer(nn.Module):
def __init__(self, config: GroupViTTextConfig):
super().__init__()
self.config = config
embed_dim = config.hidden_size
self.embeddings = GroupViTTextEmbeddings(config)
self.encoder = GroupViTTextEncoder(config)
self.final_layer_norm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
# For `pooled_output` computation
self.eos_token_id = config.eos_token_id
@add_start_docstrings_to_model_forward(GROUPVIT_TEXT_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=GroupViTTextConfig)
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, BaseModelOutputWithPooling]:
r"""
Returns:
"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if input_ids is None:
raise ValueError("You have to specify input_ids")
input_shape = input_ids.size()
input_ids = input_ids.view(-1, input_shape[-1])
hidden_states = self.embeddings(input_ids=input_ids, position_ids=position_ids)
# CLIP's text model uses causal mask, prepare it here.
# https://github.com/openai/CLIP/blob/cfcffb90e69f37bf2ff1e988237a0fbe41f33c04/clip/model.py#L324
causal_attention_mask = _create_4d_causal_attention_mask(
input_shape, hidden_states.dtype, device=hidden_states.device
)
# expand attention_mask
if attention_mask is not None:
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
attention_mask = _prepare_4d_attention_mask(attention_mask, hidden_states.dtype)
encoder_outputs = self.encoder(
inputs_embeds=hidden_states,
attention_mask=attention_mask,
causal_attention_mask=causal_attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
last_hidden_state = encoder_outputs[0]
last_hidden_state = self.final_layer_norm(last_hidden_state)
if self.eos_token_id == 2:
# The `eos_token_id` was incorrect before PR #24773: Let's keep what have been done here.
# A CLIP model with such `eos_token_id` in the config can't work correctly with extra new tokens added
# ------------------------------------------------------------
# text_embeds.shape = [batch_size, sequence_length, transformer.width]
# take features from the eot embedding (eot_token is the highest number in each sequence)
# casting to torch.int for onnx compatibility: argmax doesn't support int64 inputs with opset 14
pooled_output = last_hidden_state[
torch.arange(last_hidden_state.shape[0], device=last_hidden_state.device),
input_ids.to(dtype=torch.int, device=last_hidden_state.device).argmax(dim=-1),
]
else:
# The config gets updated `eos_token_id` from PR #24773 (so the use of exta new tokens is possible)
pooled_output = last_hidden_state[
torch.arange(last_hidden_state.shape[0], device=last_hidden_state.device),
# We need to get the first position of `eos_token_id` value (`pad_token_ids` might equal to `eos_token_id`)
# Note: we assume each sequence (along batch dim.) contains an `eos_token_id` (e.g. prepared by the tokenizer)
(input_ids.to(dtype=torch.int, device=last_hidden_state.device) == self.eos_token_id)
.int()
.argmax(dim=-1),
]
if not return_dict:
return (last_hidden_state, pooled_output) + encoder_outputs[1:]
return BaseModelOutputWithPooling(
last_hidden_state=last_hidden_state,
pooler_output=pooled_output,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
)
class GroupViTTextModel(GroupViTPreTrainedModel):
config_class = GroupViTTextConfig
def __init__(self, config: GroupViTTextConfig):
super().__init__(config)
self.text_model = GroupViTTextTransformer(config)
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self) -> nn.Module:
return self.text_model.embeddings.token_embedding
def set_input_embeddings(self, value):
self.text_model.embeddings.token_embedding = value
@add_start_docstrings_to_model_forward(GROUPVIT_TEXT_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=GroupViTTextConfig)
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, BaseModelOutputWithPooling]:
r"""
Returns:
Examples:
```python
>>> from transformers import CLIPTokenizer, GroupViTTextModel
>>> tokenizer = CLIPTokenizer.from_pretrained("nvidia/groupvit-gcc-yfcc")
>>> model = GroupViTTextModel.from_pretrained("nvidia/groupvit-gcc-yfcc")
>>> inputs = tokenizer(["a photo of a cat", "a photo of a dog"], padding=True, return_tensors="pt")
>>> outputs = model(**inputs)
>>> last_hidden_state = outputs.last_hidden_state
>>> pooled_output = outputs.pooler_output # pooled (EOS token) states
```"""
return self.text_model(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
class GroupViTVisionTransformer(nn.Module):
def __init__(self, config: GroupViTVisionConfig):
super().__init__()
self.config = config
embed_dim = config.hidden_size
self.embeddings = GroupViTVisionEmbeddings(config)
self.encoder = GroupViTVisionEncoder(config)
self.layernorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
@add_start_docstrings_to_model_forward(GROUPVIT_VISION_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=GroupViTVisionConfig)
def forward(
self,
pixel_values: Optional[torch.FloatTensor] = None,
output_hidden_states: Optional[bool] = None,
output_attentions: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, BaseModelOutputWithPooling]:
r"""
Returns:
"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if pixel_values is None:
raise ValueError("You have to specify pixel_values")
hidden_states = self.embeddings(pixel_values)
encoder_outputs = self.encoder(
hidden_states=hidden_states,
output_hidden_states=output_hidden_states,
output_attentions=output_attentions,
return_dict=return_dict,
)
last_hidden_state = encoder_outputs[0]
# normalize the last hidden state
last_hidden_state = self.layernorm(last_hidden_state)
pooled_output = last_hidden_state.mean(dim=1)
if not return_dict:
return (last_hidden_state, pooled_output) + encoder_outputs[1:]
return BaseModelOutputWithPooling(
last_hidden_state=last_hidden_state,
pooler_output=pooled_output,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
)
class GroupViTVisionModel(GroupViTPreTrainedModel):
config_class = GroupViTVisionConfig
main_input_name = "pixel_values"
def __init__(self, config: GroupViTVisionConfig):
super().__init__(config)
self.vision_model = GroupViTVisionTransformer(config)
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self) -> GroupViTPatchEmbeddings:
return self.vision_model.embeddings.patch_embeddings
@add_start_docstrings_to_model_forward(GROUPVIT_VISION_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=GroupViTVisionConfig)
def forward(
self,
pixel_values: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, BaseModelOutputWithPooling]:
r"""
Returns:
Examples:
```python
>>> from PIL import Image
>>> import requests
>>> from transformers import AutoProcessor, GroupViTVisionModel
>>> processor = AutoProcessor.from_pretrained("nvidia/groupvit-gcc-yfcc")
>>> model = GroupViTVisionModel.from_pretrained("nvidia/groupvit-gcc-yfcc")
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> inputs = processor(images=image, return_tensors="pt")
>>> outputs = model(**inputs)
>>> last_hidden_state = outputs.last_hidden_state
>>> pooled_output = outputs.pooler_output # pooled CLS states
```"""
return self.vision_model(
pixel_values=pixel_values,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
@add_start_docstrings(GROUPVIT_START_DOCSTRING)
class GroupViTModel(GroupViTPreTrainedModel):
config_class = GroupViTConfig
def __init__(self, config: GroupViTConfig):
super().__init__(config)
if not isinstance(config.text_config, GroupViTTextConfig):
raise TypeError(
"config.text_config is expected to be of type GroupViTTextConfig but is of type"
f" {type(config.text_config)}."
)
if not isinstance(config.vision_config, GroupViTVisionConfig):
raise TypeError(
"config.vision_config is expected to be of type GroupViTVisionConfig but is of type"
f" {type(config.vision_config)}."
)
text_config = config.text_config
vision_config = config.vision_config
self.projection_dim = config.projection_dim
self.projection_intermediate_dim = config.projection_intermediate_dim
self.text_embed_dim = text_config.hidden_size
self.vision_embed_dim = vision_config.hidden_size
self.text_model = GroupViTTextTransformer(text_config)
self.vision_model = GroupViTVisionTransformer(vision_config)
self.visual_projection = nn.Sequential(
nn.Linear(self.vision_embed_dim, self.projection_intermediate_dim, bias=True),
nn.BatchNorm1d(self.projection_intermediate_dim),
nn.ReLU(inplace=True),
nn.Linear(self.projection_intermediate_dim, self.projection_dim, bias=True),
)
self.text_projection = nn.Sequential(
nn.Linear(self.text_embed_dim, self.projection_intermediate_dim, bias=True),
nn.BatchNorm1d(self.projection_intermediate_dim),
nn.ReLU(inplace=True),
nn.Linear(self.projection_intermediate_dim, self.projection_dim, bias=True),
)
self.logit_scale = nn.Parameter(torch.tensor(self.config.logit_scale_init_value))
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(GROUPVIT_TEXT_INPUTS_DOCSTRING)
def get_text_features(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> torch.FloatTensor:
r"""
Returns:
text_features (`torch.FloatTensor` of shape `(batch_size, output_dim`): The text embeddings obtained by
applying the projection layer to the pooled output of [`GroupViTTextModel`].
Examples:
```python
>>> from transformers import CLIPTokenizer, GroupViTModel
>>> model = GroupViTModel.from_pretrained("nvidia/groupvit-gcc-yfcc")
>>> tokenizer = CLIPTokenizer.from_pretrained("nvidia/groupvit-gcc-yfcc")
>>> inputs = tokenizer(["a photo of a cat", "a photo of a dog"], padding=True, return_tensors="pt")
>>> text_features = model.get_text_features(**inputs)
```"""
# Use GROUPVIT model's config for some fields (if specified) instead of those of vision & text components.
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
text_outputs = self.text_model(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
pooled_output = text_outputs[1]
text_features = self.text_projection(pooled_output)
return text_features
@add_start_docstrings_to_model_forward(GROUPVIT_VISION_INPUTS_DOCSTRING)
def get_image_features(
self,
pixel_values: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> torch.FloatTensor:
r"""
Returns:
image_features (`torch.FloatTensor` of shape `(batch_size, output_dim`): The image embeddings obtained by
applying the projection layer to the pooled output of [`GroupViTVisionModel`].
Examples:
```python
>>> from PIL import Image
>>> import requests
>>> from transformers import AutoProcessor, GroupViTModel
>>> model = GroupViTModel.from_pretrained("nvidia/groupvit-gcc-yfcc")
>>> processor = AutoProcessor.from_pretrained("nvidia/groupvit-gcc-yfcc")
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> inputs = processor(images=image, return_tensors="pt")
>>> image_features = model.get_image_features(**inputs)
```"""
# Use GROUPVIT model's config for some fields (if specified) instead of those of vision & text components.
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
vision_outputs = self.vision_model(
pixel_values=pixel_values,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
pooled_output = vision_outputs[1] # pooled_output
image_features = self.visual_projection(pooled_output)
return image_features
@add_start_docstrings_to_model_forward(GROUPVIT_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=GroupViTModelOutput, config_class=GroupViTConfig)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
pixel_values: Optional[torch.FloatTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
return_loss: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
output_segmentation: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, GroupViTModelOutput]:
r"""
Returns:
Examples:
```python
>>> from PIL import Image
>>> import requests
>>> from transformers import AutoProcessor, GroupViTModel
>>> model = GroupViTModel.from_pretrained("nvidia/groupvit-gcc-yfcc")
>>> processor = AutoProcessor.from_pretrained("nvidia/groupvit-gcc-yfcc")
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> inputs = processor(
... text=["a photo of a cat", "a photo of a dog"], images=image, return_tensors="pt", padding=True
... )
>>> outputs = model(**inputs)
>>> logits_per_image = outputs.logits_per_image # this is the image-text similarity score
>>> probs = logits_per_image.softmax(dim=1) # we can take the softmax to get the label probabilities
```"""
# Use GROUPVIT model's config for some fields (if specified) instead of those of vision & text components.
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_segmentation = (
output_segmentation if output_segmentation is not None else self.config.output_segmentation
)
if output_segmentation:
output_attentions = True
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
vision_outputs = self.vision_model(
pixel_values=pixel_values,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
text_outputs = self.text_model(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
image_embeds = vision_outputs[1]
image_embeds = self.visual_projection(image_embeds)
text_embeds = text_outputs[1]
text_embeds = self.text_projection(text_embeds)
# normalized features
image_embeds = image_embeds / image_embeds.norm(dim=-1, keepdim=True)
text_embeds = text_embeds / text_embeds.norm(dim=-1, keepdim=True)
# cosine similarity as logits
logit_scale = self.logit_scale.exp()
logits_per_text = torch.matmul(text_embeds, image_embeds.t()) * logit_scale
logits_per_image = logits_per_text.t()
seg_logits = None
if output_segmentation:
# grouped features
# [batch_size_image, num_group, hidden_size]
image_group_embeds = vision_outputs[0]
# [batch_size_image*num_group, hidden_size]
image_group_embeds = self.visual_projection(image_group_embeds.reshape(-1, image_group_embeds.shape[-1]))
if output_hidden_states:
attentions = vision_outputs[3]
else:
attentions = vision_outputs[2]
# [batch_size_image, num_group, height, width]
grouping = get_grouping_from_attentions(attentions, pixel_values.shape[2:])
# normalized features
image_group_embeds = image_group_embeds / image_group_embeds.norm(dim=-1, keepdim=True)
# [batch_size_image x num_group, batch_size_text]
logits_per_image_group = torch.matmul(image_group_embeds, text_embeds.t()) * logit_scale
# [batch_size_image, batch_size_text, num_group]
logits_per_image_group = logits_per_image_group.reshape(
image_embeds.shape[0], -1, text_embeds.shape[0]
).permute(0, 2, 1)
# [batch_size_image, batch_size_text, height x width]
flatten_grouping = grouping.reshape(grouping.shape[0], grouping.shape[1], -1)
# [batch_size_image, batch_size_text, height, width]
seg_logits = torch.matmul(logits_per_image_group, flatten_grouping) * logit_scale
seg_logits = seg_logits.reshape(
seg_logits.shape[0], seg_logits.shape[1], grouping.shape[2], grouping.shape[3]
)
loss = None
if return_loss:
loss = groupvit_loss(logits_per_text)
if not return_dict:
if seg_logits is not None:
output = (
logits_per_image,
logits_per_text,
seg_logits,
text_embeds,
image_embeds,
text_outputs,
vision_outputs,
)
else:
output = (logits_per_image, logits_per_text, text_embeds, image_embeds, text_outputs, vision_outputs)
return ((loss,) + output) if loss is not None else output
return GroupViTModelOutput(
loss=loss,
logits_per_image=logits_per_image,
logits_per_text=logits_per_text,
segmentation_logits=seg_logits,
text_embeds=text_embeds,
image_embeds=image_embeds,
text_model_output=text_outputs,
vision_model_output=vision_outputs,
)
__all__ = ["GroupViTModel", "GroupViTPreTrainedModel", "GroupViTTextModel", "GroupViTVisionModel"]
```
|
============================================================================================================================================
SOURCE CODE FILE: modeling_tf_groupvit.py
LINES: 1
SIZE: 88.18 KB
PATH: scripts\freecad_env\Lib\site-packages\transformers\models\groupvit\modeling_tf_groupvit.py
ENCODING: utf-8
```py
# coding=utf-8
# Copyright 2022 NVIDIA and The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""TF 2.0 GroupViT model."""
from __future__ import annotations
import collections.abc
import math
from dataclasses import dataclass
from typing import Any, Optional, Tuple, Union
import numpy as np
import tensorflow as tf
from ...activations_tf import get_tf_activation
from ...modeling_tf_outputs import TFBaseModelOutput, TFBaseModelOutputWithPooling
from ...modeling_tf_utils import (
TFModelInputType,
TFPreTrainedModel,
get_initializer,
keras,
keras_serializable,
unpack_inputs,
)
from ...tf_utils import check_embeddings_within_bounds, shape_list, stable_softmax
from ...utils import (
ModelOutput,
add_start_docstrings,
add_start_docstrings_to_model_forward,
is_tensorflow_probability_available,
logging,
replace_return_docstrings,
)
from .configuration_groupvit import GroupViTConfig, GroupViTTextConfig, GroupViTVisionConfig
logger = logging.get_logger(__name__)
# soft dependency
if is_tensorflow_probability_available():
try:
import tensorflow_probability as tfp
# On the first call, check whether a compatible version of TensorFlow is installed
# TensorFlow Probability depends on a recent stable release of TensorFlow
_ = tfp.distributions.Normal(loc=0.0, scale=1.0)
except ImportError:
logger.error(
"GroupViT models are not usable since `tensorflow_probability` can't be loaded. "
"It seems you have `tensorflow_probability` installed with the wrong tensorflow version."
"Please try to reinstall it following the instructions here: https://github.com/tensorflow/probability."
)
else:
try:
import tensorflow_probability as tfp
# On the first call, check whether a compatible version of TensorFlow is installed
# TensorFlow Probability depends on a recent stable release of TensorFlow
_ = tfp.distributions.Normal(loc=0.0, scale=1.0)
except ImportError:
pass
_CHECKPOINT_FOR_DOC = "nvidia/groupvit-gcc-yfcc"
LARGE_NEGATIVE = -1e8
# Copied from transformers.models.bart.modeling_tf_bart._expand_mask
def _expand_mask(mask: tf.Tensor, tgt_len: Optional[int] = None):
"""
Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
"""
src_len = shape_list(mask)[1]
tgt_len = tgt_len if tgt_len is not None else src_len
one_cst = tf.constant(1.0)
mask = tf.cast(mask, dtype=one_cst.dtype)
expanded_mask = tf.tile(mask[:, None, None, :], (1, 1, tgt_len, 1))
return (one_cst - expanded_mask) * LARGE_NEGATIVE
# contrastive loss function, adapted from
# https://sachinruk.github.io/blog/pytorch/pytorch%20lightning/loss%20function/gpu/2021/03/07/CLIP.html
def contrastive_loss(logits: tf.Tensor) -> tf.Tensor:
return tf.math.reduce_mean(
keras.metrics.sparse_categorical_crossentropy(
y_true=tf.range(shape_list(logits)[0]), y_pred=logits, from_logits=True
)
)
# Copied from transformers.models.clip.modeling_tf_clip.clip_loss with clip->groupvit
def groupvit_loss(similarity: tf.Tensor) -> tf.Tensor:
caption_loss = contrastive_loss(similarity)
image_loss = contrastive_loss(tf.transpose(similarity))
return (caption_loss + image_loss) / 2.0
def hard_softmax(logits: tf.Tensor, dim: int) -> tf.Tensor:
y_soft = stable_softmax(logits, dim)
# Straight through.
index = tf.argmax(y_soft, dim)
y_hard = tf.one_hot(
index,
depth=shape_list(logits)[dim],
# TensorFlow expects axis to be -1 or between [0, 3). But received: -2
# This is why the following code snippet is used.
axis=range(len(shape_list(logits)))[dim],
dtype=y_soft.dtype,
)
ret = y_hard - tf.stop_gradient(y_soft) + y_soft
return ret
def gumbel_softmax(logits: tf.Tensor, tau: float = 1, hard: bool = False, dim: int = -1) -> tf.Tensor:
gumbel_dist = tfp.distributions.Gumbel(0.0, 1.0)
gumbels = gumbel_dist.sample(tf.shape(logits), dtype=logits.dtype)
gumbels = (logits + gumbels) / tau # ~Gumbel(logits,tau)
y_soft = stable_softmax(gumbels, dim)
if hard:
# Straight through.
index = tf.argmax(y_soft, dim)
y_hard = tf.one_hot(
index,
depth=shape_list(logits)[dim],
# TensorFlow expects axis to be -1 or between [0, 3). But received: -2
# This is why the following code snippet is used.
axis=range(len(shape_list(logits)))[dim],
dtype=y_soft.dtype,
)
ret = y_hard - tf.stop_gradient(y_soft) + y_soft
else:
# Reparametrization trick.
ret = y_soft
return ret
def resize_attention_map(attentions: tf.Tensor, height: int, width: int, align_corners: bool = False) -> tf.Tensor:
"""
Args:
attentions (`tf.Tensor`): attention map of shape [batch_size, groups, feat_height*feat_width]
height (`int`): height of the output attention map
width (`int`): width of the output attention map
align_corners (`bool`, *optional*): the `align_corner` argument for `nn.functional.interpolate`.
Returns:
`tf.Tensor`: resized attention map of shape [batch_size, groups, height, width]
"""
scale = (height * width // attentions.shape[2]) ** 0.5
if height > width:
feat_width = int(np.round(width / scale))
feat_height = shape_list(attentions)[2] // feat_width
else:
feat_height = int(np.round(height / scale))
feat_width = shape_list(attentions)[2] // feat_height
batch_size = shape_list(attentions)[0]
groups = shape_list(attentions)[1] # number of group token
# [batch_size, groups, height x width, groups] -> [batch_size, groups, height, width]
attentions = tf.reshape(attentions, (batch_size, groups, feat_height, feat_width))
attentions = tf.transpose(attentions, perm=(0, 2, 3, 1))
if align_corners:
attentions = tf.compat.v1.image.resize(
attentions,
size=(height, width),
method="bilinear",
align_corners=align_corners,
)
else:
attentions = tf.image.resize(attentions, size=(height, width), method="bilinear")
attentions = tf.transpose(attentions, perm=(0, 3, 1, 2))
return attentions
def get_grouping_from_attentions(attentions: Tuple[tf.Tensor], hw_shape: Tuple[int]) -> tf.Tensor:
"""
Args:
attentions (`tuple(tf.Tensor)`: tuple of attention maps returned by `TFGroupViTVisionTransformer`
hw_shape (`tuple(int)`): height and width of the output attention map
Returns:
`tf.Tensor`: the attention map of shape [batch_size, groups, height, width]
"""
attn_maps = []
prev_attn_masks = None
for attn_masks in attentions:
# [batch_size, num_groups, height x width] -> [batch_size, height x width, num_groups]
attn_masks = tf.transpose(attn_masks, perm=(0, 2, 1))
if prev_attn_masks is None:
prev_attn_masks = attn_masks
else:
prev_attn_masks = tf.matmul(prev_attn_masks, attn_masks)
# [batch_size, height x width, num_groups] -> [batch_size, num_groups, height x width] -> [batch_size, num_groups, height, width]
cur_attn_map = resize_attention_map(tf.transpose(prev_attn_masks, perm=(0, 2, 1)), *hw_shape)
attn_maps.append(cur_attn_map)
# [batch_size, num_groups, height, width]
final_grouping = attn_maps[-1]
return tf.stop_gradient(final_grouping)
@dataclass
class TFGroupViTModelOutput(ModelOutput):
"""
Args:
loss (`tf.Tensor` of shape `(1,)`, *optional*, returned when `return_loss` is `True`):
Contrastive loss for image-text similarity.
logits_per_image (`tf.Tensor` of shape `(image_batch_size, text_batch_size)`):
The scaled dot product scores between `image_embeds` and `text_embeds`. This represents the image-text
similarity scores.
logits_per_text (`tf.Tensor` of shape `(text_batch_size, image_batch_size)`):
The scaled dot product scores between `text_embeds` and `image_embeds`. This represents the text-image
similarity scores.
segmentation_logits (`tf.Tensor` of shape `(batch_size, config.num_labels, logits_height, logits_width)`):
Classification scores for each pixel.
<Tip warning={true}>
The logits returned do not necessarily have the same size as the `pixel_values` passed as inputs. This is
to avoid doing two interpolations and lose some quality when a user needs to resize the logits to the
original image size as post-processing. You should always check your logits shape and resize as needed.
</Tip>
text_embeds (`tf.Tensor` of shape `(batch_size, output_dim`):
The text embeddings obtained by applying the projection layer to the pooled output of
[`TFGroupViTTextModel`].
image_embeds (`tf.Tensor` of shape `(batch_size, output_dim`):
The image embeddings obtained by applying the projection layer to the pooled output of
[`TFGroupViTVisionModel`].
text_model_output (`TFBaseModelOutputWithPooling`):
The output of the [`TFGroupViTTextModel`].
vision_model_output (`TFBaseModelOutputWithPooling`):
The output of the [`TFGroupViTVisionModel`].
"""
loss: tf.Tensor | None = None
logits_per_image: Optional[tf.Tensor] = None
logits_per_text: Optional[tf.Tensor] = None
segmentation_logits: Optional[tf.Tensor] = None
text_embeds: Optional[tf.Tensor] = None
image_embeds: Optional[tf.Tensor] = None
text_model_output: TFBaseModelOutputWithPooling = None
vision_model_output: TFBaseModelOutputWithPooling = None
def to_tuple(self) -> Tuple[Any]:
return tuple(
self[k] if k not in ["text_model_output", "vision_model_output"] else getattr(self, k).to_tuple()
for k in self.keys()
)
class TFGroupViTCrossAttentionLayer(keras.layers.Layer):
def __init__(self, config: GroupViTVisionConfig, **kwargs):
super().__init__(**kwargs)
self.attn = TFGroupViTAttention(config, name="attn")
self.norm2 = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="norm2")
self.mlp = TFGroupViTMLP(config, name="mlp")
self.norm_post = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="norm_post")
self.config = config
def call(self, query: tf.Tensor, key: tf.Tensor, training: bool = False) -> tf.Tensor:
x = query
x = x + self.attn(query, encoder_hidden_states=key)[0]
x = x + self.mlp(self.norm2(x))
x = self.norm_post(x)
return x
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "attn", None) is not None:
with tf.name_scope(self.attn.name):
self.attn.build(None)
if getattr(self, "norm2", None) is not None:
with tf.name_scope(self.norm2.name):
self.norm2.build([None, None, self.config.hidden_size])
if getattr(self, "mlp", None) is not None:
with tf.name_scope(self.mlp.name):
self.mlp.build(None)
if getattr(self, "norm_post", None) is not None:
with tf.name_scope(self.norm_post.name):
self.norm_post.build([None, None, self.config.hidden_size])
class TFGroupViTAssignAttention(keras.layers.Layer):
def __init__(self, config: GroupViTVisionConfig, **kwargs):
super().__init__(**kwargs)
self.scale = config.hidden_size**-0.5
self.q_proj = keras.layers.Dense(config.hidden_size, name="q_proj")
self.k_proj = keras.layers.Dense(config.hidden_size, name="k_proj")
self.v_proj = keras.layers.Dense(config.hidden_size, name="v_proj")
self.proj = keras.layers.Dense(config.hidden_size, name="proj")
self.assign_eps = config.assign_eps
self.config = config
def get_attn(self, attn: tf.Tensor, gumbel: bool = True, hard: bool = True, training: bool = False) -> tf.Tensor:
if gumbel and training:
attn = gumbel_softmax(attn, dim=-2, hard=hard)
else:
if hard:
attn = hard_softmax(attn, dim=-2)
else:
attn = stable_softmax(attn, axis=-2)
return attn
def call(self, query: tf.Tensor, key: tf.Tensor, training: bool = False):
value = key
# [batch_size, query_length, channels]
query = self.q_proj(query)
# [batch_size, key_length, channels]
key = self.k_proj(key)
# [batch_size, key_length, channels]
value = self.v_proj(value)
# [batch_size, query_length, key_length]
raw_attn = tf.matmul(query, key, transpose_b=True) * self.scale
attn = self.get_attn(raw_attn, training=training)
soft_attn = self.get_attn(raw_attn, training=training, gumbel=False, hard=False)
attn = attn / (tf.math.reduce_sum(attn, axis=-1, keepdims=True) + self.assign_eps)
out = tf.matmul(attn, value)
out = self.proj(out)
return out, soft_attn
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "q_proj", None) is not None:
with tf.name_scope(self.q_proj.name):
self.q_proj.build([None, None, self.config.hidden_size])
if getattr(self, "k_proj", None) is not None:
with tf.name_scope(self.k_proj.name):
self.k_proj.build([None, None, self.config.hidden_size])
if getattr(self, "v_proj", None) is not None:
with tf.name_scope(self.v_proj.name):
self.v_proj.build([None, None, self.config.hidden_size])
if getattr(self, "proj", None) is not None:
with tf.name_scope(self.proj.name):
self.proj.build([None, None, self.config.hidden_size])
class TFGroupViTTokenAssign(keras.layers.Layer):
def __init__(self, config: GroupViTVisionConfig, num_group_token: int, num_output_group: int, **kwargs):
super().__init__(**kwargs)
self.num_output_group = num_output_group
# norm on group_tokens
self.norm_tokens = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="norm_tokens")
assign_mlp_ratio = (
config.assign_mlp_ratio
if isinstance(config.assign_mlp_ratio, collections.abc.Iterable)
else (config.assign_mlp_ratio, config.assign_mlp_ratio)
)
tokens_dim, channels_dim = [int(x * config.hidden_size) for x in assign_mlp_ratio]
self.mlp_inter = TFGroupViTMixerMLP(config, num_group_token, tokens_dim, num_output_group, name="mlp_inter")
self.norm_post_tokens = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="norm_post_tokens")
# norm on x
self.norm_x = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="norm_x")
self.pre_assign_attn = TFGroupViTCrossAttentionLayer(config, name="pre_assign_attn")
self.assign = TFGroupViTAssignAttention(config, name="assign")
self.norm_new_x = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="norm_new_x")
self.mlp_channels = TFGroupViTMLP(
config, config.hidden_size, channels_dim, config.hidden_size, name="mlp_channels"
)
self.config = config
def project_group_token(self, group_tokens: tf.Tensor) -> tf.Tensor:
"""
Args:
group_tokens (tf.Tensor): group tokens, [batch_size, num_group_tokens, channels]
Returns:
projected_group_tokens (tf.Tensor): [batch_size, num_output_groups, channels]
"""
# [B, num_output_groups, C] <- [B, num_group_tokens, C]
projected_group_tokens = self.mlp_inter(group_tokens)
projected_group_tokens = self.norm_post_tokens(projected_group_tokens)
return projected_group_tokens
def call(self, image_tokens: tf.Tensor, group_tokens: tf.Tensor, training: bool = False):
"""
Args:
image_tokens (`tf.Tensor`): image tokens, of shape [batch_size, input_length, channels]
group_tokens (`tf.Tensor`): group tokens, [batch_size, num_group_tokens, channels]
"""
group_tokens = self.norm_tokens(group_tokens)
image_tokens = self.norm_x(image_tokens)
# [batch_size, num_output_groups, channels]
projected_group_tokens = self.project_group_token(group_tokens)
projected_group_tokens = self.pre_assign_attn(projected_group_tokens, image_tokens)
new_image_tokens, attention = self.assign(projected_group_tokens, image_tokens)
new_image_tokens += projected_group_tokens
new_image_tokens = new_image_tokens + self.mlp_channels(self.norm_new_x(new_image_tokens))
return new_image_tokens, attention
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "norm_tokens", None) is not None:
with tf.name_scope(self.norm_tokens.name):
self.norm_tokens.build([None, None, self.config.hidden_size])
if getattr(self, "mlp_inter", None) is not None:
with tf.name_scope(self.mlp_inter.name):
self.mlp_inter.build(None)
if getattr(self, "norm_post_tokens", None) is not None:
with tf.name_scope(self.norm_post_tokens.name):
self.norm_post_tokens.build([None, None, self.config.hidden_size])
if getattr(self, "norm_x", None) is not None:
with tf.name_scope(self.norm_x.name):
self.norm_x.build([None, None, self.config.hidden_size])
if getattr(self, "pre_assign_attn", None) is not None:
with tf.name_scope(self.pre_assign_attn.name):
self.pre_assign_attn.build(None)
if getattr(self, "assign", None) is not None:
with tf.name_scope(self.assign.name):
self.assign.build(None)
if getattr(self, "norm_new_x", None) is not None:
with tf.name_scope(self.norm_new_x.name):
self.norm_new_x.build([None, None, self.config.hidden_size])
if getattr(self, "mlp_channels", None) is not None:
with tf.name_scope(self.mlp_channels.name):
self.mlp_channels.build(None)
# Adapted from transformers.models.vit.modeling_tf_vit.TFViTPatchEmbeddings with ViT->GroupViT
class TFGroupViTPatchEmbeddings(keras.layers.Layer):
"""
This class turns `pixel_values` of shape `(batch_size, num_channels, height, width)` into the initial
`hidden_states` (patch embeddings) of shape `(batch_size, seq_length, hidden_size)` to be consumed by a
Transformer.
"""
def __init__(self, config: GroupViTConfig, **kwargs):
super().__init__(**kwargs)
image_size, patch_size = config.image_size, config.patch_size
num_channels = config.num_channels
# hidden_size is a member as it will be required in the call method
self.hidden_size = config.hidden_size
image_size = image_size if isinstance(image_size, collections.abc.Iterable) else (image_size, image_size)
patch_size = patch_size if isinstance(patch_size, collections.abc.Iterable) else (patch_size, patch_size)
num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
self.image_size = image_size
self.patch_size = patch_size
self.num_patches = num_patches
self.num_channels = num_channels
self.config = config
self.projection = keras.layers.Conv2D(
filters=self.hidden_size,
kernel_size=patch_size,
strides=patch_size,
padding="valid",
data_format="channels_last",
use_bias=True,
kernel_initializer=get_initializer(self.config.initializer_range),
bias_initializer="zeros",
name="projection",
)
def call(
self, pixel_values: tf.Tensor, interpolate_pos_encoding: bool = False, training: bool = False
) -> tf.Tensor:
batch_size, num_channels, height, width = shape_list(pixel_values)
if tf.executing_eagerly() and num_channels != self.num_channels:
raise ValueError(
"Make sure that the channel dimension of the pixel values match with the one set in the configuration."
)
if (
not interpolate_pos_encoding
and tf.executing_eagerly()
and (height != self.image_size[0] or width != self.image_size[1])
):
raise ValueError(
f"Input image size ({height}*{width}) doesn't match model ({self.image_size[0]}*{self.image_size[1]})."
)
# When running on CPU, `keras.layers.Conv2D` doesn't support `NCHW` format.
# So change the input format from `NCHW` to `NHWC`.
# shape = (batch_size, in_height, in_width, in_channels=num_channels)
pixel_values = tf.transpose(pixel_values, perm=(0, 2, 3, 1))
projection = self.projection(pixel_values)
# Change the 2D spatial dimensions to a single temporal dimension.
# shape = (batch_size, num_patches, out_channels=embed_dim)
num_patches = (width // self.patch_size[1]) * (height // self.patch_size[0])
# In the TFGroupViTVisionEmbeddings the embeddings from this layer will be layer normalized
# LayerNormalization layer needs to have static last dimension (otherwise the test_keras_save_load fails with symbolic tensors)
# This is why we have used the hidden_size in the reshape method
embeddings = tf.reshape(tensor=projection, shape=(batch_size, num_patches, self.hidden_size))
return embeddings
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "projection", None) is not None:
with tf.name_scope(self.projection.name):
self.projection.build([None, None, None, self.num_channels])
# Adapted from transformers.vit.modeling_tf_vit.TFViTEmbeddings
class TFGroupViTVisionEmbeddings(keras.layers.Layer):
"""
Construct the position and patch embeddings.
"""
def __init__(self, config: GroupViTVisionConfig, **kwargs):
super().__init__(**kwargs)
self.patch_embeddings = TFGroupViTPatchEmbeddings(config, name="patch_embeddings")
self.dropout = keras.layers.Dropout(rate=config.dropout, name="dropout")
self.layernorm = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layernorm")
self.config = config
def build(self, input_shape=None):
num_patches = self.patch_embeddings.num_patches
self.position_embeddings = self.add_weight(
shape=(1, num_patches, self.config.hidden_size),
initializer="zeros",
trainable=True,
name="position_embeddings",
)
if self.built:
return
self.built = True
if getattr(self, "patch_embeddings", None) is not None:
with tf.name_scope(self.patch_embeddings.name):
self.patch_embeddings.build(None)
if getattr(self, "dropout", None) is not None:
with tf.name_scope(self.dropout.name):
self.dropout.build(None)
if getattr(self, "layernorm", None) is not None:
with tf.name_scope(self.layernorm.name):
self.layernorm.build([None, None, self.config.hidden_size])
def interpolate_pos_encoding(self, embeddings, height, width) -> tf.Tensor:
"""
This method allows to interpolate the pre-trained position encodings, to be able to use the model on higher
resolution images.
Source:
https://github.com/facebookresearch/dino/blob/de9ee3df6cf39fac952ab558447af1fa1365362a/vision_transformer.py#L174
"""
batch_size, num_patches, dim = shape_list(embeddings)
num_positions = shape_list(self.position_embeddings)[1]
if num_patches == num_positions and height == width:
return self.position_embeddings
patch_pos_embed = self.position_embeddings
h0 = height // self.config.patch_size
w0 = width // self.config.patch_size
patch_pos_embed = tf.image.resize(
images=tf.reshape(
patch_pos_embed, shape=(1, int(math.sqrt(num_positions)), int(math.sqrt(num_positions)), dim)
),
size=(h0, w0),
method="bicubic",
)
patch_pos_embed = tf.reshape(tensor=patch_pos_embed, shape=(1, -1, dim))
return patch_pos_embed
def call(
self, pixel_values: tf.Tensor, interpolate_pos_encoding: bool = False, training: bool = False
) -> tf.Tensor:
_, _, height, width = shape_list(pixel_values)
embeddings = self.patch_embeddings(pixel_values, interpolate_pos_encoding=interpolate_pos_encoding)
embeddings = self.layernorm(embeddings)
# add positional encoding to each token
if interpolate_pos_encoding:
embeddings = embeddings + self.interpolate_pos_encoding(embeddings, height, width)
else:
embeddings = embeddings + self.position_embeddings
embeddings = self.dropout(embeddings)
return embeddings
# Copied from transformers.models.clip.modeling_tf_clip.TFCLIPTextEmbeddings with CLIP->GroupViT
class TFGroupViTTextEmbeddings(keras.layers.Layer):
def __init__(self, config: GroupViTTextConfig, **kwargs):
super().__init__(**kwargs)
self.embed_dim = config.hidden_size
self.config = config
def build(self, input_shape: tf.TensorShape = None):
with tf.name_scope("token_embedding"):
self.weight = self.add_weight(
shape=(self.config.vocab_size, self.embed_dim),
initializer=get_initializer(self.config.initializer_factor * self.config.initializer_range),
trainable=True,
name="weight",
)
with tf.name_scope("position_embedding"):
self.position_embedding = self.add_weight(
shape=(self.config.max_position_embeddings, self.embed_dim),
initializer=get_initializer(self.config.initializer_factor * self.config.initializer_range),
trainable=True,
name="embeddings",
)
super().build(input_shape)
def call(
self,
input_ids: Optional[tf.Tensor] = None,
position_ids: Optional[tf.Tensor] = None,
inputs_embeds: Optional[tf.Tensor] = None,
) -> tf.Tensor:
"""
Applies embedding based on inputs tensor.
Returns:
final_embeddings (`tf.Tensor`): output embedding tensor.
"""
if input_ids is None and inputs_embeds is None:
raise ValueError("You have to specify either input_ids or inputs_embeds")
if inputs_embeds is None:
check_embeddings_within_bounds(input_ids, self.config.vocab_size)
inputs_embeds = tf.gather(params=self.weight, indices=input_ids)
input_shape = shape_list(inputs_embeds)[:-1]
if position_ids is None:
position_ids = tf.expand_dims(tf.range(start=0, limit=input_shape[-1]), axis=0)
position_embeds = tf.gather(params=self.position_embedding, indices=position_ids)
position_embeds = tf.tile(input=position_embeds, multiples=(input_shape[0], 1, 1))
final_embeddings = inputs_embeds + position_embeds
return final_embeddings
class TFGroupViTStage(keras.layers.Layer):
"""This corresponds to the `GroupingLayer` class in the GroupViT implementation."""
def __init__(
self,
config: GroupViTVisionConfig,
depth: int,
num_prev_group_token: int,
num_group_token: int,
num_output_group: int,
**kwargs,
):
super().__init__(**kwargs)
self.config = config
self.depth = depth
self.num_group_token = num_group_token
self.layers = [TFGroupViTEncoderLayer(config, name=f"layers_._{i}") for i in range(depth)]
if num_group_token > 0:
self.downsample = TFGroupViTTokenAssign(
config=config,
num_group_token=num_group_token,
num_output_group=num_output_group,
name="downsample",
)
else:
self.downsample = None
if num_prev_group_token > 0 and num_group_token > 0:
self.group_projector = [
keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="group_projector.0"),
TFGroupViTMixerMLP(
config, num_prev_group_token, config.hidden_size // 2, num_group_token, name="group_projector.1"
),
]
else:
self.group_projector = None
def build(self, input_shape=None):
if self.num_group_token > 0:
self.group_token = self.add_weight(
shape=(1, self.num_group_token, self.config.hidden_size),
initializer="zeros",
trainable=True,
name="group_token",
)
else:
self.group_token = None
if self.built:
return
self.built = True
if getattr(self, "downsample", None) is not None:
with tf.name_scope(self.downsample.name):
self.downsample.build(None)
if getattr(self, "layers", None) is not None:
for layer in self.layers:
with tf.name_scope(layer.name):
layer.build(None)
if getattr(self, "group_projector", None) is not None:
with tf.name_scope(self.group_projector[0].name):
self.group_projector[0].build([None, None, self.config.hidden_size])
with tf.name_scope(self.group_projector[1].name):
self.group_projector[1].build(None)
@property
def with_group_token(self):
return self.group_token is not None
def split_x(self, x: tf.Tensor) -> tf.Tensor:
if self.with_group_token:
return x[:, : -self.num_group_token], x[:, -self.num_group_token :]
else:
return x, None
def concat_x(self, x: tf.Tensor, group_token: tf.Tensor | None = None) -> tf.Tensor:
if group_token is None:
return x
return tf.concat([x, group_token], axis=1)
def call(
self,
hidden_states: tf.Tensor,
prev_group_token: tf.Tensor | None = None,
output_attentions: bool = False,
training: bool = False,
) -> Tuple[tf.Tensor]:
"""
Args:
hidden_states (`tf.Tensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
attention_mask (`tf.Tensor`): attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
`(config.encoder_attention_heads,)`.
output_attentions (`bool`, *optional*):
Whether or not to return the grouping tensors of Grouping block.
"""
if self.with_group_token:
group_token = tf.tile(self.group_token, multiples=(shape_list(hidden_states)[0], 1, 1))
if self.group_projector is not None:
for layer in self.group_projector:
prev_group_token = layer(prev_group_token)
group_token = group_token + prev_group_token
else:
group_token = None
x = hidden_states
cat_x = self.concat_x(x, group_token)
for layer in self.layers:
layer_out = layer(
cat_x,
attention_mask=None,
causal_attention_mask=None,
output_attentions=None,
)
cat_x = layer_out[0]
x, group_token = self.split_x(cat_x)
attention = None
if self.downsample is not None:
x, attention = self.downsample(x, group_token)
outputs = (x, group_token)
if output_attentions:
outputs = outputs + (attention,)
return outputs
class TFGroupViTMLP(keras.layers.Layer):
def __init__(
self,
config: GroupViTVisionConfig,
hidden_size: Optional[int] = None,
intermediate_size: Optional[int] = None,
output_size: Optional[int] = None,
**kwargs,
):
super().__init__(**kwargs)
self.config = config
self.activation_fn = get_tf_activation(config.hidden_act)
hidden_size = hidden_size if hidden_size is not None else config.hidden_size
intermediate_size = intermediate_size if intermediate_size is not None else config.intermediate_size
output_size = output_size if output_size is not None else hidden_size
self.fc1 = keras.layers.Dense(intermediate_size, name="fc1")
self.fc2 = keras.layers.Dense(output_size, name="fc2")
self.intermediate_size = intermediate_size
self.hidden_size = hidden_size
def call(self, hidden_states: tf.Tensor, training: bool = False) -> tf.Tensor:
hidden_states = self.fc1(hidden_states)
hidden_states = self.activation_fn(hidden_states)
hidden_states = self.fc2(hidden_states)
return hidden_states
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "fc1", None) is not None:
with tf.name_scope(self.fc1.name):
self.fc1.build([None, None, self.hidden_size])
if getattr(self, "fc2", None) is not None:
with tf.name_scope(self.fc2.name):
self.fc2.build([None, None, self.intermediate_size])
class TFGroupViTMixerMLP(TFGroupViTMLP):
def call(self, x, training: bool = False):
x = super().call(hidden_states=tf.transpose(x, perm=(0, 2, 1)))
return tf.transpose(x, perm=(0, 2, 1))
# Adapted from transformers.models.clip.modeling_tf_clip.TFCLIPAttention
class TFGroupViTAttention(keras.layers.Layer):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(self, config: GroupViTConfig, **kwargs):
super().__init__(**kwargs)
self.embed_dim = config.hidden_size
self.num_attention_heads = config.num_attention_heads
self.attention_head_size = self.embed_dim // self.num_attention_heads
if self.attention_head_size * self.num_attention_heads != self.embed_dim:
raise ValueError(
f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:"
f" {self.num_attention_heads})."
)
factor = config.initializer_factor
in_proj_std = (self.embed_dim**-0.5) * ((2 * config.num_hidden_layers) ** -0.5) * factor
out_proj_std = (self.embed_dim**-0.5) * factor
self.sqrt_att_head_size = math.sqrt(self.attention_head_size)
self.q_proj = keras.layers.Dense(
units=self.embed_dim, kernel_initializer=get_initializer(in_proj_std), name="q_proj"
)
self.k_proj = keras.layers.Dense(
units=self.embed_dim, kernel_initializer=get_initializer(in_proj_std), name="k_proj"
)
self.v_proj = keras.layers.Dense(
units=self.embed_dim, kernel_initializer=get_initializer(in_proj_std), name="v_proj"
)
self.dropout = keras.layers.Dropout(rate=config.attention_dropout)
self.out_proj = keras.layers.Dense(
units=self.embed_dim, kernel_initializer=get_initializer(out_proj_std), name="out_proj"
)
# Copied from transformers.models.bert.modeling_tf_bert.TFBertSelfAttention.transpose_for_scores
def transpose_for_scores(self, tensor: tf.Tensor, batch_size: int) -> tf.Tensor:
# Reshape from [batch_size, seq_length, all_head_size] to [batch_size, seq_length, num_attention_heads, attention_head_size]
tensor = tf.reshape(tensor=tensor, shape=(batch_size, -1, self.num_attention_heads, self.attention_head_size))
# Transpose the tensor from [batch_size, seq_length, num_attention_heads, attention_head_size] to [batch_size, num_attention_heads, seq_length, attention_head_size]
return tf.transpose(tensor, perm=[0, 2, 1, 3])
def call(
self,
hidden_states: tf.Tensor,
attention_mask: Optional[tf.Tensor] = None,
causal_attention_mask: Optional[tf.Tensor] = None,
output_attentions: Optional[bool] = None,
encoder_hidden_states: Optional[tf.Tensor] = None,
training: bool = False,
) -> Tuple[tf.Tensor]:
"""Input shape: Batch x Time x Channel"""
batch_size = shape_list(hidden_states)[0]
is_cross_attention = encoder_hidden_states is not None
mixed_query_layer = self.q_proj(inputs=hidden_states)
if is_cross_attention:
mixed_key_layer = self.k_proj(inputs=encoder_hidden_states)
mixed_value_layer = self.v_proj(inputs=encoder_hidden_states)
else:
mixed_key_layer = self.k_proj(inputs=hidden_states)
mixed_value_layer = self.v_proj(inputs=hidden_states)
query_layer = self.transpose_for_scores(mixed_query_layer, batch_size)
key_layer = self.transpose_for_scores(mixed_key_layer, batch_size)
value_layer = self.transpose_for_scores(mixed_value_layer, batch_size)
# Take the dot product between "query" and "key" to get the raw attention scores.
# (batch size, num_heads, seq_len_q, seq_len_k)
attention_scores = tf.matmul(query_layer, key_layer, transpose_b=True)
dk = tf.cast(self.sqrt_att_head_size, dtype=attention_scores.dtype)
attention_scores = tf.divide(attention_scores, dk)
# apply the causal_attention_mask first
if causal_attention_mask is not None:
# Apply the causal attention mask (precomputed for all layers in TFCLIPModel call() function)
attention_scores = tf.add(attention_scores, causal_attention_mask)
if attention_mask is not None:
# Apply the attention mask (precomputed for all layers in TFCLIPModel call() function)
attention_scores = tf.add(attention_scores, attention_mask)
# Normalize the attention scores to probabilities.
_attention_probs = stable_softmax(logits=attention_scores, axis=-1)
# This is actually dropping out entire tokens to attend to, which might
# seem a bit unusual, but is taken from the original Transformer paper.
attention_probs = self.dropout(inputs=_attention_probs)
attention_output = tf.matmul(attention_probs, value_layer)
attention_output = tf.transpose(attention_output, perm=[0, 2, 1, 3])
# (batch_size, seq_len_q, embed_dim)
attention_output = tf.reshape(tensor=attention_output, shape=(batch_size, -1, self.embed_dim))
attention_output = self.out_proj(attention_output)
# In TFBert, attention weights are returned after dropout.
# However, in CLIP, they are returned before dropout.
outputs = (attention_output, _attention_probs) if output_attentions else (attention_output,)
return outputs
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "q_proj", None) is not None:
with tf.name_scope(self.q_proj.name):
self.q_proj.build([None, None, self.embed_dim])
if getattr(self, "k_proj", None) is not None:
with tf.name_scope(self.k_proj.name):
self.k_proj.build([None, None, self.embed_dim])
if getattr(self, "v_proj", None) is not None:
with tf.name_scope(self.v_proj.name):
self.v_proj.build([None, None, self.embed_dim])
if getattr(self, "out_proj", None) is not None:
with tf.name_scope(self.out_proj.name):
self.out_proj.build([None, None, self.embed_dim])
# Copied from transformers.models.clip.modeling_tf_clip.TFCLIPEncoderLayer with CLIP->GroupViT
class TFGroupViTEncoderLayer(keras.layers.Layer):
def __init__(self, config: GroupViTConfig, **kwargs):
super().__init__(**kwargs)
self.embed_dim = config.hidden_size
self.self_attn = TFGroupViTAttention(config, name="self_attn")
self.layer_norm1 = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layer_norm1")
self.mlp = TFGroupViTMLP(config, name="mlp")
self.layer_norm2 = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layer_norm2")
def call(
self,
hidden_states: tf.Tensor,
attention_mask: tf.Tensor,
causal_attention_mask: tf.Tensor,
output_attentions: bool,
training: bool = False,
) -> Tuple[tf.Tensor]:
"""
Args:
hidden_states (`tf.Tensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
attention_mask (`tf.Tensor`): attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
causal_attention_mask (`tf.Tensor`): causal attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
output_attentions (`bool`):
Whether or not to return the attentions tensors of all attention layers. See `outputs` under returned
tensors for more detail.
"""
residual = hidden_states
hidden_states = self.layer_norm1(inputs=hidden_states)
attention_outputs = self.self_attn(
hidden_states=hidden_states,
attention_mask=attention_mask,
causal_attention_mask=causal_attention_mask,
output_attentions=output_attentions,
training=training,
)
hidden_states = attention_outputs[0]
hidden_states = residual + hidden_states
residual = hidden_states
hidden_states = self.layer_norm2(inputs=hidden_states)
hidden_states = self.mlp(hidden_states=hidden_states)
hidden_states = residual + hidden_states
outputs = (hidden_states,) + attention_outputs[1:] # add attentions if we output them
return outputs
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "self_attn", None) is not None:
with tf.name_scope(self.self_attn.name):
self.self_attn.build(None)
if getattr(self, "layer_norm1", None) is not None:
with tf.name_scope(self.layer_norm1.name):
self.layer_norm1.build([None, None, self.embed_dim])
if getattr(self, "mlp", None) is not None:
with tf.name_scope(self.mlp.name):
self.mlp.build(None)
if getattr(self, "layer_norm2", None) is not None:
with tf.name_scope(self.layer_norm2.name):
self.layer_norm2.build([None, None, self.embed_dim])
# Adapted from transformers.models.clip.modeling_tf_clip.TFGroupViTTextEncoder
class TFGroupViTTextEncoder(keras.layers.Layer):
def __init__(self, config: GroupViTTextConfig, **kwargs):
super().__init__(**kwargs)
self.layers = [TFGroupViTEncoderLayer(config, name=f"layers_._{i}") for i in range(config.num_hidden_layers)]
def call(
self,
hidden_states,
attention_mask: tf.Tensor,
causal_attention_mask: tf.Tensor,
output_attentions: bool,
output_hidden_states: bool,
return_dict: bool,
training: bool = False,
) -> Union[Tuple, TFBaseModelOutput]:
encoder_states = () if output_hidden_states else None
all_attentions = () if output_attentions else None
for idx, encoder_layer in enumerate(self.layers):
if output_hidden_states:
encoder_states = encoder_states + (hidden_states,)
layer_outputs = encoder_layer(
hidden_states,
attention_mask,
causal_attention_mask,
output_attentions=output_attentions,
)
hidden_states = layer_outputs[0]
if output_attentions:
all_attentions = all_attentions + (layer_outputs[1],)
if output_hidden_states:
encoder_states = encoder_states + (hidden_states,)
if not return_dict:
return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None)
return TFBaseModelOutput(
last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions
)
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "layers", None) is not None:
for layer in self.layers:
with tf.name_scope(layer.name):
layer.build(None)
class TFGroupViTVisionEncoder(keras.layers.Layer):
def __init__(self, config: GroupViTVisionConfig, **kwargs) -> None:
super().__init__(**kwargs)
self.stages = [
TFGroupViTStage(
config=config,
depth=config.depths[i],
num_group_token=config.num_group_tokens[i],
num_output_group=config.num_output_groups[i],
num_prev_group_token=config.num_output_groups[i - 1] if i > 0 else 0,
name=f"stages_._{i}",
)
for i in range(len(config.depths))
]
def call(
self,
hidden_states: tf.Tensor,
output_hidden_states: bool,
output_attentions: bool,
return_dict: bool,
training: bool = False,
) -> Union[tuple, TFBaseModelOutput]:
all_hidden_states = () if output_hidden_states else None
all_groupings = () if output_attentions else None
group_tokens = None
for stage in self.stages:
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
layer_outputs = stage(hidden_states, group_tokens, output_attentions)
hidden_states = layer_outputs[0]
group_tokens = layer_outputs[1]
if output_attentions and layer_outputs[2] is not None:
all_groupings = all_groupings + (layer_outputs[2],)
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if not return_dict:
return tuple(v for v in [hidden_states, all_hidden_states, all_groupings] if v is not None)
return TFBaseModelOutput(
last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_groupings
)
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "stages", None) is not None:
for layer in self.stages:
with tf.name_scope(layer.name):
layer.build(None)
# Copied from transformers.models.clip.modeling_tf_clip.TFCLIPTextTransformer with CLIPText->GroupViTText, CLIPEncoder->GroupViTTextEncoder
class TFGroupViTTextTransformer(keras.layers.Layer):
def __init__(self, config: GroupViTTextConfig, **kwargs):
super().__init__(**kwargs)
self.embeddings = TFGroupViTTextEmbeddings(config, name="embeddings")
self.encoder = TFGroupViTTextEncoder(config, name="encoder")
self.final_layer_norm = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="final_layer_norm")
# For `pooled_output` computation
self.eos_token_id = config.eos_token_id
self.embed_dim = config.hidden_size
def call(
self,
input_ids: TFModelInputType,
attention_mask: tf.Tensor,
position_ids: tf.Tensor,
output_attentions: bool,
output_hidden_states: bool,
return_dict: bool,
training: bool = False,
) -> Union[TFBaseModelOutputWithPooling, Tuple[tf.Tensor]]:
input_shape = shape_list(input_ids)
embedding_output = self.embeddings(input_ids=input_ids, position_ids=position_ids)
batch_size, seq_length = input_shape
# CLIP's text model uses causal mask, prepare it here.
# https://github.com/openai/CLIP/blob/cfcffb90e69f37bf2ff1e988237a0fbe41f33c04/clip/model.py#L324
causal_attention_mask = self._build_causal_attention_mask(batch_size, seq_length, dtype=embedding_output.dtype)
# check attention mask and invert
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
attention_mask = _expand_mask(attention_mask)
encoder_outputs = self.encoder(
hidden_states=embedding_output,
attention_mask=attention_mask,
causal_attention_mask=causal_attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
sequence_output = encoder_outputs[0]
sequence_output = self.final_layer_norm(inputs=sequence_output)
if self.eos_token_id == 2:
# The `eos_token_id` was incorrect before PR #24773: Let's keep what have been done here.
# A CLIP model with such `eos_token_id` in the config can't work correctly with extra new tokens added
# ------------------------------------------------------------
# text_embeds.shape = [batch_size, n_ctx, transformer.width]
# take features from the eot embedding (eot_token is the highest number in each sequence)
pooled_output = tf.gather_nd(
params=sequence_output,
indices=tf.stack(
values=(tf.range(input_shape[0], dtype=tf.int64), tf.math.argmax(input_ids, axis=-1)), axis=1
),
)
else:
# The config gets updated `eos_token_id` from PR #24773 (so the use of exta new tokens is possible)
pooled_output = tf.gather_nd(
params=sequence_output,
indices=tf.stack(
values=(
tf.range(input_shape[0], dtype=tf.int64),
tf.math.argmax(tf.cast(input_ids == self.eos_token_id, dtype=tf.int8), axis=-1),
),
axis=1,
),
)
if not return_dict:
return (sequence_output, pooled_output) + encoder_outputs[1:]
return TFBaseModelOutputWithPooling(
last_hidden_state=sequence_output,
pooler_output=pooled_output,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
)
def _build_causal_attention_mask(self, batch_size, seq_length, dtype=tf.float32):
# It is possible with an unspecified sequence length for seq_length to be
# a runtime value, which is unsupported by tf.constant. Per the TensorFlow
# docs, tf.fill can handle runtime dynamic shapes:
# https://www.tensorflow.org/api_docs/python/tf/fill
diag = tf.cast(tf.fill((seq_length,), 0.0), dtype)
# set an additive 2D attention mask with all places being masked
to_mask = tf.cast(tf.fill((seq_length, seq_length), -10000.0), dtype)
# set diagonal & lower triangular parts to 0 (i.e. the places not to be masked)
# TIP: think the 2D matrix as the space of (query_seq, key_seq)
to_mask = tf.linalg.band_part(to_mask, 0, -1)
# to_mask = tf.linalg.band_part(to_mask, -1, 0)
to_mask = tf.linalg.set_diag(to_mask, diagonal=diag)
return tf.broadcast_to(input=to_mask, shape=(batch_size, 1, seq_length, seq_length))
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "embeddings", None) is not None:
with tf.name_scope(self.embeddings.name):
self.embeddings.build(None)
if getattr(self, "encoder", None) is not None:
with tf.name_scope(self.encoder.name):
self.encoder.build(None)
if getattr(self, "final_layer_norm", None) is not None:
with tf.name_scope(self.final_layer_norm.name):
self.final_layer_norm.build([None, None, self.embed_dim])
# Adapted from transformers.models.clip.modeling_tf_clip.TFCLIPVisionTransformer
class TFGroupViTVisionTransformer(keras.layers.Layer):
def __init__(self, config: GroupViTVisionConfig, **kwargs):
super().__init__(**kwargs)
self.embeddings = TFGroupViTVisionEmbeddings(config, name="embeddings")
self.encoder = TFGroupViTVisionEncoder(config, name="encoder")
self.layernorm = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layernorm")
self.embed_dim = config.hidden_size
def call(
self,
pixel_values: TFModelInputType,
output_attentions: bool,
output_hidden_states: bool,
return_dict: bool,
training: bool = False,
) -> Union[Tuple, TFBaseModelOutputWithPooling]:
embedding_output = self.embeddings(pixel_values)
encoder_outputs = self.encoder(
hidden_states=embedding_output,
output_hidden_states=output_hidden_states,
output_attentions=output_attentions,
return_dict=return_dict,
)
last_hidden_state = encoder_outputs[0]
# normalize the last hidden state
last_hidden_state = self.layernorm(last_hidden_state)
pooled_output = tf.math.reduce_mean(last_hidden_state, axis=1)
if not return_dict:
return (last_hidden_state, pooled_output) + encoder_outputs[1:]
return TFBaseModelOutputWithPooling(
last_hidden_state=last_hidden_state,
pooler_output=pooled_output,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
)
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "embeddings", None) is not None:
with tf.name_scope(self.embeddings.name):
self.embeddings.build(None)
if getattr(self, "encoder", None) is not None:
with tf.name_scope(self.encoder.name):
self.encoder.build(None)
if getattr(self, "layernorm", None) is not None:
with tf.name_scope(self.layernorm.name):
self.layernorm.build([None, None, self.embed_dim])
@keras_serializable
# Copied from transformers.models.clip.modeling_tf_clip.TFCLIPTextMainLayer with CLIP->GroupViT
class TFGroupViTTextMainLayer(keras.layers.Layer):
config_class = GroupViTTextConfig
def __init__(self, config: GroupViTTextConfig, **kwargs):
super().__init__(**kwargs)
self.config = config
self.text_model = TFGroupViTTextTransformer(config, name="text_model")
def get_input_embeddings(self) -> keras.layers.Layer:
return self.text_model.embeddings
def set_input_embeddings(self, value: tf.Variable):
self.text_model.embeddings.weight = value
self.text_model.embeddings.vocab_size = shape_list(value)[0]
@unpack_inputs
def call(
self,
input_ids: TFModelInputType | None = None,
attention_mask: np.ndarray | tf.Tensor | None = None,
position_ids: np.ndarray | tf.Tensor | None = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
training: bool = False,
) -> Union[TFBaseModelOutputWithPooling, Tuple[tf.Tensor]]:
if input_ids is None:
raise ValueError("You have to specify input_ids")
input_shape = shape_list(input_ids)
if attention_mask is None:
attention_mask = tf.fill(dims=input_shape, value=1)
text_model_outputs = self.text_model(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
return text_model_outputs
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "text_model", None) is not None:
with tf.name_scope(self.text_model.name):
self.text_model.build(None)
@keras_serializable
# Copied from transformers.models.clip.modeling_tf_clip.TFCLIPVisionMainLayer with CLIP->GroupViT
class TFGroupViTVisionMainLayer(keras.layers.Layer):
config_class = GroupViTVisionConfig
def __init__(self, config: GroupViTVisionConfig, **kwargs):
super().__init__(**kwargs)
self.config = config
self.vision_model = TFGroupViTVisionTransformer(config, name="vision_model")
def get_input_embeddings(self) -> keras.layers.Layer:
return self.vision_model.embeddings
@unpack_inputs
def call(
self,
pixel_values: TFModelInputType | None = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
training: bool = False,
) -> Union[TFBaseModelOutputWithPooling, Tuple[tf.Tensor]]:
if pixel_values is None:
raise ValueError("You have to specify pixel_values")
vision_model_outputs = self.vision_model(
pixel_values=pixel_values,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
return vision_model_outputs
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "vision_model", None) is not None:
with tf.name_scope(self.vision_model.name):
self.vision_model.build(None)
@keras_serializable
# Adapted from transformers.models.clip.modeling_tf_clip.TFCLIPMainLayer
class TFGroupViTMainLayer(keras.layers.Layer):
config_class = GroupViTConfig
def __init__(self, config: GroupViTConfig, **kwargs):
super().__init__(**kwargs)
if not isinstance(config.text_config, GroupViTTextConfig):
raise TypeError(
"config.text_config is expected to be of type GroupViTTextConfig but is of type"
f" {type(config.text_config)}."
)
if not isinstance(config.vision_config, GroupViTVisionConfig):
raise TypeError(
"config.vision_config is expected to be of type GroupViTVisionConfig but is of type"
f" {type(config.vision_config)}."
)
self.config = config
text_config = config.text_config
vision_config = config.vision_config
self.projection_dim = config.projection_dim
self.projection_intermediate_dim = config.projection_intermediate_dim
self.text_embed_dim = text_config.hidden_size
self.vision_embed_dim = vision_config.hidden_size
self.text_model = TFGroupViTTextTransformer(text_config, name="text_model")
self.vision_model = TFGroupViTVisionTransformer(vision_config, name="vision_model")
self.visual_projection = [
keras.layers.Dense(self.projection_intermediate_dim, name="visual_projection.0"),
keras.layers.BatchNormalization(name="visual_projection.1", momentum=0.9, epsilon=1e-5),
keras.layers.ReLU(name="visual_projection.2"),
keras.layers.Dense(self.projection_dim, name="visual_projection.3"),
]
self.text_projection = [
keras.layers.Dense(self.projection_intermediate_dim, name="text_projection.0"),
keras.layers.BatchNormalization(name="text_projection.1", momentum=0.9, epsilon=1e-5),
keras.layers.ReLU(name="text_projection.2"),
keras.layers.Dense(self.projection_dim, name="text_projection.3"),
]
def build(self, input_shape=None):
self.logit_scale = self.add_weight(
shape=(1,),
initializer=keras.initializers.Constant(self.config.logit_scale_init_value),
trainable=True,
name="logit_scale",
)
if self.built:
return
self.built = True
if getattr(self, "text_model", None) is not None:
with tf.name_scope(self.text_model.name):
self.text_model.build(None)
if getattr(self, "vision_model", None) is not None:
with tf.name_scope(self.vision_model.name):
self.vision_model.build(None)
if getattr(self, "visual_projection", None) is not None:
with tf.name_scope(self.visual_projection[0].name):
self.visual_projection[0].build([None, None, None, self.vision_embed_dim])
with tf.name_scope(self.visual_projection[1].name):
self.visual_projection[1].build((None, self.projection_intermediate_dim))
with tf.name_scope(self.visual_projection[3].name):
self.visual_projection[3].build([None, None, None, self.projection_intermediate_dim])
if getattr(self, "text_projection", None) is not None:
with tf.name_scope(self.text_projection[0].name):
self.text_projection[0].build([None, None, None, self.text_embed_dim])
with tf.name_scope(self.text_projection[1].name):
self.text_projection[1].build((None, self.projection_intermediate_dim))
with tf.name_scope(self.text_projection[3].name):
self.text_projection[3].build([None, None, None, self.projection_intermediate_dim])
@unpack_inputs
def get_text_features(
self,
input_ids: TFModelInputType | None = None,
attention_mask: np.ndarray | tf.Tensor | None = None,
position_ids: np.ndarray | tf.Tensor | None = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
training: bool = False,
) -> tf.Tensor:
if input_ids is None:
raise ValueError("You have to specify either input_ids")
input_shape = shape_list(input_ids)
if attention_mask is None:
attention_mask = tf.fill(dims=input_shape, value=1)
text_outputs = self.text_model(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
pooled_output = text_outputs[1]
for layer in self.text_projection:
pooled_output = layer(pooled_output)
text_features = pooled_output
return text_features
@unpack_inputs
def get_image_features(
self,
pixel_values: TFModelInputType | None = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
training: bool = False,
) -> tf.Tensor:
if pixel_values is None:
raise ValueError("You have to specify pixel_values")
vision_outputs = self.vision_model(
pixel_values=pixel_values,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
pooled_output = vision_outputs[1]
for layer in self.visual_projection:
pooled_output = layer(pooled_output)
image_features = pooled_output
return image_features
@unpack_inputs
def call(
self,
input_ids: TFModelInputType | None = None,
pixel_values: TFModelInputType | None = None,
attention_mask: np.ndarray | tf.Tensor | None = None,
position_ids: np.ndarray | tf.Tensor | None = None,
return_loss: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
output_segmentation: Optional[bool] = None,
return_dict: Optional[bool] = None,
training: bool = False,
) -> Union[TFGroupViTModelOutput, Tuple[tf.Tensor]]:
if input_ids is None:
raise ValueError("You have to specify either input_ids")
if pixel_values is None:
raise ValueError("You have to specify pixel_values")
input_shape = shape_list(input_ids)
if attention_mask is None:
attention_mask = tf.fill(dims=input_shape, value=1)
if output_segmentation:
output_attentions = True
vision_outputs = self.vision_model(
pixel_values=pixel_values,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
text_outputs = self.text_model(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
image_embeds = vision_outputs[1]
for layer in self.visual_projection:
image_embeds = layer(image_embeds)
text_embeds = text_outputs[1]
for layer in self.text_projection:
text_embeds = layer(text_embeds)
# normalized features
image_embeds = image_embeds / tf.norm(image_embeds, axis=-1, keepdims=True)
text_embeds = text_embeds / tf.norm(text_embeds, axis=-1, keepdims=True)
# cosine similarity as logits
logit_scale = tf.math.exp(self.logit_scale)
logits_per_text = tf.matmul(text_embeds, image_embeds, transpose_b=True) * logit_scale
logits_per_image = tf.transpose(logits_per_text)
seg_logits = None
if output_segmentation:
# grouped features
# [batch_size_image, num_group, hidden_size]
image_group_embeds = vision_outputs[0]
# [batch_size_image*num_group, hidden_size]
image_group_embeds = tf.reshape(image_group_embeds, shape=(-1, shape_list(image_group_embeds)[-1]))
for layer in self.visual_projection:
image_group_embeds = layer(image_group_embeds)
if output_hidden_states:
attentions = vision_outputs[3]
else:
attentions = vision_outputs[2]
# [batch_size_image, num_group, height, width]
grouping = get_grouping_from_attentions(attentions, pixel_values.shape[2:])
# normalized features
image_group_embeds = image_group_embeds / tf.norm(
tensor=image_group_embeds, ord="euclidean", axis=-1, keepdims=True
)
# [batch_size_image x num_group, batch_size_text]
logits_per_image_group = tf.matmul(image_group_embeds, text_embeds, transpose_b=True) * logit_scale
# [batch_size_image, batch_size_text, num_group]
logits_per_image_group = tf.reshape(
logits_per_image_group, shape=(image_embeds.shape[0], -1, text_embeds.shape[0])
)
logits_per_image_group = tf.transpose(logits_per_image_group, perm=(0, 2, 1))
# [batch_size_image, batch_size_text, height x width]
flatten_grouping = tf.reshape(grouping, shape=(shape_list(grouping)[0], shape_list(grouping)[1], -1))
# [batch_size_image, batch_size_text, height, width]
seg_logits = tf.matmul(logits_per_image_group, flatten_grouping) * logit_scale
seg_logits = tf.reshape(
seg_logits, shape=(seg_logits.shape[0], seg_logits.shape[1], grouping.shape[2], grouping.shape[3])
)
loss = None
if return_loss:
loss = groupvit_loss(logits_per_text)[None, ...]
if not return_dict:
if seg_logits is not None:
output = (
logits_per_image,
logits_per_text,
seg_logits,
text_embeds,
image_embeds,
text_outputs,
vision_outputs,
)
else:
output = (logits_per_image, logits_per_text, text_embeds, image_embeds, text_outputs, vision_outputs)
return ((loss,) + output) if loss is not None else output
return TFGroupViTModelOutput(
loss=loss,
logits_per_image=logits_per_image,
logits_per_text=logits_per_text,
segmentation_logits=seg_logits,
text_embeds=text_embeds,
image_embeds=image_embeds,
text_model_output=text_outputs,
vision_model_output=vision_outputs,
)
class TFGroupViTPreTrainedModel(TFPreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = GroupViTConfig
base_model_prefix = "groupvit"
GROUPVIT_START_DOCSTRING = r"""
This model inherits from [`TFPreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a [keras.Model](https://www.tensorflow.org/api_docs/python/tf/keras/Model) subclass. Use it
as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and
behavior.
<Tip>
TF 2.0 models accepts two formats as inputs:
- having all inputs as keyword arguments (like PyTorch models), or
- having all inputs as a list, tuple or dict in the first positional arguments.
This second option is useful when using [`keras.Model.fit`] method which currently requires having all the
tensors in the first argument of the model call function: `model(inputs)`.
If you choose this second option, there are three possibilities you can use to gather all the input Tensors in the
first positional argument :
- a single Tensor with `input_ids` only and nothing else: `model(input_ids)`
- a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
`model([input_ids, attention_mask])` or `model([input_ids, attention_mask, token_type_ids])`
- a dictionary with one or several input Tensors associated to the input names given in the docstring:
`model({"input_ids": input_ids, "token_type_ids": token_type_ids})`
</Tip>
Args:
config ([`GroupViTConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
GROUPVIT_TEXT_INPUTS_DOCSTRING = r"""
Args:
input_ids (`np.ndarray`, `tf.Tensor`, `List[tf.Tensor]` ``Dict[str, tf.Tensor]` or `Dict[str, np.ndarray]` and each example must have the shape `({0})`):
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.__call__`] and
[`PreTrainedTokenizer.encode`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`np.ndarray` or `tf.Tensor` of shape `({0})`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
position_ids (`np.ndarray` or `tf.Tensor` of shape `({0})`, *optional*):
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
config.max_position_embeddings - 1]`.
[What are position IDs?](../glossary#position-ids)
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the
config will be used instead.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
used instead.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. This argument can be used in
eager mode, in graph mode the value will always be set to True.
training (`bool`, *optional*, defaults to `False``):
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).
"""
GROUPVIT_VISION_INPUTS_DOCSTRING = r"""
Args:
pixel_values (`np.ndarray`, `tf.Tensor`, `List[tf.Tensor]`, `Dict[str, tf.Tensor]` or `Dict[str, np.ndarray]` and each example must have the shape `(batch_size, num_channels, height, width)`):
Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
[`CLIPImageProcessor.__call__`] for details.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the
config will be used instead.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
used instead.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. This argument can be used in
eager mode, in graph mode the value will always be set to True.
training (`bool`, *optional*, defaults to `False``):
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).
"""
GROUPVIT_INPUTS_DOCSTRING = r"""
Args:
input_ids (`np.ndarray`, `tf.Tensor`, `List[tf.Tensor]` ``Dict[str, tf.Tensor]` or `Dict[str, np.ndarray]` and each example must have the shape `({0})`):
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.__call__`] and
[`PreTrainedTokenizer.encode`] for details.
[What are input IDs?](../glossary#input-ids)
pixel_values (`np.ndarray`, `tf.Tensor`, `List[tf.Tensor]` `Dict[str, tf.Tensor]` or `Dict[str, np.ndarray]` and each example must have the shape `(batch_size, num_channels, height, width)`):
Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
[`CLIPImageProcessor.__call__`] for details.
attention_mask (`np.ndarray` or `tf.Tensor` of shape `({0})`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
position_ids (`np.ndarray` or `tf.Tensor` of shape `({0})`, *optional*):
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
config.max_position_embeddings - 1]`.
[What are position IDs?](../glossary#position-ids)
return_loss (`bool`, *optional*):
Whether or not to return the contrastive loss.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the
config will be used instead.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
used instead.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. This argument can be used in
eager mode, in graph mode the value will always be set to True.
training (`bool`, *optional*, defaults to `False``):
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).
"""
class TFGroupViTTextModel(TFGroupViTPreTrainedModel):
config_class = GroupViTTextConfig
main_input_name = "input_ids"
def __init__(self, config: GroupViTTextConfig, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.groupvit = TFGroupViTTextMainLayer(config, name="groupvit")
@unpack_inputs
@add_start_docstrings_to_model_forward(GROUPVIT_TEXT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=TFBaseModelOutputWithPooling, config_class=GroupViTTextConfig)
def call(
self,
input_ids: TFModelInputType | None = None,
attention_mask: np.ndarray | tf.Tensor | None = None,
position_ids: np.ndarray | tf.Tensor | None = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
training: bool = False,
) -> Union[TFBaseModelOutputWithPooling, Tuple[tf.Tensor]]:
r"""
Returns:
Examples:
```python
>>> from transformers import CLIPTokenizer, TFGroupViTTextModel
>>> tokenizer = CLIPTokenizer.from_pretrained("nvidia/groupvit-gcc-yfcc")
>>> model = TFGroupViTTextModel.from_pretrained("nvidia/groupvit-gcc-yfcc")
>>> inputs = tokenizer(["a photo of a cat", "a photo of a dog"], padding=True, return_tensors="tf")
>>> outputs = model(**inputs)
>>> last_hidden_state = outputs.last_hidden_state
>>> pooled_output = outputs.pooler_output # pooled (EOS token) states
```"""
outputs = self.groupvit(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
return outputs
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "groupvit", None) is not None:
with tf.name_scope(self.groupvit.name):
self.groupvit.build(None)
class TFGroupViTVisionModel(TFGroupViTPreTrainedModel):
config_class = GroupViTVisionConfig
main_input_name = "pixel_values"
def __init__(self, config: GroupViTVisionConfig, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.groupvit = TFGroupViTVisionMainLayer(config, name="groupvit")
@unpack_inputs
@add_start_docstrings_to_model_forward(GROUPVIT_VISION_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=TFBaseModelOutputWithPooling, config_class=GroupViTVisionConfig)
def call(
self,
pixel_values: TFModelInputType | None = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
training: bool = False,
) -> Union[TFBaseModelOutputWithPooling, Tuple[tf.Tensor]]:
r"""
Returns:
Examples:
```python
>>> from PIL import Image
>>> import requests
>>> from transformers import AutoProcessor, TFGroupViTVisionModel
>>> processor = AutoProcessor.from_pretrained("nvidia/groupvit-gcc-yfcc")
>>> model = TFGroupViTVisionModel.from_pretrained("nvidia/groupvit-gcc-yfcc")
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> inputs = processor(images=image, return_tensors="tf")
>>> outputs = model(**inputs)
>>> last_hidden_state = outputs.last_hidden_state
>>> pooled_output = outputs.pooler_output # pooled CLS states
```"""
outputs = self.groupvit(
pixel_values=pixel_values,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
return outputs
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "groupvit", None) is not None:
with tf.name_scope(self.groupvit.name):
self.groupvit.build(None)
@add_start_docstrings(GROUPVIT_START_DOCSTRING)
class TFGroupViTModel(TFGroupViTPreTrainedModel):
config_class = GroupViTConfig
def __init__(self, config: GroupViTConfig, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.groupvit = TFGroupViTMainLayer(config, name="groupvit")
@unpack_inputs
@add_start_docstrings_to_model_forward(GROUPVIT_TEXT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
def get_text_features(
self,
input_ids: TFModelInputType | None = None,
attention_mask: np.ndarray | tf.Tensor | None = None,
position_ids: np.ndarray | tf.Tensor | None = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
training: bool = False,
) -> tf.Tensor:
r"""
Returns:
text_features (`tf.Tensor` of shape `(batch_size, output_dim`): The text embeddings obtained by applying
the projection layer to the pooled output of [`TFGroupViTTextModel`].
Examples:
```python
>>> from transformers import CLIPTokenizer, TFGroupViTModel
>>> model = TFGroupViTModel.from_pretrained("nvidia/groupvit-gcc-yfcc")
>>> tokenizer = CLIPTokenizer.from_pretrained("nvidia/groupvit-gcc-yfcc")
>>> inputs = tokenizer(["a photo of a cat", "a photo of a dog"], padding=True, return_tensors="tf")
>>> text_features = model.get_text_features(**inputs)
```"""
text_features = self.groupvit.get_text_features(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
return text_features
@unpack_inputs
@add_start_docstrings_to_model_forward(GROUPVIT_VISION_INPUTS_DOCSTRING)
def get_image_features(
self,
pixel_values: TFModelInputType | None = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
training: bool = False,
) -> tf.Tensor:
r"""
Returns:
image_features (`tf.Tensor` of shape `(batch_size, output_dim`): The image embeddings obtained by applying
the projection layer to the pooled output of [`TFGroupViTVisionModel`].
Examples:
```python
>>> from PIL import Image
>>> import requests
>>> from transformers import AutoProcessor, TFGroupViTModel
>>> model = TFGroupViTModel.from_pretrained("nvidia/groupvit-gcc-yfcc")
>>> processor = AutoProcessor.from_pretrained("nvidia/groupvit-gcc-yfcc")
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> inputs = processor(images=image, return_tensors="tf")
>>> image_features = model.get_image_features(**inputs)
```"""
image_features = self.groupvit.get_image_features(
pixel_values=pixel_values,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
return image_features
@unpack_inputs
@add_start_docstrings_to_model_forward(GROUPVIT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=TFGroupViTModelOutput, config_class=GroupViTConfig)
def call(
self,
input_ids: TFModelInputType | None = None,
pixel_values: TFModelInputType | None = None,
attention_mask: np.ndarray | tf.Tensor | None = None,
position_ids: np.ndarray | tf.Tensor | None = None,
return_loss: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
output_segmentation: Optional[bool] = None,
return_dict: Optional[bool] = None,
training: bool = False,
) -> Union[TFGroupViTModelOutput, Tuple[tf.Tensor]]:
r"""
Returns:
Examples:
```python
>>> from PIL import Image
>>> import requests
>>> from transformers import AutoProcessor, TFGroupViTModel
>>> import tensorflow as tf
>>> model = TFGroupViTModel.from_pretrained("nvidia/groupvit-gcc-yfcc")
>>> processor = AutoProcessor.from_pretrained("nvidia/groupvit-gcc-yfcc")
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> inputs = processor(
... text=["a photo of a cat", "a photo of a dog"], images=image, return_tensors="tf", padding=True
... )
>>> outputs = model(**inputs)
>>> logits_per_image = outputs.logits_per_image # this is the image-text similarity score
>>> probs = tf.math.softmax(logits_per_image, axis=1) # we can take the softmax to get the label probabilities
```"""
outputs = self.groupvit(
input_ids=input_ids,
pixel_values=pixel_values,
attention_mask=attention_mask,
position_ids=position_ids,
return_loss=return_loss,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
output_segmentation=output_segmentation,
return_dict=return_dict,
training=training,
)
return outputs
def serving_output(self, output: TFGroupViTModelOutput) -> TFGroupViTModelOutput:
# TODO: As is this currently fails with saved_model=True, because
# TensorFlow cannot trace through nested dataclasses. Reference:
# https://github.com/huggingface/transformers/pull/16886
return output
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "groupvit", None) is not None:
with tf.name_scope(self.groupvit.name):
self.groupvit.build(None)
__all__ = ["TFGroupViTModel", "TFGroupViTPreTrainedModel", "TFGroupViTTextModel", "TFGroupViTVisionModel"]
```
|
==============================================================================================================================
SOURCE CODE FILE: __init__.py
LINES: 1
SIZE: 0.97 KB
PATH: scripts\freecad_env\Lib\site-packages\transformers\models\helium\__init__.py
ENCODING: utf-8
```py
# Copyright 2024 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import _LazyModule
from ...utils.import_utils import define_import_structure
if TYPE_CHECKING:
from .configuration_helium import *
from .modeling_helium import *
else:
import sys
_file = globals()["__file__"]
sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)
```
|
==========================================================================================================================================
SOURCE CODE FILE: configuration_helium.py
LINES: 1
SIZE: 7.20 KB
PATH: scripts\freecad_env\Lib\site-packages\transformers\models\helium\configuration_helium.py
ENCODING: utf-8
```py
# coding=utf-8
# Copyright 2024 The Kyutai and HuggingFace Inc. teams. All rights reserved.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ...configuration_utils import PretrainedConfig
class HeliumConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`HeliumModel`]. It is used to instantiate an Helium
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar configuration to that of the Helium 2b model.
e.g. [kyutai/helium-2b](https://huggingface.co/kyutai/helium-2b)
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 48000):
Vocabulary size of the Helium model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`HeliumModel`]
hidden_size (`int`, *optional*, defaults to 2560):
Dimension of the hidden representations.
intermediate_size (`int`, *optional*, defaults to 7040):
Dimension of the MLP representations.
num_hidden_layers (`int`, *optional*, defaults to 24):
Number of hidden layers in the Transformer decoder.
num_attention_heads (`int`, *optional*, defaults to 20):
Number of attention heads for each attention layer in the Transformer decoder.
num_key_value_heads (`int`, *optional*, defaults to 20):
This is the number of key_value heads that should be used to implement Grouped Query Attention. If
`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
`num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
by meanpooling all the original heads within that group. For more details checkout [this
paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
`num_attention_heads`.
head_dim (`int`, *optional*, defaults to 128):
The attention head dimension.
hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
The legacy activation function. It is overwritten by the `hidden_activation`.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
max_position_embeddings (`int`, *optional*, defaults to 4096):
The maximum sequence length that this model might ever be used with.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
rms_norm_eps (`float`, *optional*, defaults to 1e-08):
The epsilon used by the rms normalization layers.
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models). Only
relevant if `config.is_decoder=True`.
tie_word_embeddings (`bool`, *optional*, defaults to `False`):
Whether to tie weight embeddings
rope_theta (`float`, *optional*, defaults to 100000.0):
The base period of the RoPE embeddings.
pad_token_id (`int`, *optional*, defaults to 3):
Padding token id.
eos_token_id (`int` | `list`, *optional*, defaults to 2):
End of stream token id.
bos_token_id (`int`, *optional*, defaults to 1):
Beginning of stream token id.
attention_bias (`bool`, *optional*, defaults to `False`):
Whether to use a bias in the query, key, value and output projection layers during self-attention.
mlp_bias (`bool`, *optional*, defaults to `False`):
Whether to use a bias in up_proj, down_proj and gate_proj layers in the MLP layers.
```python
>>> from transformers import HeliumModel, HeliumConfig
>>> # Initializing a Helium 2b style configuration
>>> configuration = HeliumConfig()
>>> # Initializing a model from the Helium 2b style configuration
>>> model = HeliumModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "helium"
keys_to_ignore_at_inference = ["past_key_values"]
base_model_tp_plan = {
"layers.*.self_attn.q_proj": "colwise",
"layers.*.self_attn.k_proj": "colwise",
"layers.*.self_attn.v_proj": "colwise",
"layers.*.self_attn.o_proj": "rowwise",
"layers.*.mlp.gate_proj": "colwise",
"layers.*.mlp.up_proj": "colwise",
"layers.*.mlp.down_proj": "rowwise",
}
base_model_pp_plan = {
"embed_tokens": (["input_ids"], ["inputs_embeds"]),
"layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
"norm": (["hidden_states"], ["hidden_states"]),
}
def __init__(
self,
vocab_size=48000,
hidden_size=2560,
intermediate_size=7040,
num_hidden_layers=24,
num_attention_heads=20,
num_key_value_heads=20,
head_dim=128,
hidden_act="silu",
attention_dropout=0.0,
max_position_embeddings=4096,
initializer_range=0.02,
rms_norm_eps=1e-8,
use_cache=True,
tie_word_embeddings=False,
rope_theta=100000.0,
pad_token_id=3,
eos_token_id=2,
bos_token_id=1,
attention_bias=False,
mlp_bias=False,
**kwargs,
):
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.num_key_value_heads = num_key_value_heads
self.head_dim = head_dim
self.hidden_act = hidden_act
self.initializer_range = initializer_range
self.rms_norm_eps = rms_norm_eps
self.use_cache = use_cache
self.rope_theta = rope_theta
self.attention_bias = attention_bias
self.attention_dropout = attention_dropout
self.mlp_bias = mlp_bias
super().__init__(
pad_token_id=pad_token_id,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
tie_word_embeddings=tie_word_embeddings,
**kwargs,
)
__all__ = ["HeliumConfig"]
```
|
=====================================================================================================================================
SOURCE CODE FILE: modeling_helium.py
LINES: 1
SIZE: 45.17 KB
PATH: scripts\freecad_env\Lib\site-packages\transformers\models\helium\modeling_helium.py
ENCODING: utf-8
```py
# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
# This file was automatically generated from src/transformers/models/helium/modular_helium.py.
# Do NOT edit this file manually as any edits will be overwritten by the generation of
# the file from the modular. If any change should be done, please apply the change to the
# modular_helium.py file directly. One of our CI enforces this.
# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
# coding=utf-8
# Copyright 2024 The Kyutai and HuggingFace Inc. teams. All rights reserved.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import math
from functools import partial
from typing import Callable, Optional, Tuple, Union
import torch
import torch.nn as nn
from ...activations import ACT2FN
from ...cache_utils import Cache, DynamicCache, StaticCache
from ...generation import GenerationMixin
from ...modeling_attn_mask_utils import AttentionMaskConverter
from ...modeling_flash_attention_utils import FlashAttentionKwargs
from ...modeling_outputs import (
BaseModelOutputWithPast,
CausalLMOutputWithPast,
SequenceClassifierOutputWithPast,
TokenClassifierOutput,
)
from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
from ...processing_utils import Unpack
from ...utils import (
LossKwargs,
add_code_sample_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
can_return_tuple,
is_torch_flex_attn_available,
logging,
replace_return_docstrings,
)
from ...utils.deprecation import deprecate_kwarg
from .configuration_helium import HeliumConfig
if is_torch_flex_attn_available():
from torch.nn.attention.flex_attention import BlockMask
from ...integrations.flex_attention import make_flex_block_causal_mask
logger = logging.get_logger(__name__)
_CHECKPOINT_FOR_DOC = "google/helium-7b"
_CONFIG_FOR_DOC = "HeliumConfig"
class HeliumRMSNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-6):
super().__init__()
self.weight = nn.Parameter(torch.ones(hidden_size))
self.variance_epsilon = eps
def forward(self, hidden_states):
input_dtype = hidden_states.dtype
hidden_states = hidden_states.to(torch.float32)
variance = hidden_states.pow(2).mean(-1, keepdim=True)
hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
return (self.weight.to(torch.float32) * hidden_states).to(input_dtype)
def extra_repr(self):
return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}"
class HeliumRotaryEmbedding(nn.Module):
def __init__(self, config: HeliumConfig, device=None):
super().__init__()
# BC: "rope_type" was originally "type"
if hasattr(config, "rope_scaling") and config.rope_scaling is not None:
self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type"))
else:
self.rope_type = "default"
self.max_seq_len_cached = config.max_position_embeddings
self.original_max_seq_len = config.max_position_embeddings
self.config = config
self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device)
self.register_buffer("inv_freq", inv_freq, persistent=False)
self.original_inv_freq = self.inv_freq
@torch.no_grad()
@dynamic_rope_update # power user: used with advanced RoPE types (e.g. dynamic rope)
def forward(self, x, position_ids):
inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device)
position_ids_expanded = position_ids[:, None, :].float()
device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
with torch.autocast(device_type=device_type, enabled=False): # Force float32
freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
emb = torch.cat((freqs, freqs), dim=-1)
cos = emb.cos() * self.attention_scaling
sin = emb.sin() * self.attention_scaling
return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
class HeliumMLP(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.hidden_size = config.hidden_size
self.intermediate_size = config.intermediate_size
self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=config.mlp_bias)
self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=config.mlp_bias)
self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=config.mlp_bias)
self.act_fn = ACT2FN[config.hidden_act]
def forward(self, x):
down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
return down_proj
def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
"""
This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
"""
batch, num_key_value_heads, slen, head_dim = hidden_states.shape
if n_rep == 1:
return hidden_states
hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
def eager_attention_forward(
module: nn.Module,
query: torch.Tensor,
key: torch.Tensor,
value: torch.Tensor,
attention_mask: Optional[torch.Tensor],
scaling: float,
dropout: float = 0.0,
**kwargs,
):
key_states = repeat_kv(key, module.num_key_value_groups)
value_states = repeat_kv(value, module.num_key_value_groups)
attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling
if attention_mask is not None:
causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
attn_weights = attn_weights + causal_mask
attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
attn_output = torch.matmul(attn_weights, value_states)
attn_output = attn_output.transpose(1, 2).contiguous()
return attn_output, attn_weights
def rotate_half(x):
"""Rotates half the hidden dims of the input."""
x1 = x[..., 0::2]
x2 = x[..., 1::2]
return torch.stack((-x2, x1), dim=-1).flatten(-2)
def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
"""Applies Rotary Position Embedding to the query and key tensors.
Args:
q (`torch.Tensor`): The query tensor.
k (`torch.Tensor`): The key tensor.
cos (`torch.Tensor`): The cosine part of the rotary embedding.
sin (`torch.Tensor`): The sine part of the rotary embedding.
position_ids (`torch.Tensor`, *optional*):
Deprecated and unused.
unsqueeze_dim (`int`, *optional*, defaults to 1):
The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
Returns:
`tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
"""
cos = cos.unsqueeze(unsqueeze_dim)
sin = sin.unsqueeze(unsqueeze_dim)
# Interleave them instead of usual shape
cos = cos[..., : cos.shape[-1] // 2].repeat_interleave(2, dim=-1)
sin = sin[..., : sin.shape[-1] // 2].repeat_interleave(2, dim=-1)
q_embed = (q * cos) + (rotate_half(q) * sin)
k_embed = (k * cos) + (rotate_half(k) * sin)
return q_embed, k_embed
class HeliumAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(self, config: HeliumConfig, layer_idx: Optional[int] = None):
super().__init__()
self.config = config
self.layer_idx = layer_idx
self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads
self.scaling = 1 / math.sqrt(self.head_dim)
self.attention_dropout = config.attention_dropout
self.is_causal = True
self.q_proj = nn.Linear(
config.hidden_size, config.num_attention_heads * self.head_dim, bias=config.attention_bias
)
self.k_proj = nn.Linear(
config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
)
self.v_proj = nn.Linear(
config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
)
self.o_proj = nn.Linear(config.hidden_size, config.hidden_size, bias=False)
def forward(
self,
hidden_states: torch.Tensor,
position_embeddings: Tuple[torch.Tensor, torch.Tensor],
attention_mask: Optional[torch.Tensor],
past_key_value: Optional[Cache] = None,
cache_position: Optional[torch.LongTensor] = None,
**kwargs: Unpack[FlashAttentionKwargs],
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
input_shape = hidden_states.shape[:-1]
hidden_shape = (*input_shape, -1, self.head_dim)
query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2)
key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2)
value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2)
cos, sin = position_embeddings
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
if past_key_value is not None:
# sin and cos are specific to RoPE models; cache_position needed for the static cache
cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
attention_interface: Callable = eager_attention_forward
if self.config._attn_implementation != "eager":
if self.config._attn_implementation == "sdpa" and kwargs.get("output_attentions", False):
logger.warning_once(
"`torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to "
'eager attention. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
)
else:
attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
attn_output, attn_weights = attention_interface(
self,
query_states,
key_states,
value_states,
attention_mask,
dropout=0.0 if not self.training else self.attention_dropout,
scaling=self.scaling,
**kwargs,
)
attn_output = attn_output.reshape(*input_shape, -1).contiguous()
attn_output = self.o_proj(attn_output)
return attn_output, attn_weights
class HeliumDecoderLayer(nn.Module):
def __init__(self, config: HeliumConfig, layer_idx: Optional[int] = None):
super().__init__()
self.hidden_size = config.hidden_size
self.self_attn = HeliumAttention(config=config, layer_idx=layer_idx)
self.mlp = HeliumMLP(config)
self.input_layernorm = HeliumRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.post_attention_layernorm = HeliumRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Cache] = None,
output_attentions: Optional[bool] = False,
use_cache: Optional[bool] = False,
cache_position: Optional[torch.LongTensor] = None,
position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, # necessary, but kept here for BC
**kwargs: Unpack[FlashAttentionKwargs],
) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
residual = hidden_states
hidden_states = self.input_layernorm(hidden_states)
# Self Attention
hidden_states, self_attn_weights = self.self_attn(
hidden_states=hidden_states,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_value=past_key_value,
output_attentions=output_attentions,
use_cache=use_cache,
cache_position=cache_position,
position_embeddings=position_embeddings,
**kwargs,
)
hidden_states = residual + hidden_states
# Fully Connected
residual = hidden_states
hidden_states = self.post_attention_layernorm(hidden_states)
hidden_states = self.mlp(hidden_states)
hidden_states = residual + hidden_states
outputs = (hidden_states,)
if output_attentions:
outputs += (self_attn_weights,)
return outputs
HELIUM_START_DOCSTRING = r"""
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.
Parameters:
config ([`HeliumConfig`]):
Model configuration class with all the parameters of the model. Initializing with a config file does not
load the weights associated with the model, only the configuration. Check out the
[`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
@add_start_docstrings(
"The bare Helium Model outputting raw hidden-states without any specific head on top.",
HELIUM_START_DOCSTRING,
)
class HeliumPreTrainedModel(PreTrainedModel):
config_class = HeliumConfig
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = ["HeliumDecoderLayer"]
_skip_keys_device_placement = ["past_key_values"]
_supports_flash_attn_2 = True
_supports_sdpa = True
_supports_flex_attn = True
_supports_cache_class = True
_supports_quantized_cache = True
_supports_static_cache = True
_supports_attention_backend = True
def _init_weights(self, module):
std = self.config.initializer_range
if isinstance(module, nn.Linear):
module.weight.data.normal_(mean=0.0, std=std)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.Embedding):
module.weight.data.normal_(mean=0.0, std=std)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
HELIUM_INPUTS_DOCSTRING = r"""
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
it.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
If `past_key_values` is used, optionally only the last `input_ids` have to be input (see
`past_key_values`).
If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
information on the default strategy.
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
config.n_positions - 1]`.
[What are position IDs?](../glossary#position-ids)
past_key_values (`Cache`, *optional*):
Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values`
returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.
It is a [`~cache_utils.Cache`] instance. For more details, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache).
If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't
have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids`
of shape `(batch_size, sequence_length)`.
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
model's internal embedding lookup matrix.
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
`past_key_values`).
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
Indices depicting the position of the input sequence tokens in the sequence. Contrarily to `position_ids`,
this tensor is not affected by padding. It is used to update the cache in the correct position and to infer
the complete sequence length.
"""
@add_start_docstrings(
"The bare Helium Model outputting raw hidden-states without any specific head on top.",
HELIUM_START_DOCSTRING,
)
class HeliumModel(HeliumPreTrainedModel):
"""
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`HeliumDecoderLayer`]
Args:
config: HeliumConfig
"""
def __init__(self, config: HeliumConfig):
super().__init__(config)
self.padding_idx = config.pad_token_id
self.vocab_size = config.vocab_size
self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
self.layers = nn.ModuleList(
[HeliumDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
)
self.norm = HeliumRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.rotary_emb = HeliumRotaryEmbedding(config)
self.gradient_checkpointing = False
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.embed_tokens
def set_input_embeddings(self, value):
self.embed_tokens = value
@can_return_tuple
@add_start_docstrings_to_model_forward(HELIUM_INPUTS_DOCSTRING)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Cache] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
cache_position: Optional[torch.LongTensor] = None,
**flash_attn_kwargs: Unpack[FlashAttentionKwargs],
) -> BaseModelOutputWithPast:
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
use_cache = use_cache if use_cache is not None else self.config.use_cache
if (input_ids is None) ^ (inputs_embeds is not None):
raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
if self.gradient_checkpointing and self.training and use_cache:
logger.warning_once(
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`."
)
use_cache = False
# TODO (joao): remove this exception in v4.56 -- it exists for users that try to pass a legacy cache
if not isinstance(past_key_values, (type(None), Cache)):
raise ValueError("The `past_key_values` should be either a `Cache` object or `None`.")
if inputs_embeds is None:
inputs_embeds = self.embed_tokens(input_ids)
if use_cache and past_key_values is None:
past_key_values = DynamicCache()
if cache_position is None:
past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
cache_position = torch.arange(
past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
)
if position_ids is None:
position_ids = cache_position.unsqueeze(0)
causal_mask = self._update_causal_mask(
attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions
)
hidden_states = inputs_embeds
# create position embeddings to be shared across the decoder layers
position_embeddings = self.rotary_emb(hidden_states, position_ids)
# decoder layers
all_hidden_states = () if output_hidden_states else None
all_self_attns = () if output_attentions else None
for decoder_layer in self.layers[: self.config.num_hidden_layers]:
if output_hidden_states:
all_hidden_states += (hidden_states,)
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
partial(decoder_layer.__call__, **flash_attn_kwargs),
hidden_states,
causal_mask,
position_ids,
past_key_values,
output_attentions,
use_cache,
cache_position,
position_embeddings,
)
else:
layer_outputs = decoder_layer(
hidden_states,
attention_mask=causal_mask,
position_ids=position_ids,
past_key_value=past_key_values,
output_attentions=output_attentions,
use_cache=use_cache,
cache_position=cache_position,
position_embeddings=position_embeddings,
**flash_attn_kwargs,
)
hidden_states = layer_outputs[0]
if output_attentions:
all_self_attns += (layer_outputs[1],)
hidden_states = self.norm(hidden_states)
# add hidden states from the last decoder layer
if output_hidden_states:
all_hidden_states += (hidden_states,)
return BaseModelOutputWithPast(
last_hidden_state=hidden_states,
past_key_values=past_key_values if use_cache else None,
hidden_states=all_hidden_states,
attentions=all_self_attns,
)
def _update_causal_mask(
self,
attention_mask: torch.Tensor,
input_tensor: torch.Tensor,
cache_position: torch.Tensor,
past_key_values: Cache,
output_attentions: bool = False,
):
if self.config._attn_implementation == "flash_attention_2":
if attention_mask is not None and (attention_mask == 0.0).any():
return attention_mask
return None
if self.config._attn_implementation == "flex_attention":
if isinstance(attention_mask, torch.Tensor):
attention_mask = make_flex_block_causal_mask(attention_mask)
if isinstance(attention_mask, BlockMask):
return attention_mask
# For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in
# order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail
# to infer the attention mask.
past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
using_static_cache = isinstance(past_key_values, StaticCache)
# When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward
if self.config._attn_implementation == "sdpa" and not using_static_cache and not output_attentions:
if AttentionMaskConverter._ignore_causal_mask_sdpa(
attention_mask,
inputs_embeds=input_tensor,
past_key_values_length=past_seen_tokens,
is_training=self.training,
):
return None
dtype, device = input_tensor.dtype, input_tensor.device
sequence_length = input_tensor.shape[1]
if using_static_cache:
target_length = past_key_values.get_max_cache_shape()
else:
target_length = (
attention_mask.shape[-1]
if isinstance(attention_mask, torch.Tensor)
else past_seen_tokens + sequence_length + 1
)
# In case the provided `attention` mask is 2D, we generate a causal mask here (4D).
causal_mask = self._prepare_4d_causal_attention_mask_with_cache_position(
attention_mask,
sequence_length=sequence_length,
target_length=target_length,
dtype=dtype,
device=device,
cache_position=cache_position,
batch_size=input_tensor.shape[0],
)
if (
self.config._attn_implementation == "sdpa"
and attention_mask is not None
and attention_mask.device.type in ["cuda", "xpu"]
and not output_attentions
):
# Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when
# using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path.
# Details: https://github.com/pytorch/pytorch/issues/110213
min_dtype = torch.finfo(dtype).min
causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype)
return causal_mask
@staticmethod
def _prepare_4d_causal_attention_mask_with_cache_position(
attention_mask: torch.Tensor,
sequence_length: int,
target_length: int,
dtype: torch.dtype,
device: torch.device,
cache_position: torch.Tensor,
batch_size: int,
**kwargs,
):
"""
Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape
`(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing.
Args:
attention_mask (`torch.Tensor`):
A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape
`(batch_size, 1, query_length, key_value_length)`.
sequence_length (`int`):
The sequence length being processed.
target_length (`int`):
The target length: when generating with static cache, the mask should be as long as the static cache,
to account for the 0 padding, the part of the cache that is not filled yet.
dtype (`torch.dtype`):
The dtype to use for the 4D attention mask.
device (`torch.device`):
The device to place the 4D attention mask on.
cache_position (`torch.Tensor`):
Indices depicting the position of the input sequence tokens in the sequence.
batch_size (`torch.Tensor`):
Batch size.
"""
if attention_mask is not None and attention_mask.dim() == 4:
# In this case we assume that the mask comes already in inverted form and requires no inversion or slicing.
causal_mask = attention_mask
else:
min_dtype = torch.finfo(dtype).min
causal_mask = torch.full(
(sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=device
)
if sequence_length != 1:
causal_mask = torch.triu(causal_mask, diagonal=1)
causal_mask *= torch.arange(target_length, device=device) > cache_position.reshape(-1, 1)
causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1)
if attention_mask is not None:
causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit
mask_length = attention_mask.shape[-1]
padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :].to(
causal_mask.device
)
padding_mask = padding_mask == 0
causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
padding_mask, min_dtype
)
return causal_mask
class KwargsForCausalLM(FlashAttentionKwargs, LossKwargs): ...
class HeliumForCausalLM(HeliumPreTrainedModel, GenerationMixin):
_tied_weights_keys = ["lm_head.weight"]
_tp_plan = {"lm_head": "colwise_rep"}
_pp_plan = {"lm_head": (["hidden_states"], ["logits"])}
def __init__(self, config: HeliumConfig):
super().__init__(config)
self.model = HeliumModel(config)
self.vocab_size = config.vocab_size
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.model.embed_tokens
def set_input_embeddings(self, value):
self.model.embed_tokens = value
def get_output_embeddings(self):
return self.lm_head
def set_output_embeddings(self, new_embeddings):
self.lm_head = new_embeddings
def set_decoder(self, decoder):
self.model = decoder
def get_decoder(self):
return self.model
@can_return_tuple
@deprecate_kwarg("num_logits_to_keep", version="4.50", new_name="logits_to_keep")
@add_start_docstrings_to_model_forward(HELIUM_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Cache] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
cache_position: Optional[torch.LongTensor] = None,
logits_to_keep: Union[int, torch.Tensor] = 0,
**kwargs: Unpack[KwargsForCausalLM],
) -> CausalLMOutputWithPast:
r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
logits_to_keep (`int` or `torch.Tensor`, *optional*):
If an `int`, compute logits for the last `logits_to_keep` tokens. If `0`, calculate logits for all
`input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that
token can save memory, which becomes pretty significant for long sequences or large vocabulary size.
If a `torch.Tensor`, must be 1D corresponding to the indices to keep in the sequence length dimension.
This is useful when using packed tensor format (single dimension for batch and sequence length).
Returns:
Example:
```python
>>> from transformers import AutoTokenizer, HeliumForCausalLM
>>> model = HeliumForCausalLM.from_pretrained("google/helium-7b")
>>> tokenizer = AutoTokenizer.from_pretrained("google/helium-7b")
>>> prompt = "What is your favorite condiment?"
>>> inputs = tokenizer(prompt, return_tensors="pt")
>>> # Generate
>>> generate_ids = model.generate(inputs.input_ids, max_length=30)
>>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
"What is your favorite condiment?"
```"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
outputs: BaseModelOutputWithPast = self.model(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
cache_position=cache_position,
**kwargs,
)
hidden_states = outputs.last_hidden_state
# Only compute necessary logits, and do not upcast them to float if we are not computing the loss
slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
logits = self.lm_head(hidden_states[:, slice_indices, :])
loss = None
if labels is not None:
loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.config.vocab_size, **kwargs)
return CausalLMOutputWithPast(
loss=loss,
logits=logits,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
@add_start_docstrings(
"""
The Helium Model transformer with a sequence classification head on top (linear layer).
[`HeliumForSequenceClassification`] uses the last token in order to do the classification, as other causal models
(e.g. GPT-2) do.
Since it does classification on the last token, it requires to know the position of the last token. If a
`pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
each row of the batch).
""",
HELIUM_START_DOCSTRING,
)
class HeliumForSequenceClassification(HeliumPreTrainedModel):
def __init__(self, config: HeliumConfig):
super().__init__(config)
self.num_labels = config.num_labels
self.model = HeliumModel(config)
self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.model.embed_tokens
def set_input_embeddings(self, value):
self.model.embed_tokens = value
@can_return_tuple
@add_start_docstrings_to_model_forward(HELIUM_INPUTS_DOCSTRING)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Cache] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
) -> SequenceClassifierOutputWithPast:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
"""
transformer_outputs: BaseModelOutputWithPast = self.model(
input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
)
hidden_states = transformer_outputs.last_hidden_state
logits = self.score(hidden_states)
if input_ids is not None:
batch_size = input_ids.shape[0]
else:
batch_size = inputs_embeds.shape[0]
if self.config.pad_token_id is None and batch_size != 1:
raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
if self.config.pad_token_id is None:
last_non_pad_token = -1
elif input_ids is not None:
# To handle both left- and right- padding, we take the rightmost token that is not equal to pad_token_id
non_pad_mask = (input_ids != self.config.pad_token_id).to(logits.device, torch.int32)
token_indices = torch.arange(input_ids.shape[-1], device=logits.device, dtype=torch.int32)
last_non_pad_token = (token_indices * non_pad_mask).argmax(-1)
else:
last_non_pad_token = -1
logger.warning_once(
f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be "
"unexpected if using padding tokens in conjunction with `inputs_embeds.`"
)
pooled_logits = logits[torch.arange(batch_size, device=logits.device), last_non_pad_token]
loss = None
if labels is not None:
loss = self.loss_function(logits=logits, labels=labels, pooled_logits=pooled_logits, config=self.config)
return SequenceClassifierOutputWithPast(
loss=loss,
logits=pooled_logits,
past_key_values=transformer_outputs.past_key_values,
hidden_states=transformer_outputs.hidden_states,
attentions=transformer_outputs.attentions,
)
@add_start_docstrings(
"""
The Helium Model transformer with a token classification head on top (a linear layer on top of the hidden-states
output) e.g. for Named-Entity-Recognition (NER) tasks.
""",
HELIUM_START_DOCSTRING,
)
class HeliumForTokenClassification(HeliumPreTrainedModel):
def __init__(self, config: HeliumConfig):
super().__init__(config)
self.num_labels = config.num_labels
self.model = HeliumModel(config)
if getattr(config, "classifier_dropout", None) is not None:
classifier_dropout = config.classifier_dropout
elif getattr(config, "hidden_dropout", None) is not None:
classifier_dropout = config.hidden_dropout
else:
classifier_dropout = 0.1
self.dropout = nn.Dropout(classifier_dropout)
self.score = nn.Linear(config.hidden_size, config.num_labels)
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.model.embed_tokens
def set_input_embeddings(self, value):
self.model.embed_tokens = value
@can_return_tuple
@add_start_docstrings_to_model_forward(HELIUM_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TokenClassifierOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Cache] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
) -> TokenClassifierOutput:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
"""
outputs: BaseModelOutputWithPast = self.model(
input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
)
sequence_output = outputs.last_hidden_state
sequence_output = self.dropout(sequence_output)
logits = self.score(sequence_output)
loss = None
if labels is not None:
loss = self.loss_function(logits, labels, self.config)
return TokenClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
__all__ = [
"HeliumPreTrainedModel",
"HeliumModel",
"HeliumForCausalLM",
"HeliumForSequenceClassification",
"HeliumForTokenClassification",
]
```
|
====================================================================================================================================
SOURCE CODE FILE: modular_helium.py
LINES: 1
SIZE: 5.80 KB
PATH: scripts\freecad_env\Lib\site-packages\transformers\models\helium\modular_helium.py
ENCODING: utf-8
```py
# coding=utf-8
# Copyright 2024 The Kyutai and HuggingFace Inc. teams. All rights reserved.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import math
from typing import Optional
import torch
import torch.nn as nn
import torch.utils.checkpoint
from ...utils import logging
from ..gemma.modeling_gemma import (
GemmaForCausalLM,
GemmaForSequenceClassification,
GemmaForTokenClassification,
)
from ..granite.modeling_granite import (
GraniteAttention,
)
from ..llama.modeling_llama import (
LlamaDecoderLayer,
LlamaMLP,
LlamaModel,
LlamaPreTrainedModel,
LlamaRotaryEmbedding,
)
from .configuration_helium import HeliumConfig
logger = logging.get_logger(__name__)
class HeliumRMSNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-6):
super().__init__()
self.weight = nn.Parameter(torch.ones(hidden_size))
self.variance_epsilon = eps
def forward(self, hidden_states):
input_dtype = hidden_states.dtype
hidden_states = hidden_states.to(torch.float32)
variance = hidden_states.pow(2).mean(-1, keepdim=True)
hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
return (self.weight.to(torch.float32) * hidden_states).to(input_dtype)
def extra_repr(self):
return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}"
class HeliumRotaryEmbedding(LlamaRotaryEmbedding):
pass
class HeliumMLP(LlamaMLP):
pass
def rotate_half(x):
"""Rotates half the hidden dims of the input."""
x1 = x[..., 0::2]
x2 = x[..., 1::2]
return torch.stack((-x2, x1), dim=-1).flatten(-2)
def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
"""Applies Rotary Position Embedding to the query and key tensors.
Args:
q (`torch.Tensor`): The query tensor.
k (`torch.Tensor`): The key tensor.
cos (`torch.Tensor`): The cosine part of the rotary embedding.
sin (`torch.Tensor`): The sine part of the rotary embedding.
position_ids (`torch.Tensor`, *optional*):
Deprecated and unused.
unsqueeze_dim (`int`, *optional*, defaults to 1):
The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
Returns:
`tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
"""
cos = cos.unsqueeze(unsqueeze_dim)
sin = sin.unsqueeze(unsqueeze_dim)
# Interleave them instead of usual shape
cos = cos[..., : cos.shape[-1] // 2].repeat_interleave(2, dim=-1)
sin = sin[..., : sin.shape[-1] // 2].repeat_interleave(2, dim=-1)
q_embed = (q * cos) + (rotate_half(q) * sin)
k_embed = (k * cos) + (rotate_half(k) * sin)
return q_embed, k_embed
class HeliumAttention(GraniteAttention):
def __init__(self, config: HeliumConfig, layer_idx: Optional[int] = None):
super().__init__(config, layer_idx)
self.o_proj = nn.Linear(config.hidden_size, config.hidden_size, bias=False)
self.scaling = 1 / math.sqrt(self.head_dim)
class HeliumDecoderLayer(LlamaDecoderLayer):
def __init__(self, config: HeliumConfig, layer_idx: Optional[int] = None):
super().__init__()
self.mlp = HeliumMLP(config)
self.input_layernorm = HeliumRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.post_attention_layernorm = HeliumRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
class HeliumPreTrainedModel(LlamaPreTrainedModel):
pass
class HeliumModel(HeliumPreTrainedModel, LlamaModel):
def __init__(self, config: HeliumConfig):
super().__init__(config)
self.layers = nn.ModuleList(
[HeliumDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
)
self.norm = HeliumRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.rotary_emb = HeliumRotaryEmbedding(config)
self.gradient_checkpointing = False
# Initialize weights and apply final processing
self.post_init()
class HeliumForCausalLM(GemmaForCausalLM):
def __init__(self, config: HeliumConfig):
super().__init__(config)
self.model = HeliumModel(config)
self.post_init()
class HeliumForSequenceClassification(GemmaForSequenceClassification):
def __init__(self, config: HeliumConfig):
super().__init__(config)
self.model = HeliumModel(config)
self.post_init()
class HeliumForTokenClassification(GemmaForTokenClassification):
def __init__(self, config: HeliumConfig):
super().__init__(config)
self.model = HeliumModel(config)
self.post_init()
__all__ = [
"HeliumPreTrainedModel",
"HeliumModel",
"HeliumForCausalLM",
"HeliumForSequenceClassification",
"HeliumForTokenClassification",
]
```
|
===============================================================================================================================
SOURCE CODE FILE: __init__.py
LINES: 1
SIZE: 0.98 KB
PATH: scripts\freecad_env\Lib\site-packages\transformers\models\herbert\__init__.py
ENCODING: utf-8
```py
# Copyright 2024 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import _LazyModule
from ...utils.import_utils import define_import_structure
if TYPE_CHECKING:
from .tokenization_herbert import *
from .tokenization_herbert_fast import *
else:
import sys
_file = globals()["__file__"]
sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)
```
|
===========================================================================================================================================
SOURCE CODE FILE: tokenization_herbert.py
LINES: 6
SIZE: 24.48 KB
PATH: scripts\freecad_env\Lib\site-packages\transformers\models\herbert\tokenization_herbert.py
ENCODING: utf-8
```py
# coding=utf-8
# Copyright 2020 The Google AI Language Team Authors, Allegro.pl, Facebook Inc. and the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
import os
import re
import unicodedata
from typing import List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer, _is_control, _is_punctuation, _is_whitespace
from ...utils import logging
logger = logging.get_logger(__name__)
VOCAB_FILES_NAMES = {
"vocab_file": "vocab.json",
"merges_file": "merges.txt",
}
# Copied from transformers.models.xlm.tokenization_xlm.get_pairs
def get_pairs(word):
"""
Return set of symbol pairs in a word. word is represented as tuple of symbols (symbols being variable-length
strings)
"""
pairs = set()
prev_char = word[0]
for char in word[1:]:
pairs.add((prev_char, char))
prev_char = char
return pairs
# Copied from transformers.models.xlm.tokenization_xlm.replace_unicode_punct
def replace_unicode_punct(text):
"""
Port of https://github.com/moses-smt/mosesdecoder/blob/master/scripts/tokenizer/replace-unicode-punctuation.perl
"""
text = text.replace("，", ",")
text = re.sub(r"。\s*", ". ", text)
text = text.replace("、", ",")
text = text.replace("”", '"')
text = text.replace("“", '"')
text = text.replace("∶", ":")
text = text.replace("：", ":")
text = text.replace("？", "?")
text = text.replace("《", '"')
text = text.replace("》", '"')
text = text.replace("）", ")")
text = text.replace("！", "!")
text = text.replace("（", "(")
text = text.replace("；", ";")
text = text.replace("１", "1")
text = text.replace("」", '"')
text = text.replace("「", '"')
text = text.replace("０", "0")
text = text.replace("３", "3")
text = text.replace("２", "2")
text = text.replace("５", "5")
text = text.replace("６", "6")
text = text.replace("９", "9")
text = text.replace("７", "7")
text = text.replace("８", "8")
text = text.replace("４", "4")
text = re.sub(r"．\s*", ". ", text)
text = text.replace("～", "~")
text = text.replace("’", "'")
text = text.replace("…", "...")
text = text.replace("━", "-")
text = text.replace("〈", "<")
text = text.replace("〉", ">")
text = text.replace("【", "[")
text = text.replace("】", "]")
text = text.replace("％", "%")
return text
# Copied from transformers.models.xlm.tokenization_xlm.remove_non_printing_char
def remove_non_printing_char(text):
"""
Port of https://github.com/moses-smt/mosesdecoder/blob/master/scripts/tokenizer/remove-non-printing-char.perl
"""
output = []
for char in text:
cat = unicodedata.category(char)
if cat.startswith("C"):
continue
output.append(char)
return "".join(output)
# Copied from transformers.models.bert.tokenization_bert.whitespace_tokenize
def whitespace_tokenize(text):
"""Runs basic whitespace cleaning and splitting on a piece of text."""
text = text.strip()
if not text:
return []
tokens = text.split()
return tokens
# Copied from transformers.models.bert.tokenization_bert.BasicTokenizer
class BasicTokenizer:
"""
Constructs a BasicTokenizer that will run basic tokenization (punctuation splitting, lower casing, etc.).
Args:
do_lower_case (`bool`, *optional*, defaults to `True`):
Whether or not to lowercase the input when tokenizing.
never_split (`Iterable`, *optional*):
Collection of tokens which will never be split during tokenization. Only has an effect when
`do_basic_tokenize=True`
tokenize_chinese_chars (`bool`, *optional*, defaults to `True`):
Whether or not to tokenize Chinese characters.
This should likely be deactivated for Japanese (see this
[issue](https://github.com/huggingface/transformers/issues/328)).
strip_accents (`bool`, *optional*):
Whether or not to strip all accents. If this option is not specified, then it will be determined by the
value for `lowercase` (as in the original BERT).
do_split_on_punc (`bool`, *optional*, defaults to `True`):
In some instances we want to skip the basic punctuation splitting so that later tokenization can capture
the full context of the words, such as contractions.
"""
def __init__(
self,
do_lower_case=True,
never_split=None,
tokenize_chinese_chars=True,
strip_accents=None,
do_split_on_punc=True,
):
if never_split is None:
never_split = []
self.do_lower_case = do_lower_case
self.never_split = set(never_split)
self.tokenize_chinese_chars = tokenize_chinese_chars
self.strip_accents = strip_accents
self.do_split_on_punc = do_split_on_punc
def tokenize(self, text, never_split=None):
"""
Basic Tokenization of a piece of text. For sub-word tokenization, see WordPieceTokenizer.
Args:
never_split (`List[str]`, *optional*)
Kept for backward compatibility purposes. Now implemented directly at the base class level (see
[`PreTrainedTokenizer.tokenize`]) List of token not to split.
"""
# union() returns a new set by concatenating the two sets.
never_split = self.never_split.union(set(never_split)) if never_split else self.never_split
text = self._clean_text(text)
# This was added on November 1st, 2018 for the multilingual and Chinese
# models. This is also applied to the English models now, but it doesn't
# matter since the English models were not trained on any Chinese data
# and generally don't have any Chinese data in them (there are Chinese
# characters in the vocabulary because Wikipedia does have some Chinese
# words in the English Wikipedia.).
if self.tokenize_chinese_chars:
text = self._tokenize_chinese_chars(text)
# prevents treating the same character with different unicode codepoints as different characters
unicode_normalized_text = unicodedata.normalize("NFC", text)
orig_tokens = whitespace_tokenize(unicode_normalized_text)
split_tokens = []
for token in orig_tokens:
if token not in never_split:
if self.do_lower_case:
token = token.lower()
if self.strip_accents is not False:
token = self._run_strip_accents(token)
elif self.strip_accents:
token = self._run_strip_accents(token)
split_tokens.extend(self._run_split_on_punc(token, never_split))
output_tokens = whitespace_tokenize(" ".join(split_tokens))
return output_tokens
def _run_strip_accents(self, text):
"""Strips accents from a piece of text."""
text = unicodedata.normalize("NFD", text)
output = []
for char in text:
cat = unicodedata.category(char)
if cat == "Mn":
continue
output.append(char)
return "".join(output)
def _run_split_on_punc(self, text, never_split=None):
"""Splits punctuation on a piece of text."""
if not self.do_split_on_punc or (never_split is not None and text in never_split):
return [text]
chars = list(text)
i = 0
start_new_word = True
output = []
while i < len(chars):
char = chars[i]
if _is_punctuation(char):
output.append([char])
start_new_word = True
else:
if start_new_word:
output.append([])
start_new_word = False
output[-1].append(char)
i += 1
return ["".join(x) for x in output]
def _tokenize_chinese_chars(self, text):
"""Adds whitespace around any CJK character."""
output = []
for char in text:
cp = ord(char)
if self._is_chinese_char(cp):
output.append(" ")
output.append(char)
output.append(" ")
else:
output.append(char)
return "".join(output)
def _is_chinese_char(self, cp):
"""Checks whether CP is the codepoint of a CJK character."""
# This defines a "chinese character" as anything in the CJK Unicode block:
# https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
#
# Note that the CJK Unicode block is NOT all Japanese and Korean characters,
# despite its name. The modern Korean Hangul alphabet is a different block,
# as is Japanese Hiragana and Katakana. Those alphabets are used to write
# space-separated words, so they are not treated specially and handled
# like the all of the other languages.
if (
(cp >= 0x4E00 and cp <= 0x9FFF)
or (cp >= 0x3400 and cp <= 0x4DBF) #
or (cp >= 0x20000 and cp <= 0x2A6DF) #
or (cp >= 0x2A700 and cp <= 0x2B73F) #
or (cp >= 0x2B740 and cp <= 0x2B81F) #
or (cp >= 0x2B820 and cp <= 0x2CEAF) #
or (cp >= 0xF900 and cp <= 0xFAFF)
or (cp >= 0x2F800 and cp <= 0x2FA1F) #
): #
return True
return False
def _clean_text(self, text):
"""Performs invalid character removal and whitespace cleanup on text."""
output = []
for char in text:
cp = ord(char)
if cp == 0 or cp == 0xFFFD or _is_control(char):
continue
if _is_whitespace(char):
output.append(" ")
else:
output.append(char)
return "".join(output)
class HerbertTokenizer(PreTrainedTokenizer):
"""
Construct a BPE tokenizer for HerBERT.
Peculiarities:
- uses BERT's pre-tokenizer: BaseTokenizer splits tokens on spaces, and also on punctuation. Each occurrence of a
punctuation character will be treated separately.
- Such pretokenized input is BPE subtokenized
This tokenizer inherits from [`XLMTokenizer`] which contains most of the methods. Users should refer to the
superclass for more information regarding methods.
"""
vocab_files_names = VOCAB_FILES_NAMES
def __init__(
self,
vocab_file,
merges_file,
tokenizer_file=None,
cls_token="<s>",
unk_token="<unk>",
pad_token="<pad>",
mask_token="<mask>",
sep_token="</s>",
bos_token="<s>",
do_lowercase_and_remove_accent=False,
additional_special_tokens=[
"<special0>",
"<special1>",
"<special2>",
"<special3>",
"<special4>",
"<special5>",
"<special6>",
"<special7>",
"<special8>",
"<special9>",
],
lang2id=None,
id2lang=None,
**kwargs,
):
try:
import sacremoses
except ImportError:
raise ImportError(
"You need to install sacremoses to use HerbertTokenizer. "
"See https://pypi.org/project/sacremoses/ for installation."
)
self.sm = sacremoses
# cache of sm.MosesPunctNormalizer instance
self.cache_moses_punct_normalizer = {}
# cache of sm.MosesTokenizer instance
self.cache_moses_tokenizer = {}
self.lang_with_custom_tokenizer = {"zh", "th", "ja"}
# True for current supported model (v1.2.0), False for XLM-17 & 100
self.do_lowercase_and_remove_accent = do_lowercase_and_remove_accent
self.lang2id = lang2id
self.id2lang = id2lang
if lang2id is not None and id2lang is not None:
assert len(lang2id) == len(id2lang)
self.ja_word_tokenizer = None
self.zh_word_tokenizer = None
with open(vocab_file, encoding="utf-8") as vocab_handle:
self.encoder = json.load(vocab_handle)
self.decoder = {v: k for k, v in self.encoder.items()}
with open(merges_file, encoding="utf-8") as merges_handle:
merges = merges_handle.read().split("\n")[:-1]
merges = [tuple(merge.split()[:2]) for merge in merges]
self.bpe_ranks = dict(zip(merges, range(len(merges))))
self.cache = {}
super().__init__(
unk_token=unk_token,
bos_token=bos_token,
sep_token=sep_token,
pad_token=pad_token,
cls_token=cls_token,
mask_token=mask_token,
additional_special_tokens=additional_special_tokens,
lang2id=lang2id,
id2lang=id2lang,
do_lowercase_and_remove_accent=do_lowercase_and_remove_accent,
tokenizer_file=None,
**kwargs,
)
self.bert_pre_tokenizer = BasicTokenizer(
do_lower_case=False,
never_split=self.all_special_tokens,
tokenize_chinese_chars=False,
strip_accents=False,
)
@property
# Copied from transformers.models.xlm.tokenization_xlm.XLMTokenizer.do_lower_case
def do_lower_case(self):
return self.do_lowercase_and_remove_accent
# Copied from transformers.models.xlm.tokenization_xlm.XLMTokenizer.moses_punct_norm
def moses_punct_norm(self, text, lang):
if lang not in self.cache_moses_punct_normalizer:
punct_normalizer = self.sm.MosesPunctNormalizer(lang=lang)
self.cache_moses_punct_normalizer[lang] = punct_normalizer
else:
punct_normalizer = self.cache_moses_punct_normalizer[lang]
return punct_normalizer.normalize(text)
# Copied from transformers.models.xlm.tokenization_xlm.XLMTokenizer.moses_tokenize
def moses_tokenize(self, text, lang):
if lang not in self.cache_moses_tokenizer:
moses_tokenizer = self.sm.MosesTokenizer(lang=lang)
self.cache_moses_tokenizer[lang] = moses_tokenizer
else:
moses_tokenizer = self.cache_moses_tokenizer[lang]
return moses_tokenizer.tokenize(text, return_str=False, escape=False)
# Copied from transformers.models.xlm.tokenization_xlm.XLMTokenizer.moses_pipeline
def moses_pipeline(self, text, lang):
text = replace_unicode_punct(text)
text = self.moses_punct_norm(text, lang)
text = remove_non_printing_char(text)
return text
# Copied from transformers.models.xlm.tokenization_xlm.XLMTokenizer.ja_tokenize
def ja_tokenize(self, text):
if self.ja_word_tokenizer is None:
try:
import Mykytea
self.ja_word_tokenizer = Mykytea.Mykytea(
f"-model {os.path.expanduser('~')}/local/share/kytea/model.bin"
)
except (AttributeError, ImportError):
logger.error(
"Make sure you install KyTea (https://github.com/neubig/kytea) and it's python wrapper"
" (https://github.com/chezou/Mykytea-python) with the following steps"
)
logger.error("1. git clone [email protected]:neubig/kytea.git && cd kytea")
logger.error("2. autoreconf -i")
logger.error("3. ./configure --prefix=$HOME/local")
logger.error("4. make && make install")
logger.error("5. pip install kytea")
raise
return list(self.ja_word_tokenizer.getWS(text))
@property
# Copied from transformers.models.xlm.tokenization_xlm.XLMTokenizer.vocab_size
def vocab_size(self):
return len(self.encoder)
# Copied from transformers.models.xlm.tokenization_xlm.XLMTokenizer.get_vocab
def get_vocab(self):
return dict(self.encoder, **self.added_tokens_encoder)
# Copied from transformers.models.xlm.tokenization_xlm.XLMTokenizer.bpe
def bpe(self, token):
word = tuple(token[:-1]) + (token[-1] + "</w>",)
if token in self.cache:
return self.cache[token]
pairs = get_pairs(word)
if not pairs:
return token + "</w>"
while True:
bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float("inf")))
if bigram not in self.bpe_ranks:
break
first, second = bigram
new_word = []
i = 0
while i < len(word):
try:
j = word.index(first, i)
except ValueError:
new_word.extend(word[i:])
break
else:
new_word.extend(word[i:j])
i = j
if word[i] == first and i < len(word) - 1 and word[i + 1] == second:
new_word.append(first + second)
i += 2
else:
new_word.append(word[i])
i += 1
new_word = tuple(new_word)
word = new_word
if len(word) == 1:
break
else:
pairs = get_pairs(word)
word = " ".join(word)
if word == "\n </w>":
word = "\n</w>"
self.cache[token] = word
return word
def _tokenize(self, text):
pre_tokens = self.bert_pre_tokenizer.tokenize(text)
split_tokens = []
for token in pre_tokens:
if token:
split_tokens.extend(list(self.bpe(token).split(" ")))
return split_tokens
# Copied from transformers.models.xlm.tokenization_xlm.XLMTokenizer._convert_token_to_id
def _convert_token_to_id(self, token):
"""Converts a token (str) in an id using the vocab."""
return self.encoder.get(token, self.encoder.get(self.unk_token))
# Copied from transformers.models.xlm.tokenization_xlm.XLMTokenizer._convert_id_to_token
def _convert_id_to_token(self, index):
"""Converts an index (integer) in a token (str) using the vocab."""
return self.decoder.get(index, self.unk_token)
# Copied from transformers.models.xlm.tokenization_xlm.XLMTokenizer.convert_tokens_to_string
def convert_tokens_to_string(self, tokens):
"""Converts a sequence of tokens (string) in a single string."""
out_string = "".join(tokens).replace("</w>", " ").strip()
return out_string
# Copied from transformers.models.xlm.tokenization_xlm.XLMTokenizer.build_inputs_with_special_tokens
def build_inputs_with_special_tokens(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. An XLM sequence has the following format:
- single sequence: `<s> X </s>`
- pair of sequences: `<s> A </s> B </s>`
Args:
token_ids_0 (`List[int]`):
List of IDs to which the special tokens will be added.
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
Returns:
`List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
"""
bos = [self.bos_token_id]
sep = [self.sep_token_id]
if token_ids_1 is None:
return bos + token_ids_0 + sep
return bos + token_ids_0 + sep + token_ids_1 + sep
# Copied from transformers.models.xlm.tokenization_xlm.XLMTokenizer.get_special_tokens_mask
def get_special_tokens_mask(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
) -> List[int]:
"""
Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
special tokens using the tokenizer `prepare_for_model` method.
Args:
token_ids_0 (`List[int]`):
List of IDs.
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
already_has_special_tokens (`bool`, *optional*, defaults to `False`):
Whether or not the token list is already formatted with special tokens for the model.
Returns:
`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
)
if token_ids_1 is not None:
return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1]
return [1] + ([0] * len(token_ids_0)) + [1]
# Copied from transformers.models.xlm.tokenization_xlm.XLMTokenizer.create_token_type_ids_from_sequences
def create_token_type_ids_from_sequences(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Create a mask from the two sequences passed to be used in a sequence-pair classification task. An XLM sequence
pair mask has the following format:
```
0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
| first sequence | second sequence |
```
If `token_ids_1` is `None`, this method only returns the first portion of the mask (0s).
Args:
token_ids_0 (`List[int]`):
List of IDs.
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
Returns:
`List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s).
"""
sep = [self.sep_token_id]
cls = [self.cls_token_id]
if token_ids_1 is None:
return len(cls + token_ids_0 + sep) * [0]
return len(cls + token_ids_0 + sep) * [0] + len(token_ids_1 + sep) * [1]
# Copied from transformers.models.xlm.tokenization_xlm.XLMTokenizer.save_vocabulary
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
if not os.path.isdir(save_directory):
logger.error(f"Vocabulary path ({save_directory}) should be a directory")
return
vocab_file = os.path.join(
save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
)
merge_file = os.path.join(
save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]
)
with open(vocab_file, "w", encoding="utf-8") as f:
f.write(json.dumps(self.encoder, indent=2, sort_keys=True, ensure_ascii=False) + "\n")
index = 0
with open(merge_file, "w", encoding="utf-8") as writer:
for bpe_tokens, token_index in sorted(self.bpe_ranks.items(), key=lambda kv: kv[1]):
if index != token_index:
logger.warning(
f"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."
" Please check that the tokenizer is not corrupted!"
)
index = token_index
writer.write(" ".join(bpe_tokens) + "\n")
index += 1
return vocab_file, merge_file
# Copied from transformers.models.xlm.tokenization_xlm.XLMTokenizer.__getstate__
def __getstate__(self):
state = self.__dict__.copy()
state["sm"] = None
return state
# Copied from transformers.models.xlm.tokenization_xlm.XLMTokenizer.__setstate__
def __setstate__(self, d):
self.__dict__ = d
try:
import sacremoses
except ImportError:
raise ImportError(
"You need to install sacremoses to use XLMTokenizer. "
"See https://pypi.org/project/sacremoses/ for installation."
)
self.sm = sacremoses
__all__ = ["HerbertTokenizer"]
```
|
================================================================================================================================================
SOURCE CODE FILE: tokenization_herbert_fast.py
LINES: 1
SIZE: 5.82 KB
PATH: scripts\freecad_env\Lib\site-packages\transformers\models\herbert\tokenization_herbert_fast.py
ENCODING: utf-8
```py
# coding=utf-8
# Copyright 2020 The Google AI Language Team Authors, Allegro.pl, Facebook Inc. and the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer
logger = logging.get_logger(__name__)
VOCAB_FILES_NAMES = {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}
class HerbertTokenizerFast(PreTrainedTokenizerFast):
"""
Construct a "Fast" BPE tokenizer for HerBERT (backed by HuggingFace's *tokenizers* library).
Peculiarities:
- uses BERT's pre-tokenizer: BertPreTokenizer splits tokens on spaces, and also on punctuation. Each occurrence of
a punctuation character will be treated separately.
This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the methods. Users should refer to the
superclass for more information regarding methods.
Args:
vocab_file (`str`):
Path to the vocabulary file.
merges_file (`str`):
Path to the merges file.
"""
vocab_files_names = VOCAB_FILES_NAMES
slow_tokenizer_class = HerbertTokenizer
def __init__(
self,
vocab_file=None,
merges_file=None,
tokenizer_file=None,
cls_token="<s>",
unk_token="<unk>",
pad_token="<pad>",
mask_token="<mask>",
sep_token="</s>",
**kwargs,
):
super().__init__(
vocab_file,
merges_file,
tokenizer_file=tokenizer_file,
cls_token=cls_token,
unk_token=unk_token,
pad_token=pad_token,
mask_token=mask_token,
sep_token=sep_token,
**kwargs,
)
def build_inputs_with_special_tokens(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. An HerBERT, like BERT sequence has the following format:
- single sequence: `<s> X </s>`
- pair of sequences: `<s> A </s> B </s>`
Args:
token_ids_0 (`List[int]`):
List of IDs to which the special tokens will be added.
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
Returns:
`List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
"""
cls = [self.cls_token_id]
sep = [self.sep_token_id]
if token_ids_1 is None:
return cls + token_ids_0 + sep
return cls + token_ids_0 + sep + token_ids_1 + sep
def get_special_tokens_mask(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
) -> List[int]:
"""
Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
special tokens using the tokenizer `prepare_for_model` method.
Args:
token_ids_0 (`List[int]`):
List of IDs.
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
already_has_special_tokens (`bool`, *optional*, defaults to `False`):
Whether or not the token list is already formatted with special tokens for the model.
Returns:
`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
)
if token_ids_1 is None:
return [1] + ([0] * len(token_ids_0)) + [1]
return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1]
def create_token_type_ids_from_sequences(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Create a mask from the two sequences passed to be used in a sequence-pair classification task. HerBERT, like
BERT sequence pair mask has the following format:
```
0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
| first sequence | second sequence |
```
Args:
token_ids_0 (`List[int]`):
List of IDs.
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
Returns:
`List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s).
"""
sep = [self.sep_token_id]
cls = [self.cls_token_id]
if token_ids_1 is None:
return len(cls + token_ids_0 + sep) * [0]
return len(cls + token_ids_0 + sep) * [0] + len(token_ids_1 + sep) * [1]
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
files = self._tokenizer.model.save(save_directory, name=filename_prefix)
return tuple(files)
__all__ = ["HerbertTokenizerFast"]
```
|
=============================================================================================================================
SOURCE CODE FILE: __init__.py
LINES: 1
SIZE: 0.97 KB
PATH: scripts\freecad_env\Lib\site-packages\transformers\models\hiera\__init__.py
ENCODING: utf-8
```py
# Copyright 2024 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import _LazyModule
from ...utils.import_utils import define_import_structure
if TYPE_CHECKING:
from .configuration_hiera import *
from .modeling_hiera import *
else:
import sys
_file = globals()["__file__"]
sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)
```
|
========================================================================================================================================
SOURCE CODE FILE: configuration_hiera.py
LINES: 1
SIZE: 9.10 KB
PATH: scripts\freecad_env\Lib\site-packages\transformers\models\hiera\configuration_hiera.py
ENCODING: utf-8
```py
# coding=utf-8
# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Hiera model configuration"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
logger = logging.get_logger(__name__)
class HieraConfig(BackboneConfigMixin, PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`HieraModel`]. It is used to instantiate a Hiera
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar configuration to that of the Hiera
[facebook/hiera-base-224](https://huggingface.co/facebook/hiera-base-224) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
embed_dim (`int`, *optional*, defaults to 96):
Dimensionality of patch embedding.
image_size (`list(int)`, *optional*, defaults to `[224, 224]`):
The size (resolution) of input in the format (height, width) for images
and (frames, height, width) for videos.
patch_size (`list(int)`, *optional*, defaults to `[7, 7]`):
The size (resolution) of each patch.
patch_stride (`list(int)`, *optional*, defaults to `[4, 4]`):
The stride of the patch.
patch_padding (`list(int)`, *optional*, defaults to `[3, 3]`):
The padding of the patch.
mlp_ratio (`float`, *optional*, defaults to 4.0):
The ratio of mlp hidden dim to embedding dim.
depths (`list(int)`, *optional*, defaults to `[2, 3, 16, 3]`):
Depth of each layer in the Transformer encoder.
num_heads (`list(int)`, *optional*, defaults to `[1, 2, 4, 8]`):
Number of attention heads in each layer of the Transformer encoder.
embed_dim_multiplier (`float`, *optional*, defaults to 2.0):
The multiplier to the dimensionality of patch embedding in each layer of the Transformer encoder.
num_query_pool (`int`, *optional*, defaults to 3):
The number of query pool stages.
query_stride (`list(int)`, *optional*, defaults to `[2, 2]`):
The stride of the query pool.
masked_unit_size (`list(int)`, *optional*, defaults to `[8, 8]`):
The size of the masked unit.
masked_unit_attention (`list(bool)`, *optional*, defaults to `[True, True, False, False]`):
Whether to use masked unit attention in each layer of the Transformer encoder.
drop_path_rate (`float`, *optional*, defaults to 0.0):
The drop path rate.
num_channels (`int`, *optional*, defaults to 3):
The number of input channels.
hidden_act (`str`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder. If string, `"gelu"`, `"relu"`,
`"selu"` and `"gelu_new"` are supported.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices and
the zero_initializer for initializing all bias vectors.
layer_norm_init (`float`, *optional*, defaults to 1.0):
The initial weight value for layer normalization layers.
layer_norm_eps (`float`, *optional*, defaults to 1e-06):
The epsilon used by the layer normalization layers.
decoder_hidden_size (`int`, *optional*):
Dimensionality of decoder embeddings for MAE pretraining.
decoder_depth (`int`, *optional*):
Depth of the decoder for MAE pretraining.
decoder_num_heads (`int`, *optional*):
Number of attention heads in each layer of the decoder for MAE pretraining.
normalize_pixel_loss (`bool`, *optional*, defaults to `True`):
Whether to normalize the pixel loss by the number of pixels.
mask_ratio (`float`, *optional*, defaults to 0.6):
The ratio of masked tokens in the input.
out_features (`List[str]`, *optional*):
If used as backbone, list of features to output. Can be any of `"stem"`, `"stage1"`, `"stage2"`, etc.
(depending on how many stages the model has). If unset and `out_indices` is set, will default to the
corresponding stages. If unset and `out_indices` is unset, will default to the last stage. Must be in the
same order as defined in the `stage_names` attribute.
out_indices (`List[int]`, *optional*):
If used as backbone, list of indices of features to output. Can be any of 0, 1, 2, etc. (depending on how
many stages the model has). If unset and `out_features` is set, will default to the corresponding stages.
If unset and `out_features` is unset, will default to the last stage. Must be in the
same order as defined in the `stage_names` attribute.
Example:
```python
>>> from transformers import HieraConfig, HieraModel
>>> # Initializing a Hiera hiera-base-patch16-224 style configuration
>>> configuration = HieraConfig()
>>> # Initializing a model (with random weights) from the hiera-base-patch16-224 style configuration
>>> model = HieraModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "hiera"
attribute_map = {"num_hidden_layers": "num_layers"}
def __init__(
self,
embed_dim=96,
image_size=[224, 224],
patch_size=[7, 7],
patch_stride=[4, 4],
patch_padding=[3, 3],
mlp_ratio=4.0,
depths=[2, 3, 16, 3],
num_heads=[1, 2, 4, 8],
embed_dim_multiplier=2.0,
num_query_pool=3,
query_stride=[2, 2],
masked_unit_size=[8, 8],
masked_unit_attention=[True, True, False, False],
drop_path_rate=0.0,
num_channels=3,
hidden_act="gelu",
initializer_range=0.02,
layer_norm_init=1.0,
layer_norm_eps=1e-6,
decoder_hidden_size=None,
decoder_depth=None,
decoder_num_heads=None,
normalize_pixel_loss=True,
mask_ratio=0.6,
out_features=None,
out_indices=None,
**kwargs,
):
super().__init__(**kwargs)
if masked_unit_size[0] % query_stride[0] ** (len(depths) - 1) != 0:
raise ValueError(
f"masked_unit_size[0] ({masked_unit_size[0]}) must be divisible by query_stride[0] ({query_stride[0]}) "
f"raised to the power of the number of layers ({len(depths) - 1})"
)
if num_query_pool >= len(depths):
raise ValueError(
f"num_query_pool ({num_query_pool}) must be less than the number of layers ({len(depths)})"
)
self.embed_dim = embed_dim
self.image_size = image_size
self.patch_size = patch_size
self.patch_stride = patch_stride
self.patch_padding = patch_padding
self.mlp_ratio = mlp_ratio
self.depths = depths
self.num_heads = num_heads
self.num_layers = len(depths)
self.embed_dim_multiplier = embed_dim_multiplier
self.num_query_pool = num_query_pool
self.query_stride = query_stride
self.masked_unit_size = masked_unit_size
self.masked_unit_attention = masked_unit_attention
self.drop_path_rate = drop_path_rate
self.num_channels = num_channels
self.hidden_act = hidden_act
self.initializer_range = initializer_range
self.layer_norm_init = layer_norm_init
self.layer_norm_eps = layer_norm_eps
self.decoder_hidden_size = decoder_hidden_size
self.decoder_depth = decoder_depth
self.decoder_num_heads = decoder_num_heads
self.normalize_pixel_loss = normalize_pixel_loss
self.mask_ratio = mask_ratio
# we set the hidden_size attribute in order to make Hiera work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
self.hidden_size = int(embed_dim * embed_dim_multiplier ** (len(depths) - 1))
self.stage_names = ["stem"] + [f"stage{idx}" for idx in range(1, len(depths) + 1)]
self._out_features, self._out_indices = get_aligned_output_features_output_indices(
out_features=out_features, out_indices=out_indices, stage_names=self.stage_names
)
__all__ = ["HieraConfig"]
```
|
===================================================================================================================================
SOURCE CODE FILE: modeling_hiera.py
LINES: 1
SIZE: 68.21 KB
PATH: scripts\freecad_env\Lib\site-packages\transformers\models\hiera\modeling_hiera.py
ENCODING: utf-8
```py
# coding=utf-8
# Copyright 2024 Meta and The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""PyTorch Hiera model."""
import math
from dataclasses import dataclass
from typing import Dict, List, Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACT2FN
from ...modeling_outputs import (
BackboneOutput,
BaseModelOutput,
BaseModelOutputWithPooling,
ImageClassifierOutput,
ModelOutput,
)
from ...modeling_utils import PreTrainedModel
from ...utils import (
add_code_sample_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
logging,
replace_return_docstrings,
torch_int,
)
from ...utils.backbone_utils import BackboneMixin
from .configuration_hiera import HieraConfig
logger = logging.get_logger(__name__)
# General docstring
_CONFIG_FOR_DOC = "HieraConfig"
# Base docstring
_CHECKPOINT_FOR_DOC = "facebook/hiera-tiny-224-hf"
_EXPECTED_OUTPUT_SHAPE = [1, 49, 768]
# Image classification docstring
_IMAGE_CLASS_CHECKPOINT = "facebook/hiera-tiny-224-in1k-hf"
_IMAGE_CLASS_EXPECTED_OUTPUT = "tabby, tabby cat"
@dataclass
class HieraEncoderOutput(ModelOutput):
"""
Hiera encoder's outputs, with potential hidden states and attentions.
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the model.
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each stage) of
shape `(batch_size, sequence_length, hidden_size)`. Thesre are the unrolled hidden states of the model.
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each stage) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
reshaped_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each stage) of
shape `(batch_size, height, width, hidden_size)`. These are the reshaped and re-rolled hidden states of the model.
Hidden-states of the model at the output of each layer plus the initial embedding outputs reshaped to
include the spatial dimensions.
"""
last_hidden_state: Optional[torch.FloatTensor] = None
hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
reshaped_hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
@dataclass
class HieraModelOutput(ModelOutput):
"""
Hiera model's outputs that also contains a pooling of the last hidden states.
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the model.
pooler_output (`torch.FloatTensor` of shape `(batch_size, hidden_size)`, *optional*, returned when `add_pooling_layer=True` is passed):
Average pooling of the last layer hidden-state.
bool_masked_pos (`torch.BoolTensor` of shape `(batch_size, sequence_length)`):
Tensor indicating which patches are masked (0) and which are not (1).
ids_restore (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Tensor containing the original index of the (shuffled) masked patches.
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each stage) of
shape `(batch_size, sequence_length, hidden_size)`. These are the unrolled hidden states of the model.
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each stage) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
reshaped_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each stage) of
shape `(batch_size, height, width, hidden_size)`. These are the reshaped and re-rolled hidden states of the model.
Hidden-states of the model at the output of each layer plus the initial embedding outputs reshaped to
include the spatial dimensions.
"""
last_hidden_state: Optional[torch.FloatTensor] = None
pooler_output: Optional[torch.FloatTensor] = None
bool_masked_pos: torch.BoolTensor = None
ids_restore: Optional[torch.LongTensor] = None
hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
reshaped_hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
@dataclass
class HieraForImageClassificationOutput(ImageClassifierOutput):
"""
Hiera image classification outputs.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, `optional`):
Loss value for the training task.
logits (`torch.FloatTensor` of shape `(batch_size, num_labels)`):
Prediction scores of the classification head (logits of the output layer).
hidden_states (`tuple(torch.FloatTensor)`, `optional`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each stage) of
shape `(batch_size, sequence_length, hidden_size)`. These are the unrolled hidden states of the model.
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, `optional`):
Tuple of `torch.FloatTensor` (one for each stage) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
reshaped_hidden_states (`tuple(torch.FloatTensor)`, `optional`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each stage) of
shape `(batch_size, height, width, hidden_size)`. These are the reshaped and re-rolled hidden states of the model.
Hidden-states of the model at the output of each layer plus the initial embedding outputs reshaped to
include the spatial dimensions.
"""
loss: Optional[torch.FloatTensor] = None
logits: Optional[torch.FloatTensor] = None
hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
reshaped_hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
@dataclass
class HieraForPreTrainingOutput(ModelOutput):
"""
Class for HieraForPreTraining's outputs, with potential hidden states and attentions.
Args:
loss (`torch.FloatTensor` of shape `(1,)`):
Pixel reconstruction loss.
logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, patch_size ** 2 * num_channels)`):
Pixel reconstruction logits.
bool_masked_pos (`torch.BoolTensor` of shape `(batch_size, sequence_length)`):
Tensor indicating which patches are masked (0) and which are not (1).
ids_restore (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Tensor containing the original index of the (shuffled) masked patches.
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer
plus the initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in
the self-attention heads.
reshaped_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
shape `(batch_size, height, width, hidden_size)`. Hidden-states of the model at the output of each layer
plus the initial embedding outputs reshaped to include the spatial dimensions.
"""
loss: Optional[torch.FloatTensor] = None
logits: Optional[torch.FloatTensor] = None
bool_masked_pos: torch.BoolTensor = None
ids_restore: Optional[torch.LongTensor] = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None
reshaped_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
class HieraPatchEmbeddings(nn.Module):
"""
This class turns `pixel_values` of shape `(batch_size, num_channels, height, width)` into the initial
`hidden_states` (patch embeddings) of shape `(batch_size, seq_length, hidden_size)` to be consumed by a
Transformer.
"""
def __init__(self, config, is_mae: bool = False):
super().__init__()
# Support any number of spatial dimensions
self.spatial_dims = len(config.patch_size)
if self.spatial_dims != 2:
raise ValueError(f"The number of dimensions of the input image should be 2, but got {self.spatial_dims}.")
self.num_channels = config.num_channels
self.image_size = config.image_size[-2:]
self.tokens_spatial_shape = [i // s for i, s in zip(config.image_size, config.patch_stride)]
self.mask_spatial_shape = [i // s for i, s in zip(self.tokens_spatial_shape, config.masked_unit_size)]
self.mask_ratio = config.mask_ratio
self.is_mae = is_mae
self.projection = nn.Conv2d(
self.num_channels,
config.embed_dim,
kernel_size=config.patch_size,
stride=config.patch_stride,
padding=config.patch_padding,
)
def masked_conv(
self, pixel_values: torch.FloatTensor, bool_masked_pos: Optional[torch.BoolTensor] = None
) -> torch.Tensor:
"""Zero-out the masked regions of the input before conv.
Prevents leakage of masked regions when using overlapping kernels.
"""
if bool_masked_pos is None:
return self.projection(pixel_values)
target_size = pixel_values.shape[2:]
# Reshape bool_masked_pos to (batch_size, 1, mask_unit_height, mask_unit_width)
bool_masked_pos = bool_masked_pos.view(pixel_values.shape[0], 1, *self.mask_spatial_shape)
bool_masked_pos = nn.functional.interpolate(bool_masked_pos.float(), size=target_size)
return self.projection(pixel_values * bool_masked_pos)
def random_masking(
self, pixel_values: torch.FloatTensor, noise: Optional[torch.FloatTensor] = None
) -> Tuple[torch.BoolTensor, torch.LongTensor]:
"""
Perform per-sample random masking by per-sample shuffling. Per-sample shuffling is done by argsort random
noise.
Args:
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`)
noise (`torch.FloatTensor` of shape `(batch_size, num_mask_units)`, *optional*) which is
mainly used for testing purposes to control randomness and maintain the reproducibility
"""
batch_size = pixel_values.shape[0]
# Tokens selected for masking at mask unit level
num_windows = math.prod(self.mask_spatial_shape)
len_keep = int(num_windows * (1 - self.mask_ratio))
if noise is None:
noise = torch.rand(batch_size, num_windows, device=pixel_values.device)
# Sort noise for each sample
ids_shuffle = torch.argsort(noise, dim=1)
# ascend: small is keep, large is remove
ids_restore = torch.argsort(ids_shuffle, dim=1).to(pixel_values.device)
# Generate the binary bool_masked_pos: 1 is *keep*, 0 is *remove*
# Note this is opposite to original MAE
bool_masked_pos = torch.zeros([batch_size, num_windows], device=pixel_values.device)
bool_masked_pos[:, :len_keep] = 1
# Unshuffle to get the binary bool_masked_pos
bool_masked_pos = torch.gather(bool_masked_pos, dim=1, index=ids_restore).bool()
return bool_masked_pos, ids_restore
def forward(
self,
pixel_values: torch.FloatTensor,
noise: Optional[torch.FloatTensor] = None,
) -> Tuple[torch.Tensor, Optional[torch.BoolTensor], Optional[torch.LongTensor]]:
(bool_masked_pos, ids_restore) = (
self.random_masking(pixel_values, noise=noise) if self.is_mae else (None, None)
)
embeddings = self.masked_conv(pixel_values, bool_masked_pos)
embeddings = embeddings.flatten(2).transpose(2, 1)
return embeddings, bool_masked_pos, ids_restore
class HieraEmbeddings(nn.Module):
"""
Construct position and patch embeddings.
"""
def __init__(self, config: HieraConfig, is_mae: bool = False) -> None:
super().__init__()
self.patch_stride = config.patch_stride
tokens_spatial_shape = [i // s for i, s in zip(config.image_size, config.patch_stride)]
self.mask_spatial_shape = [i // s for i, s in zip(tokens_spatial_shape, config.masked_unit_size)]
self.num_tokens = math.prod(tokens_spatial_shape)
self.is_mae = is_mae
self.patch_embeddings = HieraPatchEmbeddings(config, is_mae=is_mae)
self.position_embeddings = nn.Parameter(torch.zeros(1, self.num_tokens, config.embed_dim))
def interpolate_pos_encoding(
self, embeddings: torch.Tensor, pos_embeds: torch.Tensor, height: int, width: int
) -> torch.Tensor:
"""
This method allows to interpolate the pre-trained position encodings, to be able to use the model on higher resolution
images. This method is also adapted to support torch.jit tracing, no class embeddings, and different patch strides.
Adapted from:
- https://github.com/facebookresearch/dino/blob/de9ee3df6cf39fac952ab558447af1fa1365362a/vision_transformer.py#L174-L194, and
- https://github.com/facebookresearch/dinov2/blob/e1277af2ba9496fbadf7aec6eba56e8d882d1e35/dinov2/models/vision_transformer.py#L179-L211
"""
num_patches = embeddings.shape[1]
num_positions = pos_embeds.shape[1]
# always interpolate when tracing to ensure the exported model works for dynamic input shapes
if not torch.jit.is_tracing() and num_patches == num_positions and height == width:
return pos_embeds
dim = embeddings.shape[-1]
new_height = height // self.patch_stride[0]
new_width = width // self.patch_stride[1]
sqrt_num_positions = torch_int(num_positions**0.5)
pos_embeds = pos_embeds.reshape(1, sqrt_num_positions, sqrt_num_positions, dim)
pos_embeds = pos_embeds.permute(0, 3, 1, 2)
pos_embeds = nn.functional.interpolate(
pos_embeds,
size=(new_height, new_width),
mode="bicubic",
align_corners=False,
)
pos_embeds = pos_embeds.permute(0, 2, 3, 1).view(1, -1, dim)
return pos_embeds
def get_position_embedding(
self, embeddings: torch.Tensor, height: int, width: int, interpolate_pos_encoding: bool
) -> torch.FloatTensor:
return (
self.interpolate_pos_encoding(embeddings, self.position_embeddings, height, width)
if interpolate_pos_encoding
else self.position_embeddings
)
def forward(
self,
pixel_values: torch.FloatTensor,
noise: Optional[torch.FloatTensor] = None,
interpolate_pos_encoding: bool = False,
) -> Tuple[torch.Tensor, Optional[torch.BoolTensor], Optional[torch.LongTensor]]:
height, width = pixel_values.shape[-2:]
embeddings, bool_masked_pos, ids_restore = self.patch_embeddings(pixel_values, noise=noise)
embeddings = embeddings + self.get_position_embedding(embeddings, height, width, interpolate_pos_encoding)
return embeddings, bool_masked_pos, ids_restore
class HieraMaskUnitAttention(nn.Module):
"""
Computes either Mask Unit or Global Attention. Also is able to perform query pooling.
Note: this assumes the tokens have already been flattened and unrolled into mask units.
"""
def __init__(
self,
hidden_size: int,
hidden_size_output: int,
num_heads: int,
query_stride: int = 1,
window_size: int = 0,
use_mask_unit_attn: bool = False,
) -> None:
super().__init__()
self.num_heads = num_heads
self.query_stride = query_stride
self.hidden_size_output = hidden_size_output
self.head_dim = hidden_size_output // num_heads
self.scale = (self.head_dim) ** -0.5
self.qkv = nn.Linear(hidden_size, 3 * hidden_size_output)
self.proj = nn.Linear(hidden_size_output, hidden_size_output)
self.window_size = window_size
self.use_mask_unit_attn = use_mask_unit_attn
def forward(
self,
hidden_states: torch.Tensor,
head_mask: Optional[torch.FloatTensor] = None,
output_attentions: bool = False,
) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
"""Input should be of shape [batch, tokens, channels]."""
batch_size, seq_len, _ = hidden_states.shape
num_windows = 1
if self.use_mask_unit_attn:
num_windows = seq_len // (self.query_stride * self.window_size)
qkv = self.qkv(hidden_states)
qkv = qkv.reshape(batch_size, -1, num_windows, 3, self.num_heads, self.head_dim)
qkv = qkv.permute(3, 0, 4, 2, 1, 5)
query, key, value = qkv.unbind(0)
if self.query_stride > 1:
# Refer to unroll to see how this performs a maxpool-Nd
query = query.view(batch_size, self.num_heads, num_windows, self.query_stride, -1, self.head_dim)
query = query.max(dim=3).values
attn_weights = (query * self.scale) @ key.transpose(-1, -2)
attn_weights = attn_weights.softmax(dim=-1)
# Mask heads if we want to
if head_mask is not None:
attn_weights = attn_weights * head_mask
attn_output = attn_weights @ value
attn_output = attn_output.transpose(1, 3).reshape(batch_size, -1, self.hidden_size_output)
attn_output = self.proj(attn_output)
return (attn_output, attn_weights) if output_attentions else (attn_output, None)
# Copied from transformers.models.beit.modeling_beit.drop_path
def drop_path(input: torch.Tensor, drop_prob: float = 0.0, training: bool = False) -> torch.Tensor:
"""
Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
Comment by Ross Wightman: This is the same as the DropConnect impl I created for EfficientNet, etc networks,
however, the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper...
See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for changing the
layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use 'survival rate' as the
argument.
"""
if drop_prob == 0.0 or not training:
return input
keep_prob = 1 - drop_prob
shape = (input.shape[0],) + (1,) * (input.ndim - 1) # work with diff dim tensors, not just 2D ConvNets
random_tensor = keep_prob + torch.rand(shape, dtype=input.dtype, device=input.device)
random_tensor.floor_() # binarize
output = input.div(keep_prob) * random_tensor
return output
# Copied from transformers.models.beit.modeling_beit.BeitDropPath with Beit->Hiera
class HieraDropPath(nn.Module):
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks)."""
def __init__(self, drop_prob: Optional[float] = None) -> None:
super().__init__()
self.drop_prob = drop_prob
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
return drop_path(hidden_states, self.drop_prob, self.training)
def extra_repr(self) -> str:
return "p={}".format(self.drop_prob)
class HieraMlp(nn.Module):
def __init__(self, config, dim: int) -> None:
super().__init__()
self.activation_fn = ACT2FN[config.hidden_act]
self.fc1 = nn.Linear(dim, int(dim * config.mlp_ratio))
self.fc2 = nn.Linear(int(dim * config.mlp_ratio), dim)
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidden_states = self.fc1(hidden_states)
hidden_states = self.activation_fn(hidden_states)
hidden_states = self.fc2(hidden_states)
return hidden_states
class HieraLayer(nn.Module):
def __init__(
self,
config,
hidden_size: int,
hidden_size_output: int,
num_heads: int,
drop_path: float = 0.0,
query_stride: int = 1,
window_size: int = 0,
use_mask_unit_attn: bool = False,
) -> None:
super().__init__()
self.hidden_size = hidden_size
self.hidden_size_output = hidden_size_output
self.query_stride = query_stride
self.layernorm_before = nn.LayerNorm(hidden_size, eps=config.layer_norm_eps)
self.attn = HieraMaskUnitAttention(
hidden_size=hidden_size,
hidden_size_output=hidden_size_output,
num_heads=num_heads,
query_stride=query_stride,
window_size=window_size,
use_mask_unit_attn=use_mask_unit_attn,
)
self.layernorm_after = nn.LayerNorm(hidden_size_output, eps=config.layer_norm_eps)
self.mlp = HieraMlp(config, hidden_size_output)
self.drop_path = HieraDropPath(drop_path) if drop_path > 0 else nn.Identity()
if hidden_size != hidden_size_output:
self.proj = nn.Linear(hidden_size, hidden_size_output)
def forward(
self,
hidden_states: torch.Tensor,
head_mask: Optional[torch.FloatTensor] = None,
output_attentions: bool = False,
) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
batch_size, seq_len, _ = hidden_states.shape
# Attention + Q Pooling
hidden_states_norm = self.layernorm_before(hidden_states)
if self.hidden_size != self.hidden_size_output:
hidden_states = self.proj(hidden_states_norm)
# Refer to unroll to see how this performs a maxpool-Nd
hidden_states = (
hidden_states.view(batch_size, self.query_stride, -1, self.hidden_size_output).max(dim=1).values
)
(hidden_states_norm, attn_weights) = self.attn(
hidden_states_norm, head_mask, output_attentions=output_attentions
)
hidden_states = hidden_states + self.drop_path(hidden_states_norm)
residual = hidden_states
hidden_states = self.layernorm_after(hidden_states)
hidden_states = self.mlp(hidden_states)
hidden_states = residual + self.drop_path(hidden_states)
return (hidden_states, attn_weights)
class HieraStage(nn.Module):
def __init__(
self,
config,
depth: int,
hidden_size: int,
hidden_size_output: int,
num_heads: int,
drop_path: List[float],
query_stride: List[int],
window_size: int,
use_mask_unit_attn: bool,
stage_num: Optional[int] = None,
) -> None:
super().__init__()
# we need to know if the previous stage used masked attention
# mask unit or global attention.
# lag by 1 layer, so that global attention,
# applied post pooling on lower resolution
previous_stage_used_masked_attention = False
if stage_num is not None:
previous_stage_used_masked_attention = config.masked_unit_attention[stage_num - 1 if stage_num > 0 else 0]
self.layers = nn.ModuleList(
[
HieraLayer(
config=config,
hidden_size=hidden_size if i == 0 else hidden_size_output,
hidden_size_output=hidden_size_output,
num_heads=num_heads,
drop_path=drop_path[i],
query_stride=query_stride[i],
window_size=window_size,
use_mask_unit_attn=use_mask_unit_attn or (previous_stage_used_masked_attention and i == 0),
)
for i in range(depth)
]
)
def forward(
self, hidden_states: torch.Tensor, head_mask: Optional[torch.FloatTensor], output_attentions: bool = False
) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
for i, layer_module in enumerate(self.layers):
layer_head_mask = head_mask[i] if head_mask is not None else None
(hidden_states, attn_weights) = layer_module(
hidden_states, layer_head_mask, output_attentions=output_attentions
)
return hidden_states, attn_weights
def undo_windowing(hidden_states: torch.Tensor, shape: List[int], mask_unit_shape: List[int]) -> torch.Tensor:
"""
Restore spatial organization by undoing windowed organization of mask units.
Args:
hidden_states (`torch.Tensor`): The hidden states tensor of shape `[batch_size, num_mask_unit_height*num_mask_unit_width, hidden_size]`.
shape (`List[int]`): The original shape of the hidden states tensor before windowing.
mask_unit_shape (`List[int]`): The shape of the mask units used for windowing.
Returns:
torch.Tensor: The restored hidden states tensor of shape [batch_size, num_mask_unit_height*mask_unit_height, num_mask_unit_width*mask_unit_width, hidden_size].
"""
batch_size, hidden_size = hidden_states.shape[0], hidden_states.shape[-1]
# From: [batch_size, num_mask_unit_height*num_mask_unit_width, hidden_size]
# To: [batch_size, num_mask_unit_height, num_mask_unit_width, mask_unit_height, mask_unit_width, hidden_size]
num_mask_units = [s // mu for s, mu in zip(shape, mask_unit_shape)]
hidden_states = hidden_states.view(batch_size, *num_mask_units, *mask_unit_shape, hidden_size)
# From: [batch_size, num_mask_unit_height, num_mask_unit_width, mask_unit_height, mask_unit_width, hidden_size]
# To: [batch_size, num_mask_unit_height*mask_unit_height, num_mask_unit_width*mask_unit_width, hidden_size]
hidden_states = hidden_states.permute(0, 1, 3, 2, 4, 5)
hidden_states = hidden_states.reshape(batch_size, *shape, hidden_size)
return hidden_states
class HieraEncoder(nn.Module):
def __init__(self, config: HieraConfig) -> None:
super().__init__()
total_depth = sum(config.depths)
# stochastic depth decay rule
dpr = [x.item() for x in torch.linspace(0, config.drop_path_rate, total_depth)]
# query strides rule
cumulative_depths = torch.tensor(config.depths).cumsum(0).tolist()
query_pool_layer = cumulative_depths[: config.num_query_pool]
query_strides = [math.prod(config.query_stride) if i in query_pool_layer else 1 for i in range(total_depth)]
# Transformer blocks
self.stages = nn.ModuleList()
hidden_size = config.embed_dim
stage_ends = [0] + cumulative_depths
masked_unit_area = math.prod(config.masked_unit_size)
query_stride_area = math.prod(config.query_stride)
for idx_stage, depth in enumerate(config.depths):
hidden_size_output = int(config.embed_dim * config.embed_dim_multiplier**idx_stage)
stage = HieraStage(
config=config,
depth=depth,
hidden_size=hidden_size,
hidden_size_output=hidden_size_output,
num_heads=config.num_heads[idx_stage],
drop_path=dpr[stage_ends[idx_stage] : stage_ends[idx_stage + 1]],
query_stride=query_strides[stage_ends[idx_stage] : stage_ends[idx_stage + 1]],
window_size=int(masked_unit_area * query_stride_area**-idx_stage),
use_mask_unit_attn=config.masked_unit_attention[idx_stage],
stage_num=idx_stage,
)
hidden_size = hidden_size_output
self.stages.append(stage)
# Setting reroll schedule
# The first stage has to reverse everything
# The next stage has to reverse all but the first unroll, etc.
stage_size = [i // s for i, s in zip(config.image_size, config.patch_stride)]
unroll_schedule = [config.query_stride] * len(config.depths[:-1])
self.schedule = {}
for idx_stage in range(len(config.depths)):
self.schedule[idx_stage] = unroll_schedule, stage_size
if idx_stage < config.num_query_pool:
stage_size = [i // s for i, s in zip(stage_size, config.query_stride)]
unroll_schedule = unroll_schedule[1:]
self.gradient_checkpointing = False
def reroll(
self, hidden_states: torch.Tensor, stage_idx: int, bool_masked_pos: Optional[torch.BoolTensor] = None
) -> torch.Tensor:
"""
Roll the given tensor back up to spatial order assuming it's from the given block.
If no bool_masked_pos is provided returns:
- [batch_size, height, width, hidden_size]
If a bool_masked_pos is provided returns:
- [batch_size, num_mask_units, mask_unit_height, mask_unit_width, hidden_size]
"""
schedule, size = self.schedule[stage_idx]
batch_size, seq_len, hidden_size = hidden_states.shape
num_dim = len(size)
mask_unit_shape = [1] * num_dim
for strides in schedule:
# Extract the current patch from seq_len
hidden_states = hidden_states.view(
batch_size, *strides, seq_len // math.prod(strides), *mask_unit_shape, hidden_size
)
# Move that patch into the current MU
# Input: [batch_size, stride, stride, seq_len//(stride*stride), mask_unit_height, mask_unit_width, hidden_size]
# Output: [batch_size, seq_len//(stride*stride), stride, mask_unit_height, stride, mask_unit_width, hidden_size]
hidden_states = hidden_states.permute(0, 3, 1, 4, 2, 5, 6)
# Reshape to [batch_size, seq_len//(stride*stride), *mask_units, hidden_size]
for i in range(num_dim):
mask_unit_shape[i] *= strides[i]
hidden_states = hidden_states.reshape(batch_size, -1, *mask_unit_shape, hidden_size)
seq_len = hidden_states.shape[1]
# Current shape (e.g., 2d: [batch_size, #num_mask_units_height*#num_mask_units_width, mask_unit_height, mask_unit_width, hidden_size])
hidden_states = hidden_states.view(batch_size, seq_len, *mask_unit_shape, hidden_size)
# If masked, return [batch_size, num_mask_units, mask_unit_height, mask_unit_width, hidden_size]
if bool_masked_pos is not None:
return hidden_states
# If not masked, we can return [batch_size, height, width, hidden_size]
hidden_states = undo_windowing(hidden_states, size, mask_unit_shape)
return hidden_states
def forward(
self,
hidden_states: torch.Tensor,
bool_masked_pos: Optional[torch.BoolTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
output_attentions: bool = False,
output_hidden_states: bool = False,
return_dict: bool = True,
) -> Union[tuple, BaseModelOutput]:
all_hidden_states = () if output_hidden_states else None
all_reshaped_hidden_states = () if output_hidden_states else None
all_self_attentions = () if output_attentions else None
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
reshaped_hidden_states = self.reroll(hidden_states, stage_idx=0, bool_masked_pos=bool_masked_pos)
all_reshaped_hidden_states = all_reshaped_hidden_states + (reshaped_hidden_states,)
for i, stage_module in enumerate(self.stages):
layer_head_mask = head_mask[i] if head_mask is not None else None
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
stage_module.__call__, hidden_states, layer_head_mask, output_attentions
)
else:
layer_outputs = stage_module(hidden_states, layer_head_mask, output_attentions)
hidden_states = layer_outputs[0]
if output_attentions:
all_self_attentions = all_self_attentions + (layer_outputs[1],)
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
reshaped_hidden_states = self.reroll(hidden_states, stage_idx=i, bool_masked_pos=bool_masked_pos)
all_reshaped_hidden_states = all_reshaped_hidden_states + (reshaped_hidden_states,)
if not return_dict:
return tuple(
v
for v in [hidden_states, all_hidden_states, all_self_attentions, all_reshaped_hidden_states]
if v is not None
)
return HieraEncoderOutput(
last_hidden_state=hidden_states,
hidden_states=all_hidden_states,
attentions=all_self_attentions,
reshaped_hidden_states=all_reshaped_hidden_states,
)
def unroll(
hidden_states: torch.Tensor, image_shape: Tuple[int, int], patch_stride: Tuple[int, int], schedule: List[List[int]]
) -> torch.Tensor:
"""
Reorders the tokens such that patches are contiguous in memory.
E.g., given [batch_size, (height, width), hidden_size] and stride of (stride, stride), this will re-order the tokens as
[batch_size, (stride, stride, height // stride, width // stride), hidden_size]
This allows operations like Max2d to be computed as x.view(batch_size, stride*stride, -1, hidden_size).max(dim=1).
Not only is this faster, but it also makes it easy to support inputs of arbitrary
dimensions in addition to patch-wise sparsity.
Performing this operation multiple times in sequence puts entire windows as contiguous
in memory. For instance, if you applied the stride (2, 2) 3 times, entire windows of
size 8x8 would be contiguous in memory, allowing operations like mask unit attention
computed easily and efficiently, while also allowing max to be applied sequentially.
Note: This means that intermediate values of the model are not in height x width order, so they
need to be re-rolled if you want to use the intermediate values as a height x width feature map.
The last block of the network is fine though, since by then the strides are all consumed.
"""
batch_size, _, hidden_size = hidden_states.shape
size = [i // s for i, s in zip(image_shape, patch_stride)]
current_size = size
hidden_states = hidden_states.view(*([batch_size] + current_size + [hidden_size]))
for strides in schedule:
# Move patches with the given strides to the batch dimension
# Create a view of the tensor with the patch stride as separate dims
# For example in 2d: [batch_size, height // stride, stride, width // stride, stride, C]
current_size = [i // s for i, s in zip(current_size, strides)]
# initialize new_shape with [height // stride, stride, width // stride, stride]
new_shape = [item for pair in zip(current_size, strides) for item in pair]
# add batch_size and hidden_size to new_shape
new_shape = [batch_size] + new_shape + [hidden_size]
hidden_states = hidden_states.view(new_shape)
# Move the patch stride into the batch dimension
# For example in 2d: [batch_size, stride, stride, height // stride, width // stride, hidden_size]
num_dims = len(new_shape)
permute = [0] + list(range(2, num_dims - 1, 2)) + list(range(1, num_dims - 1, 2)) + [num_dims - 1]
hidden_states = hidden_states.permute(permute)
# Now finally flatten the relevant dims into the batch dimension
hidden_states = hidden_states.flatten(0, len(strides))
batch_size *= math.prod(strides)
hidden_states = hidden_states.reshape(-1, math.prod(size), hidden_size)
return hidden_states
class HieraPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = HieraConfig
base_model_prefix = "hiera"
main_input_name = "pixel_values"
supports_gradient_checkpointing = True
def _init_weights(self, module) -> None:
"""Initialize the weights"""
std = self.config.initializer_range
if isinstance(module, HieraEmbeddings):
nn.init.trunc_normal_(module.position_embeddings, std=std)
elif isinstance(module, HieraDecoder):
nn.init.trunc_normal_(module.mask_token, std=std)
nn.init.trunc_normal_(module.decoder_position_embeddings, std=std)
elif isinstance(module, (nn.Linear, nn.Conv1d, nn.Conv2d)):
nn.init.trunc_normal_(module.weight, std=std)
if module.bias is not None:
nn.init.constant_(module.bias, std)
elif isinstance(module, nn.LayerNorm):
nn.init.constant_(module.bias, std)
nn.init.constant_(module.weight, self.config.layer_norm_init)
HIERA_START_DOCSTRING = r"""
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`HieraConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
HIERA_INPUTS_DOCSTRING = r"""
Args:
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See [`BitImageProcessor.__call__`]
for details.
head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
interpolate_pos_encoding (`bool`, *optional*):
Whether to interpolate the pre-trained position encodings.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
class HieraPooler(nn.Module):
def __init__(self, config: HieraConfig):
super().__init__()
num_features = int(config.embed_dim * config.embed_dim_multiplier ** (len(config.depths) - 1))
self.layernorm = nn.LayerNorm(num_features, eps=config.layer_norm_eps)
self.pooler = nn.AdaptiveAvgPool1d(1)
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidden_states = hidden_states.transpose(1, 2)
pooled_output = self.pooler(hidden_states)
pooled_output = torch.flatten(pooled_output, 1)
pooled_output = self.layernorm(pooled_output)
return pooled_output
@add_start_docstrings(
"The bare Hiera Model transformer outputting raw hidden-states without any specific head on top.",
HIERA_START_DOCSTRING,
"""
add_pooling_layer (`bool`, *optional*, defaults to `True`):
Whether or not to apply pooling layer.
is_mae (`bool`, *optional*, defaults to `False`):
Whether or not to run the model on MAE mode.
""",
)
class HieraModel(HieraPreTrainedModel):
def __init__(self, config: HieraConfig, add_pooling_layer: bool = True, is_mae: bool = False):
super().__init__(config)
self.num_features = int(config.embed_dim * config.embed_dim_multiplier ** (len(config.depths) - 1))
self.embeddings = HieraEmbeddings(config, is_mae=is_mae)
self.encoder = HieraEncoder(config)
self.unroll_schedule = [config.query_stride] * len(config.depths[:-1])
self.pooler = HieraPooler(config) if add_pooling_layer else None
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self) -> HieraPatchEmbeddings:
return self.embeddings.patch_embeddings
def _prune_heads(self, heads_to_prune: Dict[int, List[int]]) -> None:
"""
Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
class PreTrainedModel
"""
for layer, heads in heads_to_prune.items():
self.encoder.layer[layer].attention.prune_heads(heads)
@add_start_docstrings_to_model_forward(HIERA_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=HieraModelOutput,
config_class=_CONFIG_FOR_DOC,
modality="vision",
expected_output=_EXPECTED_OUTPUT_SHAPE,
)
def forward(
self,
pixel_values: Optional[torch.Tensor] = None,
noise: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
interpolate_pos_encoding: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, BaseModelOutputWithPooling]:
r"""
noise (`torch.FloatTensor` of shape `(batch_size, num_mask_units)`, *optional*) which is
mainly used for testing purposes to control randomness and maintain the reproducibility
when is_mae is set to True.
"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if pixel_values is None:
raise ValueError("You have to specify pixel_values")
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x n_heads x N x N
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
head_mask = self.get_head_mask(head_mask, len(self.config.depths))
embedding_output, bool_masked_pos, ids_restore = self.embeddings(
pixel_values, interpolate_pos_encoding=interpolate_pos_encoding, noise=noise
)
image_shape = (pixel_values.shape[-2], pixel_values.shape[-1])
hidden_states = unroll(
embedding_output,
image_shape=image_shape,
patch_stride=self.config.patch_stride,
schedule=self.unroll_schedule,
)
# Discard masked tokens if bool_masked_pos is provided
if bool_masked_pos is not None:
mask_unit_area = math.prod(self.config.masked_unit_size)
batch_size, _, hidden_size = hidden_states.shape
positions = bool_masked_pos.unsqueeze(-1).tile(1, mask_unit_area, hidden_size)
hidden_states = hidden_states[positions]
hidden_states = hidden_states.view(batch_size, -1, hidden_size)
encoder_outputs = self.encoder(
hidden_states,
bool_masked_pos=bool_masked_pos,
head_mask=head_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = encoder_outputs[0]
pooled_output = None
if self.pooler is not None:
pooled_output = self.pooler(sequence_output)
if not return_dict:
head_outputs = (sequence_output, pooled_output) if pooled_output is not None else (sequence_output,)
head_outputs = (
head_outputs + (bool_masked_pos, ids_restore) if bool_masked_pos is not None else head_outputs
)
return head_outputs + encoder_outputs[1:]
return HieraModelOutput(
last_hidden_state=sequence_output,
pooler_output=pooled_output,
bool_masked_pos=bool_masked_pos,
ids_restore=ids_restore,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
reshaped_hidden_states=encoder_outputs.reshaped_hidden_states,
)
class HieraDecoder(nn.Module):
def __init__(self, config: HieraConfig):
super().__init__()
num_features = int(config.embed_dim * config.embed_dim_multiplier ** (len(config.depths) - 1))
tokens_spatial_shape = [i // s for i, s in zip(config.image_size, config.patch_stride)]
self.tokens_spatial_shape_final = [
i // s ** (config.num_query_pool) for i, s in zip(tokens_spatial_shape, config.query_stride)
]
self.mask_unit_spatial_shape_final = [
i // s ** (config.num_query_pool) for i, s in zip(config.masked_unit_size, config.query_stride)
]
self.decoder_embeddings = nn.Linear(num_features, config.decoder_hidden_size)
self.mask_token = nn.Parameter(torch.zeros(1, 1, config.decoder_hidden_size))
self.decoder_position_embeddings = nn.Parameter(
torch.zeros(1, math.prod(self.tokens_spatial_shape_final), config.decoder_hidden_size)
)
self.decoder_block = HieraStage(
config=config,
hidden_size=config.decoder_hidden_size,
hidden_size_output=config.decoder_hidden_size,
num_heads=config.decoder_num_heads,
depth=config.decoder_depth,
use_mask_unit_attn=False,
drop_path=[0.0] * config.decoder_depth,
query_stride=[1] * config.decoder_depth,
window_size=0,
)
self.decoder_norm = nn.LayerNorm(config.decoder_hidden_size, eps=config.layer_norm_eps)
# patch stride of prediction
self.pred_stride = config.patch_stride[-1] * (config.query_stride[-1] ** config.num_query_pool)
pred_dim = (self.pred_stride ** len(config.query_stride)) * config.num_channels
self.decoder_pred = nn.Linear(config.decoder_hidden_size, pred_dim)
def forward(
self,
encoder_hidden_states: torch.Tensor,
bool_masked_pos: torch.BoolTensor,
head_mask: Optional[torch.Tensor] = None,
output_attentions: bool = False,
) -> Tuple[torch.Tensor, torch.BoolTensor]:
# Embed tokens
hidden_states = self.decoder_embeddings(encoder_hidden_states)
# Combine visible and bool_masked_pos tokens
# hidden_states : [batch_size, num_mask_units_visible, *mask_unit_spatial_shape_final, decoder_hidden_size]
# bool_masked_pos: [batch_size, num_mask_units]
mask_unit_height, mask_unit_width, decoder_hidden_size = hidden_states.shape[2:]
batch_size, num_mask_units = bool_masked_pos.shape
decoder_hidden_states = torch.zeros(
batch_size,
num_mask_units,
mask_unit_height,
mask_unit_width,
decoder_hidden_size,
device=hidden_states.device,
dtype=hidden_states.dtype,
)
mask_tokens = self.mask_token.view(1, 1, 1, 1, -1)
bool_masked_pos = bool_masked_pos.reshape(batch_size, num_mask_units, 1, 1, 1)
bool_masked_pos = bool_masked_pos.expand(-1, -1, mask_unit_height, mask_unit_width, decoder_hidden_size)
decoder_hidden_states[bool_masked_pos] = hidden_states.flatten()
decoder_hidden_states = (
1 - bool_masked_pos.float()
) * mask_tokens + bool_masked_pos.float() * decoder_hidden_states
# Get back spatial order
hidden_states = undo_windowing(
decoder_hidden_states,
self.tokens_spatial_shape_final,
self.mask_unit_spatial_shape_final,
)
bool_masked_pos = undo_windowing(
bool_masked_pos[..., 0:1],
self.tokens_spatial_shape_final,
self.mask_unit_spatial_shape_final,
)
# Flatten
hidden_states = hidden_states.reshape(hidden_states.shape[0], -1, hidden_states.shape[-1])
bool_masked_pos = bool_masked_pos.view(hidden_states.shape[0], -1)
# Add pos embed
hidden_states = hidden_states + self.decoder_position_embeddings
# Apply decoder blocks
hidden_states, attn_weights = self.decoder_block(
hidden_states, head_mask=head_mask, output_attentions=output_attentions
)
hidden_states = self.decoder_norm(hidden_states)
# Predictor projection
hidden_states = self.decoder_pred(hidden_states)
return hidden_states, bool_masked_pos
class HieraMultiScaleHead(nn.Module):
def __init__(self, config: HieraConfig):
super().__init__()
self.mask_unit_spatial_shape_final = [
i // s ** (config.num_query_pool) for i, s in zip(config.masked_unit_size, config.query_stride)
]
self.stage_dimensions = [
int(config.embed_dim * config.embed_dim_multiplier**i) for i in range(len(config.depths))
]
current_masked_unit_size = config.masked_unit_size
self.multi_scale_fusion_heads = nn.ModuleList()
for idx in range(config.num_query_pool):
kernel = [i // s for i, s in zip(current_masked_unit_size, self.mask_unit_spatial_shape_final)]
current_masked_unit_size = [i // s for i, s in zip(current_masked_unit_size, config.query_stride)]
self.multi_scale_fusion_heads.append(
nn.Conv2d(
self.stage_dimensions[idx],
self.stage_dimensions[-1],
kernel_size=kernel,
stride=kernel,
)
)
self.multi_scale_fusion_heads.append(nn.Identity())
def apply_fusion_head(self, head: nn.Module, hidden_states: torch.Tensor) -> torch.Tensor:
if isinstance(head, nn.Identity):
return hidden_states
# Doing explicit to avoid problems with torch.fx
batch_size, num_mask_units, mask_unit_height, mask_unit_width, hidden_size = hidden_states.shape
# From: [batch_size, num_mask_units, mask_unit_height, mask_unit_width, hidden_size]
# To: head([batch_size * num_mask_units, hidden_size, mask_unit_height, mask_unit_width])
hidden_states = hidden_states.reshape(
batch_size * num_mask_units, mask_unit_height, mask_unit_width, hidden_size
)
hidden_states = hidden_states.permute(0, 3, 1, 2)
hidden_states = head(hidden_states)
# Restore original layout
hidden_states = hidden_states.permute(0, 2, 3, 1)
mask_unit_height_final, mask_unit_width_final, hidden_size = hidden_states.shape[1:]
hidden_states = hidden_states.reshape(
batch_size, num_mask_units, mask_unit_height_final, mask_unit_width_final, hidden_size
)
return hidden_states
def forward(self, feature_maps: List[torch.Tensor]) -> torch.Tensor:
# Multi-scale fusion
hidden_states = 0.0
for head, feature_map in zip(self.multi_scale_fusion_heads, feature_maps):
hidden_states = hidden_states + self.apply_fusion_head(head, feature_map)
return hidden_states
@add_start_docstrings(
"""The Hiera Model transformer with the decoder on top for self-supervised pre-training.
<Tip>
Note that we provide a script to pre-train this model on custom data in our [examples
directory](https://github.com/huggingface/transformers/tree/main/examples/pytorch/image-pretraining).
</Tip>
""",
HIERA_START_DOCSTRING,
)
class HieraForPreTraining(HieraPreTrainedModel):
def __init__(self, config: HieraConfig) -> None:
super().__init__(config)
# Encoder
self.hiera = HieraModel(config, add_pooling_layer=False, is_mae=True)
self.encoder_norm = nn.LayerNorm(self.hiera.num_features, eps=config.layer_norm_eps)
# Multi-scale fusion heads
self.multiscale_fusion = HieraMultiScaleHead(config)
# Decoder
self.decoder = HieraDecoder(config)
self.pred_stride = self.decoder.pred_stride
# Initialize weights and apply final processing
self.post_init()
def get_pixel_label_2d(self, pixel_values: torch.Tensor, bool_masked_pos: torch.BoolTensor) -> torch.Tensor:
# bool_masked_pos (boolean tensor): True means *masked*
pixel_values = pixel_values.permute(0, 2, 3, 1)
size = self.pred_stride
label = pixel_values.unfold(1, size, size).unfold(2, size, size)
label = label.flatten(1, 2).flatten(2)
label = label[bool_masked_pos]
if self.config.normalize_pixel_loss:
mean = label.mean(dim=-1, keepdim=True)
var = label.var(dim=-1, keepdim=True)
label = (label - mean) / (var + 1.0e-6) ** 0.5
return label
def forward_loss(self, pixel_values: torch.Tensor, logits: torch.Tensor, bool_masked_pos: torch.BoolTensor):
# We invert the bool_masked_pos such that 1.0 is *masked*
bool_masked_pos = ~bool_masked_pos
label = self.get_pixel_label_2d(pixel_values, bool_masked_pos)
logits = logits[bool_masked_pos]
loss = (logits - label) ** 2
loss = loss.mean()
return loss
@add_start_docstrings_to_model_forward(HIERA_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=HieraForPreTrainingOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
pixel_values: Optional[torch.Tensor] = None,
noise: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
interpolate_pos_encoding: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[tuple, HieraForPreTrainingOutput]:
r"""
noise (`torch.FloatTensor` of shape `(batch_size, num_mask_units)`, *optional*) which is
mainly used for testing purposes to control randomness and maintain the reproducibility
when is_mae is set to True.
Returns:
Examples:
```python
>>> from transformers import AutoImageProcessor, HieraForPreTraining
>>> import torch
>>> from PIL import Image
>>> import requests
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> image_processor = AutoImageProcessor.from_pretrained("facebook/hiera-tiny-224-mae-hf")
>>> model = HieraForPreTraining.from_pretrained("facebook/hiera-tiny-224-mae-hf")
>>> inputs = image_processor(images=image, return_tensors="pt")
>>> outputs = model(**inputs)
>>> logits = outputs.logits
>>> loss = outputs.loss
>>> print(list(logits.shape))
[1, 196, 768]
```"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
outputs = self.hiera(
pixel_values,
noise=noise,
head_mask=head_mask,
output_attentions=output_attentions,
output_hidden_states=True,
interpolate_pos_encoding=interpolate_pos_encoding,
return_dict=return_dict,
)
feature_maps = outputs[-1]
bool_masked_pos = outputs[1]
ids_to_restore = outputs[2]
# Take only the query pooled and last hidden states
feature_maps = feature_maps[1 : self.hiera.config.num_query_pool + 1] + (feature_maps[-1],)
fused_hidden_states = self.multiscale_fusion(feature_maps)
fused_hidden_states = self.encoder_norm(fused_hidden_states)
# Reconstruct pixel values
logits, bool_masked_pos = self.decoder(
fused_hidden_states,
bool_masked_pos=bool_masked_pos,
head_mask=head_mask,
output_attentions=output_attentions,
)
loss = self.forward_loss(pixel_values, logits, bool_masked_pos)
if not return_dict:
output = (logits, bool_masked_pos, ids_to_restore)
if output_hidden_states:
output = output + (outputs[3],)
if output_attentions:
output = output + (outputs[4],)
if output_hidden_states:
output = output + (outputs[-1],)
return ((loss,) + output) if loss is not None else output
return HieraForPreTrainingOutput(
loss=loss,
logits=logits,
bool_masked_pos=bool_masked_pos,
ids_restore=ids_to_restore,
hidden_states=outputs.hidden_states if output_hidden_states else None,
attentions=outputs.attentions,
reshaped_hidden_states=outputs.reshaped_hidden_states if output_hidden_states else None,
)
@add_start_docstrings(
"""
Hiera Model transformer with an image classification head on top (a linear layer on top of the final hidden state with
average pooling) e.g. for ImageNet.
<Tip>
Note that it's possible to fine-tune Hiera on higher resolution images than the ones it has been trained on, by
setting `interpolate_pos_encoding` to `True` in the forward of the model. This will interpolate the pre-trained
position embeddings to the higher resolution.
</Tip>
""",
HIERA_START_DOCSTRING,
)
class HieraForImageClassification(HieraPreTrainedModel):
def __init__(self, config: HieraConfig) -> None:
super().__init__(config)
self.num_labels = config.num_labels
self.hiera = HieraModel(config, add_pooling_layer=True, is_mae=False)
# Classifier head
self.classifier = (
nn.Linear(self.hiera.num_features, config.num_labels) if config.num_labels > 0 else nn.Identity()
)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(HIERA_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT,
output_type=HieraForImageClassificationOutput,
config_class=_CONFIG_FOR_DOC,
expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT,
)
def forward(
self,
pixel_values,
head_mask: Optional[torch.Tensor] = None,
labels: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
interpolate_pos_encoding: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[tuple, HieraForImageClassificationOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the image classification/regression loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
outputs = self.hiera(
pixel_values,
head_mask=head_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
interpolate_pos_encoding=interpolate_pos_encoding,
return_dict=return_dict,
)
pooled_output = outputs[1]
logits = self.classifier(pooled_output)
loss = None
if labels is not None:
# move labels to correct device to enable model parallelism
labels = labels.to(logits.device)
if self.config.problem_type is None:
if self.num_labels == 1:
self.config.problem_type = "regression"
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
self.config.problem_type = "single_label_classification"
else:
self.config.problem_type = "multi_label_classification"
if self.config.problem_type == "regression":
loss_fct = MSELoss()
if self.num_labels == 1:
loss = loss_fct(logits.squeeze(), labels.squeeze())
else:
loss = loss_fct(logits, labels)
elif self.config.problem_type == "single_label_classification":
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
elif self.config.problem_type == "multi_label_classification":
loss_fct = BCEWithLogitsLoss()
loss = loss_fct(logits, labels)
if not return_dict:
output = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return HieraForImageClassificationOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
reshaped_hidden_states=outputs.reshaped_hidden_states,
)
@add_start_docstrings(
"""
Hiera backbone, to be used with frameworks like DETR and MaskFormer.
""",
HIERA_START_DOCSTRING,
)
class HieraBackbone(HieraPreTrainedModel, BackboneMixin):
def __init__(self, config: HieraConfig):
super().__init__(config)
super()._init_backbone(config)
self.num_features = [config.embed_dim] + [
int(config.embed_dim * config.embed_dim_multiplier**i) for i in range(len(config.depths))
]
self.embeddings = HieraEmbeddings(config, is_mae=False)
self.encoder = HieraEncoder(config)
# Add layer norms to hidden states of out_features
hidden_states_norms = {}
for stage, num_channels in zip(self._out_features, self.channels):
hidden_states_norms[stage] = nn.LayerNorm(num_channels)
self.hidden_states_norms = nn.ModuleDict(hidden_states_norms)
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.embeddings.patch_embeddings
def forward(
self,
pixel_values: torch.Tensor,
output_hidden_states: Optional[bool] = None,
output_attentions: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> BackboneOutput:
"""
Returns:
Examples:
```python
>>> from transformers import AutoImageProcessor, AutoBackbone
>>> import torch
>>> from PIL import Image
>>> import requests
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> processor = AutoImageProcessor.from_pretrained("facebook/hiera-tiny-224-hf")
>>> model = AutoBackbone.from_pretrained(
... "facebook/hiera-tiny-224-hf", out_features=["stage1", "stage2", "stage3", "stage4"]
... )
>>> inputs = processor(image, return_tensors="pt")
>>> outputs = model(**inputs)
>>> feature_maps = outputs.feature_maps
>>> list(feature_maps[-1].shape)
[1, 768, 7, 7]
```"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
embedding_output, _, _ = self.embeddings(pixel_values)
outputs = self.encoder(
embedding_output,
head_mask=None,
output_attentions=output_attentions,
output_hidden_states=True,
return_dict=return_dict,
)
hidden_states = outputs[-1]
feature_maps = ()
for stage, hidden_state in zip(self.stage_names, hidden_states):
if stage in self.out_features:
batch_size, height, width, num_channels = hidden_state.shape
hidden_state = hidden_state.view(batch_size, height * width, num_channels)
hidden_state = self.hidden_states_norms[stage](hidden_state)
hidden_state = hidden_state.view(batch_size, height, width, num_channels)
hidden_state = hidden_state.permute(0, 3, 1, 2).contiguous()
feature_maps += (hidden_state,)
if not return_dict:
output = (feature_maps,)
if output_hidden_states:
output += (outputs[1],)
if output_attentions:
output += (outputs[2],)
return output
return BackboneOutput(
feature_maps=feature_maps,
hidden_states=outputs[1] if output_hidden_states else None,
attentions=outputs[2] if output_attentions else None,
)
__all__ = ["HieraForImageClassification", "HieraForPreTraining", "HieraBackbone", "HieraModel", "HieraPreTrainedModel"]
```
|
==============================================================================================================================
SOURCE CODE FILE: __init__.py
LINES: 1
SIZE: 1.01 KB
PATH: scripts\freecad_env\Lib\site-packages\transformers\models\hubert\__init__.py
ENCODING: utf-8
```py
# Copyright 2024 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import _LazyModule
from ...utils.import_utils import define_import_structure
if TYPE_CHECKING:
from .configuration_hubert import *
from .modeling_hubert import *
from .modeling_tf_hubert import *
else:
import sys
_file = globals()["__file__"]
sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)
```
|
==========================================================================================================================================
SOURCE CODE FILE: configuration_hubert.py
LINES: 1
SIZE: 14.59 KB
PATH: scripts\freecad_env\Lib\site-packages\transformers\models\hubert\configuration_hubert.py
ENCODING: utf-8
```py
# coding=utf-8
# Copyright 2021 The Fairseq Authors and The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Hubert model configuration"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
logger = logging.get_logger(__name__)
class HubertConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`HubertModel`]. It is used to instantiate an
Hubert model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a similar configuration to that of the Hubert
[facebook/hubert-base-ls960](https://huggingface.co/facebook/hubert-base-ls960) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 32):
Vocabulary size of the Hubert model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`HubertModel`]. Vocabulary size of the model. Defines the different
tokens that can be represented by the *inputs_ids* passed to the forward method of [`HubertModel`].
hidden_size (`int`, *optional*, defaults to 768):
Dimensionality of the encoder layers and the pooler layer.
num_hidden_layers (`int`, *optional*, defaults to 12):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 12):
Number of attention heads for each attention layer in the Transformer encoder.
intermediate_size (`int`, *optional*, defaults to 3072):
Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"selu"` and `"gelu_new"` are supported.
hidden_dropout(`float`, *optional*, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
activation_dropout (`float`, *optional*, defaults to 0.1):
The dropout ratio for activations inside the fully connected layer.
attention_dropout(`float`, *optional*, defaults to 0.1):
The dropout ratio for the attention probabilities.
final_dropout (`float`, *optional*, defaults to 0.1):
The dropout probability for the final projection layer of [`Wav2Vec2ForCTC`].
layerdrop (`float`, *optional*, defaults to 0.1):
The LayerDrop probability. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556) for more
details.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (`float`, *optional*, defaults to 1e-12):
The epsilon used by the layer normalization layers.
feat_extract_norm (`str`, *optional*, defaults to `"group"`):
The norm to be applied to 1D convolutional layers in feature encoder. One of `"group"` for group
normalization of only the first 1D convolutional layer or `"layer"` for layer normalization of all 1D
convolutional layers.
feat_proj_dropout (`float`, *optional*, defaults to 0.0):
The dropout probability for output of the feature encoder.
feat_proj_layer_norm (`bool`, *optional*, defaults to `True`):
Whether to apply LayerNorm to the output of the feature encoder.
feat_extract_activation (`str, `optional`, defaults to `"gelu"`):
The non-linear activation function (function or string) in the 1D convolutional layers of the feature
extractor. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"` are supported.
conv_dim (`Tuple[int]`, *optional*, defaults to `(512, 512, 512, 512, 512, 512, 512)`):
A tuple of integers defining the number of input and output channels of each 1D convolutional layer in the
feature encoder. The length of *conv_dim* defines the number of 1D convolutional layers.
conv_stride (`Tuple[int]`, *optional*, defaults to `(5, 2, 2, 2, 2, 2, 2)`):
A tuple of integers defining the stride of each 1D convolutional layer in the feature encoder. The length
of *conv_stride* defines the number of convolutional layers and has to match the length of *conv_dim*.
conv_kernel (`Tuple[int]`, *optional*, defaults to `(10, 3, 3, 3, 3, 3, 3)`):
A tuple of integers defining the kernel size of each 1D convolutional layer in the feature encoder. The
length of *conv_kernel* defines the number of convolutional layers and has to match the length of
*conv_dim*.
conv_bias (`bool`, *optional*, defaults to `False`):
Whether the 1D convolutional layers have a bias.
num_conv_pos_embeddings (`int`, *optional*, defaults to 128):
Number of convolutional positional embeddings. Defines the kernel size of 1D convolutional positional
embeddings layer.
num_conv_pos_embedding_groups (`int`, *optional*, defaults to 16):
Number of groups of 1D convolutional positional embeddings layer.
conv_pos_batch_norm (`bool`, *optional*, defaults to `False`):
Whether to use batch norm instead of weight norm in conv_pos
do_stable_layer_norm (`bool`, *optional*, defaults to `False`):
Whether do apply *stable* layer norm architecture of the Transformer encoder. `do_stable_layer_norm is
True` corresponds to applying layer norm before the attention layer, whereas `do_stable_layer_norm is
False` corresponds to applying layer norm after the attention layer.
apply_spec_augment (`bool`, *optional*, defaults to `True`):
Whether to apply *SpecAugment* data augmentation to the outputs of the feature encoder. For reference see
[SpecAugment: A Simple Data Augmentation Method for Automatic Speech
Recognition](https://arxiv.org/abs/1904.08779).
mask_time_prob (`float`, *optional*, defaults to 0.05):
Percentage (between 0 and 1) of all feature vectors along the time axis which will be masked. The masking
procecure generates ''mask_time_prob*len(time_axis)/mask_time_length'' independent masks over the axis. If
reasoning from the propability of each feature vector to be chosen as the start of the vector span to be
masked, *mask_time_prob* should be `prob_vector_start*mask_time_length`. Note that overlap may decrease the
actual percentage of masked vectors. This is only relevant if `apply_spec_augment is True`.
mask_time_length (`int`, *optional*, defaults to 10):
Length of vector span along the time axis.
mask_time_min_masks (`int`, *optional*, defaults to 2),:
The minimum number of masks of length `mask_feature_length` generated along the time axis, each time step,
irrespectively of `mask_feature_prob`. Only relevant if ''mask_time_prob*len(time_axis)/mask_time_length <
mask_time_min_masks''
mask_feature_prob (`float`, *optional*, defaults to 0.0):
Percentage (between 0 and 1) of all feature vectors along the feature axis which will be masked. The
masking procecure generates ''mask_feature_prob*len(feature_axis)/mask_time_length'' independent masks over
the axis. If reasoning from the propability of each feature vector to be chosen as the start of the vector
span to be masked, *mask_feature_prob* should be `prob_vector_start*mask_feature_length`. Note that overlap
may decrease the actual percentage of masked vectors. This is only relevant if `apply_spec_augment is
True`.
mask_feature_length (`int`, *optional*, defaults to 10):
Length of vector span along the feature axis.
mask_feature_min_masks (`int`, *optional*, defaults to 0),:
The minimum number of masks of length `mask_feature_length` generated along the feature axis, each time
step, irrespectively of `mask_feature_prob`. Only relevant if
''mask_feature_prob*len(feature_axis)/mask_feature_length < mask_feature_min_masks''
ctc_loss_reduction (`str`, *optional*, defaults to `"sum"`):
Specifies the reduction to apply to the output of `torch.nn.CTCLoss`. Only relevant when training an
instance of [`HubertForCTC`].
ctc_zero_infinity (`bool`, *optional*, defaults to `False`):
Whether to zero infinite losses and the associated gradients of `torch.nn.CTCLoss`. Infinite losses mainly
occur when the inputs are too short to be aligned to the targets. Only relevant when training an instance
of [`HubertForCTC`].
use_weighted_layer_sum (`bool`, *optional*, defaults to `False`):
Whether to use a weighted average of layer outputs with learned weights. Only relevant when using an
instance of [`HubertForSequenceClassification`].
classifier_proj_size (`int`, *optional*, defaults to 256):
Dimensionality of the projection before token mean-pooling for classification.
Example:
```python
>>> from transformers import HubertModel, HubertConfig
>>> # Initializing a Hubert facebook/hubert-base-ls960 style configuration
>>> configuration = HubertConfig()
>>> # Initializing a model from the facebook/hubert-base-ls960 style configuration
>>> model = HubertModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "hubert"
def __init__(
self,
vocab_size=32,
hidden_size=768,
num_hidden_layers=12,
num_attention_heads=12,
intermediate_size=3072,
hidden_act="gelu",
hidden_dropout=0.1,
activation_dropout=0.1,
attention_dropout=0.1,
feat_proj_layer_norm=True,
feat_proj_dropout=0.0,
final_dropout=0.1,
layerdrop=0.1,
initializer_range=0.02,
layer_norm_eps=1e-5,
feat_extract_norm="group",
feat_extract_activation="gelu",
conv_dim=(512, 512, 512, 512, 512, 512, 512),
conv_stride=(5, 2, 2, 2, 2, 2, 2),
conv_kernel=(10, 3, 3, 3, 3, 2, 2),
conv_bias=False,
num_conv_pos_embeddings=128,
num_conv_pos_embedding_groups=16,
conv_pos_batch_norm=False,
do_stable_layer_norm=False,
apply_spec_augment=True,
mask_time_prob=0.05,
mask_time_length=10,
mask_time_min_masks=2,
mask_feature_prob=0.0,
mask_feature_length=10,
mask_feature_min_masks=0,
ctc_loss_reduction="sum",
ctc_zero_infinity=False,
use_weighted_layer_sum=False,
classifier_proj_size=256,
pad_token_id=0,
bos_token_id=1,
eos_token_id=2,
**kwargs,
):
super().__init__(**kwargs, pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id)
self.hidden_size = hidden_size
self.feat_extract_norm = feat_extract_norm
self.feat_extract_activation = feat_extract_activation
self.conv_dim = list(conv_dim)
self.conv_stride = list(conv_stride)
self.conv_kernel = list(conv_kernel)
self.conv_bias = conv_bias
self.num_conv_pos_embeddings = num_conv_pos_embeddings
self.num_conv_pos_embedding_groups = num_conv_pos_embedding_groups
self.conv_pos_batch_norm = conv_pos_batch_norm
self.num_feat_extract_layers = len(self.conv_dim)
self.num_hidden_layers = num_hidden_layers
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.num_attention_heads = num_attention_heads
self.hidden_dropout = hidden_dropout
self.attention_dropout = attention_dropout
self.activation_dropout = activation_dropout
self.feat_proj_layer_norm = feat_proj_layer_norm
self.feat_proj_dropout = feat_proj_dropout
self.final_dropout = final_dropout
self.layerdrop = layerdrop
self.layer_norm_eps = layer_norm_eps
self.initializer_range = initializer_range
self.vocab_size = vocab_size
self.do_stable_layer_norm = do_stable_layer_norm
self.use_weighted_layer_sum = use_weighted_layer_sum
self.classifier_proj_size = classifier_proj_size
if (
(len(self.conv_stride) != self.num_feat_extract_layers)
or (len(self.conv_kernel) != self.num_feat_extract_layers)
or (len(self.conv_dim) != self.num_feat_extract_layers)
):
raise ValueError(
"Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="
" `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="
f" {len(self.conv_dim)}`, `len(config.conv_stride) = {len(self.conv_stride)}`,"
f" `len(config.conv_kernel) = {len(self.conv_kernel)}`."
)
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
self.apply_spec_augment = apply_spec_augment
self.mask_time_prob = mask_time_prob
self.mask_time_length = mask_time_length
self.mask_time_min_masks = mask_time_min_masks
self.mask_feature_prob = mask_feature_prob
self.mask_feature_length = mask_feature_length
self.mask_feature_min_masks = mask_feature_min_masks
# ctc loss
self.ctc_loss_reduction = ctc_loss_reduction
self.ctc_zero_infinity = ctc_zero_infinity
@property
def inputs_to_logits_ratio(self):
return functools.reduce(operator.mul, self.conv_stride, 1)
__all__ = ["HubertConfig"]
```
|
=====================================================================================================================================
SOURCE CODE FILE: modeling_hubert.py
LINES: 1
SIZE: 70.95 KB
PATH: scripts\freecad_env\Lib\site-packages\transformers\models\hubert\modeling_hubert.py
ENCODING: utf-8
```py
# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
# This file was automatically generated from src/transformers/models/hubert/modular_hubert.py.
# Do NOT edit this file manually as any edits will be overwritten by the generation of
# the file from the modular. If any change should be done, please apply the change to the
# modular_hubert.py file directly. One of our CI enforces this.
# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
import warnings
from typing import Optional, Tuple, Union
import numpy as np
import torch
import torch.nn as nn
from torch.nn import CrossEntropyLoss
from ...activations import ACT2FN
from ...integrations.deepspeed import is_deepspeed_zero3_enabled
from ...integrations.fsdp import is_fsdp_managed_module
from ...modeling_flash_attention_utils import flash_attn_supports_top_left_mask, is_flash_attn_available
from ...modeling_outputs import BaseModelOutput, CausalLMOutput, SequenceClassifierOutput
from ...modeling_utils import PreTrainedModel
from ...utils import (
add_code_sample_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
logging,
replace_return_docstrings,
)
from .configuration_hubert import HubertConfig
if is_flash_attn_available():
from ...modeling_flash_attention_utils import _flash_attention_forward
logger = logging.get_logger(__name__)
# Base docstring
_CHECKPOINT_FOR_DOC = "facebook/hubert-large-ls960-ft"
# General docstring
_CONFIG_FOR_DOC = "HubertConfig"
class HubertPositionalConvEmbedding(nn.Module):
def __init__(self, config):
super().__init__()
self.conv = nn.Conv1d(
config.hidden_size,
config.hidden_size,
kernel_size=config.num_conv_pos_embeddings,
padding=config.num_conv_pos_embeddings // 2,
groups=config.num_conv_pos_embedding_groups,
)
self.batch_norm = None
if config.conv_pos_batch_norm:
self.batch_norm = nn.BatchNorm1d(config.hidden_size)
else:
weight_norm = nn.utils.weight_norm
if hasattr(nn.utils.parametrizations, "weight_norm"):
weight_norm = nn.utils.parametrizations.weight_norm
if is_deepspeed_zero3_enabled():
import deepspeed
with deepspeed.zero.GatheredParameters(self.conv.weight, modifier_rank=0):
self.conv = weight_norm(self.conv, name="weight", dim=2)
if hasattr(self.conv, "parametrizations"):
weight_g = self.conv.parametrizations.weight.original0
weight_v = self.conv.parametrizations.weight.original1
else:
weight_g = self.conv.weight_g
weight_v = self.conv.weight_v
deepspeed.zero.register_external_parameter(self, weight_v)
deepspeed.zero.register_external_parameter(self, weight_g)
else:
self.conv = weight_norm(self.conv, name="weight", dim=2)
self.padding = HubertSamePadLayer(config.num_conv_pos_embeddings)
self.activation = ACT2FN[config.feat_extract_activation]
def forward(self, hidden_states):
hidden_states = hidden_states.transpose(1, 2)
if self.batch_norm is not None:
hidden_states = self.batch_norm(hidden_states)
hidden_states = self.conv(hidden_states)
hidden_states = self.padding(hidden_states)
hidden_states = self.activation(hidden_states)
hidden_states = hidden_states.transpose(1, 2)
return hidden_states
class HubertSamePadLayer(nn.Module):
def __init__(self, num_conv_pos_embeddings):
super().__init__()
self.num_pad_remove = 1 if num_conv_pos_embeddings % 2 == 0 else 0
def forward(self, hidden_states):
if self.num_pad_remove > 0:
hidden_states = hidden_states[:, :, : -self.num_pad_remove]
return hidden_states
class HubertNoLayerNormConvLayer(nn.Module):
def __init__(self, config, layer_id=0):
super().__init__()
self.in_conv_dim = config.conv_dim[layer_id - 1] if layer_id > 0 else 1
self.out_conv_dim = config.conv_dim[layer_id]
self.conv = nn.Conv1d(
self.in_conv_dim,
self.out_conv_dim,
kernel_size=config.conv_kernel[layer_id],
stride=config.conv_stride[layer_id],
bias=config.conv_bias,
)
self.activation = ACT2FN[config.feat_extract_activation]
def forward(self, hidden_states):
hidden_states = self.conv(hidden_states)
hidden_states = self.activation(hidden_states)
return hidden_states
class HubertLayerNormConvLayer(nn.Module):
def __init__(self, config, layer_id=0):
super().__init__()
self.in_conv_dim = config.conv_dim[layer_id - 1] if layer_id > 0 else 1
self.out_conv_dim = config.conv_dim[layer_id]
self.conv = nn.Conv1d(
self.in_conv_dim,
self.out_conv_dim,
kernel_size=config.conv_kernel[layer_id],
stride=config.conv_stride[layer_id],
bias=config.conv_bias,
)
self.layer_norm = nn.LayerNorm(self.out_conv_dim, elementwise_affine=True)
self.activation = ACT2FN[config.feat_extract_activation]
def forward(self, hidden_states):
hidden_states = self.conv(hidden_states)
hidden_states = hidden_states.transpose(-2, -1)
hidden_states = self.layer_norm(hidden_states)
hidden_states = hidden_states.transpose(-2, -1)
hidden_states = self.activation(hidden_states)
return hidden_states
class HubertGroupNormConvLayer(nn.Module):
def __init__(self, config, layer_id=0):
super().__init__()
self.in_conv_dim = config.conv_dim[layer_id - 1] if layer_id > 0 else 1
self.out_conv_dim = config.conv_dim[layer_id]
self.conv = nn.Conv1d(
self.in_conv_dim,
self.out_conv_dim,
kernel_size=config.conv_kernel[layer_id],
stride=config.conv_stride[layer_id],
bias=config.conv_bias,
)
self.activation = ACT2FN[config.feat_extract_activation]
self.layer_norm = nn.GroupNorm(num_groups=self.out_conv_dim, num_channels=self.out_conv_dim, affine=True)
def forward(self, hidden_states):
hidden_states = self.conv(hidden_states)
hidden_states = self.layer_norm(hidden_states)
hidden_states = self.activation(hidden_states)
return hidden_states
class HubertFeatureEncoder(nn.Module):
"""Construct the features from raw audio waveform"""
def __init__(self, config):
super().__init__()
if config.feat_extract_norm == "group":
conv_layers = [HubertGroupNormConvLayer(config, layer_id=0)] + [
HubertNoLayerNormConvLayer(config, layer_id=i + 1) for i in range(config.num_feat_extract_layers - 1)
]
elif config.feat_extract_norm == "layer":
conv_layers = [HubertLayerNormConvLayer(config, layer_id=i) for i in range(config.num_feat_extract_layers)]
else:
raise ValueError(
f"`config.feat_extract_norm` is {config.feat_extract_norm}, but has to be one of ['group', 'layer']"
)
self.conv_layers = nn.ModuleList(conv_layers)
self.gradient_checkpointing = False
self._requires_grad = True
def _freeze_parameters(self):
for param in self.parameters():
param.requires_grad = False
self._requires_grad = False
def forward(self, input_values):
hidden_states = input_values[:, None]
# make sure hidden_states require grad for gradient_checkpointing
if self._requires_grad and self.training:
hidden_states.requires_grad = True
for conv_layer in self.conv_layers:
if self._requires_grad and self.gradient_checkpointing and self.training:
hidden_states = self._gradient_checkpointing_func(
conv_layer.__call__,
hidden_states,
)
else:
hidden_states = conv_layer(hidden_states)
return hidden_states
class HubertFeatureProjection(nn.Module):
def __init__(self, config):
super().__init__()
self.feat_proj_layer_norm = config.feat_proj_layer_norm
if self.feat_proj_layer_norm:
self.layer_norm = nn.LayerNorm(config.conv_dim[-1], eps=config.layer_norm_eps)
self.projection = nn.Linear(config.conv_dim[-1], config.hidden_size)
self.dropout = nn.Dropout(config.feat_proj_dropout)
def forward(self, hidden_states):
# non-projected hidden states are needed for quantization
if self.feat_proj_layer_norm:
hidden_states = self.layer_norm(hidden_states)
hidden_states = self.projection(hidden_states)
hidden_states = self.dropout(hidden_states)
return hidden_states
class HubertAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(
self,
embed_dim: int,
num_heads: int,
dropout: float = 0.0,
is_decoder: bool = False,
bias: bool = True,
is_causal: bool = False,
config: Optional[HubertConfig] = None,
):
super().__init__()
self.embed_dim = embed_dim
self.num_heads = num_heads
self.dropout = dropout
self.head_dim = embed_dim // num_heads
self.config = config
if (self.head_dim * num_heads) != self.embed_dim:
raise ValueError(
f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}"
f" and `num_heads`: {num_heads})."
)
self.scaling = self.head_dim**-0.5
self.is_decoder = is_decoder
self.is_causal = is_causal
self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
def forward(
self,
hidden_states: torch.Tensor,
key_value_states: Optional[torch.Tensor] = None,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
attention_mask: Optional[torch.Tensor] = None,
layer_head_mask: Optional[torch.Tensor] = None,
output_attentions: bool = False,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
"""Input shape: Batch x Time x Channel"""
# if key_value_states are provided this layer is used as a cross-attention layer
# for the decoder
is_cross_attention = key_value_states is not None
bsz, tgt_len, _ = hidden_states.size()
# get query proj
query_states = self.q_proj(hidden_states) * self.scaling
# get key, value proj
# `past_key_value[0].shape[2] == key_value_states.shape[1]`
# is checking that the `sequence_length` of the `past_key_value` is the same as
# the provided `key_value_states` to support prefix tuning
if (
is_cross_attention
and past_key_value is not None
and past_key_value[0].shape[2] == key_value_states.shape[1]
):
# reuse k,v, cross_attentions
key_states = past_key_value[0]
value_states = past_key_value[1]
elif is_cross_attention:
# cross_attentions
key_states = self._shape(self.k_proj(key_value_states), -1, bsz)
value_states = self._shape(self.v_proj(key_value_states), -1, bsz)
elif past_key_value is not None:
# reuse k, v, self_attention
key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
key_states = torch.cat([past_key_value[0], key_states], dim=2)
value_states = torch.cat([past_key_value[1], value_states], dim=2)
else:
# self_attention
key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
if self.is_decoder:
# if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states.
# Further calls to cross_attention layer can then reuse all cross-attention
# key/value_states (first "if" case)
# if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of
# all previous decoder key/value_states. Further calls to uni-directional self-attention
# can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
# if encoder bi-directional self-attention `past_key_value` is always `None`
past_key_value = (key_states, value_states)
proj_shape = (bsz * self.num_heads, -1, self.head_dim)
query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape)
key_states = key_states.reshape(*proj_shape)
value_states = value_states.reshape(*proj_shape)
src_len = key_states.size(1)
attn_weights = torch.bmm(query_states, key_states.transpose(1, 2))
if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len):
raise ValueError(
f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is"
f" {attn_weights.size()}"
)
if attention_mask is not None:
if attention_mask.size() != (bsz, 1, tgt_len, src_len):
raise ValueError(
f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}"
)
attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask
attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
attn_weights = nn.functional.softmax(attn_weights, dim=-1)
if layer_head_mask is not None:
if layer_head_mask.size() != (self.num_heads,):
raise ValueError(
f"Head mask for a single layer should be of size {(self.num_heads,)}, but is"
f" {layer_head_mask.size()}"
)
attn_weights = layer_head_mask.view(1, -1, 1, 1) * attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
if output_attentions:
# this operation is a bit awkward, but it's required to
# make sure that attn_weights keeps its gradient.
# In order to do so, attn_weights have to be reshaped
# twice and have to be reused in the following
attn_weights_reshaped = attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
attn_weights = attn_weights_reshaped.view(bsz * self.num_heads, tgt_len, src_len)
else:
attn_weights_reshaped = None
attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)
attn_output = torch.bmm(attn_probs, value_states)
if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim):
raise ValueError(
f"`attn_output` should be of size {(bsz * self.num_heads, tgt_len, self.head_dim)}, but is"
f" {attn_output.size()}"
)
attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim)
attn_output = attn_output.transpose(1, 2)
# Use the `embed_dim` from the config (stored in the class) rather than `hidden_state` because `attn_output` can be
# partitioned across GPUs when using tensor-parallelism.
attn_output = attn_output.reshape(bsz, tgt_len, self.embed_dim)
attn_output = self.out_proj(attn_output)
return attn_output, attn_weights_reshaped, past_key_value
class HubertFlashAttention2(HubertAttention):
"""
Hubert flash attention module. This module inherits from `HubertAttention` as the weights of the module stays
untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
flash attention and deal with padding tokens in case the input contains any of them.
"""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
# TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
# flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignment, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
# Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left).
self._flash_attn_uses_top_left_mask = flash_attn_supports_top_left_mask()
def _reshape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
return tensor.view(bsz, seq_len, self.num_heads, self.head_dim)
def forward(
self,
hidden_states: torch.Tensor,
key_value_states: Optional[torch.Tensor] = None,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
attention_mask: Optional[torch.Tensor] = None,
layer_head_mask: Optional[torch.Tensor] = None,
output_attentions: bool = False,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
# HubertFlashAttention2 attention does not support output_attentions
if output_attentions:
raise ValueError("HubertFlashAttention2 attention does not support output_attentions")
# if key_value_states are provided this layer is used as a cross-attention layer
# for the decoder
is_cross_attention = key_value_states is not None
bsz, q_len, _ = hidden_states.size()
# get query proj
query_states = self._reshape(self.q_proj(hidden_states), -1, bsz)
# get key, value proj
# `past_key_value[0].shape[2] == key_value_states.shape[1]`
# is checking that the `sequence_length` of the `past_key_value` is the same as
# the provided `key_value_states` to support prefix tuning
if (
is_cross_attention
and past_key_value is not None
and past_key_value[0].shape[2] == key_value_states.shape[1]
):
# reuse k,v, cross_attentions
key_states = past_key_value[0].transpose(1, 2)
value_states = past_key_value[1].transpose(1, 2)
elif is_cross_attention:
# cross_attentions
key_states = self._reshape(self.k_proj(key_value_states), -1, bsz)
value_states = self._reshape(self.v_proj(key_value_states), -1, bsz)
elif past_key_value is not None:
# reuse k, v, self_attention
key_states = self._reshape(self.k_proj(hidden_states), -1, bsz)
value_states = self._reshape(self.v_proj(hidden_states), -1, bsz)
key_states = torch.cat([past_key_value[0].transpose(1, 2), key_states], dim=1)
value_states = torch.cat([past_key_value[1].transpose(1, 2), value_states], dim=1)
else:
# self_attention
key_states = self._reshape(self.k_proj(hidden_states), -1, bsz)
value_states = self._reshape(self.v_proj(hidden_states), -1, bsz)
if self.is_decoder:
# if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states.
# Further calls to cross_attention layer can then reuse all cross-attention
# key/value_states (first "if" case)
# if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of
# all previous decoder key/value_states. Further calls to uni-directional self-attention
# can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
# if encoder bi-directional self-attention `past_key_value` is always `None`
past_key_value = (key_states.transpose(1, 2), value_states.transpose(1, 2))
kv_seq_len = key_states.shape[-2]
if past_key_value is not None:
kv_seq_len += past_key_value[0].shape[-2]
# In PEFT, usually we cast the layer norms in float32 for training stability reasons
# therefore the input hidden states gets silently casted in float32. Hence, we need
# cast them back in the correct dtype just to be sure everything works as expected.
# This might slowdown training & inference so it is recommended to not cast the LayerNorms
# in fp32. (LlamaRMSNorm handles it correctly)
input_dtype = query_states.dtype
if input_dtype == torch.float32:
if torch.is_autocast_enabled():
target_dtype = torch.get_autocast_gpu_dtype()
# Handle the case where the model is quantized
elif hasattr(self.config, "_pre_quantization_dtype"):
target_dtype = self.config._pre_quantization_dtype
else:
target_dtype = self.q_proj.weight.dtype
logger.warning_once(
f"The input hidden states seems to be silently casted in float32, this might be related to"
f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
f" {target_dtype}."
)
query_states = query_states.to(target_dtype)
key_states = key_states.to(target_dtype)
value_states = value_states.to(target_dtype)
attn_output = _flash_attention_forward(
query_states,
key_states,
value_states,
attention_mask,
q_len,
dropout=self.dropout if self.training else 0.0,
is_causal=self.is_causal,
use_top_left_mask=self._flash_attn_uses_top_left_mask,
)
attn_output = attn_output.reshape(bsz, q_len, -1)
attn_output = self.out_proj(attn_output)
if not output_attentions:
attn_weights = None
return attn_output, attn_weights, past_key_value
class HubertSdpaAttention(HubertAttention):
def forward(
self,
hidden_states: torch.Tensor,
key_value_states: Optional[torch.Tensor] = None,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
attention_mask: Optional[torch.Tensor] = None,
layer_head_mask: Optional[torch.Tensor] = None,
output_attentions: bool = False,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
"""Input shape: Batch x Time x Channel"""
if output_attentions or layer_head_mask is not None:
# TODO: Improve this warning with e.g. `model.config._attn_implementation = "manual"` once this is implemented.
logger.warning_once(
"HubertModel is using HubertSdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True` or `layer_head_mask` not None. Falling back to the manual attention"
' implementation, but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
)
return super().forward(
hidden_states,
key_value_states=key_value_states,
past_key_value=past_key_value,
attention_mask=attention_mask,
layer_head_mask=layer_head_mask,
output_attentions=output_attentions,
)
# if key_value_states are provided this layer is used as a cross-attention layer
# for the decoder
is_cross_attention = key_value_states is not None
bsz, tgt_len, _ = hidden_states.size()
# get query proj
query_states = self.q_proj(hidden_states)
# get key, value proj
# `past_key_value[0].shape[2] == key_value_states.shape[1]`
# is checking that the `sequence_length` of the `past_key_value` is the same as
# the provided `key_value_states` to support prefix tuning
if (
is_cross_attention
and past_key_value is not None
and past_key_value[0].shape[2] == key_value_states.shape[1]
):
# reuse k,v, cross_attentions
key_states = past_key_value[0]
value_states = past_key_value[1]
elif is_cross_attention:
# cross_attentions
key_states = self._shape(self.k_proj(key_value_states), -1, bsz)
value_states = self._shape(self.v_proj(key_value_states), -1, bsz)
elif past_key_value is not None:
# reuse k, v, self_attention
key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
key_states = torch.cat([past_key_value[0], key_states], dim=2)
value_states = torch.cat([past_key_value[1], value_states], dim=2)
else:
# self_attention
key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
if self.is_decoder:
# if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states.
# Further calls to cross_attention layer can then reuse all cross-attention
# key/value_states (first "if" case)
# if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of
# all previous decoder key/value_states. Further calls to uni-directional self-attention
# can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
# if encoder bi-directional self-attention `past_key_value` is always `None`
past_key_value = (key_states, value_states)
query_states = self._shape(query_states, tgt_len, bsz)
# We dispatch to SDPA's Flash Attention or Efficient kernels via this `is_causal` if statement instead of an inline conditional assignment
# in SDPA to support both torch.compile's dynamic shapes and full graph options. An inline conditional prevents dynamic shapes from compiling.
# The tgt_len > 1 is necessary to match with AttentionMaskConverter.to_causal_4d that does not create a causal mask in case tgt_len == 1.
is_causal = True if self.is_causal and attention_mask is None and tgt_len > 1 else False
# NOTE: SDPA with memory-efficient backend is currently (torch==2.1.2) bugged when using non-contiguous inputs and a custom attn_mask,
# but we are fine here as `_shape` do call `.contiguous()`. Reference: https://github.com/pytorch/pytorch/issues/112577
attn_output = torch.nn.functional.scaled_dot_product_attention(
query_states,
key_states,
value_states,
attn_mask=attention_mask,
dropout_p=self.dropout if self.training else 0.0,
is_causal=is_causal,
)
if attn_output.size() != (bsz, self.num_heads, tgt_len, self.head_dim):
raise ValueError(
f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is"
f" {attn_output.size()}"
)
attn_output = attn_output.transpose(1, 2)
# Use the `embed_dim` from the config (stored in the class) rather than `hidden_state` because `attn_output` can be
# partitioned across GPUs when using tensor-parallelism.
attn_output = attn_output.reshape(bsz, tgt_len, self.embed_dim)
attn_output = self.out_proj(attn_output)
return attn_output, None, past_key_value
class HubertFeedForward(nn.Module):
def __init__(self, config):
super().__init__()
self.intermediate_dropout = nn.Dropout(config.activation_dropout)
self.intermediate_dense = nn.Linear(config.hidden_size, config.intermediate_size)
if isinstance(config.hidden_act, str):
self.intermediate_act_fn = ACT2FN[config.hidden_act]
else:
self.intermediate_act_fn = config.hidden_act
self.output_dense = nn.Linear(config.intermediate_size, config.hidden_size)
self.output_dropout = nn.Dropout(config.hidden_dropout)
def forward(self, hidden_states):
hidden_states = self.intermediate_dense(hidden_states)
hidden_states = self.intermediate_act_fn(hidden_states)
hidden_states = self.intermediate_dropout(hidden_states)
hidden_states = self.output_dense(hidden_states)
hidden_states = self.output_dropout(hidden_states)
return hidden_states
HUBERT_ATTENTION_CLASSES = {
"eager": HubertAttention,
"sdpa": HubertSdpaAttention,
"flash_attention_2": HubertFlashAttention2,
}
class HubertEncoderLayer(nn.Module):
def __init__(self, config):
super().__init__()
self.attention = HUBERT_ATTENTION_CLASSES[config._attn_implementation](
embed_dim=config.hidden_size,
num_heads=config.num_attention_heads,
dropout=config.attention_dropout,
is_decoder=False,
)
self.dropout = nn.Dropout(config.hidden_dropout)
self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.feed_forward = HubertFeedForward(config)
self.final_layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
def forward(self, hidden_states, attention_mask=None, output_attentions=False):
attn_residual = hidden_states
hidden_states, attn_weights, _ = self.attention(
hidden_states, attention_mask=attention_mask, output_attentions=output_attentions
)
hidden_states = self.dropout(hidden_states)
hidden_states = attn_residual + hidden_states
hidden_states = self.layer_norm(hidden_states)
hidden_states = hidden_states + self.feed_forward(hidden_states)
hidden_states = self.final_layer_norm(hidden_states)
outputs = (hidden_states,)
if output_attentions:
outputs += (attn_weights,)
return outputs
class HubertEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.pos_conv_embed = HubertPositionalConvEmbedding(config)
self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout)
self.layers = nn.ModuleList([HubertEncoderLayer(config) for _ in range(config.num_hidden_layers)])
self.gradient_checkpointing = False
self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2"
def forward(
self,
hidden_states: torch.tensor,
attention_mask: Optional[torch.Tensor] = None,
output_attentions: bool = False,
output_hidden_states: bool = False,
return_dict: bool = True,
):
all_hidden_states = () if output_hidden_states else None
all_self_attentions = () if output_attentions else None
if attention_mask is not None:
# make sure padded tokens output 0
expand_attention_mask = attention_mask.unsqueeze(-1).repeat(1, 1, hidden_states.shape[2])
hidden_states[~expand_attention_mask] = 0
if self._use_flash_attention_2:
# 2d mask is passed through the layers
attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None
else:
# extend attention_mask
attention_mask = 1.0 - attention_mask[:, None, None, :].to(dtype=hidden_states.dtype)
attention_mask = attention_mask * torch.finfo(hidden_states.dtype).min
attention_mask = attention_mask.expand(
attention_mask.shape[0], 1, attention_mask.shape[-1], attention_mask.shape[-1]
)
position_embeddings = self.pos_conv_embed(hidden_states)
hidden_states = hidden_states + position_embeddings
hidden_states = self.layer_norm(hidden_states)
hidden_states = self.dropout(hidden_states)
synced_gpus = is_deepspeed_zero3_enabled() or is_fsdp_managed_module(self)
for layer in self.layers:
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
# add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
dropout_probability = torch.rand([])
skip_the_layer = True if self.training and (dropout_probability < self.config.layerdrop) else False
if not skip_the_layer or synced_gpus:
# under fsdp or deepspeed zero3 all gpus must run in sync
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
layer.__call__,
hidden_states,
attention_mask,
output_attentions,
)
else:
layer_outputs = layer(
hidden_states, attention_mask=attention_mask, output_attentions=output_attentions
)
hidden_states = layer_outputs[0]
if skip_the_layer:
layer_outputs = (None, None)
if output_attentions:
all_self_attentions = all_self_attentions + (layer_outputs[1],)
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if not return_dict:
return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None)
return BaseModelOutput(
last_hidden_state=hidden_states,
hidden_states=all_hidden_states,
attentions=all_self_attentions,
)
class HubertAttnAdapterLayer(nn.Module):
def __init__(self, config):
"""
Implements adapter modules directly with 3D tensor weight as parameters and without using ModuleList to speed
up training throughput.
"""
super().__init__()
self.input_dim = config.adapter_attn_dim
self.hidden_dim = config.hidden_size
self.norm = nn.LayerNorm(self.hidden_dim)
self.linear_1 = nn.Linear(self.hidden_dim, self.input_dim)
self.act_fn = nn.ReLU()
self.linear_2 = nn.Linear(self.input_dim, self.hidden_dim)
def forward(self, hidden_states: torch.FloatTensor):
hidden_states = self.norm(hidden_states)
hidden_states = self.linear_1(hidden_states)
hidden_states = self.act_fn(hidden_states)
hidden_states = self.linear_2(hidden_states)
return hidden_states
class HubertEncoderLayerStableLayerNorm(nn.Module):
def __init__(self, config):
super().__init__()
self.attention = HUBERT_ATTENTION_CLASSES[config._attn_implementation](
embed_dim=config.hidden_size,
num_heads=config.num_attention_heads,
dropout=config.attention_dropout,
is_decoder=False,
)
self.dropout = nn.Dropout(config.hidden_dropout)
self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.feed_forward = HubertFeedForward(config)
self.final_layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
if getattr(config, "adapter_attn_dim", None) is not None:
self.adapter_layer = HubertAttnAdapterLayer(config)
else:
self.adapter_layer = None
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
output_attentions: bool = False,
):
attn_residual = hidden_states
hidden_states = self.layer_norm(hidden_states)
hidden_states, attn_weights, _ = self.attention(
hidden_states, attention_mask=attention_mask, output_attentions=output_attentions
)
hidden_states = self.dropout(hidden_states)
hidden_states = attn_residual + hidden_states
hidden_states = hidden_states + self.feed_forward(self.final_layer_norm(hidden_states))
if self.adapter_layer is not None:
hidden_states = hidden_states + self.adapter_layer(hidden_states)
outputs = (hidden_states,)
if output_attentions:
outputs += (attn_weights,)
return outputs
class HubertEncoderStableLayerNorm(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.pos_conv_embed = HubertPositionalConvEmbedding(config)
self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout)
self.layers = nn.ModuleList(
[HubertEncoderLayerStableLayerNorm(config) for _ in range(config.num_hidden_layers)]
)
self.gradient_checkpointing = False
self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2"
def forward(
self,
hidden_states,
attention_mask=None,
output_attentions=False,
output_hidden_states=False,
return_dict=True,
):
all_hidden_states = () if output_hidden_states else None
all_self_attentions = () if output_attentions else None
if attention_mask is not None:
# make sure padded tokens are not attended to
expand_attention_mask = attention_mask.unsqueeze(-1).repeat(1, 1, hidden_states.shape[2])
hidden_states = hidden_states * expand_attention_mask.to(dtype=hidden_states.dtype)
if self._use_flash_attention_2:
# 2d mask is passed through the layers
attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None
else:
# extend attention_mask
attention_mask = 1.0 - attention_mask[:, None, None, :].to(dtype=hidden_states.dtype)
attention_mask = attention_mask * torch.finfo(hidden_states.dtype).min
attention_mask = attention_mask.expand(
attention_mask.shape[0], 1, attention_mask.shape[-1], attention_mask.shape[-1]
)
position_embeddings = self.pos_conv_embed(hidden_states)
hidden_states = hidden_states + position_embeddings
hidden_states = self.dropout(hidden_states)
synced_gpus = is_deepspeed_zero3_enabled() or is_fsdp_managed_module(self)
for layer in self.layers:
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
# add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
dropout_probability = torch.rand([])
skip_the_layer = True if self.training and (dropout_probability < self.config.layerdrop) else False
if not skip_the_layer or synced_gpus:
# under fsdp or deepspeed zero3 all gpus must run in sync
# XXX: could optimize this like synced_gpus in generate_utils but not sure if it's worth the code complication
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
layer.__call__,
hidden_states,
attention_mask,
output_attentions,
)
else:
layer_outputs = layer(
hidden_states, attention_mask=attention_mask, output_attentions=output_attentions
)
hidden_states = layer_outputs[0]
if skip_the_layer:
layer_outputs = (None, None)
if output_attentions:
all_self_attentions = all_self_attentions + (layer_outputs[1],)
hidden_states = self.layer_norm(hidden_states)
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if not return_dict:
return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None)
return BaseModelOutput(
last_hidden_state=hidden_states,
hidden_states=all_hidden_states,
attentions=all_self_attentions,
)
class HubertPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = HubertConfig
base_model_prefix = "hubert"
main_input_name = "input_values"
supports_gradient_checkpointing = True
_supports_flash_attn_2 = True
_supports_sdpa = True
def _init_weights(self, module):
"""Initialize the weights"""
if isinstance(module, nn.Linear):
# Slightly different from the TF version which uses truncated_normal for initialization
# cf https://github.com/pytorch/pytorch/pull/5617
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, (nn.LayerNorm, nn.GroupNorm, nn.BatchNorm1d)):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
elif isinstance(module, nn.Conv1d):
if is_deepspeed_zero3_enabled():
import deepspeed
if hasattr(module, "weight_v") and hasattr(module, "weight_g"):
with deepspeed.zero.GatheredParameters([module.weight_v, module.weight_g], modifier_rank=0):
nn.init.kaiming_normal_(module.weight.data)
else:
with deepspeed.zero.GatheredParameters(module.weight, modifier_rank=0):
nn.init.kaiming_normal_(module.weight.data)
else:
nn.init.kaiming_normal_(module.weight.data)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, HubertModel):
if hasattr(module, "masked_spec_embed"):
module.masked_spec_embed.data.uniform_()
elif isinstance(module, HubertForSequenceClassification):
if hasattr(module, "layer_weights"):
module.layer_weights.data.fill_(1.0 / (self.config.num_hidden_layers + 1))
def _get_feat_extract_output_lengths(self, input_lengths: Union[torch.LongTensor, int]):
"""
Computes the output length of the convolutional layers
"""
def _conv_out_length(input_length, kernel_size, stride):
# 1D convolutional layer output length formula taken
# from https://pytorch.org/docs/stable/generated/torch.nn.Conv1d.html
return torch.div(input_length - kernel_size, stride, rounding_mode="floor") + 1
for kernel_size, stride in zip(self.config.conv_kernel, self.config.conv_stride):
input_lengths = _conv_out_length(input_lengths, kernel_size, stride)
return input_lengths
def _get_feature_vector_attention_mask(self, feature_vector_length: int, attention_mask: torch.LongTensor):
output_lengths = self._get_feat_extract_output_lengths(attention_mask.sum(-1)).to(torch.long)
batch_size = attention_mask.shape[0]
attention_mask = torch.zeros(
(batch_size, feature_vector_length), dtype=attention_mask.dtype, device=attention_mask.device
)
# these two operations makes sure that all values before the output lengths idxs are attended to
attention_mask[(torch.arange(attention_mask.shape[0], device=attention_mask.device), output_lengths - 1)] = 1
attention_mask = attention_mask.flip([-1]).cumsum(-1).flip([-1]).bool()
return attention_mask
def _compute_mask_indices(
shape: Tuple[int, int],
mask_prob: float,
mask_length: int,
attention_mask: Optional[torch.LongTensor] = None,
min_masks: int = 0,
) -> np.ndarray:
"""
Computes random mask spans for a given shape. Used to implement [SpecAugment: A Simple Data Augmentation Method for
ASR](https://arxiv.org/abs/1904.08779). Note that this method is not optimized to run on TPU and should be run on
CPU as part of the preprocessing during training.
Args:
shape: The shape for which to compute masks. This should be of a tuple of size 2 where
the first element is the batch size and the second element is the length of the axis to span.
mask_prob: The percentage of the whole axis (between 0 and 1) which will be masked. The number of
independently generated mask spans of length `mask_length` is computed by
`mask_prob*shape[1]/mask_length`. Note that due to overlaps, `mask_prob` is an upper bound and the
actual percentage will be smaller.
mask_length: size of the mask
min_masks: minimum number of masked spans
attention_mask: A (right-padded) attention mask which independently shortens the feature axis of
each batch dimension.
"""
batch_size, sequence_length = shape
if mask_length < 1:
raise ValueError("`mask_length` has to be bigger than 0.")
if mask_length > sequence_length:
raise ValueError(
f"`mask_length` has to be smaller than `sequence_length`, but got `mask_length`: {mask_length}"
f" and `sequence_length`: {sequence_length}`"
)
# epsilon is used for probabilistic rounding
epsilon = np.random.rand(1).item()
def compute_num_masked_span(input_length):
"""Given input length, compute how many spans should be masked"""
num_masked_span = int(mask_prob * input_length / mask_length + epsilon)
num_masked_span = max(num_masked_span, min_masks)
# make sure num masked span <= sequence_length
if num_masked_span * mask_length > sequence_length:
num_masked_span = sequence_length // mask_length
# make sure num_masked span is also <= input_length - (mask_length - 1)
if input_length - (mask_length - 1) < num_masked_span:
num_masked_span = max(input_length - (mask_length - 1), 0)
return num_masked_span
# compute number of masked spans in batch
input_lengths = (
attention_mask.detach().sum(-1).tolist()
if attention_mask is not None
else [sequence_length for _ in range(batch_size)]
)
# SpecAugment mask to fill
spec_aug_mask = np.zeros((batch_size, sequence_length), dtype=bool)
spec_aug_mask_idxs = []
max_num_masked_span = compute_num_masked_span(sequence_length)
if max_num_masked_span == 0:
return spec_aug_mask
for input_length in input_lengths:
# compute num of masked spans for this input
num_masked_span = compute_num_masked_span(input_length)
# get random indices to mask
spec_aug_mask_idx = np.random.choice(
np.arange(input_length - (mask_length - 1)), num_masked_span, replace=False
)
# pick first sampled index that will serve as a dummy index to pad vector
# to ensure same dimension for all batches due to probabilistic rounding
# Picking first sample just pads those vectors twice.
if len(spec_aug_mask_idx) == 0:
# this case can only happen if `input_length` is strictly smaller then
# `sequence_length` in which case the last token has to be a padding
# token which we can use as a dummy mask id
dummy_mask_idx = sequence_length - 1
else:
dummy_mask_idx = spec_aug_mask_idx[0]
spec_aug_mask_idx = np.concatenate(
[spec_aug_mask_idx, np.ones(max_num_masked_span - num_masked_span, dtype=np.int32) * dummy_mask_idx]
)
spec_aug_mask_idxs.append(spec_aug_mask_idx)
spec_aug_mask_idxs = np.array(spec_aug_mask_idxs)
# expand masked indices to masked spans
spec_aug_mask_idxs = np.broadcast_to(
spec_aug_mask_idxs[:, :, None], (batch_size, max_num_masked_span, mask_length)
)
spec_aug_mask_idxs = spec_aug_mask_idxs.reshape(batch_size, max_num_masked_span * mask_length)
# add offset to the starting indexes so that indexes now create a span
offsets = np.arange(mask_length)[None, None, :]
offsets = np.broadcast_to(offsets, (batch_size, max_num_masked_span, mask_length)).reshape(
batch_size, max_num_masked_span * mask_length
)
spec_aug_mask_idxs = spec_aug_mask_idxs + offsets
# ensure that we cannot have indices larger than sequence_length
if spec_aug_mask_idxs.max() > sequence_length - 1:
spec_aug_mask_idxs[spec_aug_mask_idxs > sequence_length - 1] = sequence_length - 1
# scatter indices to mask
np.put_along_axis(spec_aug_mask, spec_aug_mask_idxs, 1, -1)
return spec_aug_mask
HUBERT_START_DOCSTRING = r"""
Hubert was proposed in [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden
Units](https://arxiv.org/abs/2106.07447) by Wei-Ning Hsu, Benjamin Bolte, Yao-Hung Hubert Tsai, Kushal Lakhotia,
Ruslan Salakhutdinov, Abdelrahman Mohamed.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving etc.).
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`HubertConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
HUBERT_INPUTS_DOCSTRING = r"""
Args:
input_values (`torch.FloatTensor` of shape `(batch_size, sequence_length)`):
Float values of input raw speech waveform. Values can be obtained by loading a `.flac` or `.wav` audio file
into an array of type `List[float]` or a `numpy.ndarray`, *e.g.* via the soundfile library (`pip install
soundfile`). To prepare the array into `input_values`, the [`AutoProcessor`] should be used for padding and
conversion into a tensor of type `torch.FloatTensor`. See [`Wav2Vec2Processor.__call__`] for details.
attention_mask (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing convolution and attention on padding token indices. Mask values selected in `[0,
1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
<Tip warning={true}>
`attention_mask` should only be passed if the corresponding processor has `config.return_attention_mask ==
True`. For all models whose processor has `config.return_attention_mask == False`, such as
[hubert-base](https://huggingface.co/facebook/hubert-base-ls960), `attention_mask` should **not** be passed
to avoid degraded performance when doing batched inference. For such models `input_values` should simply be
padded with 0 and passed without `attention_mask`. Be aware that these models also yield slightly different
results depending on whether `input_values` is padded or not.
</Tip>
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
@add_start_docstrings(
"The bare Hubert Model transformer outputting raw hidden-states without any specific head on top.",
HUBERT_START_DOCSTRING,
)
class HubertModel(HubertPreTrainedModel):
def __init__(self, config: HubertConfig):
super().__init__(config)
self.config = config
self.feature_extractor = HubertFeatureEncoder(config)
self.feature_projection = HubertFeatureProjection(config)
# model only needs masking vector if mask prob is > 0.0
if config.mask_time_prob > 0.0 or config.mask_feature_prob > 0.0:
self.masked_spec_embed = nn.Parameter(torch.Tensor(config.hidden_size).uniform_())
if config.do_stable_layer_norm:
self.encoder = HubertEncoderStableLayerNorm(config)
else:
self.encoder = HubertEncoder(config)
# Initialize weights and apply final processing
self.post_init()
def _mask_hidden_states(
self,
hidden_states: torch.FloatTensor,
mask_time_indices: Optional[torch.FloatTensor] = None,
attention_mask: Optional[torch.LongTensor] = None,
):
"""
Masks extracted features along time axis and/or along feature axis according to
[SpecAugment](https://arxiv.org/abs/1904.08779).
"""
# `config.apply_spec_augment` can set masking to False
if not getattr(self.config, "apply_spec_augment", True):
return hidden_states
# generate indices & apply SpecAugment along time axis
batch_size, sequence_length, hidden_size = hidden_states.size()
if mask_time_indices is not None:
# apply SpecAugment along time axis with given mask_time_indices
hidden_states[mask_time_indices] = self.masked_spec_embed.to(hidden_states.dtype)
elif self.config.mask_time_prob > 0 and self.training:
mask_time_indices = _compute_mask_indices(
(batch_size, sequence_length),
mask_prob=self.config.mask_time_prob,
mask_length=self.config.mask_time_length,
attention_mask=attention_mask,
min_masks=self.config.mask_time_min_masks,
)
mask_time_indices = torch.tensor(mask_time_indices, device=hidden_states.device, dtype=torch.bool)
hidden_states[mask_time_indices] = self.masked_spec_embed.to(hidden_states.dtype)
if self.config.mask_feature_prob > 0 and self.training:
# generate indices & apply SpecAugment along feature axis
mask_feature_indices = _compute_mask_indices(
(batch_size, hidden_size),
mask_prob=self.config.mask_feature_prob,
mask_length=self.config.mask_feature_length,
min_masks=self.config.mask_feature_min_masks,
)
mask_feature_indices = torch.tensor(mask_feature_indices, device=hidden_states.device, dtype=torch.bool)
mask_feature_indices = mask_feature_indices[:, None].expand(-1, sequence_length, -1)
hidden_states[mask_feature_indices] = 0
return hidden_states
@add_start_docstrings_to_model_forward(HUBERT_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=BaseModelOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_values: Optional[torch.Tensor],
attention_mask: Optional[torch.Tensor] = None,
mask_time_indices: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, BaseModelOutput]:
"""
Returns:
Example:
```python
>>> from transformers import AutoProcessor, HubertModel
>>> from datasets import load_dataset
>>> import soundfile as sf
>>> processor = AutoProcessor.from_pretrained("facebook/hubert-large-ls960-ft")
>>> model = HubertModel.from_pretrained("facebook/hubert-large-ls960-ft")
>>> def map_to_array(batch):
... speech, _ = sf.read(batch["file"])
... batch["speech"] = speech
... return batch
>>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
>>> ds = ds.map(map_to_array)
>>> input_values = processor(ds["speech"][0], return_tensors="pt").input_values # Batch size 1
>>> hidden_states = model(input_values).last_hidden_state
```"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
extract_features = self.feature_extractor(input_values)
extract_features = extract_features.transpose(1, 2)
if attention_mask is not None:
# compute reduced attention_mask corresponding to feature vectors
attention_mask = self._get_feature_vector_attention_mask(extract_features.shape[1], attention_mask)
hidden_states = self.feature_projection(extract_features)
hidden_states = self._mask_hidden_states(hidden_states, mask_time_indices=mask_time_indices)
encoder_outputs = self.encoder(
hidden_states,
attention_mask=attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
hidden_states = encoder_outputs[0]
if not return_dict:
return (hidden_states,) + encoder_outputs[1:]
return BaseModelOutput(
last_hidden_state=hidden_states,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
)
_HIDDEN_STATES_START_POSITION = 1
_CTC_EXPECTED_OUTPUT = "'MISTER QUILTER IS THE APOSTLE OF THE MIDDLE CLASSES AND WE ARE GLAD TO WELCOME HIS GOSPEL'"
_CTC_EXPECTED_LOSS = 22.68
@add_start_docstrings(
"""Hubert Model with a `language modeling` head on top for Connectionist Temporal Classification (CTC).""",
HUBERT_START_DOCSTRING,
)
class HubertForCTC(HubertPreTrainedModel):
def __init__(self, config, target_lang: Optional[str] = None):
super().__init__(config)
self.hubert = HubertModel(config)
self.dropout = nn.Dropout(config.final_dropout)
self.target_lang = target_lang
if config.vocab_size is None:
raise ValueError(
f"You are trying to instantiate {self.__class__} with a configuration that "
"does not define the vocabulary size of the language model head. Please "
"instantiate the model as follows: `HubertForCTC.from_pretrained(..., vocab_size=vocab_size)`. "
"or define `vocab_size` of your model's configuration."
)
output_hidden_size = (
config.output_hidden_size if hasattr(config, "add_adapter") and config.add_adapter else config.hidden_size
)
self.lm_head = nn.Linear(output_hidden_size, config.vocab_size)
# Initialize weights and apply final processing
self.post_init()
def tie_weights(self):
"""
This method overwrites [`~PreTrainedModel.tie_weights`] so that adapter weights can be correctly loaded when
passing `target_lang=...` to `from_pretrained(...)`.
This method is **not** supposed to be called by the user and is prone to be changed in the future.
"""
# Note that `tie_weights` is usually used to tie input and output embedding weights. The method is re-purposed to
# correctly load adapter layers for Hubert so that we do not have to introduce a new API to
# [`PreTrainedModel`]. While slightly hacky, Hubert never has to tie input and output embeddings, so that it is
# ok to repurpose this function here.
target_lang = self.target_lang
if target_lang is not None and getattr(self.config, "adapter_attn_dim", None) is None:
raise ValueError(f"Cannot pass `target_lang`: {target_lang} if `config.adapter_attn_dim` is not defined.")
elif target_lang is None and getattr(self.config, "adapter_attn_dim", None) is not None:
logger.info("By default `target_lang` is set to 'eng'.")
elif target_lang is not None:
self.load_adapter(target_lang, force_load=True)
def freeze_feature_extractor(self):
"""
Calling this function will disable the gradient computation for the feature encoder so that its parameter will
not be updated during training.
"""
warnings.warn(
"The method `freeze_feature_extractor` is deprecated and will be removed in Transformers v5. "
"Please use the equivalent `freeze_feature_encoder` method instead.",
FutureWarning,
)
self.freeze_feature_encoder()
def freeze_feature_encoder(self):
"""
Calling this function will disable the gradient computation for the feature encoder so that its parameter will
not be updated during training.
"""
self.hubert.feature_extractor._freeze_parameters()
def freeze_base_model(self):
"""
Calling this function will disable the gradient computation for the base model so that its parameters will not
be updated during training. Only the classification head will be updated.
"""
for param in self.hubert.parameters():
param.requires_grad = False
@add_start_docstrings_to_model_forward(HUBERT_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=CausalLMOutput,
config_class=_CONFIG_FOR_DOC,
expected_output=_CTC_EXPECTED_OUTPUT,
expected_loss=_CTC_EXPECTED_LOSS,
)
def forward(
self,
input_values: Optional[torch.Tensor],
attention_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
labels: Optional[torch.Tensor] = None,
) -> Union[Tuple, CausalLMOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size, target_length)`, *optional*):
Labels for connectionist temporal classification. Note that `target_length` has to be smaller or equal to
the sequence length of the output logits. Indices are selected in `[-100, 0, ..., config.vocab_size - 1]`.
All labels set to `-100` are ignored (masked), the loss is only computed for labels in `[0, ...,
config.vocab_size - 1]`.
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if labels is not None and labels.max() >= self.config.vocab_size:
raise ValueError(f"Label values must be <= vocab_size: {self.config.vocab_size}")
outputs = self.hubert(
input_values,
attention_mask=attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
hidden_states = outputs[0]
hidden_states = self.dropout(hidden_states)
logits = self.lm_head(hidden_states)
loss = None
if labels is not None:
# retrieve loss input_lengths from attention_mask
attention_mask = (
attention_mask if attention_mask is not None else torch.ones_like(input_values, dtype=torch.long)
)
input_lengths = self._get_feat_extract_output_lengths(attention_mask.sum(-1)).to(torch.long)
# assuming that padded tokens are filled with -100
# when not being attended to
labels_mask = labels >= 0
target_lengths = labels_mask.sum(-1)
flattened_targets = labels.masked_select(labels_mask)
# ctc_loss doesn't support fp16
log_probs = nn.functional.log_softmax(logits, dim=-1, dtype=torch.float32).transpose(0, 1)
with torch.backends.cudnn.flags(enabled=False):
loss = nn.functional.ctc_loss(
log_probs,
flattened_targets,
input_lengths,
target_lengths,
blank=self.config.pad_token_id,
reduction=self.config.ctc_loss_reduction,
zero_infinity=self.config.ctc_zero_infinity,
)
if not return_dict:
output = (logits,) + outputs[_HIDDEN_STATES_START_POSITION:]
return ((loss,) + output) if loss is not None else output
return CausalLMOutput(
loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions
)
_SEQ_CLASS_CHECKPOINT = "superb/hubert-base-superb-ks"
_SEQ_CLASS_EXPECTED_OUTPUT = "'_unknown_'"
_SEQ_CLASS_EXPECTED_LOSS = 8.53
@add_start_docstrings(
"""
Hubert Model with a sequence classification head on top (a linear layer over the pooled output) for tasks like
SUPERB Keyword Spotting.
""",
HUBERT_START_DOCSTRING,
)
class HubertForSequenceClassification(HubertPreTrainedModel):
def __init__(self, config):
super().__init__(config)
if hasattr(config, "add_adapter") and config.add_adapter:
raise ValueError(
"Sequence classification does not support the use of Hubert adapters (config.add_adapter=True)"
)
self.hubert = HubertModel(config)
num_layers = config.num_hidden_layers + 1 # transformer layers + input embeddings
if config.use_weighted_layer_sum:
self.layer_weights = nn.Parameter(torch.ones(num_layers) / num_layers)
self.projector = nn.Linear(config.hidden_size, config.classifier_proj_size)
self.classifier = nn.Linear(config.classifier_proj_size, config.num_labels)
# Initialize weights and apply final processing
self.post_init()
def freeze_feature_extractor(self):
"""
Calling this function will disable the gradient computation for the feature encoder so that its parameters will
not be updated during training.
"""
warnings.warn(
"The method `freeze_feature_extractor` is deprecated and will be removed in Transformers v5. "
"Please use the equivalent `freeze_feature_encoder` method instead.",
FutureWarning,
)
self.freeze_feature_encoder()
def freeze_feature_encoder(self):
"""
Calling this function will disable the gradient computation for the feature encoder so that its parameter will
not be updated during training.
"""
self.hubert.feature_extractor._freeze_parameters()
def freeze_base_model(self):
"""
Calling this function will disable the gradient computation for the base model so that its parameters will not
be updated during training. Only the classification head will be updated.
"""
for param in self.hubert.parameters():
param.requires_grad = False
@add_start_docstrings_to_model_forward(HUBERT_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_SEQ_CLASS_CHECKPOINT,
output_type=SequenceClassifierOutput,
config_class=_CONFIG_FOR_DOC,
modality="audio",
expected_output=_SEQ_CLASS_EXPECTED_OUTPUT,
expected_loss=_SEQ_CLASS_EXPECTED_LOSS,
)
def forward(
self,
input_values: Optional[torch.Tensor],
attention_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
labels: Optional[torch.Tensor] = None,
) -> Union[Tuple, SequenceClassifierOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
output_hidden_states = True if self.config.use_weighted_layer_sum else output_hidden_states
outputs = self.hubert(
input_values,
attention_mask=attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
if self.config.use_weighted_layer_sum:
hidden_states = outputs[_HIDDEN_STATES_START_POSITION]
hidden_states = torch.stack(hidden_states, dim=1)
norm_weights = nn.functional.softmax(self.layer_weights, dim=-1)
hidden_states = (hidden_states * norm_weights.view(-1, 1, 1)).sum(dim=1)
else:
hidden_states = outputs[0]
hidden_states = self.projector(hidden_states)
if attention_mask is None:
pooled_output = hidden_states.mean(dim=1)
else:
padding_mask = self._get_feature_vector_attention_mask(hidden_states.shape[1], attention_mask)
expand_padding_mask = padding_mask.unsqueeze(-1).repeat(1, 1, hidden_states.shape[2])
hidden_states[~expand_padding_mask] = 0.0
pooled_output = hidden_states.sum(dim=1) / padding_mask.sum(dim=1).view(-1, 1)
logits = self.classifier(pooled_output)
loss = None
if labels is not None:
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.view(-1, self.config.num_labels), labels.view(-1))
if not return_dict:
output = (logits,) + outputs[_HIDDEN_STATES_START_POSITION:]
return ((loss,) + output) if loss is not None else output
return SequenceClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
__all__ = ["HubertForCTC", "HubertForSequenceClassification", "HubertModel", "HubertPreTrainedModel"]
```
|
========================================================================================================================================
SOURCE CODE FILE: modeling_tf_hubert.py
LINES: 2
SIZE: 69.12 KB
PATH: scripts\freecad_env\Lib\site-packages\transformers\models\hubert\modeling_tf_hubert.py
ENCODING: utf-8
```py
# coding=utf-8
# Copyright 2021 The Fairseq Authors and the HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""TensorFlow Hubert model."""
from __future__ import annotations
import warnings
from typing import Any, Optional, Tuple, Union
import numpy as np
import tensorflow as tf
from ...activations_tf import get_tf_activation
from ...modeling_tf_outputs import TFBaseModelOutput, TFCausalLMOutput
from ...modeling_tf_utils import (
TFPreTrainedModel,
get_initializer,
keras,
keras_serializable,
unpack_inputs,
)
from ...tf_utils import shape_list, stable_softmax
from ...utils import (
add_start_docstrings,
add_start_docstrings_to_model_forward,
logging,
replace_return_docstrings,
)
from .configuration_hubert import HubertConfig
logger = logging.get_logger(__name__)
_CONFIG_FOR_DOC = "HubertConfig"
LARGE_NEGATIVE = -1e8
# Copied from transformers.models.wav2vec2.modeling_tf_wav2vec2._sample_without_replacement
def _sample_without_replacement(distribution, num_samples):
"""
Categorical sampling without replacement is currently not implemented. The gumbel-max trick will do for now - see
https://github.com/tensorflow/tensorflow/issues/9260 for more info
"""
z = -tf.math.log(tf.random.uniform(shape_list(distribution), 0, 1))
_, indices = tf.nn.top_k(distribution + z, num_samples)
return indices
# Copied from transformers.models.wav2vec2.modeling_tf_wav2vec2._scatter_values_on_batch_indices
def _scatter_values_on_batch_indices(values, batch_indices, output_shape):
"""
Scatter function as in PyTorch with indices in format (batch_dim, indixes)
"""
indices_shape = shape_list(batch_indices)
# broadcast batch dim to indices_shape
broad_casted_batch_dims = tf.reshape(
tf.broadcast_to(tf.expand_dims(tf.range(indices_shape[0]), axis=-1), indices_shape), [1, -1]
)
# transform batch_indices to pair_indices
pair_indices = tf.transpose(tf.concat([broad_casted_batch_dims, tf.reshape(batch_indices, [1, -1])], 0))
# scatter values to pair indices
return tf.scatter_nd(pair_indices, tf.reshape(values, [-1]), output_shape)
# Copied from transformers.models.wav2vec2.modeling_tf_wav2vec2._compute_mask_indices
def _compute_mask_indices(
shape: Tuple[int, int],
mask_prob: float,
mask_length: int,
min_masks: int = 0,
) -> tf.Tensor:
"""
Computes random mask spans for a given shape
Args:
shape: the shape for which to compute masks.
should be of size 2 where first element is batch size and 2nd is timesteps
attention_mask: optional padding mask of the same size as shape, which will prevent masking padded elements
mask_prob:
probability for each token to be chosen as start of the span to be masked. this will be multiplied by
number of timesteps divided by length of mask span to mask approximately this percentage of all elements.
however due to overlaps, the actual number will be smaller (unless no_overlap is True)
mask_length: size of the mask
min_masks: minimum number of masked spans
Adapted from [fairseq's
data_utils.py](https://github.com/pytorch/fairseq/blob/e0788f7007a8473a76db573985031f3c94201e79/fairseq/data/data_utils.py#L376).
"""
batch_size, sequence_length = shape
if mask_length < 1:
raise ValueError("`mask_length` has to be bigger than 0.")
tf.debugging.assert_less(
mask_length,
sequence_length,
message=(
f"`mask_length` has to be smaller than `sequence_length`, but got `mask_length`: {mask_length} and"
f" `sequence_length`: {sequence_length}`"
),
)
# compute number of masked spans in batch
num_masked_spans = mask_prob * tf.cast(sequence_length, tf.float32) / mask_length + tf.random.uniform((1,))
num_masked_spans = tf.maximum(num_masked_spans, min_masks)
num_masked_spans = tf.cast(num_masked_spans, tf.int32)
# make sure num masked indices <= sequence_length
num_masked_spans = tf.math.minimum(sequence_length // mask_length, num_masked_spans)
num_masked_spans = tf.squeeze(num_masked_spans)
# SpecAugment mask to fill
spec_aug_mask = tf.zeros((batch_size, sequence_length), dtype=tf.int32)
# uniform distribution to sample from, make sure that offset samples are < sequence_length
uniform_dist = tf.ones((batch_size, sequence_length - (mask_length - 1)))
# get random indices to mask
spec_aug_mask_idxs = _sample_without_replacement(uniform_dist, num_masked_spans)
# expand masked indices to masked spans
spec_aug_mask_idxs = tf.expand_dims(spec_aug_mask_idxs, -1)
spec_aug_mask_idxs = tf.tile(spec_aug_mask_idxs, (1, 1, mask_length))
spec_aug_mask_idxs = tf.reshape(spec_aug_mask_idxs, (batch_size, num_masked_spans * mask_length))
offsets = tf.range(mask_length)[tf.newaxis, tf.newaxis, :]
offsets = tf.tile(offsets, (batch_size, num_masked_spans, 1))
offsets = tf.reshape(offsets, (batch_size, num_masked_spans * mask_length))
spec_aug_mask_idxs = spec_aug_mask_idxs + offsets
# scatter indices to mask
spec_aug_mask = _scatter_values_on_batch_indices(
tf.ones_like(spec_aug_mask_idxs), spec_aug_mask_idxs, tf.shape(spec_aug_mask)
)
return spec_aug_mask
# Copied from transformers.models.bart.modeling_tf_bart._expand_mask
def _expand_mask(mask: tf.Tensor, tgt_len: Optional[int] = None):
"""
Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
"""
src_len = shape_list(mask)[1]
tgt_len = tgt_len if tgt_len is not None else src_len
one_cst = tf.constant(1.0)
mask = tf.cast(mask, dtype=one_cst.dtype)
expanded_mask = tf.tile(mask[:, None, None, :], (1, 1, tgt_len, 1))
return (one_cst - expanded_mask) * LARGE_NEGATIVE
# Copied from transformers.models.wav2vec2.modeling_tf_wav2vec2.TFWav2Vec2GroupNorm with Wav2Vec2->Hubert
class TFHubertGroupNorm(keras.layers.Layer):
"""
From tensorflow-addons https://www.tensorflow.org/addons/api_docs/python/tfa/layers/GroupNormalization
"""
def __init__(
self,
groups: int = 32,
axis: int = -1,
epsilon: float = 1e-3,
center: bool = True,
scale: bool = True,
beta_initializer: keras.initializers.Initializer = "zeros",
gamma_initializer: keras.initializers.Initializer = "ones",
beta_regularizer: keras.regularizers.Regularizer = None,
gamma_regularizer: keras.regularizers.Regularizer = None,
beta_constraint: keras.constraints.Constraint = None,
gamma_constraint: keras.constraints.Constraint = None,
**kwargs,
):
super().__init__(**kwargs)
self.supports_masking = True
self.groups = groups
self.axis = axis
self.epsilon = epsilon
self.center = center
self.scale = scale
self.beta_initializer = keras.initializers.get(beta_initializer)
self.gamma_initializer = keras.initializers.get(gamma_initializer)
self.beta_regularizer = keras.regularizers.get(beta_regularizer)
self.gamma_regularizer = keras.regularizers.get(gamma_regularizer)
self.beta_constraint = keras.constraints.get(beta_constraint)
self.gamma_constraint = keras.constraints.get(gamma_constraint)
self._check_axis()
def build(self, input_shape):
self._check_if_input_shape_is_none(input_shape)
self._set_number_of_groups_for_instance_norm(input_shape)
self._check_size_of_dimensions(input_shape)
self._create_input_spec(input_shape)
self._add_gamma_weight(input_shape)
self._add_beta_weight(input_shape)
self.built = True
super().build(input_shape)
def call(self, inputs):
input_shape = keras.backend.int_shape(inputs)
tensor_input_shape = tf.shape(inputs)
reshaped_inputs, group_shape = self._reshape_into_groups(inputs, input_shape, tensor_input_shape)
normalized_inputs = self._apply_normalization(reshaped_inputs, input_shape)
is_instance_norm = (input_shape[self.axis] // self.groups) == 1
if not is_instance_norm:
outputs = tf.reshape(normalized_inputs, tensor_input_shape)
else:
outputs = normalized_inputs
return outputs
def get_config(self):
config = {
"groups": self.groups,
"axis": self.axis,
"epsilon": self.epsilon,
"center": self.center,
"scale": self.scale,
"beta_initializer": keras.initializers.serialize(self.beta_initializer),
"gamma_initializer": keras.initializers.serialize(self.gamma_initializer),
"beta_regularizer": keras.regularizers.serialize(self.beta_regularizer),
"gamma_regularizer": keras.regularizers.serialize(self.gamma_regularizer),
"beta_constraint": keras.constraints.serialize(self.beta_constraint),
"gamma_constraint": keras.constraints.serialize(self.gamma_constraint),
}
base_config = super().get_config()
return {**base_config, **config}
def compute_output_shape(self, input_shape):
return input_shape
def _reshape_into_groups(self, inputs, input_shape, tensor_input_shape):
group_shape = [tensor_input_shape[i] for i in range(len(input_shape))]
is_instance_norm = (input_shape[self.axis] // self.groups) == 1
if not is_instance_norm:
group_shape[self.axis] = input_shape[self.axis] // self.groups
group_shape.insert(self.axis, self.groups)
group_shape = tf.stack(group_shape)
reshaped_inputs = tf.reshape(inputs, group_shape)
return reshaped_inputs, group_shape
else:
return inputs, group_shape
def _apply_normalization(self, reshaped_inputs, input_shape):
group_shape = keras.backend.int_shape(reshaped_inputs)
group_reduction_axes = list(range(1, len(group_shape)))
is_instance_norm = (input_shape[self.axis] // self.groups) == 1
if not is_instance_norm:
axis = -2 if self.axis == -1 else self.axis - 1
else:
axis = -1 if self.axis == -1 else self.axis - 1
group_reduction_axes.pop(axis)
mean, variance = tf.nn.moments(reshaped_inputs, group_reduction_axes, keepdims=True)
gamma, beta = self._get_reshaped_weights(input_shape)
normalized_inputs = tf.nn.batch_normalization(
reshaped_inputs,
mean=mean,
variance=variance,
scale=gamma,
offset=beta,
variance_epsilon=self.epsilon,
)
return normalized_inputs
def _get_reshaped_weights(self, input_shape):
broadcast_shape = self._create_broadcast_shape(input_shape)
gamma = None
beta = None
if self.scale:
gamma = tf.reshape(self.gamma, broadcast_shape)
if self.center:
beta = tf.reshape(self.beta, broadcast_shape)
return gamma, beta
def _check_if_input_shape_is_none(self, input_shape):
dim = input_shape[self.axis]
if dim is None:
raise ValueError(
"Axis "
+ str(self.axis)
+ " of input tensor should have a defined dimension but the layer received an input with shape "
+ str(input_shape)
+ "."
)
def _set_number_of_groups_for_instance_norm(self, input_shape):
dim = input_shape[self.axis]
if self.groups == -1:
self.groups = dim
def _check_size_of_dimensions(self, input_shape):
dim = input_shape[self.axis]
if dim < self.groups:
raise ValueError(
"Number of groups ("
+ str(self.groups)
+ ") cannot be more than the number of channels ("
+ str(dim)
+ ")."
)
if dim % self.groups != 0:
raise ValueError(
"Number of groups ("
+ str(self.groups)
+ ") must be a multiple of the number of channels ("
+ str(dim)
+ ")."
)
def _check_axis(self):
if self.axis == 0:
raise ValueError(
"You are trying to normalize your batch axis. Do you want to use tf.layer.batch_normalization instead"
)
def _create_input_spec(self, input_shape):
dim = input_shape[self.axis]
self.input_spec = keras.layers.InputSpec(ndim=len(input_shape), axes={self.axis: dim})
def _add_gamma_weight(self, input_shape):
dim = input_shape[self.axis]
shape = (dim,)
if self.scale:
self.gamma = self.add_weight(
shape=shape,
name="gamma",
initializer=self.gamma_initializer,
regularizer=self.gamma_regularizer,
constraint=self.gamma_constraint,
)
else:
self.gamma = None
def _add_beta_weight(self, input_shape):
dim = input_shape[self.axis]
shape = (dim,)
if self.center:
self.beta = self.add_weight(
shape=shape,
name="beta",
initializer=self.beta_initializer,
regularizer=self.beta_regularizer,
constraint=self.beta_constraint,
)
else:
self.beta = None
def _create_broadcast_shape(self, input_shape):
broadcast_shape = [1] * len(input_shape)
is_instance_norm = (input_shape[self.axis] // self.groups) == 1
if not is_instance_norm:
broadcast_shape[self.axis] = input_shape[self.axis] // self.groups
broadcast_shape.insert(self.axis, self.groups)
else:
broadcast_shape[self.axis] = self.groups
return broadcast_shape
# Copied from transformers.models.wav2vec2.modeling_tf_wav2vec2.TFWav2Vec2WeightNormConv1D with Wav2Vec2->Hubert
class TFHubertWeightNormConv1D(keras.layers.Conv1D):
"""Adapted from https://www.tensorflow.org/probability/api_docs/python/tfp/layers/weight_norm/WeightNorm"""
def __init__(self, filters, kernel_size, groups, explicit_padding, **kwargs):
super().__init__(
filters=filters,
kernel_size=kernel_size,
groups=groups,
padding="valid",
use_bias=True,
bias_initializer="he_normal",
**kwargs,
)
self.explicit_padding = explicit_padding
self.filter_axis = 2
self.kernel_norm_axes = tf.constant([0, 1])
def _init_norm(self):
"""Set the norm of the weight vector."""
kernel_norm = tf.sqrt(tf.reduce_sum(tf.square(self.weight_v), axis=self.kernel_norm_axes))
self.weight_g.assign(kernel_norm[:, tf.newaxis, tf.newaxis])
def _normalize_kernel(self):
"""Generate normalized weights."""
kernel = tf.nn.l2_normalize(self.weight_v, axis=self.kernel_norm_axes) * tf.transpose(self.weight_g)
self.kernel = tf.transpose(kernel)
def build(self, input_shape):
if not self.built:
super().build(input_shape)
self.kernel = tf.Variable(tf.transpose(self.kernel), name="weight_v", trainable=True)
self.weight_v = self.kernel
self.weight_g = self.add_weight(
name="weight_g",
shape=(int(self.weight_v.shape[self.filter_axis]), 1, 1),
initializer="ones",
dtype=self.weight_v.dtype,
trainable=True,
)
self._init_norm()
self.bias = self.add_weight(name="bias", shape=(self.filters,), initializer="zeros", trainable=True)
def call(self, inputs):
# TODO Matt: Assigning to attributes in call() is deeply sinful in TensorFlow, as it should be idempotent.
# This whole layer should be replaced by a layer that doesn't inherit from Conv1D, but instead calls
# a functional 1d convolution with normalized weights that it generates (but does not store!)
self._normalize_kernel()
padded_inputs = tf.pad(inputs, ((0, 0), (self.explicit_padding, self.explicit_padding), (0, 0)))
output = super().call(padded_inputs)
return output
# Copied from transformers.models.wav2vec2.modeling_tf_wav2vec2.TFWav2Vec2NoLayerNormConvLayer with Wav2Vec2->Hubert
class TFHubertNoLayerNormConvLayer(keras.layers.Layer):
def __init__(self, config: HubertConfig, layer_id: int = 0, **kwargs: Any) -> None:
super().__init__(**kwargs)
self.in_conv_dim = config.conv_dim[layer_id] if layer_id > 0 else 1
self.out_conv_dim = config.conv_dim[layer_id]
self.conv = keras.layers.Conv1D(
filters=self.out_conv_dim,
kernel_size=config.conv_kernel[layer_id],
strides=config.conv_stride[layer_id],
use_bias=config.conv_bias,
name="conv",
)
self.activation = get_tf_activation(config.feat_extract_activation)
def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
hidden_states = self.conv(hidden_states)
hidden_states = self.activation(hidden_states)
return hidden_states
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "conv", None) is not None:
with tf.name_scope(self.conv.name):
self.conv.build([None, None, self.in_conv_dim])
# Copied from transformers.models.wav2vec2.modeling_tf_wav2vec2.TFWav2Vec2LayerNormConvLayer with Wav2Vec2->Hubert
class TFHubertLayerNormConvLayer(keras.layers.Layer):
def __init__(self, config: HubertConfig, layer_id: int = 0, **kwargs: Any) -> None:
super().__init__(**kwargs)
self.in_conv_dim = config.conv_dim[layer_id] if layer_id > 0 else 1
self.out_conv_dim = config.conv_dim[layer_id]
self.conv = keras.layers.Conv1D(
filters=self.out_conv_dim,
kernel_size=config.conv_kernel[layer_id],
strides=config.conv_stride[layer_id],
use_bias=config.conv_bias,
name="conv",
)
self.layer_norm = keras.layers.LayerNormalization(name="layer_norm", epsilon=config.layer_norm_eps)
self.activation = get_tf_activation(config.feat_extract_activation)
def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
hidden_states = self.conv(hidden_states)
hidden_states = self.layer_norm(hidden_states)
hidden_states = self.activation(hidden_states)
return hidden_states
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "conv", None) is not None:
with tf.name_scope(self.conv.name):
self.conv.build([None, None, self.in_conv_dim])
if getattr(self, "layer_norm", None) is not None:
with tf.name_scope(self.layer_norm.name):
self.layer_norm.build([None, None, self.out_conv_dim])
# Copied from transformers.models.wav2vec2.modeling_tf_wav2vec2.TFWav2Vec2GroupNormConvLayer with Wav2Vec2->Hubert
class TFHubertGroupNormConvLayer(keras.layers.Layer):
def __init__(self, config: HubertConfig, layer_id: int = 0, **kwargs: Any) -> None:
super().__init__(**kwargs)
self.in_conv_dim = config.conv_dim[layer_id] if layer_id > 0 else 1
self.out_conv_dim = config.conv_dim[layer_id]
self.conv = keras.layers.Conv1D(
filters=self.out_conv_dim,
kernel_size=config.conv_kernel[layer_id],
strides=config.conv_stride[layer_id],
use_bias=config.conv_bias,
name="conv",
)
self.activation = get_tf_activation(config.feat_extract_activation)
self.layer_norm = TFHubertGroupNorm(groups=self.out_conv_dim, epsilon=config.layer_norm_eps, name="layer_norm")
def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
hidden_states = self.conv(hidden_states)
hidden_states = self.layer_norm(hidden_states)
hidden_states = self.activation(hidden_states)
return hidden_states
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "conv", None) is not None:
with tf.name_scope(self.conv.name):
self.conv.build([None, None, self.in_conv_dim])
if getattr(self, "layer_norm", None) is not None:
with tf.name_scope(self.layer_norm.name):
self.layer_norm.build([None, None, self.out_conv_dim])
# Copied from transformers.models.wav2vec2.modeling_tf_wav2vec2.TFWav2Vec2PositionalConvEmbedding with Wav2Vec2->Hubert
class TFHubertPositionalConvEmbedding(keras.layers.Layer):
def __init__(self, config: HubertConfig, **kwargs: Any) -> None:
super().__init__(**kwargs)
self.conv = TFHubertWeightNormConv1D(
filters=config.hidden_size,
kernel_size=config.num_conv_pos_embeddings,
groups=config.num_conv_pos_embedding_groups,
explicit_padding=config.num_conv_pos_embeddings // 2,
name="conv",
)
self.padding = TFHubertSamePadLayer(config.num_conv_pos_embeddings)
self.activation = get_tf_activation(config.feat_extract_activation)
self.config = config
def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
hidden_states = self.conv(hidden_states)
hidden_states = self.padding(hidden_states)
hidden_states = self.activation(hidden_states)
return hidden_states
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "conv", None) is not None:
with tf.name_scope(self.conv.name):
self.conv.build([None, None, self.config.hidden_size])
# Copied from transformers.models.wav2vec2.modeling_tf_wav2vec2.TFWav2Vec2SamePadLayer with Wav2Vec2->Hubert
class TFHubertSamePadLayer(keras.layers.Layer):
def __init__(self, num_conv_pos_embeddings, **kwargs):
super().__init__(**kwargs)
self.num_pad_remove = 1 if num_conv_pos_embeddings % 2 == 0 else 0
def call(self, hidden_states):
if self.num_pad_remove > 0:
hidden_states = hidden_states[:, : -self.num_pad_remove, :]
return hidden_states
class TFHubertFeatureEncoder(keras.layers.Layer):
def __init__(self, config: HubertConfig, **kwargs: Any) -> None:
super().__init__(**kwargs)
if config.feat_extract_norm == "group":
conv_layers = [TFHubertGroupNormConvLayer(config, layer_id=0, name=f"conv_layers.{0}")] + [
TFHubertNoLayerNormConvLayer(config, layer_id=i + 1, name=f"conv_layers.{i + 1}")
for i in range(config.num_feat_extract_layers - 1)
]
elif config.feat_extract_norm == "layer":
conv_layers = [
TFHubertLayerNormConvLayer(config, layer_id=i, name=f"conv_layers.{i}")
for i in range(config.num_feat_extract_layers)
]
else:
raise ValueError(
f"`config.feat_extract_norm` is {config.feat_extract_norm}, but has to be one of ['group', 'layer']"
)
self.conv_layers = conv_layers
def call(self, input_values):
hidden_states = tf.expand_dims(input_values, -1)
for conv_layer in self.conv_layers:
hidden_states = conv_layer(hidden_states)
return hidden_states
def build(self, input_shape=None):
if self.built:
return
self.built = True
for conv_layer in self.conv_layers:
with tf.name_scope(conv_layer.name):
conv_layer.build(None)
class TFHubertFeatureExtractor(TFHubertFeatureEncoder):
def __init__(self, config, **kwargs):
super().__init__(config, **kwargs)
warnings.warn(
f"The class `{self.__class__.__name__}` has been depreciated "
"and will be removed in Transformers v5. "
f"Use `{self.__class__.__bases__[0].__name__}` instead.",
FutureWarning,
)
class TFHubertFeatureProjection(keras.layers.Layer):
def __init__(self, config: HubertConfig, **kwargs):
super().__init__(**kwargs)
self.layer_norm = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layer_norm")
self.projection = keras.layers.Dense(
units=config.hidden_size,
kernel_initializer=get_initializer(config.initializer_range),
bias_initializer="zeros",
name="projection",
)
self.dropout = keras.layers.Dropout(rate=config.feat_proj_dropout)
self.config = config
def call(self, hidden_states: tf.Tensor, training: bool = False) -> tf.Tensor:
hidden_states = self.layer_norm(hidden_states)
hidden_states = self.projection(hidden_states)
hidden_states = self.dropout(hidden_states, training=training)
return hidden_states
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "layer_norm", None) is not None:
with tf.name_scope(self.layer_norm.name):
self.layer_norm.build([None, None, self.config.conv_dim[-1]])
if getattr(self, "projection", None) is not None:
with tf.name_scope(self.projection.name):
self.projection.build([None, None, self.config.conv_dim[-1]])
# Copied from transformers.models.bart.modeling_tf_bart.TFBartAttention with TFBart->TFHubert
class TFHubertAttention(keras.layers.Layer):
"""Multi-headed attention from "Attention Is All You Need"""
def __init__(
self,
embed_dim: int,
num_heads: int,
dropout: float = 0.0,
is_decoder: bool = False,
bias: bool = True,
**kwargs,
):
super().__init__(**kwargs)
self.embed_dim = embed_dim
self.num_heads = num_heads
self.dropout = keras.layers.Dropout(dropout)
self.head_dim = embed_dim // num_heads
if (self.head_dim * num_heads) != self.embed_dim:
raise ValueError(
f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}"
f" and `num_heads`: {num_heads})."
)
self.scaling = self.head_dim**-0.5
self.is_decoder = is_decoder
self.k_proj = keras.layers.Dense(embed_dim, use_bias=bias, name="k_proj")
self.q_proj = keras.layers.Dense(embed_dim, use_bias=bias, name="q_proj")
self.v_proj = keras.layers.Dense(embed_dim, use_bias=bias, name="v_proj")
self.out_proj = keras.layers.Dense(embed_dim, use_bias=bias, name="out_proj")
def _shape(self, tensor: tf.Tensor, seq_len: int, bsz: int):
return tf.transpose(tf.reshape(tensor, (bsz, seq_len, self.num_heads, self.head_dim)), (0, 2, 1, 3))
def call(
self,
hidden_states: tf.Tensor,
key_value_states: tf.Tensor | None = None,
past_key_value: Tuple[Tuple[tf.Tensor]] | None = None,
attention_mask: tf.Tensor | None = None,
layer_head_mask: tf.Tensor | None = None,
training: Optional[bool] = False,
) -> Tuple[tf.Tensor, tf.Tensor | None]:
"""Input shape: Batch x Time x Channel"""
# if key_value_states are provided this layer is used as a cross-attention layer
# for the decoder
is_cross_attention = key_value_states is not None
bsz, tgt_len, embed_dim = shape_list(hidden_states)
# get query proj
query_states = self.q_proj(hidden_states) * self.scaling
# get key, value proj
if is_cross_attention and past_key_value is not None:
# reuse k,v, cross_attentions
key_states = past_key_value[0]
value_states = past_key_value[1]
elif is_cross_attention:
# cross_attentions
key_states = self._shape(self.k_proj(key_value_states), -1, bsz)
value_states = self._shape(self.v_proj(key_value_states), -1, bsz)
elif past_key_value is not None:
# reuse k, v, self_attention
key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
key_states = tf.concat([past_key_value[0], key_states], axis=2)
value_states = tf.concat([past_key_value[1], value_states], axis=2)
else:
# self_attention
key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
if self.is_decoder:
# if cross_attention save Tuple(tf.Tensor, tf.Tensor) of all cross attention key/value_states.
# Further calls to cross_attention layer can then reuse all cross-attention
# key/value_states (first "if" case)
# if uni-directional self-attention (decoder) save Tuple(tf.Tensor, tf.Tensor) of
# all previous decoder key/value_states. Further calls to uni-directional self-attention
# can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
# if encoder bi-directional self-attention `past_key_value` is always `None`
past_key_value = (key_states, value_states)
proj_shape = (bsz * self.num_heads, -1, self.head_dim)
query_states = tf.reshape(self._shape(query_states, tgt_len, bsz), proj_shape)
key_states = tf.reshape(key_states, proj_shape)
value_states = tf.reshape(value_states, proj_shape)
src_len = shape_list(key_states)[1]
attn_weights = tf.matmul(query_states, key_states, transpose_b=True)
tf.debugging.assert_equal(
shape_list(attn_weights),
[bsz * self.num_heads, tgt_len, src_len],
message=(
f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is"
f" {shape_list(attn_weights)}"
),
)
if attention_mask is not None:
tf.debugging.assert_equal(
shape_list(attention_mask),
[bsz, 1, tgt_len, src_len],
message=(
f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is"
f" {shape_list(attention_mask)}"
),
)
attention_mask = tf.cast(attention_mask, dtype=attn_weights.dtype)
attn_weights = tf.reshape(attn_weights, (bsz, self.num_heads, tgt_len, src_len)) + attention_mask
attn_weights = tf.reshape(attn_weights, (bsz * self.num_heads, tgt_len, src_len))
attn_weights = stable_softmax(attn_weights, axis=-1)
if layer_head_mask is not None:
tf.debugging.assert_equal(
shape_list(layer_head_mask),
[self.num_heads],
message=(
f"Head mask for a single layer should be of size {(self.num_heads)}, but is"
f" {shape_list(layer_head_mask)}"
),
)
attn_weights = tf.reshape(layer_head_mask, (1, -1, 1, 1)) * tf.reshape(
attn_weights, (bsz, self.num_heads, tgt_len, src_len)
)
attn_weights = tf.reshape(attn_weights, (bsz * self.num_heads, tgt_len, src_len))
attn_probs = self.dropout(attn_weights, training=training)
attn_output = tf.matmul(attn_probs, value_states)
tf.debugging.assert_equal(
shape_list(attn_output),
[bsz * self.num_heads, tgt_len, self.head_dim],
message=(
f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is"
f" {shape_list(attn_output)}"
),
)
attn_output = tf.transpose(
tf.reshape(attn_output, (bsz, self.num_heads, tgt_len, self.head_dim)), (0, 2, 1, 3)
)
attn_output = tf.reshape(attn_output, (bsz, tgt_len, embed_dim))
attn_output = self.out_proj(attn_output)
attn_weights: tf.Tensor = tf.reshape(attn_weights, (bsz, self.num_heads, tgt_len, src_len))
return attn_output, attn_weights, past_key_value
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "k_proj", None) is not None:
with tf.name_scope(self.k_proj.name):
self.k_proj.build([None, None, self.embed_dim])
if getattr(self, "q_proj", None) is not None:
with tf.name_scope(self.q_proj.name):
self.q_proj.build([None, None, self.embed_dim])
if getattr(self, "v_proj", None) is not None:
with tf.name_scope(self.v_proj.name):
self.v_proj.build([None, None, self.embed_dim])
if getattr(self, "out_proj", None) is not None:
with tf.name_scope(self.out_proj.name):
self.out_proj.build([None, None, self.embed_dim])
# Copied from transformers.models.wav2vec2.modeling_tf_wav2vec2.TFWav2Vec2FeedForward with Wav2Vec2->Hubert
class TFHubertFeedForward(keras.layers.Layer):
def __init__(self, config: HubertConfig, **kwargs):
super().__init__(**kwargs)
self.intermediate_dropout = keras.layers.Dropout(config.activation_dropout)
self.intermediate_dense = keras.layers.Dense(
units=config.intermediate_size,
kernel_initializer=get_initializer(config.initializer_range),
bias_initializer="zeros",
name="intermediate_dense",
)
self.intermediate_act_fn = get_tf_activation(config.hidden_act)
self.output_dense = keras.layers.Dense(
units=config.hidden_size,
kernel_initializer=get_initializer(config.initializer_range),
bias_initializer="zeros",
name="output_dense",
)
self.output_dropout = keras.layers.Dropout(config.hidden_dropout)
self.config = config
def call(self, hidden_states: tf.Tensor, training: bool = False) -> tf.Tensor:
hidden_states = self.intermediate_dense(hidden_states)
hidden_states = self.intermediate_act_fn(hidden_states)
hidden_states = self.intermediate_dropout(hidden_states, training=training)
hidden_states = self.output_dense(hidden_states)
hidden_states = self.output_dropout(hidden_states, training=training)
return hidden_states
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "intermediate_dense", None) is not None:
with tf.name_scope(self.intermediate_dense.name):
self.intermediate_dense.build([None, None, self.config.hidden_size])
if getattr(self, "output_dense", None) is not None:
with tf.name_scope(self.output_dense.name):
self.output_dense.build([None, None, self.config.intermediate_size])
# Copied from transformers.models.wav2vec2.modeling_tf_wav2vec2.TFWav2Vec2EncoderLayer with Wav2Vec2->Hubert
class TFHubertEncoderLayer(keras.layers.Layer):
def __init__(self, config: HubertConfig, **kwargs):
super().__init__(**kwargs)
self.attention = TFHubertAttention(
embed_dim=config.hidden_size,
num_heads=config.num_attention_heads,
dropout=config.attention_dropout,
is_decoder=False,
name="attention",
)
self.dropout = keras.layers.Dropout(config.hidden_dropout)
self.layer_norm = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layer_norm")
self.feed_forward = TFHubertFeedForward(config, name="feed_forward")
self.final_layer_norm = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="final_layer_norm")
self.config = config
def call(
self,
hidden_states: tf.Tensor,
attention_mask: tf.Tensor | None = None,
output_attentions: Optional[bool] = False,
training: bool = False,
) -> Tuple[tf.Tensor]:
attn_residual = hidden_states
hidden_states, attn_weights, _ = self.attention(
hidden_states, attention_mask=attention_mask, training=training
)
hidden_states = self.dropout(hidden_states, training=training)
hidden_states = attn_residual + hidden_states
hidden_states = self.layer_norm(hidden_states)
hidden_states = hidden_states + self.feed_forward(hidden_states)
hidden_states = self.final_layer_norm(hidden_states)
outputs = (hidden_states,)
if output_attentions:
outputs += (attn_weights,)
return outputs
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "attention", None) is not None:
with tf.name_scope(self.attention.name):
self.attention.build(None)
if getattr(self, "layer_norm", None) is not None:
with tf.name_scope(self.layer_norm.name):
self.layer_norm.build([None, None, self.config.hidden_size])
if getattr(self, "feed_forward", None) is not None:
with tf.name_scope(self.feed_forward.name):
self.feed_forward.build(None)
if getattr(self, "final_layer_norm", None) is not None:
with tf.name_scope(self.final_layer_norm.name):
self.final_layer_norm.build([None, None, self.config.hidden_size])
# Copied from transformers.models.wav2vec2.modeling_tf_wav2vec2.TFWav2Vec2EncoderLayerStableLayerNorm with Wav2Vec2->Hubert
class TFHubertEncoderLayerStableLayerNorm(keras.layers.Layer):
def __init__(self, config: HubertConfig, **kwargs):
super().__init__(**kwargs)
self.attention = TFHubertAttention(
embed_dim=config.hidden_size,
num_heads=config.num_attention_heads,
dropout=config.attention_dropout,
is_decoder=False,
name="attention",
)
self.dropout = keras.layers.Dropout(config.hidden_dropout)
self.layer_norm = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layer_norm")
self.feed_forward = TFHubertFeedForward(config, name="feed_forward")
self.final_layer_norm = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="final_layer_norm")
self.config = config
def call(
self,
hidden_states: tf.Tensor,
attention_mask: tf.Tensor | None = None,
output_attentions: Optional[bool] = False,
training: bool = False,
) -> Tuple[tf.Tensor]:
attn_residual = hidden_states
hidden_states = self.layer_norm(hidden_states)
hidden_states, attn_weights, _ = self.attention(
hidden_states, attention_mask=attention_mask, training=training
)
hidden_states = self.dropout(hidden_states, training=training)
hidden_states = attn_residual + hidden_states
hidden_states = hidden_states + self.feed_forward(self.final_layer_norm(hidden_states))
outputs = (hidden_states,)
if output_attentions:
outputs += (attn_weights,)
return outputs
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "attention", None) is not None:
with tf.name_scope(self.attention.name):
self.attention.build(None)
if getattr(self, "layer_norm", None) is not None:
with tf.name_scope(self.layer_norm.name):
self.layer_norm.build([None, None, self.config.hidden_size])
if getattr(self, "feed_forward", None) is not None:
with tf.name_scope(self.feed_forward.name):
self.feed_forward.build(None)
if getattr(self, "final_layer_norm", None) is not None:
with tf.name_scope(self.final_layer_norm.name):
self.final_layer_norm.build([None, None, self.config.hidden_size])
# Copied from transformers.models.wav2vec2.modeling_tf_wav2vec2.TFWav2Vec2Encoder with Wav2Vec2->Hubert
class TFHubertEncoder(keras.layers.Layer):
def __init__(self, config: HubertConfig, **kwargs):
super().__init__(**kwargs)
self.config = config
self.pos_conv_embed = TFHubertPositionalConvEmbedding(config, name="pos_conv_embed")
self.layer_norm = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layer_norm")
self.dropout = keras.layers.Dropout(config.hidden_dropout)
self.layer = [TFHubertEncoderLayer(config, name=f"layers.{i}") for i in range(config.num_hidden_layers)]
def call(
self,
hidden_states: tf.Tensor,
attention_mask: tf.Tensor | None = None,
output_attentions: Optional[bool] = False,
output_hidden_states: Optional[bool] = False,
return_dict: Optional[bool] = True,
training: Optional[bool] = False,
) -> Union[TFBaseModelOutput, Tuple[tf.Tensor]]:
all_hidden_states = () if output_hidden_states else None
all_self_attentions = () if output_attentions else None
if attention_mask is not None:
hidden_states = hidden_states * tf.expand_dims(attention_mask, -1)
attention_mask = _expand_mask(attention_mask)
else:
attention_mask = None
position_embeddings = self.pos_conv_embed(hidden_states)
hidden_states = hidden_states + position_embeddings
hidden_states = self.layer_norm(hidden_states)
hidden_states = self.dropout(hidden_states, training=training)
for i, layer_module in enumerate(self.layer):
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
# add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
dropout_probability = np.random.uniform(0, 1)
if training and (dropout_probability < self.config.layerdrop): # skip the layer
continue
layer_outputs = layer_module(
hidden_states=hidden_states,
attention_mask=attention_mask,
output_attentions=output_attentions,
training=training,
)
hidden_states = layer_outputs[0]
if output_attentions:
all_self_attentions = all_self_attentions + (layer_outputs[1],)
# Add last layer
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if not return_dict:
return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None)
return TFBaseModelOutput(
last_hidden_state=hidden_states,
hidden_states=all_hidden_states,
attentions=all_self_attentions,
)
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "pos_conv_embed", None) is not None:
with tf.name_scope(self.pos_conv_embed.name):
self.pos_conv_embed.build(None)
if getattr(self, "layer_norm", None) is not None:
with tf.name_scope(self.layer_norm.name):
self.layer_norm.build([None, None, self.config.hidden_size])
if getattr(self, "layer", None) is not None:
for layer in self.layer:
with tf.name_scope(layer.name):
layer.build(None)
# Copied from transformers.models.wav2vec2.modeling_tf_wav2vec2.TFWav2Vec2EncoderStableLayerNorm with Wav2Vec2->Hubert
class TFHubertEncoderStableLayerNorm(keras.layers.Layer):
def __init__(self, config: HubertConfig, **kwargs):
super().__init__(**kwargs)
self.config = config
self.pos_conv_embed = TFHubertPositionalConvEmbedding(config, name="pos_conv_embed")
self.layer_norm = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layer_norm")
self.dropout = keras.layers.Dropout(config.hidden_dropout)
self.layer = [
TFHubertEncoderLayerStableLayerNorm(config, name=f"layers.{i}") for i in range(config.num_hidden_layers)
]
def call(
self,
hidden_states: tf.Tensor,
attention_mask: tf.Tensor | None = None,
output_attentions: Optional[bool] = False,
output_hidden_states: Optional[bool] = False,
return_dict: Optional[bool] = True,
training: Optional[bool] = False,
) -> Union[TFBaseModelOutput, Tuple[tf.Tensor]]:
all_hidden_states = () if output_hidden_states else None
all_self_attentions = () if output_attentions else None
if attention_mask is not None:
hidden_states = hidden_states * tf.expand_dims(attention_mask, -1)
attention_mask = _expand_mask(attention_mask)
else:
attention_mask = None
position_embeddings = self.pos_conv_embed(hidden_states)
hidden_states = hidden_states + position_embeddings
hidden_states = self.dropout(hidden_states, training=training)
for i, layer_module in enumerate(self.layer):
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
# add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
dropout_probability = np.random.uniform(0, 1)
if training and (dropout_probability < self.config.layerdrop): # skip the layer
continue
layer_outputs = layer_module(
hidden_states=hidden_states,
attention_mask=attention_mask,
output_attentions=output_attentions,
training=training,
)
hidden_states = layer_outputs[0]
if output_attentions:
all_self_attentions = all_self_attentions + (layer_outputs[1],)
hidden_states = self.layer_norm(hidden_states)
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if not return_dict:
return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None)
return TFBaseModelOutput(
last_hidden_state=hidden_states,
hidden_states=all_hidden_states,
attentions=all_self_attentions,
)
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "pos_conv_embed", None) is not None:
with tf.name_scope(self.pos_conv_embed.name):
self.pos_conv_embed.build(None)
if getattr(self, "layer_norm", None) is not None:
with tf.name_scope(self.layer_norm.name):
self.layer_norm.build([None, None, self.config.hidden_size])
if getattr(self, "layer", None) is not None:
for layer in self.layer:
with tf.name_scope(layer.name):
layer.build(None)
@keras_serializable
class TFHubertMainLayer(keras.layers.Layer):
config_class = HubertConfig
def __init__(self, config: HubertConfig, **kwargs):
super().__init__(**kwargs)
self.config = config
self.feature_extractor = TFHubertFeatureEncoder(config, name="feature_extractor")
self.feature_projection = TFHubertFeatureProjection(config, name="feature_projection")
if config.do_stable_layer_norm:
self.encoder = TFHubertEncoderStableLayerNorm(config, name="encoder")
else:
self.encoder = TFHubertEncoder(config, name="encoder")
def build(self, input_shape=None):
self.masked_spec_embed = self.add_weight(
shape=(self.config.hidden_size,), initializer="uniform", trainable=True, name="masked_spec_embed"
)
if self.built:
return
self.built = True
if getattr(self, "feature_extractor", None) is not None:
with tf.name_scope(self.feature_extractor.name):
self.feature_extractor.build(None)
if getattr(self, "feature_projection", None) is not None:
with tf.name_scope(self.feature_projection.name):
self.feature_projection.build(None)
if getattr(self, "encoder", None) is not None:
with tf.name_scope(self.encoder.name):
self.encoder.build(None)
def _get_feat_extract_output_lengths(self, input_lengths: tf.Tensor):
"""
Computes the output length of the convolutional layers
"""
def _conv_out_length(input_length, kernel_size, stride):
# 1D convolutional layer output length formula taken
# from https://pytorch.org/docs/stable/generated/torch.nn.Conv1d.html
return (input_length - kernel_size) // stride + 1
for kernel_size, stride in zip(self.config.conv_kernel, self.config.conv_stride):
input_lengths = _conv_out_length(input_lengths, kernel_size, stride)
return input_lengths
def _mask_hidden_states(self, hidden_states: tf.Tensor, mask_time_indices: tf.Tensor | None = None):
"""
Masks extracted features along time axis and/or along feature axis according to
[SpecAugment](https://arxiv.org/abs/1904.08779).
"""
batch_size, sequence_length, hidden_size = shape_list(hidden_states)
# `config.apply_spec_augment` can set masking to False
if not getattr(self.config, "apply_spec_augment", True):
return hidden_states
if mask_time_indices is not None:
# apply SpecAugment along time axis with given mask_time_indices
hidden_states = tf.where(
tf.cast(mask_time_indices[:, :, tf.newaxis], tf.bool),
self.masked_spec_embed[tf.newaxis, tf.newaxis, :],
hidden_states,
)
elif self.config.mask_time_prob > 0:
# generate indices & apply SpecAugment along time axis
mask_time_indices = _compute_mask_indices(
(batch_size, sequence_length),
mask_prob=self.config.mask_time_prob,
mask_length=self.config.mask_time_length,
min_masks=2,
)
hidden_states = tf.where(
tf.cast(mask_time_indices[:, :, tf.newaxis], tf.bool),
self.masked_spec_embed[tf.newaxis, tf.newaxis, :],
hidden_states,
)
# apply SpecAugment along feature axis
if self.config.mask_feature_prob > 0:
mask_feature_indices = _compute_mask_indices(
(batch_size, hidden_size),
mask_prob=self.config.mask_feature_prob,
mask_length=self.config.mask_feature_length,
)
hidden_states = tf.where(mask_feature_indices[:, tf.newaxis, :], hidden_states, 0)
return hidden_states
@unpack_inputs
def call(
self,
input_values: tf.Tensor,
attention_mask: tf.Tensor | None = None,
token_type_ids: tf.Tensor | None = None,
position_ids: tf.Tensor | None = None,
head_mask: tf.Tensor | None = None,
inputs_embeds: tf.Tensor | None = None,
output_attentions: tf.Tensor | None = None,
output_hidden_states: tf.Tensor | None = None,
return_dict: Optional[bool] = None,
training: bool = False,
**kwargs: Any,
):
hidden_states = self.feature_extractor(tf.cast(input_values, tf.float32), training=training)
if attention_mask is not None:
# compute real output lengths according to convolution formula
output_lengths = self._get_feat_extract_output_lengths(tf.reduce_sum(attention_mask, -1))
attention_mask = tf.sequence_mask(
output_lengths, maxlen=shape_list(hidden_states)[1], dtype=hidden_states.dtype
)
hidden_states = self.feature_projection(hidden_states, training=training)
mask_time_indices = kwargs.get("mask_time_indices", None)
if training:
hidden_states = self._mask_hidden_states(hidden_states, mask_time_indices=mask_time_indices)
encoder_outputs = self.encoder(
hidden_states,
attention_mask=attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
hidden_states = encoder_outputs[0]
if not return_dict:
return (hidden_states,) + encoder_outputs[1:]
return TFBaseModelOutput(
last_hidden_state=hidden_states,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
)
class TFHubertPreTrainedModel(TFPreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = HubertConfig
base_model_prefix = "hubert"
main_input_name = "input_values"
@property
def input_signature(self):
return {
"input_values": tf.TensorSpec((None, 16000), tf.float32, name="input_values"),
"attention_mask": tf.TensorSpec((None, None), tf.int32, name="attention_mask"),
"token_type_ids": tf.TensorSpec((None, None), tf.int32, name="token_type_ids"),
}
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
logger.warning(
f"\n{self.__class__.__name__} has backpropagation operations that are NOT supported on CPU. If you wish "
"to train/fine-tune this model, you need a GPU or a TPU"
)
HUBERT_START_DOCSTRING = r"""
This model inherits from [`TFPreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a [keras.Model](https://www.tensorflow.org/api_docs/python/tf/keras/Model) subclass. Use it
as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and
behavior.
<Tip>
TensorFlow models and layers in `transformers` accept two formats as input:
- having all inputs as keyword arguments (like PyTorch models), or
- having all inputs as a list, tuple or dict in the first positional argument.
The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
and layers. Because of this support, when using methods like `model.fit()` things should "just work" for you - just
pass your inputs and labels in any format that `model.fit()` supports! If, however, you want to use the second
format outside of Keras methods like `fit()` and `predict()`, such as when creating your own layers or models with
the Keras `Functional` API, there are three possibilities you can use to gather all the input Tensors in the first
positional argument:
- a single Tensor with `input_values` only and nothing else: `model(input_values)`
- a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
`model([input_values, attention_mask])` or `model([input_values, attention_mask, token_type_ids])`
- a dictionary with one or several input Tensors associated to the input names given in the docstring:
`model({"input_values": input_values, "token_type_ids": token_type_ids})`
Note that when creating models and layers with
[subclassing](https://keras.io/guides/making_new_layers_and_models_via_subclassing/) then you don't need to worry
about any of this, as you can just pass inputs like you would to any other Python function!
</Tip>
Args:
config ([`HubertConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
HUBERT_INPUTS_DOCSTRING = r"""
Args:
input_values (`np.ndarray`, `tf.Tensor`, `List[tf.Tensor]` `Dict[str, tf.Tensor]` or `Dict[str, np.ndarray]` and each example must have the shape `({0})`):
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.__call__`] and
[`PreTrainedTokenizer.encode`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`np.ndarray` or `tf.Tensor` of shape `({0})`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
token_type_ids (`np.ndarray` or `tf.Tensor` of shape `({0})`, *optional*):
Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
1]`:
- 0 corresponds to a *sentence A* token,
- 1 corresponds to a *sentence B* token.
[What are token type IDs?](../glossary#token-type-ids)
position_ids (`np.ndarray` or `tf.Tensor` of shape `({0})`, *optional*):
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
config.max_position_embeddings - 1]`.
[What are position IDs?](../glossary#position-ids)
head_mask (`np.ndarray` or `tf.Tensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
inputs_embeds (`np.ndarray` or `tf.Tensor` of shape `({0}, hidden_size)`, *optional*):
Optionally, instead of passing `input_values` you can choose to directly pass an embedded representation.
This is useful if you want more control over how to convert `input_values` indices into associated vectors
than the model's internal embedding lookup matrix.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the
config will be used instead.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
used instead.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. This argument can be used in
eager mode, in graph mode the value will always be set to True.
training (`bool`, *optional*, defaults to `False``):
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).
"""
@add_start_docstrings(
"The bare TFHubert Model transformer outputing raw hidden-states without any specific head on top.",
HUBERT_START_DOCSTRING,
)
class TFHubertModel(TFHubertPreTrainedModel):
def __init__(self, config: HubertConfig, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.config = config
self.hubert = TFHubertMainLayer(config, name="hubert")
@add_start_docstrings_to_model_forward(HUBERT_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=TFBaseModelOutput, config_class=_CONFIG_FOR_DOC)
@unpack_inputs
def call(
self,
input_values: tf.Tensor,
attention_mask: tf.Tensor | None = None,
token_type_ids: tf.Tensor | None = None,
position_ids: tf.Tensor | None = None,
head_mask: tf.Tensor | None = None,
inputs_embeds: tf.Tensor | None = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
training: bool = False,
) -> Union[TFBaseModelOutput, Tuple[tf.Tensor]]:
"""
Returns:
Example:
```python
>>> from transformers import AutoProcessor, TFHubertModel
>>> from datasets import load_dataset
>>> import soundfile as sf
>>> processor = AutoProcessor.from_pretrained("facebook/hubert-large-ls960-ft")
>>> model = TFHubertModel.from_pretrained("facebook/hubert-large-ls960-ft")
>>> def map_to_array(batch):
... speech, _ = sf.read(batch["file"])
... batch["speech"] = speech
... return batch
>>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
>>> ds = ds.map(map_to_array)
>>> input_values = processor(ds["speech"][0], return_tensors="tf").input_values # Batch size 1
>>> hidden_states = model(input_values).last_hidden_state
```"""
output_hidden_states = output_hidden_states if output_hidden_states else self.config.output_hidden_states
output_attentions = output_attentions if output_attentions else self.config.output_attentions
return_dict = return_dict if return_dict else self.config.return_dict
outputs = self.hubert(
input_values=input_values,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
return outputs
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "hubert", None) is not None:
with tf.name_scope(self.hubert.name):
self.hubert.build(None)
@add_start_docstrings(
"""TFHubert Model with a `language modeling` head on top for Connectionist Temporal Classification (CTC).""",
HUBERT_START_DOCSTRING,
)
class TFHubertForCTC(TFHubertPreTrainedModel):
def __init__(self, config: HubertConfig, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.hubert = TFHubertMainLayer(config, name="hubert")
self.dropout = keras.layers.Dropout(config.final_dropout)
self.lm_head = keras.layers.Dense(config.vocab_size, name="lm_head")
self.output_hidden_size = (
config.output_hidden_size if hasattr(config, "add_adapter") and config.add_adapter else config.hidden_size
)
def freeze_feature_extractor(self):
"""
Calling this function will disable the gradient computation for the feature encoder so that its parameters will
not be updated during training.
"""
warnings.warn(
"The method `freeze_feature_extractor` is deprecated and will be removed in Transformers v5. "
"Please use the equivalent `freeze_feature_encoder` method instead.",
FutureWarning,
)
self.freeze_feature_encoder()
def freeze_feature_encoder(self):
"""
Calling this function will disable the gradient computation for the feature encoder so that its parameter will
not be updated during training.
"""
self.hubert.feature_extractor.trainable = False
@add_start_docstrings_to_model_forward(HUBERT_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=TFCausalLMOutput, config_class=_CONFIG_FOR_DOC)
@unpack_inputs
def call(
self,
input_values: tf.Tensor,
attention_mask: tf.Tensor | None = None,
token_type_ids: tf.Tensor | None = None,
position_ids: tf.Tensor | None = None,
head_mask: tf.Tensor | None = None,
inputs_embeds: tf.Tensor | None = None,
output_attentions: Optional[bool] = None,
labels: tf.Tensor | None = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
training: Optional[bool] = False,
) -> Union[TFCausalLMOutput, Tuple[tf.Tensor]]:
r"""
labels (`tf.Tensor` or `np.ndarray` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
config.vocab_size]` (see `input_values` docstring) Tokens with indices set to `-100` are ignored (masked),
the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`
Returns:
Example:
```python
>>> import tensorflow as tf
>>> from transformers import AutoProcessor, TFHubertForCTC
>>> from datasets import load_dataset
>>> import soundfile as sf
>>> processor = AutoProcessor.from_pretrained("facebook/hubert-large-ls960-ft")
>>> model = TFHubertForCTC.from_pretrained("facebook/hubert-large-ls960-ft")
>>> def map_to_array(batch):
... speech, _ = sf.read(batch["file"])
... batch["speech"] = speech
... return batch
>>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
>>> ds = ds.map(map_to_array)
>>> input_values = processor(ds["speech"][0], return_tensors="tf").input_values # Batch size 1
>>> logits = model(input_values).logits
>>> predicted_ids = tf.argmax(logits, axis=-1)
>>> transcription = processor.decode(predicted_ids[0])
>>> # compute loss
>>> target_transcription = "A MAN SAID TO THE UNIVERSE SIR I EXIST"
>>> # Pass the transcription as text to encode labels
>>> labels = processor(text=transcription, return_tensors="tf").input_values
>>> loss = model(input_values, labels=labels).loss
```"""
if labels is not None and tf.reduce_max(labels) >= self.config.vocab_size:
raise ValueError(f"Label values must be <= vocab_size: {self.config.vocab_size}")
outputs = self.hubert(
input_values=input_values,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
hidden_states = outputs[0]
hidden_states = self.dropout(hidden_states, training=training)
logits = self.lm_head(hidden_states)
if labels is not None:
attention_mask = (
attention_mask if attention_mask is not None else tf.ones_like(input_values, dtype=tf.float32)
)
input_lengths = self.hubert._get_feat_extract_output_lengths(tf.reduce_sum(attention_mask, axis=-1))
# assuming that padded tokens are filled with -100
# when not being attended to
labels_mask = tf.cast(labels >= 0, tf.int32)
target_lengths = tf.reduce_sum(labels_mask, axis=-1)
loss = tf.nn.ctc_loss(
logits=logits,
labels=labels,
logit_length=input_lengths,
label_length=target_lengths,
blank_index=self.config.pad_token_id,
logits_time_major=False,
)
if self.config.ctc_loss_reduction == "sum":
loss = tf.reduce_sum(loss)
loss = tf.reshape(loss, (1,))
if self.config.ctc_loss_reduction == "mean":
loss = tf.reduce_mean(loss)
loss = tf.reshape(loss, (1,))
else:
loss = None
if not return_dict:
output = (logits,) + outputs[1:]
return ((loss,) + output) if loss is not None else output
return TFCausalLMOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "hubert", None) is not None:
with tf.name_scope(self.hubert.name):
self.hubert.build(None)
if getattr(self, "lm_head", None) is not None:
with tf.name_scope(self.lm_head.name):
self.lm_head.build([None, None, self.output_hidden_size])
__all__ = ["TFHubertForCTC", "TFHubertModel", "TFHubertPreTrainedModel"]
```
|
====================================================================================================================================
SOURCE CODE FILE: modular_hubert.py
LINES: 1
SIZE: 16.24 KB
PATH: scripts\freecad_env\Lib\site-packages\transformers\models\hubert\modular_hubert.py
ENCODING: utf-8
```py
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ...activations import ACT2FN
from ...integrations.deepspeed import is_deepspeed_zero3_enabled
from ...modeling_outputs import BaseModelOutput, CausalLMOutput, SequenceClassifierOutput
from ...modeling_utils import PreTrainedModel
from ...utils import (
add_code_sample_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
replace_return_docstrings,
)
from ..wav2vec2.modeling_wav2vec2 import (
Wav2Vec2Encoder,
Wav2Vec2EncoderStableLayerNorm,
Wav2Vec2FeatureEncoder,
Wav2Vec2ForCTC,
Wav2Vec2ForSequenceClassification,
Wav2Vec2Model,
Wav2Vec2SamePadLayer,
)
from .configuration_hubert import HubertConfig
_HIDDEN_STATES_START_POSITION = 1
# General docstring
_CONFIG_FOR_DOC = "HubertConfig"
# Base docstring
_CHECKPOINT_FOR_DOC = "facebook/hubert-large-ls960-ft"
_EXPECTED_OUTPUT_SHAPE = [1, 292, 768]
_CTC_EXPECTED_OUTPUT = "'MISTER QUILTER IS THE APOSTLE OF THE MIDDLE CLASSES AND WE ARE GLAD TO WELCOME HIS GOSPEL'"
_CTC_EXPECTED_LOSS = 22.68
_SEQ_CLASS_CHECKPOINT = "superb/hubert-base-superb-ks"
_SEQ_CLASS_EXPECTED_OUTPUT = "'_unknown_'"
_SEQ_CLASS_EXPECTED_LOSS = 8.53
class HubertPositionalConvEmbedding(nn.Module):
def __init__(self, config):
super().__init__()
self.conv = nn.Conv1d(
config.hidden_size,
config.hidden_size,
kernel_size=config.num_conv_pos_embeddings,
padding=config.num_conv_pos_embeddings // 2,
groups=config.num_conv_pos_embedding_groups,
)
self.batch_norm = None
if config.conv_pos_batch_norm:
self.batch_norm = nn.BatchNorm1d(config.hidden_size)
else:
weight_norm = nn.utils.weight_norm
if hasattr(nn.utils.parametrizations, "weight_norm"):
weight_norm = nn.utils.parametrizations.weight_norm
if is_deepspeed_zero3_enabled():
import deepspeed
with deepspeed.zero.GatheredParameters(self.conv.weight, modifier_rank=0):
self.conv = weight_norm(self.conv, name="weight", dim=2)
if hasattr(self.conv, "parametrizations"):
weight_g = self.conv.parametrizations.weight.original0
weight_v = self.conv.parametrizations.weight.original1
else:
weight_g = self.conv.weight_g
weight_v = self.conv.weight_v
deepspeed.zero.register_external_parameter(self, weight_v)
deepspeed.zero.register_external_parameter(self, weight_g)
else:
self.conv = weight_norm(self.conv, name="weight", dim=2)
self.padding = HubertSamePadLayer(config.num_conv_pos_embeddings)
self.activation = ACT2FN[config.feat_extract_activation]
def forward(self, hidden_states):
hidden_states = hidden_states.transpose(1, 2)
if self.batch_norm is not None:
hidden_states = self.batch_norm(hidden_states)
hidden_states = self.conv(hidden_states)
hidden_states = self.padding(hidden_states)
hidden_states = self.activation(hidden_states)
hidden_states = hidden_states.transpose(1, 2)
return hidden_states
class HubertSamePadLayer(Wav2Vec2SamePadLayer):
pass
class HubertFeatureEncoder(Wav2Vec2FeatureEncoder):
pass
class HubertFeatureProjection(nn.Module):
def __init__(self, config):
super().__init__()
self.feat_proj_layer_norm = config.feat_proj_layer_norm
if self.feat_proj_layer_norm:
self.layer_norm = nn.LayerNorm(config.conv_dim[-1], eps=config.layer_norm_eps)
self.projection = nn.Linear(config.conv_dim[-1], config.hidden_size)
self.dropout = nn.Dropout(config.feat_proj_dropout)
def forward(self, hidden_states):
# non-projected hidden states are needed for quantization
if self.feat_proj_layer_norm:
hidden_states = self.layer_norm(hidden_states)
hidden_states = self.projection(hidden_states)
hidden_states = self.dropout(hidden_states)
return hidden_states
class HubertEncoder(Wav2Vec2Encoder):
pass
class HubertEncoderStableLayerNorm(Wav2Vec2EncoderStableLayerNorm):
pass
class HubertPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = HubertConfig
base_model_prefix = "hubert"
main_input_name = "input_values"
supports_gradient_checkpointing = True
_supports_flash_attn_2 = True
_supports_sdpa = True
def _init_weights(self, module):
"""Initialize the weights"""
if isinstance(module, nn.Linear):
# Slightly different from the TF version which uses truncated_normal for initialization
# cf https://github.com/pytorch/pytorch/pull/5617
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, (nn.LayerNorm, nn.GroupNorm, nn.BatchNorm1d)):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
elif isinstance(module, nn.Conv1d):
if is_deepspeed_zero3_enabled():
import deepspeed
if hasattr(module, "weight_v") and hasattr(module, "weight_g"):
with deepspeed.zero.GatheredParameters([module.weight_v, module.weight_g], modifier_rank=0):
nn.init.kaiming_normal_(module.weight.data)
else:
with deepspeed.zero.GatheredParameters(module.weight, modifier_rank=0):
nn.init.kaiming_normal_(module.weight.data)
else:
nn.init.kaiming_normal_(module.weight.data)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, HubertModel):
if hasattr(module, "masked_spec_embed"):
module.masked_spec_embed.data.uniform_()
elif isinstance(module, HubertForSequenceClassification):
if hasattr(module, "layer_weights"):
module.layer_weights.data.fill_(1.0 / (self.config.num_hidden_layers + 1))
def _get_feat_extract_output_lengths(self, input_lengths: Union[torch.LongTensor, int]):
"""
Computes the output length of the convolutional layers
"""
def _conv_out_length(input_length, kernel_size, stride):
# 1D convolutional layer output length formula taken
# from https://pytorch.org/docs/stable/generated/torch.nn.Conv1d.html
return torch.div(input_length - kernel_size, stride, rounding_mode="floor") + 1
for kernel_size, stride in zip(self.config.conv_kernel, self.config.conv_stride):
input_lengths = _conv_out_length(input_lengths, kernel_size, stride)
return input_lengths
def _get_feature_vector_attention_mask(self, feature_vector_length: int, attention_mask: torch.LongTensor):
output_lengths = self._get_feat_extract_output_lengths(attention_mask.sum(-1)).to(torch.long)
batch_size = attention_mask.shape[0]
attention_mask = torch.zeros(
(batch_size, feature_vector_length), dtype=attention_mask.dtype, device=attention_mask.device
)
# these two operations makes sure that all values before the output lengths idxs are attended to
attention_mask[(torch.arange(attention_mask.shape[0], device=attention_mask.device), output_lengths - 1)] = 1
attention_mask = attention_mask.flip([-1]).cumsum(-1).flip([-1]).bool()
return attention_mask
HUBERT_START_DOCSTRING = r"""
Hubert was proposed in [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden
Units](https://arxiv.org/abs/2106.07447) by Wei-Ning Hsu, Benjamin Bolte, Yao-Hung Hubert Tsai, Kushal Lakhotia,
Ruslan Salakhutdinov, Abdelrahman Mohamed.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving etc.).
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`HubertConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
HUBERT_INPUTS_DOCSTRING = r"""
Args:
input_values (`torch.FloatTensor` of shape `(batch_size, sequence_length)`):
Float values of input raw speech waveform. Values can be obtained by loading a `.flac` or `.wav` audio file
into an array of type `List[float]` or a `numpy.ndarray`, *e.g.* via the soundfile library (`pip install
soundfile`). To prepare the array into `input_values`, the [`AutoProcessor`] should be used for padding and
conversion into a tensor of type `torch.FloatTensor`. See [`Wav2Vec2Processor.__call__`] for details.
attention_mask (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing convolution and attention on padding token indices. Mask values selected in `[0,
1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
<Tip warning={true}>
`attention_mask` should only be passed if the corresponding processor has `config.return_attention_mask ==
True`. For all models whose processor has `config.return_attention_mask == False`, such as
[hubert-base](https://huggingface.co/facebook/hubert-base-ls960), `attention_mask` should **not** be passed
to avoid degraded performance when doing batched inference. For such models `input_values` should simply be
padded with 0 and passed without `attention_mask`. Be aware that these models also yield slightly different
results depending on whether `input_values` is padded or not.
</Tip>
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
@add_start_docstrings(
"The bare Hubert Model transformer outputting raw hidden-states without any specific head on top.",
HUBERT_START_DOCSTRING,
)
class HubertModel(Wav2Vec2Model, HubertPreTrainedModel):
def __init__(self, config: HubertConfig):
super().__init__(config)
self.config = config
self.feature_extractor = HubertFeatureEncoder(config)
self.feature_projection = HubertFeatureProjection(config)
if config.mask_time_prob > 0.0 or config.mask_feature_prob > 0.0:
self.masked_spec_embed = nn.Parameter(torch.Tensor(config.hidden_size).uniform_())
if config.do_stable_layer_norm:
self.encoder = HubertEncoderStableLayerNorm(config)
else:
self.encoder = HubertEncoder(config)
# Initialize weights and apply final processing
self.post_init()
del self.adapter
def freeze_feature_extractor(self):
raise AttributeError("Not needed for Hubert")
def freeze_feature_encoder(self):
raise AttributeError("Not needed for Hubert")
@add_start_docstrings_to_model_forward(HUBERT_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=BaseModelOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_values: Optional[torch.Tensor],
attention_mask: Optional[torch.Tensor] = None,
mask_time_indices: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, BaseModelOutput]:
"""
Returns:
Example:
```python
>>> from transformers import AutoProcessor, HubertModel
>>> from datasets import load_dataset
>>> import soundfile as sf
>>> processor = AutoProcessor.from_pretrained("facebook/hubert-large-ls960-ft")
>>> model = HubertModel.from_pretrained("facebook/hubert-large-ls960-ft")
>>> def map_to_array(batch):
... speech, _ = sf.read(batch["file"])
... batch["speech"] = speech
... return batch
>>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
>>> ds = ds.map(map_to_array)
>>> input_values = processor(ds["speech"][0], return_tensors="pt").input_values # Batch size 1
>>> hidden_states = model(input_values).last_hidden_state
```"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
extract_features = self.feature_extractor(input_values)
extract_features = extract_features.transpose(1, 2)
if attention_mask is not None:
# compute reduced attention_mask corresponding to feature vectors
attention_mask = self._get_feature_vector_attention_mask(extract_features.shape[1], attention_mask)
hidden_states = self.feature_projection(extract_features)
hidden_states = self._mask_hidden_states(hidden_states, mask_time_indices=mask_time_indices)
encoder_outputs = self.encoder(
hidden_states,
attention_mask=attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
hidden_states = encoder_outputs[0]
if not return_dict:
return (hidden_states,) + encoder_outputs[1:]
return BaseModelOutput(
last_hidden_state=hidden_states,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
)
@add_start_docstrings(
"""Hubert Model with a `language modeling` head on top for Connectionist Temporal Classification (CTC).""",
HUBERT_START_DOCSTRING,
)
class HubertForCTC(Wav2Vec2ForCTC):
pass
@add_start_docstrings_to_model_forward(HUBERT_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=CausalLMOutput,
config_class=_CONFIG_FOR_DOC,
expected_output=_CTC_EXPECTED_OUTPUT,
expected_loss=_CTC_EXPECTED_LOSS,
)
def forward(self, **super_kwargs):
super().forward(**super_kwargs)
@add_start_docstrings(
"""
Hubert Model with a sequence classification head on top (a linear layer over the pooled output) for tasks like
SUPERB Keyword Spotting.
""",
HUBERT_START_DOCSTRING,
)
class HubertForSequenceClassification(Wav2Vec2ForSequenceClassification):
pass
@add_start_docstrings_to_model_forward(HUBERT_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_SEQ_CLASS_CHECKPOINT,
output_type=SequenceClassifierOutput,
config_class=_CONFIG_FOR_DOC,
modality="audio",
expected_output=_SEQ_CLASS_EXPECTED_OUTPUT,
expected_loss=_SEQ_CLASS_EXPECTED_LOSS,
)
def forward(self, **super_kwargs):
super().forward(**super_kwargs)
__all__ = ["HubertForCTC", "HubertForSequenceClassification", "HubertModel", "HubertPreTrainedModel"]
```
|
=============================================================================================================================
SOURCE CODE FILE: __init__.py
LINES: 1
SIZE: 0.97 KB
PATH: scripts\freecad_env\Lib\site-packages\transformers\models\ibert\__init__.py
ENCODING: utf-8
```py
# Copyright 2024 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import _LazyModule
from ...utils.import_utils import define_import_structure
if TYPE_CHECKING:
from .configuration_ibert import *
from .modeling_ibert import *
else:
import sys
_file = globals()["__file__"]
sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)
```
|
========================================================================================================================================
SOURCE CODE FILE: configuration_ibert.py
LINES: 1
SIZE: 6.93 KB
PATH: scripts\freecad_env\Lib\site-packages\transformers\models\ibert\configuration_ibert.py
ENCODING: utf-8
```py
# coding=utf-8
# Copyright 2021 The I-BERT Authors (Sehoon Kim, Amir Gholami, Zhewei Yao,
# Michael Mahoney, Kurt Keutzer - UC Berkeley) and The HuggingFace Inc. team.
# Copyright (c) 20121, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""I-BERT configuration"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
logger = logging.get_logger(__name__)
class IBertConfig(PretrainedConfig):
"""
This is the configuration class to store the configuration of a [`IBertModel`]. It is used to instantiate a I-BERT
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar configuration to that of the IBERT
[kssteven/ibert-roberta-base](https://huggingface.co/kssteven/ibert-roberta-base) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 30522):
Vocabulary size of the I-BERT model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`IBertModel`]
hidden_size (`int`, *optional*, defaults to 768):
Dimensionality of the encoder layers and the pooler layer.
num_hidden_layers (`int`, *optional*, defaults to 12):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 12):
Number of attention heads for each attention layer in the Transformer encoder.
intermediate_size (`int`, *optional*, defaults to 3072):
Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
hidden_act (`str` or `Callable`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"silu"` and `"gelu_new"` are supported.
hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
The dropout ratio for the attention probabilities.
max_position_embeddings (`int`, *optional*, defaults to 512):
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).
type_vocab_size (`int`, *optional*, defaults to 2):
The vocabulary size of the `token_type_ids` passed when calling [`IBertModel`]
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (`float`, *optional*, defaults to 1e-12):
The epsilon used by the layer normalization layers.
position_embedding_type (`str`, *optional*, defaults to `"absolute"`):
Type of position embedding. Choose one of `"absolute"`, `"relative_key"`, `"relative_key_query"`. For
positional embeddings use `"absolute"`. For more information on `"relative_key"`, please refer to
[Self-Attention with Relative Position Representations (Shaw et al.)](https://arxiv.org/abs/1803.02155).
For more information on `"relative_key_query"`, please refer to *Method 4* in [Improve Transformer Models
with Better Relative Position Embeddings (Huang et al.)](https://arxiv.org/abs/2009.13658).
quant_mode (`bool`, *optional*, defaults to `False`):
Whether to quantize the model or not.
force_dequant (`str`, *optional*, defaults to `"none"`):
Force dequantize specific nonlinear layer. Dequatized layers are then executed with full precision.
`"none"`, `"gelu"`, `"softmax"`, `"layernorm"` and `"nonlinear"` are supported. As deafult, it is set as
`"none"`, which does not dequantize any layers. Please specify `"gelu"`, `"softmax"`, or `"layernorm"` to
dequantize GELU, Softmax, or LayerNorm, respectively. `"nonlinear"` will dequantize all nonlinear layers,
i.e., GELU, Softmax, and LayerNorm.
"""
model_type = "ibert"
def __init__(
self,
vocab_size=30522,
hidden_size=768,
num_hidden_layers=12,
num_attention_heads=12,
intermediate_size=3072,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
type_vocab_size=2,
initializer_range=0.02,
layer_norm_eps=1e-12,
pad_token_id=1,
bos_token_id=0,
eos_token_id=2,
position_embedding_type="absolute",
quant_mode=False,
force_dequant="none",
**kwargs,
):
super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.hidden_act = hidden_act
self.intermediate_size = intermediate_size
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.type_vocab_size = type_vocab_size
self.initializer_range = initializer_range
self.layer_norm_eps = layer_norm_eps
self.position_embedding_type = position_embedding_type
self.quant_mode = quant_mode
self.force_dequant = force_dequant
class IBertOnnxConfig(OnnxConfig):
@property
def inputs(self) -> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
dynamic_axis = {0: "batch", 1: "choice", 2: "sequence"}
else:
dynamic_axis = {0: "batch", 1: "sequence"}
return OrderedDict(
[
("input_ids", dynamic_axis),
("attention_mask", dynamic_axis),
]
)
__all__ = ["IBertConfig", "IBertOnnxConfig"]
```
|
===================================================================================================================================
SOURCE CODE FILE: modeling_ibert.py
LINES: 1
SIZE: 55.85 KB
PATH: scripts\freecad_env\Lib\site-packages\transformers\models\ibert\modeling_ibert.py
ENCODING: utf-8
```py
# coding=utf-8
# Copyright 2021 The I-BERT Authors (Sehoon Kim, Amir Gholami, Zhewei Yao,
# Michael Mahoney, Kurt Keutzer - UC Berkeley) and The HuggingFace Inc. team.
# Copyright (c) 20121, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""PyTorch I-BERT model."""
import math
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import gelu
from ...modeling_outputs import (
BaseModelOutputWithPastAndCrossAttentions,
BaseModelOutputWithPoolingAndCrossAttentions,
MaskedLMOutput,
MultipleChoiceModelOutput,
QuestionAnsweringModelOutput,
SequenceClassifierOutput,
TokenClassifierOutput,
)
from ...modeling_utils import PreTrainedModel
from ...pytorch_utils import find_pruneable_heads_and_indices, prune_linear_layer
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_ibert import IBertConfig
from .quant_modules import IntGELU, IntLayerNorm, IntSoftmax, QuantAct, QuantEmbedding, QuantLinear
logger = logging.get_logger(__name__)
_CHECKPOINT_FOR_DOC = "kssteven/ibert-roberta-base"
_CONFIG_FOR_DOC = "IBertConfig"
class IBertEmbeddings(nn.Module):
"""
Same as BertEmbeddings with a tiny tweak for positional embeddings indexing.
"""
def __init__(self, config):
super().__init__()
self.quant_mode = config.quant_mode
self.embedding_bit = 8
self.embedding_act_bit = 16
self.act_bit = 8
self.ln_input_bit = 22
self.ln_output_bit = 32
self.word_embeddings = QuantEmbedding(
config.vocab_size,
config.hidden_size,
padding_idx=config.pad_token_id,
weight_bit=self.embedding_bit,
quant_mode=self.quant_mode,
)
self.token_type_embeddings = QuantEmbedding(
config.type_vocab_size, config.hidden_size, weight_bit=self.embedding_bit, quant_mode=self.quant_mode
)
# position_ids (1, len position emb) is contiguous in memory and exported when serialized
self.register_buffer(
"position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False
)
self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
# End copy
self.padding_idx = config.pad_token_id
self.position_embeddings = QuantEmbedding(
config.max_position_embeddings,
config.hidden_size,
padding_idx=self.padding_idx,
weight_bit=self.embedding_bit,
quant_mode=self.quant_mode,
)
# Integer-only addition between embeddings
self.embeddings_act1 = QuantAct(self.embedding_act_bit, quant_mode=self.quant_mode)
self.embeddings_act2 = QuantAct(self.embedding_act_bit, quant_mode=self.quant_mode)
# self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
# any TensorFlow checkpoint file
self.LayerNorm = IntLayerNorm(
config.hidden_size,
eps=config.layer_norm_eps,
output_bit=self.ln_output_bit,
quant_mode=self.quant_mode,
force_dequant=config.force_dequant,
)
self.output_activation = QuantAct(self.act_bit, quant_mode=self.quant_mode)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(
self, input_ids=None, token_type_ids=None, position_ids=None, inputs_embeds=None, past_key_values_length=0
):
if position_ids is None:
if input_ids is not None:
# Create the position ids from the input token ids. Any padded tokens remain padded.
position_ids = create_position_ids_from_input_ids(
input_ids, self.padding_idx, past_key_values_length
).to(input_ids.device)
else:
position_ids = self.create_position_ids_from_inputs_embeds(inputs_embeds)
if input_ids is not None:
input_shape = input_ids.size()
else:
input_shape = inputs_embeds.size()[:-1]
if token_type_ids is None:
token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device)
if inputs_embeds is None:
inputs_embeds, inputs_embeds_scaling_factor = self.word_embeddings(input_ids)
else:
inputs_embeds_scaling_factor = None
token_type_embeddings, token_type_embeddings_scaling_factor = self.token_type_embeddings(token_type_ids)
embeddings, embeddings_scaling_factor = self.embeddings_act1(
inputs_embeds,
inputs_embeds_scaling_factor,
identity=token_type_embeddings,
identity_scaling_factor=token_type_embeddings_scaling_factor,
)
if self.position_embedding_type == "absolute":
position_embeddings, position_embeddings_scaling_factor = self.position_embeddings(position_ids)
embeddings, embeddings_scaling_factor = self.embeddings_act1(
embeddings,
embeddings_scaling_factor,
identity=position_embeddings,
identity_scaling_factor=position_embeddings_scaling_factor,
)
embeddings, embeddings_scaling_factor = self.LayerNorm(embeddings, embeddings_scaling_factor)
embeddings = self.dropout(embeddings)
embeddings, embeddings_scaling_factor = self.output_activation(embeddings, embeddings_scaling_factor)
return embeddings, embeddings_scaling_factor
def create_position_ids_from_inputs_embeds(self, inputs_embeds):
"""
We are provided embeddings directly. We cannot infer which are padded so just generate sequential position ids.
Args:
inputs_embeds: torch.Tensor
Returns: torch.Tensor
"""
input_shape = inputs_embeds.size()[:-1]
sequence_length = input_shape[1]
position_ids = torch.arange(
self.padding_idx + 1, sequence_length + self.padding_idx + 1, dtype=torch.long, device=inputs_embeds.device
)
return position_ids.unsqueeze(0).expand(input_shape)
class IBertSelfAttention(nn.Module):
def __init__(self, config):
super().__init__()
if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
raise ValueError(
f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention "
f"heads ({config.num_attention_heads})"
)
self.quant_mode = config.quant_mode
self.weight_bit = 8
self.bias_bit = 32
self.act_bit = 8
self.num_attention_heads = config.num_attention_heads
self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
self.all_head_size = self.num_attention_heads * self.attention_head_size
# Q, K, V Linear layers
self.query = QuantLinear(
config.hidden_size,
self.all_head_size,
bias=True,
weight_bit=self.weight_bit,
bias_bit=self.bias_bit,
quant_mode=self.quant_mode,
per_channel=True,
)
self.key = QuantLinear(
config.hidden_size,
self.all_head_size,
bias=True,
weight_bit=self.weight_bit,
bias_bit=self.bias_bit,
quant_mode=self.quant_mode,
per_channel=True,
)
self.value = QuantLinear(
config.hidden_size,
self.all_head_size,
bias=True,
weight_bit=self.weight_bit,
bias_bit=self.bias_bit,
quant_mode=self.quant_mode,
per_channel=True,
)
# Requantization (32bit -> 8bit) for Q, K, V activations
self.query_activation = QuantAct(self.act_bit, quant_mode=self.quant_mode)
self.key_activation = QuantAct(self.act_bit, quant_mode=self.quant_mode)
self.value_activation = QuantAct(self.act_bit, quant_mode=self.quant_mode)
self.output_activation = QuantAct(self.act_bit, quant_mode=self.quant_mode)
self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
if self.position_embedding_type != "absolute":
raise ValueError("I-BERT only supports 'absolute' for `config.position_embedding_type`")
self.softmax = IntSoftmax(self.act_bit, quant_mode=self.quant_mode, force_dequant=config.force_dequant)
def transpose_for_scores(self, x):
new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
x = x.view(*new_x_shape)
return x.permute(0, 2, 1, 3)
def forward(
self,
hidden_states,
hidden_states_scaling_factor,
attention_mask=None,
head_mask=None,
output_attentions=False,
):
# Projection
mixed_query_layer, mixed_query_layer_scaling_factor = self.query(hidden_states, hidden_states_scaling_factor)
mixed_key_layer, mixed_key_layer_scaling_factor = self.key(hidden_states, hidden_states_scaling_factor)
mixed_value_layer, mixed_value_layer_scaling_factor = self.value(hidden_states, hidden_states_scaling_factor)
# Requantization
query_layer, query_layer_scaling_factor = self.query_activation(
mixed_query_layer, mixed_query_layer_scaling_factor
)
key_layer, key_layer_scaling_factor = self.key_activation(mixed_key_layer, mixed_key_layer_scaling_factor)
value_layer, value_layer_scaling_factor = self.value_activation(
mixed_value_layer, mixed_value_layer_scaling_factor
)
# Transpose
query_layer = self.transpose_for_scores(query_layer)
key_layer = self.transpose_for_scores(key_layer)
value_layer = self.transpose_for_scores(value_layer)
# Take the dot product between "query" and "key" to get the raw attention scores.
attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
scale = math.sqrt(self.attention_head_size)
attention_scores = attention_scores / scale
if self.quant_mode:
attention_scores_scaling_factor = query_layer_scaling_factor * key_layer_scaling_factor / scale
else:
attention_scores_scaling_factor = None
if attention_mask is not None:
# Apply the attention mask is (precomputed for all layers in IBertModel forward() function)
attention_scores = attention_scores + attention_mask
# Normalize the attention scores to probabilities.
attention_probs, attention_probs_scaling_factor = self.softmax(
attention_scores, attention_scores_scaling_factor
)
# This is actually dropping out entire tokens to attend to, which might
# seem a bit unusual, but is taken from the original Transformer paper.
attention_probs = self.dropout(attention_probs)
# Mask heads if we want to
if head_mask is not None:
attention_probs = attention_probs * head_mask
context_layer = torch.matmul(attention_probs, value_layer)
if attention_probs_scaling_factor is not None:
context_layer_scaling_factor = attention_probs_scaling_factor * value_layer_scaling_factor
else:
context_layer_scaling_factor = None
context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
context_layer = context_layer.view(*new_context_layer_shape)
# requantization: 32-bit -> 8-bit
context_layer, context_layer_scaling_factor = self.output_activation(
context_layer, context_layer_scaling_factor
)
outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
output_scaling_factor = (
(context_layer_scaling_factor, attention_probs_scaling_factor)
if output_attentions
else (context_layer_scaling_factor,)
)
return outputs, output_scaling_factor
class IBertSelfOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.quant_mode = config.quant_mode
self.act_bit = 8
self.weight_bit = 8
self.bias_bit = 32
self.ln_input_bit = 22
self.ln_output_bit = 32
self.dense = QuantLinear(
config.hidden_size,
config.hidden_size,
bias=True,
weight_bit=self.weight_bit,
bias_bit=self.bias_bit,
quant_mode=self.quant_mode,
per_channel=True,
)
self.ln_input_act = QuantAct(self.ln_input_bit, quant_mode=self.quant_mode)
self.LayerNorm = IntLayerNorm(
config.hidden_size,
eps=config.layer_norm_eps,
output_bit=self.ln_output_bit,
quant_mode=self.quant_mode,
force_dequant=config.force_dequant,
)
self.output_activation = QuantAct(self.act_bit, quant_mode=self.quant_mode)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, hidden_states, hidden_states_scaling_factor, input_tensor, input_tensor_scaling_factor):
hidden_states, hidden_states_scaling_factor = self.dense(hidden_states, hidden_states_scaling_factor)
hidden_states = self.dropout(hidden_states)
hidden_states, hidden_states_scaling_factor = self.ln_input_act(
hidden_states,
hidden_states_scaling_factor,
identity=input_tensor,
identity_scaling_factor=input_tensor_scaling_factor,
)
hidden_states, hidden_states_scaling_factor = self.LayerNorm(hidden_states, hidden_states_scaling_factor)
hidden_states, hidden_states_scaling_factor = self.output_activation(
hidden_states, hidden_states_scaling_factor
)
return hidden_states, hidden_states_scaling_factor
class IBertAttention(nn.Module):
def __init__(self, config):
super().__init__()
self.quant_mode = config.quant_mode
self.self = IBertSelfAttention(config)
self.output = IBertSelfOutput(config)
self.pruned_heads = set()
def prune_heads(self, heads):
if len(heads) == 0:
return
heads, index = find_pruneable_heads_and_indices(
heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads
)
# Prune linear layers
self.self.query = prune_linear_layer(self.self.query, index)
self.self.key = prune_linear_layer(self.self.key, index)
self.self.value = prune_linear_layer(self.self.value, index)
self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
# Update hyper params and store pruned heads
self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads
self.pruned_heads = self.pruned_heads.union(heads)
def forward(
self,
hidden_states,
hidden_states_scaling_factor,
attention_mask=None,
head_mask=None,
output_attentions=False,
):
self_outputs, self_outputs_scaling_factor = self.self(
hidden_states,
hidden_states_scaling_factor,
attention_mask,
head_mask,
output_attentions,
)
attention_output, attention_output_scaling_factor = self.output(
self_outputs[0], self_outputs_scaling_factor[0], hidden_states, hidden_states_scaling_factor
)
outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them
outputs_scaling_factor = (attention_output_scaling_factor,) + self_outputs_scaling_factor[1:]
return outputs, outputs_scaling_factor
class IBertIntermediate(nn.Module):
def __init__(self, config):
super().__init__()
self.quant_mode = config.quant_mode
self.act_bit = 8
self.weight_bit = 8
self.bias_bit = 32
self.dense = QuantLinear(
config.hidden_size,
config.intermediate_size,
bias=True,
weight_bit=self.weight_bit,
bias_bit=self.bias_bit,
quant_mode=self.quant_mode,
per_channel=True,
)
if config.hidden_act != "gelu":
raise ValueError("I-BERT only supports 'gelu' for `config.hidden_act`")
self.intermediate_act_fn = IntGELU(quant_mode=self.quant_mode, force_dequant=config.force_dequant)
self.output_activation = QuantAct(self.act_bit, quant_mode=self.quant_mode)
def forward(self, hidden_states, hidden_states_scaling_factor):
hidden_states, hidden_states_scaling_factor = self.dense(hidden_states, hidden_states_scaling_factor)
hidden_states, hidden_states_scaling_factor = self.intermediate_act_fn(
hidden_states, hidden_states_scaling_factor
)
# Requantization: 32bit -> 8-bit
hidden_states, hidden_states_scaling_factor = self.output_activation(
hidden_states, hidden_states_scaling_factor
)
return hidden_states, hidden_states_scaling_factor
class IBertOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.quant_mode = config.quant_mode
self.act_bit = 8
self.weight_bit = 8
self.bias_bit = 32
self.ln_input_bit = 22
self.ln_output_bit = 32
self.dense = QuantLinear(
config.intermediate_size,
config.hidden_size,
bias=True,
weight_bit=self.weight_bit,
bias_bit=self.bias_bit,
quant_mode=self.quant_mode,
per_channel=True,
)
self.ln_input_act = QuantAct(self.ln_input_bit, quant_mode=self.quant_mode)
self.LayerNorm = IntLayerNorm(
config.hidden_size,
eps=config.layer_norm_eps,
output_bit=self.ln_output_bit,
quant_mode=self.quant_mode,
force_dequant=config.force_dequant,
)
self.output_activation = QuantAct(self.act_bit, quant_mode=self.quant_mode)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, hidden_states, hidden_states_scaling_factor, input_tensor, input_tensor_scaling_factor):
hidden_states, hidden_states_scaling_factor = self.dense(hidden_states, hidden_states_scaling_factor)
hidden_states = self.dropout(hidden_states)
hidden_states, hidden_states_scaling_factor = self.ln_input_act(
hidden_states,
hidden_states_scaling_factor,
identity=input_tensor,
identity_scaling_factor=input_tensor_scaling_factor,
)
hidden_states, hidden_states_scaling_factor = self.LayerNorm(hidden_states, hidden_states_scaling_factor)
hidden_states, hidden_states_scaling_factor = self.output_activation(
hidden_states, hidden_states_scaling_factor
)
return hidden_states, hidden_states_scaling_factor
class IBertLayer(nn.Module):
def __init__(self, config):
super().__init__()
self.quant_mode = config.quant_mode
self.act_bit = 8
self.seq_len_dim = 1
self.attention = IBertAttention(config)
self.intermediate = IBertIntermediate(config)
self.output = IBertOutput(config)
self.pre_intermediate_act = QuantAct(self.act_bit, quant_mode=self.quant_mode)
self.pre_output_act = QuantAct(self.act_bit, quant_mode=self.quant_mode)
def forward(
self,
hidden_states,
hidden_states_scaling_factor,
attention_mask=None,
head_mask=None,
output_attentions=False,
):
self_attention_outputs, self_attention_outputs_scaling_factor = self.attention(
hidden_states,
hidden_states_scaling_factor,
attention_mask,
head_mask,
output_attentions=output_attentions,
)
attention_output = self_attention_outputs[0]
attention_output_scaling_factor = self_attention_outputs_scaling_factor[0]
outputs = self_attention_outputs[1:] # add self attentions if we output attention weights
layer_output, layer_output_scaling_factor = self.feed_forward_chunk(
attention_output, attention_output_scaling_factor
)
outputs = (layer_output,) + outputs
return outputs
def feed_forward_chunk(self, attention_output, attention_output_scaling_factor):
attention_output, attention_output_scaling_factor = self.pre_intermediate_act(
attention_output, attention_output_scaling_factor
)
intermediate_output, intermediate_output_scaling_factor = self.intermediate(
attention_output, attention_output_scaling_factor
)
intermediate_output, intermediate_output_scaling_factor = self.pre_output_act(
intermediate_output, intermediate_output_scaling_factor
)
layer_output, layer_output_scaling_factor = self.output(
intermediate_output, intermediate_output_scaling_factor, attention_output, attention_output_scaling_factor
)
return layer_output, layer_output_scaling_factor
class IBertEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.quant_mode = config.quant_mode
self.layer = nn.ModuleList([IBertLayer(config) for _ in range(config.num_hidden_layers)])
def forward(
self,
hidden_states,
hidden_states_scaling_factor,
attention_mask=None,
head_mask=None,
output_attentions=False,
output_hidden_states=False,
return_dict=True,
):
all_hidden_states = () if output_hidden_states else None
all_self_attentions = () if output_attentions else None
all_cross_attentions = None # `config.add_cross_attention` is not supported
next_decoder_cache = None # `config.use_cache` is not supported
for i, layer_module in enumerate(self.layer):
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
layer_head_mask = head_mask[i] if head_mask is not None else None
layer_outputs = layer_module(
hidden_states,
hidden_states_scaling_factor,
attention_mask,
layer_head_mask,
output_attentions,
)
hidden_states = layer_outputs[0]
if output_attentions:
all_self_attentions = all_self_attentions + (layer_outputs[1],)
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if not return_dict:
return tuple(
v
for v in [
hidden_states,
next_decoder_cache,
all_hidden_states,
all_self_attentions,
all_cross_attentions,
]
if v is not None
)
return BaseModelOutputWithPastAndCrossAttentions(
last_hidden_state=hidden_states,
past_key_values=next_decoder_cache,
hidden_states=all_hidden_states,
attentions=all_self_attentions,
cross_attentions=all_cross_attentions,
)
class IBertPooler(nn.Module):
def __init__(self, config):
super().__init__()
self.quant_mode = config.quant_mode
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.activation = nn.Tanh()
def forward(self, hidden_states):
# We "pool" the model by simply taking the hidden state corresponding
# to the first token.
first_token_tensor = hidden_states[:, 0]
pooled_output = self.dense(first_token_tensor)
pooled_output = self.activation(pooled_output)
return pooled_output
class IBertPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = IBertConfig
base_model_prefix = "ibert"
def _init_weights(self, module):
"""Initialize the weights"""
if isinstance(module, (QuantLinear, nn.Linear)):
# Slightly different from the TF version which uses truncated_normal for initialization
# cf https://github.com/pytorch/pytorch/pull/5617
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, (QuantEmbedding, nn.Embedding)):
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
elif isinstance(module, (IntLayerNorm, nn.LayerNorm)):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
elif isinstance(module, IBertLMHead):
module.bias.data.zero_()
def resize_token_embeddings(self, new_num_tokens=None):
raise NotImplementedError("`resize_token_embeddings` is not supported for I-BERT.")
IBERT_START_DOCSTRING = r"""
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.
Parameters:
config ([`IBertConfig`]): Model configuration class with all the parameters of the
model. Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
IBERT_INPUTS_DOCSTRING = r"""
Args:
input_ids (`torch.LongTensor` of shape `({0})`):
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*):
Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
1]`:
- 0 corresponds to a *sentence A* token,
- 1 corresponds to a *sentence B* token.
[What are token type IDs?](../glossary#token-type-ids)
position_ids (`torch.LongTensor` of shape `({0})`, *optional*):
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
config.max_position_embeddings - 1]`.
[What are position IDs?](../glossary#position-ids)
head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
model's internal embedding lookup matrix.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
@add_start_docstrings(
"The bare I-BERT Model transformer outputting raw hidden-states without any specific head on top.",
IBERT_START_DOCSTRING,
)
class IBertModel(IBertPreTrainedModel):
"""
The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of
cross-attention is added between the self-attention layers, following the architecture described in [Attention is
all you need](https://arxiv.org/abs/1706.03762) by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit,
Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin.
"""
def __init__(self, config, add_pooling_layer=True):
super().__init__(config)
self.config = config
self.quant_mode = config.quant_mode
self.embeddings = IBertEmbeddings(config)
self.encoder = IBertEncoder(config)
self.pooler = IBertPooler(config) if add_pooling_layer else None
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.embeddings.word_embeddings
def set_input_embeddings(self, value):
self.embeddings.word_embeddings = value
def _prune_heads(self, heads_to_prune):
"""
Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
class PreTrainedModel
"""
for layer, heads in heads_to_prune.items():
self.encoder.layer[layer].attention.prune_heads(heads)
@add_start_docstrings_to_model_forward(IBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=BaseModelOutputWithPoolingAndCrossAttentions,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[BaseModelOutputWithPoolingAndCrossAttentions, Tuple[torch.FloatTensor]]:
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
elif input_ids is not None:
self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
input_shape = input_ids.size()
elif inputs_embeds is not None:
input_shape = inputs_embeds.size()[:-1]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
batch_size, seq_length = input_shape
device = input_ids.device if input_ids is not None else inputs_embeds.device
if attention_mask is None:
attention_mask = torch.ones(((batch_size, seq_length)), device=device)
if token_type_ids is None:
token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
# ourselves in which case we just need to make it broadcastable to all heads.
extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape)
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x n_heads x N x N
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
embedding_output, embedding_output_scaling_factor = self.embeddings(
input_ids=input_ids,
position_ids=position_ids,
token_type_ids=token_type_ids,
inputs_embeds=inputs_embeds,
)
encoder_outputs = self.encoder(
embedding_output,
embedding_output_scaling_factor,
attention_mask=extended_attention_mask,
head_mask=head_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = encoder_outputs[0]
pooled_output = self.pooler(sequence_output) if self.pooler is not None else None
if not return_dict:
return (sequence_output, pooled_output) + encoder_outputs[1:]
return BaseModelOutputWithPoolingAndCrossAttentions(
last_hidden_state=sequence_output,
pooler_output=pooled_output,
past_key_values=encoder_outputs.past_key_values,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
cross_attentions=encoder_outputs.cross_attentions,
)
@add_start_docstrings("""I-BERT Model with a `language modeling` head on top.""", IBERT_START_DOCSTRING)
class IBertForMaskedLM(IBertPreTrainedModel):
_tied_weights_keys = ["lm_head.decoder.bias", "lm_head.decoder.weight"]
def __init__(self, config):
super().__init__(config)
self.ibert = IBertModel(config, add_pooling_layer=False)
self.lm_head = IBertLMHead(config)
# Initialize weights and apply final processing
self.post_init()
def get_output_embeddings(self):
return self.lm_head.decoder
def set_output_embeddings(self, new_embeddings):
self.lm_head.decoder = new_embeddings
self.lm_head.bias = new_embeddings.bias
@add_start_docstrings_to_model_forward(IBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=MaskedLMOutput,
config_class=_CONFIG_FOR_DOC,
mask="<mask>",
)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[MaskedLMOutput, Tuple[torch.FloatTensor]]:
r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the
loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`
kwargs (`Dict[str, any]`, *optional*, defaults to `{}`):
Used to hide legacy arguments that have been deprecated.
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.ibert(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = outputs[0]
prediction_scores = self.lm_head(sequence_output)
masked_lm_loss = None
if labels is not None:
loss_fct = CrossEntropyLoss()
masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
if not return_dict:
output = (prediction_scores,) + outputs[2:]
return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
return MaskedLMOutput(
loss=masked_lm_loss,
logits=prediction_scores,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
class IBertLMHead(nn.Module):
"""I-BERT Head for masked language modeling."""
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.decoder = nn.Linear(config.hidden_size, config.vocab_size)
self.bias = nn.Parameter(torch.zeros(config.vocab_size))
self.decoder.bias = self.bias
def forward(self, features, **kwargs):
x = self.dense(features)
x = gelu(x)
x = self.layer_norm(x)
# project back to size of vocabulary with bias
x = self.decoder(x)
return x
def _tie_weights(self) -> None:
# For accelerate compatibility and to not break backward compatibility
if self.decoder.bias.device.type == "meta":
self.decoder.bias = self.bias
else:
# To tie those two weights if they get disconnected (on TPU or when the bias is resized)
self.bias = self.decoder.bias
@add_start_docstrings(
"""
I-BERT Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled
output) e.g. for GLUE tasks.
""",
IBERT_START_DOCSTRING,
)
class IBertForSequenceClassification(IBertPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.ibert = IBertModel(config, add_pooling_layer=False)
self.classifier = IBertClassificationHead(config)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(IBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=SequenceClassifierOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[SequenceClassifierOutput, Tuple[torch.FloatTensor]]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.ibert(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = outputs[0]
logits = self.classifier(sequence_output)
loss = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
self.config.problem_type = "regression"
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
self.config.problem_type = "single_label_classification"
else:
self.config.problem_type = "multi_label_classification"
if self.config.problem_type == "regression":
loss_fct = MSELoss()
if self.num_labels == 1:
loss = loss_fct(logits.squeeze(), labels.squeeze())
else:
loss = loss_fct(logits, labels)
elif self.config.problem_type == "single_label_classification":
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
elif self.config.problem_type == "multi_label_classification":
loss_fct = BCEWithLogitsLoss()
loss = loss_fct(logits, labels)
if not return_dict:
output = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return SequenceClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
@add_start_docstrings(
"""
I-BERT Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a
softmax) e.g. for RocStories/SWAG tasks.
""",
IBERT_START_DOCSTRING,
)
class IBertForMultipleChoice(IBertPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.ibert = IBertModel(config)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.classifier = nn.Linear(config.hidden_size, 1)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(IBERT_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=MultipleChoiceModelOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[MultipleChoiceModelOutput, Tuple[torch.FloatTensor]]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the multiple choice classification loss. Indices should be in `[0, ...,
num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See
`input_ids` above)
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1]
flat_input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None
flat_position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None
flat_token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None
flat_attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None
flat_inputs_embeds = (
inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1))
if inputs_embeds is not None
else None
)
outputs = self.ibert(
flat_input_ids,
position_ids=flat_position_ids,
token_type_ids=flat_token_type_ids,
attention_mask=flat_attention_mask,
head_mask=head_mask,
inputs_embeds=flat_inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
pooled_output = outputs[1]
pooled_output = self.dropout(pooled_output)
logits = self.classifier(pooled_output)
reshaped_logits = logits.view(-1, num_choices)
loss = None
if labels is not None:
loss_fct = CrossEntropyLoss()
loss = loss_fct(reshaped_logits, labels)
if not return_dict:
output = (reshaped_logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return MultipleChoiceModelOutput(
loss=loss,
logits=reshaped_logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
@add_start_docstrings(
"""
I-BERT Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for
Named-Entity-Recognition (NER) tasks.
""",
IBERT_START_DOCSTRING,
)
class IBertForTokenClassification(IBertPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.ibert = IBertModel(config, add_pooling_layer=False)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.classifier = nn.Linear(config.hidden_size, config.num_labels)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(IBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TokenClassifierOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[TokenClassifierOutput, Tuple[torch.FloatTensor]]:
r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.ibert(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = outputs[0]
sequence_output = self.dropout(sequence_output)
logits = self.classifier(sequence_output)
loss = None
if labels is not None:
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
if not return_dict:
output = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return TokenClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
class IBertClassificationHead(nn.Module):
"""Head for sentence-level classification tasks."""
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.out_proj = nn.Linear(config.hidden_size, config.num_labels)
def forward(self, features, **kwargs):
hidden_states = features[:, 0, :] # take <s> token (equiv. to [CLS])
hidden_states = self.dropout(hidden_states)
hidden_states = self.dense(hidden_states)
hidden_states = torch.tanh(hidden_states)
hidden_states = self.dropout(hidden_states)
hidden_states = self.out_proj(hidden_states)
return hidden_states
@add_start_docstrings(
"""
I-BERT Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear
layers on top of the hidden-states output to compute `span start logits` and `span end logits`).
""",
IBERT_START_DOCSTRING,
)
class IBertForQuestionAnswering(IBertPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.ibert = IBertModel(config, add_pooling_layer=False)
self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(IBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=QuestionAnsweringModelOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
start_positions: Optional[torch.LongTensor] = None,
end_positions: Optional[torch.LongTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[QuestionAnsweringModelOutput, Tuple[torch.FloatTensor]]:
r"""
start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for position (index) of the start of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
are not taken into account for computing the loss.
end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for position (index) of the end of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
are not taken into account for computing the loss.
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.ibert(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = outputs[0]
logits = self.qa_outputs(sequence_output)
start_logits, end_logits = logits.split(1, dim=-1)
start_logits = start_logits.squeeze(-1).contiguous()
end_logits = end_logits.squeeze(-1).contiguous()
total_loss = None
if start_positions is not None and end_positions is not None:
# If we are on multi-GPU, split add a dimension
if len(start_positions.size()) > 1:
start_positions = start_positions.squeeze(-1)
if len(end_positions.size()) > 1:
end_positions = end_positions.squeeze(-1)
# sometimes the start/end positions are outside our model inputs, we ignore these terms
ignored_index = start_logits.size(1)
start_positions = start_positions.clamp(0, ignored_index)
end_positions = end_positions.clamp(0, ignored_index)
loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
start_loss = loss_fct(start_logits, start_positions)
end_loss = loss_fct(end_logits, end_positions)
total_loss = (start_loss + end_loss) / 2
if not return_dict:
output = (start_logits, end_logits) + outputs[2:]
return ((total_loss,) + output) if total_loss is not None else output
return QuestionAnsweringModelOutput(
loss=total_loss,
start_logits=start_logits,
end_logits=end_logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
def create_position_ids_from_input_ids(input_ids, padding_idx, past_key_values_length=0):
"""
Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols
are ignored. This is modified from fairseq's *utils.make_positions*.
Args:
input_ids (`torch.LongTensor`):
Indices of input sequence tokens in the vocabulary.
Returns: torch.Tensor
"""
# The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA.
mask = input_ids.ne(padding_idx).int()
incremental_indices = (torch.cumsum(mask, dim=1).type_as(mask) + past_key_values_length) * mask
return incremental_indices.long() + padding_idx
__all__ = [
"IBertForMaskedLM",
"IBertForMultipleChoice",
"IBertForQuestionAnswering",
"IBertForSequenceClassification",
"IBertForTokenClassification",
"IBertModel",
"IBertPreTrainedModel",
]
```
|
==================================================================================================================================
SOURCE CODE FILE: quant_modules.py
LINES: 1
SIZE: 29.37 KB
PATH: scripts\freecad_env\Lib\site-packages\transformers\models\ibert\quant_modules.py
ENCODING: utf-8
```py
# coding=utf-8
# Copyright 2021 The I-BERT Authors (Sehoon Kim, Amir Gholami, Zhewei Yao,
# Michael Mahoney, Kurt Keutzer - UC Berkeley) and The HuggingFace Inc. team.
# Copyright (c) 20121, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import decimal
import numpy as np
import torch
from torch import nn
from torch.autograd import Function
from ...utils import logging
logger = logging.get_logger(__name__)
class QuantEmbedding(nn.Module):
"""
Quantized version of `torch.nn.Embedding`. Adds quantization-specific arguments on top of `torch.nn.Embedding`.
Args:
weight_bit (`int`, *optional*, defaults to `8`):
Bitwidth for the quantized weight.
momentum (`float`, *optional*, defaults to `0.95`):
Momentum for updating the activation quantization range.
quant_mode (`bool`, *optional*, defaults to `False`):
Whether or not the layer is quantized.
"""
def __init__(
self,
num_embeddings,
embedding_dim,
padding_idx=None,
max_norm=None,
norm_type=2.0,
scale_grad_by_freq=False,
sparse=False,
_weight=None,
weight_bit=8,
momentum=0.95,
quant_mode=False,
):
super().__init__()
self.num_ = num_embeddings
self.dim = embedding_dim
self.padding_idx = padding_idx
self.max_norm = max_norm
self.norm_type = norm_type
self.scale_grad_by_freq = scale_grad_by_freq
self.sparse = sparse
self.weight = nn.Parameter(torch.zeros([num_embeddings, embedding_dim]))
self.register_buffer("weight_scaling_factor", torch.zeros(1))
self.register_buffer("weight_integer", torch.zeros_like(self.weight))
self.weight_bit = weight_bit
self.momentum = momentum
self.quant_mode = quant_mode
self.percentile_mode = False
self.weight_function = SymmetricQuantFunction.apply
def forward(self, x, positions=None, incremental_state=None):
if not self.quant_mode:
return (
nn.functional.embedding(
x,
self.weight,
self.padding_idx,
self.max_norm,
self.norm_type,
self.scale_grad_by_freq,
self.sparse,
),
None,
)
w = self.weight
w_transform = w.data.detach()
w_min = w_transform.min().expand(1)
w_max = w_transform.max().expand(1)
self.weight_scaling_factor = symmetric_linear_quantization_params(self.weight_bit, w_min, w_max, False)
self.weight_integer = self.weight_function(
self.weight, self.weight_bit, self.percentile_mode, self.weight_scaling_factor
)
emb_int = nn.functional.embedding(
x,
self.weight_integer,
self.padding_idx,
self.max_norm,
self.norm_type,
self.scale_grad_by_freq,
self.sparse,
)
return emb_int * self.weight_scaling_factor, self.weight_scaling_factor
class QuantAct(nn.Module):
"""
Quantizes the given activation.
Args:
activation_bit (`int`):
Bitwidth for the quantized activation.
act_range_momentum (`float`, *optional*, defaults to `0.95`):
Momentum for updating the activation quantization range.
per_channel (`bool`, *optional*, defaults to `False`):
Whether to or not use channel-wise quantization.
channel_len (`int`, *optional*):
Specify the channel length when set the *per_channel* True.
quant_mode (`bool`, *optional*, defaults to `False`):
Whether or not the layer is quantized.
"""
def __init__(self, activation_bit, act_range_momentum=0.95, per_channel=False, channel_len=None, quant_mode=False):
super().__init__()
self.activation_bit = activation_bit
self.act_range_momentum = act_range_momentum
self.quant_mode = quant_mode
self.per_channel = per_channel
self.percentile = False
self.act_function = SymmetricQuantFunction.apply
if not self.per_channel:
self.register_buffer("x_min", torch.zeros(1))
self.register_buffer("x_max", torch.zeros(1))
self.register_buffer("act_scaling_factor", torch.zeros(1))
self.x_min -= 1e-5
self.x_max += 1e-5
else:
raise NotImplementedError("per-channel mode is not currently supported for activation.")
def __repr__(self):
return (
f"{self.__class__.__name__}(activation_bit={self.activation_bit}, "
f"quant_mode: {self.quant_mode}, Act_min: {self.x_min.item():.2f}, "
f"Act_max: {self.x_max.item():.2f})"
)
def forward(
self,
x,
pre_act_scaling_factor=None,
identity=None,
identity_scaling_factor=None,
specified_min=None,
specified_max=None,
):
x_act = x if identity is None else identity + x
# collect running stats if training
if self.training:
assert not self.percentile, "percentile mode is not currently supported for activation."
assert not self.per_channel, "per-channel mode is not currently supported for activation."
x_min = x_act.data.min()
x_max = x_act.data.max()
assert x_max.isnan().sum() == 0 and x_min.isnan().sum() == 0, (
"NaN detected when computing min/max of the activation"
)
# Initialization
if self.x_min.min() > -1.1e-5 and self.x_max.max() < 1.1e-5:
self.x_min = self.x_min + x_min
self.x_max = self.x_max + x_max
# exponential moving average (EMA)
# use momentum to prevent the quantized values change greatly every iteration
elif self.act_range_momentum == -1:
self.x_min = torch.min(self.x_min, x_min)
self.x_max = torch.max(self.x_max, x_max)
else:
self.x_min = self.x_min * self.act_range_momentum + x_min * (1 - self.act_range_momentum)
self.x_max = self.x_max * self.act_range_momentum + x_max * (1 - self.act_range_momentum)
if not self.quant_mode:
return x_act, None
x_min = self.x_min if specified_min is None else specified_min
x_max = self.x_max if specified_max is None else specified_max
self.act_scaling_factor = symmetric_linear_quantization_params(
self.activation_bit, x_min, x_max, per_channel=self.per_channel
)
if pre_act_scaling_factor is None:
# this is for the input quantization
quant_act_int = self.act_function(x, self.activation_bit, self.percentile, self.act_scaling_factor)
else:
quant_act_int = FixedPointMul.apply(
x,
pre_act_scaling_factor,
self.activation_bit,
self.act_scaling_factor,
identity,
identity_scaling_factor,
)
correct_output_scale = self.act_scaling_factor.view(-1)
return quant_act_int * correct_output_scale, self.act_scaling_factor
class QuantLinear(nn.Module):
"""
Quantized version of `torch.nn.Linear`. Adds quantization-specific arguments on top of `torch.nn.Linear`.
Args:
weight_bit (`int`, *optional*, defaults to `8`):
Bitwidth for the quantized weight.
bias_bit (`int`, *optional*, defaults to `32`):
Bitwidth for the quantized bias.
per_channel (`bool`, *optional*, defaults to `False`):
Whether or not to use channel-wise quantization.
quant_mode (`bool`, *optional*, defaults to `False`):
Whether or not the layer is quantized.
"""
def __init__(
self, in_features, out_features, bias=True, weight_bit=8, bias_bit=32, per_channel=False, quant_mode=False
):
super().__init__()
self.in_features = in_features
self.out_features = out_features
self.weight = nn.Parameter(torch.zeros([out_features, in_features]))
self.register_buffer("weight_integer", torch.zeros_like(self.weight))
self.register_buffer("fc_scaling_factor", torch.zeros(self.out_features))
if bias:
self.bias = nn.Parameter(torch.zeros(out_features))
self.register_buffer("bias_integer", torch.zeros_like(self.bias))
self.weight_bit = weight_bit
self.quant_mode = quant_mode
self.per_channel = per_channel
self.bias_bit = bias_bit
self.quant_mode = quant_mode
self.percentile_mode = False
self.weight_function = SymmetricQuantFunction.apply
def __repr__(self):
s = super().__repr__()
s = f"({s} weight_bit={self.weight_bit}, quant_mode={self.quant_mode})"
return s
def forward(self, x, prev_act_scaling_factor=None):
if not self.quant_mode:
return nn.functional.linear(x, weight=self.weight, bias=self.bias), None
# assert that prev_act_scaling_factor is a scalar tensor
assert prev_act_scaling_factor is not None and prev_act_scaling_factor.shape == (1,), (
"Input activation to the QuantLinear layer should be globally (non-channel-wise) quantized. "
"Please add a QuantAct layer with `per_channel = True` before this QuantAct layer"
)
w = self.weight
w_transform = w.data.detach()
if self.per_channel:
w_min, _ = torch.min(w_transform, dim=1, out=None)
w_max, _ = torch.max(w_transform, dim=1, out=None)
else:
w_min = w_transform.min().expand(1)
w_max = w_transform.max().expand(1)
self.fc_scaling_factor = symmetric_linear_quantization_params(self.weight_bit, w_min, w_max, self.per_channel)
self.weight_integer = self.weight_function(
self.weight, self.weight_bit, self.percentile_mode, self.fc_scaling_factor
)
bias_scaling_factor = self.fc_scaling_factor * prev_act_scaling_factor
if self.bias is not None:
self.bias_integer = self.weight_function(self.bias, self.bias_bit, False, bias_scaling_factor)
prev_act_scaling_factor = prev_act_scaling_factor.view(1, -1)
x_int = x / prev_act_scaling_factor
return (
nn.functional.linear(x_int, weight=self.weight_integer, bias=self.bias_integer) * bias_scaling_factor,
bias_scaling_factor,
)
class IntGELU(nn.Module):
"""
Quantized version of `torch.nn.GELU`. Adds quantization-specific arguments on top of `torch.nn.GELU`.
Args:
quant_mode (`bool`, *optional*, defaults to `False`):
Whether or not the layer is quantized.
force_dequant (`str`, *optional*, defaults to `"none"`):
Force dequantize the layer if either "gelu" or "nonlinear" is given.
"""
def __init__(self, quant_mode=True, force_dequant="none"):
super().__init__()
self.quant_mode = quant_mode
if force_dequant in ["nonlinear", "gelu"]:
logger.info("Force dequantize gelu")
self.quant_mode = False
if not self.quant_mode:
self.activation_fn = nn.GELU()
self.k = 1.4142
self.const = 14 # dummy integer constant
self.coeff = [-0.2888, -1.769, 1] # a(x+b)**2 + c
self.coeff[2] /= self.coeff[0]
def int_erf(self, x_int, scaling_factor):
b_int = torch.floor(self.coeff[1] / scaling_factor)
c_int = torch.floor(self.coeff[2] / scaling_factor**2)
sign = torch.sign(x_int)
abs_int = torch.min(torch.abs(x_int), -b_int)
y_int = sign * ((abs_int + b_int) ** 2 + c_int)
scaling_factor = scaling_factor**2 * self.coeff[0]
# avoid overflow
y_int = floor_ste.apply(y_int / 2**self.const)
scaling_factor = scaling_factor * 2**self.const
return y_int, scaling_factor
def forward(self, x, scaling_factor=None):
if not self.quant_mode:
return self.activation_fn(x), None
x_int = x / scaling_factor
sigmoid_int, sigmoid_scaling_factor = self.int_erf(x_int, scaling_factor / self.k)
shift_int = 1.0 // sigmoid_scaling_factor
x_int = x_int * (sigmoid_int + shift_int)
scaling_factor = scaling_factor * sigmoid_scaling_factor / 2
return x_int * scaling_factor, scaling_factor
class IntSoftmax(nn.Module):
"""
Quantized version of `torch.nn.Softmax`. Adds quantization-specific arguments on top of `torch.nn.Softmax`.
Args:
output_bit (`int`):
Bitwidth for the layer output activation.
quant_mode (`bool`, *optional*, defaults to `False`):
Whether or not the layer is quantized.
force_dequant (`str`, *optional*, defaults to `"none"`):
Force dequantize the layer if either "softmax" or "nonlinear" is given.
"""
def __init__(self, output_bit, quant_mode=False, force_dequant="none"):
super().__init__()
self.output_bit = output_bit
self.max_bit = 32
self.quant_mode = quant_mode
if force_dequant in ["nonlinear", "softmax"]:
logger.info("Force dequantize softmax")
self.quant_mode = False
self.act = QuantAct(16, quant_mode=self.quant_mode)
self.x0 = -0.6931 # -ln2
self.const = 30 # dummy integer constant
self.coef = [0.35815147, 0.96963238, 1.0] # ax**2 + bx + c
self.coef[1] /= self.coef[0]
self.coef[2] /= self.coef[0]
def int_polynomial(self, x_int, scaling_factor):
with torch.no_grad():
b_int = torch.floor(self.coef[1] / scaling_factor)
c_int = torch.floor(self.coef[2] / scaling_factor**2)
z = (x_int + b_int) * x_int + c_int
scaling_factor = self.coef[0] * scaling_factor**2
return z, scaling_factor
def int_exp(self, x_int, scaling_factor):
with torch.no_grad():
x0_int = torch.floor(self.x0 / scaling_factor)
x_int = torch.max(x_int, self.const * x0_int)
q = floor_ste.apply(x_int / x0_int)
r = x_int - x0_int * q
exp_int, exp_scaling_factor = self.int_polynomial(r, scaling_factor)
exp_int = torch.clamp(floor_ste.apply(exp_int * 2 ** (self.const - q)), min=0)
scaling_factor = exp_scaling_factor / 2**self.const
return exp_int, scaling_factor
def forward(self, x, scaling_factor):
if not self.quant_mode:
return nn.functional.softmax(x, dim=-1), None
x_int = x / scaling_factor
x_int_max, _ = x_int.max(dim=-1, keepdim=True)
x_int = x_int - x_int_max
exp_int, exp_scaling_factor = self.int_exp(x_int, scaling_factor)
# Avoid overflow
exp, exp_scaling_factor = self.act(exp_int, exp_scaling_factor)
exp_int = exp / exp_scaling_factor
exp_int_sum = exp_int.sum(dim=-1, keepdim=True)
factor = floor_ste.apply(2**self.max_bit / exp_int_sum)
exp_int = floor_ste.apply(exp_int * factor / 2 ** (self.max_bit - self.output_bit))
scaling_factor = 1 / 2**self.output_bit
return exp_int * scaling_factor, scaling_factor
class IntLayerNorm(nn.Module):
"""
Quantized version of `torch.nn.LayerNorm`. Adds quantization-specific arguments on top of `torch.nn.LayerNorm`.
Args:
output_bit (`int`, *optional*, defaults to `8`):
Bitwidth for the layer output activation.
quant_mode (`bool`, *optional*, defaults to `False`):
Whether or not the layer is quantized.
force_dequant (`str`, *optional*, defaults to `"none"`):
Force dequantize the layer if either "layernorm" or "nonlinear" is given.
"""
def __init__(self, normalized_shape, eps, output_bit=8, quant_mode=False, force_dequant="none"):
super().__init__()
self.normalized_shape = normalized_shape
self.eps = eps
self.weight = nn.Parameter(torch.zeros(normalized_shape))
self.bias = nn.Parameter(torch.zeros(normalized_shape))
self.quant_mode = quant_mode
if force_dequant in ["nonlinear", "layernorm"]:
logger.info("Force dequantize layernorm")
self.quant_mode = False
self.register_buffer("shift", torch.zeros(1))
self.output_bit = output_bit
self.max_bit = 32
self.dim_sqrt = None
self.activation = QuantAct(self.output_bit, quant_mode=self.quant_mode)
def set_shift(self, y_int):
with torch.no_grad():
y_sq_int = y_int**2
var_int = torch.sum(y_sq_int, axis=2, keepdim=True)
shift = (torch.log2(torch.sqrt(var_int / 2**self.max_bit)).ceil()).max()
shift_old = self.shift
self.shift = torch.max(self.shift, shift)
logger.info(f"Dynamic shift adjustment: {int(shift_old)} -> {int(self.shift)}")
def overflow_fallback(self, y_int):
"""
This fallback function is called when overflow is detected during training time, and adjusts the `self.shift`
to avoid overflow in the subsequent runs.
"""
self.set_shift(y_int) # adjusts `self.shift`
y_int_shifted = floor_ste.apply(y_int / 2**self.shift)
y_sq_int = y_int_shifted**2
var_int = torch.sum(y_sq_int, axis=2, keepdim=True)
return var_int
def forward(self, x, scaling_factor=None):
if not self.quant_mode:
mean = x.mean(axis=2, keepdim=True)
y = x - mean
var = torch.mean(y**2, axis=2, keepdim=True)
x = y / torch.sqrt(self.eps + var)
x = x * self.weight + self.bias
return x, None
# compute sqrt of the feature dimension if it is the first run
if self.dim_sqrt is None:
n = torch.tensor(x.shape[2], dtype=torch.float)
self.dim_sqrt = torch.sqrt(n).to(x.device)
# Normalization: computes mean and variance(std)
x_int = x / scaling_factor
mean_int = round_ste.apply(x_int.mean(axis=2, keepdim=True))
y_int = x_int - mean_int
y_int_shifted = floor_ste.apply(y_int / 2**self.shift)
y_sq_int = y_int_shifted**2
var_int = torch.sum(y_sq_int, axis=2, keepdim=True)
# overflow handling in training time
if self.training:
# if overflow is detected
if var_int.max() >= 2**self.max_bit:
var_int = self.overflow_fallback(y_int)
assert var_int.max() < 2**self.max_bit + 0.1, (
"Error detected in overflow handling: "
"`var_int` exceeds `self.max_bit` (the maximum possible bit width)"
)
# To be replaced with integer-sqrt kernel that produces the same output
std_int = floor_ste.apply(torch.sqrt(var_int)) * 2**self.shift
factor = floor_ste.apply(2**31 / std_int)
y_int = floor_ste.apply(y_int * factor / 2)
scaling_factor = self.dim_sqrt / 2**30
# scaling and shifting
bias = self.bias.data.detach() / (self.weight.data.detach())
bias_int = floor_ste.apply(bias / scaling_factor)
y_int = y_int + bias_int
scaling_factor = scaling_factor * self.weight
x = y_int * scaling_factor
return x, scaling_factor
def get_percentile_min_max(input, lower_percentile, upper_percentile, output_tensor=False):
"""
Calculate the percentile max and min values in a given tensor
Args:
input (`torch.Tensor`):
The target tensor to calculate percentile max and min.
lower_percentile (`float`):
If 0.1, means we return the value of the smallest 0.1% value in the tensor as percentile min.
upper_percentile (`float`):
If 99.9, means we return the value of the largest 0.1% value in the tensor as percentile max.
output_tensor (`bool`, *optional*, defaults to `False`):
If True, this function returns tensors, otherwise it returns values.
Returns:
`Tuple(torch.Tensor, torch.Tensor)`: Percentile min and max value of *input*
"""
input_length = input.shape[0]
lower_index = round(input_length * (1 - lower_percentile * 0.01))
upper_index = round(input_length * upper_percentile * 0.01)
upper_bound = torch.kthvalue(input, k=upper_index).values
if lower_percentile == 0:
lower_bound = upper_bound * 0
# lower_index += 1
else:
lower_bound = -torch.kthvalue(-input, k=lower_index).values
if not output_tensor:
lower_bound = lower_bound.item()
upper_bound = upper_bound.item()
return lower_bound, upper_bound
def linear_quantize(input, scale, zero_point, inplace=False):
"""
Quantize single-precision input tensor to integers with the given scaling factor and zeropoint.
Args:
input (`torch.Tensor`):
Single-precision input tensor to be quantized.
scale (`torch.Tensor`):
Scaling factor for quantization.
zero_pint (`torch.Tensor`):
Shift for quantization.
inplace (`bool`, *optional*, defaults to `False`):
Whether to compute inplace or not.
Returns:
`torch.Tensor`: Linearly quantized value of *input* according to *scale* and *zero_point*.
"""
# reshape scale and zeropoint for convolutional weights and activation
if len(input.shape) == 4:
scale = scale.view(-1, 1, 1, 1)
zero_point = zero_point.view(-1, 1, 1, 1)
# reshape scale and zeropoint for linear weights
elif len(input.shape) == 2:
scale = scale.view(-1, 1)
zero_point = zero_point.view(-1, 1)
else:
scale = scale.view(-1)
zero_point = zero_point.view(-1)
# quantized = float / scale + zero_point
if inplace:
input.mul_(1.0 / scale).add_(zero_point).round_()
return input
return torch.round(1.0 / scale * input + zero_point)
def symmetric_linear_quantization_params(num_bits, saturation_min, saturation_max, per_channel=False):
"""
Compute the scaling factor with the given quantization range for symmetric quantization.
Args:
saturation_min (`torch.Tensor`):
Lower bound for quantization range.
saturation_max (`torch.Tensor`):
Upper bound for quantization range.
per_channel (`bool`, *optional*, defaults to `False`):
Whether to or not use channel-wise quantization.
Returns:
`torch.Tensor`: Scaling factor that linearly quantizes the given range between *saturation_min* and
*saturation_max*.
"""
# in this part, we do not need any gradient computation,
# in order to enforce this, we put torch.no_grad()
with torch.no_grad():
n = 2 ** (num_bits - 1) - 1
if per_channel:
scale, _ = torch.max(torch.stack([saturation_min.abs(), saturation_max.abs()], dim=1), dim=1)
scale = torch.clamp(scale, min=1e-8) / n
else:
scale = max(saturation_min.abs(), saturation_max.abs())
scale = torch.clamp(scale, min=1e-8) / n
return scale
class SymmetricQuantFunction(Function):
"""
Class to quantize the given floating-point values using symmetric quantization with given range and bitwidth.
"""
@staticmethod
def forward(ctx, x, k, percentile_mode, scale):
"""
Args:
x (`torch.Tensor`):
Floating point tensor to be quantized.
k (`int`):
Quantization bitwidth.
percentile_mode (`bool`):
Whether or not to use percentile calibration.
scale (`torch.Tensor`):
Pre-calculated scaling factor for *x*. Note that the current implementation of SymmetricQuantFunction
requires pre-calculated scaling factor.
Returns:
`torch.Tensor`: Symmetric-quantized value of *input*.
"""
zero_point = torch.tensor(0.0, device=scale.device)
n = 2 ** (k - 1) - 1
new_quant_x = linear_quantize(x, scale, zero_point, inplace=False)
new_quant_x = torch.clamp(new_quant_x, -n, n - 1)
ctx.scale = scale
return new_quant_x
@staticmethod
def backward(ctx, grad_output):
scale = ctx.scale
if len(grad_output.shape) == 4:
scale = scale.view(-1, 1, 1, 1)
# reshape scale and zeropoint for linear weights
elif len(grad_output.shape) == 2:
scale = scale.view(-1, 1)
else:
scale = scale.view(-1)
return grad_output.clone() / scale, None, None, None, None
class floor_ste(Function):
"""
Straight-through Estimator(STE) for torch.floor()
"""
@staticmethod
def forward(ctx, x):
return torch.floor(x)
@staticmethod
def backward(ctx, grad_output):
return grad_output.clone()
class round_ste(Function):
"""
Straight-through Estimator(STE) for torch.round()
"""
@staticmethod
def forward(ctx, x):
return torch.round(x)
@staticmethod
def backward(ctx, grad_output):
return grad_output.clone()
def batch_frexp(inputs, max_bit=31):
"""
Decompose the scaling factor into mantissa and twos exponent.
Args:
scaling_factor (`torch.Tensor`):
Target scaling factor to decompose.
Returns:
``Tuple(torch.Tensor, torch.Tensor)`: mantisa and exponent
"""
shape_of_input = inputs.size()
# trans the input to be a 1-d tensor
inputs = inputs.view(-1)
output_m, output_e = np.frexp(inputs.cpu().numpy())
tmp_m = []
for m in output_m:
int_m_shifted = int(
decimal.Decimal(m * (2**max_bit)).quantize(decimal.Decimal("1"), rounding=decimal.ROUND_HALF_UP)
)
tmp_m.append(int_m_shifted)
output_m = np.array(tmp_m)
output_e = float(max_bit) - output_e
return (
torch.from_numpy(output_m).to(inputs.device).view(shape_of_input),
torch.from_numpy(output_e).to(inputs.device).view(shape_of_input),
)
class FixedPointMul(Function):
"""
Function to perform fixed-point arithmetic that can match integer arithmetic on hardware.
Args:
pre_act (`torch.Tensor`):
Input tensor.
pre_act_scaling_factor (`torch.Tensor`):
Scaling factor of the input tensor *pre_act*.
bit_num (`int`):
Quantization bitwidth.
z_scaling_factor (`torch.Tensor`):
Scaling factor of the output tensor.
identity (`torch.Tensor`, *optional*):
Identity tensor, if exists.
identity_scaling_factor (`torch.Tensor`, *optional*):
Scaling factor of the identity tensor *identity*, if exists.
Returns:
`torch.Tensor`: Output tensor(*pre_act* if *identity* is not given, otherwise the addition of *pre_act* and
*identity*), whose scale is rescaled to *z_scaling_factor*.
"""
@staticmethod
def forward(
ctx,
pre_act,
pre_act_scaling_factor,
bit_num,
z_scaling_factor,
identity=None,
identity_scaling_factor=None,
):
if len(pre_act_scaling_factor.shape) == 3:
reshape = lambda x: x # noqa: E731
else:
reshape = lambda x: x.view(1, 1, -1) # noqa: E731
ctx.identity = identity
n = 2 ** (bit_num - 1) - 1
with torch.no_grad():
pre_act_scaling_factor = reshape(pre_act_scaling_factor)
if identity is not None:
identity_scaling_factor = reshape(identity_scaling_factor)
ctx.z_scaling_factor = z_scaling_factor
z_int = torch.round(pre_act / pre_act_scaling_factor)
_A = pre_act_scaling_factor.type(torch.double)
_B = (z_scaling_factor.type(torch.float)).type(torch.double)
new_scale = _A / _B
new_scale = reshape(new_scale)
m, e = batch_frexp(new_scale)
output = z_int.type(torch.double) * m.type(torch.double)
output = torch.round(output / (2.0**e))
if identity is not None:
# needs addition of identity activation
wx_int = torch.round(identity / identity_scaling_factor)
_A = identity_scaling_factor.type(torch.double)
_B = (z_scaling_factor.type(torch.float)).type(torch.double)
new_scale = _A / _B
new_scale = reshape(new_scale)
m1, e1 = batch_frexp(new_scale)
output1 = wx_int.type(torch.double) * m1.type(torch.double)
output1 = torch.round(output1 / (2.0**e1))
output = output1 + output
return torch.clamp(output.type(torch.float), -n - 1, n)
@staticmethod
def backward(ctx, grad_output):
identity_grad = None
if ctx.identity is not None:
identity_grad = grad_output.clone() / ctx.z_scaling_factor
return grad_output.clone() / ctx.z_scaling_factor, None, None, None, None, identity_grad, None
```
|
================================================================================================================================
SOURCE CODE FILE: __init__.py
LINES: 1
SIZE: 1.06 KB
PATH: scripts\freecad_env\Lib\site-packages\transformers\models\idefics2\__init__.py
ENCODING: utf-8
```py
# Copyright 2024 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import _LazyModule
from ...utils.import_utils import define_import_structure
if TYPE_CHECKING:
from .configuration_idefics2 import *
from .image_processing_idefics2 import *
from .modeling_idefics2 import *
from .processing_idefics2 import *
else:
import sys
_file = globals()["__file__"]
sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)
```
|
==============================================================================================================================================
SOURCE CODE FILE: configuration_idefics2.py
LINES: 1
SIZE: 11.74 KB
PATH: scripts\freecad_env\Lib\site-packages\transformers\models\idefics2\configuration_idefics2.py
ENCODING: utf-8
```py
# coding=utf-8
# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Idefics2 model configuration"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING, AutoConfig
logger = logging.get_logger(__name__)
class Idefics2VisionConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`Idefics2VisionModel`]. It is used to instantiate a
Idefics2 vision encoder according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will yield a similar configuration to that of the SigLIP checkpoint
[google/siglip-base-patch16-224](https://huggingface.co/google/siglip-base-patch16-224) used in the Idefics2 model
[HuggingFaceM4/idefics2-8b](https://huggingface.co/HuggingFaceM4/idefics2-8b).
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
hidden_size (`int`, *optional*, defaults to 768):
Dimensionality of the encoder layers and the pooler layer.
intermediate_size (`int`, *optional*, defaults to 3072):
Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
num_hidden_layers (`int`, *optional*, defaults to 12):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 12):
Number of attention heads for each attention layer in the Transformer encoder.
num_channels (`int`, *optional*, defaults to 3):
Number of channels in the input images.
image_size (`int`, *optional*, defaults to 224):
The size (resolution) of each image.
patch_size (`int`, *optional*, defaults to 32):
The size (resolution) of each patch.
hidden_act (`str` or `function`, *optional*, defaults to `"gelu_pytorch_tanh"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"selu"` and `"gelu_new"` `"quick_gelu"` are supported.
layer_norm_eps (`float`, *optional*, defaults to 1e-06):
The epsilon used by the layer normalization layers.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation for initializing all weight matrices in the model.
Example:
```python
>>> from transformers.models.idefics2.modeling_idefics2 import Idefics2VisionTransformer
>>> from transformers.models.idefics2.configuration_idefics2 import Idefics2VisionConfig
>>> # Initializing a Idefics2VisionConfig with google/siglip-base-patch16-224 style configuration
>>> configuration = Idefics2VisionConfig()
>>> # Initializing a Idefics2VisionTransformer (with random weights) from the google/siglip-base-patch16-224 style configuration
>>> model = Idefics2VisionTransformer(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "idefics2_vision"
base_config_key = "vision_config"
def __init__(
self,
hidden_size=768,
intermediate_size=3072,
num_hidden_layers=12,
num_attention_heads=12,
num_channels=3,
image_size=224,
patch_size=32,
hidden_act="gelu_pytorch_tanh",
layer_norm_eps=1e-6,
attention_dropout=0.0,
initializer_range=0.02,
**kwargs,
):
super().__init__(**kwargs)
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.num_channels = num_channels
self.patch_size = patch_size
self.image_size = image_size
self.attention_dropout = attention_dropout
self.layer_norm_eps = layer_norm_eps
self.hidden_act = hidden_act
self.initializer_range = initializer_range
class Idefics2PerceiverConfig(PretrainedConfig):
r"""
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
The non-linear activation function (function or string) in the perceiver block.
hidden_size (`int`, *optional*, defaults to 4096):
Dimension of the hidden representations.
rms_norm_eps (`float`, *optional*, defaults to 1e-06):
The epsilon used by the rms normalization layers.
resampler_n_latents (`int`, *optional*, defaults to 64):
Number of latent embeddings to resample ("compress") the input sequence to (usually < 128).
resampler_depth (`int`, *optional*, defaults to 3):
Depth of the Perceiver Resampler (Transformer w/ cross attention). Should be shallow (<= 3).
resampler_n_heads (`int`, *optional*, defaults to 16):
Number of heads in each Transformer block (for multi-headed self-attention).
resampler_head_dim (`int`, *optional*, defaults to 96):
Dimensionality of each head projection in the Transformer block.
num_key_value_heads (`int`, *optional*, defaults to 4):
Number of key-value heads in the perceiver attention block.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
"""
model_type = "idefics2_perceiver"
def __init__(
self,
hidden_act="silu",
hidden_size=4096,
rms_norm_eps=1e-06,
resampler_n_latents=64,
resampler_depth=3,
resampler_n_heads=16,
resampler_head_dim=96,
num_key_value_heads=4,
attention_dropout=0.0,
**kwargs,
):
self.hidden_act = hidden_act
self.hidden_size = hidden_size
self.rms_norm_eps = rms_norm_eps
self.resampler_n_latents = resampler_n_latents
self.resampler_depth = resampler_depth
self.resampler_n_heads = resampler_n_heads
self.num_key_value_heads = num_key_value_heads
self.resampler_head_dim = resampler_head_dim
self.attention_dropout = attention_dropout
if self.num_key_value_heads > self.resampler_n_heads:
raise ValueError(
f"num_key_value_heads={self.num_key_value_heads} must be less than or equal to"
f" resampler_n_heads={self.resampler_n_heads}"
)
super().__init__(**kwargs)
class Idefics2Config(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`Idefics2Model`]. It is used to instantiate a
Idefics2 model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will yield a similar configuration to that of the model of the Idefics2
[HuggingFaceM4/idefics2-8b](https://huggingface.co/HuggingFaceM4/idefics2-8b) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should cache the key/value pairs of the attention mechanism.
image_token_id (`int`, *optional*, defaults to 32001):
The id of the "image" token.
tie_word_embeddings (`bool`, *optional*, defaults to `False`):
Whether or not to tie the word embeddings with the token embeddings.
vision_config (`IdeficsVisionConfig` or `dict`, *optional*):
Custom vision config or dict
perceiver_config (`IdeficsPerceiverConfig` or `dict`, *optional*):
Custom perceiver config or dict
text_config (`MistralConfig` or `dict`, *optional*):
Custom text config or dict for the text model
Example:
```python
>>> from transformers import Idefics2Model, Idefics2Config
>>> # Initializing configuration
>>> configuration = Idefics2Config()
>>> # Initializing a model from the configuration
>>> model = Idefics2Model(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "idefics2"
sub_configs = {
"text_config": AutoConfig,
"perceiver_config": Idefics2PerceiverConfig,
"vision_config": Idefics2VisionConfig,
}
def __init__(
self,
use_cache=True,
image_token_id=32_001,
tie_word_embeddings=False,
vision_config=None,
perceiver_config=None,
text_config=None,
**kwargs,
):
self.image_token_id = image_token_id
self.use_cache = use_cache
self.tie_word_embeddings = tie_word_embeddings
if perceiver_config is None:
self.perceiver_config = Idefics2PerceiverConfig()
logger.info("perciver_config is None, using default perceiver config")
elif isinstance(perceiver_config, dict):
self.perceiver_config = Idefics2PerceiverConfig(**perceiver_config)
elif isinstance(perceiver_config, Idefics2PerceiverConfig):
self.perceiver_config = perceiver_config
if vision_config is None:
self.vision_config = Idefics2VisionConfig()
logger.info("vision_config is None, using default vision config")
elif isinstance(vision_config, dict):
self.vision_config = Idefics2VisionConfig(**vision_config)
elif isinstance(vision_config, Idefics2VisionConfig):
self.vision_config = vision_config
if isinstance(text_config, dict):
text_config["model_type"] = text_config["model_type"] if "model_type" in text_config else "mistral"
text_config = CONFIG_MAPPING[text_config["model_type"]](**text_config)
elif text_config is None:
logger.info("text_config is None, using default text config")
text_config = CONFIG_MAPPING["mistral"](
max_position_embeddings=4096 * 8,
rms_norm_eps=1e-5,
# None in the original configuration_mistral, we set it to the unk_token_id
pad_token_id=0,
tie_word_embeddings=False,
)
self.text_config = text_config
if self.text_config.hidden_size != self.perceiver_config.hidden_size:
self.perceiver_config.hidden_size = self.text_config.hidden_size
self.perceiver_config.rms_norm_eps = self.text_config.rms_norm_eps
logger.warning_once(
"Perceiver config has a different `hidden_size` than text config, which means default values were used. "
"In your model's config on the hub, add `hidden_size` and `rms_norm_eps` keys under the `perceiver_config` dict. "
)
super().__init__(**kwargs, tie_word_embeddings=tie_word_embeddings)
__all__ = ["Idefics2Config"]
```
|
=================================================================================================================================================
SOURCE CODE FILE: image_processing_idefics2.py
LINES: 1
SIZE: 25.83 KB
PATH: scripts\freecad_env\Lib\site-packages\transformers\models\idefics2\image_processing_idefics2.py
ENCODING: utf-8
```py
# coding=utf-8
# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Any, Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import PaddingMode, pad, resize, to_channel_dimension_format
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
get_image_size,
infer_channel_dimension_format,
is_scaled_image,
make_nested_list_of_images,
to_numpy_array,
valid_images,
validate_preprocess_arguments,
)
from ...utils import TensorType, is_vision_available, logging
logger = logging.get_logger(__name__)
if is_vision_available():
import PIL
from PIL import Image
def get_resize_output_image_size(image, size, input_data_format) -> Tuple[int, int]:
"""
Get the output size of the image after resizing given a dictionary specifying the max and min sizes.
Args:
image (`np.ndarray`):
Image to resize.
size (`Dict[str, int]`):
Size of the output image containing the keys "shortest_edge" and "longest_edge".
input_data_format (`ChannelDimension` or `str`):
The channel dimension format of the input image.
Returns:
The output size of the image after resizing.
"""
height, width = get_image_size(image, channel_dim=input_data_format)
min_len = size["shortest_edge"]
max_len = size["longest_edge"]
aspect_ratio = width / height
if width >= height and width > max_len:
width = max_len
height = int(width / aspect_ratio)
elif height > width and height > max_len:
height = max_len
width = int(height * aspect_ratio)
height = max(height, min_len)
width = max(width, min_len)
return height, width
# Copied from transformers.models.detr.image_processing_detr.max_across_indices
def max_across_indices(values: Iterable[Any]) -> List[Any]:
"""
Return the maximum value across all indices of an iterable of values.
"""
return [max(values_i) for values_i in zip(*values)]
def get_max_height_width(
images_list: List[List[np.ndarray]], input_data_format: Optional[Union[str, ChannelDimension]] = None
) -> List[int]:
"""
Get the maximum height and width across all images in a batch.
"""
if input_data_format is None:
input_data_format = infer_channel_dimension_format(images_list[0][0])
image_sizes = []
for images in images_list:
for image in images:
image_sizes.append(get_image_size(image, channel_dim=input_data_format))
max_height, max_width = max_across_indices(image_sizes)
return (max_height, max_width)
# Copied from transformers.models.detr.image_processing_detr.make_pixel_mask
def make_pixel_mask(
image: np.ndarray, output_size: Tuple[int, int], input_data_format: Optional[Union[str, ChannelDimension]] = None
) -> np.ndarray:
"""
Make a pixel mask for the image, where 1 indicates a valid pixel and 0 indicates padding.
Args:
image (`np.ndarray`):
Image to make the pixel mask for.
output_size (`Tuple[int, int]`):
Output size of the mask.
"""
input_height, input_width = get_image_size(image, channel_dim=input_data_format)
mask = np.zeros(output_size, dtype=np.int64)
mask[:input_height, :input_width] = 1
return mask
# FIXME Amy: merge this function with the one in image_transforms.py
def convert_to_rgb(image: ImageInput) -> ImageInput:
"""
Converts an image to RGB format. Only converts if the image is of type PIL.Image.Image, otherwise returns the image
as is.
Args:
image (Image):
The image to convert.
"""
if not isinstance(image, PIL.Image.Image):
return image
# `image.convert("RGB")` would only work for .jpg images, as it creates a wrong background
# for transparent images. The call to `alpha_composite` handles this case
if image.mode == "RGB":
return image
image_rgba = image.convert("RGBA")
background = Image.new("RGBA", image_rgba.size, (255, 255, 255))
alpha_composite = Image.alpha_composite(background, image_rgba)
alpha_composite = alpha_composite.convert("RGB")
return alpha_composite
class Idefics2ImageProcessor(BaseImageProcessor):
r"""
Constructs a Idefics image processor.
Args:
do_convert_rgb (`bool`, *optional*, defaults to `True`):
Whether to convert the image to RGB. This is useful if the input image is of a different format e.g. RGBA.
Only has an effect if the input image is in the PIL format.
do_resize (`bool`, *optional*, defaults to `True`):
Whether to resize the image. The longest edge of the image is resized to be <= `size["longest_edge"]`, with the
shortest edge resized to keep the input aspect ratio, with a minimum size of `size["shortest_edge"]`.
size (`Dict`, *optional*):
Controls the size of the output image. This is a dictionary containing the keys "shortest_edge" and "longest_edge".
resample (`Resampling`, *optional*, defaults to `Resampling.BILINEAR`):
Resampling filter to use when resizing the image.
do_rescale (`bool`, *optional*, defaults to `True`):
Whether to rescale the image. If set to `True`, the image is rescaled to have pixel values between 0 and 1.
rescale_factor (`float`, *optional*, defaults to `1/255`):
Rescale factor to rescale the image by if `do_rescale` is set to `True`.
do_normalize (`bool`, *optional*, defaults to `True`):
Whether to normalize the image. If set to `True`, the image is normalized to have a mean of `image_mean` and
a standard deviation of `image_std`.
image_mean (`float` or `List[float]`, *optional*, defaults to `IDEFICS_STANDARD_MEAN`):
Mean to use if normalizing the image. This is a float or list of floats the length of the number of
channels in the image. Can be overridden by the `image_mean` parameter in the `preprocess` method. Can be
overridden by the `image_mean` parameter in the `preprocess` method.
image_std (`float` or `List[float]`, *optional*, defaults to `IDEFICS_STANDARD_STD`):
Standard deviation to use if normalizing the image. This is a float or list of floats the length of the
number of channels in the image. Can be overridden by the `image_std` parameter in the `preprocess` method.
Can be overridden by the `image_std` parameter in the `preprocess` method.
do_pad (`bool`, *optional*, defaults to `True`):
Whether or not to pad the images to the largest height and width in the batch and number of images per
sample in the batch, such that the returned tensor is of shape (batch_size, max_num_images, num_channels, max_height, max_width).
do_image_splitting (`bool`, *optional*, defaults to `False`):
Whether to split the image into a sequence 4 equal sub-images concatenated with the original image. That
strategy was first introduced in https://arxiv.org/abs/2311.06607.
"""
model_input_names = ["pixel_values", "pixel_attention_mask"]
def __init__(
self,
do_convert_rgb: bool = True,
do_resize: bool = True,
size: Dict[str, int] = None,
resample: PILImageResampling = PILImageResampling.BILINEAR,
do_rescale: bool = True,
rescale_factor: float = 1 / 255,
do_normalize: bool = True,
image_mean: Optional[Union[float, List[float]]] = None,
image_std: Optional[Union[float, List[float]]] = None,
do_pad: bool = True,
do_image_splitting: bool = False,
**kwargs,
) -> None:
super().__init__(**kwargs)
self.do_convert_rgb = do_convert_rgb
self.do_resize = do_resize
self.size = size if size is not None else {"shortest_edge": 378, "longest_edge": 980}
self.resample = resample
self.do_rescale = do_rescale
self.rescale_factor = rescale_factor
self.do_normalize = do_normalize
self.image_mean = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
self.image_std = image_std if image_std is not None else IMAGENET_STANDARD_STD
self.do_pad = do_pad
self.do_image_splitting = do_image_splitting
def resize(
self,
image: np.ndarray,
size: Dict[str, int],
resample: PILImageResampling = PILImageResampling.BILINEAR,
data_format: Optional[Union[str, ChannelDimension]] = None,
input_data_format: Optional[Union[str, ChannelDimension]] = None,
**kwargs,
) -> np.ndarray:
"""
Resize an image. The shortest edge of the image is resized to size["shortest_edge"], with the longest edge
resized to keep the input aspect ratio.
Args:
image (`np.ndarray`):
Image to resize.
size (`Dict[str, int]`):
Size of the output image.
resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BICUBIC`):
Resampling filter to use when resiizing the image.
data_format (`str` or `ChannelDimension`, *optional*):
The channel dimension format of the image. If not provided, it will be the same as the input image.
input_data_format (`ChannelDimension` or `str`, *optional*):
The channel dimension format of the input image. If not provided, it will be inferred.
"""
if "shortest_edge" in size and "longest_edge" in size:
size = get_resize_output_image_size(image, size, input_data_format)
elif "height" in size and "width" in size:
size = (size["height"], size["width"])
else:
raise ValueError(
"size must be a dictionary with keys 'shortest_edge' and 'longest_edge' or 'height' and 'width'."
)
return resize(
image, size, resample=resample, data_format=data_format, input_data_format=input_data_format, **kwargs
)
# Copied from transformers.models.vilt.image_processing_vilt.ViltImageProcessor._pad_image
def _pad_image(
self,
image: np.ndarray,
output_size: Tuple[int, int],
constant_values: Union[float, Iterable[float]] = 0,
data_format: Optional[ChannelDimension] = None,
input_data_format: Optional[Union[str, ChannelDimension]] = None,
) -> np.ndarray:
"""
Pad an image with zeros to the given size.
"""
input_height, input_width = get_image_size(image, channel_dim=input_data_format)
output_height, output_width = output_size
pad_bottom = output_height - input_height
pad_right = output_width - input_width
padding = ((0, pad_bottom), (0, pad_right))
padded_image = pad(
image,
padding,
mode=PaddingMode.CONSTANT,
constant_values=constant_values,
data_format=data_format,
input_data_format=input_data_format,
)
return padded_image
def pad(
self,
images: List[np.ndarray],
constant_values: Union[float, Iterable[float]] = 0,
return_pixel_mask: bool = True,
return_tensors: Optional[Union[str, TensorType]] = None,
data_format: Optional[ChannelDimension] = None,
input_data_format: Optional[Union[str, ChannelDimension]] = None,
) -> BatchFeature:
"""
For a list of images, for each images, pads a batch of images to the bottom and right of the image with zeros to the size of largest height and width.
For each sample in the batch, pads the sample with empty images to the max_number of images per sample in the batch. Optionally returns a pixel mask.
Args:
images (`np.ndarray`):
List of list of images to pad. Pads to the largest height and width in the batch.
constant_values (`float` or `Iterable[float]`, *optional*):
The value to use for the padding if `mode` is `"constant"`.
return_pixel_mask (`bool`, *optional*, defaults to `True`):
Whether to return a pixel mask.
return_tensors (`str` or `TensorType`, *optional*):
The type of tensors to return. Can be one of:
- Unset: Return a list of `np.ndarray`.
- `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`.
- `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
- `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`.
- `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`.
data_format (`str` or `ChannelDimension`, *optional*):
The channel dimension format of the image. If not provided, it will be the same as the input image.
input_data_format (`ChannelDimension` or `str`, *optional*):
The channel dimension format of the input image. If not provided, it will be inferred.
"""
pad_size = get_max_height_width(images, input_data_format=input_data_format)
batch_size = len(images)
max_num_images = max(len(images_) for images_ in images)
input_data_format = (
infer_channel_dimension_format(images[0][0]) if input_data_format is None else input_data_format
)
data_format = input_data_format if data_format is None else data_format
def empty_image(size, input_data_format):
if input_data_format == ChannelDimension.FIRST:
return np.zeros((3, *size), dtype=np.uint8)
elif input_data_format == ChannelDimension.LAST:
return np.zeros((*size, 3), dtype=np.uint8)
raise ValueError("Invalid channel dimension format.")
padded_images_list = [
[empty_image(pad_size, data_format) for _ in range(max_num_images)] for _ in range(batch_size)
]
padded_masks = [[np.zeros(pad_size) for _ in range(max_num_images)] for _ in range(batch_size)]
for batch_idx in range(batch_size):
for sample_idx, image in enumerate(images[batch_idx]):
padded_images_list[batch_idx][sample_idx] = self._pad_image(
image,
pad_size,
constant_values=constant_values,
data_format=data_format,
input_data_format=input_data_format,
)
padded_masks[batch_idx][sample_idx] = make_pixel_mask(
image, output_size=pad_size, input_data_format=input_data_format
)
padded_masks = padded_masks if return_pixel_mask else None
return padded_images_list, padded_masks
def _crop(
self,
im: np.ndarray,
w1: int,
h1: int,
w2: int,
h2: int,
input_data_format: Optional[Union[str, ChannelDimension]] = None,
) -> np.ndarray:
if input_data_format == ChannelDimension.FIRST:
return im[:, h1:h2, w1:w2]
elif input_data_format == ChannelDimension.LAST:
return im[h1:h2, w1:w2, :]
def split_image(
self,
image: np.ndarray,
input_data_format: Optional[Union[str, ChannelDimension]] = None,
):
"""
Split an image into 4 equal sub-images, and the concatenate that sequence with the original image.
That means that a single image becomes a sequence of 5 images.
This is a "trick" to spend more compute on each image with no changes in the vision encoder.
Args:
image (`np.ndarray`):
Images to split.
input_data_format (`ChannelDimension` or `str`, *optional*):
The channel dimension format of the input image. If not provided, it will be inferred.
"""
height, width = get_image_size(image, input_data_format)
mid_width = width // 2
mid_height = height // 2
return [
self._crop(image, 0, 0, mid_width, mid_height, input_data_format),
self._crop(image, mid_width, 0, width, mid_height, input_data_format),
self._crop(image, 0, mid_height, mid_width, height, input_data_format),
self._crop(image, mid_width, mid_height, width, height, input_data_format),
image,
]
def preprocess(
self,
images: ImageInput,
do_convert_rgb: Optional[bool] = None,
do_resize: Optional[bool] = None,
size: Optional[Dict[str, int]] = None,
resample: PILImageResampling = None,
do_rescale: Optional[bool] = None,
rescale_factor: Optional[float] = None,
do_normalize: Optional[bool] = None,
image_mean: Optional[Union[float, List[float]]] = None,
image_std: Optional[Union[float, List[float]]] = None,
do_pad: Optional[bool] = None,
do_image_splitting: Optional[bool] = None,
return_tensors: Optional[Union[str, TensorType]] = None,
input_data_format: Optional[ChannelDimension] = None,
data_format: Optional[ChannelDimension] = ChannelDimension.FIRST,
):
"""
Preprocess a batch of images.
Args:
images (`ImageInput`):
A list of images to preprocess.
do_convert_rgb (`bool`, *optional*, defaults to `self.do_convert_rgb`):
Whether to convert the image to RGB.
do_resize (`bool`, *optional*, defaults to `self.do_resize`):
Whether to resize the image.
size (`Dict[str, int]`, *optional*, defaults to `self.size`):
Size of the image after resizing. Shortest edge of the image is resized to size["shortest_edge"], with
the longest edge resized to keep the input aspect ratio.
resample (`int`, *optional*, defaults to `self.resample`):
Resampling filter to use if resizing the image. This can be one of the enum `PILImageResampling`. Only
has an effect if `do_resize` is set to `True`.
do_rescale (`bool`, *optional*, defaults to `self.do_rescale`):
Whether to rescale the image.
rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`):
Rescale factor to rescale the image by if `do_rescale` is set to `True`.
do_normalize (`bool`, *optional*, defaults to `self.do_normalize`):
Whether to normalize the image.
image_mean (`float` or `List[float]`, *optional*, defaults to `self.image_mean`):
Image mean to use for normalization. Only has an effect if `do_normalize` is set to `True`.
image_std (`float` or `List[float]`, *optional*, defaults to `self.image_std`):
Image standard deviation to use for normalization. Only has an effect if `do_normalize` is set to
`True`.
do_pad (`bool`, *optional*, defaults to `self.do_pad`):
Whether or not to pad the images to the largest height and width in the batch.
do_image_splitting (`bool`, *optional*, defaults to `self.do_image_splitting`):
Whether to split the image into a sequence 4 equal sub-images concatenated with the original image. That
strategy was first introduced in https://arxiv.org/abs/2311.06607.
return_tensors (`str` or `TensorType`, *optional*):
The type of tensors to return. Can be one of:
- Unset: Return a list of `np.ndarray`.
- `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`.
- `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
- `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`.
- `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`.
data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`):
The channel dimension format for the output image. Can be one of:
- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
- Unset: Use the channel dimension format of the input image.
input_data_format (`ChannelDimension` or `str`, *optional*):
The channel dimension format for the input image. If unset, the channel dimension format is inferred
from the input image. Can be one of:
- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
- `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
"""
do_resize = do_resize if do_resize is not None else self.do_resize
size = size if size is not None else self.size
resample = resample if resample is not None else self.resample
do_rescale = do_rescale if do_rescale is not None else self.do_rescale
rescale_factor = rescale_factor if rescale_factor is not None else self.rescale_factor
do_normalize = do_normalize if do_normalize is not None else self.do_normalize
image_mean = image_mean if image_mean is not None else self.image_mean
image_std = image_std if image_std is not None else self.image_std
do_convert_rgb = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
do_pad = do_pad if do_pad is not None else self.do_pad
do_image_splitting = do_image_splitting if do_image_splitting is not None else self.do_image_splitting
images_list = make_nested_list_of_images(images)
if not valid_images(images_list[0]):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
"torch.Tensor, tf.Tensor or jax.ndarray."
)
validate_preprocess_arguments(
do_rescale=do_rescale,
rescale_factor=rescale_factor,
do_normalize=do_normalize,
image_mean=image_mean,
image_std=image_std,
do_resize=do_resize,
size=size,
resample=resample,
)
if do_convert_rgb:
images_list = [[convert_to_rgb(image) for image in images] for images in images_list]
# All transformations expect numpy arrays.
images_list = [[to_numpy_array(image) for image in images] for images in images_list]
if do_rescale and is_scaled_image(images_list[0][0]):
logger.warning_once(
"It looks like you are trying to rescale already rescaled images. If the input"
" images have pixel values between 0 and 1, set `do_rescale=False` to avoid rescaling them again."
)
if input_data_format is None:
# We assume that all images have the same channel dimension format.
input_data_format = infer_channel_dimension_format(images_list[0][0])
if do_image_splitting:
new_images_list = []
for images in images_list:
new_images = []
for image in images:
new_images.extend(self.split_image(image, input_data_format))
new_images_list.append(new_images)
images_list = new_images_list
if do_resize:
images_list = [
[
self.resize(image=image, size=size, resample=resample, input_data_format=input_data_format)
for image in images
]
for images in images_list
]
if do_rescale:
images_list = [
[
self.rescale(image=image, scale=rescale_factor, input_data_format=input_data_format)
for image in images
]
for images in images_list
]
if do_normalize:
images_list = [
[
self.normalize(image=image, mean=image_mean, std=image_std, input_data_format=input_data_format)
for image in images
]
for images in images_list
]
pixel_attention_mask = None
if do_pad:
images_list, pixel_attention_mask = self.pad(
images_list, return_pixel_mask=True, return_tensors=return_tensors, input_data_format=input_data_format
)
if data_format is not None:
images_list = [
[
to_channel_dimension_format(image, data_format, input_channel_dim=input_data_format)
for image in images
]
for images in images_list
]
data = {"pixel_values": np.array(images_list) if do_pad else images_list} # Faster tensor conversion
if pixel_attention_mask is not None:
data["pixel_attention_mask"] = np.array(pixel_attention_mask) if do_pad else pixel_attention_mask
return BatchFeature(data=data, tensor_type=return_tensors)
__all__ = ["Idefics2ImageProcessor"]
```
|
=========================================================================================================================================
SOURCE CODE FILE: modeling_idefics2.py
LINES: 8
SIZE: 69.62 KB
PATH: scripts\freecad_env\Lib\site-packages\transformers\models\idefics2\modeling_idefics2.py
ENCODING: utf-8
```py
# coding=utf-8
# Copyright 2024 the HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""PyTorch Idefics2 model."""
from dataclasses import dataclass
from typing import Callable, List, Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import CrossEntropyLoss
from ...activations import ACT2FN
from ...cache_utils import Cache, DynamicCache
from ...generation import GenerationMixin
from ...modeling_attn_mask_utils import _prepare_4d_attention_mask
from ...modeling_outputs import BaseModelOutput, ModelOutput
from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
from ...utils import (
add_start_docstrings,
add_start_docstrings_to_model_forward,
logging,
replace_return_docstrings,
)
from ...utils.deprecation import deprecate_kwarg
from ..auto import AutoModel
from .configuration_idefics2 import Idefics2Config, Idefics2PerceiverConfig, Idefics2VisionConfig
logger = logging.get_logger(__name__)
_CONFIG_FOR_DOC = "Idefics2Config"
@dataclass
class Idefics2BaseModelOutputWithPast(ModelOutput):
"""
Base class for Idefics2 model's outputs that may also contain a past key/values (to speed up sequential decoding).
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the model.
If `past_key_values` is used only the last hidden-state of the sequences of shape `(batch_size, 1,
hidden_size)` is output.
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
`(batch_size, num_heads, sequence_length, embed_size_per_head)`) and optionally if
`config.is_encoder_decoder=True` 2 additional tensors of shape `(batch_size, num_heads,
encoder_sequence_length, embed_size_per_head)`.
Contains pre-computed hidden-states (key and values in the self-attention blocks and optionally if
`config.is_encoder_decoder=True` in the cross-attention blocks) that can be used (see `past_key_values`
input) to speed up sequential decoding.
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
image_hidden_states (`tuple(torch.FloatTensor)`, *optional*):
Tuple of `torch.FloatTensor` (one for the output of the image embeddings, `(batch_size, num_images,
sequence_length, hidden_size)`.
image_hidden_states of the model produced by the vision encoder, and optionally by the perceiver
"""
last_hidden_state: Optional[torch.FloatTensor] = None
past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None
image_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
@dataclass
# Copied from transformers.models.idefics.modeling_idefics.IdeficsCausalLMOutputWithPast with Idefics->Idefics2
class Idefics2CausalLMOutputWithPast(ModelOutput):
"""
Base class for Idefics2 causal language model (or autoregressive) outputs.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Language modeling loss (for next-token prediction).
logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
`(batch_size, num_heads, sequence_length, embed_size_per_head)`)
Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
`past_key_values` input) to speed up sequential decoding.
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
image_hidden_states (`tuple(torch.FloatTensor)`, *optional*):
Tuple of `torch.FloatTensor` (one for the output of the image embeddings, `(batch_size, num_images,
sequence_length, hidden_size)`.
image_hidden_states of the model produced by the vision encoder, and optionally by the perceiver
"""
loss: Optional[torch.FloatTensor] = None
logits: Optional[torch.FloatTensor] = None
past_key_values: Optional[List[torch.FloatTensor]] = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None
image_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
class Idefics2VisionEmbeddings(nn.Module):
"""
This is a modified version of `siglip.modelign_siglip.SiglipVisionEmbeddings` to enable images of variable
resolution.
The modifications are adapted from [Patch n' Pack: NaViT, a Vision Transformer for any Aspect Ratio and Resolution](https://arxiv.org/abs/2307.06304)
which allows treating images in their native aspect ratio and without the need to resize them to the same
fixed size. In particular, we start from the original pre-trained SigLIP model
(which uses images of fixed-size square images) and adapt it by training on images of variable resolutions.
"""
def __init__(self, config: Idefics2VisionConfig):
super().__init__()
self.embed_dim = config.hidden_size
self.image_size = config.image_size
self.patch_size = config.patch_size
self.patch_embedding = nn.Conv2d(
in_channels=config.num_channels,
out_channels=self.embed_dim,
kernel_size=self.patch_size,
stride=self.patch_size,
padding="valid",
)
self.num_patches_per_side = self.image_size // self.patch_size
self.num_patches = self.num_patches_per_side**2
self.num_positions = self.num_patches
self.position_embedding = nn.Embedding(self.num_positions, self.embed_dim)
def forward(self, pixel_values: torch.FloatTensor, patch_attention_mask: torch.BoolTensor) -> torch.Tensor:
batch_size, _, max_im_h, max_im_w = pixel_values.shape
patch_embeds = self.patch_embedding(pixel_values)
embeddings = patch_embeds.flatten(2).transpose(1, 2)
max_nb_patches_h, max_nb_patches_w = max_im_h // self.patch_size, max_im_w // self.patch_size
boundaries = torch.arange(1 / self.num_patches_per_side, 1.0, 1 / self.num_patches_per_side)
position_ids = torch.full(size=(batch_size, max_nb_patches_h * max_nb_patches_w), fill_value=0)
for batch_idx, p_attn_mask in enumerate(patch_attention_mask):
nb_patches_h = p_attn_mask[:, 0].sum()
nb_patches_w = p_attn_mask[0].sum()
fractional_coords_h = torch.arange(0, 1 - 1e-6, 1 / nb_patches_h)
fractional_coords_w = torch.arange(0, 1 - 1e-6, 1 / nb_patches_w)
bucket_coords_h = torch.bucketize(fractional_coords_h, boundaries, right=True)
bucket_coords_w = torch.bucketize(fractional_coords_w, boundaries, right=True)
pos_ids = (bucket_coords_h[:, None] * self.num_patches_per_side + bucket_coords_w).flatten()
position_ids[batch_idx][p_attn_mask.view(-1).cpu()] = pos_ids
position_ids = position_ids.to(self.position_embedding.weight.device)
embeddings = embeddings + self.position_embedding(position_ids)
return embeddings
def eager_attention_forward(
module: nn.Module,
query: torch.Tensor,
key: torch.Tensor,
value: torch.Tensor,
attention_mask: Optional[torch.Tensor],
scaling: float,
dropout: float = 0.0,
**kwargs,
):
if hasattr(module, "num_key_value_groups"):
key = repeat_kv(key, module.num_key_value_groups)
value = repeat_kv(value, module.num_key_value_groups)
attn_weights = torch.matmul(query, key.transpose(2, 3)) * scaling
if attention_mask is not None:
causal_mask = attention_mask[:, :, :, : key.shape[-2]]
attn_weights = attn_weights + causal_mask
attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
attn_output = torch.matmul(attn_weights, value)
attn_output = attn_output.transpose(1, 2).contiguous()
return attn_output, attn_weights
# Copied from transformers.models.siglip.modeling_siglip.SiglipAttention with Siglip->Idefics2Vision
class Idefics2VisionAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
# Copied from transformers.models.clip.modeling_clip.CLIPAttention.__init__
def __init__(self, config):
super().__init__()
self.config = config
self.embed_dim = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_heads != self.embed_dim:
raise ValueError(
f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:"
f" {self.num_heads})."
)
self.scale = self.head_dim**-0.5
self.dropout = config.attention_dropout
self.k_proj = nn.Linear(self.embed_dim, self.embed_dim)
self.v_proj = nn.Linear(self.embed_dim, self.embed_dim)
self.q_proj = nn.Linear(self.embed_dim, self.embed_dim)
self.out_proj = nn.Linear(self.embed_dim, self.embed_dim)
# Ignore copy
self.is_causal = False
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = False,
) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
"""Input shape: Batch x Time x Channel"""
batch_size, seq_length, embed_dim = hidden_states.shape
queries = self.q_proj(hidden_states)
keys = self.k_proj(hidden_states)
values = self.v_proj(hidden_states)
queries = queries.view(batch_size, seq_length, self.num_heads, self.head_dim).transpose(1, 2)
keys = keys.view(batch_size, seq_length, self.num_heads, self.head_dim).transpose(1, 2)
values = values.view(batch_size, seq_length, self.num_heads, self.head_dim).transpose(1, 2)
attention_interface: Callable = eager_attention_forward
if self.config._attn_implementation != "eager":
if self.config._attn_implementation == "sdpa" and output_attentions:
logger.warning_once(
"`torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to "
'eager attention. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
)
else:
attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
attn_output, attn_weights = attention_interface(
self,
queries,
keys,
values,
attention_mask,
is_causal=self.is_causal,
scaling=self.scale,
dropout=0.0 if not self.training else self.dropout,
)
attn_output = attn_output.reshape(batch_size, seq_length, embed_dim).contiguous()
attn_output = self.out_proj(attn_output)
if not output_attentions:
attn_weights = None
return attn_output, attn_weights
# Copied from transformers.models.siglip.modeling_siglip.SiglipMLP with Siglip->Idefics2Vision
class Idefics2VisionMLP(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.activation_fn = ACT2FN[config.hidden_act]
self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size)
self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size)
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidden_states = self.fc1(hidden_states)
hidden_states = self.activation_fn(hidden_states)
hidden_states = self.fc2(hidden_states)
return hidden_states
class Idefics2MLP(nn.Module):
def __init__(
self,
hidden_size: int,
intermediate_size: int,
output_size: int,
hidden_act: str,
):
super().__init__()
self.gate_proj = nn.Linear(hidden_size, intermediate_size, bias=False)
self.up_proj = nn.Linear(hidden_size, intermediate_size, bias=False)
self.down_proj = nn.Linear(intermediate_size, output_size, bias=False)
self.act_fn = ACT2FN[hidden_act]
def forward(self, x):
return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
# Copied from transformers.models.siglip.modeling_siglip.SiglipMultiheadAttentionPoolingHead with Siglip->Idefics2
class Idefics2MultiheadAttentionPoolingHead(nn.Module):
"""Multihead Attention Pooling."""
def __init__(self, config: Idefics2VisionConfig):
super().__init__()
self.probe = nn.Parameter(torch.randn(1, 1, config.hidden_size))
self.attention = torch.nn.MultiheadAttention(config.hidden_size, config.num_attention_heads, batch_first=True)
self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
# Ignore copy
self.mlp = Idefics2MLP(
hidden_size=config.hidden_size,
intermediate_size=config.intermediate_size,
hidden_act=config.hidden_act,
output_size=config.hidden_size,
)
def forward(self, hidden_state):
batch_size = hidden_state.shape[0]
probe = self.probe.repeat(batch_size, 1, 1)
hidden_state = self.attention(probe, hidden_state, hidden_state)[0]
residual = hidden_state
hidden_state = self.layernorm(hidden_state)
hidden_state = residual + self.mlp(hidden_state)
return hidden_state[:, 0]
class Idefics2EncoderLayer(nn.Module):
def __init__(self, config: Idefics2VisionConfig):
super().__init__()
self.embed_dim = config.hidden_size
self.self_attn = Idefics2VisionAttention(config)
self.layer_norm1 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
self.mlp = Idefics2VisionMLP(config)
self.layer_norm2 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
# Copied from transformers.models.siglip.modeling_siglip.SiglipEncoderLayer.forward
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: torch.Tensor,
output_attentions: Optional[bool] = False,
) -> Tuple[torch.FloatTensor]:
"""
Args:
hidden_states (`torch.FloatTensor`):
Input to the layer of shape `(batch, seq_len, embed_dim)`.
attention_mask (`torch.FloatTensor`):
Attention mask of shape `(batch, 1, q_len, k_v_seq_len)` where padding elements are indicated by very large negative values.
output_attentions (`bool`, *optional*, defaults to `False`):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
"""
residual = hidden_states
hidden_states = self.layer_norm1(hidden_states)
hidden_states, attn_weights = self.self_attn(
hidden_states=hidden_states,
attention_mask=attention_mask,
output_attentions=output_attentions,
)
hidden_states = residual + hidden_states
residual = hidden_states
hidden_states = self.layer_norm2(hidden_states)
hidden_states = self.mlp(hidden_states)
hidden_states = residual + hidden_states
outputs = (hidden_states,)
if output_attentions:
outputs += (attn_weights,)
return outputs
# Copied from transformers.models.siglip.modeling_siglip.SiglipEncoder with Siglip->Idefics2
class Idefics2Encoder(nn.Module):
"""
Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a
[`Idefics2EncoderLayer`].
Args:
config: Idefics2Config
"""
def __init__(self, config: Idefics2Config):
super().__init__()
self.config = config
self.layers = nn.ModuleList([Idefics2EncoderLayer(config) for _ in range(config.num_hidden_layers)])
self.gradient_checkpointing = False
# Ignore copy
def forward(
self,
inputs_embeds,
attention_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, BaseModelOutput]:
r"""
Args:
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
This is useful if you want more control over how to convert `input_ids` indices into associated vectors
than the model's internal embedding lookup matrix.
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
for more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
encoder_states = () if output_hidden_states else None
all_attentions = () if output_attentions else None
hidden_states = inputs_embeds
for encoder_layer in self.layers:
if output_hidden_states:
encoder_states = encoder_states + (hidden_states,)
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
encoder_layer.__call__,
hidden_states,
attention_mask,
output_attentions,
)
else:
layer_outputs = encoder_layer(
hidden_states,
attention_mask,
output_attentions=output_attentions,
)
hidden_states = layer_outputs[0]
if output_attentions:
all_attentions = all_attentions + (layer_outputs[1],)
if output_hidden_states:
encoder_states = encoder_states + (hidden_states,)
if not return_dict:
return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None)
return BaseModelOutput(
last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions
)
IDEFICS2_START_DOCSTRING = r"""
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.
Parameters:
config ([`Idefics2Config`] or [`Idefics2VisionConfig`]):
Model configuration class with all the parameters of the model. Initializing with a config file does not
load the weights associated with the model, only the configuration. Check out the
[`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
@add_start_docstrings(
"The bare Idefics2 Model outputting raw hidden-states without any specific head on top.",
IDEFICS2_START_DOCSTRING,
)
class Idefics2PreTrainedModel(PreTrainedModel):
config_class = Idefics2Config
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = ["Idefics2VisionAttention", "Idefics2MLP", "Idefics2PerceiverLayer", "Idefics2DecoderLayer"]
_skip_keys_device_placement = "past_key_values"
_supports_flash_attn_2 = True
_supports_sdpa = True
_supports_flex_attn = True
_supports_cache_class = True
def _init_weights(self, module):
std = (
self.config.initializer_range
if hasattr(self.config, "initializer_range")
else self.config.get_text_config().initializer_range
)
if hasattr(module, "class_embedding"):
module.class_embedding.data.normal_(mean=0.0, std=std)
if isinstance(module, (nn.Linear, nn.Conv2d)):
module.weight.data.normal_(mean=0.0, std=std)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.Embedding):
module.weight.data.normal_(mean=0.0, std=std)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
IDEFICS2_INPUTS_DOCSTRING = r"""
Args:
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)):
The tensors corresponding to the input images. Pixel values can be obtained using
[`AutoImageProcessor`]. See [`CLIPImageProcessor.__call__`] for details ([]`LlavaProcessor`] uses
[`CLIPImageProcessor`] for processing images).
pixel_attention_mask (`torch.Tensor` of shape `(batch_size, image_size, image_size)`, *optional*):
Mask to avoid performing attention on padding pixel indices.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
@add_start_docstrings(
"""Idefics2 vision encoder model that returnss raw image embeddings.""",
IDEFICS2_START_DOCSTRING,
)
class Idefics2VisionTransformer(Idefics2PreTrainedModel):
config_class = Idefics2VisionConfig
_supports_sdpa = True
_supports_flash_attention_2 = True
_supports_flex_attn = True
def __init__(self, config: Idefics2VisionConfig):
super().__init__(config)
embed_dim = config.hidden_size
self.config = config
self.embeddings = Idefics2VisionEmbeddings(config)
self.encoder = Idefics2Encoder(config)
self.post_layernorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2"
def get_input_embeddings(self):
return self.embeddings
def set_input_embeddings(self, value):
self.embeddings = value
def forward(
self,
pixel_values,
patch_attention_mask: Optional[torch.BoolTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, BaseModelOutput]:
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
batch_size = pixel_values.size(0)
if patch_attention_mask is None:
patch_size = self.config.patch_size
patch_attention_mask = torch.ones(
(
batch_size,
pixel_values.size(2) // patch_size,
pixel_values.size(3) // patch_size,
)
)
patch_attention_mask = patch_attention_mask.to(dtype=torch.bool, device=pixel_values.device)
hidden_states = self.embeddings(pixel_values=pixel_values, patch_attention_mask=patch_attention_mask)
patch_attention_mask = patch_attention_mask.view(batch_size, -1)
# The call to `_upad_input` in `_flash_attention_forward` is expensive
# So when the `patch_attention_mask` is full of 1s (i.e. attending to the whole sequence),
# avoiding passing the attention_mask, which is equivalent to attending to the full sequence
if not torch.any(~patch_attention_mask):
patch_attention_mask = None
elif not self._use_flash_attention_2:
patch_attention_mask = _prepare_4d_attention_mask(patch_attention_mask, hidden_states.dtype)
encoder_outputs = self.encoder(
inputs_embeds=hidden_states,
attention_mask=patch_attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
last_hidden_state = encoder_outputs[0]
last_hidden_state = self.post_layernorm(last_hidden_state)
if not return_dict:
return (last_hidden_state,) + encoder_outputs[1:]
return BaseModelOutput(
last_hidden_state=last_hidden_state,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
)
# Copied from transformers.models.llama.modeling_llama.repeat_kv
def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
"""
This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
"""
batch, num_key_value_heads, slen, head_dim = hidden_states.shape
if n_rep == 1:
return hidden_states
hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
# Copied from transformers.models.llama.modeling_llama.LlamaRMSNorm with Llama->Idefics2
class Idefics2RMSNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-6):
"""
Idefics2RMSNorm is equivalent to T5LayerNorm
"""
super().__init__()
self.weight = nn.Parameter(torch.ones(hidden_size))
self.variance_epsilon = eps
def forward(self, hidden_states):
input_dtype = hidden_states.dtype
hidden_states = hidden_states.to(torch.float32)
variance = hidden_states.pow(2).mean(-1, keepdim=True)
hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
return self.weight * hidden_states.to(input_dtype)
def extra_repr(self):
return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}"
class Idefics2PerceiverAttention(nn.Module):
def __init__(self, config, layer_idx: Optional[int] = None) -> None:
"""Perceiver Cross-Attention Module --> let long-form inputs be `context`, resampled embeddings be `latents`"""
super().__init__()
self.config = config
self.layer_idx = None
self.hidden_size = config.hidden_size
self.num_heads = config.resampler_n_heads
self.head_dim = config.resampler_head_dim
self.num_key_value_heads = config.num_key_value_heads
self.num_key_value_groups = self.num_heads // self.num_key_value_heads
self.attention_dropout = config.attention_dropout
self.scaling = self.head_dim**-0.5
self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
self.is_causal = False
def forward(
self,
latents: torch.Tensor,
context: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
output_attentions: bool = False,
use_cache: bool = False,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
"""
Runs Perceiver Self-Attention, with special (context, latents) appended along the `seq` dimension!
Args:
latents (`torch.Tensor`): Tensor of shape [bsz, n_latents, embed_dim] representing fixed length latents to compress to.
context (`torch.Tensor`): Tensor of shape [bsz, seq, embed_dim] representing long-form context to resample.
attention_mask (`torch.Tensor`, *optional*): Tensor of shape [bsz, 1, seq, n_latents] representing attention mask.
position_ids (`torch.LongTensor`, *optional*): Tensor of shape [bsz, seq] representing position indices of each input token.
past_key_value (`Tuple[torch.Tensor]`, *optional*): Tuple of tensors containing cached key and value states.
output_attentions (`bool`, *optional*, defaults to `False`): Whether to return attention weights.
use_cache (`bool`, *optional*, defaults to `False`): Whether to use past_key_value for caching.
"""
bsz, q_len, _ = latents.size()
kv_seq_len = q_len + context.size()[1]
hidden_states = torch.concat([context, latents], dim=-2)
queries = self.q_proj(latents)
keys = self.k_proj(hidden_states)
values = self.v_proj(hidden_states)
queries = queries.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
keys = keys.view(bsz, kv_seq_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
values = values.view(bsz, kv_seq_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
past_key_value = getattr(self, "past_key_value", past_key_value)
if past_key_value is not None:
keys, values = past_key_value.update(keys, values, self.layer_idx)
attention_interface: Callable = eager_attention_forward
if self.config._attn_implementation != "eager":
if self.config._attn_implementation == "sdpa" and output_attentions:
logger.warning_once(
"`torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to "
'eager attention. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
)
else:
attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
attn_output, attn_weights = attention_interface(
self,
queries,
keys,
values,
attention_mask,
is_causal=self.is_causal,
scaling=self.scaling,
dropout=0.0 if not self.training else self.attention_dropout,
)
attn_output = attn_output.reshape(bsz, q_len, self.num_heads * self.head_dim)
attn_output = self.o_proj(attn_output)
if not output_attentions:
attn_weights = None
return attn_output, attn_weights, past_key_value
class Idefics2PerceiverLayer(nn.Module):
def __init__(self, config, layer_idx: int):
super().__init__()
self.hidden_size = config.hidden_size
self.n_latents = config.resampler_n_latents
self.depth = config.resampler_depth
self.rms_norm_eps = config.rms_norm_eps
self.input_latents_norm = Idefics2RMSNorm(self.hidden_size, eps=self.rms_norm_eps)
self.input_context_norm = Idefics2RMSNorm(self.hidden_size, eps=self.rms_norm_eps)
self.self_attn = Idefics2PerceiverAttention(config, layer_idx=layer_idx)
self.post_attention_layernorm = Idefics2RMSNorm(self.hidden_size, eps=self.rms_norm_eps)
self.mlp = Idefics2MLP(
hidden_size=config.hidden_size,
intermediate_size=config.hidden_size * 4,
output_size=config.hidden_size,
hidden_act=config.hidden_act,
)
def forward(
self,
latents: torch.Tensor,
context: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
output_attentions: Optional[bool] = False,
use_cache: Optional[bool] = False,
**kwargs,
) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
"""
Args:
latents (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
context (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
`(batch, sequence_length)` where padding elements are indicated by 0.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
(see `past_key_values`).
past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
"""
residual = latents
latents = self.input_latents_norm(latents)
context = self.input_context_norm(context)
latents, self_attn_weights, present_key_value = self.self_attn(
latents=latents,
context=context,
attention_mask=attention_mask,
)
latents = residual + latents
residual = latents
latents = self.post_attention_layernorm(latents)
latents = self.mlp(latents)
latents = residual + latents
outputs = (latents,)
if output_attentions:
outputs += (self_attn_weights,)
if use_cache:
outputs += (present_key_value,)
return outputs
IDEFICS2_INPUTS_DOCSTRING = r"""
Args:
context (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_dim)`):
The hidden states of the image after vision encoder and modality projection.
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
"""
@add_start_docstrings(
"Idefics2 perceiver resampler model that performs `depth` blocks of cross-attention with a fixed ",
"`n_latents` inputs to decrease embedding sequence length. The Resampler acts as a form of learned pooling and ",
"is derived from [Perceiver: General Perception with Iterative Attention](https://arxiv.org/abs/2103.03206)",
IDEFICS2_START_DOCSTRING,
)
class Idefics2PerceiverResampler(Idefics2PreTrainedModel):
config_class = Idefics2PerceiverConfig
_supports_sdpa = True
_supports_flash_attention_2 = True
_supports_flex_attn = True
def __init__(self, config) -> None:
super().__init__(config)
self.hidden_size = config.hidden_size
self.hidden_act = config.hidden_act
self.n_latents = config.resampler_n_latents
self.depth = config.resampler_depth
self.rms_norm_eps = config.rms_norm_eps
# Create Latents for Perceiver
self.latents = nn.Parameter(torch.ones(self.n_latents, self.hidden_size))
# Create Transformer Blocks
self.layers = nn.ModuleList([Idefics2PerceiverLayer(config, idx) for idx in range(self.depth)])
self.norm = Idefics2RMSNorm(self.hidden_size, eps=self.rms_norm_eps)
self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2"
def forward(
self,
context: torch.Tensor,
attention_mask: torch.Tensor,
) -> torch.Tensor:
# seq embed -> bsz seq embed
latents = self.latents.unsqueeze(0).expand((context.shape[0], *self.latents.size()))
latent_attention_mask = torch.ones(
(attention_mask.size(0), latents.size(1)), dtype=attention_mask.dtype, device=attention_mask.device
)
attention_mask = torch.cat([attention_mask, latent_attention_mask], dim=-1)
attention_mask = (
_prepare_4d_attention_mask(attention_mask, latents.dtype, tgt_len=self.n_latents)
if not self._use_flash_attention_2
else attention_mask
)
compressed_context = latents
for perceiver_layer in self.layers:
layer_outputs = perceiver_layer(
compressed_context,
context,
attention_mask=attention_mask,
position_ids=None,
past_key_value=None,
output_attentions=False,
use_cache=False,
)
compressed_context = layer_outputs[0]
compressed_context = self.norm(compressed_context)
return compressed_context
class Idefics2Connector(nn.Module):
def __init__(self, config):
super().__init__()
self.modality_projection = Idefics2MLP(
hidden_size=config.vision_config.hidden_size,
intermediate_size=config.text_config.intermediate_size,
output_size=config.text_config.hidden_size,
hidden_act=config.text_config.hidden_act,
)
self.perceiver_resampler = Idefics2PerceiverResampler._from_config(config.perceiver_config)
def forward(self, image_hidden_states, attention_mask):
image_hidden_states = self.modality_projection(image_hidden_states)
image_hidden_states = self.perceiver_resampler(context=image_hidden_states, attention_mask=attention_mask)
return image_hidden_states
IDEFICS2_INPUTS_DOCSTRING = r"""
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
it.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
`past_key_values`).
If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
information on the default strategy.
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
config.n_positions - 1]`. [What are position IDs?](../glossary#position-ids)
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
`(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
`(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
`decoder_input_ids` of shape `(batch_size, sequence_length)`.
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
model's internal embedding lookup matrix.
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)):
The tensors corresponding to the input images. Pixel values can be obtained using
[`AutoImageProcessor`]. See [`CLIPImageProcessor.__call__`] for details ([]`LlavaProcessor`] uses
[`CLIPImageProcessor`] for processing images).
pixel_attention_mask (`torch.Tensor` of shape `(batch_size, image_size, image_size)`, *optional*):
Mask to avoid performing attention on padding pixel indices.
image_hidden_states (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`):
The hidden states of the image encoder after modality projection and perceiver resampling.
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
`past_key_values`).
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
Indices depicting the position of the input sequence tokens in the sequence. Contrarily to `position_ids`,
this tensor is not affected by padding. It is used to update the cache in the correct position and to infer
the complete sequence length.
"""
@add_start_docstrings(
"""Idefics2 model consisting of a SIGLIP vision encoder and Mistral language decoder""",
IDEFICS2_START_DOCSTRING,
)
class Idefics2Model(Idefics2PreTrainedModel):
def __init__(self, config: Idefics2Config):
super().__init__(config)
self.padding_idx = self.config.text_config.pad_token_id
self.vocab_size = self.config.text_config.vocab_size
self.vision_model = Idefics2VisionTransformer._from_config(config.vision_config)
self.connector = Idefics2Connector(config)
self.text_model = AutoModel.from_config(config.text_config)
self.image_seq_len = config.perceiver_config.resampler_n_latents
self.image_token_id = self.config.image_token_id
self._use_flash_attention_2 = config.text_config._attn_implementation == "flash_attention_2"
self.post_init()
def enable_input_require_grads(self):
"""
Enables the gradients for the input embeddings.
This is useful for lora when using gradient checkpointing.
c.f. https://github.com/huggingface/peft/issues/1402#issuecomment-1913675032
Override to set output.requires_grad = True for both the decoder's and vision model's embeddings.
"""
def get_lowest_module(module):
if len(list(module.children())) == 0:
# If the module has no children, it is a leaf module (e.g., Linear, Conv2d, etc.)
return module
else:
# Recursively call the function on each child module
return get_lowest_module(list(module.children())[0])
def make_inputs_require_grads(module, input, output):
output.requires_grad_(True)
self._text_require_grads_hook = self.get_input_embeddings().register_forward_hook(make_inputs_require_grads)
self._vision_require_grads_hook = get_lowest_module(self.vision_model).register_forward_hook(
make_inputs_require_grads
)
def disable_input_require_grads(self):
self._text_require_grads_hook.remove()
self._vision_require_grads_hook.remove()
def get_input_embeddings(self):
return self.text_model.get_input_embeddings()
def set_input_embeddings(self, value):
self.text_model.set_input_embeddings(value)
def inputs_merger(
self,
input_ids: torch.LongTensor,
inputs_embeds: Optional[torch.Tensor],
image_hidden_states: Optional[torch.Tensor],
):
"""
This method aims at merging the token embeddings with the image hidden states into one single sequence of vectors that are fed to the transformer LM.
The merging happens as follows:
- The text token sequence is: `tok_1 tok_2 tok_3 <fake_token_around_image> <image> <image> ... <image> <fake_token_around_image> tok_4`.
- We get the image hidden states for the image through the vision encoder (and potentially the perceiver), and that hidden state is then projected into the text embedding space.
We thus have a sequence of image hidden states of size (1, image_seq_len, hidden_dim), where 1 is for batch_size of 1 image and hidden_dim is the hidden_dim of the LM transformer.
- The merging happens so that we obtain the following sequence: `vector_tok_1 vector_tok_2 vector_tok_3 vector_fake_tok_around_image {sequence of image_seq_len image hidden states} vector_fake_toke_around_image vector_tok_4`. That sequence is fed to the LM.
- To fit the format of that sequence, `input_ids`, `input_embeds`, `attention_mask` are all 3 adapted to insert the image hidden states.
"""
num_images, _, vision_hidden_size = image_hidden_states.shape
special_image_token_mask = input_ids == self.image_token_id
new_inputs_embeds = inputs_embeds.clone()
reshaped_image_hidden_states = image_hidden_states.view(-1, vision_hidden_size)
new_inputs_embeds[special_image_token_mask] = reshaped_image_hidden_states.to(new_inputs_embeds.device)
return new_inputs_embeds
@add_start_docstrings_to_model_forward(
"""
Inputs fed to the model can have an arbitrary number of images. To account for this, pixel_values fed to
the model have image padding -> (batch_size, max_num_images, 3, max_heights, max_widths) where
max_num_images is the maximum number of images among the batch_size samples in the batch.
Padding images are not needed beyond padding the pixel_values at the entrance of the model.
For efficiency, we only pass through the vision_model's forward the real images by
discarding the padding images i.e. pixel_values of size (image_batch_size, 3, height, width) where
image_batch_size would be 7 when num_images_per_sample=[1, 3, 1, 2] and max_num_images would be 3.
""",
IDEFICS2_INPUTS_DOCSTRING,
)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
pixel_values: Optional[torch.FloatTensor] = None,
pixel_attention_mask: Optional[torch.BoolTensor] = None,
image_hidden_states: Optional[torch.FloatTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
cache_position: Optional[torch.LongTensor] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, Idefics2BaseModelOutputWithPast]:
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
use_cache = use_cache if use_cache is not None else self.config.use_cache
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if self.training and self.text_model.gradient_checkpointing and use_cache:
logger.warning_once(
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
)
use_cache = False
# retrieve input_ids and inputs_embeds
if input_ids is not None:
batch_size, seq_length = input_ids.shape
elif inputs_embeds is not None:
batch_size, seq_length, _ = inputs_embeds.shape
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
past_seen_tokens = 0
# kept for BC (non `Cache` `past_key_values` inputs)
return_legacy_cache = False
if use_cache:
if not isinstance(past_key_values, Cache):
return_legacy_cache = True
if past_key_values is None:
past_key_values = DynamicCache()
else:
past_key_values = DynamicCache.from_legacy_cache(past_key_values)
logger.warning_once(
"We detected that you are passing `past_key_values` as a tuple of tuples. This is deprecated and "
"will be removed in v4.47. Please convert your cache or use an appropriate `Cache` class "
"(https://huggingface.co/docs/transformers/kv_cache#legacy-cache-format)"
)
past_seen_tokens = past_key_values.get_seq_length()
if inputs_embeds is not None and input_ids is None and past_seen_tokens == 0:
raise ValueError("When first calling the model, if input_embeds are passed, input_ids should not be None.")
if inputs_embeds is None:
inputs_embeds = self.text_model.get_input_embeddings()(input_ids)
# START VISUAL INPUTS INTEGRATION
if pixel_values is not None and image_hidden_states is not None:
raise ValueError("You cannot specify both pixel_values and image_hidden_states at the same time")
elif pixel_values is not None:
batch_size, num_images, num_channels, height, width = pixel_values.shape
pixel_values = pixel_values.to(dtype=self.dtype) # fp16 compatibility
pixel_values = pixel_values.view(batch_size * num_images, *pixel_values.shape[2:])
# Remove padding images - padding images are full 0.
nb_values_per_image = pixel_values.shape[1:].numel()
real_images_inds = (pixel_values == 0.0).sum(dim=(-1, -2, -3)) != nb_values_per_image
pixel_values = pixel_values[real_images_inds].contiguous()
# Handle the vision attention mask
if pixel_attention_mask is None:
pixel_attention_mask = torch.ones(
size=(pixel_values.size(0), pixel_values.size(2), pixel_values.size(3)),
dtype=torch.bool,
device=pixel_values.device,
)
else:
# Remove padding images from the mask/pP p
pixel_attention_mask = pixel_attention_mask.view(
batch_size * num_images, *pixel_attention_mask.shape[2:]
)
pixel_attention_mask = pixel_attention_mask[real_images_inds].contiguous()
patch_size = self.config.vision_config.patch_size
patches_subgrid = pixel_attention_mask.unfold(dimension=1, size=patch_size, step=patch_size)
patches_subgrid = patches_subgrid.unfold(dimension=2, size=patch_size, step=patch_size)
patch_attention_mask = (patches_subgrid.sum(dim=(-1, -2)) == patch_size * patch_size).bool()
# Get sequence from the vision encoder
image_hidden_states = self.vision_model(
pixel_values=pixel_values,
patch_attention_mask=patch_attention_mask,
).last_hidden_state
# Modality projection & resampling
image_hidden_states = self.connector(
image_hidden_states, attention_mask=patch_attention_mask.view(pixel_values.size(0), -1)
)
elif image_hidden_states is not None:
image_hidden_states = image_hidden_states.to(dtype=self.dtype, device=input_ids.device)
if past_seen_tokens == 0 and inputs_embeds is not None and image_hidden_states is not None:
# When we generate, we don't want to replace the potential image_token_id that we generated by images
# that simply don't exist
inputs_embeds = self.inputs_merger(
input_ids=input_ids,
inputs_embeds=inputs_embeds,
image_hidden_states=image_hidden_states,
)
outputs = self.text_model(
inputs_embeds=inputs_embeds,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
cache_position=cache_position,
return_dict=return_dict,
)
if return_legacy_cache and use_cache:
outputs.past_key_values = outputs.past_key_values.to_legacy_cache()
if not return_dict:
return tuple(v for v in [*outputs, image_hidden_states] if v is not None)
return Idefics2BaseModelOutputWithPast(
last_hidden_state=outputs.last_hidden_state,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
image_hidden_states=image_hidden_states,
)
@add_start_docstrings(
"""The Idefics2 Model with a language modeling head. It is made up a SigLIP vision encoder, with a language modeling head on top. """,
IDEFICS2_START_DOCSTRING,
)
class Idefics2ForConditionalGeneration(Idefics2PreTrainedModel, GenerationMixin):
_tied_weights_keys = ["lm_head.weight"]
def __init__(self, config):
super().__init__(config)
self.model = Idefics2Model(config)
self.image_token_id = self.config.image_token_id
self.lm_head = nn.Linear(config.text_config.hidden_size, config.text_config.vocab_size, bias=False)
self.vocab_size = config.text_config.vocab_size
# Initialize weights and apply final processing
self.post_init()
def enable_input_require_grads(self):
"""
Enables the gradients for the input embeddings. This is useful for fine-tuning adapter weights while keeping
the model weights fixed.
"""
def make_inputs_require_grads(module, input, output):
output.requires_grad_(True)
self._text_require_grads_hook = self.get_input_embeddings().register_forward_hook(make_inputs_require_grads)
self._vision_require_grads_hook = self.model.vision_model.get_input_embeddings().register_forward_hook(
make_inputs_require_grads
)
def disable_input_require_grads(self):
self._text_require_grads_hook.remove()
self._vision_require_grads_hook.remove()
def get_input_embeddings(self):
return self.model.text_model.get_input_embeddings()
def set_input_embeddings(self, value):
self.model.text_model.set_input_embeddings(value)
def get_output_embeddings(self):
return self.lm_head
def set_output_embeddings(self, new_embeddings):
self.lm_head = new_embeddings
@deprecate_kwarg("num_logits_to_keep", version="4.50", new_name="logits_to_keep")
@add_start_docstrings_to_model_forward(IDEFICS2_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=Idefics2CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
pixel_values: Optional[torch.FloatTensor] = None,
pixel_attention_mask: Optional[torch.BoolTensor] = None,
image_hidden_states: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
cache_position: Optional[torch.LongTensor] = None,
logits_to_keep: Union[int, torch.Tensor] = 0,
) -> Union[Tuple, Idefics2CausalLMOutputWithPast]:
r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
config.vocab_size]` or `model.image_token_id` (where `model` is your instance of `Idefics2ForConditionalGeneration`).
Tokens with indices set to `model.image_token_id` are ignored (masked), the loss is only
computed for the tokens with labels in `[0, ..., config.vocab_size]`.
logits_to_keep (`int` or `torch.Tensor`, *optional*):
If an `int`, compute logits for the last `logits_to_keep` tokens. If `0`, calculate logits for all
`input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that
token can save memory, which becomes pretty significant for long sequences or large vocabulary size.
If a `torch.Tensor`, must be 1D corresponding to the indices to keep in the sequence length dimension.
This is useful when using packed tensor format (single dimension for batch and sequence length).
Returns:
Example:
```python
>>> import requests
>>> import torch
>>> from PIL import Image
>>> from io import BytesIO
>>> from transformers import AutoProcessor, AutoModelForVision2Seq
>>> from transformers.image_utils import load_image
>>> # Note that passing the image urls (instead of the actual pil images) to the processor is also possible
>>> image1 = load_image("https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg")
>>> image2 = load_image("https://cdn.britannica.com/59/94459-050-DBA42467/Skyline-Chicago.jpg")
>>> image3 = load_image("https://cdn.britannica.com/68/170868-050-8DDE8263/Golden-Gate-Bridge-San-Francisco.jpg")
>>> processor = AutoProcessor.from_pretrained("HuggingFaceM4/idefics2-8b-base")
>>> model = AutoModelForVision2Seq.from_pretrained("HuggingFaceM4/idefics2-8b-base", device_map="auto")
>>> BAD_WORDS_IDS = processor.tokenizer(["<image>", "<fake_token_around_image>"], add_special_tokens=False).input_ids
>>> EOS_WORDS_IDS = [processor.tokenizer.eos_token_id]
>>> # Create inputs
>>> prompts = [
... "<image>In this image, we can see the city of New York, and more specifically the Statue of Liberty.<image>In this image,",
... "In which city is that bridge located?<image>",
... ]
>>> images = [[image1, image2], [image3]]
>>> inputs = processor(images=images, text=prompts, padding=True, return_tensors="pt").to("cuda")
>>> # Generate
>>> generated_ids = model.generate(**inputs, bad_words_ids=BAD_WORDS_IDS, max_new_tokens=20)
>>> generated_texts = processor.batch_decode(generated_ids, skip_special_tokens=True)
>>> print(generated_texts)
['In this image, we can see the city of New York, and more specifically the Statue of Liberty. In this image, we can see the city of New York, and more specifically the Statue of Liberty.\n\n', 'In which city is that bridge located?\n\nThe bridge is located in the city of Pittsburgh, Pennsylvania.\n\n\nThe bridge is']
```"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
outputs = self.model(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
pixel_values=pixel_values,
pixel_attention_mask=pixel_attention_mask,
image_hidden_states=image_hidden_states,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
cache_position=cache_position,
return_dict=return_dict,
)
hidden_states = outputs[0]
# Only compute necessary logits, and do not upcast them to float if we are not computing the loss
slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
logits = self.lm_head(hidden_states[:, slice_indices, :])
loss = None
if labels is not None:
# Upcast to float if we need to compute the loss to avoid potential precision issues
logits = logits.float()
labels = labels.to(logits.device)
# Shift so that tokens < n predict n
if attention_mask is not None:
# we use the input attention mask to shift the logits and labels, because it is 2D.
# we also crop attn mask in case it is longer, which happens in PrefixTuning with peft
shift_attention_mask = attention_mask[:, -(logits.shape[1] - 1) :].to(logits.device)
shift_logits = logits[..., :-1, :][shift_attention_mask != 0].contiguous()
shift_labels = labels[..., 1:][shift_attention_mask != 0].contiguous()
else:
shift_logits = logits[..., :-1, :].contiguous()
shift_labels = labels[..., 1:].contiguous()
# Flatten the tokens
loss_fct = CrossEntropyLoss()
loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))
if not return_dict:
output = (logits,) + outputs[1:]
return (loss,) + output if loss is not None else output
return Idefics2CausalLMOutputWithPast(
loss=loss,
logits=logits,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
image_hidden_states=outputs.image_hidden_states,
)
def prepare_inputs_for_generation(
self,
input_ids,
past_key_values=None,
attention_mask=None,
inputs_embeds=None,
cache_position=None,
pixel_values=None,
pixel_attention_mask=None,
image_hidden_states=None,
logits_to_keep=None,
**kwargs,
):
# Overwritten -- there are mutually exclusive inputs (if the logic to make `image_hidden_states` take
# precedence is moved to the model, we can remove this fn)
model_inputs = super().prepare_inputs_for_generation(
input_ids,
past_key_values=past_key_values,
attention_mask=attention_mask,
inputs_embeds=inputs_embeds,
cache_position=cache_position,
pixel_values=pixel_values,
pixel_attention_mask=pixel_attention_mask,
image_hidden_states=image_hidden_states,
logits_to_keep=logits_to_keep,
**kwargs,
)
# if `inputs_embeds` are passed, we only want to use them in the 1st generation step
# but IDEFICS requires both ids and embeds to be present
if inputs_embeds is not None and cache_position[0] == 0:
model_inputs["input_ids"] = input_ids
if image_hidden_states is not None:
model_inputs["pixel_values"] = None
model_inputs["pixel_attention_mask"] = None
return model_inputs
def _update_model_kwargs_for_generation(self, outputs, model_kwargs, is_encoder_decoder, **kwargs):
model_kwargs = super()._update_model_kwargs_for_generation(
outputs=outputs,
model_kwargs=model_kwargs,
is_encoder_decoder=is_encoder_decoder,
**kwargs,
)
# Get the precomputed image_hidden_states
model_kwargs["image_hidden_states"] = outputs.image_hidden_states
return model_kwargs
@staticmethod
# Copied from transformers.models.opt.modeling_opt.OPTForCausalLM._reorder_cache
def _reorder_cache(past_key_values, beam_idx):
reordered_past = ()
for layer_past in past_key_values:
reordered_past += (
tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
)
return reordered_past
__all__ = ["Idefics2ForConditionalGeneration", "Idefics2PreTrainedModel", "Idefics2Model"]
```
|
===========================================================================================================================================
SOURCE CODE FILE: processing_idefics2.py
LINES: 1
SIZE: 12.45 KB
PATH: scripts\freecad_env\Lib\site-packages\transformers\models\idefics2\processing_idefics2.py
ENCODING: utf-8
```py
# coding=utf-8
# Copyright 2024 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Processor class for IDEFICS2.
"""
from itertools import accumulate
from typing import TYPE_CHECKING, List, Optional, Union
from ...feature_extraction_utils import BatchFeature
from ...image_utils import ImageInput, is_valid_image, load_image
from ...processing_utils import (
ImagesKwargs,
ProcessingKwargs,
ProcessorMixin,
Unpack,
_validate_images_text_input_order,
)
from ...tokenization_utils_base import AddedToken, TextInput
from ...utils import logging
if TYPE_CHECKING:
from ...tokenization_utils_base import PreTokenizedInput
logger = logging.get_logger(__name__)
def is_url(val) -> bool:
return isinstance(val, str) and val.startswith("http")
def is_image_or_image_url(elem):
return is_url(elem) or is_valid_image(elem)
class Idefics2ImagesKwargs(ImagesKwargs, total=False):
image_seq_len: Optional[int]
class Idefics2ProcessorKwargs(ProcessingKwargs, total=False):
images_kwargs: Idefics2ImagesKwargs
_defaults = {
"text_kwargs": {
"add_special_tokens": True,
"padding": False,
"is_split_into_words": False,
},
"images_kwargs": {},
}
class Idefics2Processor(ProcessorMixin):
r"""
Constructs a IDEFICS2 processor which wraps a LLama tokenizer and IDEFICS2 image processor into a single processor.
[`IdeficsProcessor`] offers all the functionalities of [`Idefics2ImageProcessor`] and [`LlamaTokenizerFast`]. See
the docstring of [`~IdeficsProcessor.__call__`] and [`~IdeficsProcessor.decode`] for more information.
Args:
image_processor (`Idefics2ImageProcessor`):
An instance of [`Idefics2ImageProcessor`]. The image processor is a required input.
tokenizer (`PreTrainedTokenizerBase`, *optional*):
An instance of [`PreTrainedTokenizerBase`]. This should correspond with the model's text model. The tokenizer is a required input.
image_seq_len (`int`, *optional*, defaults to 64):
The length of the image sequence i.e. the number of <image> tokens per image in the input.
This parameter is used to build the string from the input prompt and image tokens and should match the
config.perceiver_config.resampler_n_latents value for the model used.
chat_template (`str`, *optional*): A Jinja template which will be used to convert lists of messages
in a chat into a tokenizable string.
"""
attributes = ["image_processor", "tokenizer"]
valid_kwargs = ["image_seq_len", "chat_template"]
image_processor_class = "Idefics2ImageProcessor"
tokenizer_class = "AutoTokenizer"
def __init__(
self, image_processor, tokenizer=None, image_seq_len: int = 64, chat_template: Optional[str] = None, **kwargs
):
if image_processor is None:
raise ValueError("You need to specify an `image_processor`.")
if tokenizer is None:
raise ValueError("You need to specify a `tokenizer`.")
if not hasattr(tokenizer, "image_token"):
self.fake_image_token = AddedToken("<fake_token_around_image>", normalized=False, special=True)
self.image_token = AddedToken("<image>", normalized=False, special=True)
tokens_to_add = {"additional_special_tokens": [self.fake_image_token, self.image_token]}
tokenizer.add_special_tokens(tokens_to_add)
else:
self.fake_image_token = tokenizer.image_boundary_token
self.image_token = tokenizer.image_token
self.end_of_utterance_token = AddedToken("<end_of_utterance>", normalized=False, special=True)
tokenizer.add_special_tokens({"additional_special_tokens": [self.end_of_utterance_token]})
self.image_seq_len = image_seq_len
super().__init__(image_processor, tokenizer, chat_template=chat_template)
def _extract_images_from_prompts(self, prompts):
prompt_images = []
for prompt in prompts:
images = []
for elem in prompt:
if is_valid_image(elem):
images.append(elem)
elif is_url(elem):
images.append(load_image(elem))
prompt_images.append(images)
return prompt_images
def __call__(
self,
images: Union[ImageInput, List[ImageInput], List[List[ImageInput]]] = None,
text: Union[TextInput, "PreTokenizedInput", List[TextInput], List["PreTokenizedInput"]] = None,
audio=None,
videos=None,
**kwargs: Unpack[Idefics2ProcessorKwargs],
) -> BatchFeature:
"""
Processes the input prompts and returns a BatchEncoding.
Example:
```python
>>> import requests
>>> from transformers import Idefics2Processor
>>> from transformers.image_utils import load_image
>>> processor = Idefics2Processor.from_pretrained("HuggingFaceM4/idefics2-8b", image_seq_len=2)
>>> processor.image_processor.do_image_splitting = False # Force as False to simplify the example
>>> url1 = "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
>>> url2 = "https://cdn.britannica.com/59/94459-050-DBA42467/Skyline-Chicago.jpg"
>>> image1, image2 = load_image(url1), load_image(url2)
>>> images = [[image1], [image2]]
>>> text = [
... "<image>In this image, we see",
... "bla bla bla<image>",
... ]
>>> outputs = processor(images=images, text=text, return_tensors="pt", padding=True)
>>> input_ids = outputs.input_ids
>>> input_tokens = processor.tokenizer.batch_decode(input_ids)
>>> print(input_tokens)
['<s><fake_token_around_image><image><image><fake_token_around_image> In this image, we see', '<s> bla bla bla<fake_token_around_image><image><image><fake_token_around_image>']
```
Args:
images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `List[PIL.Image.Image]`, `List[np.ndarray]`, `List[torch.Tensor]`, *optional*):
The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch
tensor. If is of type `List[ImageInput]`, it's assumed that this is for a single prompt i.e. of batch size 1.
text (`Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]]`, *optional*):
The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
(pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
`is_split_into_words=True` (to lift the ambiguity with a batch of sequences).
Wherever an image token, `<image>` is encountered it is expanded to
`<fake_token_around_image>` + `<image>` * `image_seq_len` * <fake_token_around_image>`.
return_tensors (`Union[str, TensorType]`, *optional*):
If set, will return tensors of a particular framework. See [`PreTrainedTokenizerFast.__call__`] for more
information.
"""
if text is None and images is None:
raise ValueError("You must provide either `text` or `images`.")
# check if images and text inputs are reversed for BC
images, text = _validate_images_text_input_order(images, text)
output_kwargs = self._merge_kwargs(
Idefics2ProcessorKwargs,
tokenizer_init_kwargs=self.tokenizer.init_kwargs,
**kwargs,
)
image_seq_len = output_kwargs["images_kwargs"].pop("image_seq_len", None)
image_seq_len = image_seq_len if image_seq_len is not None else self.image_seq_len
n_images_in_text = []
inputs = BatchFeature()
if text is not None:
if isinstance(text, str):
text = [text]
elif not isinstance(text, list) and not isinstance(text[0], str):
raise ValueError("Invalid input text. Please provide a string, or a list of strings")
# Replace the image token with fake tokens around the expanded image token sequence of length `image_seq_len`
fake_image_token = self.fake_image_token.content
image_token = self.image_token.content
image_str = f"{fake_image_token}{image_token * image_seq_len}{fake_image_token}"
if self.image_processor.do_image_splitting:
# A single image token is split into 4 patches + 1 original image
image_str = image_str * 5
prompt_strings = []
for sample in text:
n_images_in_text.append(sample.count(image_token))
sample = sample.replace(image_token, image_str)
# Remove any double fake tokens if images are adjacent
sample = sample.replace(f"{fake_image_token}{fake_image_token}", f"{fake_image_token}")
prompt_strings.append(sample)
text_inputs = self.tokenizer(prompt_strings, **output_kwargs["text_kwargs"])
inputs.update(text_inputs)
if images is not None:
if is_image_or_image_url(images):
images = [[images]]
elif isinstance(images, (list, tuple)) and is_image_or_image_url(images[0]):
if text is not None:
if sum(n_images_in_text) != len(images):
raise ValueError(
f"The total number of {image_token} tokens in the prompts should be the same as the number of images passed."
f" Found {sum(n_images_in_text)} {image_token} tokens and {len(images)} images."
)
# Reorganize the images to match the prompts
cumsum_images_in_text = [0] + list(accumulate(n_images_in_text))
images = [
images[cumsum_images_in_text[i] : cumsum_images_in_text[i + 1]]
for i in range(len(n_images_in_text))
]
else:
images = [images]
elif (
not isinstance(images, (list, tuple))
and not isinstance(images[0], (list, tuple))
and not is_image_or_image_url(images[0][0])
):
raise ValueError(
"Invalid input images. Please provide a single image or a list of images or a list of list of images."
)
n_images_in_images = [len(sample) for sample in images]
if text is not None and not n_images_in_images == n_images_in_text:
raise ValueError(
f"The number of images in the text {n_images_in_text} and images {n_images_in_images} should be the same."
)
# Load images if they are URLs
images = [[load_image(im) for im in sample] for sample in images]
image_inputs = self.image_processor(images, **output_kwargs["images_kwargs"])
inputs.update(image_inputs)
return inputs
def batch_decode(self, *args, **kwargs):
"""
This method forwards all its arguments to LlamaTokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please
refer to the docstring of this method for more information.
"""
return self.tokenizer.batch_decode(*args, **kwargs)
def decode(self, *args, **kwargs):
"""
This method forwards all its arguments to LlamaTokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to
the docstring of this method for more information.
"""
return self.tokenizer.decode(*args, **kwargs)
@property
def model_input_names(self):
tokenizer_input_names = self.tokenizer.model_input_names
image_processor_input_names = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))
__all__ = ["Idefics2Processor"]
```
|
================================================================================================================================
SOURCE CODE FILE: __init__.py
LINES: 1
SIZE: 1.06 KB
PATH: scripts\freecad_env\Lib\site-packages\transformers\models\idefics3\__init__.py
ENCODING: utf-8
```py
# Copyright 2024 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import _LazyModule
from ...utils.import_utils import define_import_structure
if TYPE_CHECKING:
from .configuration_idefics3 import *
from .image_processing_idefics3 import *
from .modeling_idefics3 import *
from .processing_idefics3 import *
else:
import sys
_file = globals()["__file__"]
sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)
```
|
==============================================================================================================================================
SOURCE CODE FILE: configuration_idefics3.py
LINES: 1
SIZE: 8.40 KB
PATH: scripts\freecad_env\Lib\site-packages\transformers\models\idefics3\configuration_idefics3.py
ENCODING: utf-8
```py
# coding=utf-8
# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Idefics3 model configuration"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING, AutoConfig
logger = logging.get_logger(__name__)
class Idefics3VisionConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`Idefics3VisionModel`]. It is used to instantiate a
Idefics3 vision encoder according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will yield a similar configuration to that of the SigLIP checkpoint
[google/siglip-base-patch16-224](https://huggingface.co/google/siglip-base-patch16-224) used in the Idefics3 model
[HuggingFaceM4/Idefics3-8B-Llama3](https://huggingface.co/HuggingFaceM4/Idefics3-8B-Llama3).
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
hidden_size (`int`, *optional*, defaults to 1152):
Dimensionality of the encoder layers and the pooler layer.
intermediate_size (`int`, *optional*, defaults to 3072):
Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
num_hidden_layers (`int`, *optional*, defaults to 12):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 16):
Number of attention heads for each attention layer in the Transformer encoder.
num_channels (`int`, *optional*, defaults to 3):
Number of channels in the input images.
image_size (`int`, *optional*, defaults to 224):
The size (resolution) of each image.
patch_size (`int`, *optional*, defaults to 32):
The size (resolution) of each patch.
hidden_act (`str` or `function`, *optional*, defaults to `"gelu_pytorch_tanh"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"selu"` and `"gelu_new"` `"quick_gelu"` are supported.
layer_norm_eps (`float`, *optional*, defaults to 1e-06):
The epsilon used by the layer normalization layers.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
Example:
```python
>>> from transformers.models.idefics3.modeling_idefics3 import Idefics3VisionTransformer
>>> from transformers.models.idefics3.configuration_idefics3 import Idefics3VisionConfig
>>> # Initializing a Idefics3VisionConfig with google/siglip-base-patch16-224 style configuration
>>> configuration = Idefics3VisionConfig()
>>> # Initializing a Idefics3VisionTransformer (with random weights) from the google/siglip-base-patch16-224 style configuration
>>> model = Idefics3VisionTransformer(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "idefics3_vision"
base_config_key = "vision_config"
def __init__(
self,
hidden_size=1152,
intermediate_size=3072,
num_hidden_layers=12,
num_attention_heads=16,
num_channels=3,
image_size=224,
patch_size=32,
hidden_act="gelu_pytorch_tanh",
layer_norm_eps=1e-6,
attention_dropout=0.0,
initializer_range=0.02,
**kwargs,
):
super().__init__(**kwargs)
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.num_channels = num_channels
self.patch_size = patch_size
self.image_size = image_size
self.attention_dropout = attention_dropout
self.layer_norm_eps = layer_norm_eps
self.hidden_act = hidden_act
self.initializer_range = initializer_range
class Idefics3Config(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`Idefics3Model`]. It is used to instantiate a
Idefics3 model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will yield a similar configuration to that of the model of the Idefics3
[HuggingFaceM4/Idefics3-8B-Llama3](https://huggingface.co/HuggingFaceM4/Idefics3-8B-Llama3) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should cache the key/value pairs of the attention mechanism. Only
relevant if `config.is_decoder=True`.
image_token_id (`int`, *optional*, defaults to 128257):
The id of the "image" token.
tie_word_embeddings (`bool`, *optional*, defaults to `False`):
Whether or not to tie the word embeddings with the token embeddings.
vision_config (`IdeficsVisionConfig` or `dict`, *optional*, defaults to `IdeficsVisionConfig`):
Custom vision config or dict for the vision tower
text_config (`PretrainedConfig` or `dict`, *optional*, defaults to `LlamaConfig`):
Custom text config or dict for the text model
scale_factor (`int`, *optional*, defaults to 2):
The scale factor for the image encoder.
pad_token_id (`int`, *optional*, defaults to 128002):
The id of the padding token.
Example:
```python
>>> from transformers import Idefics3Model, Idefics3Config
>>> # Initializing configuration
>>> configuration = Idefics3Config()
>>> # Initializing a model from the configuration
>>> model = Idefics3Model(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "idefics3"
sub_configs = {"text_config": AutoConfig, "vision_config": Idefics3VisionConfig}
def __init__(
self,
use_cache=True,
image_token_id=128257,
tie_word_embeddings=False,
vision_config=None,
text_config=None,
scale_factor=2,
pad_token_id=128_002,
**kwargs,
):
self.image_token_id = image_token_id
self.use_cache = use_cache
self.tie_word_embeddings = tie_word_embeddings
if vision_config is None:
self.vision_config = Idefics3VisionConfig()
logger.info("vision_config is None, using default vision config")
elif isinstance(vision_config, dict):
self.vision_config = Idefics3VisionConfig(**vision_config)
elif isinstance(vision_config, Idefics3VisionConfig):
self.vision_config = vision_config
if isinstance(text_config, dict):
text_config["model_type"] = text_config["model_type"] if "model_type" in text_config else "llama"
text_config = CONFIG_MAPPING[text_config["model_type"]](**text_config)
elif text_config is None:
logger.info("text_config is None, using default text config")
text_config = CONFIG_MAPPING["llama"](
rms_norm_eps=1e-5,
pad_token_id=pad_token_id,
tie_word_embeddings=False,
)
self.text_config = text_config
self.scale_factor = scale_factor
super().__init__(**kwargs, pad_token_id=pad_token_id, tie_word_embeddings=tie_word_embeddings)
__all__ = ["Idefics3Config", "Idefics3VisionConfig"]
```
|
=================================================================================================================================================
SOURCE CODE FILE: image_processing_idefics3.py
LINES: 1
SIZE: 40.35 KB
PATH: scripts\freecad_env\Lib\site-packages\transformers\models\idefics3\image_processing_idefics3.py
ENCODING: utf-8
```py
# coding=utf-8
# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import math
from typing import Any, Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import PaddingMode, pad, to_channel_dimension_format, to_pil_image
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
get_image_size,
infer_channel_dimension_format,
is_scaled_image,
make_nested_list_of_images,
to_numpy_array,
valid_images,
validate_preprocess_arguments,
)
from ...utils import TensorType, is_vision_available, logging
logger = logging.get_logger(__name__)
MAX_IMAGE_SIZE = 4096 # 4k resolution as absolute maximum
if is_vision_available():
import PIL
from PIL import Image
def _resize_output_size_rescale_to_max_len(
height: int, width: int, min_len: Optional[int] = 1, max_len: Optional[int] = None
) -> Tuple[int, int]:
"""
Get the output size of the image after resizing given a dictionary specifying the max and min sizes.
Args:
height (`int`):
Height of the input image.
width (`int`):
Width of the input image.
min_len (`int`, *optional*, defaults to 1):
Minimum size of the output image.
max_len (`int`, *optional*, defaults to the maximum size of the image):
Maximum size of the output image.
Returns:
The output size of the image after resizing.
"""
max_len = max(height, width) if max_len is None else max_len
aspect_ratio = width / height
if width >= height:
width = max_len
height = int(width / aspect_ratio)
if height % 2 != 0:
height += 1
elif height > width:
height = max_len
width = int(height * aspect_ratio)
if width % 2 != 0:
width += 1
# Avoid resizing to a size smaller than min_len
height = max(height, min_len)
width = max(width, min_len)
return height, width
def _resize_output_size_scale_below_upper_bound(
height: int, width: int, max_len: Optional[Dict[str, int]] = None
) -> Tuple[int, int]:
"""
Get the output size of the image after resizing given a dictionary specifying the max and min sizes.
Args:
height (`int`):
Height of the input image.
width (`int`):
Width of the input image.
max_len (`Dict[str, int]`, *optional*, defaults to the maximum size of the image):
Defines the maximum dimensions of the image.
Returns:
The output size of the image after resizing.
"""
max_len = max(height, width) if max_len is None else max_len
aspect_ratio = width / height
if width >= height and width > max_len:
width = max_len
height = int(width / aspect_ratio)
elif height > width and height > max_len:
height = max_len
width = int(height * aspect_ratio)
# Avoid resizing to a size smaller than 1
height = max(height, 1)
width = max(width, 1)
return height, width
def get_resize_output_image_size(
image,
resolution_max_side: int,
input_data_format: Optional[Union[str, ChannelDimension]] = None,
) -> Tuple[int, int]:
"""
Get the output size of the image after resizing given a dictionary specifying the max and min sizes.
Args:
image (`np.ndarray`):
Image to resize.
resolution_max_side (`int`):
The longest edge of the image will be resized to this value. The shortest edge will be resized to keep the
input aspect ratio.
input_data_format (`ChannelDimension` or `str`):
The channel dimension format of the input image.
Returns:
The output size of the image after resizing.
"""
height, width = get_image_size(image, channel_dim=input_data_format)
# Find the output size, when rescaling the longest edge to max_len and preserving the aspect ratio
height, width = _resize_output_size_rescale_to_max_len(height, width, max_len=resolution_max_side)
# Find the output size when scaling the image to be below the MAX_IMAGE_SIZE
height, width = _resize_output_size_scale_below_upper_bound(height, width, max_len=MAX_IMAGE_SIZE)
return height, width
# Copied from transformers.models.detr.image_processing_detr.max_across_indices
def max_across_indices(values: Iterable[Any]) -> List[Any]:
"""
Return the maximum value across all indices of an iterable of values.
"""
return [max(values_i) for values_i in zip(*values)]
def get_max_height_width(
images_list: List[List[np.ndarray]], input_data_format: Optional[Union[str, ChannelDimension]] = None
) -> List[int]:
"""
Get the maximum height and width across all images in a batch.
"""
if input_data_format is None:
input_data_format = infer_channel_dimension_format(images_list[0][0], num_channels=(1, 3, 4))
max_height = max_width = float("-inf")
for images in images_list:
for image in images:
height, width = get_image_size(image, channel_dim=input_data_format)
max_height = max(height, max_height)
max_width = max(width, max_width)
return (max_height, max_width)
# Copied from transformers.models.detr.image_processing_detr.make_pixel_mask
def make_pixel_mask(
image: np.ndarray, output_size: Tuple[int, int], input_data_format: Optional[Union[str, ChannelDimension]] = None
) -> np.ndarray:
"""
Make a pixel mask for the image, where 1 indicates a valid pixel and 0 indicates padding.
Args:
image (`np.ndarray`):
Image to make the pixel mask for.
output_size (`Tuple[int, int]`):
Output size of the mask.
"""
input_height, input_width = get_image_size(image, channel_dim=input_data_format)
mask = np.zeros(output_size, dtype=np.int64)
mask[:input_height, :input_width] = 1
return mask
def convert_to_rgb(
image: np.ndarray,
palette: Optional[PIL.ImagePalette.ImagePalette] = None,
data_format: Optional[Union[str, ChannelDimension]] = None,
input_data_format: Optional[Union[str, ChannelDimension]] = None,
) -> ImageInput:
"""
Converts an image to RGB format.
Args:
image (`np.ndarray`):
The image to convert.
palette (List[int], *optional*):
The palette to use if given.
data_format (ChannelDimension or str, *optional*):
The channel dimension format for the output image. If not provided, it will be the same as the input image.
input_data_format (ChannelDimension or str, *optional*):
The channel dimension format of the input image.
"""
if input_data_format is None:
input_data_format = infer_channel_dimension_format(image, num_channels=(1, 3, 4))
# For all transformations, we want to keep the same data format as the input image unless otherwise specified.
# The resized image from PIL will always have channels last, so find the input format first.
data_format = input_data_format if data_format is None else data_format
mode = "P" if palette is not None else None
image = to_pil_image(image, image_mode=mode, input_data_format=input_data_format)
if image.mode == "P" and palette is not None:
image.putpalette(palette)
image_rgba = image.convert("RGBA")
background = Image.new("RGBA", image_rgba.size, (255, 255, 255))
alpha_composite = Image.alpha_composite(background, image_rgba)
alpha_composite = alpha_composite.convert("RGB")
output_array = np.array(alpha_composite)
# The image is always in channels last format after converting from a PIL image
output_array = to_channel_dimension_format(output_array, data_format, input_channel_dim=ChannelDimension.LAST)
return output_array
# FIXME Amy: make a more general crop function that isn't just centre crop
def _crop(
image: np.ndarray,
w1: int,
h1: int,
w2: int,
h2: int,
data_format: Optional[Union[str, ChannelDimension]] = None,
) -> np.ndarray:
if data_format is None:
data_format = infer_channel_dimension_format(image, num_channels=(1, 3, 4))
if data_format == ChannelDimension.FIRST:
image = image[:, h1:h2, w1:w2]
elif data_format == ChannelDimension.LAST:
image = image[h1:h2, w1:w2, :]
else:
raise ValueError("Invalid channel dimension format.")
return image
class Idefics3ImageProcessor(BaseImageProcessor):
r"""
Constructs a Idefics3 image processor.
Args:
do_convert_rgb (`bool`, *optional*, defaults to `True`):
Whether to convert the image to RGB. This is useful if the input image is of a different format e.g. RGBA.
Only has an effect if the input image is in the PIL format.
do_resize (`bool`, *optional*, defaults to `True`):
Whether to resize the image. The longest edge of the image is resized to be <= `size["longest_edge"]`, with the
shortest edge resized to keep the input aspect ratio.
size (`Dict`, *optional*, defaults to `{"longest_edge": 4 * 364}`):
Controls the size of the output image. This is a dictionary containing the key "longest_edge".
The image will be resized such that the longest edge is <= `size["longest_edge"]` and the shortest edge is resized
to keep the input aspect ratio.
resample (`Resampling`, *optional*, defaults to `Resampling.LANCZOS`):
Resampling filter to use when resizing the image.
do_image_splitting (`bool`, *optional*, defaults to `True`):
Whether to split the image into sub-images concatenated with the original image. They are split into patches
such that each patch has a size of `max_image_size["height"]` x `max_image_size["width"]`.
max_image_size (`Dict`, *optional*, defaults to `{"longest_edge": 364}`):
Maximum resolution of the patches of images accepted by the model. This is a dictionary containing the key "longest_edge".
do_rescale (`bool`, *optional*, defaults to `True`):
Whether to rescale the image. If set to `True`, the image is rescaled to have pixel values between 0 and 1.
rescale_factor (`float`, *optional*, defaults to `1/255`):
Rescale factor to rescale the image by if `do_rescale` is set to `True`.
do_normalize (`bool`, *optional*, defaults to `True`):
Whether to normalize the image. If set to `True`, the image is normalized to have a mean of `image_mean` and
a standard deviation of `image_std`.
image_mean (`float` or `List[float]`, *optional*, defaults to `IDEFICS_STANDARD_MEAN`):
Mean to use if normalizing the image. This is a float or list of floats the length of the number of
channels in the image. Can be overridden by the `image_mean` parameter in the `preprocess` method. Can be
overridden by the `image_mean` parameter in the `preprocess` method.
image_std (`float` or `List[float]`, *optional*, defaults to `IDEFICS_STANDARD_STD`):
Standard deviation to use if normalizing the image. This is a float or list of floats the length of the
number of channels in the image. Can be overridden by the `image_std` parameter in the `preprocess` method.
Can be overridden by the `image_std` parameter in the `preprocess` method.
do_pad (`bool`, *optional*, defaults to `True`):
Whether or not to pad the images to the largest height and width in the batch and number of images per
sample in the batch, such that the returned tensor is of shape (batch_size, max_num_images, num_channels, max_height, max_width).
"""
model_input_names = ["pixel_values", "pixel_attention_mask"]
def __init__(
self,
do_convert_rgb: bool = True,
do_resize: bool = True,
size: Dict[str, int] = None,
resample: PILImageResampling = PILImageResampling.LANCZOS,
do_image_splitting: bool = True,
max_image_size: Dict[str, int] = None,
do_rescale: bool = True,
rescale_factor: float = 1 / 255,
do_normalize: bool = True,
image_mean: Optional[Union[float, List[float]]] = None,
image_std: Optional[Union[float, List[float]]] = None,
do_pad: bool = True,
**kwargs,
) -> None:
super().__init__(**kwargs)
self.do_convert_rgb = do_convert_rgb
self.do_resize = do_resize
self.size = size if size is not None else {"longest_edge": 4 * 364}
self.resample = resample
self.do_image_splitting = do_image_splitting
self.max_image_size = max_image_size if max_image_size is not None else {"longest_edge": 364}
self.do_rescale = do_rescale
self.rescale_factor = rescale_factor
self.do_normalize = do_normalize
self.image_mean = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
self.image_std = image_std if image_std is not None else IMAGENET_STANDARD_STD
self.do_pad = do_pad
def resize(
self,
image: np.ndarray,
size: Dict[str, int],
resample: PILImageResampling = PILImageResampling.LANCZOS,
data_format: Optional[Union[str, ChannelDimension]] = None,
input_data_format: Optional[Union[str, ChannelDimension]] = None,
**kwargs,
) -> np.ndarray:
"""
Resize an image. The longest edge of the image is resized to size["longest_edge"], with the shortest edge
resized to keep the input aspect ratio. Can also be used with size["height"] and size["width"].
Args:
image (`np.ndarray`):
Image to resize.
size (`Dict[str, int]`):
Size of the output image.
resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.LANCZOS`):
Resampling filter to use when resizing the image.
data_format (`ChannelDimension` or `str`, *optional*):
The channel dimension format of the output image. If not provided, it will be the same as the input image.
input_data_format (`ChannelDimension` or `str`, *optional*):
The channel dimension format of the input image. If not provided, it will be inferred.
"""
if input_data_format is None:
input_data_format = infer_channel_dimension_format(image, num_channels=(1, 3, 4))
# For all transformations, we want to keep the same data format as the input image unless otherwise specified.
# The resized image from PIL will always have channels last, so find the input format first.
data_format = input_data_format if data_format is None else data_format
if "longest_edge" in size:
size = get_resize_output_image_size(
image, resolution_max_side=size["longest_edge"], input_data_format=input_data_format
)
elif "height" in size and "width" in size:
size = (size["height"], size["width"])
else:
raise ValueError("size must be a dictionary with key 'longest_edge' or 'height' and 'width'.")
image_mode = None
if image.ndim == 2 or image.shape[-1] == 1:
image_mode = "P"
image = to_pil_image(image, image_mode=image_mode, input_data_format=input_data_format)
resized_image = image.resize((size[1], size[0]), resample=resample)
resized_image = np.array(resized_image)
# If the input image channel dimension was of size 1, then it is dropped when converting to a PIL image
# so we need to add it back if necessary.
resized_image = np.expand_dims(resized_image, axis=-1) if resized_image.ndim == 2 else resized_image
# The image is always in channels last format after converting from a PIL image
resized_image = to_channel_dimension_format(
resized_image, data_format, input_channel_dim=ChannelDimension.LAST
)
return resized_image
def split_image(
self,
image,
max_image_size: Dict[str, int],
resample: PILImageResampling = PILImageResampling.LANCZOS,
data_format: Optional[Union[str, ChannelDimension]] = None,
input_data_format: Optional[Union[str, ChannelDimension]] = None,
):
"""
Split an image into squares of side max_image_size and the original image resized to max_image_size.
That means that a single image becomes a sequence of images.
This is a "trick" to spend more compute on each image with no changes in the vision encoder.
1) If one side of the original image is larger than `max_image_size`, resize it to `max_image_size` while preserving the aspect ratio.
2) Divide the resulting image into `ceil(height / max_image_size)` x `ceil(width / max_image_size)`
sub-images of the same size each (image_size, image_size). Typically, 364x364.
3) Returns the list of the crops and the original image, in addition to the number of splits for the height and the width.
Args:
image (`np.ndarray`):
Images to split.
max_image_size (`Dict[str, int]`):
Maximum size of the output image. If the image is larger than this size, it will be split into
patches of this size, and the original image will be concatenated with the patches, resized to max_size.
resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.LANCZOS`):
Resampling filter to use when resizing the image.
data_format (`ChannelDimension` or `str`, *optional*):
The channel dimension format of the output image. If not provided, it will be the same as the input image.
input_data_format (`ChannelDimension` or `str`, *optional*):
The channel dimension format of the input image. If not provided, it will be inferred.
"""
height, width = get_image_size(image, channel_dim=input_data_format)
max_height = max_width = max_image_size["longest_edge"]
frames = []
if height > max_height or width > max_width:
# Calculate the number of splits
num_splits_h = math.ceil(height / max_height)
num_splits_w = math.ceil(width / max_width)
# Calculate the optimal width and height for the sub-images
optimal_height = math.ceil(height / num_splits_h)
optimal_width = math.ceil(width / num_splits_w)
# Iterate through each row and column
for r in range(num_splits_h):
for c in range(num_splits_w):
# Calculate the starting point of the crop
start_x = c * optimal_width
start_y = r * optimal_height
# Calculate the ending point of the crop
end_x = min(start_x + optimal_width, width)
end_y = min(start_y + optimal_height, height)
# Crop the image
cropped_image = _crop(
image,
start_x,
start_y,
end_x,
end_y,
data_format=data_format,
)
frames.append(cropped_image)
# For the global image at the end, we resize it to match the max_image_size, for cpu memory efficiency
global_image_height, global_image_width = max_height, max_width
if height != global_image_height or width != global_image_width:
image = self.resize(
image,
{"height": global_image_height, "width": global_image_width},
resample=resample,
input_data_format=data_format,
)
else:
num_splits_h, num_splits_w = 0, 0
frames.append(image)
return frames, num_splits_h, num_splits_w
def resize_for_vision_encoder(
self,
image: np.ndarray,
vision_encoder_max_size: int,
resample: PILImageResampling = PILImageResampling.LANCZOS,
data_format: Optional[Union[str, ChannelDimension]] = None,
input_data_format: Optional[Union[str, ChannelDimension]] = None,
):
"""
Resize images to be multiples of `vision_encoder_max_size` while preserving the aspect ratio.
Args:
image (`np.ndarray`):
Images to resize.
vision_encoder_max_size (`int`):
Maximum size of the output image. If the image is larger than this size, it will be split into
patches of this size, and the original image will be concatenated with the patches, resized to max_size.
resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.LANCZOS`):
Resampling filter to use when resizing the image.
data_format (`ChannelDimension` or `str`, *optional*):
The channel dimension format of the output image. If not provided, it will be the same as the input image.
input_data_format (`ChannelDimension` or `str`, *optional*):
The channel dimension format of the input image. If not provided, it will be inferred
"""
height, width = get_image_size(image, channel_dim=input_data_format)
aspect_ratio = width / height
if width >= height:
width = math.ceil(width / vision_encoder_max_size) * vision_encoder_max_size
height = int(width / aspect_ratio)
height = math.ceil(height / vision_encoder_max_size) * vision_encoder_max_size
elif height > width:
height = math.ceil(height / vision_encoder_max_size) * vision_encoder_max_size
width = int(height * aspect_ratio)
width = math.ceil(width / vision_encoder_max_size) * vision_encoder_max_size
new_size = {"height": height, "width": width}
return self.resize(
image, size=new_size, resample=resample, input_data_format=input_data_format, data_format=data_format
)
def _pad_image(
self,
image: np.ndarray,
output_size: Tuple[int, int],
constant_values: Union[float, Iterable[float]] = 0,
data_format: Optional[ChannelDimension] = None,
input_data_format: Optional[Union[str, ChannelDimension]] = None,
) -> np.ndarray:
"""
Pad an image with zeros to the given size.
"""
input_height, input_width = get_image_size(image, channel_dim=input_data_format)
output_height, output_width = output_size
pad_bottom = output_height - input_height
pad_right = output_width - input_width
padding = ((0, pad_bottom), (0, pad_right))
padded_image = pad(
image,
padding,
mode=PaddingMode.CONSTANT,
constant_values=constant_values,
data_format=data_format,
input_data_format=input_data_format,
)
return padded_image
def pad(
self,
images: List[np.ndarray],
constant_values: Union[float, Iterable[float]] = 0,
return_pixel_mask: bool = True,
return_tensors: Optional[Union[str, TensorType]] = None,
data_format: Optional[ChannelDimension] = None,
input_data_format: Optional[Union[str, ChannelDimension]] = None,
) -> BatchFeature:
"""
For a list of images, for each images, pads a batch of images to the bottom and right of the image with zeros to the size of largest height and width.
For each sample in the batch, pads the sample with empty images to the max_number of images per sample in the batch. Optionally returns a pixel mask.
Args:
images (`List[np.ndarray]`):
List of list of images to pad. Pads to the largest height and width in the batch.
constant_values (`float` or `Iterable[float]`, *optional*):
The value to use for the padding if `mode` is `"constant"`.
return_pixel_mask (`bool`, *optional*, defaults to `True`):
Whether to return a pixel mask.
return_tensors (`str` or `TensorType`, *optional*):
The type of tensors to return. Can be one of:
- Unset: Return a list of `np.ndarray`.
- `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`.
- `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
- `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`.
- `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`.
data_format (`str` or `ChannelDimension`, *optional*):
The channel dimension format of the image. If not provided, it will be the same as the input image.
input_data_format (`ChannelDimension` or `str`, *optional*):
The channel dimension format of the input image. If not provided, it will be inferred.
"""
pad_size = get_max_height_width(images, input_data_format=input_data_format)
batch_size = len(images)
max_num_images = max(len(images_) for images_ in images)
input_data_format = (
infer_channel_dimension_format(images[0][0], num_channels=(1, 3, 4))
if input_data_format is None
else input_data_format
)
data_format = input_data_format if data_format is None else data_format
if input_data_format == ChannelDimension.FIRST:
n_channels = images[0][0].shape[0]
elif input_data_format == ChannelDimension.LAST:
n_channels = images[0][0].shape[-1]
else:
raise ValueError("Invalid channel dimension format.")
def empty_image(size, input_data_format):
if input_data_format == ChannelDimension.FIRST:
return np.zeros((n_channels, *size), dtype=np.uint8)
elif input_data_format == ChannelDimension.LAST:
return np.zeros((*size, n_channels), dtype=np.uint8)
padded_images_list = [
[empty_image(pad_size, data_format) for _ in range(max_num_images)] for _ in range(batch_size)
]
padded_masks = [[np.zeros(pad_size) for _ in range(max_num_images)] for _ in range(batch_size)]
for batch_idx in range(batch_size):
for sample_idx, image in enumerate(images[batch_idx]):
padded_images_list[batch_idx][sample_idx] = self._pad_image(
image,
pad_size,
constant_values=constant_values,
data_format=data_format,
input_data_format=input_data_format,
)
padded_masks[batch_idx][sample_idx] = make_pixel_mask(
image, output_size=pad_size, input_data_format=input_data_format
)
padded_masks = padded_masks if return_pixel_mask else None
return padded_images_list, padded_masks
def preprocess(
self,
images: ImageInput,
do_convert_rgb: Optional[bool] = None,
do_resize: Optional[bool] = None,
size: Optional[Dict[str, int]] = None,
resample: PILImageResampling = None,
do_image_splitting: Optional[bool] = None,
do_rescale: Optional[bool] = None,
max_image_size: Optional[Dict[str, int]] = None,
rescale_factor: Optional[float] = None,
do_normalize: Optional[bool] = None,
image_mean: Optional[Union[float, List[float]]] = None,
image_std: Optional[Union[float, List[float]]] = None,
do_pad: Optional[bool] = None,
return_tensors: Optional[Union[str, TensorType]] = None,
return_row_col_info: bool = False,
data_format: Optional[ChannelDimension] = ChannelDimension.FIRST,
input_data_format: Optional[Union[str, ChannelDimension]] = None,
):
"""
Preprocess a batch of images.
Args:
images (`ImageInput`):
A list of images to preprocess.
do_convert_rgb (`bool`, *optional*, defaults to `self.do_convert_rgb`):
Whether to convert the image to RGB.
do_resize (`bool`, *optional*, defaults to `self.do_resize`):
Whether to resize the image.
size (`Dict[str, int]`, *optional*, defaults to `self.size`):
Size of the image after resizing. With the longest edge resized to keep the input aspect ratio.
resample (`int`, *optional*, defaults to `self.resample`):
Resampling filter to use if resizing the image. This can be one of the enum `PILImageResampling`. Only
has an effect if `do_resize` is set to `True`.
do_image_splitting (`bool`, *optional*, defaults to `self.do_image_splitting`):
Whether to split the image into sub-images concatenated with the original image. They are split into patches
such that each patch has a size of `max_image_size["height"]` x `max_image_size["width"]`.
max_image_size (`Dict`, *optional*, defaults to `self.max_image_size`):
Maximum resolution of the images. If the image is larger than this size, the image is split into patches.
do_rescale (`bool`, *optional*, defaults to `self.do_rescale`):
Whether to rescale the image.
rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`):
Rescale factor to rescale the image by if `do_rescale` is set to `True`.
do_normalize (`bool`, *optional*, defaults to `self.do_normalize`):
Whether to normalize the image.
image_mean (`float` or `List[float]`, *optional*, defaults to `self.image_mean`):
Image mean to use for normalization. Only has an effect if `do_normalize` is set to `True`.
image_std (`float` or `List[float]`, *optional*, defaults to `self.image_std`):
Image standard deviation to use for normalization. Only has an effect if `do_normalize` is set to
`True`.
do_pad (`bool`, *optional*, defaults to `self.do_pad`):
Whether or not to pad the images to the largest height and width in the batch.
return_tensors (`str` or `TensorType`, *optional*):
The type of tensors to return. Can be one of:
- Unset: Return a list of `np.ndarray`.
- `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`.
- `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
- `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`.
- `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`.
return_row_col_info (`bool`, *optional*, default to `False`):
Whether to return the number of rows and columns of the split images. This is used for the
`Idefics3Processor` to generate prompt strings based on the number of rows and columns.
data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`):
The channel dimension format for the output image. Can be one of:
- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
- Unset: Use the channel dimension format of the input image.
input_data_format (`ChannelDimension` or `str`, *optional*):
The channel dimension format for the input image. If unset, the channel dimension format is inferred
from the input image. Can be one of:
- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
- `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
"""
do_resize = do_resize if do_resize is not None else self.do_resize
size = size if size is not None else self.size
resample = resample if resample is not None else self.resample
do_rescale = do_rescale if do_rescale is not None else self.do_rescale
rescale_factor = rescale_factor if rescale_factor is not None else self.rescale_factor
do_image_splitting = do_image_splitting if do_image_splitting is not None else self.do_image_splitting
max_image_size = max_image_size if max_image_size is not None else self.max_image_size
do_normalize = do_normalize if do_normalize is not None else self.do_normalize
image_mean = image_mean if image_mean is not None else self.image_mean
image_std = image_std if image_std is not None else self.image_std
do_convert_rgb = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
do_pad = do_pad if do_pad is not None else self.do_pad
images_list = make_nested_list_of_images(images)
if not valid_images(images_list[0]):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
"torch.Tensor, tf.Tensor or jax.ndarray."
)
validate_preprocess_arguments(
do_rescale=do_rescale,
rescale_factor=rescale_factor,
do_normalize=do_normalize,
image_mean=image_mean,
image_std=image_std,
do_resize=do_resize,
size=size,
resample=resample,
)
# save the palettes for conversion to RGB
palettes_list = [
[im.getpalette() if isinstance(im, Image.Image) and im.mode == "P" else None for im in images]
for images in images_list
]
# All transformations expect numpy arrays.
images_list = [[to_numpy_array(image) for image in images] for images in images_list]
# Extra channel dimension for grayscale images
if input_data_format in [ChannelDimension.LAST, None]:
images_list = [
[np.expand_dims(img, axis=-1) if img.ndim == 2 else img for img in images] for images in images_list
]
elif input_data_format == ChannelDimension.FIRST:
images_list = [
[np.expand_dims(img, axis=0) if img.ndim == 2 else img for img in images] for images in images_list
]
if do_rescale and is_scaled_image(images_list[0][0]):
logger.warning_once(
"It looks like you are trying to rescale already rescaled images. If the input"
" images have pixel values between 0 and 1, set `do_rescale=False` to avoid rescaling them again."
)
# We assume that all images have the same channel dimension format.
if input_data_format is None:
input_data_format = infer_channel_dimension_format(images_list[0][0], num_channels=(1, 3, 4))
if do_resize:
images_list = [
[
self.resize(image=image, size=size, resample=resample, input_data_format=input_data_format)
for image in images
]
for images in images_list
]
if do_image_splitting:
# We first resize both height and width of each image to the nearest max_image_size multiple, disregarding the aspect ratio
# for size=(10, max_image_size) -> rescaled_size=(max_image_size, max_image_size)
# for size=(11, max_image_size+1) -> rescaled_size=(max_image_size, max_image_size*2)
images_list = [
[
self.resize_for_vision_encoder(
image, max_image_size["longest_edge"], resample=resample, input_data_format=input_data_format
)
for image in images
]
for images in images_list
]
images_list_split_arrays = []
palettes_list_split_arrays = []
images_list_rows = []
images_list_cols = []
for images, palettes in zip(images_list, palettes_list):
split_image_arrays = []
split_palettes_arrays = []
image_rows = []
image_cols = []
for image, palette in zip(images, palettes):
split_image_array, rows, cols = self.split_image(
image,
max_image_size=max_image_size,
input_data_format=input_data_format,
)
split_image_arrays.extend(split_image_array)
split_palettes_arrays.extend([palette] * len(split_image_array))
image_rows.append(rows)
image_cols.append(cols)
images_list_split_arrays.append(split_image_arrays)
palettes_list_split_arrays.append(split_palettes_arrays)
images_list_rows.append(image_rows)
images_list_cols.append(image_cols)
images_list = images_list_split_arrays
palettes_list = palettes_list_split_arrays
else:
# We square the images to max_image_size
images_list = [
[
self.resize(
image=image,
size={"height": max_image_size["longest_edge"], "width": max_image_size["longest_edge"]},
resample=resample,
input_data_format=input_data_format,
)
for image in images
]
for images in images_list
]
images_list_rows = [[0] * len(images) for images in images_list]
images_list_cols = [[0] * len(images) for images in images_list]
if do_convert_rgb:
images_list = [
[convert_to_rgb(img, palette) for img, palette in zip(images, palettes)]
for images, palettes in zip(images_list, palettes_list)
]
if do_rescale:
images_list = [
[self.rescale(image, rescale_factor, input_data_format=input_data_format) for image in images]
for images in images_list
]
if do_normalize:
images_list = [
[
self.normalize(image=image, mean=image_mean, std=image_std, input_data_format=input_data_format)
for image in images
]
for images in images_list
]
pixel_attention_mask = None
if do_pad:
images_list, pixel_attention_mask = self.pad(
images_list, return_pixel_mask=True, return_tensors=return_tensors, input_data_format=input_data_format
)
if data_format is not None:
images_list = [
[
to_channel_dimension_format(image, data_format, input_channel_dim=input_data_format)
for image in images
]
for images in images_list
]
# Faster tensor conversion
data = {"pixel_values": np.array(images_list) if do_pad and return_tensors is not None else images_list}
if pixel_attention_mask is not None:
data["pixel_attention_mask"] = (
np.array(pixel_attention_mask) if do_pad and return_tensors is not None else pixel_attention_mask
)
encoding = BatchFeature(data=data, tensor_type=return_tensors)
# This is needed for generating correct text inputs in the processor - we don't pad to the max number of images
if return_row_col_info:
encoding["rows"] = images_list_rows
encoding["cols"] = images_list_cols
return encoding
__all__ = ["Idefics3ImageProcessor"]
```
|
=========================================================================================================================================
SOURCE CODE FILE: modeling_idefics3.py
LINES: 1
SIZE: 58.54 KB
PATH: scripts\freecad_env\Lib\site-packages\transformers\models\idefics3\modeling_idefics3.py
ENCODING: utf-8
```py
# coding=utf-8
# Copyright 2024 the HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""PyTorch Idefics3 model."""
from dataclasses import dataclass
from typing import Callable, List, Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import CrossEntropyLoss
from ...activations import ACT2FN
from ...cache_utils import DynamicCache
from ...generation import GenerationMixin
from ...modeling_attn_mask_utils import _prepare_4d_attention_mask
from ...modeling_outputs import BaseModelOutput, ModelOutput
from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
from ...utils import (
add_start_docstrings,
add_start_docstrings_to_model_forward,
logging,
replace_return_docstrings,
)
from ..auto import AutoModel
from .configuration_idefics3 import Idefics3Config, Idefics3VisionConfig
logger = logging.get_logger(__name__)
_CONFIG_FOR_DOC = "Idefics3Config"
@dataclass
class Idefics3BaseModelOutputWithPast(ModelOutput):
"""
Base class for Idefics3 model's outputs that may also contain a past key/values (to speed up sequential decoding).
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the model.
If `past_key_values` is used only the last hidden-state of the sequences of shape `(batch_size, 1,
hidden_size)` is output.
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
`(batch_size, num_heads, sequence_length, embed_size_per_head)`) and optionally if
`config.is_encoder_decoder=True` 2 additional tensors of shape `(batch_size, num_heads,
encoder_sequence_length, embed_size_per_head)`.
Contains pre-computed hidden-states (key and values in the self-attention blocks and optionally if
`config.is_encoder_decoder=True` in the cross-attention blocks) that can be used (see `past_key_values`
input) to speed up sequential decoding.
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
image_hidden_states (`tuple(torch.FloatTensor)`, *optional*):
Tuple of `torch.FloatTensor` (one for the output of the image embeddings, `(batch_size, num_images,
sequence_length, hidden_size)`.
image_hidden_states of the model produced by the vision encoder
"""
last_hidden_state: Optional[torch.FloatTensor] = None
past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None
image_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
@dataclass
class Idefics3CausalLMOutputWithPast(ModelOutput):
"""
Base class for Idefics causal language model (or autoregressive) outputs.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Language modeling loss (for next-token prediction).
logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
`(batch_size, num_heads, sequence_length, embed_size_per_head)`)
Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
`past_key_values` input) to speed up sequential decoding.
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
image_hidden_states (`tuple(torch.FloatTensor)`, *optional*):
Tuple of `torch.FloatTensor` (one for the output of the image embeddings, `(batch_size, num_images,
sequence_length, hidden_size)`.
image_hidden_states of the model produced by the vision encoder
"""
loss: Optional[torch.FloatTensor] = None
logits: Optional[torch.FloatTensor] = None
past_key_values: Optional[List[torch.FloatTensor]] = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None
image_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
# Copied from transformers.models.idefics2.modeling_idefics2.Idefics2VisionEmbeddings with Idefics2->Idefics3
class Idefics3VisionEmbeddings(nn.Module):
"""
This is a modified version of `siglip.modelign_siglip.SiglipVisionEmbeddings` to enable images of variable
resolution.
The modifications are adapted from [Patch n' Pack: NaViT, a Vision Transformer for any Aspect Ratio and Resolution](https://arxiv.org/abs/2307.06304)
which allows treating images in their native aspect ratio and without the need to resize them to the same
fixed size. In particular, we start from the original pre-trained SigLIP model
(which uses images of fixed-size square images) and adapt it by training on images of variable resolutions.
"""
def __init__(self, config: Idefics3VisionConfig):
super().__init__()
self.embed_dim = config.hidden_size
self.image_size = config.image_size
self.patch_size = config.patch_size
self.patch_embedding = nn.Conv2d(
in_channels=config.num_channels,
out_channels=self.embed_dim,
kernel_size=self.patch_size,
stride=self.patch_size,
padding="valid",
)
self.num_patches_per_side = self.image_size // self.patch_size
self.num_patches = self.num_patches_per_side**2
self.num_positions = self.num_patches
self.position_embedding = nn.Embedding(self.num_positions, self.embed_dim)
def forward(self, pixel_values: torch.FloatTensor, patch_attention_mask: torch.BoolTensor) -> torch.Tensor:
batch_size, _, max_im_h, max_im_w = pixel_values.shape
patch_embeds = self.patch_embedding(pixel_values)
embeddings = patch_embeds.flatten(2).transpose(1, 2)
max_nb_patches_h, max_nb_patches_w = max_im_h // self.patch_size, max_im_w // self.patch_size
boundaries = torch.arange(1 / self.num_patches_per_side, 1.0, 1 / self.num_patches_per_side)
position_ids = torch.full(size=(batch_size, max_nb_patches_h * max_nb_patches_w), fill_value=0)
for batch_idx, p_attn_mask in enumerate(patch_attention_mask):
nb_patches_h = p_attn_mask[:, 0].sum()
nb_patches_w = p_attn_mask[0].sum()
fractional_coords_h = torch.arange(0, 1 - 1e-6, 1 / nb_patches_h)
fractional_coords_w = torch.arange(0, 1 - 1e-6, 1 / nb_patches_w)
bucket_coords_h = torch.bucketize(fractional_coords_h, boundaries, right=True)
bucket_coords_w = torch.bucketize(fractional_coords_w, boundaries, right=True)
pos_ids = (bucket_coords_h[:, None] * self.num_patches_per_side + bucket_coords_w).flatten()
position_ids[batch_idx][p_attn_mask.view(-1).cpu()] = pos_ids
position_ids = position_ids.to(self.position_embedding.weight.device)
embeddings = embeddings + self.position_embedding(position_ids)
return embeddings
# Copied from transformers.models.siglip.modeling_siglip.eager_attention_forward
def eager_attention_forward(
module: nn.Module,
query: torch.Tensor,
key: torch.Tensor,
value: torch.Tensor,
attention_mask: Optional[torch.Tensor],
scaling: float,
dropout: float = 0.0,
**kwargs,
):
attn_weights = torch.matmul(query, key.transpose(-1, -2)) * scaling
if attention_mask is not None:
attn_weights = attn_weights + attention_mask
attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
attn_output = torch.matmul(attn_weights, value)
attn_output = attn_output.transpose(1, 2).contiguous()
return attn_output, attn_weights
# Copied from transformers.models.siglip.modeling_siglip.SiglipAttention with Siglip->Idefics3Vision
class Idefics3VisionAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
# Copied from transformers.models.clip.modeling_clip.CLIPAttention.__init__
def __init__(self, config):
super().__init__()
self.config = config
self.embed_dim = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_heads != self.embed_dim:
raise ValueError(
f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:"
f" {self.num_heads})."
)
self.scale = self.head_dim**-0.5
self.dropout = config.attention_dropout
self.k_proj = nn.Linear(self.embed_dim, self.embed_dim)
self.v_proj = nn.Linear(self.embed_dim, self.embed_dim)
self.q_proj = nn.Linear(self.embed_dim, self.embed_dim)
self.out_proj = nn.Linear(self.embed_dim, self.embed_dim)
# Ignore copy
self.is_causal = False
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = False,
) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
"""Input shape: Batch x Time x Channel"""
batch_size, seq_length, embed_dim = hidden_states.shape
queries = self.q_proj(hidden_states)
keys = self.k_proj(hidden_states)
values = self.v_proj(hidden_states)
queries = queries.view(batch_size, seq_length, self.num_heads, self.head_dim).transpose(1, 2)
keys = keys.view(batch_size, seq_length, self.num_heads, self.head_dim).transpose(1, 2)
values = values.view(batch_size, seq_length, self.num_heads, self.head_dim).transpose(1, 2)
attention_interface: Callable = eager_attention_forward
if self.config._attn_implementation != "eager":
if self.config._attn_implementation == "sdpa" and output_attentions:
logger.warning_once(
"`torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to "
'eager attention. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
)
else:
attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
attn_output, attn_weights = attention_interface(
self,
queries,
keys,
values,
attention_mask,
is_causal=self.is_causal,
scaling=self.scale,
dropout=0.0 if not self.training else self.dropout,
)
attn_output = attn_output.reshape(batch_size, seq_length, embed_dim).contiguous()
attn_output = self.out_proj(attn_output)
if not output_attentions:
attn_weights = None
return attn_output, attn_weights
# Copied from transformers.models.siglip.modeling_siglip.SiglipMLP with Siglip->Idefics3Vision
class Idefics3VisionMLP(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.activation_fn = ACT2FN[config.hidden_act]
self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size)
self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size)
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidden_states = self.fc1(hidden_states)
hidden_states = self.activation_fn(hidden_states)
hidden_states = self.fc2(hidden_states)
return hidden_states
class Idefics3SimpleMLP(nn.Module):
def __init__(self, config):
super().__init__()
input_size = config.vision_config.hidden_size * (config.scale_factor**2)
output_size = config.text_config.hidden_size
self.proj = nn.Linear(input_size, output_size, bias=False)
def forward(self, x):
return self.proj(x)
# Copied from transformers.models.idefics2.modeling_idefics2.Idefics2EncoderLayer with Idefics2->Idefics3
class Idefics3EncoderLayer(nn.Module):
def __init__(self, config: Idefics3VisionConfig):
super().__init__()
self.embed_dim = config.hidden_size
self.self_attn = Idefics3VisionAttention(config)
self.layer_norm1 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
self.mlp = Idefics3VisionMLP(config)
self.layer_norm2 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
# Copied from transformers.models.siglip.modeling_siglip.SiglipEncoderLayer.forward
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: torch.Tensor,
output_attentions: Optional[bool] = False,
) -> Tuple[torch.FloatTensor]:
"""
Args:
hidden_states (`torch.FloatTensor`):
Input to the layer of shape `(batch, seq_len, embed_dim)`.
attention_mask (`torch.FloatTensor`):
Attention mask of shape `(batch, 1, q_len, k_v_seq_len)` where padding elements are indicated by very large negative values.
output_attentions (`bool`, *optional*, defaults to `False`):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
"""
residual = hidden_states
hidden_states = self.layer_norm1(hidden_states)
hidden_states, attn_weights = self.self_attn(
hidden_states=hidden_states,
attention_mask=attention_mask,
output_attentions=output_attentions,
)
hidden_states = residual + hidden_states
residual = hidden_states
hidden_states = self.layer_norm2(hidden_states)
hidden_states = self.mlp(hidden_states)
hidden_states = residual + hidden_states
outputs = (hidden_states,)
if output_attentions:
outputs += (attn_weights,)
return outputs
# Copied from transformers.models.siglip.modeling_siglip.SiglipEncoder with Siglip->Idefics3
class Idefics3Encoder(nn.Module):
"""
Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a
[`Idefics3EncoderLayer`].
Args:
config: Idefics3Config
"""
def __init__(self, config: Idefics3Config):
super().__init__()
self.config = config
self.layers = nn.ModuleList([Idefics3EncoderLayer(config) for _ in range(config.num_hidden_layers)])
self.gradient_checkpointing = False
# Ignore copy
def forward(
self,
inputs_embeds,
attention_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, BaseModelOutput]:
r"""
Args:
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
This is useful if you want more control over how to convert `input_ids` indices into associated vectors
than the model's internal embedding lookup matrix.
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
for more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
encoder_states = () if output_hidden_states else None
all_attentions = () if output_attentions else None
hidden_states = inputs_embeds
for encoder_layer in self.layers:
if output_hidden_states:
encoder_states = encoder_states + (hidden_states,)
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
encoder_layer.__call__,
hidden_states,
attention_mask,
output_attentions,
)
else:
layer_outputs = encoder_layer(
hidden_states,
attention_mask,
output_attentions=output_attentions,
)
hidden_states = layer_outputs[0]
if output_attentions:
all_attentions = all_attentions + (layer_outputs[1],)
if output_hidden_states:
encoder_states = encoder_states + (hidden_states,)
if not return_dict:
return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None)
return BaseModelOutput(
last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions
)
# Copied from transformers.models.llama.modeling_llama.repeat_kv
def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
"""
This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
"""
batch, num_key_value_heads, slen, head_dim = hidden_states.shape
if n_rep == 1:
return hidden_states
hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
# Copied from transformers.models.llama.modeling_llama.LlamaRMSNorm with Llama->Idefics3
class Idefics3RMSNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-6):
"""
Idefics3RMSNorm is equivalent to T5LayerNorm
"""
super().__init__()
self.weight = nn.Parameter(torch.ones(hidden_size))
self.variance_epsilon = eps
def forward(self, hidden_states):
input_dtype = hidden_states.dtype
hidden_states = hidden_states.to(torch.float32)
variance = hidden_states.pow(2).mean(-1, keepdim=True)
hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
return self.weight * hidden_states.to(input_dtype)
def extra_repr(self):
return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}"
class Idefics3Connector(nn.Module):
def __init__(self, config):
super().__init__()
self.scale_factor = config.scale_factor
self.modality_projection = Idefics3SimpleMLP(config)
def pixel_shuffle(self, x, scale_factor=2):
bsz, seq, embed_dim = x.size()
height = width = int(seq**0.5)
x = x.view(bsz, height, width, embed_dim)
x = x.view(bsz, height, int(width / scale_factor), embed_dim * scale_factor)
x = x.permute(0, 2, 1, 3)
x = x.reshape(bsz, int(width / scale_factor), int(height / scale_factor), embed_dim * (scale_factor**2))
x = x.permute(0, 2, 1, 3)
x = x.reshape(bsz, int(seq / (scale_factor**2)), embed_dim * (scale_factor**2))
return x
def forward(self, image_hidden_states):
image_hidden_states = self.pixel_shuffle(image_hidden_states, self.scale_factor)
image_hidden_states = self.modality_projection(image_hidden_states)
return image_hidden_states
IDEFICS3_START_DOCSTRING = r"""
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.
Parameters:
config ([`Idefics3Config`] or [`Idefics3VisionConfig`]):
Model configuration class with all the parameters of the model. Initializing with a config file does not
load the weights associated with the model, only the configuration. Check out the
[`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
@add_start_docstrings(
"The bare Idefics3 Model outputting raw hidden-states without any specific head on top.",
IDEFICS3_START_DOCSTRING,
)
class Idefics3PreTrainedModel(PreTrainedModel):
config_class = Idefics3Config
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = ["Idefics3VisionAttention", "Idefics3DecoderLayer"]
_skip_keys_device_placement = "past_key_values"
_supports_flash_attn_2 = True
_supports_sdpa = True
_supports_flex_attn = True
_supports_cache_class = True
# Copied from transformers.models.idefics2.modeling_idefics2.Idefics2PreTrainedModel._init_weights
def _init_weights(self, module):
std = (
self.config.initializer_range
if hasattr(self.config, "initializer_range")
else self.config.get_text_config().initializer_range
)
if hasattr(module, "class_embedding"):
module.class_embedding.data.normal_(mean=0.0, std=std)
if isinstance(module, (nn.Linear, nn.Conv2d)):
module.weight.data.normal_(mean=0.0, std=std)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.Embedding):
module.weight.data.normal_(mean=0.0, std=std)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
IDEFICS3_VISION_START_DOCSTRING = r"""
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.
Parameters:
config ([`Idefics3VisionConfig`]):
Model configuration class with all the parameters of the model. Initializing with a config file does not
load the weights associated with the model, only the configuration. Check out the
[`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
@add_start_docstrings(
"The Idefics3 Vision Transformer Model outputting raw image embedding.",
IDEFICS3_VISION_START_DOCSTRING,
)
class Idefics3VisionTransformer(Idefics3PreTrainedModel):
config_class = Idefics3VisionConfig
_supports_sdpa = True
_supports_flash_attention_2 = True
_supports_flex_attn = True
def __init__(self, config: Idefics3VisionConfig):
super().__init__(config)
embed_dim = config.hidden_size
self.embeddings = Idefics3VisionEmbeddings(config)
self.encoder = Idefics3Encoder(config)
self.patch_size = config.patch_size
self.post_layernorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2"
# Copied from transformers.models.idefics2.modeling_idefics2.Idefics2VisionTransformer.get_input_embeddings
def get_input_embeddings(self):
return self.embeddings
# Copied from transformers.models.idefics2.modeling_idefics2.Idefics2VisionTransformer.set_input_embeddings
def set_input_embeddings(self, value):
self.embeddings = value
def forward(
self,
pixel_values,
patch_attention_mask: Optional[torch.BoolTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, BaseModelOutput]:
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
batch_size = pixel_values.size(0)
if patch_attention_mask is None:
patch_size = self.patch_size
patch_attention_mask = torch.ones(
(
batch_size,
pixel_values.size(2) // patch_size,
pixel_values.size(3) // patch_size,
)
)
patch_attention_mask = patch_attention_mask.to(dtype=torch.bool, device=pixel_values.device)
hidden_states = self.embeddings(pixel_values=pixel_values, patch_attention_mask=patch_attention_mask)
patch_attention_mask = patch_attention_mask.view(batch_size, -1)
# The call to `_upad_input` in `_flash_attention_forward` is expensive
# So when the `patch_attention_mask` is full of 1s (i.e. attending to the whole sequence),
# avoiding passing the attention_mask, which is equivalent to attending to the full sequence
if not torch.any(~patch_attention_mask):
patch_attention_mask = None
elif not self._use_flash_attention_2:
patch_attention_mask = _prepare_4d_attention_mask(patch_attention_mask, hidden_states.dtype)
encoder_outputs = self.encoder(
inputs_embeds=hidden_states,
attention_mask=patch_attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
last_hidden_state = encoder_outputs[0]
last_hidden_state = self.post_layernorm(last_hidden_state)
if not return_dict:
return (last_hidden_state,) + encoder_outputs[1:]
return BaseModelOutput(
last_hidden_state=last_hidden_state,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
)
IDEFICS3_INPUTS_DOCSTRING = r"""
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
it.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
`past_key_values`).
If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
information on the default strategy.
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
config.n_positions - 1]`. [What are position IDs?](../glossary#position-ids)
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
`(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
`(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
`decoder_input_ids` of shape `(batch_size, sequence_length)`.
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
model's internal embedding lookup matrix.
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)):
The tensors corresponding to the input images. Pixel values can be obtained using
[`AutoImageProcessor`]. See [`CLIPImageProcessor.__call__`] for details ([]`LlavaProcessor`] uses
[`CLIPImageProcessor`] for processing images).
pixel_attention_mask (`torch.Tensor` of shape `(batch_size, image_size, image_size)`, *optional*):
Mask to avoid performing attention on padding pixel indices.
image_hidden_states (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`):
The hidden states of the image encoder after modality projection.
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
`past_key_values`).
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
Indices depicting the position of the input sequence tokens in the sequence. Contrarily to `position_ids`,
this tensor is not affected by padding. It is used to update the cache in the correct position and to infer
the complete sequence length.
"""
@add_start_docstrings(
"""Idefics3 model consisting of a SIGLIP vision encoder and Llama3 language decoder""",
IDEFICS3_START_DOCSTRING,
)
class Idefics3Model(Idefics3PreTrainedModel):
def __init__(self, config: Idefics3Config):
super().__init__(config)
self.padding_idx = self.config.text_config.pad_token_id
self.vocab_size = self.config.text_config.vocab_size
self.vision_model = Idefics3VisionTransformer._from_config(config.vision_config)
self.connector = Idefics3Connector(config)
self.text_model = AutoModel.from_config(config.text_config)
self.image_seq_len = int(
((config.vision_config.image_size // config.vision_config.patch_size) ** 2) / (config.scale_factor**2)
)
self.image_token_id = self.config.image_token_id
self._use_flash_attention_2 = config.text_config._attn_implementation == "flash_attention_2"
self.post_init()
# Copied from transformers.models.idefics2.modeling_idefics2.Idefics2Model.enable_input_require_grads
def enable_input_require_grads(self):
"""
Enables the gradients for the input embeddings.
This is useful for lora when using gradient checkpointing.
c.f. https://github.com/huggingface/peft/issues/1402#issuecomment-1913675032
Override to set output.requires_grad = True for both the decoder's and vision model's embeddings.
"""
def get_lowest_module(module):
if len(list(module.children())) == 0:
# If the module has no children, it is a leaf module (e.g., Linear, Conv2d, etc.)
return module
else:
# Recursively call the function on each child module
return get_lowest_module(list(module.children())[0])
def make_inputs_require_grads(module, input, output):
output.requires_grad_(True)
self._text_require_grads_hook = self.get_input_embeddings().register_forward_hook(make_inputs_require_grads)
self._vision_require_grads_hook = get_lowest_module(self.vision_model).register_forward_hook(
make_inputs_require_grads
)
# Copied from transformers.models.idefics2.modeling_idefics2.Idefics2Model.disable_input_require_grads
def disable_input_require_grads(self):
self._text_require_grads_hook.remove()
self._vision_require_grads_hook.remove()
# Copied from transformers.models.idefics2.modeling_idefics2.Idefics2Model.get_input_embeddings
def get_input_embeddings(self):
return self.text_model.get_input_embeddings()
# Copied from transformers.models.idefics2.modeling_idefics2.Idefics2Model.set_input_embeddings
def set_input_embeddings(self, value):
self.text_model.set_input_embeddings(value)
def inputs_merger(
self,
input_ids: torch.LongTensor,
inputs_embeds: Optional[torch.Tensor],
image_hidden_states: Optional[torch.Tensor],
):
"""
This method aims at merging the token embeddings with the image hidden states into one single sequence of vectors that are fed to the transformer LM.
The merging happens as follows:
- The text token sequence is: `tok_1 tok_2 tok_3 <fake_token_around_image> <image> <image> ... <image> <fake_token_around_image> tok_4`.
- We get the image hidden states for the image through the vision encoder and that hidden state, after a pixel shuffle operation, is then projected into the text embedding space.
We thus have a sequence of image hidden states of size (1, image_seq_len, hidden_dim), where 1 is for batch_size of 1 image and hidden_dim is the hidden_dim of the LM transformer.
- The merging happens so that we obtain the following sequence: `vector_tok_1 vector_tok_2 vector_tok_3 vector_fake_tok_around_image {sequence of image_seq_len image hidden states} vector_fake_toke_around_image vector_tok_4`. That sequence is fed to the LM.
- To fit the format of that sequence, `input_ids`, `input_embeds`, `attention_mask` are all 3 adapted to insert the image hidden states.
"""
num_images, _, vision_hidden_size = image_hidden_states.shape
special_image_token_mask = input_ids == self.image_token_id
# Fixes RuntimeError: a leaf Variable that requires grad is being used in an in-place operation.
new_inputs_embeds = inputs_embeds.clone()
reshaped_image_hidden_states = image_hidden_states.view(-1, vision_hidden_size)
# cast to the dtype of the input_embeds to support quantized models
reshaped_image_hidden_states = reshaped_image_hidden_states.to(inputs_embeds.device, inputs_embeds.dtype)
new_inputs_embeds[special_image_token_mask] = reshaped_image_hidden_states
return new_inputs_embeds
@add_start_docstrings_to_model_forward(
"""
Inputs fed to the model can have an arbitrary number of images. To account for this, pixel_values fed to
the model have image padding -> (batch_size, max_num_images, 3, max_heights, max_widths) where
max_num_images is the maximum number of images among the batch_size samples in the batch.
Padding images are not needed beyond padding the pixel_values at the entrance of the model.
For efficiency, we only pass through the vision_model's forward the real images by
discarding the padding images i.e. pixel_values of size (image_batch_size, 3, height, width) where
image_batch_size would be 7 when num_images_per_sample=[1, 3, 1, 2] and max_num_images would be 3.
""",
IDEFICS3_INPUTS_DOCSTRING,
)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
pixel_values: Optional[torch.FloatTensor] = None,
pixel_attention_mask: Optional[torch.BoolTensor] = None,
image_hidden_states: Optional[torch.FloatTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
cache_position: Optional[torch.LongTensor] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, Idefics3BaseModelOutputWithPast]:
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
use_cache = use_cache if use_cache is not None else self.config.use_cache
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if self.training and self.text_model.gradient_checkpointing and use_cache:
logger.warning_once(
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
)
use_cache = False
# retrieve input_ids and inputs_embeds
if input_ids is not None:
batch_size, seq_length = input_ids.shape
elif inputs_embeds is not None:
batch_size, seq_length, _ = inputs_embeds.shape
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
past_seen_tokens = 0
if use_cache:
if past_key_values is None:
past_key_values = DynamicCache()
past_seen_tokens = past_key_values.get_seq_length()
if inputs_embeds is not None and input_ids is None and past_seen_tokens == 0:
raise ValueError("When first calling the model, if input_embeds are passed, input_ids should not be None.")
if inputs_embeds is None:
inputs_embeds = self.text_model.get_input_embeddings()(input_ids).to(self.device)
# START VISUAL INPUTS INTEGRATION
if pixel_values is not None and image_hidden_states is not None:
raise ValueError("You cannot specify both pixel_values and image_hidden_states at the same time")
elif pixel_values is not None:
batch_size, num_images, num_channels, height, width = pixel_values.shape
pixel_values = pixel_values.to(dtype=self.dtype) # fp16 compatibility
pixel_values = pixel_values.view(batch_size * num_images, *pixel_values.shape[2:])
# Remove padding images - padding images are full 0.
nb_values_per_image = pixel_values.shape[1:].numel()
real_images_inds = (pixel_values == 0.0).sum(dim=(-1, -2, -3)) != nb_values_per_image
pixel_values = pixel_values[real_images_inds].contiguous()
# Handle the vision attention mask
if pixel_attention_mask is None:
pixel_attention_mask = torch.ones(
size=(pixel_values.size(0), pixel_values.size(2), pixel_values.size(3)),
dtype=torch.bool,
device=pixel_values.device,
)
else:
# Remove padding images from the mask
pixel_attention_mask = pixel_attention_mask.view(
batch_size * num_images, *pixel_attention_mask.shape[2:]
)
pixel_attention_mask = pixel_attention_mask[real_images_inds].contiguous()
patch_size = self.config.vision_config.patch_size
patches_subgrid = pixel_attention_mask.unfold(dimension=1, size=patch_size, step=patch_size)
patches_subgrid = patches_subgrid.unfold(dimension=2, size=patch_size, step=patch_size)
patch_attention_mask = (patches_subgrid.sum(dim=(-1, -2)) > 0).bool()
# Get sequence from the vision encoder
image_hidden_states = self.vision_model(
pixel_values=pixel_values,
patch_attention_mask=patch_attention_mask,
).last_hidden_state
# Modality projection & resampling
image_hidden_states = self.connector(image_hidden_states)
elif image_hidden_states is not None:
image_hidden_states = image_hidden_states.to(dtype=self.dtype, device=input_ids.device)
if past_seen_tokens == 0 and inputs_embeds is not None and image_hidden_states is not None:
# When we generate, we don't want to replace the potential image_token_id that we generated by images
# that simply don't exist
inputs_embeds = self.inputs_merger(
input_ids=input_ids,
inputs_embeds=inputs_embeds,
image_hidden_states=image_hidden_states,
)
outputs = self.text_model(
inputs_embeds=inputs_embeds,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
cache_position=cache_position,
return_dict=return_dict,
)
if not return_dict:
return tuple(v for v in [*outputs, image_hidden_states] if v is not None)
return Idefics3BaseModelOutputWithPast(
last_hidden_state=outputs.last_hidden_state,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
image_hidden_states=image_hidden_states,
)
@add_start_docstrings(
"""The Idefics3 Model with a language modeling head. It is made up a SigLIP vision encoder, with a language modeling head on top. """,
IDEFICS3_START_DOCSTRING,
)
class Idefics3ForConditionalGeneration(Idefics3PreTrainedModel, GenerationMixin):
_tied_weights_keys = ["lm_head.weight"]
# Copied from transformers.models.idefics2.modeling_idefics2.Idefics2ForConditionalGeneration.__init__ with Idefics2->Idefics3
def __init__(self, config):
super().__init__(config)
self.model = Idefics3Model(config)
self.image_token_id = self.config.image_token_id
self.lm_head = nn.Linear(config.text_config.hidden_size, config.text_config.vocab_size, bias=False)
self.vocab_size = config.text_config.vocab_size
# Initialize weights and apply final processing
self.post_init()
# Copied from transformers.models.idefics2.modeling_idefics2.Idefics2ForConditionalGeneration.enable_input_require_grads
def enable_input_require_grads(self):
"""
Enables the gradients for the input embeddings. This is useful for fine-tuning adapter weights while keeping
the model weights fixed.
"""
def make_inputs_require_grads(module, input, output):
output.requires_grad_(True)
self._text_require_grads_hook = self.get_input_embeddings().register_forward_hook(make_inputs_require_grads)
self._vision_require_grads_hook = self.model.vision_model.get_input_embeddings().register_forward_hook(
make_inputs_require_grads
)
# Copied from transformers.models.idefics2.modeling_idefics2.Idefics2ForConditionalGeneration.disable_input_require_grads
def disable_input_require_grads(self):
self._text_require_grads_hook.remove()
self._vision_require_grads_hook.remove()
# Copied from transformers.models.idefics2.modeling_idefics2.Idefics2ForConditionalGeneration.get_input_embeddings
def get_input_embeddings(self):
return self.model.text_model.get_input_embeddings()
# Copied from transformers.models.idefics2.modeling_idefics2.Idefics2ForConditionalGeneration.set_input_embeddings
def set_input_embeddings(self, value):
self.model.text_model.set_input_embeddings(value)
# Copied from transformers.models.idefics2.modeling_idefics2.Idefics2ForConditionalGeneration.get_output_embeddings
def get_output_embeddings(self):
return self.lm_head
# Copied from transformers.models.idefics2.modeling_idefics2.Idefics2ForConditionalGeneration.set_output_embeddings
def set_output_embeddings(self, new_embeddings):
self.lm_head = new_embeddings
@add_start_docstrings_to_model_forward(IDEFICS3_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=Idefics3CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
pixel_values: Optional[torch.FloatTensor] = None,
pixel_attention_mask: Optional[torch.BoolTensor] = None,
image_hidden_states: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
cache_position: Optional[torch.LongTensor] = None,
return_dict: Optional[bool] = None,
logits_to_keep: Union[int, torch.Tensor] = 0,
) -> Union[Tuple, Idefics3CausalLMOutputWithPast]:
r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
config.vocab_size]` or `model.image_token_id` (where `model` is your instance of `Idefics3ForConditionalGeneration`).
Tokens with indices set to `model.image_token_id` are ignored (masked), the loss is only
computed for the tokens with labels in `[0, ..., config.vocab_size]`.
logits_to_keep (`int` or `torch.Tensor`, *optional*):
If an `int`, compute logits for the last `logits_to_keep` tokens. If `0`, calculate logits for all
`input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that
token can save memory, which becomes pretty significant for long sequences or large vocabulary size.
If a `torch.Tensor`, must be 1D corresponding to the indices to keep in the sequence length dimension.
This is useful when using packed tensor format (single dimension for batch and sequence length).
Returns:
Example:
```python
>>> import requests
>>> import torch
>>> from PIL import Image
>>> from io import BytesIO
>>> from transformers import AutoProcessor, AutoModelForVision2Seq
>>> from transformers.image_utils import load_image
>>> # Note that passing the image urls (instead of the actual pil images) to the processor is also possible
>>> image1 = load_image("https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg")
>>> image2 = load_image("https://cdn.britannica.com/59/94459-050-DBA42467/Skyline-Chicago.jpg")
>>> image3 = load_image("https://cdn.britannica.com/68/170868-050-8DDE8263/Golden-Gate-Bridge-San-Francisco.jpg")
>>> processor = AutoProcessor.from_pretrained("HuggingFaceM4/Idefics3-8B-Llama3")
>>> model = AutoModelForVision2Seq.from_pretrained("HuggingFaceM4/Idefics3-8B-Llama3", torch_dtype=torch.bfloat16, device_map="auto")
>>> # Create inputs
>>> messages = [
... {
... "role": "user",
... "content": [
... {"type": "image"},
... {"type": "text", "text": "In this image, we can see the city of New York, and more specifically the Statue of Liberty."},
... {"type": "image"},
... {"type": "text", "text": "What can we see in this image?"},
... ]
... },
... {
... "role": "user",
... "content": [
... {"type": "image"},
... {"type": "text", "text": "In which city is that bridge located?"},
... ]
... }
... ]
>>> prompts = [processor.apply_chat_template([message], add_generation_prompt=True) for message in messages]
>>> images = [[image1, image2], [image3]]
>>> inputs = processor(text=prompts, images=images, padding=True, return_tensors="pt").to(model.device)
>>> # Generate
>>> generated_ids = model.generate(**inputs, max_new_tokens=256)
>>> generated_texts = processor.batch_decode(generated_ids, skip_special_tokens=True)
>>> print(generated_texts[0])
Assistant: There are buildings, trees, lights, and water visible in this image.
>>> print(generated_texts[1])
Assistant: The bridge is in San Francisco.
```"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
outputs = self.model(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
pixel_values=pixel_values,
pixel_attention_mask=pixel_attention_mask,
image_hidden_states=image_hidden_states,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
cache_position=cache_position,
return_dict=return_dict,
)
hidden_states = outputs[0]
# Only compute necessary logits, and do not upcast them to float if we are not computing the loss
slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
logits = self.lm_head(hidden_states[:, slice_indices, :])
loss = None
if labels is not None:
# Upcast to float if we need to compute the loss to avoid potential precision issues
logits = logits.float()
labels = labels.to(logits.device)
# Shift so that tokens < n predict n
if attention_mask is not None:
# we use the input attention mask to shift the logits and labels, because it is 2D.
# we also crop attn mask in case it is longer, which happens in PrefixTuning with peft
shift_attention_mask = attention_mask[:, -(logits.shape[1] - 1) :].to(logits.device)
shift_logits = logits[..., :-1, :][shift_attention_mask != 0].contiguous()
shift_labels = labels[..., 1:][shift_attention_mask != 0].contiguous()
else:
shift_logits = logits[..., :-1, :].contiguous()
shift_labels = labels[..., 1:].contiguous()
# Flatten the tokens
loss_fct = CrossEntropyLoss()
loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))
if not return_dict:
output = (logits,) + outputs[1:]
return (loss,) + output if loss is not None else output
return Idefics3CausalLMOutputWithPast(
loss=loss,
logits=logits,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
image_hidden_states=outputs.image_hidden_states,
)
# Copied from transformers.models.idefics2.modeling_idefics2.Idefics2ForConditionalGeneration.prepare_inputs_for_generation
def prepare_inputs_for_generation(
self,
input_ids,
past_key_values=None,
attention_mask=None,
inputs_embeds=None,
cache_position=None,
pixel_values=None,
pixel_attention_mask=None,
image_hidden_states=None,
logits_to_keep=None,
**kwargs,
):
# Overwritten -- there are mutually exclusive inputs (if the logic to make `image_hidden_states` take
# precedence is moved to the model, we can remove this fn)
model_inputs = super().prepare_inputs_for_generation(
input_ids,
past_key_values=past_key_values,
attention_mask=attention_mask,
inputs_embeds=inputs_embeds,
cache_position=cache_position,
pixel_values=pixel_values,
pixel_attention_mask=pixel_attention_mask,
image_hidden_states=image_hidden_states,
logits_to_keep=logits_to_keep,
**kwargs,
)
# if `inputs_embeds` are passed, we only want to use them in the 1st generation step
# but IDEFICS requires both ids and embeds to be present
if inputs_embeds is not None and cache_position[0] == 0:
model_inputs["input_ids"] = input_ids
if image_hidden_states is not None:
model_inputs["pixel_values"] = None
model_inputs["pixel_attention_mask"] = None
return model_inputs
# Copied from transformers.models.idefics2.modeling_idefics2.Idefics2ForConditionalGeneration._update_model_kwargs_for_generation
def _update_model_kwargs_for_generation(self, outputs, model_kwargs, is_encoder_decoder, **kwargs):
model_kwargs = super()._update_model_kwargs_for_generation(
outputs=outputs,
model_kwargs=model_kwargs,
is_encoder_decoder=is_encoder_decoder,
**kwargs,
)
# Get the precomputed image_hidden_states
model_kwargs["image_hidden_states"] = outputs.image_hidden_states
return model_kwargs
__all__ = ["Idefics3ForConditionalGeneration", "Idefics3PreTrainedModel", "Idefics3Model", "Idefics3VisionTransformer"]
```
|
===========================================================================================================================================
SOURCE CODE FILE: processing_idefics3.py
LINES: 6
SIZE: 16.34 KB
PATH: scripts\freecad_env\Lib\site-packages\transformers\models\idefics3\processing_idefics3.py
ENCODING: utf-8
```py
# coding=utf-8
# Copyright 2024 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Processor class for Idefics3.
"""
import re
from itertools import accumulate
from typing import TYPE_CHECKING, Dict, List, Optional, Union
from ...feature_extraction_utils import BatchFeature
from ...image_utils import ImageInput, is_valid_image, load_image
from ...processing_utils import ImagesKwargs, ProcessingKwargs, ProcessorMixin, Unpack
from ...tokenization_utils_base import AddedToken, BatchEncoding, TextInput
from ...utils import logging
if TYPE_CHECKING:
from ...tokenization_utils_base import PreTokenizedInput
logger = logging.get_logger(__name__)
def is_url(val) -> bool:
return isinstance(val, str) and val.startswith("http")
def is_image_or_image_url(elem):
return is_url(elem) or is_valid_image(elem)
def _prompt_split_image(image_seq_len, image_rows, image_cols, fake_token_around_image, image_token, global_img_token):
"""Prompt with expanded image tokens for when the image is split into patches."""
text_split_images = ""
for n_h in range(image_rows):
for n_w in range(image_cols):
text_split_images += (
f"{fake_token_around_image}" + f"<row_{n_h + 1}_col_{n_w + 1}>" + f"{image_token}" * image_seq_len
)
text_split_images += "\n"
text_split_images += (
f"\n{fake_token_around_image}"
+ f"{global_img_token}"
+ f"{image_token}" * image_seq_len
+ f"{fake_token_around_image}"
)
return text_split_images
def _prompt_single_image(image_seq_len, fake_token_around_image, image_token, global_img_token):
"""Prompt with expanded image tokens for a single image."""
return (
f"{fake_token_around_image}"
+ f"{global_img_token}"
+ f"{image_token}" * image_seq_len
+ f"{fake_token_around_image}"
)
def get_image_prompt_string(
image_rows, image_cols, image_seq_len, fake_token_around_image, image_token, global_img_token
):
if image_rows == 0 and image_cols == 0:
return _prompt_single_image(
image_seq_len,
fake_token_around_image=fake_token_around_image,
image_token=image_token,
global_img_token=global_img_token,
)
return _prompt_split_image(
image_seq_len, image_rows, image_cols, fake_token_around_image, image_token, global_img_token
)
class Idefics3ImagesKwargs(ImagesKwargs, total=False):
return_row_col_info: Optional[bool]
max_image_size: Optional[Dict[str, int]]
class Idefics3ProcessorKwargs(ProcessingKwargs, total=False):
images_kwargs: Idefics3ImagesKwargs
_defaults = {
"text_kwargs": {
"add_special_tokens": True,
"padding": False,
"is_split_into_words": False,
},
"images_kwargs": {
"return_row_col_info": True,
},
}
Idefics3ProcessorKwargs.__annotations__["images_kwargs"] = Idefics3ImagesKwargs # python 3.8 compatibility
class Idefics3Processor(ProcessorMixin):
r"""
Constructs a Idefics3 processor which wraps a LLama tokenizer and Idefics3 image processor into a single processor.
[`Idefics3Processor`] offers all the functionalities of [`Idefics3ImageProcessor`] and [`Idefics3TokenizerFast`]. See
the docstring of [`~IdeficsProcessor.__call__`] and [`~IdeficsProcessor.decode`] for more information.
Args:
image_processor (`Idefics3ImageProcessor`):
An instance of [`Idefics3ImageProcessor`]. The image processor is a required input.
tokenizer (`PreTrainedTokenizerBase`, *optional*):
An instance of [`PreTrainedTokenizerBase`]. This should correspond with the model's text model. The tokenizer is a required input.
image_seq_len (`int`, *optional*, defaults to 169):
The length of the image sequence i.e. the number of <image> tokens per image in the input.
This parameter is used to build the string from the input prompt and image tokens and should match the
value the model used. It is computed as: image_seq_len = int(((image_size // patch_size) ** 2) / (scale_factor**2))
chat_template (`str`, *optional*): A Jinja template which will be used to convert lists of messages
in a chat into a tokenizable string.
"""
attributes = ["image_processor", "tokenizer"]
valid_kwargs = ["image_seq_len", "chat_template"]
image_processor_class = "Idefics3ImageProcessor"
tokenizer_class = "AutoTokenizer"
def __init__(
self, image_processor, tokenizer=None, image_seq_len: int = 169, chat_template: Optional[str] = None, **kwargs
):
if image_processor is None:
raise ValueError("You need to specify an `image_processor`.")
if tokenizer is None:
raise ValueError("You need to specify a `tokenizer`.")
self.fake_image_token = AddedToken("<fake_token_around_image>", normalized=False, special=True)
self.image_token = AddedToken("<image>", normalized=False, special=True)
self.end_of_utterance_token = AddedToken("<end_of_utterance>", normalized=False, special=True)
self.global_image_tag = "<global-img>" # https://github.com/huggingface/transformers/pull/32473/files/8063e5e17362571b693f1db95167f5443a3be1b2#r1734825341
self.image_seq_len = image_seq_len
# This regex matches one or more occurrences of <global-img> tags (optionally surrounded by newline characters)
# or <row_x_col_y> tags (where x and y are digits, also optionally surrounded by newline characters).
self._regex_to_remove_extra_special_tokens = re.compile(r"(\n?<global-img>\n?|<row_\d+_col_\d+>\n?)+")
tokens_to_add = {
"additional_special_tokens": [
self.fake_image_token,
self.image_token,
self.end_of_utterance_token,
]
}
tokenizer.add_special_tokens(tokens_to_add)
super().__init__(image_processor, tokenizer, chat_template=chat_template, **kwargs)
def _extract_images_from_prompts(self, prompts):
prompt_images = []
for prompt in prompts:
images = []
for elem in prompt:
if is_valid_image(elem):
images.append(elem)
elif is_url(elem):
images.append(load_image(elem))
prompt_images.append(images)
return prompt_images
def __call__(
self,
images: Union[ImageInput, List[ImageInput], List[List[ImageInput]]] = None,
text: Union[TextInput, "PreTokenizedInput", List[TextInput], List["PreTokenizedInput"]] = None,
audio=None,
videos=None,
image_seq_len: Optional[int] = None,
**kwargs: Unpack[Idefics3ProcessorKwargs],
) -> BatchEncoding:
"""
Processes the input prompts and returns a BatchEncoding.
Example:
```python
>>> import requests
>>> from transformers import Idefics3Processor
>>> from transformers.image_utils import load_image
>>> processor = Idefics3Processor.from_pretrained("HuggingFaceM4/Idefics3-8B-Llama3")
>>> processor.image_processor.do_image_splitting = False # Force as False to simplify the example
>>> url1 = "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
>>> url2 = "https://cdn.britannica.com/59/94459-050-DBA42467/Skyline-Chicago.jpg"
>>> image1, image2 = load_image(url1), load_image(url2)
>>> images = [[image1], [image2]]
>>> text = [
... "<image>In this image, we see",
... "bla bla bla<image>",
... ]
>>> outputs = processor(images=images, text=text, return_tensors="pt", padding=True)
>>> input_ids = outputs.input_ids
>>> input_tokens = processor.tokenizer.batch_decode(input_ids)
>>> print(input_tokens)
['<|begin_of_text|><fake_token_around_image><global-img>((<image>)*169)<fake_token_around_image> In this image, we see', '<|reserved_special_token_0|><|reserved_special_token_0|><|reserved_special_token_0|><|begin_of_text|>bla bla bla<fake_token_around_image><global-img>((<image>)*169)<fake_token_around_image>']
```
Args:
images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `List[PIL.Image.Image]`, `List[np.ndarray]`, `List[torch.Tensor]`, *optional*):
The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch
tensor. If is of type `List[ImageInput]`, it's assumed that this is for a single prompt i.e. of batch size 1.
text (`Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]]`, *optional*):
The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
(pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
`is_split_into_words=True` (to lift the ambiguity with a batch of sequences).
Wherever an image token, `<image>` is encountered it is expanded to
`<fake_token_around_image>` + `<row_x_col_y>` + `<image>` * `image_seq_len` * <fake_token_around_image>`.
image_seq_len (`int`, *optional*):
The length of the image sequence. If not provided, the default value of self.image_seq_len is used.
image_seq_len should be equal to int(((image_size // patch_size) ** 2) / (scale_factor**2))
return_tensors (`Union[str, TensorType]`, *optional*):
If set, will return tensors of a particular framework. See [`PreTrainedTokenizerFast.__call__`] for more
information.
"""
if text is None and images is None:
raise ValueError("You must provide either `text` or `images`.")
output_kwargs = self._merge_kwargs(
Idefics3ProcessorKwargs,
tokenizer_init_kwargs=self.tokenizer.init_kwargs,
**kwargs,
)
image_seq_len = image_seq_len if image_seq_len is not None else self.image_seq_len
n_images_in_text = []
n_images_in_images = []
inputs = BatchFeature()
if text is not None:
if isinstance(text, str):
text = [text]
elif not isinstance(text, list) and not isinstance(text[0], str):
raise ValueError("Invalid input text. Please provide a string, or a list of strings")
n_images_in_text = [sample.count(self.image_token.content) for sample in text]
if images is not None:
if is_image_or_image_url(images):
images = [[images]]
elif isinstance(images, (list, tuple)) and is_image_or_image_url(images[0]):
if text is not None:
if sum(n_images_in_text) != len(images):
raise ValueError(
f"The total number of {self.image_token.content} tokens in the prompts should be the same as the number of images passed."
f" Found {sum(n_images_in_text)} {self.image_token.content} tokens and {len(images)} images."
)
# Reorganize the images to match the prompts
cumsum_images_in_text = [0] + list(accumulate(n_images_in_text))
images = [
images[cumsum_images_in_text[i] : cumsum_images_in_text[i + 1]]
for i in range(len(n_images_in_text))
]
else:
images = [images]
elif (
not isinstance(images, (list, tuple))
and not isinstance(images[0], (list, tuple))
and not is_image_or_image_url(images[0][0])
):
raise ValueError(
"Invalid input images. Please provide a single image or a list of images or a list of list of images."
)
n_images_in_images = [len(sample) for sample in images]
# Load images if they are URLs
images = [[load_image(im) if is_url(im) else im for im in sample] for sample in images]
image_inputs = self.image_processor(images, **output_kwargs["images_kwargs"])
inputs.update(image_inputs)
if text is not None:
if n_images_in_images != n_images_in_text:
raise ValueError(
f"The number of images in the text {n_images_in_text} and images {n_images_in_images} should be the same."
)
image_rows = inputs.pop("rows", [[0] * len(text)])
image_cols = inputs.pop("cols", [[0] * len(text)])
fake_image_token = self.fake_image_token.content
image_token = self.image_token.content
global_img_token = self.global_image_tag
prompt_strings = []
for sample, sample_rows, sample_cols in zip(text, image_rows, image_cols):
# Replace the image token with fake tokens around the expanded image token sequence of length `image_seq_len`
image_prompt_strings = []
for n_rows, n_cols in zip(sample_rows, sample_cols):
image_prompt_string = get_image_prompt_string(
n_rows,
n_cols,
image_seq_len,
image_token=image_token,
fake_token_around_image=fake_image_token,
global_img_token=global_img_token,
)
image_prompt_strings.append(image_prompt_string)
split_sample = sample.split(image_token)
if len(split_sample) == 0:
raise ValueError("The image token should be present in the text.")
# Place in the image prompt strings where the image tokens are
sample = split_sample[0]
for i, image_prompt_string in enumerate(image_prompt_strings):
sample += image_prompt_string + split_sample[i + 1]
prompt_strings.append(sample)
text_inputs = self.tokenizer(text=prompt_strings, **output_kwargs["text_kwargs"])
inputs.update(text_inputs)
elif text is not None:
if any(n_images_in_text):
raise ValueError(
f"Found {sum(n_images_in_text)} {self.image_token.content} tokens in the text but no images were passed."
)
text_inputs = self.tokenizer(text=text, **output_kwargs["text_kwargs"])
inputs.update(text_inputs)
return inputs
def batch_decode(self, *args, **kwargs):
"""
This method forwards all its arguments to Idefics3TokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please
refer to the docstring of this method for more information.
"""
batched_decode_output = self.tokenizer.batch_decode(*args, **kwargs)
return [self._regex_to_remove_extra_special_tokens.sub("<image>", s) for s in batched_decode_output]
def decode(self, *args, **kwargs):
"""
This method forwards all its arguments to Idefics3TokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to
the docstring of this method for more information.
"""
decode_output = self.tokenizer.decode(*args, **kwargs)
return self._regex_to_remove_extra_special_tokens.sub("<image>", decode_output)
@property
def model_input_names(self):
tokenizer_input_names = self.tokenizer.model_input_names
image_processor_input_names = self.image_processor.model_input_names
return list(dict.fromkeys(image_processor_input_names + tokenizer_input_names))
__all__ = ["Idefics3Processor"]
```
|
===============================================================================================================================
SOURCE CODE FILE: __init__.py
LINES: 1
SIZE: 1.09 KB
PATH: scripts\freecad_env\Lib\site-packages\transformers\models\idefics\__init__.py
ENCODING: utf-8
```py
# Copyright 2024 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import _LazyModule
from ...utils.import_utils import define_import_structure
if TYPE_CHECKING:
from .configuration_idefics import *
from .image_processing_idefics import *
from .modeling_idefics import *
from .modeling_tf_idefics import *
from .processing_idefics import *
else:
import sys
_file = globals()["__file__"]
sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)
```
|
============================================================================================================================================
SOURCE CODE FILE: configuration_idefics.py
LINES: 1
SIZE: 14.95 KB
PATH: scripts\freecad_env\Lib\site-packages\transformers\models\idefics\configuration_idefics.py
ENCODING: utf-8
```py
# coding=utf-8
# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
#
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
# and OPT implementations in this library. It has been modified from its
# original forms to accommodate minor architectural differences compared
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Idefics model configuration"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
logger = logging.get_logger(__name__)
class IdeficsVisionConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`IdeficsModel`]. It is used to instantiate an
Idefics model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a similar configuration to that of the Idefics-9B.
e.g. [HuggingFaceM4/idefics-9b](https://huggingface.co/HuggingFaceM4/idefics-9b)
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
embed_dim (`int`, *optional*, defaults to 768):
Dimensionality of the encoder layers and the pooler layer. (elsewhere referred to as `hidden_size`)
image_size (`int`, *optional*, defaults to 224):
The size (resolution) of each image.
intermediate_size (`int`, *optional*, defaults to 5120):
Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
patch_size (`int`, *optional*, defaults to 14):
The size (resolution) of each patch.
num_hidden_layers (`int`, *optional*, defaults to 32):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 16):
Number of attention heads for each attention layer in the Transformer encoder.
num_channels (`int`, *optional*, defaults to 3):
Number of image channels.
hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"selu"` and `"gelu_new"` `"quick_gelu"` are supported.
layer_norm_eps (`float`, *optional*, defaults to 1e-05):
The epsilon used by the layer normalization layers.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
initializer_factor (`float`, *optional*, defaults to 1.0):
A factor for initializing all weight matrices (should be kept to 1.0, used internally for initialization
testing).
"""
model_type = "idefics_vision"
attribute_map = {
"hidden_size": "embed_dim",
}
def __init__(
self,
embed_dim=768,
image_size=224,
intermediate_size=5120,
patch_size=14,
num_hidden_layers=32,
num_attention_heads=16,
num_channels=3,
hidden_act="gelu",
layer_norm_eps=1e-5,
attention_dropout=0.0,
initializer_range=0.02,
initializer_factor=1.0,
**kwargs,
):
self.embed_dim = embed_dim
self.image_size = image_size
self.intermediate_size = intermediate_size
self.patch_size = patch_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.num_channels = num_channels
self.layer_norm_eps = layer_norm_eps
self.attention_dropout = attention_dropout
self.initializer_range = initializer_range
self.initializer_factor = initializer_factor
self.hidden_act = hidden_act
super().__init__(**kwargs)
class IdeficsPerceiverConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`IdeficsModel`]. It is used to instantiate an
Idefics model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a similar configuration to that of the Idefics-9B.
e.g. [HuggingFaceM4/idefics-9b](https://huggingface.co/HuggingFaceM4/idefics-9b)
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
use_resampler (`bool`, *optional*, defaults to `False`):
Whether or not to use the resampler
resampler_n_latents (`int`, *optional*, defaults to 64):
Number of latent embeddings to resample ("compress") the input sequence to (usually < 128).
resampler_depth (`int`, *optional*, defaults to 6):
Depth of the Perceiver Resampler (Transformer w/ cross attention). Should be shallow (< 3).
resampler_n_heads (`int`, *optional*, defaults to 16):
Number of heads in each Transformer block (for multi-headed self-attention).
resampler_head_dim (`int`, *optional*, defaults to 96):
Dimensionality of each head projection in the Transformer block.
qk_layer_norms_perceiver (`bool`, *optional*, defaults to `False`):
Whether or not to use qk layer norms in perceiver
"""
model_type = "idefics_perciever"
def __init__(
self,
use_resampler=False,
resampler_n_latents=64,
resampler_depth=6,
resampler_n_heads=16,
resampler_head_dim=96,
qk_layer_norms_perceiver=False,
**kwargs,
):
self.use_resampler = use_resampler
self.resampler_n_latents = resampler_n_latents
self.resampler_depth = resampler_depth
self.resampler_n_heads = resampler_n_heads
self.resampler_head_dim = resampler_head_dim
self.qk_layer_norms_perceiver = qk_layer_norms_perceiver
super().__init__(**kwargs)
class IdeficsConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`IdeficsModel`]. It is used to instantiate an
Idefics model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a similar configuration to that of the Idefics-9B.
e.g. [HuggingFaceM4/idefics-9b](https://huggingface.co/HuggingFaceM4/idefics-9b)
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
additional_vocab_size (`int`, *optional*, defaults to 0):
Additional vocabulary size of the model, typically for the special "<img>" token. Additional vocab tokens
are always trainable whereas regular vocab tokens can be frozen or not.
vocab_size (`int`, *optional*, defaults to 32000):
Vocabulary size of the Idefics model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`~IdeficsModel`]
hidden_size (`int`, *optional*, defaults to 4096):
Dimension of the hidden representations.
intermediate_size (`int`, *optional*, defaults to 11008):
Dimension of the MLP representations.
num_hidden_layers (`int`, *optional*, defaults to 32):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 32):
Number of attention heads for each attention layer in the Transformer encoder.
dropout (`float`, *optional*, defaults to 0.0):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
The non-linear activation function (function or string) in the decoder.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
alpha_initializer (`str`, *optional*, defaults to `"zeros"`):
Initialization type for the alphas.
alphas_initializer_range (`float`, *optional*, defaults to 0.0):
The standard deviation of the truncated_normal_initializer for initializing the alphas in the Gated Cross
Attention.
alpha_type (`str`, *optional*, defaults to `"float"`):
Whether the gating alphas should be vectors or single floats.
rms_norm_eps (`float`, *optional*, defaults to 1e-6):
The epsilon used by the rms normalization layers.
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models). Only
relevant if `config.is_decoder=True`.
pad_token_id (`int`, *optional*, defaults to 0)
Padding token id.
bos_token_id (`int`, *optional*, defaults to 1)
Beginning of stream token id.
eos_token_id (`int`, *optional*, defaults to 2)
End of stream token id.
tie_word_embeddings(`bool`, *optional*, defaults to `False`):
Whether to tie weight embeddings
cross_layer_interval (`int`, *optional*, default to 1)
Interval for cross attention (from text to image) layers.
qk_layer_norms (`bool`, *optional*, defaults to `False`): Whether to add layer norm after q and k
freeze_text_layers (`bool`, *optional*, defaults to `True`): Whether to freeze text layers
freeze_text_module_exceptions (`bool`, *optional*, defaults to `[]`):
Exceptions to freezing text layers when `freeze_text_layers` is `True`
freeze_lm_head (`bool`, *optional*, defaults to `False`): Whether to freeze lm head
freeze_vision_layers (`bool`, *optional*, defaults to `True`): Whether to freeze vision layers
freeze_vision_module_exceptions (`bool`, *optional*, defaults to `[]`):
Exceptions to freezing vision layers when `freeze_vision_layers` is `True`
use_resampler (`bool`, *optional*, defaults to `False`): Whether to use the Resampler
vision_config (`IdeficsVisionConfig`, *optional*): Custom vision config or dict
perceiver_config (`IdeficsPerceiverConfig`, *optional*): Custom perceiver config or dict
Example:
```python
>>> from transformers import IdeficsModel, IdeficsConfig
>>> # Initializing a Idefics idefics-9b style configuration
>>> configuration = IdeficsConfig()
>>> # Initializing a model from the idefics-9b style configuration
>>> model = IdeficsModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "idefics"
sub_configs = {"perceiver_config": IdeficsPerceiverConfig, "vision_config": IdeficsVisionConfig}
def __init__(
self,
vocab_size=32000,
additional_vocab_size=0,
hidden_size=4096,
intermediate_size=11008,
num_hidden_layers=32,
num_attention_heads=32,
dropout=0.0,
hidden_act="silu",
initializer_range=0.02,
alpha_initializer="zeros",
alphas_initializer_range=0.0,
alpha_type="float",
rms_norm_eps=1e-6,
use_cache=True,
pad_token_id=0,
bos_token_id=1,
eos_token_id=2,
tie_word_embeddings=False,
cross_layer_interval=1,
qk_layer_norms=False,
freeze_text_layers=True,
freeze_text_module_exceptions=[],
freeze_lm_head=False,
freeze_vision_layers=True,
freeze_vision_module_exceptions=[],
use_resampler=False,
vision_config=None,
perceiver_config=None,
**kwargs,
):
self.vocab_size = vocab_size
self.additional_vocab_size = additional_vocab_size
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.dropout = dropout
self.hidden_act = hidden_act
self.initializer_range = initializer_range
self.alpha_initializer = alpha_initializer
self.alphas_initializer_range = alphas_initializer_range
self.alpha_type = alpha_type
self.rms_norm_eps = rms_norm_eps
self.use_cache = use_cache
self.cross_layer_interval = cross_layer_interval
self.qk_layer_norms = qk_layer_norms
self.freeze_vision_layers = freeze_vision_layers
self.freeze_text_layers = freeze_text_layers
self.freeze_text_module_exceptions = freeze_text_module_exceptions
self.freeze_vision_module_exceptions = freeze_vision_module_exceptions
self.freeze_lm_head = freeze_lm_head
self.use_resampler = use_resampler
if perceiver_config is None:
self.perceiver_config = IdeficsPerceiverConfig()
elif isinstance(perceiver_config, dict):
self.perceiver_config = IdeficsPerceiverConfig(**perceiver_config)
elif isinstance(perceiver_config, IdeficsPerceiverConfig):
self.perceiver_config = perceiver_config
if vision_config is None:
self.vision_config = IdeficsVisionConfig()
elif isinstance(vision_config, dict):
self.vision_config = IdeficsVisionConfig(**vision_config)
elif isinstance(vision_config, IdeficsVisionConfig):
self.vision_config = vision_config
super().__init__(
pad_token_id=pad_token_id,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
tie_word_embeddings=tie_word_embeddings,
**kwargs,
)
# IMPORTANT: Do not do any __init__ args-based checks in the constructor, since
# PretrainedConfig.from_dict first instantiates the class with the config dict and only then
# updates the config object with `kwargs` from from_pretrained, so during the instantiation
# of this object many attributes have default values and haven't yet been overridden.
# Do any required checks inside `from_pretrained` once the superclass' `from_pretrained` was run.
__all__ = ["IdeficsConfig"]
```
|
===============================================================================================================================================
SOURCE CODE FILE: image_processing_idefics.py
LINES: 1
SIZE: 7.62 KB
PATH: scripts\freecad_env\Lib\site-packages\transformers\models\idefics\image_processing_idefics.py
ENCODING: utf-8
```py
# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Image processor class for Idefics."""
from typing import Callable, Dict, List, Optional, Union
from PIL import Image
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_torch_available
IDEFICS_STANDARD_MEAN = [0.48145466, 0.4578275, 0.40821073]
IDEFICS_STANDARD_STD = [0.26862954, 0.26130258, 0.27577711]
def convert_to_rgb(image):
# `image.convert("RGB")` would only work for .jpg images, as it creates a wrong background
# for transparent images. The call to `alpha_composite` handles this case
if image.mode == "RGB":
return image
image_rgba = image.convert("RGBA")
background = Image.new("RGBA", image_rgba.size, (255, 255, 255))
alpha_composite = Image.alpha_composite(background, image_rgba)
alpha_composite = alpha_composite.convert("RGB")
return alpha_composite
class IdeficsImageProcessor(BaseImageProcessor):
r"""
Constructs a Idefics image processor.
Args:
image_size (`int`, *optional*, defaults to 224):
Resize to image size
image_mean (`float` or `List[float]`, *optional*, defaults to `IDEFICS_STANDARD_MEAN`):
Mean to use if normalizing the image. This is a float or list of floats the length of the number of
channels in the image. Can be overridden by the `image_mean` parameter in the `preprocess` method. Can be
overridden by the `image_mean` parameter in the `preprocess` method.
image_std (`float` or `List[float]`, *optional*, defaults to `IDEFICS_STANDARD_STD`):
Standard deviation to use if normalizing the image. This is a float or list of floats the length of the
number of channels in the image. Can be overridden by the `image_std` parameter in the `preprocess` method.
Can be overridden by the `image_std` parameter in the `preprocess` method.
image_num_channels (`int`, *optional*, defaults to 3):
Number of image channels.
"""
model_input_names = ["pixel_values"]
def __init__(
self,
image_size: int = 224,
image_mean: Optional[Union[float, List[float]]] = None,
image_std: Optional[Union[float, List[float]]] = None,
image_num_channels: Optional[int] = 3,
**kwargs,
) -> None:
super().__init__(**kwargs)
self.image_size = image_size
self.image_num_channels = image_num_channels
self.image_mean = image_mean
self.image_std = image_std
def preprocess(
self,
images: ImageInput,
image_num_channels: Optional[int] = 3,
image_size: Optional[Dict[str, int]] = None,
image_mean: Optional[Union[float, List[float]]] = None,
image_std: Optional[Union[float, List[float]]] = None,
transform: Callable = None,
return_tensors: Optional[Union[str, TensorType]] = TensorType.PYTORCH,
**kwargs,
) -> TensorType:
"""
Preprocess a batch of images.
Args:
images (`ImageInput`):
A list of images to preprocess.
image_size (`int`, *optional*, defaults to `self.image_size`):
Resize to image size
image_num_channels (`int`, *optional*, defaults to `self.image_num_channels`):
Number of image channels.
image_mean (`float` or `List[float]`, *optional*, defaults to `IDEFICS_STANDARD_MEAN`):
Mean to use if normalizing the image. This is a float or list of floats the length of the number of
channels in the image. Can be overridden by the `image_mean` parameter in the `preprocess` method. Can
be overridden by the `image_mean` parameter in the `preprocess` method.
image_std (`float` or `List[float]`, *optional*, defaults to `IDEFICS_STANDARD_STD`):
Standard deviation to use if normalizing the image. This is a float or list of floats the length of the
number of channels in the image. Can be overridden by the `image_std` parameter in the `preprocess`
method. Can be overridden by the `image_std` parameter in the `preprocess` method.
transform (`Callable`, *optional*, defaults to `None`):
A custom transform function that accepts a single image can be passed for training. For example,
`torchvision.Compose` can be used to compose multiple transforms. If `None` - an inference mode is
assumed - and then a preset of inference-specific transforms will be applied to the images
Returns:
a PyTorch tensor of the processed images
"""
image_size = image_size if image_size is not None else self.image_size
image_num_channels = image_num_channels if image_num_channels is not None else self.image_num_channels
image_mean = image_mean if image_mean is not None else self.image_mean
image_std = image_std if image_std is not None else self.image_std
size = (image_size, image_size)
if isinstance(images, list) and len(images) == 0:
return []
images = make_list_of_images(images)
if not valid_images(images):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
"torch.Tensor, tf.Tensor or jax.ndarray."
)
# For training a user needs to pass their own set of transforms as a Callable.
# For reference this is what was used in the original IDEFICS training:
# transform = transforms.Compose([
# convert_to_rgb,
# transforms.RandomResizedCrop((size, size), scale=(0.9, 1.0), interpolation=transforms.InterpolationMode.BICUBIC),
# transforms.ToTensor(),
# transforms.Normalize(mean=image_mean, std=image_std),
# ])
if transform is not None:
if not is_torch_available():
raise ImportError("To pass in `transform` torch must be installed")
import torch
images = [transform(x) for x in images]
return torch.stack(images)
# for inference we do the exact transforms that were used to train IDEFICS
images = [convert_to_rgb(x) for x in images]
# further transforms expect numpy arrays
images = [to_numpy_array(x) for x in images]
images = [resize(x, size, resample=PILImageResampling.BICUBIC) for x in images]
images = [self.rescale(image=image, scale=1 / 255) for image in images]
images = [self.normalize(x, mean=image_mean, std=image_std) for x in images]
images = [to_channel_dimension_format(x, ChannelDimension.FIRST) for x in images]
images = BatchFeature(data={"pixel_values": images}, tensor_type=return_tensors)["pixel_values"]
return images
__all__ = ["IdeficsImageProcessor"]
```
|
=======================================================================================================================================
SOURCE CODE FILE: modeling_idefics.py
LINES: 4
SIZE: 78.74 KB
PATH: scripts\freecad_env\Lib\site-packages\transformers\models\idefics\modeling_idefics.py
ENCODING: utf-8
```py
# coding=utf-8
# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
#
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
# and OPT implementations in this library. It has been modified from its
# original forms to accommodate minor architectural differences compared
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""PyTorch Idefics model."""
from dataclasses import dataclass
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
from torch import nn
from torch.nn import CrossEntropyLoss
from ...activations import ACT2FN
from ...cache_utils import Cache, DynamicCache, StaticCache
from ...generation import GenerationMixin
from ...modeling_attn_mask_utils import AttentionMaskConverter
from ...modeling_outputs import ModelOutput
from ...modeling_utils import PretrainedConfig, PreTrainedModel
from ...pytorch_utils import ALL_LAYERNORM_LAYERS
from ...utils import (
add_start_docstrings,
add_start_docstrings_to_model_forward,
is_torch_flex_attn_available,
logging,
replace_return_docstrings,
)
from .configuration_idefics import IdeficsConfig
from .perceiver import IdeficsPerceiverResampler
from .vision import IdeficsVisionTransformer
if is_torch_flex_attn_available():
from torch.nn.attention.flex_attention import BlockMask
from ...integrations.flex_attention import make_flex_block_causal_mask
logger = logging.get_logger(__name__)
_CONFIG_FOR_DOC = "IdeficsConfig"
@dataclass
class IdeficsBaseModelOutputWithPast(ModelOutput):
"""
Base class for Idefics model's outputs that may also contain a past key/values (to speed up sequential decoding).
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the model.
If `past_key_values` is used only the last hidden-state of the sequences of shape `(batch_size, 1,
hidden_size)` is output.
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
`(batch_size, num_heads, sequence_length, embed_size_per_head)`) and optionally if
`config.is_encoder_decoder=True` 2 additional tensors of shape `(batch_size, num_heads,
encoder_sequence_length, embed_size_per_head)`.
Contains pre-computed hidden-states (key and values in the self-attention blocks and optionally if
`config.is_encoder_decoder=True` in the cross-attention blocks) that can be used (see `past_key_values`
input) to speed up sequential decoding.
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
image_hidden_states (`tuple(torch.FloatTensor)`, *optional*):
Tuple of `torch.FloatTensor` (one for the output of the image embeddings, `(batch_size, num_images,
sequence_length, hidden_size)`.
image_hidden_states of the model produced by the vision encoder, and optionally by the perceiver
"""
last_hidden_state: Optional[torch.FloatTensor] = None
past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None
image_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
@dataclass
class IdeficsCausalLMOutputWithPast(ModelOutput):
"""
Base class for Idefics causal language model (or autoregressive) outputs.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Language modeling loss (for next-token prediction).
logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
`(batch_size, num_heads, sequence_length, embed_size_per_head)`)
Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
`past_key_values` input) to speed up sequential decoding.
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
image_hidden_states (`tuple(torch.FloatTensor)`, *optional*):
Tuple of `torch.FloatTensor` (one for the output of the image embeddings, `(batch_size, num_images,
sequence_length, hidden_size)`.
image_hidden_states of the model produced by the vision encoder, and optionally by the perceiver
"""
loss: Optional[torch.FloatTensor] = None
logits: Optional[torch.FloatTensor] = None
past_key_values: Optional[List[torch.FloatTensor]] = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None
image_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
def expand_inputs_for_generation(
input_ids,
expand_size=1,
is_encoder_decoder=False,
attention_mask=None,
encoder_outputs=None,
**model_kwargs,
):
expanded_return_idx = (
torch.arange(input_ids.shape[0]).view(-1, 1).repeat(1, expand_size).view(-1).to(input_ids.device)
)
input_ids = input_ids.index_select(0, expanded_return_idx)
model_kwargs["pixel_values"] = model_kwargs.get("pixel_values", None)
model_kwargs["image_encoder_embeddings"] = model_kwargs.get("image_encoder_embeddings", None)
model_kwargs["perceiver_embeddings"] = model_kwargs.get("perceiver_embeddings", None)
model_kwargs["image_attention_mask"] = model_kwargs.get("image_attention_mask", None)
if "token_type_ids" in model_kwargs:
token_type_ids = model_kwargs["token_type_ids"]
model_kwargs["token_type_ids"] = token_type_ids.index_select(0, expanded_return_idx)
if attention_mask is not None:
model_kwargs["attention_mask"] = attention_mask.index_select(0, expanded_return_idx)
if model_kwargs["image_attention_mask"] is not None:
model_kwargs["image_attention_mask"] = model_kwargs["image_attention_mask"].index_select(
0, expanded_return_idx
)
if model_kwargs["pixel_values"] is not None:
model_kwargs["pixel_values"] = model_kwargs["pixel_values"].index_select(0, expanded_return_idx)
elif model_kwargs["image_encoder_embeddings"] is not None:
model_kwargs["image_encoder_embeddings"] = model_kwargs["image_encoder_embeddings"].index_select(
0, expanded_return_idx
)
elif model_kwargs["perceiver_embeddings"] is not None:
model_kwargs["perceiver_embeddings"] = model_kwargs["perceiver_embeddings"].index_select(
0, expanded_return_idx
)
return input_ids, model_kwargs
def freeze_model(model, module_exceptions=[]):
mapping = {
"LayerNorm": nn.LayerNorm,
"Linear": nn.Linear,
"Embedding": nn.Embedding,
}
module_exceptions_mapped = [mapping[m] for m in module_exceptions]
for module in model.modules():
if module_exceptions and any(isinstance(module, t) for t in module_exceptions_mapped):
module.requires_grad_(True) # Explicitely setting it to true to avoid any mistakes
else:
module.requires_grad_(False)
return model
class IdeficsDecoupledEmbedding(nn.Embedding):
# Derived from https://pytorch.org/docs/stable/_modules/torch/nn/modules/sparse.html#Embedding
"""
Implements a decoupling of parameters to allow freezing (or not) a subset of the embeddings. In practise, the
regular `weight` can be trained or frozen (i.e. `partially_freeze=True`), and if `num_additional_embeddings` > 0,
then it will create `num_additional_embeddings` additional parameters that are always trained. If
`num_additional_embeddings=0`, then the module defaults back to the regular behavior of `nn.Embedding`.
"""
def __init__(
self,
num_embeddings,
num_additional_embeddings,
embedding_dim,
partially_freeze: Optional[bool] = False,
device=None,
dtype=None,
padding_idx=None,
**kwargs,
) -> None:
"""
Args:
num_embeddings (`int`):
Size of the dictionary of embeddings
num_additional_embeddings (`int`):
Number of additional embeddings. Only useful when you `partially_freeze=True`.
embedding_dim (`int`):
The size of each embedding vector
partially_freeze: (`bool`, *optional*, defaults to `False`):
If `True`, the regular `weight` will be frozen. `additional_weight` is never frozen.
padding_idx (`int`, *optional*):
The padding index (needs to be less than num_embeddings)
Note: there are a lot of other parameters to initialize a standard `nn.Embedding` such as `padding_idx`,
`max_norm` or `norm_type`. We are not supporting these.
"""
if padding_idx is not None and padding_idx > num_embeddings:
raise ValueError(f"padding_idx must be within num_embeddings. Got {padding_idx} and {num_embeddings}")
super().__init__(
num_embeddings=num_embeddings,
embedding_dim=embedding_dim,
device=device,
dtype=dtype,
padding_idx=padding_idx,
**kwargs,
)
self.num_embeddings = num_embeddings
self.padding_idx = padding_idx
self.num_additional_embeddings = num_additional_embeddings
self.partially_freeze = partially_freeze
if partially_freeze:
self.weight.requires_grad_(False)
if self.num_additional_embeddings > 0:
self.additional_embedding = nn.Embedding(
num_embeddings=self.num_additional_embeddings,
embedding_dim=embedding_dim,
device=device,
dtype=dtype,
)
def forward(self, input_ids):
"""
we have 2 embeddings, with different indices - one pretrained self.weight and another
self.additional_embedding.weight that is being trained.
in order to make a lookup of the input ids, we:
1. find out the indices of the entries belonging to the 2nd embedding
2. extract those values while subtracting the size of the first embedding (num_embeddings), since the 2nd
embedding starts from 0 and not num_embeddings
3. perform the 2nd embedding lookup
4. now we handle the 1st embedding, we overwrite indices belonging to the 2nd embedding with a padding index
5. perform the 1st embedding lookup
6. now we overwrite the values in the 1st embedding lookup with the values of the 2nd embedding lookup
note: for the 1st embedding lookup we could have looked up only the low indices and not do the padding, but
then we have to create a new tensor and populate it with 2 tensors that are spread out across various indices -
i.e. not a simple concat - I haven't benchmarked the complex case if it's any faster, given that seqlens are
usually relatively short it's probably not faster or if faster not by much - but might be a good idea to
measure.
"""
if self.num_additional_embeddings == 0:
return F.embedding(input_ids, self.weight)
# Clone so that we don't modify the original input_ids later on
input_ids = input_ids.clone()
additional_vocab_indices = torch.where(input_ids >= self.num_embeddings)
input_ids_additional_vocab = input_ids[additional_vocab_indices]
additional_embeddings = self.additional_embedding(input_ids_additional_vocab - self.num_embeddings)
# for successful lookup replace input_ids with 0, the results of these will be discarded anyway
input_ids[additional_vocab_indices] = 0
full_vector = F.embedding(input_ids, self.weight)
# overwrite the records with high indices
full_vector[additional_vocab_indices] = additional_embeddings
return full_vector
def extra_repr(self) -> str:
return "num_embeddings={}, num_additional_embeddings={}, embedding_dim={}, partially_freeze={}".format(
self.num_embeddings,
self.num_additional_embeddings,
self.embedding_dim,
self.partially_freeze,
)
class IdeficsDecoupledLinear(nn.Linear):
# Derived from https://pytorch.org/docs/stable/_modules/torch/nn/modules/linear.html#Linear
"""
Implements a decoupling of parameters to allow freezing (or not) a subset of the parameters. In practise, the
regular `weight` can be trained or frozen (i.e. `partially_freeze=True`), and if `out_additional_features` > 0,
then it will create `out_additional_features * in_features` additional parameters that are always trained. If
`out_additional_features=0`, then the module defaults back to the regular behavior of `nn.Linear`.
"""
def __init__(
self,
in_features: int,
out_features: int,
out_additional_features: int = 0,
bias: bool = True,
partially_freeze: bool = True,
device=None,
dtype=None,
) -> None:
"""
out_additional_features: int. Number of additional trainable dimensions. Only makes sense when
`partially_freeze=True`. partially_freeze: bool. If True, the regular `weight` will be frozen and extra
parameters (if any) will be trainable. If False, default to the regular behavior of nn.Linear.
"""
super().__init__(in_features, out_features, bias, device, dtype)
self.out_additional_features = out_additional_features
self.partially_freeze = partially_freeze
self.in_features = in_features
self.out_features = out_features
if partially_freeze:
self.weight.requires_grad_(False)
if bias:
self.bias.requires_grad_(False)
if out_additional_features > 0:
self.additional_fc = nn.Linear(
in_features=in_features,
out_features=out_additional_features,
bias=bias,
device=device,
dtype=dtype,
)
def forward(self, input: torch.Tensor) -> torch.Tensor:
output = F.linear(input, self.weight, self.bias)
if self.out_additional_features > 0:
additional_features = self.additional_fc(input)
output = torch.cat((output, additional_features), -1)
return output
def extra_repr(self) -> str:
"""Overwriting `nn.Linear.extra_repr` to include new parameters."""
return "in_features={}, out_features={}, out_additional_features={}, bias={}, partially_freeze={}".format(
self.in_features,
self.out_features,
self.out_additional_features,
self.bias is not None,
self.partially_freeze,
)
# this was adapted from LlamaRMSNorm
class IdeficsRMSNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-6):
"""
IdeficsRMSNorm is equivalent to T5LayerNorm
"""
super().__init__()
self.weight = nn.Parameter(torch.ones(hidden_size))
self.variance_epsilon = eps
def forward(self, hidden_states):
variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
# convert into half-precision if necessary
if self.weight.dtype in [torch.float16, torch.bfloat16]:
hidden_states = hidden_states.to(self.weight.dtype)
return self.weight * hidden_states
def extra_repr(self):
return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}"
ALL_LAYERNORM_LAYERS.append(IdeficsRMSNorm)
# this was adapted from LlamaRotaryEmbedding
class IdeficsEmbedding(torch.nn.Module):
def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
super().__init__()
self.dim = dim
self.max_position_embeddings = max_position_embeddings
self.base = base
inv_freq = 1.0 / (
self.base
** (torch.arange(0, self.dim, 2, dtype=torch.int64).to(device=device, dtype=torch.float) / self.dim)
)
self.register_buffer("inv_freq", inv_freq, persistent=False)
# Build here to make `torch.jit.trace` work.
self._set_cos_sin_cache(
seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype()
)
def _set_cos_sin_cache(self, seq_len, device, dtype):
self.max_seq_len_cached = seq_len
t = torch.arange(self.max_seq_len_cached, device=device, dtype=torch.int64).type_as(self.inv_freq)
freqs = torch.einsum("i,j->ij", t, self.inv_freq)
# Different from paper, but it uses a different permutation in order to obtain the same calculation
emb = torch.cat((freqs, freqs), dim=-1)
self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
def forward(self, x, seq_len=None):
# x: [bs, num_attention_heads, seq_len, head_size]
if seq_len > self.max_seq_len_cached:
self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype)
return (
self.cos_cached[:seq_len].to(dtype=x.dtype),
self.sin_cached[:seq_len].to(dtype=x.dtype),
)
def rotate_half(x):
"""Rotates half the hidden dims of the input."""
x1 = x[..., : x.shape[-1] // 2]
x2 = x[..., x.shape[-1] // 2 :]
return torch.cat((-x2, x1), dim=-1)
def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1):
"""Applies Rotary Position Embedding to the query and key tensors.
Args:
q (`torch.Tensor`): The query tensor.
k (`torch.Tensor`): The key tensor.
cos (`torch.Tensor`): The cosine part of the rotary embedding.
sin (`torch.Tensor`): The sine part of the rotary embedding.
position_ids (`torch.Tensor`):
The position indices of the tokens corresponding to the query and key tensors. For example, this can be
used to pass offsetted position ids when working with a KV-cache.
unsqueeze_dim (`int`, *optional*, defaults to 1):
The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
Returns:
`tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
"""
cos = cos[position_ids].unsqueeze(unsqueeze_dim)
sin = sin[position_ids].unsqueeze(unsqueeze_dim)
q_embed = (q * cos) + (rotate_half(q) * sin)
k_embed = (k * cos) + (rotate_half(k) * sin)
return q_embed, k_embed
# this was adapted from LlamaMLP
class IdeficsMLP(nn.Module):
def __init__(
self,
hidden_size: int,
intermediate_size: int,
hidden_act: str,
):
super().__init__()
self.gate_proj = nn.Linear(hidden_size, intermediate_size, bias=False)
self.down_proj = nn.Linear(intermediate_size, hidden_size, bias=False)
self.up_proj = nn.Linear(hidden_size, intermediate_size, bias=False)
self.act_fn = ACT2FN[hidden_act]
def forward(self, x):
return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
# this was adapted from LlamaAttention
class IdeficsAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(
self,
hidden_size: int,
num_heads: int,
dropout: float = 0.0,
is_cross_attention: bool = False,
config: PretrainedConfig = None,
qk_layer_norms: bool = False,
layer_idx: Optional[int] = None,
):
super().__init__()
self.hidden_size = hidden_size
self.num_heads = num_heads
self.head_dim = hidden_size // num_heads
self.dropout = dropout
self.is_causal = True
self.layer_idx = layer_idx
if layer_idx is None:
logger.warning_once(
f"Instantiating {self.__class__.__name__} without passing a `layer_idx` is not recommended and will "
"lead to errors during the forward call if caching is used. Please make sure to provide a `layer_idx` "
"when creating this class."
)
if (self.head_dim * num_heads) != self.hidden_size:
raise ValueError(
f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
f" and `num_heads`: {num_heads})."
)
self.is_cross_attention = is_cross_attention
if not hasattr(nn.functional, "scaled_dot_product_attention"):
raise ValueError("this model requires pytorch 2.0 or higher")
if self.is_cross_attention:
kv_input_dim = (
self.hidden_size if not hasattr(config.vision_config, "embed_dim") else config.vision_config.embed_dim
)
self.q_proj = nn.Linear(
self.hidden_size,
num_heads * self.head_dim,
bias=False,
)
self.k_proj = nn.Linear(kv_input_dim, num_heads * self.head_dim, bias=False)
self.v_proj = nn.Linear(
kv_input_dim,
num_heads * self.head_dim,
bias=False,
)
else:
self.q_proj = nn.Linear(
self.hidden_size,
num_heads * self.head_dim,
bias=False,
)
self.k_proj = nn.Linear(
self.hidden_size,
num_heads * self.head_dim,
bias=False,
)
self.v_proj = nn.Linear(
self.hidden_size,
num_heads * self.head_dim,
bias=False,
)
self.o_proj = nn.Linear(
num_heads * self.head_dim,
hidden_size,
bias=False,
)
self.rotary_emb = IdeficsEmbedding(self.head_dim)
self.qk_layer_norms = qk_layer_norms
if self.qk_layer_norms:
self.q_layer_norm = IdeficsRMSNorm(self.head_dim, eps=config.rms_norm_eps)
self.k_layer_norm = IdeficsRMSNorm(self.head_dim, eps=config.rms_norm_eps)
def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
def forward(
self,
hidden_states: torch.Tensor,
key_value_states: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
output_attentions: bool = False,
use_cache: bool = False,
cache_position: Optional[torch.LongTensor] = None,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
# if key_value_states are provided this layer is used as a cross-attention layer
is_cross_attention = self.is_cross_attention or key_value_states is not None
bsz, q_len, _ = hidden_states.size()
query_states = self.q_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
if not is_cross_attention:
key_states = self.k_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
value_states = self.v_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
else:
_, kv_len, _ = key_value_states.size() # Note that, in this case, `kv_len` == `kv_seq_len`
key_states = self.k_proj(key_value_states).view(bsz, kv_len, self.num_heads, self.head_dim).transpose(1, 2)
value_states = (
self.v_proj(key_value_states).view(bsz, kv_len, self.num_heads, self.head_dim).transpose(1, 2)
)
kv_seq_len = key_states.shape[-2]
if past_key_value is not None:
kv_seq_len += cache_position[0]
if not is_cross_attention:
cos, sin = self.rotary_emb(value_states, seq_len=max(kv_seq_len, q_len))
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
# [bsz, nh, t, hd]
if past_key_value is not None:
# sin and cos are specific to RoPE models; cache_position needed for the static cache
cache_kwargs = {"cache_position": cache_position}
key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
if self.qk_layer_norms:
query_states = self.q_layer_norm(query_states)
key_states = self.k_layer_norm(key_states)
causal_mask = attention_mask
if attention_mask is not None:
causal_mask = causal_mask[:, :, :, : key_states.shape[-2]]
# SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with custom attn_mask,
# Reference: https://github.com/pytorch/pytorch/issues/112577.
if query_states.device.type == "cuda" and attention_mask is not None:
query_states = query_states.contiguous()
key_states = key_states.contiguous()
value_states = value_states.contiguous()
# We dispatch to SDPA's Flash Attention or Efficient kernels via this `is_causal` if statement instead of an inline conditional assignment
# in SDPA to support both torch.compile's dynamic shapes and full graph options. An inline conditional prevents dynamic shapes from compiling.
# The q_len > 1 is necessary to match with AttentionMaskConverter.to_causal_4d that does not create a causal mask in case q_len == 1.
is_causal = True if self.is_causal and causal_mask is None and q_len > 1 else False
attn_output = torch.nn.functional.scaled_dot_product_attention(
query_states,
key_states,
value_states,
attn_mask=causal_mask,
dropout_p=self.dropout if self.training else 0.0,
is_causal=is_causal,
)
if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
raise ValueError(
f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
f" {attn_output.size()}"
)
attn_output = attn_output.transpose(1, 2)
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
attn_output = self.o_proj(attn_output)
attn_weights = None
if output_attentions:
logger.warning_once(
"attn_weights are not extracted in scaled_dot_product_attention. The model returns None instead"
)
return attn_output, attn_weights, past_key_value
# this was adapted from LlamaDecoderLayer
class IdeficsDecoderLayer(nn.Module):
def __init__(self, config: IdeficsConfig, layer_idx: Optional[int] = None):
super().__init__()
self.hidden_size = config.hidden_size
self.self_attn = IdeficsAttention(
hidden_size=self.hidden_size,
num_heads=config.num_attention_heads,
dropout=config.dropout,
config=config,
layer_idx=layer_idx,
)
self.mlp = IdeficsMLP(
hidden_size=self.hidden_size,
intermediate_size=config.intermediate_size,
hidden_act=config.hidden_act,
)
self.input_layernorm = IdeficsRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.post_attention_layernorm = IdeficsRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.dropout = config.dropout
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
output_attentions: Optional[bool] = False,
use_cache: Optional[bool] = False,
cache_position: Optional[torch.LongTensor] = None,
) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
"""
Args:
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
(see `past_key_values`).
past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
"""
residual = hidden_states
hidden_states = self.input_layernorm(hidden_states)
# Self Attention
hidden_states, self_attn_weights, present_key_value = self.self_attn(
hidden_states=hidden_states,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_value=past_key_value,
output_attentions=output_attentions,
use_cache=use_cache,
cache_position=cache_position,
)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = residual + hidden_states
# Fully Connected
residual = hidden_states
hidden_states = self.post_attention_layernorm(hidden_states)
hidden_states = self.mlp(hidden_states)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = residual + hidden_states
outputs = (hidden_states,)
if output_attentions:
outputs += (self_attn_weights,)
if use_cache:
outputs += (present_key_value,)
return outputs
class IdeficsGatedCrossAttentionLayer(nn.Module):
def __init__(self, config: IdeficsConfig, layer_idx: Optional[int] = None):
super().__init__()
self.hidden_size = config.hidden_size
self.cross_attn = IdeficsAttention(
hidden_size=self.hidden_size,
num_heads=config.num_attention_heads,
is_cross_attention=True,
dropout=config.dropout,
config=config,
qk_layer_norms=config.qk_layer_norms,
layer_idx=layer_idx,
)
self.mlp = IdeficsMLP(
hidden_size=self.hidden_size,
intermediate_size=config.intermediate_size,
hidden_act=config.hidden_act,
)
self.input_layernorm = IdeficsRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.post_attention_layernorm = IdeficsRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.config = config.dropout
self.act_cross_attn = nn.Tanh()
self.act_dense = nn.Tanh()
if config.alpha_initializer == "zeros":
if config.alpha_type == "vector":
self.alpha_cross_attn = nn.Parameter(torch.zeros(1, 1, self.hidden_size))
self.alpha_dense = nn.Parameter(torch.zeros(1, 1, self.hidden_size))
elif config.alpha_type == "float":
self.alpha_cross_attn = nn.Parameter(torch.zeros(1))
self.alpha_dense = nn.Parameter(torch.zeros(1))
else:
raise ValueError(f"Unknown value for `alpha_type` ({config.alpha_type})")
elif config.alpha_initializer == "ones":
if config.alpha_type == "vector":
self.alpha_cross_attn = nn.Parameter(torch.ones(1, 1, self.hidden_size))
self.alpha_dense = nn.Parameter(torch.ones(1, 1, self.hidden_size))
elif config.alpha_type == "float":
self.alpha_cross_attn = nn.Parameter(torch.ones(1))
self.alpha_dense = nn.Parameter(torch.ones(1))
else:
raise ValueError(f"Unknown value for `alpha_type` ({config.alpha_type})")
elif config.alpha_initializer in {"normal", "gaussian", "random"}:
if config.alpha_type == "vector":
self.alpha_cross_attn = nn.Parameter(
torch.normal(mean=0.0, std=config.alphas_initializer_range, size=(1, 1, self.hidden_size))
)
self.alpha_dense = nn.Parameter(
torch.normal(mean=0.0, std=config.alphas_initializer_range, size=(1, 1, self.hidden_size))
)
elif config.alpha_type == "float":
self.alpha_cross_attn = nn.Parameter(
torch.normal(mean=0.0, std=config.alphas_initializer_range, size=(1))
)
self.alpha_dense = nn.Parameter(torch.normal(mean=0.0, std=config.alphas_initializer_range, size=(1)))
else:
raise ValueError(f"Unknown value for `alpha_type` ({config.alpha_type})")
else:
raise NotImplementedError(f"Alpha initialization scheme {config.alpha_initializer} not yet implemented!")
if not (hasattr(self, "alpha_cross_attn") and hasattr(self, "alpha_dense")):
raise ValueError("Alpha parameters not initialized correctly!")
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
image_hidden_states: Optional[torch.Tensor] = None,
image_attention_mask: Optional[torch.Tensor] = None,
cross_attention_gate: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = False,
use_cache: Optional[bool] = False,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
"""
Args:
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
image_attention_mask (`torch.FloatTensor`, *optional*): image attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
cross_attention_gate (`torch.FloatTensor`, *optional*):
gate of size `(batch, seq_len)` used to zero-out cross-attention output for tokens attending no images.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
(see `past_key_values`).
past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
"""
if image_hidden_states is None:
raise ValueError(
"`image_hidden_states` is required for Idefics cross attention module which are visual features to be"
" conditioned on."
)
if cross_attention_gate is None:
raise ValueError(
"`cross_attention_gate` is required for Idefics cross attention module to zero-out the cross-attention hidden_states attending to no images."
)
if past_key_value is not None:
raise NotImplementedError("Past key value states are not implemented for Idefics cross attention module.")
residual = hidden_states
hidden_states = self.input_layernorm(hidden_states)
# Self Attention
hidden_states, self_attn_weights, present_key_value = self.cross_attn(
hidden_states=hidden_states,
key_value_states=image_hidden_states,
attention_mask=image_attention_mask,
output_attentions=output_attentions,
)
hidden_states = nn.functional.dropout(hidden_states, p=self.config, training=self.training)
# Fill in zeros for cross_attention hidden_states of tokens attending to no images
hidden_states = hidden_states.masked_fill((cross_attention_gate == 0)[:, :, None], 0.0)
hidden_states = residual + self.act_cross_attn(self.alpha_cross_attn) * hidden_states
# Fully Connected
residual = hidden_states
hidden_states = self.post_attention_layernorm(hidden_states)
hidden_states = self.mlp(hidden_states)
hidden_states = nn.functional.dropout(hidden_states, p=self.config, training=self.training)
hidden_states = residual + self.act_dense(self.alpha_dense) * hidden_states
outputs = (hidden_states,)
if output_attentions:
outputs += (self_attn_weights,)
if use_cache:
outputs += (present_key_value,)
return outputs
LLAMA_START_DOCSTRING = r"""
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.
Parameters:
config ([`IdeficsConfig`]):
Model configuration class with all the parameters of the model. Initializing with a config file does not
load the weights associated with the model, only the configuration. Check out the
[`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
@add_start_docstrings(
"The bare LLaMA Model outputting raw hidden-states without any specific head on top.",
LLAMA_START_DOCSTRING,
)
class IdeficsPreTrainedModel(PreTrainedModel):
config_class = IdeficsConfig
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = ["IdeficsDecoderLayer", "IdeficsGatedCrossAttentionLayer"]
_supports_sdpa = True
_supports_cache_class = True
_supports_static_cache = False # IDEFICS cannot compile due to dynamic control flow when checking inputs
def _init_weights(self, module):
# important: this ported version of Idefics isn't meant for training from scratch - only
# inference and fine-tuning - so the proper init weights code has been removed - the m4 code
# base should be used for training from scratch and it contains the correct code.
std = self.config.initializer_range
if isinstance(module, nn.Linear):
module.weight.data.normal_(mean=0.0, std=std)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.Embedding):
module.weight.data.normal_(mean=0.0, std=std)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
LLAMA_INPUTS_DOCSTRING = r"""
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
it.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
`past_key_values`).
If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
information on the default strategy.
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
config.n_positions - 1]`. [What are position IDs?](../glossary#position-ids)
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
`(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
`(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
`decoder_input_ids` of shape `(batch_size, sequence_length)`.
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
model's internal embedding lookup matrix.
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
`past_key_values`).
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
Indices depicting the position of the input sequence tokens in the sequence. Contrarily to `position_ids`,
this tensor is not affected by padding. It is used to update the cache in the correct position and to infer
the complete sequence length.
"""
@add_start_docstrings(
"The bare LLaMA Model outputting raw hidden-states without any specific head on top.",
LLAMA_START_DOCSTRING,
)
class IdeficsModel(IdeficsPreTrainedModel):
"""
Transformer decoder consisting of `config.num_hidden_layers` layers. Each layer is a [`IdeficsDecoderLayer`]
Args:
config: IdeficsConfig
"""
def __init__(self, config: IdeficsConfig):
super().__init__(config)
self.config = config
self.padding_idx = config.pad_token_id
self.vocab_size = config.vocab_size
self.embed_tokens = IdeficsDecoupledEmbedding(
num_embeddings=config.vocab_size,
num_additional_embeddings=config.additional_vocab_size,
embedding_dim=config.hidden_size,
partially_freeze=config.freeze_text_layers,
padding_idx=self.padding_idx,
)
self.image_size = config.vision_config.image_size
self.vision_config = config.vision_config
self.vision_model = IdeficsVisionTransformer(config.vision_config)
# Perceiver Resampler
if config.use_resampler:
perceiver_config = config.perceiver_config
self.perceiver_resampler = IdeficsPerceiverResampler(
config,
config.vision_config.embed_dim,
perceiver_config.resampler_depth,
perceiver_config.resampler_n_heads,
perceiver_config.resampler_head_dim,
perceiver_config.resampler_n_latents,
)
self.layers = nn.ModuleList(
[IdeficsDecoderLayer(config, layer_idx=i) for i in range(config.num_hidden_layers)]
)
self.cross_layer_interval = config.cross_layer_interval
num_cross_layers = config.num_hidden_layers // self.cross_layer_interval
self.gated_cross_attn_layers = nn.ModuleList(
[IdeficsGatedCrossAttentionLayer(config, layer_idx=i) for i in range(num_cross_layers)]
)
self.gradient_checkpointing = False
self.norm = IdeficsRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
# Initialize weights and apply final processing
self.post_init()
self.freeze_relevant_params(config)
def freeze_relevant_params(self, config=None):
if config is None:
config = self.config
if config.freeze_text_layers:
self.freeze_text_layers(config.freeze_text_module_exceptions)
if config.freeze_vision_layers:
freeze_model(self.vision_model, module_exceptions=config.freeze_vision_module_exceptions)
def freeze_text_layers(self, module_exceptions=[]):
for module in [self.layers, self.norm]:
freeze_model(module, module_exceptions=module_exceptions)
def freeze_vision_layers(self, module_exceptions=[]):
freeze_model(self.vision_model, module_exceptions=module_exceptions)
def get_input_embeddings(self):
return self.embed_tokens
def set_input_embeddings(self, value):
self.embed_tokens = value
@add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
pixel_values: Optional[torch.FloatTensor] = None,
image_encoder_embeddings: Optional[torch.FloatTensor] = None,
perceiver_embeddings: Optional[torch.FloatTensor] = None,
image_attention_mask: Optional[torch.Tensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
interpolate_pos_encoding: Optional[bool] = False,
return_dict: Optional[bool] = None,
cache_position: Optional[torch.LongTensor] = None,
) -> Union[Tuple, IdeficsBaseModelOutputWithPast]:
device = input_ids.device if input_ids is not None else inputs_embeds.device
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
use_cache = use_cache if use_cache is not None else self.config.use_cache
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if (input_ids is None) ^ (inputs_embeds is not None):
raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
if self.gradient_checkpointing and self.training and use_cache:
logger.warning_once(
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`."
)
use_cache = False
if inputs_embeds is None:
inputs_embeds = self.embed_tokens(input_ids)
# kept for BC (non `Cache` `past_key_values` inputs)
return_legacy_cache = False
if use_cache and not isinstance(past_key_values, Cache):
return_legacy_cache = True
if past_key_values is None:
past_key_values = DynamicCache()
else:
past_key_values = DynamicCache.from_legacy_cache(past_key_values)
logger.warning_once(
"We detected that you are passing `past_key_values` as a tuple of tuples. This is deprecated and "
"will be removed in v4.47. Please convert your cache or use an appropriate `Cache` class "
"(https://huggingface.co/docs/transformers/kv_cache#legacy-cache-format)"
)
batch_size, seq_length, _ = inputs_embeds.shape
past_key_values_length = past_key_values.get_seq_length() if past_key_values is not None else 0
seq_length_with_past = seq_length + past_key_values_length
if cache_position is None:
cache_position = torch.arange(
past_key_values_length, past_key_values_length + inputs_embeds.shape[1], device=inputs_embeds.device
)
if attention_mask is not None and position_ids is None:
# create position_ids on the fly for batch generation
position_ids = attention_mask.long().cumsum(-1) - 1
position_ids.masked_fill_(attention_mask == 0, 1)
position_ids = position_ids[:, -seq_length:]
elif position_ids is None:
position_ids = cache_position.unsqueeze(0)
if sum([x is None for x in [pixel_values, image_encoder_embeddings, perceiver_embeddings]]) != 2:
raise ValueError(
"Exactly 1 of pixel_values, image_encoder_embeddings or perceiver_embeddings has to be not-None."
)
elif pixel_values is not None:
pixel_values = pixel_values.to(dtype=self.dtype, device=device) # fp16 compatibility
batch_size, num_images = pixel_values.shape[:2]
pixel_values = pixel_values.contiguous().view(batch_size * num_images, *pixel_values.shape[2:])
# Get sequence from the vision encoder
image_hidden_states = self.vision_model(
pixel_values=pixel_values, interpolate_pos_encoding=interpolate_pos_encoding
).last_hidden_state
elif image_encoder_embeddings is not None:
batch_size, num_images, image_seq_len, image_hidden_size = image_encoder_embeddings.size()
image_hidden_states = image_encoder_embeddings.to(dtype=self.dtype, device=device)
image_hidden_states = image_hidden_states.view(batch_size * num_images, image_seq_len, image_hidden_size)
if self.config.use_resampler:
if perceiver_embeddings is None:
perceiver_embeddings = self.perceiver_resampler(image_hidden_states)
image_seq_len, image_hidden_size = perceiver_embeddings.size(1), perceiver_embeddings.size(2)
else:
batch_size, num_images, image_seq_len, image_hidden_size = perceiver_embeddings.size()
image_hidden_states = perceiver_embeddings
elif perceiver_embeddings is None:
image_seq_len, image_hidden_size = image_hidden_states.size(1), image_hidden_states.size(2)
else:
raise ValueError("If `perceiver_embeddings` are passed, use_resampler should be True")
image_hidden_states = image_hidden_states.view(batch_size, num_images * image_seq_len, image_hidden_size)
# # Hack to use the model in full language modeling mode
# image_attention_mask = torch.zeros(batch_size, seq_length, 1, dtype=torch.long, device=image_hidden_states.device)
# Make image_attention_mask compatible with hidden states
text_seq_len = image_attention_mask.size(1)
image_attention_mask = image_attention_mask.unsqueeze(-1)
image_attention_mask = image_attention_mask.repeat(1, 1, 1, image_seq_len)
image_attention_mask = image_attention_mask.view(batch_size, text_seq_len, num_images * image_seq_len)
if image_hidden_states is not None:
image_batch_size, image_sequence_length, _ = image_hidden_states.size()
image_hidden_shape = (image_batch_size, image_sequence_length)
if image_attention_mask is None:
image_attention_mask = torch.ones(image_hidden_shape, device=device)
image_attention_mask = self.invert_attention_mask(image_attention_mask)
else:
image_attention_mask = None
# cross_attention_gate:
# For any tokens attending to no images, the hidden_states comming out of the cross-attention should be zeroed-out.
# `image_attention_mask` has shape [bsz, 1, num_images, hidden_size] with elements equal to either 0.0 or a very negative number.
# If any of the elements are 0.0, then the token is attending to at least one image and the gate value is 1. Otherwise the gate value is 0.
# `cross_attention_gate` has shape [bsz, seq_len] with elements equal to either 0.0 or 1.0.
cross_attention_gate = ((((image_attention_mask == 0.0).any(dim=-1)).to(dtype=self.dtype)).squeeze(dim=1)).to(
device
)
# embed positions
if attention_mask is None:
attention_mask = torch.ones(
(batch_size, seq_length_with_past), dtype=torch.bool, device=inputs_embeds.device
)
attention_mask = self._update_causal_mask(
attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions
)
hidden_states = inputs_embeds
# decoder layers
all_hidden_states = () if output_hidden_states else None
all_self_attns = () if output_attentions else None
next_decoder_cache = None
for idx, decoder_layer in enumerate(self.layers):
if output_hidden_states:
all_hidden_states += (hidden_states,)
def vblock(
main_block,
hidden_states,
attention_mask,
position_ids,
past_key_value,
image_hidden_states,
image_attention_mask,
cross_attention_gate,
output_attentions,
use_cache,
layer_idx,
cross_layer_interval,
gated_cross_attn_layers,
cache_position,
):
# TODO(ls): Add cross attention values to respective lists
if layer_idx % cross_layer_interval == 0:
xblock = gated_cross_attn_layers[layer_idx // cross_layer_interval]
outputs = xblock(
hidden_states,
attention_mask=attention_mask,
image_hidden_states=image_hidden_states,
image_attention_mask=image_attention_mask,
cross_attention_gate=cross_attention_gate,
output_attentions=output_attentions,
use_cache=use_cache,
past_key_value=None, # not implemented
)
hidden_states = outputs[0]
layer_outputs = main_block(
hidden_states,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_value=past_key_value,
output_attentions=output_attentions,
use_cache=use_cache,
cache_position=cache_position,
)
return layer_outputs
if self.gradient_checkpointing and self.training:
past_key_values = None
if use_cache:
logger.warning_once(
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
)
use_cache = False
layer_outputs = self._gradient_checkpointing_func(
vblock,
decoder_layer,
hidden_states,
attention_mask,
position_ids,
past_key_values,
image_hidden_states,
image_attention_mask,
cross_attention_gate,
output_attentions,
use_cache,
idx,
self.cross_layer_interval,
self.gated_cross_attn_layers,
cache_position,
)
else:
layer_outputs = vblock(
decoder_layer,
hidden_states,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_value=past_key_values,
image_hidden_states=image_hidden_states,
image_attention_mask=image_attention_mask,
cross_attention_gate=cross_attention_gate,
output_attentions=output_attentions,
use_cache=use_cache,
layer_idx=idx,
cross_layer_interval=self.cross_layer_interval,
gated_cross_attn_layers=self.gated_cross_attn_layers,
cache_position=cache_position,
)
hidden_states = layer_outputs[0]
if use_cache:
next_decoder_cache = layer_outputs[2 if output_attentions else 1]
if output_attentions:
all_self_attns += (layer_outputs[1],)
hidden_states = self.norm(hidden_states)
# add hidden states from the last decoder layer
if output_hidden_states:
all_hidden_states += (hidden_states,)
next_cache = next_decoder_cache if use_cache else None
if return_legacy_cache:
next_cache = next_cache.to_legacy_cache()
image_hidden_states = image_hidden_states.view(batch_size, num_images, image_seq_len, image_hidden_size)
if not return_dict:
return tuple(
v
for v in [hidden_states, next_cache, all_hidden_states, all_self_attns, image_hidden_states]
if v is not None
)
return IdeficsBaseModelOutputWithPast(
last_hidden_state=hidden_states,
past_key_values=next_cache,
hidden_states=all_hidden_states,
attentions=all_self_attns,
image_hidden_states=image_hidden_states,
)
# Copied from transformers.models.llama.modeling_llama.LlamaModel._update_causal_mask
def _update_causal_mask(
self,
attention_mask: torch.Tensor,
input_tensor: torch.Tensor,
cache_position: torch.Tensor,
past_key_values: Cache,
output_attentions: bool = False,
):
if self.config._attn_implementation == "flash_attention_2":
if attention_mask is not None and (attention_mask == 0.0).any():
return attention_mask
return None
if self.config._attn_implementation == "flex_attention":
if isinstance(attention_mask, torch.Tensor):
attention_mask = make_flex_block_causal_mask(attention_mask)
if isinstance(attention_mask, BlockMask):
return attention_mask
# For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in
# order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail
# to infer the attention mask.
past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
using_static_cache = isinstance(past_key_values, StaticCache)
# When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward
if self.config._attn_implementation == "sdpa" and not using_static_cache and not output_attentions:
if AttentionMaskConverter._ignore_causal_mask_sdpa(
attention_mask,
inputs_embeds=input_tensor,
past_key_values_length=past_seen_tokens,
is_training=self.training,
):
return None
dtype, device = input_tensor.dtype, input_tensor.device
sequence_length = input_tensor.shape[1]
if using_static_cache:
target_length = past_key_values.get_max_cache_shape()
else:
target_length = (
attention_mask.shape[-1]
if isinstance(attention_mask, torch.Tensor)
else past_seen_tokens + sequence_length + 1
)
# In case the provided `attention` mask is 2D, we generate a causal mask here (4D).
causal_mask = self._prepare_4d_causal_attention_mask_with_cache_position(
attention_mask,
sequence_length=sequence_length,
target_length=target_length,
dtype=dtype,
device=device,
cache_position=cache_position,
batch_size=input_tensor.shape[0],
)
if (
self.config._attn_implementation == "sdpa"
and attention_mask is not None
and attention_mask.device.type in ["cuda", "xpu"]
and not output_attentions
):
# Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when
# using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path.
# Details: https://github.com/pytorch/pytorch/issues/110213
min_dtype = torch.finfo(dtype).min
causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype)
return causal_mask
@staticmethod
# Copied from transformers.models.llama.modeling_llama.LlamaModel._prepare_4d_causal_attention_mask_with_cache_position
def _prepare_4d_causal_attention_mask_with_cache_position(
attention_mask: torch.Tensor,
sequence_length: int,
target_length: int,
dtype: torch.dtype,
device: torch.device,
cache_position: torch.Tensor,
batch_size: int,
**kwargs,
):
"""
Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape
`(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing.
Args:
attention_mask (`torch.Tensor`):
A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape
`(batch_size, 1, query_length, key_value_length)`.
sequence_length (`int`):
The sequence length being processed.
target_length (`int`):
The target length: when generating with static cache, the mask should be as long as the static cache,
to account for the 0 padding, the part of the cache that is not filled yet.
dtype (`torch.dtype`):
The dtype to use for the 4D attention mask.
device (`torch.device`):
The device to place the 4D attention mask on.
cache_position (`torch.Tensor`):
Indices depicting the position of the input sequence tokens in the sequence.
batch_size (`torch.Tensor`):
Batch size.
"""
if attention_mask is not None and attention_mask.dim() == 4:
# In this case we assume that the mask comes already in inverted form and requires no inversion or slicing.
causal_mask = attention_mask
else:
min_dtype = torch.finfo(dtype).min
causal_mask = torch.full(
(sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=device
)
if sequence_length != 1:
causal_mask = torch.triu(causal_mask, diagonal=1)
causal_mask *= torch.arange(target_length, device=device) > cache_position.reshape(-1, 1)
causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1)
if attention_mask is not None:
causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit
mask_length = attention_mask.shape[-1]
padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :].to(
causal_mask.device
)
padding_mask = padding_mask == 0
causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
padding_mask, min_dtype
)
return causal_mask
class IdeficsForVisionText2Text(IdeficsPreTrainedModel, GenerationMixin):
_keys_to_ignore_on_load_missing = [r"lm_head.weight"]
_tied_weights_keys = ["model.embed_tokens.weight", "lm_head.weight"]
def __init__(self, config, vision_model=None):
super().__init__(config)
self.model = IdeficsModel(config)
self.lm_head = IdeficsDecoupledLinear(
in_features=config.hidden_size,
out_features=config.vocab_size,
out_additional_features=config.additional_vocab_size,
bias=False,
partially_freeze=config.freeze_lm_head,
)
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.model.embed_tokens
def set_input_embeddings(self, value):
self.model.embed_tokens = value
def get_output_embeddings(self):
return self.lm_head
def set_output_embeddings(self, new_embeddings):
self.lm_head = new_embeddings
def set_decoder(self, decoder):
self.model = decoder
def get_decoder(self):
return self.model
def tie_weights(self):
"""
Overwrite `transformers.modeling_utils.PreTrainedModel.tie_weights` to handle the case of
IdeficsDecoupledLinear and IdeficsDecoupledEmbedding.
"""
output_embeddings = self.get_output_embeddings()
input_embeddings = self.get_input_embeddings()
if getattr(self.config, "tie_word_embeddings", True):
output_embeddings.weight = input_embeddings.weight
if input_embeddings.num_additional_embeddings > 0:
assert output_embeddings.out_additional_features == input_embeddings.num_additional_embeddings
output_embeddings.additional_fc.weight = input_embeddings.additional_embedding.weight
if hasattr(output_embeddings, "out_features") and hasattr(input_embeddings, "num_embeddings"):
output_embeddings.out_features = input_embeddings.num_embeddings
if hasattr(output_embeddings, "out_additional_features") and hasattr(
input_embeddings, "num_additional_embeddings"
):
output_embeddings.out_additional_features = input_embeddings.num_additional_embeddings
@add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=IdeficsCausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
pixel_values: Optional[torch.FloatTensor] = None,
image_encoder_embeddings: Optional[torch.FloatTensor] = None,
perceiver_embeddings: Optional[torch.FloatTensor] = None,
image_attention_mask: Optional[torch.Tensor] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
interpolate_pos_encoding: Optional[bool] = False,
return_dict: Optional[bool] = None,
cache_position: Optional[torch.LongTensor] = None,
) -> Union[Tuple, IdeficsCausalLMOutputWithPast]:
r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
Returns:
Example:
```python
>>> from transformers import AutoProcessor, IdeficsForVisionText2Text
>>> model = IdeficsForVisionText2Text.from_pretrained("HuggingFaceM4/idefics-9b")
>>> processor = AutoProcessor.from_pretrained("HuggingFaceM4/idefics-9b")
>>> dogs_image_url_1 = "https://huggingface.co/datasets/hf-internal-testing/fixtures_nlvr2/raw/main/image1.jpeg"
>>> dogs_image_url_2 = "https://huggingface.co/datasets/hf-internal-testing/fixtures_nlvr2/raw/main/image2.jpeg"
>>> prompts = [
... [
... "User:",
... dogs_image_url_1,
... "Describe this image.\nAssistant: An image of two dogs.\n",
... "User:",
... dogs_image_url_2,
... "Describe this image.\nAssistant:",
... ]
... ]
>>> inputs = processor(prompts, return_tensors="pt")
>>> generate_ids = model.generate(**inputs, max_new_tokens=6)
>>> processor.batch_decode(generate_ids, skip_special_tokens=True)
```"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
outputs = self.model(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
pixel_values=pixel_values,
image_encoder_embeddings=image_encoder_embeddings,
perceiver_embeddings=perceiver_embeddings,
image_attention_mask=image_attention_mask,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
interpolate_pos_encoding=interpolate_pos_encoding,
return_dict=return_dict,
cache_position=cache_position,
)
hidden_states = outputs[0]
logits = self.lm_head(hidden_states)
loss = None
if labels is not None:
labels = labels.to(logits.device)
# Shift so that tokens < n predict n
if attention_mask is not None:
# we use the input attention mask to shift the logits and labels, because it is 2D.
# we also crop attn mask in case it is longer, which happens in PrefixTuning with peft
shift_attention_mask = attention_mask[:, -(logits.shape[1] - 1) :].to(logits.device)
shift_logits = logits[..., :-1, :][shift_attention_mask != 0].contiguous()
shift_labels = labels[..., 1:][shift_attention_mask != 0].contiguous()
else:
shift_logits = logits[..., :-1, :].contiguous()
shift_labels = labels[..., 1:].contiguous()
# Flatten the tokens
loss_fct = CrossEntropyLoss()
loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))
if not return_dict:
output = (logits,) + outputs[1:]
return (loss,) + output if loss is not None else output
return IdeficsCausalLMOutputWithPast(
loss=loss,
logits=logits,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
image_hidden_states=outputs.image_hidden_states,
)
def prepare_inputs_for_generation(
self,
input_ids,
attention_mask=None,
position_ids=None,
inputs_embeds=None,
past_key_values=None,
cache_position=None,
pixel_values=None,
image_hidden_states=None,
image_attention_mask=None,
use_cache=None,
**kwargs,
):
# Overwritten -- custom processing based on `config.use_resampler`
images_kwargs = {}
if image_hidden_states is not None:
if self.config.use_resampler:
images_kwargs["perceiver_embeddings"] = image_hidden_states
else:
images_kwargs["image_encoder_embeddings"] = image_hidden_states
else:
images_kwargs["pixel_values"] = pixel_values
images_kwargs["interpolate_pos_encoding"] = kwargs.pop("interpolate_pos_encoding", False)
model_inputs = super().prepare_inputs_for_generation(
input_ids,
past_key_values=past_key_values,
attention_mask=attention_mask,
inputs_embeds=inputs_embeds,
cache_position=cache_position,
position_ids=position_ids,
use_cache=use_cache,
image_attention_mask=image_attention_mask,
**images_kwargs,
**kwargs,
)
if image_attention_mask is not None and inputs_embeds is None:
seq_length = model_inputs["input_ids"].shape[1]
model_inputs["image_attention_mask"] = image_attention_mask[:, -seq_length:]
return model_inputs
def _update_model_kwargs_for_generation(
self,
outputs: ModelOutput,
model_kwargs: Dict[str, Any],
is_encoder_decoder: bool = False,
**kwargs,
) -> Dict[str, Any]:
model_kwargs = super()._update_model_kwargs_for_generation(
outputs,
model_kwargs,
is_encoder_decoder,
**kwargs,
)
if "image_attention_mask" in model_kwargs:
image_attention_mask = model_kwargs["image_attention_mask"]
last_mask = image_attention_mask[:, -1, :].unsqueeze(1)
if model_kwargs.get("use_cache", True):
model_kwargs["image_attention_mask"] = last_mask
else:
model_kwargs["image_attention_mask"] = torch.cat([image_attention_mask, last_mask], dim=1)
# Get the precomputed image_hidden_states
model_kwargs["image_hidden_states"] = outputs.image_hidden_states
return model_kwargs
@staticmethod
def _reorder_cache(past, beam_idx):
reordered_past = ()
for layer_past in past:
reordered_past += (tuple(past_state.index_select(0, beam_idx) for past_state in layer_past),)
return reordered_past
__all__ = ["IdeficsForVisionText2Text", "IdeficsModel", "IdeficsPreTrainedModel"]
```
|
==========================================================================================================================================
SOURCE CODE FILE: modeling_tf_idefics.py
LINES: 2
SIZE: 78.43 KB
PATH: scripts\freecad_env\Lib\site-packages\transformers\models\idefics\modeling_tf_idefics.py
ENCODING: utf-8
```py
# coding=utf-8
# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
#
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
# and OPT implementations in this library. It has been modified from its
# original forms to accommodate minor architectural differences compared
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""TF 2.0 Idefics model."""
from __future__ import annotations
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union
import tensorflow as tf
from ... import TFPreTrainedModel
from ...activations_tf import get_tf_activation
from ...modeling_tf_outputs import ModelOutput
from ...modeling_tf_utils import (
TFCausalLanguageModelingLoss,
TFModelInputType,
keras_serializable,
shape_list,
unpack_inputs,
)
from ...tf_utils import invert_attention_mask, scaled_dot_product_attention
from ...utils import (
add_start_docstrings,
add_start_docstrings_to_model_forward,
logging,
replace_return_docstrings,
)
from .configuration_idefics import IdeficsConfig
from .perceiver_tf import TFIdeficsPerceiverResampler
from .vision_tf import TFIdeficsVisionTransformer
logger = logging.get_logger(__name__)
_CONFIG_FOR_DOC = "IdeficsConfig"
@dataclass
class TFIdeficsBaseModelOutputWithPast(ModelOutput):
"""
Base class for Idefics model's outputs that may also contain a past key/values (to speed up sequential decoding).
Args:
last_hidden_state (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the model.
If `past_key_values` is used only the last hidden-state of the sequences of shape `(batch_size, 1,
hidden_size)` is output.
past_key_values (`tuple(tuple(tf.Tensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
Tuple of `tuple(tf.Tensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
`(batch_size, num_heads, sequence_length, embed_size_per_head)`) and optionally if
`config.is_encoder_decoder=True` 2 additional tensors of shape `(batch_size, num_heads,
encoder_sequence_length, embed_size_per_head)`.
Contains pre-computed hidden-states (key and values in the self-attention blocks and optionally if
`config.is_encoder_decoder=True` in the cross-attention blocks) that can be used (see `past_key_values`
input) to speed up sequential decoding.
hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `tf.Tensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
image_hidden_states (`tuple(tf.Tensor)`, *optional*):
Tuple of `tf.Tensor` (one for the output of the image embeddings, `(batch_size, num_images,
sequence_length, hidden_size)`.
image_hidden_states of the model produced by the vision encoder, and optionally by the perceiver
"""
last_hidden_state: Optional[tf.Tensor] = None
past_key_values: Optional[Tuple[Tuple[tf.Tensor]]] = None
hidden_states: Optional[Tuple[tf.Tensor]] = None
attentions: Optional[Tuple[tf.Tensor]] = None
image_hidden_states: Optional[Tuple[tf.Tensor]] = None
@dataclass
class TFIdeficsCausalLMOutputWithPast(ModelOutput):
"""
Base class for Idefics causal language model (or autoregressive) outputs.
Args:
loss (`tf.Tensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Language modeling loss (for next-token prediction).
logits (`tf.Tensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
past_key_values (`tuple(tuple(tf.Tensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
Tuple of `tuple(tf.Tensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
`(batch_size, num_heads, sequence_length, embed_size_per_head)`)
Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
`past_key_values` input) to speed up sequential decoding.
hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `tf.Tensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
image_hidden_states (`tuple(tf.Tensor)`, *optional*):
Tuple of `tf.Tensor` (one for the output of the image embeddings, `(batch_size, num_images,
sequence_length, hidden_size)`.
image_hidden_states of the model produced by the vision encoder, and optionally by the perceiver
"""
loss: Optional[tf.Tensor] = None
logits: Optional[tf.Tensor] = None
past_key_values: Optional[List[tf.Tensor]] = None
hidden_states: Optional[Tuple[tf.Tensor]] = None
attentions: Optional[Tuple[tf.Tensor]] = None
image_hidden_states: Optional[Tuple[tf.Tensor]] = None
def expand_inputs_for_generation(
input_ids,
expand_size=1,
is_encoder_decoder=False,
attention_mask=None,
encoder_outputs=None,
**model_kwargs,
):
expanded_return_idx = tf.reshape(tf.repeat(tf.range(tf.shape(input_ids)[0]), expand_size), [-1])
input_ids = tf.gather(input_ids, expanded_return_idx)
model_kwargs["pixel_values"] = model_kwargs.get("pixel_values", None)
model_kwargs["image_encoder_embeddings"] = model_kwargs.get("image_encoder_embeddings", None)
model_kwargs["perceiver_embeddings"] = model_kwargs.get("perceiver_embeddings", None)
model_kwargs["image_attention_mask"] = model_kwargs.get("image_attention_mask", None)
if "token_type_ids" in model_kwargs:
token_type_ids = model_kwargs["token_type_ids"]
model_kwargs["token_type_ids"] = tf.gather(token_type_ids, expanded_return_idx)
if attention_mask is not None:
model_kwargs["attention_mask"] = tf.gather(attention_mask, expanded_return_idx)
if model_kwargs["image_attention_mask"] is not None:
model_kwargs["image_attention_mask"] = tf.gather(model_kwargs["image_attention_mask"], expanded_return_idx)
if model_kwargs["pixel_values"] is not None:
model_kwargs["pixel_values"] = tf.gather(model_kwargs["pixel_values"], expanded_return_idx)
elif model_kwargs["image_encoder_embeddings"] is not None:
model_kwargs["image_encoder_embeddings"] = tf.gather(
model_kwargs["image_encoder_embeddings"], expanded_return_idx
)
elif model_kwargs["perceiver_embeddings"] is not None:
model_kwargs["perceiver_embeddings"] = tf.gather(model_kwargs["perceiver_embeddings"], expanded_return_idx)
return input_ids, model_kwargs
def update_model_kwargs_for_generation(outputs, model_kwargs):
# must have this key set to at least None
if "past_key_values" in outputs:
model_kwargs["past_key_values"] = outputs.past_key_values
else:
model_kwargs["past_key_values"] = None
# update token_type_ids with last value
if "token_type_ids" in model_kwargs:
token_type_ids = model_kwargs["token_type_ids"]
model_kwargs["token_type_ids"] = tf.concat([token_type_ids, token_type_ids[:, -1:, ...]], axis=-1)
# update attention masks
if "attention_mask" in model_kwargs:
attention_mask = model_kwargs["attention_mask"]
model_kwargs["attention_mask"] = tf.concat(
[attention_mask, tf.ones_like(attention_mask[:, -1:, ...])], axis=-1
)
if "image_attention_mask" in model_kwargs:
image_attention_mask = model_kwargs["image_attention_mask"]
last_mask = image_attention_mask[:, -1:, ...]
model_kwargs["image_attention_mask"] = last_mask
# Get the precomputed image_hidden_states
model_kwargs["image_hidden_states"] = outputs.image_hidden_states
return model_kwargs
def prepare_inputs_for_generation(input_ids, past_key_values=None, **kwargs):
token_type_ids = kwargs.get("token_type_ids", None)
# only last token for inputs_ids if past is defined in kwargs
if past_key_values is not None:
input_ids = input_ids[:, -1:]
if token_type_ids is not None:
token_type_ids = token_type_ids[:, -1:]
attention_mask = kwargs.get("attention_mask", None)
position_ids = kwargs.get("position_ids", None)
if attention_mask is not None and position_ids is None:
# create position_ids on the fly for batch generation
position_ids = tf.math.cumsum(tf.cast(attention_mask, dtype=tf.int64), axis=-1) - 1
position_ids = tf.where(attention_mask == 0, 1, position_ids)
if past_key_values is not None:
position_ids = position_ids[:, -1:]
pixel_values = kwargs.get("pixel_values", None)
image_encoder_embeddings = kwargs.get("image_encoder_embeddings", None)
perceiver_embeddings = kwargs.get("perceiver_embeddings", None)
image_attention_mask = kwargs.get("image_attention_mask", None)
interpolate_pos_encoding = kwargs.get("interpolate_pos_encoding", False)
return {
"input_ids": input_ids,
"past_key_values": past_key_values,
"use_cache": kwargs.get("use_cache"),
"position_ids": position_ids,
"attention_mask": attention_mask,
"token_type_ids": token_type_ids,
"pixel_values": pixel_values,
"image_encoder_embeddings": image_encoder_embeddings,
"perceiver_embeddings": perceiver_embeddings,
"image_attention_mask": image_attention_mask,
"interpolate_pos_encoding": interpolate_pos_encoding,
}
def freeze_model(model, module_exceptions=[]):
mapping = {
"LayerNorm": tf.keras.layers.LayerNormalization,
"Dense": tf.keras.layers.Dense,
"Embedding": tf.keras.layers.Embedding,
}
module_exceptions_mapped = [mapping[m] for m in module_exceptions]
if not hasattr(model, "layers"):
model.trainable = False # It is just a layer
return model
for layer in model.layers:
if module_exceptions and any(isinstance(layer, t) for t in module_exceptions_mapped):
layer.trainable = True # Explicitly setting it to true to avoid any mistakes
else:
layer.trainable = False
return model
class TFIdeficsDecoupledEmbedding(tf.keras.layers.Embedding):
"""
Implements a decoupling of parameters to allow freezing (or not) a subset of the embeddings. In practise, the
regular `weight` can be trained or frozen (i.e. `partially_freeze=True`), and if `num_additional_embeddings` > 0,
then it will create `num_additional_embeddings` additional parameters that are always trained. If
`num_additional_embeddings=0`, then the module defaults back to the regular behavior of `tf.keras.layers.Embedding`.
"""
def __init__(
self,
num_embeddings,
num_additional_embeddings,
embedding_dim,
partially_freeze: Optional[bool] = False,
dtype=None,
**kwargs,
) -> None:
"""
Args:
num_embeddings (`int`):
Size of the dictionary of embeddings
num_additional_embeddings (`int`):
Number of additional embeddings. Only useful when you `partially_freeze=True`.
embedding_dim (`int`):
The size of each embedding vector
partially_freeze: (`bool`, *optional*, defaults to `False`):
If `True`, the regular `weight` will be frozen. `additional_weight` is never frozen.
Note: there are a lot of other parameters to initialize a standard `tf.keras.layers.Embedding` such as `mask_zero`,
`input_length` or `embeddings_initializer`. We are not supporting these.
"""
super().__init__(
input_dim=num_embeddings,
output_dim=embedding_dim,
dtype=dtype,
**kwargs,
)
self.num_embeddings = num_embeddings
self.num_additional_embeddings = num_additional_embeddings
self.partially_freeze = partially_freeze
if partially_freeze:
self.trainable = False
if self.num_additional_embeddings > 0:
self.additional_embedding = tf.keras.layers.Embedding(
input_dim=self.num_additional_embeddings,
output_dim=embedding_dim,
dtype=dtype,
name="additional_embedding",
)
def call(self, input_ids):
"""
we have 2 embeddings, with different indices - one pretrained self.weight and another
self.additional_embedding.weight that is being trained.
in order to make a lookup of the input ids, we:
1. find out the indices of the entries belonging to the 2nd embedding
2. extract those values while subtracting the size of the first embedding (num_embeddings), since the 2nd
embedding starts from 0 and not num_embeddings
3. perform the 2nd embedding lookup
4. now we handle the 1st embedding, we overwrite indices belonging to the 2nd embedding with a padding index
5. perform the 1st embedding lookup
6. now we overwrite the values in the 1st embedding lookup with the values of the 2nd embedding lookup
note: for the 1st embedding lookup we could have looked up only the low indices and not do the padding, but
then we have to create a new tensor and populate it with 2 tensors that are spread out across various indices -
i.e. not a simple concat - I haven't benchmarked the complex case if it's any faster, given that seqlens are
usually relatively short it's probably not faster or if faster not by much - but might be a good idea to
measure.
"""
if self.num_additional_embeddings == 0:
return super().call(input_ids)
# Clone so that we don't modify the original input_ids later on
input_ids = tf.identity(input_ids)
additional_vocab_indices = tf.where(input_ids >= self.num_embeddings)
input_ids_additional_vocab = tf.gather_nd(input_ids, additional_vocab_indices)
additional_embeddings = self.additional_embedding(input_ids_additional_vocab - self.num_embeddings)
# for successful lookup replace input_ids with 0, the results of these will be discarded anyway
input_ids = tf.tensor_scatter_nd_update(
input_ids,
additional_vocab_indices,
# tensor filled with 0, having the same length as additional_vocab_indices
tf.zeros(tf.shape(additional_vocab_indices)[0], dtype=input_ids.dtype),
)
full_vector = super().call(input_ids)
# overwrite the records with high indices
full_vector = tf.tensor_scatter_nd_update(full_vector, additional_vocab_indices, additional_embeddings)
return full_vector
def extra_repr(self) -> str:
return "num_embeddings={}, num_additional_embeddings={}, embedding_dim={}, partially_freeze={}".format(
self.num_embeddings,
self.num_additional_embeddings,
self.output_dim,
self.partially_freeze,
)
class TFIdeficsDecoupledLinear(tf.keras.layers.Layer):
"""
Implements a decoupling of parameters to allow freezing (or not) a subset of the parameters. In practise, the
regular `weight` can be trained or frozen (i.e. `partially_freeze=True`), and if `out_additional_features` > 0,
then it will create `out_additional_features * in_features` additional parameters that are always trained. If
`out_additional_features=0`, then the module defaults back to the regular behavior of `tf.keras.layers.Dense`.
"""
def __init__(
self,
in_features: int,
out_features: int,
out_additional_features: int = 0,
bias: bool = True,
partially_freeze: bool = True,
**kwargs,
) -> None:
"""
out_additional_features: int. Number of additional trainable dimensions. Only makes sense when
`partially_freeze=True`. partially_freeze: bool. If True, the regular `weight` will be frozen and extra
parameters (if any) will be trainable. If False, default to the regular behavior of tf.keras.layers.Dense.
"""
super().__init__(**kwargs)
self.out_additional_features = out_additional_features
self.partially_freeze = partially_freeze
self.in_features = in_features
self.out_features = out_features
self.use_bias = bias
if out_additional_features > 0:
self.additional_fc = tf.keras.layers.Dense(
units=out_additional_features, use_bias=bias, name="additional_fc"
)
def call(self, inputs: tf.Tensor) -> tf.Tensor:
output = tf.linalg.matmul(a=inputs, b=self.weight, transpose_b=True)
if self.bias is not None:
output = tf.nn.bias_add(output, self.bias)
if self.out_additional_features > 0:
additional_features = self.additional_fc(inputs)
output = tf.concat([output, additional_features], axis=-1)
return output
def get_config(self):
config = super().get_config()
config.update(
{
"in_features": self.in_features,
"out_features": self.out_features,
"out_additional_features": self.out_additional_features,
"bias": self.bias is not None,
"partially_freeze": self.partially_freeze,
}
)
return config
def extra_repr(self) -> str:
"""Overwriting `nn.Linear.extra_repr` to include new parameters."""
return "in_features={}, out_features={}, out_additional_features={}, bias={}, partially_freeze={}".format(
self.in_features,
self.out_features,
self.out_additional_features,
self.bias is not None,
self.partially_freeze,
)
@classmethod
def from_config(cls, config):
return cls(**config)
def build(self, input_shape=None):
if self.built:
return
self.built = True
self.weight = self.add_weight(
shape=(self.out_features, self.in_features), trainable=not self.partially_freeze, name="weight"
)
if self.use_bias:
self.bias = self.add_weight(shape=(self.out_features,), trainable=not self.partially_freeze, name="bias")
else:
self.bias = None
if getattr(self, "additional_fc", None) is not None:
with tf.name_scope(self.additional_fc.name):
self.additional_fc.build(self.in_features)
def _make_causal_mask(input_ids_shape, dtype, past_key_values_length=0):
"""
Make causal mask used for bi-directional self-attention, supporting both static and dynamic shapes.
"""
bsz, tgt_len = input_ids_shape
# Create a matrix where only the lower triangle and diagonal are filled with zeros (causal mask)
mask = tf.fill((tgt_len, tgt_len), tf.dtypes.as_dtype(dtype).min)
mask_cond = tf.range(tgt_len)
mask = tf.where(mask_cond[:, None] >= mask_cond[None, :], 0.0, mask)
if past_key_values_length > 0:
mask = tf.concat([tf.zeros((tgt_len, past_key_values_length), dtype=dtype), mask], axis=-1)
if bsz is None:
# When batch size is dynamic, expand and tile
# so we can compile a functional model
mask = tf.expand_dims(mask, 0)
mask = tf.expand_dims(mask, 0) # shape: (1, 1, tgt_len, tgt_len + past_key_values_length)
mask = tf.tile(mask, [bsz, 1, 1, 1])
else:
# When batch size is static, directly use broadcast_to
mask = tf.broadcast_to(mask[None, None, :, :], (bsz, 1, tgt_len, tgt_len + past_key_values_length))
return mask
def _expand_mask(mask, dtype, tgt_len=None):
"""
Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
"""
bsz, src_len = shape_list(mask)
tgt_len = tgt_len if tgt_len is not None else src_len
expanded_mask = tf.expand_dims(tf.expand_dims(mask, 1), 1)
expanded_mask = tf.broadcast_to(expanded_mask, [bsz, 1, tgt_len, src_len])
inverted_mask = 1.0 - tf.cast(expanded_mask, dtype)
return tf.where(
tf.cast(inverted_mask, bool), tf.fill(dims=shape_list(inverted_mask), value=tf.float32.min), inverted_mask
)
class TFIdeficsRMSNorm(tf.keras.layers.Layer):
def __init__(self, hidden_size, eps=1e-6, **kwargs):
"""
TFIdeficsRMSNorm is equivalent to T5LayerNorm
"""
super().__init__(**kwargs)
self.hidden_size = hidden_size
self.variance_epsilon = eps
def build(self, input_shape):
if self.built:
return
self.built = True
self.weight = self.add_weight(name="weight", shape=[self.hidden_size], initializer="ones")
super().build(input_shape)
def call(self, hidden_states):
variance = tf.math.reduce_mean(tf.math.square(tf.cast(hidden_states, tf.float32)), axis=-1, keepdims=True)
hidden_states = hidden_states * tf.math.rsqrt(variance + self.variance_epsilon)
# convert into half-precision if necessary
if self.weight.dtype in [tf.float16, tf.bfloat16]:
hidden_states = tf.cast(hidden_states, self.weight.dtype)
return self.weight * hidden_states
class TFIdeficsEmbedding(tf.keras.layers.Layer):
def __init__(self, dim, max_position_embeddings=2048, base=10000, **kwargs):
super().__init__(**kwargs)
self.dim = dim
self.max_position_embeddings = max_position_embeddings
self.base = base
self.inv_freq = tf.constant(
1.0 / (self.base ** (tf.range(start=0, limit=self.dim, delta=2, dtype=tf.float32) / self.dim))
)
def _compute_cos_sin(self, seq_len):
t = tf.range(seq_len, dtype=self.inv_freq.dtype)
freqs = tf.einsum("i, j -> ij", t, self.inv_freq) # Outer multiplication
emb = tf.concat((freqs, freqs), axis=-1)
return tf.cos(emb), tf.sin(emb)
def call(self, x, seq_len=None):
# x: [bs, num_attention_heads, seq_len, head_size]
if seq_len is None:
seq_len = shape_list(x)[2]
return self._compute_cos_sin(seq_len=seq_len)
def rotate_half(x):
"""Rotates half the hidden dims of the input."""
x1 = x[..., : x.shape[-1] // 2]
x2 = x[..., x.shape[-1] // 2 :]
return tf.concat((-x2, x1), axis=-1)
def apply_rotary_pos_emb(q, k, cos, sin, position_ids):
cos = tf.gather(cos, position_ids) # [seq_len, dim] -> [batch_size, 1, seq_len, head_dim]
sin = tf.gather(sin, position_ids)
cos = tf.expand_dims(cos, 1)
sin = tf.expand_dims(sin, 1)
q_embed = (q * cos) + (rotate_half(q) * sin)
k_embed = (k * cos) + (rotate_half(k) * sin)
return q_embed, k_embed
class TFIdeficsMLP(tf.keras.layers.Layer):
def __init__(
self,
hidden_size: int,
intermediate_size: int,
hidden_act: str,
**kwargs,
):
super().__init__(**kwargs)
self.gate_proj = tf.keras.layers.Dense(intermediate_size, use_bias=False, name="gate_proj")
self.down_proj = tf.keras.layers.Dense(hidden_size, use_bias=False, name="down_proj")
self.up_proj = tf.keras.layers.Dense(intermediate_size, use_bias=False, name="up_proj")
self.act_fn = get_tf_activation(hidden_act)
self.intermediate_size = intermediate_size
self.hidden_size = hidden_size
def call(self, x):
return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "gate_proj", None) is not None:
with tf.name_scope(self.gate_proj.name):
self.gate_proj.build(self.hidden_size)
if getattr(self, "down_proj", None) is not None:
with tf.name_scope(self.down_proj.name):
self.down_proj.build(self.intermediate_size)
if getattr(self, "up_proj", None) is not None:
with tf.name_scope(self.up_proj.name):
self.up_proj.build(self.hidden_size)
class TFIdeficsAttention(tf.keras.layers.Layer):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(
self,
hidden_size: int,
num_heads: int,
dropout: float = 0.0,
is_cross_attention: bool = False,
config: IdeficsConfig = None,
qk_layer_norms: bool = False,
**kwargs,
):
super().__init__(**kwargs)
self.hidden_size = hidden_size
self.num_heads = num_heads
self.head_dim = hidden_size // num_heads
self.dropout = dropout
self.config = config
self.is_causal = True
if (self.head_dim * num_heads) != self.hidden_size:
raise ValueError(
f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
f" and `num_heads`: {num_heads})."
)
self.is_cross_attention = is_cross_attention
self.q_proj = tf.keras.layers.Dense(
num_heads * self.head_dim,
use_bias=False,
name="q_proj",
)
self.k_proj = tf.keras.layers.Dense(
num_heads * self.head_dim,
use_bias=False,
name="k_proj",
)
self.v_proj = tf.keras.layers.Dense(
num_heads * self.head_dim,
use_bias=False,
name="v_proj",
)
self.o_proj = tf.keras.layers.Dense(
hidden_size,
use_bias=False,
name="o_proj",
)
self.rotary_emb = TFIdeficsEmbedding(self.head_dim, name="rotary_emb")
self.qk_layer_norms = qk_layer_norms
if self.qk_layer_norms:
self.q_layer_norm = TFIdeficsRMSNorm(self.head_dim, eps=config.rms_norm_eps, name="q_layer_norm")
self.k_layer_norm = TFIdeficsRMSNorm(self.head_dim, eps=config.rms_norm_eps, name="k_layer_norm")
def _shape(self, tensor: tf.Tensor, seq_len: int, bsz: int):
return tf.transpose(tf.reshape(tensor, (bsz, seq_len, self.num_heads, self.head_dim)), perm=[0, 2, 1, 3])
def call(
self,
hidden_states: tf.Tensor,
key_value_states: Optional[tf.Tensor] = None,
attention_mask: Optional[tf.Tensor] = None,
position_ids: Optional[tf.Tensor] = None,
past_key_value: Optional[Tuple[tf.Tensor]] = None,
output_attentions: bool = False,
use_cache: bool = False,
) -> Tuple[tf.Tensor, Optional[tf.Tensor], Optional[Tuple[tf.Tensor]]]:
# if key_value_states are provided this layer is used as a cross-attention layer
is_cross_attention = self.is_cross_attention or key_value_states is not None
bsz, q_len, _ = shape_list(hidden_states)
query_states = self._shape(self.q_proj(hidden_states), q_len, bsz)
if not is_cross_attention:
key_states = self._shape(self.k_proj(hidden_states), q_len, bsz)
value_states = self._shape(self.v_proj(hidden_states), q_len, bsz)
else:
_, kv_len, _ = shape_list(key_value_states) # Note that, in this case, `kv_len` == `kv_seq_len`
key_states = self._shape(self.k_proj(key_value_states), kv_len, bsz)
value_states = self._shape(self.v_proj(key_value_states), kv_len, bsz)
kv_seq_len = shape_list(key_states)[-2]
if past_key_value is not None:
kv_seq_len += shape_list(past_key_value[0])[-2]
if not is_cross_attention:
# Below is to allow symbolic tensors compilation
if tf.is_tensor(kv_seq_len):
seq_len = tf.reduce_max(kv_seq_len, q_len)
else:
seq_len = max(kv_seq_len, q_len)
cos, sin = self.rotary_emb(value_states, seq_len)
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
# [bsz, nh, t, hd]
if past_key_value is not None:
# reuse k, v, self_attention
key_states = tf.concat([past_key_value[0], key_states], axis=2)
value_states = tf.concat([past_key_value[1], value_states], axis=2)
past_key_value = (key_states, value_states) if use_cache else None
if self.qk_layer_norms:
query_states = self.q_layer_norm(query_states)
key_states = self.k_layer_norm(key_states)
tf.debugging.assert_equal(
tf.shape(attention_mask),
[bsz, 1, q_len, kv_seq_len],
message=f"Attention weights should be of size {[bsz, 1, q_len, kv_seq_len]}, but is {tf.shape(attention_mask)}",
)
attn_output = scaled_dot_product_attention(
query_states,
key_states,
value_states,
attn_mask=attention_mask,
# The q_len > 1 is necessary to match with AttentionMaskConverter.to_causal_4d that does not create a causal mask in case q_len == 1.
is_causal=self.is_causal and attention_mask is None and q_len > 1,
)
tf.debugging.assert_equal(
tf.shape(attn_output),
[bsz, self.num_heads, q_len, self.head_dim],
message=f"Attention weights should be of size {[bsz, self.num_heads, q_len, self.head_dim]}, but is {tf.shape(attn_output)}",
)
attn_output = tf.reshape(tf.transpose(attn_output, perm=[0, 2, 1, 3]), (bsz, q_len, self.hidden_size))
attn_output = self.o_proj(attn_output)
attn_weights = None
if output_attentions:
logger.warning_once(
"attn_weights are not extracted in scaled_dot_product_attention. The model returns None instead"
)
return attn_output, attn_weights, past_key_value
def build(self, input_shape=None):
if self.built:
return
self.built = True
if self.is_cross_attention:
kv_input_dim = (
self.hidden_size
if not hasattr(self.config.vision_config, "embed_dim")
else self.config.vision_config.embed_dim
)
else:
kv_input_dim = self.hidden_size
if getattr(self, "o_proj", None) is not None:
with tf.name_scope(self.o_proj.name):
self.o_proj.build(self.num_heads * self.head_dim)
if getattr(self, "q_proj", None) is not None:
with tf.name_scope(self.q_proj.name):
self.q_proj.build(self.hidden_size)
if getattr(self, "k_proj", None) is not None:
with tf.name_scope(self.k_proj.name):
self.k_proj.build(kv_input_dim)
if getattr(self, "v_proj", None) is not None:
with tf.name_scope(self.v_proj.name):
self.v_proj.build(kv_input_dim)
if getattr(self, "rotary_emb", None) is not None:
with tf.name_scope(self.rotary_emb.name):
self.rotary_emb.build(None)
class TFIdeficsDecoderLayer(tf.keras.layers.Layer):
def __init__(self, config: IdeficsConfig, **kwargs):
super().__init__(**kwargs)
self.hidden_size = config.hidden_size
self.self_attn = TFIdeficsAttention(
hidden_size=self.hidden_size,
num_heads=config.num_attention_heads,
dropout=config.dropout,
config=config,
name="self_attn",
)
self.mlp = TFIdeficsMLP(
hidden_size=self.hidden_size,
intermediate_size=config.intermediate_size,
hidden_act=config.hidden_act,
name="mlp",
)
self.input_layernorm = TFIdeficsRMSNorm(config.hidden_size, eps=config.rms_norm_eps, name="input_layernorm")
self.post_attention_layernorm = TFIdeficsRMSNorm(
config.hidden_size, eps=config.rms_norm_eps, name="post_attention_layernorm"
)
self.dropout = config.dropout
def call(
self,
hidden_states: tf.Tensor,
attention_mask: Optional[tf.Tensor] = None,
position_ids: Optional[tf.Tensor] = None,
past_key_value: Optional[Tuple[tf.Tensor]] = None,
output_attentions: Optional[bool] = False,
use_cache: Optional[bool] = False,
training=False,
) -> Tuple[tf.Tensor, Optional[Tuple[tf.Tensor, tf.Tensor]]]:
"""
Args:
hidden_states (`tf.Tensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
attention_mask (`tf.Tensor`, *optional*): attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
(see `past_key_values`).
past_key_value (`Tuple(tf.Tensor)`, *optional*): cached past key and value projection states
"""
residual = hidden_states
hidden_states = self.input_layernorm(hidden_states)
# Self Attention
hidden_states, self_attn_weights, present_key_value = self.self_attn(
hidden_states=hidden_states,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_value=past_key_value,
output_attentions=output_attentions,
use_cache=use_cache,
)
hidden_states = tf.nn.dropout(hidden_states, rate=self.dropout)
hidden_states = residual + hidden_states
# Fully Connected
residual = hidden_states
hidden_states = self.post_attention_layernorm(hidden_states)
hidden_states = self.mlp(hidden_states)
hidden_states = tf.nn.dropout(hidden_states, rate=self.dropout)
hidden_states = residual + hidden_states
outputs = (hidden_states,)
if output_attentions:
outputs += (self_attn_weights,)
if use_cache:
outputs += (present_key_value,)
return outputs
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "self_attn", None) is not None:
with tf.name_scope(self.self_attn.name):
self.self_attn.build(None)
if getattr(self, "mlp", None) is not None:
with tf.name_scope(self.mlp.name):
self.mlp.build(None)
if getattr(self, "input_layernorm", None) is not None:
with tf.name_scope(self.input_layernorm.name):
self.input_layernorm.build(None)
if getattr(self, "post_attention_layernorm", None) is not None:
with tf.name_scope(self.post_attention_layernorm.name):
self.post_attention_layernorm.build(None)
class TFIdeficsGatedCrossAttentionLayer(tf.keras.layers.Layer):
def __init__(self, config: IdeficsConfig, **kwargs):
super().__init__(**kwargs)
self.hidden_size = config.hidden_size
self.cross_attn = TFIdeficsAttention(
hidden_size=self.hidden_size,
num_heads=config.num_attention_heads,
is_cross_attention=True,
dropout=config.dropout,
config=config,
qk_layer_norms=config.qk_layer_norms,
name="cross_attn",
)
self.mlp = TFIdeficsMLP(
hidden_size=self.hidden_size,
intermediate_size=config.intermediate_size,
hidden_act=config.hidden_act,
name="mlp",
)
self.input_layernorm = TFIdeficsRMSNorm(config.hidden_size, eps=config.rms_norm_eps, name="input_layernorm")
self.post_attention_layernorm = TFIdeficsRMSNorm(
config.hidden_size, eps=config.rms_norm_eps, name="post_attention_layernorm"
)
self.config = config.dropout
self.act_cross_attn = tf.keras.activations.tanh
self.act_dense = tf.keras.activations.tanh
self.alpha_initializer = config.alpha_initializer
self.alpha_type = config.alpha_type
self.alphas_initializer_range = config.alphas_initializer_range
def build(self, input_shape):
if self.built:
return
self.built = True
if self.alpha_initializer == "zeros":
if self.alpha_type == "vector":
self.alpha_cross_attn = self.add_weight(
shape=(1, 1, self.hidden_size), initializer="zeros", trainable=True, name="alpha_cross_attn"
)
self.alpha_dense = self.add_weight(
shape=(1, 1, self.hidden_size), initializer="zeros", trainable=True, name="alpha_dense"
)
elif self.alpha_type == "float":
self.alpha_cross_attn = self.add_weight(
shape=(1,), initializer="zeros", trainable=True, name="alpha_cross_attn"
)
self.alpha_dense = self.add_weight(shape=(1,), initializer="zeros", trainable=True, name="alpha_dense")
else:
raise ValueError(f"Unknown value for `alpha_type` ({self.alpha_type})")
elif self.alpha_initializer == "ones":
if self.alpha_type == "vector":
self.alpha_cross_attn = self.add_weight(
shape=(1, 1, self.hidden_size), initializer="ones", trainable=True, name="alpha_cross_attn"
)
self.alpha_dense = self.add_weight(
shape=(1, 1, self.hidden_size), initializer="ones", trainable=True, name="alpha_dense"
)
elif self.alpha_type == "float":
self.alpha_cross_attn = self.add_weight(
shape=(1,), initializer="ones", trainable=True, name="alpha_cross_attn"
)
self.alpha_dense = self.add_weight(shape=(1,), initializer="ones", trainable=True, name="alpha_dense")
else:
raise ValueError(f"Unknown value for `alpha_type` ({self.alpha_type})")
elif self.alpha_initializer in {"normal", "gaussian", "random"}:
if self.alpha_type == "vector":
self.alpha_cross_attn = self.add_weight(
shape=(1, 1, self.hidden_size),
initializer=tf.keras.initializers.RandomNormal(mean=0.0, stddev=self.alphas_initializer_range),
trainable=True,
name="alpha_cross_attn",
)
self.alpha_dense = self.add_weight(
shape=(1, 1, self.hidden_size),
initializer=tf.keras.initializers.RandomNormal(mean=0.0, stddev=self.alphas_initializer_range),
trainable=True,
name="alpha_dense",
)
elif self.alpha_type == "float":
self.alpha_cross_attn = self.add_weight(
shape=(1,),
initializer=tf.keras.initializers.RandomNormal(mean=0.0, stddev=self.alphas_initializer_range),
trainable=True,
name="alpha_type",
)
self.alpha_dense = self.add_weight(
shape=(1,),
initializer=tf.keras.initializers.RandomNormal(mean=0.0, stddev=self.alphas_initializer_range),
trainable=True,
name="alpha_dense",
)
else:
raise ValueError(f"Unknown value for `alpha_type` ({self.alpha_type})")
else:
raise NotImplementedError(f"Alpha initialization scheme {self.alpha_initializer} not yet implemented!")
if not (hasattr(self, "alpha_cross_attn") and hasattr(self, "alpha_dense")):
raise ValueError("Alpha parameters not initialized correctly!")
with tf.name_scope(self.cross_attn.name):
self.cross_attn.build(None)
with tf.name_scope(self.mlp.name):
self.mlp.build(None)
with tf.name_scope(self.input_layernorm.name):
self.input_layernorm.build(None)
with tf.name_scope(self.post_attention_layernorm.name):
self.post_attention_layernorm.build(None)
super().build(input_shape)
def call(
self,
hidden_states: tf.Tensor,
attention_mask: Optional[tf.Tensor] = None,
image_hidden_states: Optional[tf.Tensor] = None,
image_attention_mask: Optional[tf.Tensor] = None,
cross_attention_gate: Optional[tf.Tensor] = None,
output_attentions: Optional[bool] = False,
use_cache: Optional[bool] = False,
past_key_value: Optional[Tuple[tf.Tensor]] = None,
) -> Tuple[tf.Tensor, Optional[Tuple[tf.Tensor, tf.Tensor]]]:
"""
Args:
hidden_states (`tf.Tensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
attention_mask (`tf.Tensor`, *optional*): attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
(see `past_key_values`).
past_key_value (`Tuple(tf.Tensor)`, *optional*): cached past key and value projection states
no_images (`bool`, *optional*, defaults to `False`): If `True` the vision part is ignored
"""
if image_hidden_states is None:
raise ValueError(
"`image_hidden_states` is required for Idefics cross attention module which are visual features to be"
" conditioned on."
)
if cross_attention_gate is None:
raise ValueError(
"`cross_attention_gate` is required for Idefics cross attention module to zero-out the cross-attention hidden_states attending to no images."
)
if past_key_value is not None:
raise NotImplementedError("Past key value states are not implemented for Idefics cross attention module.")
residual = hidden_states
hidden_states = self.input_layernorm(hidden_states)
# Self Attention
hidden_states, self_attn_weights, present_key_value = self.cross_attn(
hidden_states=hidden_states,
key_value_states=image_hidden_states,
attention_mask=image_attention_mask,
output_attentions=output_attentions,
)
hidden_states = tf.nn.dropout(hidden_states, rate=self.config)
mask = tf.cast(cross_attention_gate == 0, dtype=hidden_states.dtype)
# Expand dimensions of mask to match hidden_states
mask = tf.expand_dims(mask, -1)
hidden_states = tf.where(
tf.broadcast_to(mask, tf.shape(hidden_states)) == 1, tf.zeros_like(hidden_states), hidden_states
)
# when there are no images the model is used in pure language mode
# gate = 0 if no_images else 1
hidden_states = residual + self.act_cross_attn(self.alpha_cross_attn) * hidden_states
# Fully Connected
residual = hidden_states
hidden_states = self.post_attention_layernorm(hidden_states)
hidden_states = self.mlp(hidden_states)
hidden_states = tf.nn.dropout(hidden_states, rate=self.config)
hidden_states = residual + self.act_dense(self.alpha_dense) * hidden_states
outputs = (hidden_states,)
if output_attentions:
outputs += (self_attn_weights,)
if use_cache:
outputs += (present_key_value,)
return outputs
LLAMA_START_DOCSTRING = r"""
This model inherits from [`TFPreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a TensorFlow [tf.keras.layers.Layer](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer) subclass.
Use it as a regular TensorFlow Layer and refer to the TensorFlow documentation for all matter related to general usage
and behavior.
Parameters:
config ([`IdeficsConfig`]):
Model configuration class with all the parameters of the model. Initializing with a config file does not
load the weights associated with the model, only the configuration. Check out the
[`~TFPreTrainedModel.from_pretrained`] method to load the model weights.
"""
@add_start_docstrings(
"The bare LLaMA Model outputting raw hidden-states without any specific head on top.",
LLAMA_START_DOCSTRING,
)
class TFIdeficsPreTrainedModel(TFPreTrainedModel):
config_class = IdeficsConfig
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = ["TFIdeficsDecoderLayer", "TFIdeficsGatedCrossAttentionLayer"]
LLAMA_INPUTS_DOCSTRING = r"""
Args:
input_ids (`tf.Tensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
it.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
`past_key_values`).
If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
information on the default strategy.
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
position_ids (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
config.n_positions - 1]`. [What are position IDs?](../glossary#position-ids)
past_key_values (`tuple(tuple(tf.Tensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
Tuple of `tuple(tf.Tensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
`(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
`(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
`decoder_input_ids` of shape `(batch_size, sequence_length)`.
inputs_embeds (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
model's internal embedding lookup matrix.
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
`past_key_values`).
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
@add_start_docstrings(
"The bare LLaMA Model outputting raw hidden-states without any specific head on top.",
LLAMA_START_DOCSTRING,
)
@keras_serializable
class TFIdeficsMainLayer(tf.keras.layers.Layer):
"""
Transformer decoder consisting of `config.num_hidden_layers` layers. Each layer is a [`IdeficsDecoderLayer`]
Args:
config: IdeficsConfig
"""
config_class = IdeficsConfig
def __init__(self, config: IdeficsConfig, add_pooling_year: bool = True, **kwargs):
super().__init__(**kwargs)
self.config = config
self.padding_idx = config.pad_token_id
self.vocab_size = config.vocab_size
self.embed_tokens = TFIdeficsDecoupledEmbedding(
num_embeddings=config.vocab_size,
num_additional_embeddings=config.additional_vocab_size,
embedding_dim=config.hidden_size,
partially_freeze=config.freeze_text_layers,
name="embed_tokens",
)
self.image_size = config.vision_config.image_size
self.vision_config = config.vision_config
self.vision_model = TFIdeficsVisionTransformer(config.vision_config, name="vision_model")
# Perceiver Resampler
if config.use_resampler:
perceiver_config = config.perceiver_config
self.perceiver_resampler = TFIdeficsPerceiverResampler(
config,
config.vision_config.embed_dim,
perceiver_config.resampler_depth,
perceiver_config.resampler_n_heads,
perceiver_config.resampler_head_dim,
perceiver_config.resampler_n_latents,
name="perceiver_resampler",
)
self.decoder_layers = [
TFIdeficsDecoderLayer(config, name=f"layers.{i}") for i in range(config.num_hidden_layers)
]
self.cross_layer_interval = config.cross_layer_interval
num_cross_layers = config.num_hidden_layers // self.cross_layer_interval
self.gated_cross_attn_layers = [
TFIdeficsGatedCrossAttentionLayer(config, name=f"gated_cross_attn_layers.{i}")
for i in range(num_cross_layers)
]
self.gradient_checkpointing = False
self.norm = TFIdeficsRMSNorm(config.hidden_size, eps=config.rms_norm_eps, name="norm")
self.gradient_checkpointing = False
self.freeze_relevant_params(config)
def freeze_relevant_params(self, config=None):
if config is None:
config = self.config
if config.freeze_text_layers:
self.freeze_text_layers(config.freeze_text_module_exceptions)
if config.freeze_vision_layers:
freeze_model(self.vision_model, module_exceptions=config.freeze_vision_module_exceptions)
def freeze_text_layers(self, module_exceptions=[]):
for module in [self.decoder_layers, self.norm]:
freeze_model(module, module_exceptions=module_exceptions)
def freeze_vision_layers(self, module_exceptions=[]):
freeze_model(self.vision_model, module_exceptions=module_exceptions)
def get_input_embeddings(self):
return self.embed_tokens
def set_input_embeddings(self, value):
self.embed_tokens = value
def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length):
# create causal mask
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
combined_attention_mask = None
# if input_shape[-1] > 1:
combined_attention_mask = _make_causal_mask(
input_shape,
inputs_embeds.dtype,
past_key_values_length=past_key_values_length,
)
if attention_mask is not None:
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1])
combined_attention_mask = (
expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask
)
return combined_attention_mask
@unpack_inputs
@add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
def call(
self,
input_ids: TFModelInputType | None = None,
attention_mask: Optional[tf.Tensor] = None,
position_ids: Optional[tf.Tensor] = None,
past_key_values: Optional[List[tf.Tensor]] = None,
inputs_embeds: Optional[tf.Tensor] = None,
pixel_values: Optional[tf.Tensor] = None,
image_encoder_embeddings: Optional[tf.Tensor] = None,
perceiver_embeddings: Optional[tf.Tensor] = None,
image_attention_mask: Optional[tf.Tensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
interpolate_pos_encoding: Optional[bool] = False,
return_dict: Optional[bool] = None,
training: Optional[bool] = None,
) -> Union[TFIdeficsBaseModelOutputWithPast, Tuple[tf.Tensor]]:
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
use_cache = use_cache if use_cache is not None else self.config.use_cache
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
# retrieve input_ids and inputs_embeds
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
elif input_ids is not None:
batch_size, seq_length = shape_list(input_ids)
elif inputs_embeds is not None:
batch_size, seq_length, _ = shape_list(inputs_embeds)
else:
raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
seq_length_with_past = seq_length
past_key_values_length = 0
if past_key_values is not None:
past_key_values_length = shape_list(past_key_values[0][0])[2]
seq_length_with_past = seq_length_with_past + past_key_values_length
if attention_mask is not None and position_ids is None:
# create position_ids on the fly for batch generation
position_ids = tf.math.cumsum(tf.cast(attention_mask, dtype=tf.int32), axis=-1) - 1
position_ids = tf.where(attention_mask == 0, 1, position_ids)
elif position_ids is None:
position_ids = tf.range(past_key_values_length, seq_length + past_key_values_length, dtype=tf.int32)
position_ids = tf.expand_dims(position_ids, 0)
no_images = False
if (
sum((int(pixel_values is None), int(image_encoder_embeddings is None), int(perceiver_embeddings is None)))
!= 2
):
raise ValueError(
"Exactly 1 of pixel_values, image_encoder_embeddings or perceiver_embeddings has to be not-None."
)
elif pixel_values is not None:
no_images = tf.reduce_sum(tf.cast(pixel_values, dtype=tf.int32)) == 0
pixel_values = tf.cast(pixel_values, dtype=self.dtype) # fp16 compatibility
# Below hack is because when cross-loading pytorch weights, there is an
# initial forward pass with dummy input and code below is here to handle that
if len(pixel_values.shape) == 4:
batch_size = shape_list(pixel_values)[0]
num_images = shape_list(pixel_values)[0]
# pixel_values = tf.reshape(pixel_values, [batch_size * num_images, *pixel_values.shape[1:]])
elif len(pixel_values.shape) == 5:
batch_size, num_images = shape_list(pixel_values)[:2]
pixel_values = tf.reshape(pixel_values, [batch_size * num_images, *pixel_values.shape[2:]])
# Get sequence from the vision encoder
image_hidden_states = self.vision_model(
pixel_values=pixel_values, interpolate_pos_encoding=interpolate_pos_encoding
).last_hidden_state
elif image_encoder_embeddings is not None:
batch_size, num_images, image_seq_len, image_hidden_size = shape_list(image_encoder_embeddings)
image_hidden_states = tf.cast(image_encoder_embeddings, dtype=self.dtype)
image_hidden_states = tf.reshape(
image_hidden_states, (batch_size * num_images, image_seq_len, image_hidden_size)
)
if self.config.use_resampler:
if perceiver_embeddings is None:
perceiver_embeddings = self.perceiver_resampler(image_hidden_states)
image_seq_len, image_hidden_size = shape_list(perceiver_embeddings)[1:3]
else:
batch_size, num_images, image_seq_len, image_hidden_size = shape_list(perceiver_embeddings)
image_hidden_states = perceiver_embeddings
elif perceiver_embeddings is None:
image_seq_len, image_hidden_size = shape_list(image_hidden_states)[1:3]
else:
raise ValueError("If `perceiver_embeddings` are passed, use_resampler should be True")
image_hidden_states = tf.reshape(
image_hidden_states, (batch_size, num_images * image_seq_len, image_hidden_size)
)
# # Hack to use the model in full language modeling mode
# image_attention_mask = tf.zeros((batch_size, seq_length, 1), dtype=tf.int32)
# this is to account for the dummy inputs
if pixel_values is not None and len(pixel_values.shape) == 4 and image_attention_mask is None:
image_attention_mask = tf.zeros((batch_size, seq_length, 1), dtype=tf.int32)
text_seq_len = shape_list(image_attention_mask)[1]
image_attention_mask = tf.expand_dims(image_attention_mask, -1)
image_attention_mask = tf.repeat(image_attention_mask, repeats=image_seq_len)
image_attention_mask = tf.reshape(image_attention_mask, (batch_size, text_seq_len, num_images * image_seq_len))
if image_hidden_states is not None:
image_batch_size, image_sequence_length, _ = shape_list(image_hidden_states)
image_hidden_shape = (image_batch_size, image_sequence_length)
if image_attention_mask is None:
image_attention_mask = tf.ones(image_hidden_shape, dtype=tf.int32)
image_attention_mask = invert_attention_mask(image_attention_mask)
else:
image_attention_mask = None
cross_attention_gate = tf.squeeze(
tf.cast(tf.reduce_any(image_attention_mask == 0, axis=-1), dtype=self.dtype), axis=1
)
if inputs_embeds is None:
inputs_embeds = self.embed_tokens(input_ids)
# embed positions
if attention_mask is None:
attention_mask = tf.ones((batch_size, seq_length_with_past), dtype=tf.bool)
attention_mask = self._prepare_decoder_attention_mask(
attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
)
hidden_states = inputs_embeds
if self.gradient_checkpointing and training:
if use_cache:
logger.warning_once(
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
)
use_cache = False
# decoder layers
all_hidden_states = () if output_hidden_states else None
all_self_attns = () if output_attentions else None
next_decoder_cache = () if use_cache else None
for idx, decoder_layer in enumerate(self.decoder_layers):
if output_hidden_states:
all_hidden_states += (hidden_states,)
past_key_value = past_key_values[idx] if past_key_values is not None else None
def vblock(
main_block,
hidden_states,
attention_mask,
position_ids,
past_key_value,
image_hidden_states,
image_attention_mask,
cross_attention_gate,
output_attentions,
use_cache,
layer_idx,
cross_layer_interval,
gated_cross_attn_layers,
):
# TODO(ls): Add cross attention values to respective lists
if layer_idx % cross_layer_interval == 0:
xblock = gated_cross_attn_layers[layer_idx // cross_layer_interval]
outputs = xblock(
hidden_states,
attention_mask=attention_mask,
image_hidden_states=image_hidden_states,
image_attention_mask=image_attention_mask,
cross_attention_gate=cross_attention_gate,
output_attentions=output_attentions,
use_cache=use_cache,
past_key_value=None, # not implemented
)
hidden_states = outputs[0]
layer_outputs = main_block(
hidden_states,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_value=past_key_value,
output_attentions=output_attentions,
use_cache=use_cache,
)
return layer_outputs
if self.gradient_checkpointing and training:
past_key_value = None
if use_cache:
logger.warning_once(
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
)
use_cache = False
layer_outputs = tf.recompute_grad(
vblock,
decoder_layer,
hidden_states,
attention_mask,
position_ids,
past_key_value,
image_hidden_states,
image_attention_mask,
output_attentions,
use_cache,
no_images,
idx,
self.cross_layer_interval,
self.gated_cross_attn_layers,
)
else:
layer_outputs = vblock(
decoder_layer,
hidden_states,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_value=past_key_value,
image_hidden_states=image_hidden_states,
image_attention_mask=image_attention_mask,
cross_attention_gate=cross_attention_gate,
output_attentions=output_attentions,
use_cache=use_cache,
layer_idx=idx,
cross_layer_interval=self.cross_layer_interval,
gated_cross_attn_layers=self.gated_cross_attn_layers,
)
hidden_states = layer_outputs[0]
if use_cache:
next_decoder_cache += (layer_outputs[2 if output_attentions else 1],)
if output_attentions:
all_self_attns += (layer_outputs[1],)
hidden_states = self.norm(hidden_states)
# add hidden states from the last decoder layer
if output_hidden_states:
all_hidden_states += (hidden_states,)
next_cache = next_decoder_cache if use_cache else None
image_hidden_states = tf.reshape(
image_hidden_states, (batch_size, num_images, image_seq_len, image_hidden_size)
)
if not return_dict:
return tuple(
v
for v in [hidden_states, next_cache, all_hidden_states, all_self_attns, image_hidden_states]
if v is not None
)
return TFIdeficsBaseModelOutputWithPast(
last_hidden_state=hidden_states,
past_key_values=next_cache,
hidden_states=all_hidden_states,
attentions=all_self_attns,
image_hidden_states=image_hidden_states,
)
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "embed_tokens", None) is not None:
with tf.name_scope(self.embed_tokens.name):
self.embed_tokens.build(None)
if getattr(self, "vision_model", None) is not None:
with tf.name_scope(self.vision_model.name):
self.vision_model.build(None)
if getattr(self, "norm", None) is not None:
with tf.name_scope(self.norm.name):
self.norm.build(None)
if getattr(self, "perceiver_resampler", None) is not None:
with tf.name_scope(self.perceiver_resampler.name):
self.perceiver_resampler.build(None)
if getattr(self, "decoder_layers", None) is not None:
for layer in self.decoder_layers:
with tf.name_scope(layer.name):
layer.build(None)
if getattr(self, "gated_cross_attn_layers", None) is not None:
for layer in self.gated_cross_attn_layers:
with tf.name_scope(layer.name):
layer.build(None)
class TFIdeficsModel(TFIdeficsPreTrainedModel):
def __init__(self, config: IdeficsConfig, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.model = TFIdeficsMainLayer(config, name="model")
def call(
self,
input_ids: TFModelInputType | None = None,
attention_mask: Optional[tf.Tensor] = None,
position_ids: Optional[tf.Tensor] = None,
past_key_values: Optional[List[tf.Tensor]] = None,
inputs_embeds: Optional[tf.Tensor] = None,
pixel_values: Optional[tf.Tensor] = None,
image_encoder_embeddings: Optional[tf.Tensor] = None,
perceiver_embeddings: Optional[tf.Tensor] = None,
image_attention_mask: Optional[tf.Tensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
interpolate_pos_encoding: Optional[bool] = False,
return_dict: Optional[bool] = None,
training: Optional[bool] = None,
) -> Union[TFIdeficsBaseModelOutputWithPast, Tuple[tf.Tensor]]:
outputs = self.model(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
pixel_values=pixel_values,
image_encoder_embeddings=image_encoder_embeddings,
perceiver_embeddings=perceiver_embeddings,
image_attention_mask=image_attention_mask,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
interpolate_pos_encoding=interpolate_pos_encoding,
return_dict=return_dict,
training=training,
)
return outputs
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "model", None) is not None:
with tf.name_scope(self.model.name):
self.model.build(None)
class TFIdeficsForVisionText2Text(TFPreTrainedModel, TFCausalLanguageModelingLoss):
_keys_to_ignore_on_load_missing = [r"lm_head.weight"]
_tied_weights_keys = ["model.embed_tokens.weight", "lm_head.weight"]
config_class = IdeficsConfig
def __init__(self, config, vision_model=None, **kwargs):
super().__init__(config, **kwargs)
self.model = TFIdeficsMainLayer(config, name="model")
self.lm_head = TFIdeficsDecoupledLinear(
config.hidden_size,
config.vocab_size,
config.additional_vocab_size,
bias=False,
partially_freeze=config.freeze_lm_head,
name="lm_head",
)
def get_input_embeddings(self):
return self.model.embed_tokens
def set_input_embeddings(self, value):
self.model.embed_tokens = value
def get_output_embeddings(self):
return self.lm_head
def set_output_embeddings(self, new_embeddings):
self.lm_head = new_embeddings
def set_decoder(self, decoder):
self.model = decoder
def get_decoder(self):
return self.model
def tie_weights(self):
"""
Overwrite `transformers.modeling_utils.PreTrainedModel.tie_weights` to handle the case of
IdeficsDecoupledLinear and IdeficsDecoupledEmbedding.
"""
output_embeddings = self.get_output_embeddings()
input_embeddings = self.get_input_embeddings()
if getattr(self.config, "tie_word_embeddings", True):
output_embeddings.weight = input_embeddings.weight
if input_embeddings.num_additional_embeddings > 0:
assert output_embeddings.out_additional_features == input_embeddings.num_additional_embeddings
output_embeddings.additional_fc.weight = input_embeddings.additional_embedding.weight
if hasattr(output_embeddings, "out_features") and hasattr(input_embeddings, "num_embeddings"):
output_embeddings.out_features = input_embeddings.num_embeddings
if hasattr(output_embeddings, "out_additional_features") and hasattr(
input_embeddings, "num_additional_embeddings"
):
output_embeddings.out_additional_features = input_embeddings.num_additional_embeddings
@unpack_inputs
@add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=TFIdeficsCausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
def call(
self,
input_ids: TFModelInputType | None = None,
attention_mask: Optional[tf.Tensor] = None,
position_ids: Optional[tf.Tensor] = None,
past_key_values: Optional[List[tf.Tensor]] = None,
inputs_embeds: Optional[tf.Tensor] = None,
pixel_values: Optional[tf.Tensor] = None,
image_encoder_embeddings: Optional[tf.Tensor] = None,
perceiver_embeddings: Optional[tf.Tensor] = None,
image_attention_mask: Optional[tf.Tensor] = None,
labels: Optional[tf.Tensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
interpolate_pos_encoding: Optional[bool] = False,
return_dict: Optional[bool] = None,
training=False,
) -> Union[TFIdeficsCausalLMOutputWithPast, Tuple[tf.Tensor]]:
r"""
labels (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
Returns:
Example:
```python
>> from transformers import AutoTokenizer, TFIdeficsForVisionText2Text
>> model = TFIdeficsForVisionText2Text.from_pretrained("HuggingFaceM4/idefics-9b")
>> tokenizer = AutoTokenizer.from_pretrained("HuggingFaceM4/idefics-9b")
>> prompt = "Hey, are you consciours? Can you talk to me?"
>> inputs = tokenizer(prompt, return_tensors="tf")
>> # Generate
>> generate_ids = model.generate(inputs.input_ids, max_length=30)
>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
"Hey, are you consciours? Can you talk to me?\nI'm not consciours, but I can talk to you."
```"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
outputs = self.model(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
pixel_values=pixel_values,
image_encoder_embeddings=image_encoder_embeddings,
perceiver_embeddings=perceiver_embeddings,
image_attention_mask=image_attention_mask,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
interpolate_pos_encoding=interpolate_pos_encoding,
return_dict=return_dict,
training=training,
)
hidden_states = outputs[0]
logits = self.lm_head(hidden_states)
loss = None
if labels is not None:
# Shift so that tokens < n predict n
if attention_mask is not None:
shift_attention_mask = attention_mask[..., 1:]
shift_logits = logits[..., :-1, :][shift_attention_mask != 0]
shift_labels = labels[..., 1:][shift_attention_mask != 0]
else:
shift_logits = logits[..., :-1, :]
shift_labels = labels[..., 1:]
# Flatten the tokens
loss = self.hf_compute_loss(
labels=tf.reshape(shift_labels, [-1]), logits=tf.reshape(shift_logits, [-1, shift_logits.shape[-1]])
)
if not return_dict:
output = (logits,) + outputs[1:]
return (loss,) + output if loss is not None else output
return TFIdeficsCausalLMOutputWithPast(
loss=loss,
logits=logits,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
image_hidden_states=outputs.image_hidden_states,
)
def prepare_inputs_for_generation(self, input_ids, past=None, **kwargs):
image_hidden_states = kwargs.pop("image_hidden_states", None)
if image_hidden_states is not None:
if self.config.use_resampler:
kwargs["perceiver_embeddings"] = image_hidden_states
else:
kwargs["image_encoder_embeddings"] = image_hidden_states
kwargs["pixel_values"] = None
inputs = prepare_inputs_for_generation(input_ids, past=past, **kwargs)
unwanted_kwargs = ["token_type_ids"]
for kwarg in unwanted_kwargs:
inputs.pop(kwarg, None)
return inputs
@staticmethod
def _expand_inputs_for_generation(
*args,
**model_kwargs,
):
return expand_inputs_for_generation(*args, **model_kwargs)
@staticmethod
def _update_model_kwargs_for_generation(outputs, model_kwargs, is_encoder_decoder):
return update_model_kwargs_for_generation(outputs, model_kwargs)
@staticmethod
def _reorder_cache(past, beam_idx):
reordered_past = ()
for layer_past in past:
reordered_past += (tuple(tf.gather(past_state, beam_idx) for past_state in layer_past),)
return reordered_past
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "model", None) is not None:
with tf.name_scope(self.model.name):
self.model.build(None)
if getattr(self, "lm_head", None) is not None:
with tf.name_scope(self.lm_head.name):
self.lm_head.build(None)
__all__ = ["TFIdeficsForVisionText2Text", "TFIdeficsModel", "TFIdeficsPreTrainedModel"]
```
|
================================================================================================================================
SOURCE CODE FILE: perceiver.py
LINES: 1
SIZE: 9.21 KB
PATH: scripts\freecad_env\Lib\site-packages\transformers\models\idefics\perceiver.py
ENCODING: utf-8
```py
# This code was adapted from https://github.com/lucidrains/flamingo-pytorch licensed under the MIT License.
#
# MIT License
#
# Copyright (c) 2020 The Google AI Language Team Authors, The HuggingFace Inc. team and github/lonePatient
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
"""
Generic interface to various configurations of the Perceiver Resampler, that simply takes in a series of (potentially
time-indexed) contextual embeddings, and "resamples" (compresses) them down to a pre-specified number of latents! Note
that the Perceiver in general resamples based solely off the *long-range* context; there's a nice opportunity here to
prime the Perceiver Resampler with say a single layer's worth of language embeddings (the target domain), and use that
to softly "retrieve & compress" what we need --> this would be a novel contribution we should explore.
References:
- DeepMind's Flamingo: https://www.deepmind.com/blog/tackling-multiple-tasks-with-a-single-visual-language-model
- Code borrowed w/ love from: https://github.com/lucidrains/flamingo-pytorch
"""
from typing import Optional, Tuple
import torch
import torch.nn as nn
from .configuration_idefics import IdeficsConfig
class IdeficsPerceiverResampler(nn.Module):
def __init__(
self, config: IdeficsConfig, embed_dim: int, depth: int, n_heads: int, head_dim: int, n_latents: int
) -> None:
"""
Instantiates a Perceiver Resampler that operates over a sequence of embeddings (say from a ResNet or ViT or
MAE) of a given dimension, performs `depth` blocks of cross-attention with a fixed `n_latents` inputs, then
returns a Tensor of shape [bsz, n_latents, embed_dim]. :param embed_dim: Dimensionality of embeddings being fed
to the Perceiver Resampler (also dimensionality of latent embeddings *returned* by the Perceiver Resampler.
Could be e.g., VIT embed_dim, ResNet pool dim, and so on.
Args:
config (`IdeficsConfig`): config object
embed_dim (`int`): The size of each embedding vector
depth (`int`): Depth of the Perceiver Resampler (Transformer w/ cross attention). Should be shallow (< 3).
n_heads (`int`): Number of heads in each Transformer block (for multi-headed self-attention).
head_dim (`int`): Dimensionality of each head projection in the Transformer block.
n_latents (`int`):
Number of latent embeddings to resample ("compress") the input sequence to (usually < 128).
"""
super().__init__()
self.embed_dim, self.n_heads, self.head_dim, self.n_latents = embed_dim, n_heads, head_dim, n_latents
self.qk_layer_norms = config.perceiver_config.qk_layer_norms_perceiver
# Create Latents for Perceiver
self.latents = nn.Parameter(torch.randn(self.n_latents, self.embed_dim), requires_grad=True)
self.intermediate_dim = (
self.embed_dim * 4
if not hasattr(config.vision_config, "embed_dim")
else config.vision_config.embed_dim * 4
)
# Create Transformer Blocks
self.blocks = nn.ModuleList(
[
nn.ModuleList(
[
IdeficsPerceiverAttention(self.embed_dim, self.n_heads, self.head_dim, self.qk_layer_norms),
IdeficsMLP(self.intermediate_dim, config),
]
)
for _ in range(depth)
]
)
self.layer_norm = nn.LayerNorm(self.embed_dim)
def forward(self, context: torch.Tensor) -> torch.Tensor:
"""Resample arbitrary length context & *compress* down to self.n_latents latent embeddings"""
# einsum.repeat(self.latents, "seq embed -> bsz seq embed", bsz=context.shape[0])
latents = self.latents.repeat(context.shape[0], 1, 1)
# Feed through Perceiver Attention blocks...
for attn, ff in self.blocks:
latents = attn(context, latents) + latents
latents = ff(latents) + latents
return self.layer_norm(latents)
class IdeficsPerceiverAttention(nn.Module):
def __init__(self, embed_dim: int, n_heads: int, head_dim: int, qk_layer_norms: bool) -> None:
"""Perceiver Cross-Attention Module --> let long-form inputs be `context`, resampled embeddings be `latents`"""
super().__init__()
self.embed_dim, self.n_heads, self.head_dim = embed_dim, n_heads, head_dim
self.qk_layer_norms = qk_layer_norms
# Normalization & Scaling
self.context_layer_norm = nn.LayerNorm(self.embed_dim)
self.latents_layer_norm = nn.LayerNorm(self.embed_dim)
if self.qk_layer_norms:
self.q_layer_norm = nn.LayerNorm(self.head_dim)
self.k_layer_norm = nn.LayerNorm(self.head_dim)
self.qk_scale = self.head_dim**-0.5
# Q, K, V Projection (no bias -- detail from Perceiver/Flamingo Papers).
self.q_proj = nn.Linear(self.embed_dim, self.n_heads * self.head_dim, bias=False)
self.k_proj = nn.Linear(self.embed_dim, self.n_heads * self.head_dim, bias=False)
self.v_proj = nn.Linear(self.embed_dim, self.n_heads * self.head_dim, bias=False)
self.output_proj = nn.Linear(self.n_heads * self.head_dim, embed_dim, bias=False)
def forward(self, context: torch.Tensor, latents: torch.Tensor) -> torch.Tensor:
"""
Runs Perceiver Self-Attention, with special (context, latents) appended along the `seq` dimension!
Args:
context (`torch.Tensor`):
Tensor of shape `[bsz, seq, embed_dim]` representing long-form context to resample.
latents (`torch.Tensor`):
Tensor of shape `[bsz, n_latents, embed_dim]` representing fixed length latents to compress to.
Returns:
`torch.Tensor`: Tensor of shape `[bsz, n_latents, embed_dim]` representing attention over latents w/ cross
from context.
"""
context = self.context_layer_norm(context)
latents = self.latents_layer_norm(latents)
batch_size, seq_length, embed_dim = context.shape[:3]
# Query, Key, Value Projections --> Note that in Flamingo, latents are *concatenated* with context prior to attn!
# Note: This results in queries w/ `seq = n_latents`, and keys, values with `seq = len(context) + n_latents`
q = self.q_proj(latents)
k = self.k_proj(torch.cat([context, latents], dim=-2))
v = self.v_proj(torch.cat([context, latents], dim=-2))
# Multiheaded Self-Attention w/ stable softmax (subtract per-row max -- `amax` -- before softmax call)
# =>> `attn` should be a 2D matrix of shape [n_latents x (context + n_latents)]
# einsum.rearrange(x, "bsz seq (heads embed) -> bsz heads seq embed", heads=self.n_heads)
q, k, v = [x.reshape(batch_size, x.shape[1], self.n_heads, self.head_dim).transpose(1, 2) for x in (q, k, v)]
if self.qk_layer_norms:
q = self.q_layer_norm(q)
k = self.k_layer_norm(k)
scores = torch.einsum("... i d, ... j d -> ... i j", q * self.qk_scale, k)
stabilized_scores = scores - (scores.amax(dim=-1, keepdim=True).detach())
attn = stabilized_scores.softmax(dim=-1)
# Attend & project back to output...
resampled = torch.einsum("... i j, ... j d -> ... i d", attn, v)
# einsum.rearrange(resampled, "bsz heads seq embed -> bsz seq (heads embed)", heads=self.n_heads)
return self.output_proj(resampled.transpose(1, 2).flatten(-2))
class IdeficsMLP(nn.Module):
def __init__(self, intermediate_size, config: IdeficsConfig):
"""Simple MLP block with intermediate_size and embedding size"""
super().__init__()
self.embed_dim = config.vision_config.embed_dim
self.ln = nn.LayerNorm(self.embed_dim)
self.fc = nn.Linear(self.embed_dim, intermediate_size, bias=False)
self.act = nn.ReLU()
self.c_proj = nn.Linear(intermediate_size, self.embed_dim, bias=False)
def forward(self, hidden_states: Optional[Tuple[torch.FloatTensor]]) -> torch.FloatTensor:
hidden_states = self.ln(hidden_states)
hidden_states = self.fc(hidden_states)
hidden_states = self.act(hidden_states)
hidden_states = self.c_proj(hidden_states)
return hidden_states
```
|
===================================================================================================================================
SOURCE CODE FILE: perceiver_tf.py
LINES: 1
SIZE: 9.77 KB
PATH: scripts\freecad_env\Lib\site-packages\transformers\models\idefics\perceiver_tf.py
ENCODING: utf-8
```py
# This code was adapted from https://github.com/lucidrains/flamingo-pytorch licensed under the MIT License.
#
# MIT License
#
# Copyright (c) 2020 The Google AI Language Team Authors, The HuggingFace Inc. team and github/lonePatient
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
"""
Generic interface to various configurations of the Perceiver Resampler, that simply takes in a series of (potentially
time-indexed) contextual embeddings, and "resamples" (compresses) them down to a pre-specified number of latents! Note
that the Perceiver in general resamples based solely off the *long-range* context; there's a nice opportunity here to
prime the Perceiver Resampler with say a single layer's worth of language embeddings (the target domain), and use that
to softly "retrieve & compress" what we need --> this would be a novel contribution we should explore.
References:
- DeepMind's Flamingo: https://www.deepmind.com/blog/tackling-multiple-tasks-with-a-single-visual-language-model
- Code borrowed w/ love from: https://github.com/lucidrains/flamingo-pytorch
"""
from typing import Optional, Tuple
import tensorflow as tf
from ...modeling_tf_utils import shape_list
from .configuration_idefics import IdeficsConfig
class TFIdeficsPerceiverResampler(tf.keras.layers.Layer):
def __init__(
self, config: IdeficsConfig, embed_dim: int, depth: int, n_heads: int, head_dim: int, n_latents: int, **kwargs
) -> None:
"""
Instantiates a Perceiver Resampler that operates over a sequence of embeddings (say from a ResNet or ViT or
MAE) of a given dimension, performs `depth` blocks of cross-attention with a fixed `n_latents` inputs, then
returns a Tensor of shape [bsz, n_latents, embed_dim]. :param embed_dim: Dimensionality of embeddings being fed
to the Perceiver Resampler (also dimensionality of latent embeddings *returned* by the Perceiver Resampler.
Could be e.g., VIT embed_dim, ResNet pool dim, and so on.
Args:
config (`IdeficsConfig`): config object
embed_dim (`int`): The size of each embedding vector
depth (`int`): Depth of the Perceiver Resampler (Transformer w/ cross attention). Should be shallow (< 3).
n_heads (`int`): Number of heads in each Transformer block (for multi-headed self-attention).
head_dim (`int`): Dimensionality of each head projection in the Transformer block.
n_latents (`int`):
Number of latent embeddings to resample ("compress") the input sequence to (usually < 128).
"""
super().__init__(**kwargs)
self.embed_dim, self.n_heads, self.head_dim, self.n_latents = embed_dim, n_heads, head_dim, n_latents
self.qk_layer_norms = config.perceiver_config.qk_layer_norms_perceiver
self.intermediate_dim = (
self.embed_dim * 4
if not hasattr(config.vision_config, "embed_dim")
else config.vision_config.embed_dim * 4
)
# Create Transformer Blocks
self.blocks = []
for i in range(depth):
self.blocks.append(
[
TFIdeficsPerceiverAttention(
self.embed_dim, self.n_heads, self.head_dim, self.qk_layer_norms, name=f"blocks.{i}.0"
),
TFIdeficsMLP(self.intermediate_dim, config, name=f"blocks.{i}.1"),
]
)
self.layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="layer_norm")
def build(self, input_shape):
# Create Latents for Perceiver
self.latents = self.add_weight(
shape=(self.n_latents, self.embed_dim), initializer="random_normal", trainable=True, name="latents"
)
super().build(input_shape)
def call(self, context: tf.Tensor) -> tf.Tensor:
"""Resample arbitrary length context & *compress* down to self.n_latents latent embeddings"""
# tf.repeat(self.latents, "seq embed -> bsz seq embed", bsz=context.shape[0])
latents = tf.expand_dims(self.latents, axis=0)
latents = tf.tile(latents, [tf.shape(context)[0], 1, 1])
# Feed through Perceiver Attention blocks...
for attn, ff in self.blocks:
latents = attn(context, latents) + latents
latents = ff(latents) + latents
return self.layer_norm(latents)
class TFIdeficsPerceiverAttention(tf.keras.layers.Layer):
def __init__(self, embed_dim: int, n_heads: int, head_dim: int, qk_layer_norms: bool, **kwargs) -> None:
"""Perceiver Cross-Attention Module --> let long-form inputs be `context`, resampled embeddings be `latents`"""
super().__init__(**kwargs)
self.embed_dim, self.n_heads, self.head_dim = embed_dim, n_heads, head_dim
self.qk_layer_norms = qk_layer_norms
# Normalization & Scaling
self.context_layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="context_layer_norm")
self.latents_layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="latents_layer_norm")
if self.qk_layer_norms:
self.q_layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="q_layer_norm")
self.k_layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="k_layer_norm")
self.qk_scale = self.head_dim**-0.5
# Q, K, V Projection (no bias -- detail from Perceiver/Flamingo Papers).
self.q_proj = tf.keras.layers.Dense(self.n_heads * self.head_dim, use_bias=False, name="q_proj")
self.k_proj = tf.keras.layers.Dense(self.n_heads * self.head_dim, use_bias=False, name="k_proj")
self.v_proj = tf.keras.layers.Dense(self.n_heads * self.head_dim, use_bias=False, name="v_proj")
self.output_proj = tf.keras.layers.Dense(embed_dim, use_bias=False, name="output_proj")
def call(self, context: tf.Tensor, latents: tf.Tensor) -> tf.Tensor:
"""
Runs Perceiver Self-Attention, with special (context, latents) appended along the `seq` dimension!
Args:
context (`tf.Tensor`):
Tensor of shape `[bsz, seq, embed_dim]` representing long-form context to resample.
latents (`tf.Tensor`):
Tensor of shape `[bsz, n_latents, embed_dim]` representing fixed length latents to compress to.
Returns:
`tf.Tensor`: Tensor of shape `[bsz, n_latents, embed_dim]` representing attention over latents w/ cross
from context.
"""
context = self.context_layer_norm(context)
latents = self.latents_layer_norm(latents)
batch_size, seq_length, embed_dim = shape_list(context)
# Query, Key, Value Projections --> Note that in Flamingo, latents are *concatenated* with context prior to attn!
# Note: This results in queries w/ `seq = n_latents`, and keys, values with `seq = len(context) + n_latents`
q = self.q_proj(latents)
k = self.k_proj(tf.concat([context, latents], axis=-2))
v = self.v_proj(tf.concat([context, latents], axis=-2))
# Multiheaded Self-Attention w/ stable softmax (subtract per-row max -- `amax` -- before softmax call)
# =>> `attn` should be a 2D matrix of shape [n_latents x (context + n_latents)]
q, k, v = [
tf.transpose(tf.reshape(x, (batch_size, x.shape[1], self.n_heads, self.head_dim)), perm=[0, 2, 1, 3])
for x in (q, k, v)
]
if self.qk_layer_norms:
q = self.q_layer_norm(q)
k = self.k_layer_norm(k)
scores = tf.einsum("... i d, ... j d -> ... i j", q * self.qk_scale, k)
stabilized_scores = scores - tf.reduce_max(scores, axis=-1, keepdims=True)
attn = tf.nn.softmax(stabilized_scores, axis=-1)
# Attend & project back to output...
resampled = tf.einsum("... i j, ... j d -> ... i d", attn, v)
return self.output_proj(
tf.reshape(tf.transpose(resampled, perm=[0, 2, 1, 3]), (batch_size, -1, self.n_heads * self.head_dim))
)
class TFIdeficsMLP(tf.keras.layers.Layer):
def __init__(self, intermediate_size, config: IdeficsConfig, **kwargs):
"""Simple MLP block with intermediate_size and embedding size"""
super().__init__(**kwargs)
self.embed_dim = config.vision_config.embed_dim
self.ln = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="ln")
self.fc = tf.keras.layers.Dense(intermediate_size, use_bias=False, name="fc")
self.act = tf.keras.layers.ReLU(name="act")
self.c_proj = tf.keras.layers.Dense(self.embed_dim, use_bias=False, name="c_proj")
def call(self, hidden_states: Optional[Tuple[tf.Tensor]]) -> tf.Tensor:
hidden_states = self.ln(hidden_states)
hidden_states = self.fc(hidden_states)
hidden_states = self.act(hidden_states)
hidden_states = self.c_proj(hidden_states)
return hidden_states
```
|
=========================================================================================================================================
SOURCE CODE FILE: processing_idefics.py
LINES: 4
SIZE: 23.31 KB
PATH: scripts\freecad_env\Lib\site-packages\transformers\models\idefics\processing_idefics.py
ENCODING: utf-8
```py
# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Processor class for IDEFICS.
"""
from typing import Callable, Dict, List, Optional, Union
from urllib.parse import urlparse
from ...feature_extraction_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import (
ImagesKwargs,
ProcessingKwargs,
ProcessorMixin,
TextKwargs,
Unpack,
_validate_images_text_input_order,
)
from ...tokenization_utils_base import PreTokenizedInput, TextInput
from ...utils import is_tf_available, is_torch_available
from ...utils.deprecation import deprecate_kwarg
if is_torch_available():
import torch
if is_tf_available():
import tensorflow as tf
IMAGE_TOKEN = "<image>"
class IdeficsImagesKwargs(ImagesKwargs, total=False):
transform: Optional[Callable]
image_size: Optional[Dict[str, int]]
image_mean: Optional[Union[float, List[float]]]
image_std: Optional[Union[float, List[float]]]
class IdeficsTextKwargs(TextKwargs, total=False):
add_eos_token: Optional[bool]
add_end_of_utterance_token: Optional[bool]
class IdeficsProcessorKwargs(ProcessingKwargs, total=False):
text_kwargs: IdeficsTextKwargs
images_kwargs: IdeficsImagesKwargs
_defaults = {
"text_kwargs": {
"add_special_tokens": False,
"padding": "longest",
"add_eos_token": False,
},
"images_kwargs": {},
"common_kwargs": {"return_tensors": "pt"},
}
# copied from m4.training.packing
def incremental_to_binary_attention_mask(incremental_mask, return_tensors, num_classes=-1):
# Set elements >= num_classes to -1
if num_classes != -1:
if return_tensors == "pt":
incremental_mask[incremental_mask >= num_classes] = -1
elif return_tensors == "tf":
incremental_mask = tf.where(incremental_mask >= num_classes, -1, incremental_mask)
# Create mask for negative values
if return_tensors == "pt":
negatives = incremental_mask == -1
incremental_mask[negatives] = 0
attn_mask = torch.nn.functional.one_hot(incremental_mask, num_classes=num_classes)
attn_mask[negatives, :] = 0
elif return_tensors == "tf":
negatives = tf.equal(incremental_mask, -1)
incremental_mask = tf.where(negatives, 0, incremental_mask)
attn_mask = tf.one_hot(incremental_mask, depth=num_classes)
# Reshape 'negatives' to add an extra dimension, making it [batch_size, seq_length, 1]
negatives_expanded = tf.expand_dims(negatives, -1)
attn_mask = tf.where(negatives_expanded, tf.zeros_like(attn_mask), attn_mask)
return attn_mask
# copied from m4.training.packing
def image_attention_mask_for_packed_input_ids(input_ids, tokenizer, return_tensors):
if return_tensors == "pt":
return image_attention_mask_for_packed_input_ids_pt(input_ids, tokenizer)
elif return_tensors == "tf":
return image_attention_mask_for_packed_input_ids_tf(input_ids, tokenizer)
def image_attention_mask_for_packed_input_ids_pt(input_ids, tokenizer):
image_attention_mask = torch.full_like(input_ids, fill_value=-1)
next_image_attention_mask = torch.full_like(input_ids, fill_value=-1)
image_token_id = tokenizer.convert_tokens_to_ids(IMAGE_TOKEN)
eod_token_id = tokenizer.eos_token_id
for batch_idx in range(input_ids.size(0)):
count = -1
seen_eod = False
for idx, token_id in enumerate(input_ids[batch_idx]):
if token_id == image_token_id:
count += 1
image_attention_mask[batch_idx][idx] = count
seen_eod = False
else:
image_attention_mask[batch_idx][idx] = count
if seen_eod:
image_attention_mask[batch_idx][idx] = -1
if token_id == eod_token_id:
seen_eod = True
for batch_idx in range(input_ids.size(0)):
count = -1
seen_eod = False
for idx in range(input_ids[batch_idx].size(0) - 1, -1, -1):
token_id = input_ids[batch_idx][idx]
if token_id == image_token_id:
count += 1
next_image_attention_mask[batch_idx][idx] = count
seen_eod = False
else:
next_image_attention_mask[batch_idx][idx] = count
if token_id == eod_token_id:
seen_eod = True
if seen_eod:
next_image_attention_mask[batch_idx][idx] = -1
non_negative_indices = next_image_attention_mask[batch_idx] != -1
next_image_attention_mask[batch_idx][non_negative_indices] -= count
next_image_attention_mask[batch_idx][non_negative_indices] *= -1
return image_attention_mask, next_image_attention_mask
def image_attention_mask_for_packed_input_ids_tf(input_ids, tokenizer):
image_token_id = tokenizer.convert_tokens_to_ids(IMAGE_TOKEN)
eod_token_id = tokenizer.eos_token_id
batch_size = tf.shape(input_ids)[0]
image_attention_mask = tf.fill(tf.shape(input_ids), -1)
next_image_attention_mask = tf.fill(tf.shape(input_ids), -1)
for batch_idx in range(batch_size):
count = -1
seen_eod = False
seq_length = tf.shape(input_ids)[1]
for idx in range(seq_length - 1, -1, -1):
token_id = input_ids[batch_idx, idx].numpy()
if token_id == image_token_id:
count += 1
indices = [[batch_idx, idx]]
updates = [count]
image_attention_mask = tf.tensor_scatter_nd_update(image_attention_mask, indices, updates)
next_image_attention_mask = tf.tensor_scatter_nd_update(next_image_attention_mask, indices, updates)
elif token_id == eod_token_id and not seen_eod:
seen_eod = True
count = 0
indices = [[batch_idx, idx]]
updates = [count]
next_image_attention_mask = tf.tensor_scatter_nd_update(next_image_attention_mask, indices, updates)
if seen_eod and token_id != eod_token_id:
indices = [[batch_idx, idx]]
updates = [-1]
next_image_attention_mask = tf.tensor_scatter_nd_update(next_image_attention_mask, indices, updates)
return image_attention_mask, next_image_attention_mask
def is_url(string):
"""Checks if the passed string contains a valid url and nothing else. e.g. if space is included it's immediately
invalidated the url"""
if " " in string:
return False
result = urlparse(string)
return all([result.scheme, result.netloc])
class IdeficsProcessor(ProcessorMixin):
r"""
Constructs a IDEFICS processor which wraps a LLama tokenizer and IDEFICS image processor into a single processor.
[`IdeficsProcessor`] offers all the functionalities of [`IdeficsImageProcessor`] and [`LlamaTokenizerFast`]. See
the docstring of [`~IdeficsProcessor.__call__`] and [`~IdeficsProcessor.decode`] for more information.
Args:
image_processor (`IdeficsImageProcessor`):
An instance of [`IdeficsImageProcessor`]. The image processor is a required input.
tokenizer (`LlamaTokenizerFast`):
An instance of [`LlamaTokenizerFast`]. The tokenizer is a required input.
image_size (`int`, *optional*, defaults to 224):
Image size (assuming a square image)
add_end_of_utterance_token (`str`, *optional*):
The string representation of token representing end of utterance
"""
attributes = ["image_processor", "tokenizer"]
valid_kwargs = ["image_size", "add_end_of_utterance_token"]
image_processor_class = "IdeficsImageProcessor"
tokenizer_class = "LlamaTokenizerFast"
def __init__(self, image_processor, tokenizer=None, image_size=224, add_end_of_utterance_token=None, **kwargs):
if image_processor is None:
raise ValueError("You need to specify an `image_processor`.")
if tokenizer is None:
raise ValueError("You need to specify a `tokenizer`.")
super().__init__(image_processor, tokenizer)
self.current_processor = self.image_processor
self.image_token_id = (
tokenizer.image_token_id
if hasattr(tokenizer, "image_token")
else tokenizer.convert_tokens_to_ids(IMAGE_TOKEN)
)
self.default_image_dims = (
self.image_processor.image_num_channels,
self.image_processor.image_size,
self.image_processor.image_size,
)
self.tokenizer_was_trained_with_end_of_utterance_token = (
True
if "<end_of_utterance>" in self.tokenizer.special_tokens_map.get("additional_special_tokens", [])
else False
)
@deprecate_kwarg(old_name="prompts", version="5.0.0", new_name="text", raise_if_both_names=True)
def __call__(
self,
images: Union[ImageInput, List[ImageInput], str, List[str], List[List[str]]] = None,
text: Union[
TextInput,
PreTokenizedInput,
List[TextInput],
List[PreTokenizedInput],
List[List[TextInput]],
List[List[PreTokenizedInput]],
] = None,
audio=None,
videos=None,
**kwargs: Unpack[IdeficsProcessorKwargs],
) -> BatchFeature:
"""This method takes batched or non-batched prompts made of text and images and converts them into prompts that
the model was trained on and prepares the image pixel values for the model to process.
Args:
images (`Union[ImageInput, List[ImageInput], str, List[str], List[List[str]]]`):
either a single image or a batched list of images - can be passed in when text contains only text prompts,
in order to use the image-text-to-text behavior.
text (`Union[List[TextInput], [List[List[TextInput]]]]`):
either a single prompt or a batched list of prompts - see the detailed description immediately after
the end of the arguments doc section.
return_tensors (`str` or `TensorType`, *optional*, defaults to `TensorType.PYTORCH`):
The type of tensors to return. Can be one of:
- `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
Returns:
a dict with entries: `input_ids`, `attention_mask`, `pixel_values`, `image_attention_mask` which can be
directly passed to `model.generate`
Detailed explanation:
Each entry in `text` is either a text to be passed as is or an image that will be processed.
An image can be either an image object (`PIL.Image`) or a url from which the image can be retrieved.
When the processor encounters an image it'll inject `<fake_token_around_image><image><fake_token_around_image>`
entry into the prompt.
Example:
```python
checkpoint = "HuggingFaceM4/idefics-9b"
processor = AutoProcessor.from_pretrained(checkpoint)
url = "https://hips.hearstapps.com/hmg-prod/images/cute-photos-of-cats-in-grass-1593184777.jpg"
img = processor.image_processor.fetch_images([url])[0]
prompts = [
"User:",
img,
"Describe this image.\nAssistant: An image of two kittens in grass.\n",
"User:",
"https://hips.hearstapps.com/hmg-prod/images/dog-puns-1581708208.jpg",
"Describe this image.\nAssistant:",
]
inputs = processor(text=prompts, return_tensors="pt")
generated_ids = model.generate(**inputs, max_length=100)
generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
```
In this example the `prompts` will be converted into:
```
<s>User:<fake_token_around_image><image><fake_token_around_image>Describe this image.
Assistant: An image of two kittens in grass.
User:<fake_token_around_image><image><fake_token_around_image>Describe this image.
Assistant:'
```
and the two images will be massaged using [`IdeficsImageProcessor.__call__`] method and placed inside the
`pixel_values` dict entry of the return value.
This example also examplifies that images can be passed as objects or as text urls. It can be seen that the
first image is passed as object and the second one as a url.
To do training do:
```python
image_transform = transforms.Compose(
[
transforms.RandomResizedCrop(
(w, h), scale=(0.9, 1.0), interpolation=transforms.InterpolationMode.BICUBIC
),
transforms.ToTensor(),
transforms.Normalize(mean=self.image_mean, std=self.image_std),
]
)
inputs = processor(text=prompts, transform=image_transform, return_tensors="pt")
```
In order to help debug prompt generation enable `debug=True` which will show you what's happening.
"""
if images is None and text is None:
raise ValueError("You need to specify either `text` or `images` and `text`.")
# check if images and text inputs are reversed for BC
images, text = _validate_images_text_input_order(images, text)
if images is None:
# assuming the user wants to use the old behavior with prompts as the only argument
prompts = text
elif text is not None:
# Assuming image-text-to-text behavior:
# Check if batched images are provided
if not isinstance(images, (list, tuple)):
images = [images]
if isinstance(text, str):
text = [text]
# Check if batched images and text are in the correct format
if isinstance(text, (list, tuple)) and len(text) != len(images):
raise ValueError(
"When providing both images and text arguments, the number of text prompts should be the same as the number of images."
"If you want to have several images per prompt, images should be nested as such: images=[[img1, img2], [img3, img4], ...] for text=[prompt1, prompt2, ...]."
)
# Check that only text is present in the prompts
if not all(isinstance(i, str) for i in text):
raise ValueError("When using the image-text-to-text behavior, the prompts should only contain text.")
if isinstance(images[0], (list, tuple)):
# if nested images, nest text as well
text = [[i] for i in text]
prompts = list(zip(images, text))
output_kwargs = self._merge_kwargs(
IdeficsProcessorKwargs,
tokenizer_init_kwargs=self.tokenizer.init_kwargs,
**kwargs,
)
add_eos_token = output_kwargs["text_kwargs"].pop("add_eos_token", False)
add_end_of_utterance_token = output_kwargs["text_kwargs"].pop("add_end_of_utterance_token", None)
# if the value isn't overriden by the user, check if the tokenizer was trained with this token and then use it
if add_end_of_utterance_token is None:
add_end_of_utterance_token = self.tokenizer_was_trained_with_end_of_utterance_token
# turn non-batched prompts into batched
if not any(isinstance(i, (list, tuple)) for i in prompts):
prompts = [prompts]
fake_token = "<fake_token_around_image>"
image_token = "<image>"
end_of_utterance_token = "<end_of_utterance>"
def image_tokens(last_was_image):
if last_was_image:
return image_token + fake_token
else:
return fake_token + image_token + fake_token
all_prompts = []
all_images = []
for sample in prompts:
# the model was trained on samples starting with <s>
full_text = f"{self.tokenizer.bos_token}"
# an image can either be an image object in the item or the url, everything else is a verbatim prompt text
image_objects = []
last_was_image = False
last_was_text = False
for i, item in enumerate(sample):
if i > 0:
last_was_text = True if not last_was_image else False
if isinstance(item, str):
item = item.strip(" ")
if is_url(item):
image = self.image_processor.fetch_images(item)
full_text += image_tokens(last_was_image)
image_objects.append(image)
last_was_image = True
else:
# we add end_of_utterance_token between each subsequent text prompts (but not at the last one!)
if add_end_of_utterance_token and last_was_text:
full_text += end_of_utterance_token
full_text += item
last_was_image = False
else:
# must be an image obj
full_text += image_tokens(last_was_image)
image_objects.append(item)
last_was_image = True
if add_eos_token:
full_text += self.tokenizer.eos_token
image_objects = self.image_processor(image_objects, **output_kwargs["images_kwargs"])
all_prompts.append(full_text)
all_images.append(image_objects)
# For BC
return_tensors = output_kwargs["text_kwargs"].pop("return_tensors", "pt")
text_encoding = self.tokenizer(all_prompts, **output_kwargs["text_kwargs"])
all_texts = text_encoding["input_ids"]
all_attention_masks = text_encoding["attention_mask"]
# max_num_images has to be at least 1 even when there are no images
max_num_images = max(len(x) for x in all_images)
max_num_images = max(1, max_num_images)
at_least_one_image = sum(len(x) for x in all_images) > 0
output_input_ids = []
output_images = []
output_attention_masks = []
for text_single, attention_mask, extracted_images in zip(all_texts, all_attention_masks, all_images):
padded_input_ids = text_single
image_count = padded_input_ids.count(self.image_token_id)
local_max_num_images = min(image_count, max_num_images)
current_images = extracted_images[:local_max_num_images]
if len(current_images) > 0:
if return_tensors == "pt":
padded_image_tensor = torch.zeros(max_num_images, *current_images.size()[1:])
padded_image_tensor[: current_images.size(0)] = current_images
elif return_tensors == "tf":
# Assuming current_images is a TensorFlow tensor
# Get the shape of current_images, excluding the first dimension
image_shape = tf.shape(current_images)[1:]
# Create a shape for the padded_image_tensor
padded_shape = tf.concat([[max_num_images], image_shape], axis=0)
# Create the padded_image_tensor of zeros
padded_image_tensor = tf.zeros(padded_shape, dtype=current_images.dtype)
# Get the number of images (assuming current_images has shape [num_images, height, width, channels])
num_images = tf.shape(current_images)[0]
# Update the padded_image_tensor with the values from current_images
indices = tf.reshape(tf.range(num_images), (-1, 1))
updates = current_images
padded_image_tensor = tf.tensor_scatter_nd_update(padded_image_tensor, indices, updates)
else:
if return_tensors == "pt":
padded_image_tensor = torch.zeros(max_num_images, *self.default_image_dims)
elif return_tensors == "tf":
padded_image_tensor = tf.zeros((max_num_images, *self.default_image_dims))
output_images.append(padded_image_tensor)
if return_tensors == "pt":
output_input_ids.append(torch.tensor(padded_input_ids))
output_attention_masks.append(torch.tensor(attention_mask))
elif return_tensors == "tf":
output_input_ids.append(tf.convert_to_tensor(padded_input_ids, dtype=tf.int32))
output_attention_masks.append(attention_mask)
if return_tensors == "pt":
output_input_ids = torch.stack(output_input_ids)
output_images = torch.stack(output_images)
output_attention_masks = torch.stack(output_attention_masks)
elif return_tensors == "tf":
output_input_ids = tf.stack(output_input_ids)
output_images = tf.stack(output_images)
output_attention_masks = tf.stack(output_attention_masks)
if at_least_one_image:
image_attention_mask, _ = image_attention_mask_for_packed_input_ids(
output_input_ids, self.tokenizer, return_tensors
)
image_attention_mask = incremental_to_binary_attention_mask(
image_attention_mask, return_tensors, num_classes=max_num_images
)
else:
# in full language mode we set the image mask to all-0s
if return_tensors == "pt":
image_attention_mask = torch.zeros(
output_input_ids.shape[0], output_input_ids.shape[1], 1, dtype=torch.bool
)
elif return_tensors == "tf":
image_attention_mask = tf.zeros(
(output_input_ids.shape[0], output_input_ids.shape[1], 1), dtype=tf.bool
)
return BatchFeature(
data={
"input_ids": output_input_ids,
"attention_mask": output_attention_masks,
"pixel_values": output_images,
"image_attention_mask": image_attention_mask,
}
)
def batch_decode(self, *args, **kwargs):
"""
This method forwards all its arguments to LlamaTokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please
refer to the docstring of this method for more information.
"""
return self.tokenizer.batch_decode(*args, **kwargs)
def decode(self, *args, **kwargs):
"""
This method forwards all its arguments to LlamaTokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to
the docstring of this method for more information.
"""
return self.tokenizer.decode(*args, **kwargs)
@property
def model_input_names(self):
tokenizer_input_names = self.tokenizer.model_input_names
image_processor_input_names = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))
__all__ = ["IdeficsProcessor"]
```
|
=============================================================================================================================
SOURCE CODE FILE: vision.py
LINES: 1
SIZE: 21.98 KB
PATH: scripts\freecad_env\Lib\site-packages\transformers\models\idefics\vision.py
ENCODING: utf-8
```py
# coding=utf-8
# Copyright 2021 The OpenAI Team Authors and The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""PyTorch IdeficsVision model: a copy of CLIPVisionModel using a simpler config object"""
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from ...activations import ACT2FN
from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling
from ...utils import ModelOutput, logging
from .configuration_idefics import IdeficsVisionConfig
logger = logging.get_logger(__name__)
@dataclass
class IdeficsVisionModelOutput(ModelOutput):
"""
Base class for vision model's outputs that also contains image embeddings of the pooling of the last hidden states.
Args:
image_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim)` *optional* returned when model is initialized with `with_projection=True`):
The image embeddings obtained by applying the projection layer to the pooler_output.
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the model.
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
"""
image_embeds: Optional[torch.FloatTensor] = None
last_hidden_state: Optional[torch.FloatTensor] = None
hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
# Adapted from transformers.models.clip.modeling_clip.CLIPVisionEmbeddings
class IdeficsVisionEmbeddings(nn.Module):
def __init__(self, config: IdeficsVisionConfig):
super().__init__()
self.config = config
self.embed_dim = config.hidden_size
self.image_size = config.image_size
self.patch_size = config.patch_size
self.class_embedding = nn.Parameter(torch.randn(self.embed_dim))
self.patch_embedding = nn.Conv2d(
in_channels=config.num_channels,
out_channels=self.embed_dim,
kernel_size=self.patch_size,
stride=self.patch_size,
bias=False,
)
self.num_patches = (self.image_size // self.patch_size) ** 2
self.num_positions = self.num_patches + 1
self.position_embedding = nn.Embedding(self.num_positions, self.embed_dim)
self.register_buffer("position_ids", torch.arange(self.num_positions).expand((1, -1)), persistent=False)
# Heavily inspired from https://github.com/huggingface/transformers/blob/v4.33.0/src/transformers/models/vit/modeling_vit.py#L82
def interpolate_pos_encoding(self, embeddings: torch.Tensor, height: int, width: int) -> torch.Tensor:
"""
This method allows to interpolate the pre-trained position encodings, to be able to use the model on higher
resolution images.
Source:
https://github.com/facebookresearch/dino/blob/de9ee3df6cf39fac952ab558447af1fa1365362a/vision_transformer.py#L174
"""
num_patches = embeddings.shape[1] - 1
pos_embed = self.position_embedding(self.position_ids)
num_positions = pos_embed.shape[1] - 1
if num_patches == num_positions and height == width:
return pos_embed
class_pos_embed = pos_embed[:, 0]
patch_pos_embed = pos_embed[:, 1:]
embed_dim = embeddings.shape[-1]
num_h_patches = height // self.config.patch_size
num_w_patches = width // self.config.patch_size
# we add a small number to avoid floating point error in the interpolation
# see discussion at https://github.com/facebookresearch/dino/issues/8
num_h_patches, num_w_patches = num_h_patches + 0.1, num_w_patches + 0.1
sqrt_num_positions = math.sqrt(num_positions)
patch_pos_embed = patch_pos_embed.reshape(1, int(sqrt_num_positions), int(sqrt_num_positions), embed_dim)
patch_pos_embed = patch_pos_embed.permute(0, 3, 1, 2)
fp32_upcasting = patch_pos_embed.dtype == torch.bfloat16
if fp32_upcasting:
logger.warning_once(
"Upcasting patch_pos_embed to fp32 for interpolation since `upsample_bicubic2d_out_frame` in nn.functional.interpolate "
"is not implemented for 'torch.bfloat16' dtype. This will result in a slight overhead."
)
patch_pos_embed = patch_pos_embed.to(torch.float)
patch_pos_embed = nn.functional.interpolate(
patch_pos_embed,
scale_factor=(num_h_patches / sqrt_num_positions, num_w_patches / sqrt_num_positions),
mode="bicubic",
align_corners=False,
)
if fp32_upcasting:
patch_pos_embed = patch_pos_embed.to(torch.bfloat16)
if int(num_h_patches) != patch_pos_embed.shape[-2] or int(num_w_patches) != patch_pos_embed.shape[-1]:
raise ValueError(
f"Number of patches for images ({int(num_h_patches), int(num_w_patches)}) don't match the "
f"shape of position embedding ({patch_pos_embed.shape[-2], patch_pos_embed.shape[-1]})"
)
patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, embed_dim)
return torch.cat((class_pos_embed.unsqueeze(0), patch_pos_embed), dim=1)
def forward(self, pixel_values: torch.FloatTensor, interpolate_pos_encoding: bool = False) -> torch.Tensor:
batch_size, num_channels, height, width = pixel_values.shape
if not interpolate_pos_encoding:
if height != self.image_size or width != self.image_size:
raise ValueError(
f"Input image size ({height}*{width}) doesn't match model"
f" ({self.image_size}*{self.image_size}). You should try to set `interpolate_pos_encoding=True`"
)
target_dtype = self.patch_embedding.weight.dtype
patch_embeds = self.patch_embedding(pixel_values.to(dtype=target_dtype)) # shape = [*, width, grid, grid]
patch_embeds = patch_embeds.flatten(2).transpose(1, 2)
class_embeds = self.class_embedding.expand(batch_size, 1, -1)
embeddings = torch.cat([class_embeds, patch_embeds], dim=1)
# add positional encoding to each token
if interpolate_pos_encoding:
embeddings = embeddings + self.interpolate_pos_encoding(embeddings, height, width)
else:
embeddings = embeddings + self.position_embedding(self.position_ids)
return embeddings
# Copied from transformers.models.clip.modeling_clip.CLIPAttention with CLIP->IdeficsVision
class IdeficsVisionAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(self, config):
super().__init__()
self.config = config
self.embed_dim = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_heads != self.embed_dim:
raise ValueError(
f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:"
f" {self.num_heads})."
)
self.scale = self.head_dim**-0.5
self.dropout = config.attention_dropout
self.k_proj = nn.Linear(self.embed_dim, self.embed_dim)
self.v_proj = nn.Linear(self.embed_dim, self.embed_dim)
self.q_proj = nn.Linear(self.embed_dim, self.embed_dim)
self.out_proj = nn.Linear(self.embed_dim, self.embed_dim)
def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
causal_attention_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = False,
) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
"""Input shape: Batch x Time x Channel"""
bsz, tgt_len, embed_dim = hidden_states.size()
# get query proj
query_states = self.q_proj(hidden_states) * self.scale
key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
proj_shape = (bsz * self.num_heads, -1, self.head_dim)
query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape)
key_states = key_states.view(*proj_shape)
value_states = value_states.view(*proj_shape)
src_len = key_states.size(1)
attn_weights = torch.bmm(query_states, key_states.transpose(1, 2))
if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len):
raise ValueError(
f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is"
f" {attn_weights.size()}"
)
# apply the causal_attention_mask first
if causal_attention_mask is not None:
if causal_attention_mask.size() != (bsz, 1, tgt_len, src_len):
raise ValueError(
f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is"
f" {causal_attention_mask.size()}"
)
attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + causal_attention_mask
attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
if attention_mask is not None:
if attention_mask.size() != (bsz, 1, tgt_len, src_len):
raise ValueError(
f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}"
)
attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask
attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
attn_weights = nn.functional.softmax(attn_weights, dim=-1)
if output_attentions:
# this operation is a bit awkward, but it's required to
# make sure that attn_weights keeps its gradient.
# In order to do so, attn_weights have to reshaped
# twice and have to be reused in the following
attn_weights_reshaped = attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
attn_weights = attn_weights_reshaped.view(bsz * self.num_heads, tgt_len, src_len)
else:
attn_weights_reshaped = None
attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)
attn_output = torch.bmm(attn_probs, value_states)
if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim):
raise ValueError(
f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is"
f" {attn_output.size()}"
)
attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim)
attn_output = attn_output.transpose(1, 2)
attn_output = attn_output.reshape(bsz, tgt_len, embed_dim)
attn_output = self.out_proj(attn_output)
return attn_output, attn_weights_reshaped
# Copied from transformers.models.clip.modeling_clip.CLIPMLP with CLIP->IdeficsVision
class IdeficsVisionMLP(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.activation_fn = ACT2FN[config.hidden_act]
self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size)
self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size)
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidden_states = self.fc1(hidden_states)
hidden_states = self.activation_fn(hidden_states)
hidden_states = self.fc2(hidden_states)
return hidden_states
# Copied from transformers.models.altclip.modeling_altclip.AltCLIPEncoderLayer with AltCLIP->IdeficsVision
class IdeficsVisionEncoderLayer(nn.Module):
def __init__(self, config: IdeficsVisionConfig):
super().__init__()
self.embed_dim = config.hidden_size
self.self_attn = IdeficsVisionAttention(config)
self.layer_norm1 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
self.mlp = IdeficsVisionMLP(config)
self.layer_norm2 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: torch.Tensor,
causal_attention_mask: torch.Tensor,
output_attentions: Optional[bool] = False,
) -> Tuple[torch.FloatTensor]:
"""
Args:
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
attention_mask (`torch.FloatTensor`): attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
`(config.encoder_attention_heads,)`.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
"""
residual = hidden_states
hidden_states = self.layer_norm1(hidden_states)
hidden_states, attn_weights = self.self_attn(
hidden_states=hidden_states,
attention_mask=attention_mask,
causal_attention_mask=causal_attention_mask,
output_attentions=output_attentions,
)
hidden_states = residual + hidden_states
residual = hidden_states
hidden_states = self.layer_norm2(hidden_states)
hidden_states = self.mlp(hidden_states)
hidden_states = residual + hidden_states
outputs = (hidden_states,)
if output_attentions:
outputs += (attn_weights,)
return outputs
# Copied from transformers.models.altclip.modeling_altclip.AltCLIPEncoder with AltCLIP->IdeficsVision
class IdeficsVisionEncoder(nn.Module):
"""
Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a
[`IdeficsVisionEncoderLayer`].
Args:
config: IdeficsVisionConfig
"""
def __init__(self, config: IdeficsVisionConfig):
super().__init__()
self.config = config
self.layers = nn.ModuleList([IdeficsVisionEncoderLayer(config) for _ in range(config.num_hidden_layers)])
self.gradient_checkpointing = False
def forward(
self,
inputs_embeds,
attention_mask: Optional[torch.Tensor] = None,
causal_attention_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, BaseModelOutput]:
r"""
Args:
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
This is useful if you want more control over how to convert `input_ids` indices into associated vectors
than the model's internal embedding lookup matrix.
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
causal_attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Causal mask for the text model. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
for more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
encoder_states = () if output_hidden_states else None
all_attentions = () if output_attentions else None
hidden_states = inputs_embeds
for idx, encoder_layer in enumerate(self.layers):
if output_hidden_states:
encoder_states = encoder_states + (hidden_states,)
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
encoder_layer.__call__,
hidden_states,
attention_mask,
causal_attention_mask,
output_attentions,
)
else:
layer_outputs = encoder_layer(
hidden_states,
attention_mask,
causal_attention_mask,
output_attentions=output_attentions,
)
hidden_states = layer_outputs[0]
if output_attentions:
all_attentions = all_attentions + (layer_outputs[1],)
if output_hidden_states:
encoder_states = encoder_states + (hidden_states,)
if not return_dict:
return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None)
return BaseModelOutput(
last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions
)
# Adapted from transformers.models.clip.modeling_clip.CLIPVisionTransformer
class IdeficsVisionTransformer(nn.Module):
def __init__(self, config: IdeficsVisionConfig):
super().__init__()
self.config = config
embed_dim = config.hidden_size
self.embeddings = IdeficsVisionEmbeddings(config)
self.pre_layrnorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
self.encoder = IdeficsVisionEncoder(config)
self.post_layernorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
# Adapted from transformers.models.clip.modeling_clip.CLIPVisionTransformer.forward
def forward(
self,
pixel_values: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
interpolate_pos_encoding: Optional[bool] = False,
return_dict: Optional[bool] = None,
) -> Union[Tuple, BaseModelOutputWithPooling]:
r"""
Returns:
"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if pixel_values is None:
raise ValueError("You have to specify pixel_values")
hidden_states = self.embeddings(pixel_values, interpolate_pos_encoding=interpolate_pos_encoding)
hidden_states = self.pre_layrnorm(hidden_states)
encoder_outputs = self.encoder(
inputs_embeds=hidden_states,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
last_hidden_state = encoder_outputs[0]
pooled_output = last_hidden_state[:, 0, :]
pooled_output = self.post_layernorm(pooled_output)
if not return_dict:
return (last_hidden_state, pooled_output) + encoder_outputs[1:]
return BaseModelOutputWithPooling(
last_hidden_state=last_hidden_state,
pooler_output=pooled_output,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
)
```
|
================================================================================================================================
SOURCE CODE FILE: vision_tf.py
LINES: 1
SIZE: 25.41 KB
PATH: scripts\freecad_env\Lib\site-packages\transformers\models\idefics\vision_tf.py
ENCODING: utf-8
```py
# coding=utf-8
# Copyright 2021 The OpenAI Team Authors and The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""TF IdeficsVision model: a copy of CLIPVisionModel using a simpler config object"""
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import tensorflow as tf
from ...activations_tf import get_tf_activation
from ...modeling_tf_outputs import TFBaseModelOutput, TFBaseModelOutputWithPooling
from ...modeling_tf_utils import TFPreTrainedModel, shape_list
from ...tf_utils import flatten
from ...utils import ModelOutput, logging
from .configuration_idefics import IdeficsVisionConfig
logger = logging.get_logger(__name__)
@dataclass
class TFIdeficsVisionModelOutput(ModelOutput):
"""
Base class for vision model's outputs that also contains image embeddings of the pooling of the last hidden states.
Args:
image_embeds (`tf.Tensor` of shape `(batch_size, output_dim)` *optional* returned when model is initialized with `with_projection=True`):
The image embeddings obtained by applying the projection layer to the pooler_output.
last_hidden_state (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the model.
hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `tf.Tensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
"""
image_embeds: Optional[tf.Tensor] = None
last_hidden_state: Optional[tf.Tensor] = None
hidden_states: Optional[Tuple[tf.Tensor]] = None
attentions: Optional[Tuple[tf.Tensor]] = None
class TFIdeficsVisionEmbeddings(tf.keras.layers.Layer):
def __init__(self, config: IdeficsVisionConfig, **kwargs):
super().__init__(**kwargs)
self.config = config
self.embed_dim = config.hidden_size
self.image_size = config.image_size
self.patch_size = config.patch_size
self.patch_embedding = tf.keras.layers.Conv2D(
filters=self.embed_dim,
kernel_size=self.patch_size,
strides=self.patch_size,
use_bias=False,
padding="valid",
data_format="channels_last",
name="patch_embedding",
)
self.num_patches = (self.image_size // self.patch_size) ** 2
self.num_positions = self.num_patches + 1
self.position_embedding = tf.keras.layers.Embedding(
self.num_positions, self.embed_dim, name="position_embedding"
)
# self.position_ids = tf.range(self.num_positions)[tf.newaxis, :]
def interpolate_pos_encoding(self, embeddings: tf.Tensor, height: int, width: int) -> tf.Tensor:
num_patches = shape_list(embeddings)[1] - 1
pos_embed = self.position_embedding(self.position_ids)
num_positions = shape_list(pos_embed)[1] - 1
if num_patches == num_positions and height == width:
return pos_embed
class_pos_embed = pos_embed[:, 0]
patch_pos_embed = pos_embed[:, 1:]
embed_dim = shape_list(embeddings)[-1]
num_h_patches = height // self.config.patch_size
num_w_patches = width // self.config.patch_size
num_h_patches, num_w_patches = num_h_patches + 0.1, num_w_patches + 0.1
sqrt_num_positions = math.sqrt(float(num_positions))
patch_pos_embed = tf.reshape(patch_pos_embed, (1, int(sqrt_num_positions), int(sqrt_num_positions), embed_dim))
scale_height = num_h_patches / sqrt_num_positions
scale_width = num_w_patches / sqrt_num_positions
original_height = tf.cast(tf.shape(patch_pos_embed)[1], tf.float32)
original_width = tf.cast(tf.shape(patch_pos_embed)[2], tf.float32)
# Apply scaling
new_height = tf.cast(original_height * scale_height, tf.int32)
new_width = tf.cast(original_width * scale_width, tf.int32)
patch_pos_embed = tf.image.resize(
patch_pos_embed, size=[new_height, new_width], method=tf.image.ResizeMethod.BICUBIC
)
if (
int(num_h_patches) != shape_list(patch_pos_embed)[-3]
or int(num_w_patches) != shape_list(patch_pos_embed)[-2]
):
raise ValueError(
f"Number of patches for images ({int(num_h_patches), int(num_w_patches)}) don't match the "
f"shape of position embedding ({shape_list(patch_pos_embed)[-2], shape_list(patch_pos_embed)[-1]})"
)
patch_pos_embed = tf.reshape(patch_pos_embed, (1, -1, embed_dim))
return tf.concat((class_pos_embed[tf.newaxis, :], patch_pos_embed), axis=1)
def call(self, pixel_values: tf.Tensor, interpolate_pos_encoding: bool = False) -> tf.Tensor:
# Input `pixel_values` is NCHW format which doesn't run on CPU so first thing we do is
# transpose it to change it to NHWC. We don't care to transpose it back because
# the Conv2D layer is only hit once for each query
if isinstance(pixel_values, dict):
pixel_values = pixel_values["pixel_values"]
pixel_values = tf.transpose(pixel_values, perm=(0, 2, 3, 1))
batch_size, height, width, num_channels = shape_list(pixel_values)
if not interpolate_pos_encoding:
if height != self.image_size or width != self.image_size:
raise ValueError(
f"Input image size ({height}*{width}) doesn't match model"
f" ({self.image_size}*{self.image_size}). You should try to set `interpolate_pos_encoding=True`"
)
patch_embeds = self.patch_embedding(pixel_values) # shape = [*, width, grid, grid]
# Change the 2D spatial dimensions to a single temporal dimension.
# shape = (batch_size, num_patches, out_channels=embed_dim)
patch_embeds = flatten(patch_embeds, 1, 2)
class_embeds = tf.broadcast_to(
self.class_embedding[tf.newaxis, tf.newaxis, :], [batch_size, 1, self.embed_dim]
)
embeddings = tf.concat([class_embeds, patch_embeds], axis=1)
# add positional encoding to each token
if interpolate_pos_encoding:
embeddings = embeddings + self.interpolate_pos_encoding(embeddings, height, width)
else:
embeddings = embeddings + self.position_embedding(self.position_ids)
return embeddings
def build(self, input_shape=None):
if self.built:
return
self.built = True
self.position_ids = tf.range(self.num_positions, name="self.position_ids")[tf.newaxis, :]
self.class_embedding = self.add_weight(shape=(self.embed_dim,), name="class_embedding")
if getattr(self, "patch_embedding", None) is not None:
with tf.name_scope(self.patch_embedding.name):
self.patch_embedding.build([None, None, None, self.config.num_channels])
if getattr(self, "position_embedding", None) is not None:
with tf.name_scope(self.position_embedding.name):
self.position_embedding.build(None)
class TFIdeficsVisionAttention(tf.keras.layers.Layer):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.config = config
self.embed_dim = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_heads != self.embed_dim:
raise ValueError(
f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:"
f" {self.num_heads})."
)
self.scale = self.head_dim**-0.5
self.dropout = config.attention_dropout
self.k_proj = tf.keras.layers.Dense(self.embed_dim, name="k_proj")
self.v_proj = tf.keras.layers.Dense(self.embed_dim, name="v_proj")
self.q_proj = tf.keras.layers.Dense(self.embed_dim, name="q_proj")
self.out_proj = tf.keras.layers.Dense(self.embed_dim, name="out_proj")
def _shape(self, tensor: tf.Tensor, seq_len: int, bsz: int):
return tf.transpose(tf.reshape(tensor, (bsz, seq_len, self.num_heads, self.head_dim)), perm=[0, 2, 1, 3])
def call(
self,
hidden_states: tf.Tensor,
attention_mask: Optional[tf.Tensor] = None,
causal_attention_mask: Optional[tf.Tensor] = None,
output_attentions: Optional[bool] = False,
) -> Tuple[tf.Tensor, Optional[tf.Tensor], Optional[Tuple[tf.Tensor]]]:
"""Input shape: Batch x Time x Channel"""
bsz, tgt_len, embed_dim = shape_list(hidden_states)
# get query proj
query_states = self.q_proj(hidden_states) * self.scale
key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
proj_shape = (bsz * self.num_heads, -1, self.head_dim)
query_states = tf.reshape(self._shape(query_states, tgt_len, bsz), proj_shape)
key_states = tf.reshape(key_states, proj_shape)
value_states = tf.reshape(value_states, proj_shape)
src_len = shape_list(key_states)[1]
attn_weights = tf.linalg.matmul(query_states, key_states, transpose_b=True)
tf.debugging.assert_equal(
tf.shape(attn_weights),
[bsz * self.num_heads, tgt_len, src_len],
message=f"Attention weights should be of size {[bsz * self.num_heads, tgt_len, src_len]}, but is {tf.shape(attn_weights)}",
)
# apply the causal_attention_mask first
if causal_attention_mask is not None:
if shape_list(causal_attention_mask) != [bsz, 1, tgt_len, src_len]:
raise ValueError(
f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is"
f" {shape_list(causal_attention_mask)}"
)
attn_weights = tf.reshape(attn_weights, (bsz, self.num_heads, tgt_len, src_len)) + causal_attention_mask
attn_weights = tf.reshape(attn_weights, (bsz * self.num_heads, tgt_len, src_len))
if attention_mask is not None:
if shape_list(attention_mask) != [bsz, 1, tgt_len, src_len]:
raise ValueError(
f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {shape_list(attention_mask)}"
)
attn_weights = tf.reshape(attn_weights, (bsz, self.num_heads, tgt_len, src_len)) + attention_mask
attn_weights = tf.reshape(attn_weights, (bsz * self.num_heads, tgt_len, src_len))
attn_weights = tf.nn.softmax(attn_weights, axis=-1)
if output_attentions:
# this operation is a bit akward, but it's required to
# make sure that attn_weights keeps its gradient.
# In order to do so, attn_weights have to reshaped
# twice and have to be reused in the following
attn_weights_reshaped = tf.reshape(attn_weights, (bsz, self.num_heads, tgt_len, src_len))
attn_weights = tf.reshape(attn_weights_reshaped, (bsz * self.num_heads, tgt_len, src_len))
else:
attn_weights_reshaped = None
attn_probs = tf.nn.dropout(attn_weights, rate=self.dropout)
attn_output = tf.linalg.matmul(attn_probs, value_states)
tf.debugging.assert_equal(
tf.shape(attn_output),
[bsz * self.num_heads, tgt_len, self.head_dim],
message=f"Attention weights should be of size {[bsz * self.num_heads, tgt_len, self.head_dim]}, but is {tf.shape(attn_output)}",
)
attn_output = tf.reshape(attn_output, (bsz, self.num_heads, tgt_len, self.head_dim))
attn_output = tf.transpose(attn_output, perm=[0, 2, 1, 3])
attn_output = tf.reshape(attn_output, (bsz, tgt_len, embed_dim))
attn_output = self.out_proj(attn_output)
return attn_output, attn_weights_reshaped
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "k_proj", None) is not None:
with tf.name_scope(self.k_proj.name):
self.k_proj.build((self.embed_dim, self.embed_dim))
if getattr(self, "v_proj", None) is not None:
with tf.name_scope(self.v_proj.name):
self.v_proj.build((self.embed_dim, self.embed_dim))
if getattr(self, "q_proj", None) is not None:
with tf.name_scope(self.q_proj.name):
self.q_proj.build((self.embed_dim, self.embed_dim))
if getattr(self, "out_proj", None) is not None:
with tf.name_scope(self.out_proj.name):
self.out_proj.build((self.embed_dim, self.embed_dim))
class TFIdeficsVisionMLP(tf.keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.config = config
self.activation_fn = get_tf_activation(config.hidden_act)
self.fc1 = tf.keras.layers.Dense(config.intermediate_size, name="fc1")
self.fc2 = tf.keras.layers.Dense(config.hidden_size, name="fc2")
def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
hidden_states = self.fc1(hidden_states)
hidden_states = self.activation_fn(hidden_states)
hidden_states = self.fc2(hidden_states)
return hidden_states
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "fc1", None) is not None:
with tf.name_scope(self.fc1.name):
self.fc1.build(self.config.hidden_size)
if getattr(self, "fc2", None) is not None:
with tf.name_scope(self.fc2.name):
self.fc2.build(self.config.intermediate_size)
class TFIdeficsVisionEncoderLayer(tf.keras.layers.Layer):
def __init__(self, config: IdeficsVisionConfig, **kwargs):
super().__init__(**kwargs)
self.embed_dim = config.hidden_size
self.self_attn = TFIdeficsVisionAttention(config, name="self_attn")
self.layer_norm1 = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layer_norm1")
self.mlp = TFIdeficsVisionMLP(config, name="mlp")
self.layer_norm2 = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layer_norm2")
def call(
self,
hidden_states: tf.Tensor,
attention_mask: tf.Tensor,
causal_attention_mask: tf.Tensor,
output_attentions: Optional[bool] = False,
) -> Tuple[tf.Tensor]:
"""
Args:
hidden_states (`tf.Tensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
attention_mask (`tf.Tensor`): attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
`(config.encoder_attention_heads,)`.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
"""
residual = hidden_states
hidden_states = self.layer_norm1(hidden_states)
hidden_states, attn_weights = self.self_attn(
hidden_states=hidden_states,
attention_mask=attention_mask,
causal_attention_mask=causal_attention_mask,
output_attentions=output_attentions,
)
hidden_states = residual + hidden_states
residual = hidden_states
hidden_states = self.layer_norm2(hidden_states)
hidden_states = self.mlp(hidden_states)
hidden_states = residual + hidden_states
outputs = (hidden_states,)
if output_attentions:
outputs += (attn_weights,)
return outputs
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "layer_norm1", None) is not None:
with tf.name_scope(self.layer_norm1.name):
self.layer_norm1.build([None, None, self.embed_dim])
if getattr(self, "layer_norm2", None) is not None:
with tf.name_scope(self.layer_norm2.name):
self.layer_norm2.build([None, None, self.embed_dim])
class TFIdeficsVisionEncoder(tf.keras.layers.Layer):
"""
Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a
[`TFIdeficsVisionEncoderLayer`].
Args:
config: IdeficsVisionConfig
"""
def __init__(self, config: IdeficsVisionConfig, **kwargs):
super().__init__(**kwargs)
self.config = config
self.layers = [
TFIdeficsVisionEncoderLayer(config, name=f"layers.{i}") for i in range(config.num_hidden_layers)
]
self.gradient_checkpointing = False
def call(
self,
inputs_embeds,
attention_mask: Optional[tf.Tensor] = None,
causal_attention_mask: Optional[tf.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
training: Optional[bool] = None,
) -> Union[Tuple, TFBaseModelOutput]:
r"""
Args:
inputs_embeds (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
This is useful if you want more control over how to convert `input_ids` indices into associated vectors
than the model's internal embedding lookup matrix.
attention_mask (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
causal_attention_mask (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Causal mask for the text model. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
for more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
encoder_states = () if output_hidden_states else None
all_attentions = () if output_attentions else None
hidden_states = inputs_embeds
for idx, encoder_layer in enumerate(self.layers):
if output_hidden_states:
encoder_states = encoder_states + (hidden_states,)
if self.gradient_checkpointing and training:
def create_custom_forward(module):
def custom_forward(*inputs):
return module(*inputs, output_attentions)
return custom_forward
layer_outputs = tf.recompute_grad(
create_custom_forward(encoder_layer),
hidden_states,
attention_mask,
causal_attention_mask,
)
else:
layer_outputs = encoder_layer(
hidden_states,
attention_mask,
causal_attention_mask,
output_attentions=output_attentions,
)
hidden_states = layer_outputs[0]
if output_attentions:
all_attentions = all_attentions + (layer_outputs[1],)
if output_hidden_states:
encoder_states = encoder_states + (hidden_states,)
if not return_dict:
return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None)
return TFBaseModelOutput(
last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions
)
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "layers", None) is not None:
for layer in self.layers:
with tf.name_scope(layer.name):
layer.build(None)
class TFIdeficsVisionTransformer(TFPreTrainedModel):
def __init__(self, config: IdeficsVisionConfig, **kwargs):
super().__init__(config, **kwargs)
self.config = config
self.embed_dim = config.hidden_size
self.embeddings = TFIdeficsVisionEmbeddings(config, name="embeddings")
self.pre_layrnorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="pre_layrnorm")
self.encoder = TFIdeficsVisionEncoder(config, name="encoder")
self.post_layernorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="post_layernorm")
# Adapted from transformers.models.clip.modeling_clip.CLIPVisionTransformer.forward
def call(
self,
pixel_values: Optional[tf.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
interpolate_pos_encoding: Optional[bool] = False,
return_dict: Optional[bool] = None,
training: Optional[bool] = False,
) -> Union[Tuple, TFBaseModelOutputWithPooling]:
r"""
Returns:
"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if pixel_values is None:
raise ValueError("You have to specify pixel_values")
hidden_states = self.embeddings(pixel_values, interpolate_pos_encoding=interpolate_pos_encoding)
hidden_states = self.pre_layrnorm(hidden_states)
encoder_outputs = self.encoder(
inputs_embeds=hidden_states,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
last_hidden_state = encoder_outputs[0]
pooled_output = last_hidden_state[:, 0, :]
pooled_output = self.post_layernorm(pooled_output)
if not return_dict:
return (last_hidden_state, pooled_output) + encoder_outputs[1:]
return TFBaseModelOutputWithPooling(
last_hidden_state=last_hidden_state,
pooler_output=pooled_output,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
)
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "embeddings", None) is not None:
with tf.name_scope(self.embeddings.name):
self.embeddings.build(None)
if getattr(self, "pre_layrnorm", None) is not None:
with tf.name_scope(self.pre_layrnorm.name):
self.pre_layrnorm.build([None, None, self.embed_dim])
if getattr(self, "encoder", None) is not None:
with tf.name_scope(self.encoder.name):
self.encoder.build(None)
if getattr(self, "post_layernorm", None) is not None:
with tf.name_scope(self.post_layernorm.name):
self.post_layernorm.build([None, self.embed_dim])
```
|
=============================================================================================================================
SOURCE CODE FILE: __init__.py
LINES: 1
SIZE: 0.97 KB
PATH: scripts\freecad_env\Lib\site-packages\transformers\models\ijepa\__init__.py
ENCODING: utf-8
```py
# Copyright 2024 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import _LazyModule
from ...utils.import_utils import define_import_structure
if TYPE_CHECKING:
from .configuration_ijepa import *
from .modeling_ijepa import *
else:
import sys
_file = globals()["__file__"]
sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)
```
|
========================================================================================================================================
SOURCE CODE FILE: configuration_ijepa.py
LINES: 1
SIZE: 5.32 KB
PATH: scripts\freecad_env\Lib\site-packages\transformers\models\ijepa\configuration_ijepa.py
ENCODING: utf-8
```py
# coding=utf-8
# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""I-JEPA model configuration"""
from ...configuration_utils import PretrainedConfig
class IJepaConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`IJepaModel`]. It is used to instantiate an IJEPA
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar configuration to that of the I-JEPA
[facebook/ijepa_vith14_1k](https://huggingface.co/facebook/ijepa_vith14_1k) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
hidden_size (`int`, *optional*, defaults to 768):
Dimensionality of the encoder layers and the pooler layer.
num_hidden_layers (`int`, *optional*, defaults to 12):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 12):
Number of attention heads for each attention layer in the Transformer encoder.
intermediate_size (`int`, *optional*, defaults to 3072):
Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"selu"` and `"gelu_new"` are supported.
hidden_dropout_prob (`float`, *optional*, defaults to 0.0):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_probs_dropout_prob (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (`float`, *optional*, defaults to 1e-12):
The epsilon used by the layer normalization layers.
image_size (`int`, *optional*, defaults to 224):
The size (resolution) of each image.
patch_size (`int`, *optional*, defaults to 16):
The size (resolution) of each patch.
num_channels (`int`, *optional*, defaults to 3):
The number of input channels.
qkv_bias (`bool`, *optional*, defaults to `True`):
Whether to add a bias to the queries, keys and values.
pooler_output_size (`int`, *optional*):
Dimensionality of the pooler layer. If None, defaults to `hidden_size`.
pooler_act (`str`, *optional*, defaults to `"tanh"`):
The activation function to be used by the pooler. Keys of ACT2FN are supported for Flax and
Pytorch, and elements of https://www.tensorflow.org/api_docs/python/tf/keras/activations are
supported for Tensorflow.
Example:
```python
>>> from transformers import IJepaConfig, IJepaModel
>>> # Initializing a IJEPA ijepa-base-patch16-224 style configuration
>>> configuration = IJepaConfig()
>>> # Initializing a model (with random weights) from the ijepa-base-patch16-224 style configuration
>>> model = IJepaModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "ijepa"
def __init__(
self,
hidden_size=768,
num_hidden_layers=12,
num_attention_heads=12,
intermediate_size=3072,
hidden_act="gelu",
hidden_dropout_prob=0.0,
attention_probs_dropout_prob=0.0,
initializer_range=0.02,
layer_norm_eps=1e-12,
image_size=224,
patch_size=16,
num_channels=3,
qkv_bias=True,
pooler_output_size=None,
pooler_act="tanh",
**kwargs,
):
super().__init__(**kwargs)
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.initializer_range = initializer_range
self.layer_norm_eps = layer_norm_eps
self.image_size = image_size
self.patch_size = patch_size
self.num_channels = num_channels
self.qkv_bias = qkv_bias
self.pooler_output_size = pooler_output_size if pooler_output_size else hidden_size
self.pooler_act = pooler_act
__all__ = ["IJepaConfig"]
```
|
===================================================================================================================================
SOURCE CODE FILE: modeling_ijepa.py
LINES: 1
SIZE: 30.38 KB
PATH: scripts\freecad_env\Lib\site-packages\transformers\models\ijepa\modeling_ijepa.py
ENCODING: utf-8
```py
# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
# This file was automatically generated from src/transformers/models/ijepa/modular_ijepa.py.
# Do NOT edit this file manually as any edits will be overwritten by the generation of
# the file from the modular. If any change should be done, please apply the change to the
# modular_ijepa.py file directly. One of our CI enforces this.
# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
import collections.abc
from typing import Callable, Dict, List, Optional, Set, Tuple, Union
import torch
import torch.nn as nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACT2FN
from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling, ImageClassifierOutput
from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
from ...pytorch_utils import find_pruneable_heads_and_indices, prune_linear_layer
from ...utils import (
add_code_sample_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
logging,
torch_int,
)
from .configuration_ijepa import IJepaConfig
logger = logging.get_logger(__name__)
_CHECKPOINT_FOR_DOC = "facebook/ijepa_vith14_1k"
# General docstring
_CONFIG_FOR_DOC = "IJepaConfig"
class IJepaPatchEmbeddings(nn.Module):
"""
This class turns `pixel_values` of shape `(batch_size, num_channels, height, width)` into the initial
`hidden_states` (patch embeddings) of shape `(batch_size, seq_length, hidden_size)` to be consumed by a
Transformer.
"""
def __init__(self, config):
super().__init__()
image_size, patch_size = config.image_size, config.patch_size
num_channels, hidden_size = config.num_channels, config.hidden_size
image_size = image_size if isinstance(image_size, collections.abc.Iterable) else (image_size, image_size)
patch_size = patch_size if isinstance(patch_size, collections.abc.Iterable) else (patch_size, patch_size)
num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
self.image_size = image_size
self.patch_size = patch_size
self.num_channels = num_channels
self.num_patches = num_patches
self.projection = nn.Conv2d(num_channels, hidden_size, kernel_size=patch_size, stride=patch_size)
def forward(self, pixel_values: torch.Tensor, interpolate_pos_encoding: bool = False) -> torch.Tensor:
batch_size, num_channels, height, width = pixel_values.shape
if num_channels != self.num_channels:
raise ValueError(
"Make sure that the channel dimension of the pixel values match with the one set in the configuration."
f" Expected {self.num_channels} but got {num_channels}."
)
if not interpolate_pos_encoding:
if height != self.image_size[0] or width != self.image_size[1]:
raise ValueError(
f"Input image size ({height}*{width}) doesn't match model"
f" ({self.image_size[0]}*{self.image_size[1]})."
)
embeddings = self.projection(pixel_values).flatten(2).transpose(1, 2)
return embeddings
class IJepaEmbeddings(nn.Module):
"""
Construct the CLS token, position and patch embeddings. Optionally, also the mask token.
"""
def __init__(self, config: IJepaConfig, use_mask_token: bool = False) -> None:
super().__init__()
self.mask_token = nn.Parameter(torch.zeros(1, 1, config.hidden_size)) if use_mask_token else None
self.patch_embeddings = IJepaPatchEmbeddings(config)
num_patches = self.patch_embeddings.num_patches
self.position_embeddings = nn.Parameter(torch.randn(1, num_patches, config.hidden_size))
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.patch_size = config.patch_size
self.config = config
def interpolate_pos_encoding(self, embeddings: torch.Tensor, height: int, width: int) -> torch.Tensor:
"""
This method allows to interpolate the pre-trained position encodings, to be able to use the model on higher resolution
images. This method is also adapted to support torch.jit tracing.
Adapted from:
- https://github.com/facebookresearch/dino/blob/de9ee3df6cf39fac952ab558447af1fa1365362a/vision_transformer.py#L174-L194, and
- https://github.com/facebookresearch/dinov2/blob/e1277af2ba9496fbadf7aec6eba56e8d882d1e35/dinov2/models/vision_transformer.py#L179-L211
"""
num_patches = embeddings.shape[1]
num_positions = self.position_embeddings.shape[1]
# always interpolate when tracing to ensure the exported model works for dynamic input shapes
if not torch.jit.is_tracing() and num_patches == num_positions and height == width:
return self.position_embeddings
patch_pos_embed = self.position_embeddings
dim = embeddings.shape[-1]
new_height = height // self.patch_size
new_width = width // self.patch_size
sqrt_num_positions = torch_int(num_positions**0.5)
patch_pos_embed = patch_pos_embed.reshape(1, sqrt_num_positions, sqrt_num_positions, dim)
patch_pos_embed = patch_pos_embed.permute(0, 3, 1, 2)
patch_pos_embed = nn.functional.interpolate(
patch_pos_embed,
size=(new_height, new_width),
mode="bicubic",
align_corners=False,
)
patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim)
return patch_pos_embed
def forward(
self,
pixel_values: torch.Tensor,
bool_masked_pos: Optional[torch.BoolTensor] = None,
interpolate_pos_encoding: bool = False,
) -> torch.Tensor:
batch_size, _, height, width = pixel_values.shape
embeddings = self.patch_embeddings(pixel_values, interpolate_pos_encoding=interpolate_pos_encoding)
if bool_masked_pos is not None:
seq_length = embeddings.shape[1]
mask_tokens = self.mask_token.expand(batch_size, seq_length, -1)
# replace the masked visual tokens by mask_tokens
mask = bool_masked_pos.unsqueeze(-1).type_as(mask_tokens)
embeddings = embeddings * (1.0 - mask) + mask_tokens * mask
# add positional encoding to each token
if interpolate_pos_encoding:
embeddings = embeddings + self.interpolate_pos_encoding(embeddings, height, width)
else:
embeddings = embeddings + self.position_embeddings
embeddings = self.dropout(embeddings)
return embeddings
class IJepaPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = IJepaConfig
base_model_prefix = "ijepa"
main_input_name = "pixel_values"
supports_gradient_checkpointing = True
_no_split_modules = ["IJepaEmbeddings", "IJepaLayer"]
_supports_sdpa = True
_supports_flash_attn_2 = True
def _init_weights(self, module: Union[nn.Linear, nn.Conv2d, nn.LayerNorm]) -> None:
"""Initialize the weights"""
if isinstance(module, (nn.Linear, nn.Conv2d)):
# Upcast the input in `fp32` and cast it back to desired `dtype` to avoid
# `trunc_normal_cpu` not implemented in `half` issues
module.weight.data = nn.init.trunc_normal_(
module.weight.data.to(torch.float32), mean=0.0, std=self.config.initializer_range
).to(module.weight.dtype)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.LayerNorm):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
elif isinstance(module, IJepaEmbeddings):
module.position_embeddings.data = nn.init.trunc_normal_(
module.position_embeddings.data.to(torch.float32),
mean=0.0,
std=self.config.initializer_range,
).to(module.position_embeddings.dtype)
if module.mask_token is not None:
module.mask_token.data.zero_()
def eager_attention_forward(
module: nn.Module,
query: torch.Tensor,
key: torch.Tensor,
value: torch.Tensor,
attention_mask: Optional[torch.Tensor],
scaling: float,
dropout: float = 0.0,
**kwargs,
):
# Take the dot product between "query" and "key" to get the raw attention scores.
attn_weights = torch.matmul(query, key.transpose(-1, -2)) * scaling
# Normalize the attention scores to probabilities.
attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
# This is actually dropping out entire tokens to attend to, which might
# seem a bit unusual, but is taken from the original Transformer paper.
attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
# Mask heads if we want to
if attention_mask is not None:
attn_weights = attn_weights * attention_mask
attn_output = torch.matmul(attn_weights, value)
attn_output = attn_output.transpose(1, 2).contiguous()
return attn_output, attn_weights
class IJepaSelfAttention(nn.Module):
def __init__(self, config: IJepaConfig) -> None:
super().__init__()
if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
raise ValueError(
f"The hidden size {config.hidden_size} is not a multiple of the number of attention "
f"heads {config.num_attention_heads}."
)
self.config = config
self.num_attention_heads = config.num_attention_heads
self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
self.all_head_size = self.num_attention_heads * self.attention_head_size
self.dropout_prob = config.attention_probs_dropout_prob
self.scaling = self.attention_head_size**-0.5
self.is_causal = False
self.query = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias)
self.key = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias)
self.value = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias)
def transpose_for_scores(self, x: torch.Tensor) -> torch.Tensor:
new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
x = x.view(new_x_shape)
return x.permute(0, 2, 1, 3)
def forward(
self, hidden_states, head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False
) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]:
key_layer = self.transpose_for_scores(self.key(hidden_states))
value_layer = self.transpose_for_scores(self.value(hidden_states))
query_layer = self.transpose_for_scores(self.query(hidden_states))
attention_interface: Callable = eager_attention_forward
if self.config._attn_implementation != "eager":
if self.config._attn_implementation == "sdpa" and output_attentions:
logger.warning_once(
"`torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to "
'eager attention. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
)
else:
attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
context_layer, attention_probs = attention_interface(
self,
query_layer,
key_layer,
value_layer,
head_mask,
is_causal=self.is_causal,
scaling=self.scaling,
dropout=0.0 if not self.training else self.dropout_prob,
)
new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
context_layer = context_layer.reshape(new_context_layer_shape)
outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
return outputs
class IJepaSelfOutput(nn.Module):
"""
The residual connection is defined in IJepaLayer instead of here (as is the case with other models), due to the
layernorm applied before each block.
"""
def __init__(self, config: IJepaConfig) -> None:
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
hidden_states = self.dense(hidden_states)
hidden_states = self.dropout(hidden_states)
return hidden_states
class IJepaAttention(nn.Module):
def __init__(self, config: IJepaConfig) -> None:
super().__init__()
self.attention = IJepaSelfAttention(config)
self.output = IJepaSelfOutput(config)
self.pruned_heads = set()
def prune_heads(self, heads: Set[int]) -> None:
if len(heads) == 0:
return
heads, index = find_pruneable_heads_and_indices(
heads, self.attention.num_attention_heads, self.attention.attention_head_size, self.pruned_heads
)
# Prune linear layers
self.attention.query = prune_linear_layer(self.attention.query, index)
self.attention.key = prune_linear_layer(self.attention.key, index)
self.attention.value = prune_linear_layer(self.attention.value, index)
self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
# Update hyper params and store pruned heads
self.attention.num_attention_heads = self.attention.num_attention_heads - len(heads)
self.attention.all_head_size = self.attention.attention_head_size * self.attention.num_attention_heads
self.pruned_heads = self.pruned_heads.union(heads)
def forward(
self,
hidden_states: torch.Tensor,
head_mask: Optional[torch.Tensor] = None,
output_attentions: bool = False,
) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]:
self_outputs = self.attention(hidden_states, head_mask, output_attentions)
attention_output = self.output(self_outputs[0], hidden_states)
outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them
return outputs
class IJepaIntermediate(nn.Module):
def __init__(self, config: IJepaConfig) -> None:
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
if isinstance(config.hidden_act, str):
self.intermediate_act_fn = ACT2FN[config.hidden_act]
else:
self.intermediate_act_fn = config.hidden_act
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidden_states = self.dense(hidden_states)
hidden_states = self.intermediate_act_fn(hidden_states)
return hidden_states
class IJepaOutput(nn.Module):
def __init__(self, config: IJepaConfig) -> None:
super().__init__()
self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
hidden_states = self.dense(hidden_states)
hidden_states = self.dropout(hidden_states)
hidden_states = hidden_states + input_tensor
return hidden_states
class IJepaLayer(nn.Module):
"""This corresponds to the Block class in the timm implementation."""
def __init__(self, config: IJepaConfig) -> None:
super().__init__()
self.chunk_size_feed_forward = config.chunk_size_feed_forward
self.seq_len_dim = 1
self.attention = IJepaAttention(config)
self.intermediate = IJepaIntermediate(config)
self.output = IJepaOutput(config)
self.layernorm_before = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.layernorm_after = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
def forward(
self,
hidden_states: torch.Tensor,
head_mask: Optional[torch.Tensor] = None,
output_attentions: bool = False,
) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]:
self_attention_outputs = self.attention(
self.layernorm_before(hidden_states), # in IJepa, layernorm is applied before self-attention
head_mask,
output_attentions=output_attentions,
)
attention_output = self_attention_outputs[0]
outputs = self_attention_outputs[1:] # add self attentions if we output attention weights
# first residual connection
hidden_states = attention_output + hidden_states
# in IJepa, layernorm is also applied after self-attention
layer_output = self.layernorm_after(hidden_states)
layer_output = self.intermediate(layer_output)
# second residual connection is done here
layer_output = self.output(layer_output, hidden_states)
outputs = (layer_output,) + outputs
return outputs
class IJepaEncoder(nn.Module):
def __init__(self, config: IJepaConfig) -> None:
super().__init__()
self.config = config
self.layer = nn.ModuleList([IJepaLayer(config) for _ in range(config.num_hidden_layers)])
self.gradient_checkpointing = False
def forward(
self,
hidden_states: torch.Tensor,
head_mask: Optional[torch.Tensor] = None,
output_attentions: bool = False,
output_hidden_states: bool = False,
return_dict: bool = True,
) -> Union[tuple, BaseModelOutput]:
all_hidden_states = () if output_hidden_states else None
all_self_attentions = () if output_attentions else None
for i, layer_module in enumerate(self.layer):
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
layer_head_mask = head_mask[i] if head_mask is not None else None
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
layer_module.__call__,
hidden_states,
layer_head_mask,
output_attentions,
)
else:
layer_outputs = layer_module(hidden_states, layer_head_mask, output_attentions)
hidden_states = layer_outputs[0]
if output_attentions:
all_self_attentions = all_self_attentions + (layer_outputs[1],)
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if not return_dict:
return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None)
return BaseModelOutput(
last_hidden_state=hidden_states,
hidden_states=all_hidden_states,
attentions=all_self_attentions,
)
class IJepaPooler(nn.Module):
def __init__(self, config: IJepaConfig):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.pooler_output_size)
self.activation = ACT2FN[config.pooler_act]
def forward(self, hidden_states):
# We "pool" the model by simply taking the hidden state corresponding
# to the first token.
first_token_tensor = hidden_states[:, 0]
pooled_output = self.dense(first_token_tensor)
pooled_output = self.activation(pooled_output)
return pooled_output
IJEPA_INPUTS_DOCSTRING = r"""
Args:
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See [`IJepaImageProcessor.__call__`]
for details.
head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
interpolate_pos_encoding (`bool`, *optional*):
Whether to interpolate the pre-trained position encodings.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
_EXPECTED_OUTPUT_SHAPE = [1, 256, 1280]
IJEPA_START_DOCSTRING = r"""
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`IJepaConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
@add_start_docstrings(
"The bare IJepa Model transformer outputting raw hidden-states without any specific head on top.",
IJEPA_START_DOCSTRING,
)
class IJepaModel(IJepaPreTrainedModel):
def __init__(self, config: IJepaConfig, add_pooling_layer: bool = False, use_mask_token: bool = False):
super().__init__(config)
self.config = config
self.embeddings = IJepaEmbeddings(config, use_mask_token=use_mask_token)
self.encoder = IJepaEncoder(config)
self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.pooler = IJepaPooler(config) if add_pooling_layer else None
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self) -> IJepaPatchEmbeddings:
return self.embeddings.patch_embeddings
def _prune_heads(self, heads_to_prune: Dict[int, List[int]]) -> None:
"""
Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
class PreTrainedModel
"""
for layer, heads in heads_to_prune.items():
self.encoder.layer[layer].attention.prune_heads(heads)
@add_start_docstrings_to_model_forward(IJEPA_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=BaseModelOutputWithPooling,
config_class=_CONFIG_FOR_DOC,
modality="vision",
expected_output=_EXPECTED_OUTPUT_SHAPE,
)
def forward(
self,
pixel_values: Optional[torch.Tensor] = None,
bool_masked_pos: Optional[torch.BoolTensor] = None,
head_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
interpolate_pos_encoding: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, BaseModelOutputWithPooling]:
r"""
bool_masked_pos (`torch.BoolTensor` of shape `(batch_size, num_patches)`, *optional*):
Boolean masked positions. Indicates which patches are masked (1) and which aren't (0).
"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if pixel_values is None:
raise ValueError("You have to specify pixel_values")
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x n_heads x N x N
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
# TODO: maybe have a cleaner way to cast the input (from `ImageProcessor` side?)
expected_dtype = self.embeddings.patch_embeddings.projection.weight.dtype
if pixel_values.dtype != expected_dtype:
pixel_values = pixel_values.to(expected_dtype)
embedding_output = self.embeddings(
pixel_values, bool_masked_pos=bool_masked_pos, interpolate_pos_encoding=interpolate_pos_encoding
)
encoder_outputs = self.encoder(
embedding_output,
head_mask=head_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = encoder_outputs[0]
sequence_output = self.layernorm(sequence_output)
pooled_output = self.pooler(sequence_output) if self.pooler is not None else None
if not return_dict:
head_outputs = (sequence_output, pooled_output) if pooled_output is not None else (sequence_output,)
return head_outputs + encoder_outputs[1:]
return BaseModelOutputWithPooling(
last_hidden_state=sequence_output,
pooler_output=pooled_output,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
)
_IMAGE_CLASS_CHECKPOINT = "facebook/ijepa_vith14_1k"
_IMAGE_CLASS_EXPECTED_OUTPUT = "Egyptian cat"
@add_start_docstrings(
"""
IJepa Model transformer with an image classification head on top (a linear layer on top of the final hidden states)
e.g. for ImageNet.
<Tip>
Note that it's possible to fine-tune IJepa on higher resolution images than the ones it has been trained on, by
setting `interpolate_pos_encoding` to `True` in the forward of the model. This will interpolate the pre-trained
position embeddings to the higher resolution.
</Tip>
""",
IJEPA_START_DOCSTRING,
)
class IJepaForImageClassification(IJepaPreTrainedModel):
def __init__(self, config: IJepaConfig) -> None:
super().__init__(config)
self.num_labels = config.num_labels
self.ijepa = IJepaModel(config, add_pooling_layer=False)
# Classifier head
self.classifier = nn.Linear(config.hidden_size, config.num_labels) if config.num_labels > 0 else nn.Identity()
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(IJEPA_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT,
output_type=ImageClassifierOutput,
config_class=_CONFIG_FOR_DOC,
expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT,
)
def forward(
self,
pixel_values: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
labels: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
interpolate_pos_encoding: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[tuple, ImageClassifierOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the image classification/regression loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.ijepa(
pixel_values,
head_mask=head_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
interpolate_pos_encoding=interpolate_pos_encoding,
return_dict=return_dict,
)
sequence_output = outputs[0]
logits = self.classifier(sequence_output.mean(dim=1))
loss = None
if labels is not None:
# move labels to correct device to enable model parallelism
labels = labels.to(logits.device)
if self.config.problem_type is None:
if self.num_labels == 1:
self.config.problem_type = "regression"
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
self.config.problem_type = "single_label_classification"
else:
self.config.problem_type = "multi_label_classification"
if self.config.problem_type == "regression":
loss_fct = MSELoss()
if self.num_labels == 1:
loss = loss_fct(logits.squeeze(), labels.squeeze())
else:
loss = loss_fct(logits, labels)
elif self.config.problem_type == "single_label_classification":
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
elif self.config.problem_type == "multi_label_classification":
loss_fct = BCEWithLogitsLoss()
loss = loss_fct(logits, labels)
if not return_dict:
output = (logits,) + outputs[1:]
return ((loss,) + output) if loss is not None else output
return ImageClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
__all__ = ["IJepaPreTrainedModel", "IJepaModel", "IJepaForImageClassification"]
```
|
==================================================================================================================================
SOURCE CODE FILE: modular_ijepa.py
LINES: 1
SIZE: 10.09 KB
PATH: scripts\freecad_env\Lib\site-packages\transformers\models\ijepa\modular_ijepa.py
ENCODING: utf-8
```py
from typing import Optional, Union
import torch
import torch.nn as nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from transformers.models.ijepa.configuration_ijepa import IJepaConfig
from ...modeling_outputs import ImageClassifierOutput
from ...modeling_utils import PreTrainedModel
from ...utils import (
add_start_docstrings,
torch_int,
)
from ..vit.modeling_vit import (
ViTEmbeddings,
ViTForImageClassification,
ViTModel,
)
_CHECKPOINT_FOR_DOC = "facebook/ijepa_vith14_1k"
class IJepaEmbeddings(ViTEmbeddings):
def __init__(self, config: IJepaConfig, use_mask_token: bool = False) -> None:
super().__init__(config, use_mask_token)
# Remove cls_token from IJepaEmbeddings, as it is not used in the model
del self.cls_token
num_patches = self.patch_embeddings.num_patches
self.position_embeddings = nn.Parameter(torch.randn(1, num_patches, config.hidden_size))
def interpolate_pos_encoding(self, embeddings: torch.Tensor, height: int, width: int) -> torch.Tensor:
"""
This method allows to interpolate the pre-trained position encodings, to be able to use the model on higher resolution
images. This method is also adapted to support torch.jit tracing.
Adapted from:
- https://github.com/facebookresearch/dino/blob/de9ee3df6cf39fac952ab558447af1fa1365362a/vision_transformer.py#L174-L194, and
- https://github.com/facebookresearch/dinov2/blob/e1277af2ba9496fbadf7aec6eba56e8d882d1e35/dinov2/models/vision_transformer.py#L179-L211
"""
num_patches = embeddings.shape[1]
num_positions = self.position_embeddings.shape[1]
# always interpolate when tracing to ensure the exported model works for dynamic input shapes
if not torch.jit.is_tracing() and num_patches == num_positions and height == width:
return self.position_embeddings
patch_pos_embed = self.position_embeddings
dim = embeddings.shape[-1]
new_height = height // self.patch_size
new_width = width // self.patch_size
sqrt_num_positions = torch_int(num_positions**0.5)
patch_pos_embed = patch_pos_embed.reshape(1, sqrt_num_positions, sqrt_num_positions, dim)
patch_pos_embed = patch_pos_embed.permute(0, 3, 1, 2)
patch_pos_embed = nn.functional.interpolate(
patch_pos_embed,
size=(new_height, new_width),
mode="bicubic",
align_corners=False,
)
patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim)
return patch_pos_embed
def forward(
self,
pixel_values: torch.Tensor,
bool_masked_pos: Optional[torch.BoolTensor] = None,
interpolate_pos_encoding: bool = False,
) -> torch.Tensor:
batch_size, _, height, width = pixel_values.shape
embeddings = self.patch_embeddings(pixel_values, interpolate_pos_encoding=interpolate_pos_encoding)
if bool_masked_pos is not None:
seq_length = embeddings.shape[1]
mask_tokens = self.mask_token.expand(batch_size, seq_length, -1)
# replace the masked visual tokens by mask_tokens
mask = bool_masked_pos.unsqueeze(-1).type_as(mask_tokens)
embeddings = embeddings * (1.0 - mask) + mask_tokens * mask
# add positional encoding to each token
if interpolate_pos_encoding:
embeddings = embeddings + self.interpolate_pos_encoding(embeddings, height, width)
else:
embeddings = embeddings + self.position_embeddings
embeddings = self.dropout(embeddings)
return embeddings
class IJepaPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = IJepaConfig
base_model_prefix = "ijepa"
main_input_name = "pixel_values"
supports_gradient_checkpointing = True
_no_split_modules = ["IJepaEmbeddings", "IJepaLayer"]
_supports_sdpa = True
_supports_flash_attn_2 = True
def _init_weights(self, module: Union[nn.Linear, nn.Conv2d, nn.LayerNorm]) -> None:
"""Initialize the weights"""
if isinstance(module, (nn.Linear, nn.Conv2d)):
# Upcast the input in `fp32` and cast it back to desired `dtype` to avoid
# `trunc_normal_cpu` not implemented in `half` issues
module.weight.data = nn.init.trunc_normal_(
module.weight.data.to(torch.float32), mean=0.0, std=self.config.initializer_range
).to(module.weight.dtype)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.LayerNorm):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
elif isinstance(module, IJepaEmbeddings):
module.position_embeddings.data = nn.init.trunc_normal_(
module.position_embeddings.data.to(torch.float32),
mean=0.0,
std=self.config.initializer_range,
).to(module.position_embeddings.dtype)
if module.mask_token is not None:
module.mask_token.data.zero_()
_EXPECTED_OUTPUT_SHAPE = [1, 256, 1280]
IJEPA_START_DOCSTRING = r"""
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`IJepaConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
@add_start_docstrings(
"The bare IJepa Model transformer outputting raw hidden-states without any specific head on top.",
IJEPA_START_DOCSTRING,
)
class IJepaModel(IJepaPreTrainedModel, ViTModel):
def __init__(self, config: IJepaConfig, add_pooling_layer: bool = False, use_mask_token: bool = False):
super().__init__(config)
self.config = config
self.embeddings = IJepaEmbeddings(config, use_mask_token=use_mask_token)
_IMAGE_CLASS_CHECKPOINT = "facebook/ijepa_vith14_1k"
_IMAGE_CLASS_EXPECTED_OUTPUT = "Egyptian cat"
@add_start_docstrings(
"""
IJepa Model transformer with an image classification head on top (a linear layer on top of the final hidden states)
e.g. for ImageNet.
<Tip>
Note that it's possible to fine-tune IJepa on higher resolution images than the ones it has been trained on, by
setting `interpolate_pos_encoding` to `True` in the forward of the model. This will interpolate the pre-trained
position embeddings to the higher resolution.
</Tip>
""",
IJEPA_START_DOCSTRING,
)
class IJepaForImageClassification(IJepaPreTrainedModel, ViTForImageClassification):
def __init__(self, config: IJepaConfig):
super().__init__(config)
self.ijepa = IJepaModel(config, add_pooling_layer=False)
self.post_init()
def forward(
self,
pixel_values: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
labels: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
interpolate_pos_encoding: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[tuple, ImageClassifierOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the image classification/regression loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.ijepa(
pixel_values,
head_mask=head_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
interpolate_pos_encoding=interpolate_pos_encoding,
return_dict=return_dict,
)
sequence_output = outputs[0]
logits = self.classifier(sequence_output.mean(dim=1))
loss = None
if labels is not None:
# move labels to correct device to enable model parallelism
labels = labels.to(logits.device)
if self.config.problem_type is None:
if self.num_labels == 1:
self.config.problem_type = "regression"
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
self.config.problem_type = "single_label_classification"
else:
self.config.problem_type = "multi_label_classification"
if self.config.problem_type == "regression":
loss_fct = MSELoss()
if self.num_labels == 1:
loss = loss_fct(logits.squeeze(), labels.squeeze())
else:
loss = loss_fct(logits, labels)
elif self.config.problem_type == "single_label_classification":
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
elif self.config.problem_type == "multi_label_classification":
loss_fct = BCEWithLogitsLoss()
loss = loss_fct(logits, labels)
if not return_dict:
output = (logits,) + outputs[1:]
return ((loss,) + output) if loss is not None else output
return ImageClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
__all__ = [
"IJepaPreTrainedModel",
"IJepaModel",
"IJepaForImageClassification",
]
```
|
================================================================================================================================
SOURCE CODE FILE: __init__.py
LINES: 1
SIZE: 1.06 KB
PATH: scripts\freecad_env\Lib\site-packages\transformers\models\imagegpt\__init__.py
ENCODING: utf-8
```py
# Copyright 2024 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import _LazyModule
from ...utils.import_utils import define_import_structure
if TYPE_CHECKING:
from .configuration_imagegpt import *
from .feature_extraction_imagegpt import *
from .image_processing_imagegpt import *
from .modeling_imagegpt import *
else:
import sys
_file = globals()["__file__"]
sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)
```
|
==============================================================================================================================================
SOURCE CODE FILE: configuration_imagegpt.py
LINES: 1
SIZE: 8.57 KB
PATH: scripts\freecad_env\Lib\site-packages\transformers\models\imagegpt\configuration_imagegpt.py
ENCODING: utf-8
```py
# coding=utf-8
# Copyright 2021 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""OpenAI ImageGPT configuration"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, TensorType
logger = logging.get_logger(__name__)
class ImageGPTConfig(PretrainedConfig):
"""
This is the configuration class to store the configuration of a [`ImageGPTModel`] or a [`TFImageGPTModel`]. It is
used to instantiate a GPT-2 model according to the specified arguments, defining the model architecture.
Instantiating a configuration with the defaults will yield a similar configuration to that of the ImageGPT
[openai/imagegpt-small](https://huggingface.co/openai/imagegpt-small) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 512):
Vocabulary size of the GPT-2 model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`ImageGPTModel`] or [`TFImageGPTModel`].
n_positions (`int`, *optional*, defaults to 32*32):
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).
n_embd (`int`, *optional*, defaults to 512):
Dimensionality of the embeddings and hidden states.
n_layer (`int`, *optional*, defaults to 24):
Number of hidden layers in the Transformer encoder.
n_head (`int`, *optional*, defaults to 8):
Number of attention heads for each attention layer in the Transformer encoder.
n_inner (`int`, *optional*, defaults to None):
Dimensionality of the inner feed-forward layers. `None` will set it to 4 times n_embd
activation_function (`str`, *optional*, defaults to `"quick_gelu"`):
Activation function (can be one of the activation functions defined in src/transformers/activations.py).
Defaults to "quick_gelu".
resid_pdrop (`float`, *optional*, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
embd_pdrop (`int`, *optional*, defaults to 0.1):
The dropout ratio for the embeddings.
attn_pdrop (`float`, *optional*, defaults to 0.1):
The dropout ratio for the attention.
layer_norm_epsilon (`float`, *optional*, defaults to 1e-5):
The epsilon to use in the layer normalization layers.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
scale_attn_weights (`bool`, *optional*, defaults to `True`):
Scale attention weights by dividing by sqrt(hidden_size)..
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models).
scale_attn_by_inverse_layer_idx (`bool`, *optional*, defaults to `False`):
Whether to additionally scale attention weights by `1 / layer_idx + 1`.
reorder_and_upcast_attn (`bool`, *optional*, defaults to `False`):
Whether to scale keys (K) prior to computing attention (dot-product) and upcast attention
dot-product/softmax to float() when training with mixed precision.
Example:
```python
>>> from transformers import ImageGPTConfig, ImageGPTModel
>>> # Initializing a ImageGPT configuration
>>> configuration = ImageGPTConfig()
>>> # Initializing a model (with random weights) from the configuration
>>> model = ImageGPTModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "imagegpt"
keys_to_ignore_at_inference = ["past_key_values"]
attribute_map = {
"hidden_size": "n_embd",
"max_position_embeddings": "n_positions",
"num_attention_heads": "n_head",
"num_hidden_layers": "n_layer",
}
def __init__(
self,
vocab_size=512 + 1, # add one for start of sentence (sos) token
n_positions=32 * 32,
n_embd=512,
n_layer=24,
n_head=8,
n_inner=None,
activation_function="quick_gelu",
resid_pdrop=0.1,
embd_pdrop=0.1,
attn_pdrop=0.1,
layer_norm_epsilon=1e-5,
initializer_range=0.02,
scale_attn_weights=True,
use_cache=True,
tie_word_embeddings=False,
scale_attn_by_inverse_layer_idx=False,
reorder_and_upcast_attn=False,
**kwargs,
):
self.vocab_size = vocab_size
self.n_positions = n_positions
self.n_embd = n_embd
self.n_layer = n_layer
self.n_head = n_head
self.n_inner = n_inner
self.activation_function = activation_function
self.resid_pdrop = resid_pdrop
self.embd_pdrop = embd_pdrop
self.attn_pdrop = attn_pdrop
self.layer_norm_epsilon = layer_norm_epsilon
self.initializer_range = initializer_range
self.scale_attn_weights = scale_attn_weights
self.use_cache = use_cache
self.scale_attn_by_inverse_layer_idx = scale_attn_by_inverse_layer_idx
self.reorder_and_upcast_attn = reorder_and_upcast_attn
self.tie_word_embeddings = tie_word_embeddings
super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs)
class ImageGPTOnnxConfig(OnnxConfig):
@property
def inputs(self) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("input_ids", {0: "batch", 1: "sequence"}),
]
)
def generate_dummy_inputs(
self,
preprocessor: "FeatureExtractionMixin",
batch_size: int = 1,
seq_length: int = -1,
is_pair: bool = False,
framework: Optional["TensorType"] = None,
num_channels: int = 3,
image_width: int = 32,
image_height: int = 32,
) -> Mapping[str, Any]:
"""
Generate inputs to provide to the ONNX exporter for the specific framework
Args:
preprocessor ([`PreTrainedTokenizerBase`] or [`FeatureExtractionMixin`]):
The preprocessor associated with this model configuration.
batch_size (`int`, *optional*, defaults to -1):
The batch size to export the model for (-1 means dynamic axis).
num_choices (`int`, *optional*, defaults to -1):
The number of candidate answers provided for multiple choice task (-1 means dynamic axis).
seq_length (`int`, *optional*, defaults to -1):
The sequence length to export the model for (-1 means dynamic axis).
is_pair (`bool`, *optional*, defaults to `False`):
Indicate if the input is a pair (sentence 1, sentence 2)
framework (`TensorType`, *optional*, defaults to `None`):
The framework (PyTorch or TensorFlow) that the tokenizer will generate tensors for.
num_channels (`int`, *optional*, defaults to 3):
The number of channels of the generated images.
image_width (`int`, *optional*, defaults to 40):
The width of the generated images.
image_height (`int`, *optional*, defaults to 40):
The height of the generated images.
Returns:
Mapping[str, Tensor] holding the kwargs to provide to the model's forward function
"""
input_image = self._generate_dummy_images(batch_size, num_channels, image_height, image_width)
inputs = dict(preprocessor(images=input_image, return_tensors=framework))
return inputs
__all__ = ["ImageGPTConfig", "ImageGPTOnnxConfig"]
```
|
===================================================================================================================================================
SOURCE CODE FILE: feature_extraction_imagegpt.py
LINES: 1
SIZE: 1.21 KB
PATH: scripts\freecad_env\Lib\site-packages\transformers\models\imagegpt\feature_extraction_imagegpt.py
ENCODING: utf-8
```py
# coding=utf-8
# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Feature extractor class for ImageGPT."""
import warnings
from ...utils import logging
from .image_processing_imagegpt import ImageGPTImageProcessor
logger = logging.get_logger(__name__)
class ImageGPTFeatureExtractor(ImageGPTImageProcessor):
def __init__(self, *args, **kwargs) -> None:
warnings.warn(
"The class ImageGPTFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
" Please use ImageGPTImageProcessor instead.",
FutureWarning,
)
super().__init__(*args, **kwargs)
__all__ = ["ImageGPTFeatureExtractor"]
```
|
=================================================================================================================================================
SOURCE CODE FILE: image_processing_imagegpt.py
LINES: 1
SIZE: 14.03 KB
PATH: scripts\freecad_env\Lib\site-packages\transformers\models\imagegpt\image_processing_imagegpt.py
ENCODING: utf-8
```py
# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Image processor class for ImageGPT."""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
ImageInput,
PILImageResampling,
infer_channel_dimension_format,
is_scaled_image,
make_list_of_images,
to_numpy_array,
valid_images,
validate_preprocess_arguments,
)
from ...utils import TensorType, filter_out_non_signature_kwargs, is_vision_available, logging
if is_vision_available():
import PIL
logger = logging.get_logger(__name__)
def squared_euclidean_distance(a, b):
b = b.T
a2 = np.sum(np.square(a), axis=1)
b2 = np.sum(np.square(b), axis=0)
ab = np.matmul(a, b)
d = a2[:, None] - 2 * ab + b2[None, :]
return d
def color_quantize(x, clusters):
x = x.reshape(-1, 3)
d = squared_euclidean_distance(x, clusters)
return np.argmin(d, axis=1)
class ImageGPTImageProcessor(BaseImageProcessor):
r"""
Constructs a ImageGPT image processor. This image processor can be used to resize images to a smaller resolution
(such as 32x32 or 64x64), normalize them and finally color quantize them to obtain sequences of "pixel values"
(color clusters).
Args:
clusters (`np.ndarray` or `List[List[int]]`, *optional*):
The color clusters to use, of shape `(n_clusters, 3)` when color quantizing. Can be overriden by `clusters`
in `preprocess`.
do_resize (`bool`, *optional*, defaults to `True`):
Whether to resize the image's dimensions to `(size["height"], size["width"])`. Can be overridden by
`do_resize` in `preprocess`.
size (`Dict[str, int]` *optional*, defaults to `{"height": 256, "width": 256}`):
Size of the image after resizing. Can be overridden by `size` in `preprocess`.
resample (`PILImageResampling`, *optional*, defaults to `Resampling.BILINEAR`):
Resampling filter to use if resizing the image. Can be overridden by `resample` in `preprocess`.
do_normalize (`bool`, *optional*, defaults to `True`):
Whether to normalize the image pixel value to between [-1, 1]. Can be overridden by `do_normalize` in
`preprocess`.
do_color_quantize (`bool`, *optional*, defaults to `True`):
Whether to color quantize the image. Can be overridden by `do_color_quantize` in `preprocess`.
"""
model_input_names = ["pixel_values"]
def __init__(
self,
# clusters is a first argument to maintain backwards compatibility with the old ImageGPTImageProcessor
clusters: Optional[Union[List[List[int]], np.ndarray]] = None,
do_resize: bool = True,
size: Dict[str, int] = None,
resample: PILImageResampling = PILImageResampling.BILINEAR,
do_normalize: bool = True,
do_color_quantize: bool = True,
**kwargs,
) -> None:
super().__init__(**kwargs)
size = size if size is not None else {"height": 256, "width": 256}
size = get_size_dict(size)
self.clusters = np.array(clusters) if clusters is not None else None
self.do_resize = do_resize
self.size = size
self.resample = resample
self.do_normalize = do_normalize
self.do_color_quantize = do_color_quantize
# Copied from transformers.models.vit.image_processing_vit.ViTImageProcessor.resize
def resize(
self,
image: np.ndarray,
size: Dict[str, int],
resample: PILImageResampling = PILImageResampling.BILINEAR,
data_format: Optional[Union[str, ChannelDimension]] = None,
input_data_format: Optional[Union[str, ChannelDimension]] = None,
**kwargs,
) -> np.ndarray:
"""
Resize an image to `(size["height"], size["width"])`.
Args:
image (`np.ndarray`):
Image to resize.
size (`Dict[str, int]`):
Dictionary in the format `{"height": int, "width": int}` specifying the size of the output image.
resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BILINEAR`):
`PILImageResampling` filter to use when resizing the image e.g. `PILImageResampling.BILINEAR`.
data_format (`ChannelDimension` or `str`, *optional*):
The channel dimension format for the output image. If unset, the channel dimension format of the input
image is used. Can be one of:
- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
- `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
input_data_format (`ChannelDimension` or `str`, *optional*):
The channel dimension format for the input image. If unset, the channel dimension format is inferred
from the input image. Can be one of:
- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
- `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
Returns:
`np.ndarray`: The resized image.
"""
size = get_size_dict(size)
if "height" not in size or "width" not in size:
raise ValueError(f"The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}")
output_size = (size["height"], size["width"])
return resize(
image,
size=output_size,
resample=resample,
data_format=data_format,
input_data_format=input_data_format,
**kwargs,
)
def normalize(
self,
image: np.ndarray,
data_format: Optional[Union[str, ChannelDimension]] = None,
input_data_format: Optional[Union[str, ChannelDimension]] = None,
) -> np.ndarray:
"""
Normalizes an images' pixel values to between [-1, 1].
Args:
image (`np.ndarray`):
Image to normalize.
data_format (`str` or `ChannelDimension`, *optional*):
The channel dimension format of the image. If not provided, it will be the same as the input image.
input_data_format (`ChannelDimension` or `str`, *optional*):
The channel dimension format of the input image. If not provided, it will be inferred.
"""
image = rescale(image=image, scale=1 / 127.5, data_format=data_format, input_data_format=input_data_format)
image = image - 1
return image
@filter_out_non_signature_kwargs()
def preprocess(
self,
images: ImageInput,
do_resize: Optional[bool] = None,
size: Dict[str, int] = None,
resample: PILImageResampling = None,
do_normalize: Optional[bool] = None,
do_color_quantize: Optional[bool] = None,
clusters: Optional[Union[List[List[int]], np.ndarray]] = None,
return_tensors: Optional[Union[str, TensorType]] = None,
data_format: Optional[Union[str, ChannelDimension]] = ChannelDimension.FIRST,
input_data_format: Optional[Union[str, ChannelDimension]] = None,
) -> PIL.Image.Image:
"""
Preprocess an image or batch of images.
Args:
images (`ImageInput`):
Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If
passing in images with pixel values between 0 and 1, set `do_normalize=False`.
do_resize (`bool`, *optional*, defaults to `self.do_resize`):
Whether to resize the image.
size (`Dict[str, int]`, *optional*, defaults to `self.size`):
Size of the image after resizing.
resample (`int`, *optional*, defaults to `self.resample`):
Resampling filter to use if resizing the image. This can be one of the enum `PILImageResampling`, Only
has an effect if `do_resize` is set to `True`.
do_normalize (`bool`, *optional*, defaults to `self.do_normalize`):
Whether to normalize the image
do_color_quantize (`bool`, *optional*, defaults to `self.do_color_quantize`):
Whether to color quantize the image.
clusters (`np.ndarray` or `List[List[int]]`, *optional*, defaults to `self.clusters`):
Clusters used to quantize the image of shape `(n_clusters, 3)`. Only has an effect if
`do_color_quantize` is set to `True`.
return_tensors (`str` or `TensorType`, *optional*):
The type of tensors to return. Can be one of:
- Unset: Return a list of `np.ndarray`.
- `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`.
- `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
- `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`.
- `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`.
data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`):
The channel dimension format for the output image. Can be one of:
- `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
- `ChannelDimension.LAST`: image in (height, width, num_channels) format.
Only has an effect if `do_color_quantize` is set to `False`.
input_data_format (`ChannelDimension` or `str`, *optional*):
The channel dimension format for the input image. If unset, the channel dimension format is inferred
from the input image. Can be one of:
- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
- `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
"""
do_resize = do_resize if do_resize is not None else self.do_resize
size = size if size is not None else self.size
size = get_size_dict(size)
resample = resample if resample is not None else self.resample
do_normalize = do_normalize if do_normalize is not None else self.do_normalize
do_color_quantize = do_color_quantize if do_color_quantize is not None else self.do_color_quantize
clusters = clusters if clusters is not None else self.clusters
clusters = np.array(clusters)
images = make_list_of_images(images)
if not valid_images(images):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
"torch.Tensor, tf.Tensor or jax.ndarray."
)
# Here, normalize() is using a constant factor to divide pixel values.
# hence, the method does not need iamge_mean and image_std.
validate_preprocess_arguments(
do_resize=do_resize,
size=size,
resample=resample,
)
if do_color_quantize and clusters is None:
raise ValueError("Clusters must be specified if do_color_quantize is True.")
# All transformations expect numpy arrays.
images = [to_numpy_array(image) for image in images]
if do_normalize and is_scaled_image(images[0]):
logger.warning_once(
"It looks like you are trying to rescale already rescaled images. If you wish to do this, "
"make sure to set `do_normalize` to `False` and that pixel values are between [-1, 1].",
)
if input_data_format is None:
# We assume that all images have the same channel dimension format.
input_data_format = infer_channel_dimension_format(images[0])
if do_resize:
images = [
self.resize(image=image, size=size, resample=resample, input_data_format=input_data_format)
for image in images
]
if do_normalize:
images = [self.normalize(image=image, input_data_format=input_data_format) for image in images]
if do_color_quantize:
images = [to_channel_dimension_format(image, ChannelDimension.LAST, input_data_format) for image in images]
# color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
images = np.array(images)
images = color_quantize(images, clusters).reshape(images.shape[:-1])
# flatten to (batch_size, height*width)
batch_size = images.shape[0]
images = images.reshape(batch_size, -1)
# We need to convert back to a list of images to keep consistent behaviour across processors.
images = list(images)
else:
images = [
to_channel_dimension_format(image, data_format, input_channel_dim=input_data_format)
for image in images
]
data = {"input_ids": images}
return BatchFeature(data=data, tensor_type=return_tensors)
__all__ = ["ImageGPTImageProcessor"]
```
|
=========================================================================================================================================
SOURCE CODE FILE: modeling_imagegpt.py
LINES: 1
SIZE: 50.99 KB
PATH: scripts\freecad_env\Lib\site-packages\transformers\models\imagegpt\modeling_imagegpt.py
ENCODING: utf-8
```py
# coding=utf-8
# Copyright 2021 The OpenAI Team Authors and HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""PyTorch OpenAI ImageGPT model."""
import math
import os
import warnings
from typing import Any, Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.cuda.amp import autocast
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACT2FN
from ...generation import GenerationMixin
from ...modeling_outputs import (
BaseModelOutputWithPastAndCrossAttentions,
CausalLMOutputWithCrossAttentions,
SequenceClassifierOutputWithPast,
)
from ...modeling_utils import PreTrainedModel
from ...pytorch_utils import Conv1D, find_pruneable_heads_and_indices, prune_conv1d_layer
from ...utils import (
add_start_docstrings,
add_start_docstrings_to_model_forward,
logging,
replace_return_docstrings,
torch_float,
)
from .configuration_imagegpt import ImageGPTConfig
logger = logging.get_logger(__name__)
_CHECKPOINT_FOR_DOC = "openai/imagegpt-small"
_CONFIG_FOR_DOC = "ImageGPTConfig"
def load_tf_weights_in_imagegpt(model, config, imagegpt_checkpoint_path):
"""
Load tf checkpoints in a pytorch model
"""
try:
import re
import tensorflow as tf
except ImportError:
logger.error(
"Loading a TensorFlow model in PyTorch, requires TensorFlow to be installed. Please see "
"https://www.tensorflow.org/install/ for installation instructions."
)
raise
tf_path = os.path.abspath(imagegpt_checkpoint_path)
logger.info("Converting TensorFlow checkpoint from {}".format(tf_path))
# Load weights from TF model
init_vars = tf.train.list_variables(tf_path)
names = []
arrays = []
for name, shape in init_vars:
logger.info("Loading TF weight {} with shape {}".format(name, shape))
array = tf.train.load_variable(tf_path, name)
names.append(name)
arrays.append(array.squeeze())
for name, array in zip(names, arrays):
name = name[6:] # skip "model/"
name = name.split("/")
# adam_v and adam_m are variables used in AdamWeightDecayOptimizer to calculated m and v
# which are not required for using pretrained model
if any(
n in ["adam_v", "adam_m", "AdamWeightDecayOptimizer", "AdamWeightDecayOptimizer_1", "global_step"]
for n in name
) or name[-1] in ["_step"]:
logger.info("Skipping {}".format("/".join(name)))
continue
pointer = model
if name[-1] not in ["wtet"]:
pointer = getattr(pointer, "transformer")
for m_name in name:
if re.fullmatch(r"[A-Za-z]+\d+", m_name):
scope_names = re.split(r"(\d+)", m_name)
else:
scope_names = [m_name]
if scope_names[0] == "w" or scope_names[0] == "g":
pointer = getattr(pointer, "weight")
elif scope_names[0] == "b":
pointer = getattr(pointer, "bias")
elif scope_names[0] == "wpe" or scope_names[0] == "wte":
pointer = getattr(pointer, scope_names[0])
pointer = getattr(pointer, "weight")
elif scope_names[0] in ["q_proj", "k_proj", "v_proj"]:
pointer = getattr(pointer, "c_attn")
pointer = getattr(pointer, "weight")
elif len(name) == 3 and name[1] == "attn" and scope_names[0] == "c_proj":
pointer = getattr(pointer, scope_names[0])
pointer = getattr(pointer, "weight")
elif scope_names[0] == "wtet":
pointer = getattr(pointer, "lm_head")
pointer = getattr(pointer, "weight")
elif scope_names[0] == "sos":
pointer = getattr(pointer, "wte")
pointer = getattr(pointer, "weight")
else:
pointer = getattr(pointer, scope_names[0])
if len(scope_names) >= 2:
num = int(scope_names[1])
pointer = pointer[num]
if len(name) > 1 and name[1] == "attn" or name[-1] == "wtet" or name[-1] == "sos" or name[-1] == "wte":
pass # array is used to initialize only part of the pointer so sizes won't match
else:
try:
assert pointer.shape == array.shape
except AssertionError as e:
e.args += (pointer.shape, array.shape)
raise
logger.info("Initialize PyTorch weight {}".format(name))
if name[-1] == "q_proj":
pointer.data[:, : config.n_embd] = torch.from_numpy(array.reshape(config.n_embd, config.n_embd)).T
elif name[-1] == "k_proj":
pointer.data[:, config.n_embd : 2 * config.n_embd] = torch.from_numpy(
array.reshape(config.n_embd, config.n_embd)
).T
elif name[-1] == "v_proj":
pointer.data[:, 2 * config.n_embd :] = torch.from_numpy(array.reshape(config.n_embd, config.n_embd)).T
elif len(name) == 3 and name[1] == "attn" and name[2] == "c_proj":
pointer.data = torch.from_numpy(array.reshape(config.n_embd, config.n_embd))
elif name[-1] == "wtet":
pointer.data = torch.from_numpy(array)
elif name[-1] == "wte":
pointer.data[: config.vocab_size - 1, :] = torch.from_numpy(array)
elif name[-1] == "sos":
pointer.data[-1] = torch.from_numpy(array)
else:
pointer.data = torch.from_numpy(array)
return model
class ImageGPTLayerNorm(nn.Module):
def __init__(self, hidden_size: Tuple[int], eps: float = 1e-5):
super().__init__()
self.eps = eps
self.weight = nn.Parameter(torch.Tensor(hidden_size))
def forward(self, tensor: torch.Tensor) -> torch.Tensor:
# input is not mean centered
tensor = tensor / torch.sqrt(torch.mean(torch.square(tensor), axis=-1, keepdim=True) + self.eps)
tensor = tensor * self.weight
return tensor
class ImageGPTAttention(nn.Module):
def __init__(self, config, is_cross_attention: Optional[bool] = False, layer_idx: Optional[int] = None):
super().__init__()
max_positions = config.max_position_embeddings
self.register_buffer(
"bias",
torch.tril(torch.ones((max_positions, max_positions), dtype=torch.bool)).view(
1, 1, max_positions, max_positions
),
persistent=False,
)
self.register_buffer("masked_bias", torch.tensor(-1e4), persistent=False)
self.embed_dim = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
self.split_size = self.embed_dim
if self.head_dim * self.num_heads != self.embed_dim:
raise ValueError(
f"`embed_dim` must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:"
f" {self.num_heads})."
)
self.scale_attn_weights = config.scale_attn_weights
self.is_cross_attention = is_cross_attention
# Layer-wise attention scaling, reordering, and upcasting
self.scale_attn_by_inverse_layer_idx = config.scale_attn_by_inverse_layer_idx
self.layer_idx = layer_idx
self.reorder_and_upcast_attn = config.reorder_and_upcast_attn
if self.is_cross_attention:
self.c_attn = Conv1D(2 * self.embed_dim, self.embed_dim)
self.q_attn = Conv1D(self.embed_dim, self.embed_dim)
else:
self.c_attn = Conv1D(3 * self.embed_dim, self.embed_dim)
self.c_proj = Conv1D(self.embed_dim, self.embed_dim)
self.attn_dropout = nn.Dropout(config.attn_pdrop)
self.resid_dropout = nn.Dropout(config.resid_pdrop)
self.pruned_heads = set()
def prune_heads(self, heads):
if len(heads) == 0:
return
heads, index = find_pruneable_heads_and_indices(heads, self.num_heads, self.head_dim, self.pruned_heads)
index_attn = torch.cat([index, index + self.split_size, index + (2 * self.split_size)])
# Prune conv1d layers
self.c_attn = prune_conv1d_layer(self.c_attn, index_attn, dim=1)
self.c_proj = prune_conv1d_layer(self.c_proj, index, dim=0)
# Update hyper params
self.split_size = (self.split_size // self.num_heads) * (self.num_heads - len(heads))
self.num_heads = self.num_heads - len(heads)
self.pruned_heads = self.pruned_heads.union(heads)
def _attn(self, query, key, value, attention_mask=None, head_mask=None):
attn_weights = torch.matmul(query, key.transpose(-1, -2))
if self.scale_attn_weights:
attn_weights = attn_weights / torch_float(value.size(-1) ** 0.5)
# Layer-wise attention scaling
if self.scale_attn_by_inverse_layer_idx:
attn_weights = attn_weights / float(self.layer_idx + 1)
if not self.is_cross_attention:
# if only "normal" attention layer implements causal mask
query_length, key_length = query.size(-2), key.size(-2)
causal_mask = self.bias[:, :, key_length - query_length : key_length, :key_length]
mask_value = torch.finfo(attn_weights.dtype).min
# Need to be a tensor, otherwise we get error: `RuntimeError: expected scalar type float but found double`.
# Need to be on the same device, otherwise `RuntimeError: ..., x and y to be on the same device`
mask_value = torch.tensor(mask_value, dtype=attn_weights.dtype, device=attn_weights.device)
attn_weights = torch.where(causal_mask, attn_weights, mask_value)
if attention_mask is not None:
# Apply the attention mask
attn_weights = attn_weights + attention_mask
attn_weights = nn.Softmax(dim=-1)(attn_weights)
# Downcast (if necessary) back to V's dtype (if in mixed-precision) -- No-Op otherwise
attn_weights = attn_weights.type(value.dtype)
attn_weights = self.attn_dropout(attn_weights)
# Mask heads if we want to
if head_mask is not None:
attn_weights = attn_weights * head_mask
attn_output = torch.matmul(attn_weights, value)
return attn_output, attn_weights
def _upcast_and_reordered_attn(self, query, key, value, attention_mask=None, head_mask=None):
# Use `torch.baddbmm` (a bit more efficient w/ alpha param for scaling -- from Megatron-LM)
bsz, num_heads, q_seq_len, dk = query.size()
_, _, k_seq_len, _ = key.size()
# Preallocate attn_weights for `baddbmm`
attn_weights = torch.empty(bsz * num_heads, q_seq_len, k_seq_len, dtype=torch.float32, device=query.device)
# Compute Scale Factor
scale_factor = 1.0
if self.scale_attn_weights:
scale_factor /= float(value.size(-1)) ** 0.5
if self.scale_attn_by_inverse_layer_idx:
scale_factor /= float(self.layer_idx + 1)
# Upcast (turn off autocast) and reorder (Scale K by 1 / root(dk))
with autocast(enabled=False):
q, k = query.reshape(-1, q_seq_len, dk), key.transpose(-1, -2).reshape(-1, dk, k_seq_len)
attn_weights = torch.baddbmm(attn_weights, q.float(), k.float(), beta=0, alpha=scale_factor)
attn_weights = attn_weights.reshape(bsz, num_heads, q_seq_len, k_seq_len)
if not self.is_cross_attention:
# if only "normal" attention layer implements causal mask
query_length, key_length = query.size(-2), key.size(-2)
causal_mask = self.bias[:, :, key_length - query_length : key_length, :key_length]
mask_value = torch.finfo(attn_weights.dtype).min
# Need to be a tensor, otherwise we get error: `RuntimeError: expected scalar type float but found double`.
# Need to be on the same device, otherwise `RuntimeError: ..., x and y to be on the same device`
mask_value = torch.tensor(mask_value, dtype=attn_weights.dtype, device=attn_weights.device)
attn_weights = torch.where(causal_mask, attn_weights, mask_value)
if attention_mask is not None:
# Apply the attention mask
attn_weights = attn_weights + attention_mask
attn_weights = nn.Softmax(dim=-1)(attn_weights)
# Downcast (if necessary) back to V's dtype (if in mixed-precision) -- No-Op if otherwise
if attn_weights.dtype != torch.float32:
raise RuntimeError("Error with upcasting, attn_weights does not have dtype torch.float32")
attn_weights = attn_weights.type(value.dtype)
attn_weights = self.attn_dropout(attn_weights)
# Mask heads if we want to
if head_mask is not None:
attn_weights = attn_weights * head_mask
attn_output = torch.matmul(attn_weights, value)
return attn_output, attn_weights
def _split_heads(self, tensor, num_heads, attn_head_size):
"""
Splits hidden_size dim into attn_head_size and num_heads
"""
new_shape = tensor.size()[:-1] + (num_heads, attn_head_size)
tensor = tensor.view(*new_shape)
return tensor.permute(0, 2, 1, 3) # (batch, head, seq_length, head_features)
def _merge_heads(self, tensor, num_heads, attn_head_size):
"""
Merges attn_head_size dim and num_attn_heads dim into hidden_size
"""
tensor = tensor.permute(0, 2, 1, 3).contiguous()
new_shape = tensor.size()[:-2] + (num_heads * attn_head_size,)
return tensor.view(new_shape)
def forward(
self,
hidden_states: torch.Tensor,
layer_past: Optional[bool] = None,
attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
encoder_attention_mask: Optional[torch.Tensor] = None,
use_cache: Optional[bool] = False,
output_attentions: Optional[bool] = False,
) -> tuple:
if encoder_hidden_states is not None:
if not hasattr(self, "q_attn"):
raise ValueError(
"If class is used as cross attention, the weights `q_attn` have to be defined. "
"Please make sure to instantiate class with `ImageGPTAttention(..., is_cross_attention=True)`."
)
query = self.q_attn(hidden_states)
key, value = self.c_attn(encoder_hidden_states).split(self.split_size, dim=2)
attention_mask = encoder_attention_mask
else:
query, key, value = self.c_attn(hidden_states).split(self.split_size, dim=2)
query = self._split_heads(query, self.num_heads, self.head_dim)
key = self._split_heads(key, self.num_heads, self.head_dim)
value = self._split_heads(value, self.num_heads, self.head_dim)
if layer_past is not None:
past_key, past_value = layer_past
key = torch.cat((past_key, key), dim=-2)
value = torch.cat((past_value, value), dim=-2)
if use_cache is True:
present = (key, value)
else:
present = None
if self.reorder_and_upcast_attn:
attn_output, attn_weights = self._upcast_and_reordered_attn(query, key, value, attention_mask, head_mask)
else:
attn_output, attn_weights = self._attn(query, key, value, attention_mask, head_mask)
attn_output = self._merge_heads(attn_output, self.num_heads, self.head_dim)
attn_output = self.c_proj(attn_output)
attn_output = self.resid_dropout(attn_output)
outputs = (attn_output, present)
if output_attentions:
outputs += (attn_weights,)
return outputs # a, present, (attentions)
class ImageGPTMLP(nn.Module):
def __init__(self, intermediate_size, config):
super().__init__()
embed_dim = config.hidden_size
self.c_fc = Conv1D(intermediate_size, embed_dim)
self.c_proj = Conv1D(embed_dim, intermediate_size)
self.act = ACT2FN[config.activation_function]
self.dropout = nn.Dropout(config.resid_pdrop)
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidden_states = self.c_fc(hidden_states)
hidden_states = self.act(hidden_states)
hidden_states = self.c_proj(hidden_states)
hidden_states = self.dropout(hidden_states)
return hidden_states
class ImageGPTBlock(nn.Module):
def __init__(self, config, layer_idx=None):
super().__init__()
hidden_size = config.hidden_size
inner_dim = config.n_inner if config.n_inner is not None else 4 * hidden_size
self.ln_1 = ImageGPTLayerNorm(hidden_size, eps=config.layer_norm_epsilon)
self.attn = ImageGPTAttention(config, layer_idx=layer_idx)
self.ln_2 = ImageGPTLayerNorm(hidden_size, eps=config.layer_norm_epsilon)
if config.add_cross_attention:
self.crossattention = ImageGPTAttention(config, is_cross_attention=True, layer_idx=layer_idx)
self.ln_cross_attn = ImageGPTLayerNorm(hidden_size, eps=config.layer_norm_epsilon)
self.mlp = ImageGPTMLP(inner_dim, config)
def forward(
self,
hidden_states: torch.Tensor,
layer_past: Optional[bool] = None,
attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
encoder_attention_mask: Optional[torch.Tensor] = None,
use_cache: Optional[bool] = False,
output_attentions: Optional[bool] = False,
) -> tuple:
residual = hidden_states
hidden_states = self.ln_1(hidden_states)
attn_outputs = self.attn(
hidden_states,
layer_past=layer_past,
attention_mask=attention_mask,
head_mask=head_mask,
use_cache=use_cache,
output_attentions=output_attentions,
)
attn_output = attn_outputs[0] # output_attn: a, present, (attentions)
outputs = attn_outputs[1:]
# residual connection
hidden_states = attn_output + residual
if encoder_hidden_states is not None:
# add one self-attention block for cross-attention
if not hasattr(self, "crossattention"):
raise ValueError(
f"If `encoder_hidden_states` are passed, {self} has to be instantiated with "
"cross-attention layers by setting `config.add_cross_attention=True`"
)
residual = hidden_states
hidden_states = self.ln_cross_attn(hidden_states)
cross_attn_outputs = self.crossattention(
hidden_states,
attention_mask=attention_mask,
head_mask=head_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
output_attentions=output_attentions,
)
attn_output = cross_attn_outputs[0]
# residual connection
hidden_states = residual + attn_output
outputs = outputs + cross_attn_outputs[2:] # add cross attentions if we output attention weights
residual = hidden_states
hidden_states = self.ln_2(hidden_states)
feed_forward_hidden_states = self.mlp(hidden_states)
# residual connection
hidden_states = residual + feed_forward_hidden_states
outputs = (hidden_states,) + (outputs if use_cache else outputs[1:])
return outputs # hidden_states, present, (attentions, cross_attentions)
class ImageGPTPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = ImageGPTConfig
load_tf_weights = load_tf_weights_in_imagegpt
base_model_prefix = "transformer"
main_input_name = "input_ids"
supports_gradient_checkpointing = True
_no_split_modules = ["ImageGPTBlock"]
def __init__(self, *inputs, **kwargs):
super().__init__(*inputs, **kwargs)
def _init_weights(self, module):
"""Initialize the weights."""
if isinstance(module, (nn.Linear, Conv1D)):
# Slightly different from the TF version which uses truncated_normal for initialization
# cf https://github.com/pytorch/pytorch/pull/5617
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.Embedding):
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
elif isinstance(module, ImageGPTLayerNorm):
module.weight.data.fill_(1.0)
# Reinitialize selected weights subject to the OpenAI GPT-2 Paper Scheme:
# > A modified initialization which accounts for the accumulation on the residual path with model depth. Scale
# > the weights of residual layers at initialization by a factor of 1/√N where N is the # of residual layers.
# > -- GPT-2 :: https://openai.com/blog/better-language-models/
#
# Reference (Megatron-LM): https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/model/gpt_model.py
for name, p in module.named_parameters():
if "c_proj" in name and "weight" in name:
# Special Scaled Initialization --> There are 2 Layer Norms per Transformer Block
p.data.normal_(mean=0.0, std=(self.config.initializer_range / math.sqrt(2 * self.config.n_layer)))
IMAGEGPT_START_DOCSTRING = r"""
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.
Parameters:
config ([`ImageGPTConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
IMAGEGPT_INPUTS_DOCSTRING = r"""
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
`input_ids_length` = `sequence_length` if `past_key_values` is `None` else
`past_key_values[0][0].shape[-2]` (`sequence_length` of input past key value states). Indices of input
sequence tokens in the vocabulary.
If `past_key_values` is used, only `input_ids` that do not have their past calculated should be passed as
`input_ids`.
Indices can be obtained using [`AutoImageProcessor`]. See [`ImageGPTImageProcessor.__call__`] for details.
past_key_values (`Tuple[Tuple[torch.Tensor]]` of length `config.n_layers`):
Contains precomputed hidden-states (key and values in the attention blocks) as computed by the model (see
`past_key_values` output below). Can be used to speed up sequential decoding. The `input_ids` which have
their past given to this model should not be passed as `input_ids` as they have already been computed.
attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
token_type_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
1]`:
- 0 corresponds to a *sentence A* token,
- 1 corresponds to a *sentence B* token.
[What are token type IDs?](../glossary#token-type-ids)
position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
config.max_position_embeddings - 1]`.
[What are position IDs?](../glossary#position-ids)
head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
model's internal embedding lookup matrix.
If `past_key_values` is used, optionally only the last `inputs_embeds` have to be input (see
`past_key_values`).
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
`past_key_values`).
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
@add_start_docstrings(
"The bare ImageGPT Model transformer outputting raw hidden-states without any specific head on top.",
IMAGEGPT_START_DOCSTRING,
)
class ImageGPTModel(ImageGPTPreTrainedModel):
def __init__(self, config: ImageGPTConfig):
super().__init__(config)
self.embed_dim = config.hidden_size
self.wte = nn.Embedding(config.vocab_size, self.embed_dim)
self.wpe = nn.Embedding(config.max_position_embeddings, self.embed_dim)
self.drop = nn.Dropout(config.embd_pdrop)
self.h = nn.ModuleList([ImageGPTBlock(config, layer_idx=i) for i in range(config.num_hidden_layers)])
self.ln_f = ImageGPTLayerNorm(self.embed_dim, eps=config.layer_norm_epsilon)
# Model parallel
self.model_parallel = False
self.device_map = None
self.gradient_checkpointing = False
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.wte
def set_input_embeddings(self, new_embeddings):
self.wte = new_embeddings
def _prune_heads(self, heads_to_prune):
"""
Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer}
"""
for layer, heads in heads_to_prune.items():
self.h[layer].attn.prune_heads(heads)
@add_start_docstrings_to_model_forward(IMAGEGPT_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=BaseModelOutputWithPastAndCrossAttentions, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
attention_mask: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
encoder_attention_mask: Optional[torch.Tensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
**kwargs: Any,
) -> Union[Tuple, BaseModelOutputWithPastAndCrossAttentions]:
r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set
`labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100`
are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]`
Returns:
Examples:
```python
>>> from transformers import AutoImageProcessor, ImageGPTModel
>>> from PIL import Image
>>> import requests
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> image_processor = AutoImageProcessor.from_pretrained("openai/imagegpt-small")
>>> model = ImageGPTModel.from_pretrained("openai/imagegpt-small")
>>> inputs = image_processor(images=image, return_tensors="pt")
>>> outputs = model(**inputs)
>>> last_hidden_states = outputs.last_hidden_state
```"""
if "pixel_values" in kwargs:
warnings.warn(
"The `pixel_values` argument is deprecated and will be removed in v4.47, use `input_ids` instead.",
FutureWarning,
)
if input_ids is not None:
raise ValueError(
"You cannot pass both `pixel_values` and `input_ids`. Please make sure to only pass `input_ids`."
)
input_ids = kwargs.pop("pixel_values")
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
use_cache = use_cache if use_cache is not None else self.config.use_cache
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
elif input_ids is not None:
self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
input_shape = input_ids.size()
input_ids = input_ids.view(-1, input_shape[-1])
batch_size = input_ids.shape[0]
elif inputs_embeds is not None:
input_shape = inputs_embeds.size()[:-1]
batch_size = inputs_embeds.shape[0]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
device = input_ids.device if input_ids is not None else inputs_embeds.device
if token_type_ids is not None:
token_type_ids = token_type_ids.view(-1, input_shape[-1])
if past_key_values is None:
past_length = 0
past_key_values = tuple([None] * len(self.h))
else:
past_length = past_key_values[0][0].size(-2)
if position_ids is None:
position_ids = torch.arange(past_length, input_shape[-1] + past_length, dtype=torch.long, device=device)
position_ids = position_ids.unsqueeze(0)
# ImageGPTAttention mask.
if attention_mask is not None:
if batch_size <= 0:
raise ValueError("batch_size has to be defined and > 0")
attention_mask = attention_mask.view(batch_size, -1)
# We create a 3D attention mask from a 2D tensor mask.
# Sizes are [batch_size, 1, 1, to_seq_length]
# So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
# this attention mask is more simple than the triangular masking of causal attention
# used in OpenAI GPT, we just need to prepare the broadcast dimension here.
attention_mask = attention_mask[:, None, None, :]
# Since attention_mask is 1.0 for positions we want to attend and 0.0 for
# masked positions, this operation will create a tensor which is 0.0 for
# positions we want to attend and the dtype's smallest value for masked positions.
# Since we are adding it to the raw scores before the softmax, this is
# effectively the same as removing these entirely.
attention_mask = attention_mask.to(dtype=self.dtype) # fp16 compatibility
attention_mask = (1.0 - attention_mask) * torch.finfo(self.dtype).min
# If a 2D or 3D attention mask is provided for the cross-attention
# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
if self.config.add_cross_attention and encoder_hidden_states is not None:
encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size()
encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
if encoder_attention_mask is None:
encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device)
encoder_attention_mask = self.invert_attention_mask(encoder_attention_mask)
else:
encoder_attention_mask = None
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x n_heads x N x N
# head_mask has shape n_layer x batch x n_heads x N x N
head_mask = self.get_head_mask(head_mask, self.config.n_layer)
if inputs_embeds is None:
inputs_embeds = self.wte(input_ids)
position_embeds = self.wpe(position_ids)
hidden_states = inputs_embeds + position_embeds.to(inputs_embeds.device)
if token_type_ids is not None:
token_type_embeds = self.wte(token_type_ids)
hidden_states = hidden_states + token_type_embeds
hidden_states = self.drop(hidden_states)
output_shape = input_shape + (hidden_states.size(-1),)
if self.gradient_checkpointing and self.training:
if use_cache:
logger.warning_once(
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
)
use_cache = False
presents = () if use_cache else None
all_self_attentions = () if output_attentions else None
all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None
all_hidden_states = () if output_hidden_states else None
for i, (block, layer_past) in enumerate(zip(self.h, past_key_values)):
# Model parallel
if self.model_parallel:
torch.cuda.set_device(hidden_states.device)
# Ensure layer_past is on same device as hidden_states (might not be correct)
if layer_past is not None:
layer_past = tuple(past_state.to(hidden_states.device) for past_state in layer_past)
# Ensure that attention_mask is always on the same device as hidden_states
if attention_mask is not None:
attention_mask = attention_mask.to(hidden_states.device)
if isinstance(head_mask, torch.Tensor):
head_mask = head_mask.to(hidden_states.device)
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if self.gradient_checkpointing and self.training:
outputs = self._gradient_checkpointing_func(
block.__call__,
hidden_states,
None,
attention_mask,
head_mask[i],
encoder_hidden_states,
encoder_attention_mask,
use_cache,
output_attentions,
)
else:
outputs = block(
hidden_states,
layer_past=layer_past,
attention_mask=attention_mask,
head_mask=head_mask[i],
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
use_cache=use_cache,
output_attentions=output_attentions,
)
hidden_states = outputs[0]
if use_cache is True:
presents = presents + (outputs[1],)
if output_attentions:
all_self_attentions = all_self_attentions + (outputs[2 if use_cache else 1],)
if self.config.add_cross_attention:
all_cross_attentions = all_cross_attentions + (outputs[3 if use_cache else 2],)
# Model Parallel: If it's the last layer for that device, put things on the next device
if self.model_parallel:
for k, v in self.device_map.items():
if i == v[-1] and "cuda:" + str(k) != self.last_device:
hidden_states = hidden_states.to("cuda:" + str(k + 1))
hidden_states = self.ln_f(hidden_states)
hidden_states = hidden_states.view(*output_shape)
# Add last hidden state
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if not return_dict:
return tuple(
v
for v in [hidden_states, presents, all_hidden_states, all_self_attentions, all_cross_attentions]
if v is not None
)
return BaseModelOutputWithPastAndCrossAttentions(
last_hidden_state=hidden_states,
past_key_values=presents,
hidden_states=all_hidden_states,
attentions=all_self_attentions,
cross_attentions=all_cross_attentions,
)
@add_start_docstrings(
"""
The ImageGPT Model transformer with a language modeling head on top (linear layer with weights tied to the input
embeddings).
""",
IMAGEGPT_START_DOCSTRING,
)
class ImageGPTForCausalImageModeling(ImageGPTPreTrainedModel, GenerationMixin):
_tied_weights_keys = ["lm_head.weight"]
def __init__(self, config: ImageGPTConfig):
super().__init__(config)
self.transformer = ImageGPTModel(config)
self.lm_head = nn.Linear(config.n_embd, config.vocab_size - 1, bias=False)
# Model parallel
self.model_parallel = False
self.device_map = None
# Initialize weights and apply final processing
self.post_init()
def get_output_embeddings(self):
return self.lm_head
def set_output_embeddings(self, new_embeddings):
self.lm_head = new_embeddings
@add_start_docstrings_to_model_forward(IMAGEGPT_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=CausalLMOutputWithCrossAttentions, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
attention_mask: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
encoder_attention_mask: Optional[torch.Tensor] = None,
labels: Optional[torch.Tensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
**kwargs: Any,
) -> Union[Tuple, CausalLMOutputWithCrossAttentions]:
r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set
`labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100`
are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]`
Returns:
Examples:
```python
>>> from transformers import AutoImageProcessor, ImageGPTForCausalImageModeling
>>> import torch
>>> import matplotlib.pyplot as plt
>>> import numpy as np
>>> image_processor = AutoImageProcessor.from_pretrained("openai/imagegpt-small")
>>> model = ImageGPTForCausalImageModeling.from_pretrained("openai/imagegpt-small")
>>> device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
>>> model.to(device) # doctest: +IGNORE_RESULT
>>> # unconditional generation of 8 images
>>> batch_size = 4
>>> context = torch.full((batch_size, 1), model.config.vocab_size - 1) # initialize with SOS token
>>> context = context.to(device)
>>> output = model.generate(
... input_ids=context, max_length=model.config.n_positions + 1, temperature=1.0, do_sample=True, top_k=40
... )
>>> clusters = image_processor.clusters
>>> height = image_processor.size["height"]
>>> width = image_processor.size["width"]
>>> samples = output[:, 1:].detach().cpu().numpy()
>>> samples_img = [
... np.reshape(np.rint(127.5 * (clusters[s] + 1.0)), [height, width, 3]).astype(np.uint8) for s in samples
... ] # convert color cluster tokens back to pixels
>>> f, axes = plt.subplots(1, batch_size, dpi=300)
>>> for img, ax in zip(samples_img, axes): # doctest: +IGNORE_RESULT
... ax.axis("off")
... ax.imshow(img)
```"""
if "pixel_values" in kwargs:
warnings.warn(
"The `pixel_values` argument is deprecated and will be removed in v4.47, use `input_ids` instead.",
FutureWarning,
)
if input_ids is not None:
raise ValueError(
"You cannot pass both `pixel_values` and `input_ids`. Please make sure to only pass `input_ids`."
)
input_ids = kwargs.pop("pixel_values")
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
transformer_outputs = self.transformer(
input_ids,
past_key_values=past_key_values,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
hidden_states = transformer_outputs[0]
lm_logits = self.lm_head(hidden_states)
loss = None
if labels is not None:
# Shift so that tokens < n predict n
shift_logits = lm_logits[..., :-1, :].contiguous()
shift_labels = labels[..., 1:].contiguous()
# Flatten the tokens
loss_fct = CrossEntropyLoss()
loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))
if not return_dict:
output = (lm_logits,) + transformer_outputs[1:]
return ((loss,) + output) if loss is not None else output
return CausalLMOutputWithCrossAttentions(
loss=loss,
logits=lm_logits,
past_key_values=transformer_outputs.past_key_values,
hidden_states=transformer_outputs.hidden_states,
attentions=transformer_outputs.attentions,
cross_attentions=transformer_outputs.cross_attentions,
)
@staticmethod
def _reorder_cache(
past_key_values: Tuple[Tuple[torch.Tensor]], beam_idx: torch.Tensor
) -> Tuple[Tuple[torch.Tensor]]:
"""
This function is used to re-order the `past_key_values` cache if [`~PreTrainedModel.beam_search`] or
[`~PreTrainedModel.beam_sample`] is called. This is required to match `past_key_values` with the correct
beam_idx at every generation step.
"""
return tuple(
tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past)
for layer_past in past_key_values
)
@add_start_docstrings(
"""
The ImageGPT Model transformer with an image classification head on top (linear layer).
[`ImageGPTForImageClassification`] average-pools the hidden states in order to do the classification.
""",
IMAGEGPT_START_DOCSTRING,
)
class ImageGPTForImageClassification(ImageGPTPreTrainedModel):
def __init__(self, config: ImageGPTConfig):
super().__init__(config)
self.num_labels = config.num_labels
self.transformer = ImageGPTModel(config)
self.score = nn.Linear(config.n_embd, self.num_labels, bias=False)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(IMAGEGPT_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=SequenceClassifierOutputWithPast, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
attention_mask: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
labels: Optional[torch.Tensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
**kwargs: Any,
) -> Union[Tuple, SequenceClassifierOutputWithPast]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
Returns:
Examples:
```python
>>> from transformers import AutoImageProcessor, ImageGPTForImageClassification
>>> from PIL import Image
>>> import requests
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> image_processor = AutoImageProcessor.from_pretrained("openai/imagegpt-small")
>>> model = ImageGPTForImageClassification.from_pretrained("openai/imagegpt-small")
>>> inputs = image_processor(images=image, return_tensors="pt")
>>> outputs = model(**inputs)
>>> logits = outputs.logits
```"""
if "pixel_values" in kwargs:
warnings.warn(
"The `pixel_values` argument is deprecated and will be removed in v4.47, use `input_ids` instead.",
FutureWarning,
)
if input_ids is not None:
raise ValueError(
"You cannot pass both `pixel_values` and `input_ids`. Please make sure to only pass `input_ids`."
)
input_ids = kwargs.pop("pixel_values")
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
transformer_outputs = self.transformer(
input_ids,
past_key_values=past_key_values,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
hidden_states = transformer_outputs[0]
# average-pool the hidden states along the sequence dimension
pooled_hidden_states = hidden_states.mean(dim=1)
# project from (batch_size, hidden_size) to (batch_size, num_labels)
logits = self.score(pooled_hidden_states)
loss = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
self.config.problem_type = "regression"
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
self.config.problem_type = "single_label_classification"
else:
self.config.problem_type = "multi_label_classification"
if self.config.problem_type == "regression":
loss_fct = MSELoss()
if self.num_labels == 1:
loss = loss_fct(logits.squeeze(), labels.squeeze())
else:
loss = loss_fct(logits, labels)
elif self.config.problem_type == "single_label_classification":
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
elif self.config.problem_type == "multi_label_classification":
loss_fct = BCEWithLogitsLoss()
loss = loss_fct(logits, labels)
if not return_dict:
output = (logits,) + transformer_outputs[1:]
return ((loss,) + output) if loss is not None else output
return SequenceClassifierOutputWithPast(
loss=loss,
logits=logits,
past_key_values=transformer_outputs.past_key_values,
hidden_states=transformer_outputs.hidden_states,
attentions=transformer_outputs.attentions,
)
__all__ = [
"ImageGPTForCausalImageModeling",
"ImageGPTForImageClassification",
"ImageGPTModel",
"ImageGPTPreTrainedModel",
"load_tf_weights_in_imagegpt",
]
```
|
================================================================================================================================
SOURCE CODE FILE: __init__.py
LINES: 1
SIZE: 0.97 KB
PATH: scripts\freecad_env\Lib\site-packages\transformers\models\informer\__init__.py
ENCODING: utf-8
```py
# Copyright 2024 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import _LazyModule
from ...utils.import_utils import define_import_structure
if TYPE_CHECKING:
from .configuration_informer import *
from .modeling_informer import *
else:
import sys
_file = globals()["__file__"]
sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)
```
|
==============================================================================================================================================
SOURCE CODE FILE: configuration_informer.py
LINES: 1
SIZE: 12.15 KB
PATH: scripts\freecad_env\Lib\site-packages\transformers\models\informer\configuration_informer.py
ENCODING: utf-8
```py
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Informer model configuration"""
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
logger = logging.get_logger(__name__)
class InformerConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of an [`InformerModel`]. It is used to instantiate an
Informer model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a similar configuration to that of the Informer
[huggingface/informer-tourism-monthly](https://huggingface.co/huggingface/informer-tourism-monthly) architecture.
Configuration objects inherit from [`PretrainedConfig`] can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
prediction_length (`int`):
The prediction length for the decoder. In other words, the prediction horizon of the model. This value is
typically dictated by the dataset and we recommend to set it appropriately.
context_length (`int`, *optional*, defaults to `prediction_length`):
The context length for the encoder. If `None`, the context length will be the same as the
`prediction_length`.
distribution_output (`string`, *optional*, defaults to `"student_t"`):
The distribution emission head for the model. Could be either "student_t", "normal" or "negative_binomial".
loss (`string`, *optional*, defaults to `"nll"`):
The loss function for the model corresponding to the `distribution_output` head. For parametric
distributions it is the negative log likelihood (nll) - which currently is the only supported one.
input_size (`int`, *optional*, defaults to 1):
The size of the target variable which by default is 1 for univariate targets. Would be > 1 in case of
multivariate targets.
scaling (`string` or `bool`, *optional* defaults to `"mean"`):
Whether to scale the input targets via "mean" scaler, "std" scaler or no scaler if `None`. If `True`, the
scaler is set to "mean".
lags_sequence (`list[int]`, *optional*, defaults to `[1, 2, 3, 4, 5, 6, 7]`):
The lags of the input time series as covariates often dictated by the frequency of the data. Default is
`[1, 2, 3, 4, 5, 6, 7]` but we recommend to change it based on the dataset appropriately.
num_time_features (`int`, *optional*, defaults to 0):
The number of time features in the input time series.
num_dynamic_real_features (`int`, *optional*, defaults to 0):
The number of dynamic real valued features.
num_static_categorical_features (`int`, *optional*, defaults to 0):
The number of static categorical features.
num_static_real_features (`int`, *optional*, defaults to 0):
The number of static real valued features.
cardinality (`list[int]`, *optional*):
The cardinality (number of different values) for each of the static categorical features. Should be a list
of integers, having the same length as `num_static_categorical_features`. Cannot be `None` if
`num_static_categorical_features` is > 0.
embedding_dimension (`list[int]`, *optional*):
The dimension of the embedding for each of the static categorical features. Should be a list of integers,
having the same length as `num_static_categorical_features`. Cannot be `None` if
`num_static_categorical_features` is > 0.
d_model (`int`, *optional*, defaults to 64):
Dimensionality of the transformer layers.
encoder_layers (`int`, *optional*, defaults to 2):
Number of encoder layers.
decoder_layers (`int`, *optional*, defaults to 2):
Number of decoder layers.
encoder_attention_heads (`int`, *optional*, defaults to 2):
Number of attention heads for each attention layer in the Transformer encoder.
decoder_attention_heads (`int`, *optional*, defaults to 2):
Number of attention heads for each attention layer in the Transformer decoder.
encoder_ffn_dim (`int`, *optional*, defaults to 32):
Dimension of the "intermediate" (often named feed-forward) layer in encoder.
decoder_ffn_dim (`int`, *optional*, defaults to 32):
Dimension of the "intermediate" (often named feed-forward) layer in decoder.
activation_function (`str` or `function`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder and decoder. If string, `"gelu"` and
`"relu"` are supported.
dropout (`float`, *optional*, defaults to 0.1):
The dropout probability for all fully connected layers in the encoder, and decoder.
encoder_layerdrop (`float`, *optional*, defaults to 0.1):
The dropout probability for the attention and fully connected layers for each encoder layer.
decoder_layerdrop (`float`, *optional*, defaults to 0.1):
The dropout probability for the attention and fully connected layers for each decoder layer.
attention_dropout (`float`, *optional*, defaults to 0.1):
The dropout probability for the attention probabilities.
activation_dropout (`float`, *optional*, defaults to 0.1):
The dropout probability used between the two layers of the feed-forward networks.
num_parallel_samples (`int`, *optional*, defaults to 100):
The number of samples to generate in parallel for each time step of inference.
init_std (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated normal weight initialization distribution.
use_cache (`bool`, *optional*, defaults to `True`):
Whether to use the past key/values attentions (if applicable to the model) to speed up decoding.
attention_type (`str`, *optional*, defaults to "prob"):
Attention used in encoder. This can be set to "prob" (Informer's ProbAttention) or "full" (vanilla
transformer's canonical self-attention).
sampling_factor (`int`, *optional*, defaults to 5):
ProbSparse sampling factor (only makes affect when `attention_type`="prob"). It is used to control the
reduced query matrix (Q_reduce) input length.
distil (`bool`, *optional*, defaults to `True`):
Whether to use distilling in encoder.
Example:
```python
>>> from transformers import InformerConfig, InformerModel
>>> # Initializing an Informer configuration with 12 time steps for prediction
>>> configuration = InformerConfig(prediction_length=12)
>>> # Randomly initializing a model (with random weights) from the configuration
>>> model = InformerModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "informer"
attribute_map = {
"hidden_size": "d_model",
"num_attention_heads": "encoder_attention_heads",
"num_hidden_layers": "encoder_layers",
}
def __init__(
self,
prediction_length: Optional[int] = None,
context_length: Optional[int] = None,
distribution_output: str = "student_t",
loss: str = "nll",
input_size: int = 1,
lags_sequence: List[int] = None,
scaling: Optional[Union[str, bool]] = "mean",
num_dynamic_real_features: int = 0,
num_static_real_features: int = 0,
num_static_categorical_features: int = 0,
num_time_features: int = 0,
cardinality: Optional[List[int]] = None,
embedding_dimension: Optional[List[int]] = None,
d_model: int = 64,
encoder_ffn_dim: int = 32,
decoder_ffn_dim: int = 32,
encoder_attention_heads: int = 2,
decoder_attention_heads: int = 2,
encoder_layers: int = 2,
decoder_layers: int = 2,
is_encoder_decoder: bool = True,
activation_function: str = "gelu",
dropout: float = 0.05,
encoder_layerdrop: float = 0.1,
decoder_layerdrop: float = 0.1,
attention_dropout: float = 0.1,
activation_dropout: float = 0.1,
num_parallel_samples: int = 100,
init_std: float = 0.02,
use_cache=True,
# Informer arguments
attention_type: str = "prob",
sampling_factor: int = 5,
distil: bool = True,
**kwargs,
):
# time series specific configuration
self.prediction_length = prediction_length
self.context_length = context_length or prediction_length
self.distribution_output = distribution_output
self.loss = loss
self.input_size = input_size
self.num_time_features = num_time_features
self.lags_sequence = lags_sequence if lags_sequence is not None else [1, 2, 3, 4, 5, 6, 7]
self.scaling = scaling
self.num_dynamic_real_features = num_dynamic_real_features
self.num_static_real_features = num_static_real_features
self.num_static_categorical_features = num_static_categorical_features
# set cardinality
if cardinality and num_static_categorical_features > 0:
if len(cardinality) != num_static_categorical_features:
raise ValueError(
"The cardinality should be a list of the same length as `num_static_categorical_features`"
)
self.cardinality = cardinality
else:
self.cardinality = [0]
# set embedding_dimension
if embedding_dimension and num_static_categorical_features > 0:
if len(embedding_dimension) != num_static_categorical_features:
raise ValueError(
"The embedding dimension should be a list of the same length as `num_static_categorical_features`"
)
self.embedding_dimension = embedding_dimension
else:
self.embedding_dimension = [min(50, (cat + 1) // 2) for cat in self.cardinality]
self.num_parallel_samples = num_parallel_samples
# Transformer architecture configuration
self.feature_size = input_size * len(self.lags_sequence) + self._number_of_features
self.d_model = d_model
self.encoder_attention_heads = encoder_attention_heads
self.decoder_attention_heads = decoder_attention_heads
self.encoder_ffn_dim = encoder_ffn_dim
self.decoder_ffn_dim = decoder_ffn_dim
self.encoder_layers = encoder_layers
self.decoder_layers = decoder_layers
self.dropout = dropout
self.attention_dropout = attention_dropout
self.activation_dropout = activation_dropout
self.encoder_layerdrop = encoder_layerdrop
self.decoder_layerdrop = decoder_layerdrop
self.activation_function = activation_function
self.init_std = init_std
self.use_cache = use_cache
# Informer
self.attention_type = attention_type
self.sampling_factor = sampling_factor
self.distil = distil
super().__init__(is_encoder_decoder=is_encoder_decoder, **kwargs)
@property
def _number_of_features(self) -> int:
return (
sum(self.embedding_dimension)
+ self.num_dynamic_real_features
+ self.num_time_features
+ self.num_static_real_features
+ self.input_size * 2 # the log1p(abs(loc)) and log(scale) features
)
__all__ = ["InformerConfig"]
```
|
=========================================================================================================================================
SOURCE CODE FILE: modeling_informer.py
LINES: 1
SIZE: 99.18 KB
PATH: scripts\freecad_env\Lib\site-packages\transformers\models\informer\modeling_informer.py
ENCODING: utf-8
```py
# coding=utf-8
# Copyright 2023 Amazon and The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""PyTorch Informer model."""
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from torch import nn
from ...activations import ACT2FN
from ...modeling_attn_mask_utils import _prepare_4d_attention_mask, _prepare_4d_causal_attention_mask
from ...modeling_outputs import (
BaseModelOutput,
BaseModelOutputWithPastAndCrossAttentions,
SampleTSPredictionOutput,
Seq2SeqTSModelOutput,
Seq2SeqTSPredictionOutput,
)
from ...modeling_utils import PreTrainedModel
from ...time_series_utils import NegativeBinomialOutput, NormalOutput, StudentTOutput
from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
from .configuration_informer import InformerConfig
logger = logging.get_logger(__name__)
_CONFIG_FOR_DOC = "InformerConfig"
# Copied from transformers.models.time_series_transformer.modeling_time_series_transformer.TimeSeriesFeatureEmbedder with TimeSeries->Informer
class InformerFeatureEmbedder(nn.Module):
"""
Embed a sequence of categorical features.
Args:
cardinalities (`list[int]`):
List of cardinalities of the categorical features.
embedding_dims (`list[int]`):
List of embedding dimensions of the categorical features.
"""
def __init__(self, cardinalities: List[int], embedding_dims: List[int]) -> None:
super().__init__()
self.num_features = len(cardinalities)
self.embedders = nn.ModuleList([nn.Embedding(c, d) for c, d in zip(cardinalities, embedding_dims)])
def forward(self, features: torch.Tensor) -> torch.Tensor:
if self.num_features > 1:
# we slice the last dimension, giving an array of length
# self.num_features with shape (N,T) or (N)
cat_feature_slices = torch.chunk(features, self.num_features, dim=-1)
else:
cat_feature_slices = [features]
return torch.cat(
[
embed(cat_feature_slice.squeeze(-1))
for embed, cat_feature_slice in zip(self.embedders, cat_feature_slices)
],
dim=-1,
)
# Copied from transformers.models.time_series_transformer.modeling_time_series_transformer.TimeSeriesStdScaler with TimeSeriesTransformer->Informer,TimeSeries->Informer
class InformerStdScaler(nn.Module):
"""
Standardize features by calculating the mean and scaling along the first dimension, and then normalizes it by
subtracting from the mean and dividing by the standard deviation.
"""
def __init__(self, config: InformerConfig):
super().__init__()
self.dim = config.scaling_dim if hasattr(config, "scaling_dim") else 1
self.keepdim = config.keepdim if hasattr(config, "keepdim") else True
self.minimum_scale = config.minimum_scale if hasattr(config, "minimum_scale") else 1e-5
def forward(
self, data: torch.Tensor, observed_indicator: torch.Tensor
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
"""
Parameters:
data (`torch.Tensor` of shape `(batch_size, sequence_length, num_input_channels)`):
input for Batch norm calculation
observed_indicator (`torch.BoolTensor` of shape `(batch_size, sequence_length, num_input_channels)`):
Calculating the scale on the observed indicator.
Returns:
tuple of `torch.Tensor` of shapes
(`(batch_size, sequence_length, num_input_channels)`,`(batch_size, 1, num_input_channels)`,
`(batch_size, 1, num_input_channels)`)
"""
denominator = observed_indicator.sum(self.dim, keepdim=self.keepdim)
denominator = denominator.clamp_min(1.0)
loc = (data * observed_indicator).sum(self.dim, keepdim=self.keepdim) / denominator
variance = (((data - loc) * observed_indicator) ** 2).sum(self.dim, keepdim=self.keepdim) / denominator
scale = torch.sqrt(variance + self.minimum_scale)
return (data - loc) / scale, loc, scale
# Copied from transformers.models.time_series_transformer.modeling_time_series_transformer.TimeSeriesMeanScaler with TimeSeriesTransformer->Informer,TimeSeries->Informer
class InformerMeanScaler(nn.Module):
"""
Computes a scaling factor as the weighted average absolute value along the first dimension, and scales the data
accordingly.
"""
def __init__(self, config: InformerConfig):
super().__init__()
self.dim = config.scaling_dim if hasattr(config, "scaling_dim") else 1
self.keepdim = config.keepdim if hasattr(config, "keepdim") else True
self.minimum_scale = config.minimum_scale if hasattr(config, "minimum_scale") else 1e-10
self.default_scale = config.default_scale if hasattr(config, "default_scale") else None
def forward(
self, data: torch.Tensor, observed_indicator: torch.Tensor
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
"""
Parameters:
data (`torch.Tensor` of shape `(batch_size, sequence_length, num_input_channels)`):
input for Batch norm calculation
observed_indicator (`torch.BoolTensor` of shape `(batch_size, sequence_length, num_input_channels)`):
Calculating the scale on the observed indicator.
Returns:
tuple of `torch.Tensor` of shapes
(`(batch_size, sequence_length, num_input_channels)`,`(batch_size, 1, num_input_channels)`,
`(batch_size, 1, num_input_channels)`)
"""
ts_sum = (data * observed_indicator).abs().sum(self.dim, keepdim=True)
num_observed = observed_indicator.sum(self.dim, keepdim=True)
scale = ts_sum / torch.clamp(num_observed, min=1)
# If `default_scale` is provided, we use it, otherwise we use the scale
# of the batch.
if self.default_scale is None:
batch_sum = ts_sum.sum(dim=0)
batch_observations = torch.clamp(num_observed.sum(0), min=1)
default_scale = torch.squeeze(batch_sum / batch_observations)
else:
default_scale = self.default_scale * torch.ones_like(scale)
# apply default scale where there are no observations
scale = torch.where(num_observed > 0, scale, default_scale)
# ensure the scale is at least `self.minimum_scale`
scale = torch.clamp(scale, min=self.minimum_scale)
scaled_data = data / scale
if not self.keepdim:
scale = scale.squeeze(dim=self.dim)
return scaled_data, torch.zeros_like(scale), scale
# Copied from transformers.models.time_series_transformer.modeling_time_series_transformer.TimeSeriesNOPScaler with TimeSeriesTransformer->Informer,TimeSeries->Informer
class InformerNOPScaler(nn.Module):
"""
Assigns a scaling factor equal to 1 along the first dimension, and therefore applies no scaling to the input data.
"""
def __init__(self, config: InformerConfig):
super().__init__()
self.dim = config.scaling_dim if hasattr(config, "scaling_dim") else 1
self.keepdim = config.keepdim if hasattr(config, "keepdim") else True
def forward(
self, data: torch.Tensor, observed_indicator: Optional[torch.Tensor] = None
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
"""
Parameters:
data (`torch.Tensor` of shape `(batch_size, sequence_length, num_input_channels)`):
input for Batch norm calculation
Returns:
tuple of `torch.Tensor` of shapes
(`(batch_size, sequence_length, num_input_channels)`,`(batch_size, 1, num_input_channels)`,
`(batch_size, 1, num_input_channels)`)
"""
scale = torch.ones_like(data, requires_grad=False).mean(dim=self.dim, keepdim=self.keepdim)
loc = torch.zeros_like(data, requires_grad=False).mean(dim=self.dim, keepdim=self.keepdim)
return data, loc, scale
# Copied from transformers.models.time_series_transformer.modeling_time_series_transformer.weighted_average
def weighted_average(input_tensor: torch.Tensor, weights: Optional[torch.Tensor] = None, dim=None) -> torch.Tensor:
"""
Computes the weighted average of a given tensor across a given `dim`, masking values associated with weight zero,
meaning instead of `nan * 0 = nan` you will get `0 * 0 = 0`.
Args:
input_tensor (`torch.FloatTensor`):
Input tensor, of which the average must be computed.
weights (`torch.FloatTensor`, *optional*):
Weights tensor, of the same shape as `input_tensor`.
dim (`int`, *optional*):
The dim along which to average `input_tensor`.
Returns:
`torch.FloatTensor`: The tensor with values averaged along the specified `dim`.
"""
if weights is not None:
weighted_tensor = torch.where(weights != 0, input_tensor * weights, torch.zeros_like(input_tensor))
sum_weights = torch.clamp(weights.sum(dim=dim) if dim else weights.sum(), min=1.0)
return (weighted_tensor.sum(dim=dim) if dim else weighted_tensor.sum()) / sum_weights
else:
return input_tensor.mean(dim=dim)
# Copied from transformers.models.time_series_transformer.modeling_time_series_transformer.nll
def nll(input: torch.distributions.Distribution, target: torch.Tensor) -> torch.Tensor:
"""
Computes the negative log likelihood loss from input distribution with respect to target.
"""
return -input.log_prob(target)
# Copied from transformers.models.marian.modeling_marian.MarianSinusoidalPositionalEmbedding with Marian->Informer
class InformerSinusoidalPositionalEmbedding(nn.Embedding):
"""This module produces sinusoidal positional embeddings of any length."""
def __init__(self, num_positions: int, embedding_dim: int, padding_idx: Optional[int] = None) -> None:
super().__init__(num_positions, embedding_dim)
def _init_weight(self):
"""
Identical to the XLM create_sinusoidal_embeddings except features are not interleaved. The cos features are in
the 2nd half of the vector. [dim // 2:]
"""
n_pos, dim = self.weight.shape
position_enc = np.array(
[[pos / np.power(10000, 2 * (j // 2) / dim) for j in range(dim)] for pos in range(n_pos)]
)
out = torch.empty(n_pos, dim, dtype=self.weight.dtype, requires_grad=False)
sentinel = dim // 2 if dim % 2 == 0 else (dim // 2) + 1
out[:, 0:sentinel] = torch.FloatTensor(np.sin(position_enc[:, 0::2]))
out[:, sentinel:] = torch.FloatTensor(np.cos(position_enc[:, 1::2]))
self.weight = nn.Parameter(out, requires_grad=False)
@torch.no_grad()
def forward(self, input_ids_shape: torch.Size, past_key_values_length: int = 0) -> torch.Tensor:
"""`input_ids_shape` is expected to be [bsz x seqlen]."""
bsz, seq_len = input_ids_shape[:2]
positions = torch.arange(
past_key_values_length, past_key_values_length + seq_len, dtype=torch.long, device=self.weight.device
)
return super().forward(positions)
# Copied from transformers.models.time_series_transformer.modeling_time_series_transformer.TimeSeriesValueEmbedding with TimeSeries->Info
class InformerValueEmbedding(nn.Module):
def __init__(self, feature_size, d_model):
super().__init__()
self.value_projection = nn.Linear(in_features=feature_size, out_features=d_model, bias=False)
def forward(self, x):
return self.value_projection(x)
# Copied from transformers.models.bart.modeling_bart.BartAttention with Bart->Informer
class InformerAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(
self,
embed_dim: int,
num_heads: int,
dropout: float = 0.0,
is_decoder: bool = False,
bias: bool = True,
is_causal: bool = False,
config: Optional[InformerConfig] = None,
):
super().__init__()
self.embed_dim = embed_dim
self.num_heads = num_heads
self.dropout = dropout
self.head_dim = embed_dim // num_heads
self.config = config
if (self.head_dim * num_heads) != self.embed_dim:
raise ValueError(
f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}"
f" and `num_heads`: {num_heads})."
)
self.scaling = self.head_dim**-0.5
self.is_decoder = is_decoder
self.is_causal = is_causal
self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
def forward(
self,
hidden_states: torch.Tensor,
key_value_states: Optional[torch.Tensor] = None,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
attention_mask: Optional[torch.Tensor] = None,
layer_head_mask: Optional[torch.Tensor] = None,
output_attentions: bool = False,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
"""Input shape: Batch x Time x Channel"""
# if key_value_states are provided this layer is used as a cross-attention layer
# for the decoder
is_cross_attention = key_value_states is not None
bsz, tgt_len, _ = hidden_states.size()
# get query proj
query_states = self.q_proj(hidden_states) * self.scaling
# get key, value proj
# `past_key_value[0].shape[2] == key_value_states.shape[1]`
# is checking that the `sequence_length` of the `past_key_value` is the same as
# the provided `key_value_states` to support prefix tuning
if (
is_cross_attention
and past_key_value is not None
and past_key_value[0].shape[2] == key_value_states.shape[1]
):
# reuse k,v, cross_attentions
key_states = past_key_value[0]
value_states = past_key_value[1]
elif is_cross_attention:
# cross_attentions
key_states = self._shape(self.k_proj(key_value_states), -1, bsz)
value_states = self._shape(self.v_proj(key_value_states), -1, bsz)
elif past_key_value is not None:
# reuse k, v, self_attention
key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
key_states = torch.cat([past_key_value[0], key_states], dim=2)
value_states = torch.cat([past_key_value[1], value_states], dim=2)
else:
# self_attention
key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
if self.is_decoder:
# if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states.
# Further calls to cross_attention layer can then reuse all cross-attention
# key/value_states (first "if" case)
# if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of
# all previous decoder key/value_states. Further calls to uni-directional self-attention
# can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
# if encoder bi-directional self-attention `past_key_value` is always `None`
past_key_value = (key_states, value_states)
proj_shape = (bsz * self.num_heads, -1, self.head_dim)
query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape)
key_states = key_states.reshape(*proj_shape)
value_states = value_states.reshape(*proj_shape)
src_len = key_states.size(1)
attn_weights = torch.bmm(query_states, key_states.transpose(1, 2))
if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len):
raise ValueError(
f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is"
f" {attn_weights.size()}"
)
if attention_mask is not None:
if attention_mask.size() != (bsz, 1, tgt_len, src_len):
raise ValueError(
f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}"
)
attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask
attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
attn_weights = nn.functional.softmax(attn_weights, dim=-1)
if layer_head_mask is not None:
if layer_head_mask.size() != (self.num_heads,):
raise ValueError(
f"Head mask for a single layer should be of size {(self.num_heads,)}, but is"
f" {layer_head_mask.size()}"
)
attn_weights = layer_head_mask.view(1, -1, 1, 1) * attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
if output_attentions:
# this operation is a bit awkward, but it's required to
# make sure that attn_weights keeps its gradient.
# In order to do so, attn_weights have to be reshaped
# twice and have to be reused in the following
attn_weights_reshaped = attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
attn_weights = attn_weights_reshaped.view(bsz * self.num_heads, tgt_len, src_len)
else:
attn_weights_reshaped = None
attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)
attn_output = torch.bmm(attn_probs, value_states)
if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim):
raise ValueError(
f"`attn_output` should be of size {(bsz * self.num_heads, tgt_len, self.head_dim)}, but is"
f" {attn_output.size()}"
)
attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim)
attn_output = attn_output.transpose(1, 2)
# Use the `embed_dim` from the config (stored in the class) rather than `hidden_state` because `attn_output` can be
# partitioned across GPUs when using tensor-parallelism.
attn_output = attn_output.reshape(bsz, tgt_len, self.embed_dim)
attn_output = self.out_proj(attn_output)
return attn_output, attn_weights_reshaped, past_key_value
class InformerProbSparseAttention(nn.Module):
"""Probabilistic Attention mechanism to select the "active"
queries rather than the "lazy" queries and provides a sparse Transformer thus mitigating the quadratic compute and
memory requirements of vanilla attention"""
def __init__(
self,
embed_dim: int,
num_heads: int,
dropout: float = 0.0,
is_decoder: bool = False,
sampling_factor: int = 5,
bias: bool = True,
):
super().__init__()
self.factor = sampling_factor
self.embed_dim = embed_dim
self.num_heads = num_heads
self.dropout = dropout
self.head_dim = embed_dim // num_heads
if (self.head_dim * num_heads) != self.embed_dim:
raise ValueError(
f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}"
f" and `num_heads`: {num_heads})."
)
self.scaling = self.head_dim**-0.5
self.is_decoder = is_decoder
self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
def forward(
self,
hidden_states: torch.Tensor,
key_value_states: Optional[torch.Tensor] = None,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
attention_mask: Optional[torch.Tensor] = None,
layer_head_mask: Optional[torch.Tensor] = None,
output_attentions: bool = False,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
"""Input shape: Batch x Time x Channel"""
# if key_value_states are provided this layer is used as a cross-attention layer
# for the decoder
is_cross_attention = key_value_states is not None
bsz, tgt_len, _ = hidden_states.size()
# get query proj
query_states = self.q_proj(hidden_states) * self.scaling
# get key, value proj
# `past_key_value[0].shape[2] == key_value_states.shape[1]`
# is checking that the `sequence_length` of the `past_key_value` is the same as
# the provided `key_value_states` to support prefix tuning
if (
is_cross_attention
and past_key_value is not None
and past_key_value[0].shape[2] == key_value_states.shape[1]
):
# reuse k,v, cross_attentions
key_states = past_key_value[0]
value_states = past_key_value[1]
elif is_cross_attention:
# cross_attentions
key_states = self._shape(self.k_proj(key_value_states), -1, bsz)
value_states = self._shape(self.v_proj(key_value_states), -1, bsz)
elif past_key_value is not None:
# reuse k, v, self_attention
key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
key_states = torch.cat([past_key_value[0], key_states], dim=2)
value_states = torch.cat([past_key_value[1], value_states], dim=2)
else:
# self_attention
key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
if self.is_decoder:
# if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states.
# Further calls to cross_attention layer can then reuse all cross-attention
# key/value_states (first "if" case)
# if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of
# all previous decoder key/value_states. Further calls to uni-directional self-attention
# can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
# if encoder bi-directional self-attention `past_key_value` is always `None`
past_key_value = (key_states, value_states)
proj_shape = (bsz * self.num_heads, -1, self.head_dim)
query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape)
key_states = key_states.reshape(*proj_shape)
value_states = value_states.reshape(*proj_shape)
key_states_time_length = key_states.size(1) # L_K
log_key_states_time_length = np.ceil(np.log1p(key_states_time_length)).astype("int").item() # log_L_K
query_states_time_length = query_states.size(1) # L_Q
log_query_states_time_length = np.ceil(np.log1p(query_states_time_length)).astype("int").item() # log_L_Q
u_part = min(self.factor * query_states_time_length * log_key_states_time_length, key_states_time_length)
u = min(self.factor * log_query_states_time_length, query_states_time_length)
if key_states_time_length > 0:
index_sample = torch.randint(0, key_states_time_length, (u_part,))
k_sample = key_states[:, index_sample, :]
else:
k_sample = key_states
queries_keys_sample = torch.bmm(query_states, k_sample.transpose(1, 2)) # Q_K_sampled
# find the Top_k query with sparsity measurement
if u > 0:
sparsity_measurement = queries_keys_sample.max(dim=-1)[0] - torch.div(
queries_keys_sample.sum(dim=-1), key_states_time_length
) # M
top_u_sparsity_measurement = sparsity_measurement.topk(u, sorted=False)[1] # M_top
# calculate q_reduce: query_states[:, top_u_sparsity_measurement]
dim_for_slice = torch.arange(query_states.size(0)).unsqueeze(-1)
q_reduce = query_states[dim_for_slice, top_u_sparsity_measurement]
else:
q_reduce = query_states
top_u_sparsity_measurement = None
# Use q_reduce to calculate attention weights
attn_weights = torch.bmm(q_reduce, key_states.transpose(1, 2))
src_len = key_states.size(1)
if attn_weights.size() != (bsz * self.num_heads, u, src_len):
raise ValueError(
f"Attention weights should be of size {(bsz * self.num_heads, u, src_len)}, but is"
f" {attn_weights.size()}"
)
if attention_mask is not None:
if attention_mask.size() != (bsz, 1, tgt_len, src_len):
raise ValueError(
f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}"
)
prob_mask = attention_mask.expand(bsz, self.num_heads, tgt_len, src_len).reshape(
bsz * self.num_heads, tgt_len, src_len
)
if top_u_sparsity_measurement is not None:
dim_for_slice = torch.arange(prob_mask.size(0)).unsqueeze(-1)
prob_mask = prob_mask[dim_for_slice, top_u_sparsity_measurement, :]
attn_weights = attn_weights.view(bsz, self.num_heads, u, src_len) + prob_mask.view(
bsz, self.num_heads, u, src_len
)
attn_weights = attn_weights.view(bsz * self.num_heads, u, src_len)
attn_weights = nn.functional.softmax(attn_weights, dim=-1)
if layer_head_mask is not None:
if layer_head_mask.size() != (self.num_heads,):
raise ValueError(
f"Head mask for a single layer should be of size {(self.num_heads,)}, but is"
f" {layer_head_mask.size()}"
)
attn_weights = layer_head_mask.view(1, -1, 1, 1) * attn_weights.view(bsz, self.num_heads, u, src_len)
attn_weights = attn_weights.view(bsz * self.num_heads, u, src_len)
if output_attentions:
# this operation is a bit awkward, but it's required to
# make sure that attn_weights keeps its gradient.
# In order to do so, attn_weights have to be reshaped
# twice and have to be reused in the following
attn_weights_reshaped = attn_weights.view(bsz, self.num_heads, u, src_len)
attn_weights = attn_weights_reshaped.view(bsz * self.num_heads, u, src_len)
else:
attn_weights_reshaped = None
attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)
attn_output = torch.bmm(attn_probs, value_states)
# calculate context for updating the attn_output, based on:
# https://github.com/zhouhaoyi/Informer2020/blob/ac59c7447135473fb2aafeafe94395f884d5c7a5/models/attn.py#L74
if self.is_decoder:
# cast to float32 before operation to avoid overflow
context = value_states.cumsum(dim=-2, dtype=torch.float32).to(value_states.dtype)
else:
v_mean_dim_time = value_states.mean(dim=-2)
context = (
v_mean_dim_time.unsqueeze(dim=1)
.expand(bsz * self.num_heads, query_states_time_length, v_mean_dim_time.size(-1))
.clone()
)
if top_u_sparsity_measurement is not None:
# update context: copy the attention output to the context at top_u_sparsity_measurement index
dim_for_slice = torch.arange(context.size(0)).unsqueeze(-1)
context[dim_for_slice, top_u_sparsity_measurement, :] = attn_output
attn_output = context
if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim):
raise ValueError(
f"`attn_output` should be of size {(bsz * self.num_heads, tgt_len, self.head_dim)}, but is"
f" {attn_output.size()}"
)
attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim)
attn_output = attn_output.transpose(1, 2)
# Use the `embed_dim` from the config (stored in the class) rather than `hidden_state` because `attn_output` can be
# partitioned across GPUs when using tensor-parallelism.
attn_output = attn_output.reshape(bsz, tgt_len, self.embed_dim)
attn_output = self.out_proj(attn_output)
return attn_output, attn_weights_reshaped, past_key_value
# source: https://github.com/zhouhaoyi/Informer2020/blob/main/models/encoder.py
class InformerConvLayer(nn.Module):
def __init__(self, c_in):
super().__init__()
self.downConv = nn.Conv1d(
in_channels=c_in,
out_channels=c_in,
kernel_size=3,
padding=1,
padding_mode="circular",
)
self.norm = nn.BatchNorm1d(c_in)
self.activation = nn.ELU()
self.maxPool = nn.MaxPool1d(kernel_size=3, stride=2, padding=1)
def forward(self, x):
x = self.downConv(x.permute(0, 2, 1))
x = self.norm(x)
x = self.activation(x)
x = self.maxPool(x)
x = x.transpose(1, 2)
return x
class InformerEncoderLayer(nn.Module):
def __init__(self, config: InformerConfig):
super().__init__()
self.embed_dim = config.d_model
if config.attention_type == "prob":
self.self_attn = InformerProbSparseAttention(
embed_dim=self.embed_dim,
num_heads=config.encoder_attention_heads,
dropout=config.attention_dropout,
sampling_factor=config.sampling_factor,
)
else:
self.self_attn = InformerAttention(
embed_dim=self.embed_dim,
num_heads=config.encoder_attention_heads,
dropout=config.attention_dropout,
)
self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
self.dropout = config.dropout
self.activation_fn = ACT2FN[config.activation_function]
self.activation_dropout = config.activation_dropout
self.fc1 = nn.Linear(self.embed_dim, config.encoder_ffn_dim)
self.fc2 = nn.Linear(config.encoder_ffn_dim, self.embed_dim)
self.final_layer_norm = nn.LayerNorm(self.embed_dim)
def forward(
self,
hidden_states: torch.FloatTensor,
attention_mask: torch.FloatTensor,
layer_head_mask: torch.FloatTensor,
output_attentions: Optional[bool] = False,
) -> Tuple[torch.FloatTensor, Optional[torch.FloatTensor]]:
"""
Args:
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
attention_mask (`torch.FloatTensor`): attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
layer_head_mask (`torch.FloatTensor`): mask for attention heads in a given layer of size
`(encoder_attention_heads,)`.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
"""
residual = hidden_states
hidden_states, attn_weights, _ = self.self_attn(
hidden_states=hidden_states,
attention_mask=attention_mask,
layer_head_mask=layer_head_mask,
output_attentions=output_attentions,
)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = residual + hidden_states
hidden_states = self.self_attn_layer_norm(hidden_states)
residual = hidden_states
hidden_states = self.activation_fn(self.fc1(hidden_states))
hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)
hidden_states = self.fc2(hidden_states)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = residual + hidden_states
hidden_states = self.final_layer_norm(hidden_states)
if hidden_states.dtype == torch.float16 and (
torch.isinf(hidden_states).any() or torch.isnan(hidden_states).any()
):
clamp_value = torch.finfo(hidden_states.dtype).max - 1000
hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)
outputs = (hidden_states,)
if output_attentions:
outputs += (attn_weights,)
return outputs
class InformerDecoderLayer(nn.Module):
def __init__(self, config: InformerConfig):
super().__init__()
self.embed_dim = config.d_model
if config.attention_type == "prob":
self.self_attn = InformerProbSparseAttention(
embed_dim=self.embed_dim,
num_heads=config.decoder_attention_heads,
dropout=config.attention_dropout,
sampling_factor=config.sampling_factor,
is_decoder=True,
)
else:
self.self_attn = InformerAttention(
embed_dim=self.embed_dim,
num_heads=config.decoder_attention_heads,
dropout=config.attention_dropout,
is_decoder=True,
)
self.dropout = config.dropout
self.activation_fn = ACT2FN[config.activation_function]
self.activation_dropout = config.activation_dropout
self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
self.encoder_attn = InformerAttention(
self.embed_dim,
config.decoder_attention_heads,
dropout=config.attention_dropout,
is_decoder=True,
)
self.encoder_attn_layer_norm = nn.LayerNorm(self.embed_dim)
self.fc1 = nn.Linear(self.embed_dim, config.decoder_ffn_dim)
self.fc2 = nn.Linear(config.decoder_ffn_dim, self.embed_dim)
self.final_layer_norm = nn.LayerNorm(self.embed_dim)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
encoder_attention_mask: Optional[torch.Tensor] = None,
layer_head_mask: Optional[torch.Tensor] = None,
cross_attn_layer_head_mask: Optional[torch.Tensor] = None,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
output_attentions: Optional[bool] = False,
use_cache: Optional[bool] = True,
) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
"""
Args:
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
attention_mask (`torch.FloatTensor`): attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
encoder_hidden_states (`torch.FloatTensor`):
cross attention input to the layer of shape `(batch, seq_len, embed_dim)`
encoder_attention_mask (`torch.FloatTensor`): encoder attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
layer_head_mask (`torch.FloatTensor`): mask for attention heads in a given layer of size
`(encoder_attention_heads,)`.
cross_attn_layer_head_mask (`torch.FloatTensor`): mask for cross-attention heads in a given layer of
size `(decoder_attention_heads,)`.
past_key_value (`Tuple(torch.FloatTensor)`): cached past key and value projection states
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
"""
residual = hidden_states
# Self Attention
# decoder uni-directional self-attention cached key/values tuple is at positions 1,2
self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
# add present self-attn cache to positions 1,2 of present_key_value tuple
hidden_states, self_attn_weights, present_key_value = self.self_attn(
hidden_states=hidden_states,
past_key_value=self_attn_past_key_value,
attention_mask=attention_mask,
layer_head_mask=layer_head_mask,
output_attentions=output_attentions,
)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = residual + hidden_states
hidden_states = self.self_attn_layer_norm(hidden_states)
# Cross-Attention Block
cross_attn_present_key_value = None
cross_attn_weights = None
if encoder_hidden_states is not None:
residual = hidden_states
# cross_attn cached key/values tuple is at positions 3,4 of present_key_value tuple
cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None
hidden_states, cross_attn_weights, cross_attn_present_key_value = self.encoder_attn(
hidden_states=hidden_states,
key_value_states=encoder_hidden_states,
attention_mask=encoder_attention_mask,
layer_head_mask=cross_attn_layer_head_mask,
past_key_value=cross_attn_past_key_value,
output_attentions=output_attentions,
)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = residual + hidden_states
hidden_states = self.encoder_attn_layer_norm(hidden_states)
# add cross-attn to positions 3,4 of present_key_value tuple
present_key_value = present_key_value + cross_attn_present_key_value
# Fully Connected
residual = hidden_states
hidden_states = self.activation_fn(self.fc1(hidden_states))
hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)
hidden_states = self.fc2(hidden_states)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = residual + hidden_states
hidden_states = self.final_layer_norm(hidden_states)
outputs = (hidden_states,)
if output_attentions:
outputs += (self_attn_weights, cross_attn_weights)
if use_cache:
outputs += (present_key_value,)
return outputs
class InformerPreTrainedModel(PreTrainedModel):
config_class = InformerConfig
base_model_prefix = "model"
main_input_name = "past_values"
supports_gradient_checkpointing = True
def _init_weights(self, module):
std = self.config.init_std
if isinstance(module, (nn.Linear, nn.Conv1d)):
module.weight.data.normal_(mean=0.0, std=std)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, InformerSinusoidalPositionalEmbedding):
module._init_weight()
elif isinstance(module, nn.Embedding):
module.weight.data.normal_(mean=0.0, std=std)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
INFORMER_START_DOCSTRING = r"""
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.
Parameters:
config ([`TimeSeriesTransformerConfig`]):
Model configuration class with all the parameters of the model. Initializing with a config file does not
load the weights associated with the model, only the configuration. Check out the
[`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
INFORMER_INPUTS_DOCSTRING = r"""
Args:
past_values (`torch.FloatTensor` of shape `(batch_size, sequence_length)` or `(batch_size, sequence_length, input_size)`):
Past values of the time series, that serve as context in order to predict the future. The sequence size of
this tensor must be larger than the `context_length` of the model, since the model will use the larger size
to construct lag features, i.e. additional values from the past which are added in order to serve as "extra
context".
The `sequence_length` here is equal to `config.context_length` + `max(config.lags_sequence)`, which if no
`lags_sequence` is configured, is equal to `config.context_length` + 7 (as by default, the largest
look-back index in `config.lags_sequence` is 7). The property `_past_length` returns the actual length of
the past.
The `past_values` is what the Transformer encoder gets as input (with optional additional features, such as
`static_categorical_features`, `static_real_features`, `past_time_features` and lags).
Optionally, missing values need to be replaced with zeros and indicated via the `past_observed_mask`.
For multivariate time series, the `input_size` > 1 dimension is required and corresponds to the number of
variates in the time series per time step.
past_time_features (`torch.FloatTensor` of shape `(batch_size, sequence_length, num_features)`):
Required time features, which the model internally will add to `past_values`. These could be things like
"month of year", "day of the month", etc. encoded as vectors (for instance as Fourier features). These
could also be so-called "age" features, which basically help the model know "at which point in life" a
time-series is. Age features have small values for distant past time steps and increase monotonically the
more we approach the current time step. Holiday features are also a good example of time features.
These features serve as the "positional encodings" of the inputs. So contrary to a model like BERT, where
the position encodings are learned from scratch internally as parameters of the model, the Time Series
Transformer requires to provide additional time features. The Time Series Transformer only learns
additional embeddings for `static_categorical_features`.
Additional dynamic real covariates can be concatenated to this tensor, with the caveat that these features
must but known at prediction time.
The `num_features` here is equal to `config.`num_time_features` + `config.num_dynamic_real_features`.
past_observed_mask (`torch.BoolTensor` of shape `(batch_size, sequence_length)` or `(batch_size, sequence_length, input_size)`, *optional*):
Boolean mask to indicate which `past_values` were observed and which were missing. Mask values selected in
`[0, 1]`:
- 1 for values that are **observed**,
- 0 for values that are **missing** (i.e. NaNs that were replaced by zeros).
static_categorical_features (`torch.LongTensor` of shape `(batch_size, number of static categorical features)`, *optional*):
Optional static categorical features for which the model will learn an embedding, which it will add to the
values of the time series.
Static categorical features are features which have the same value for all time steps (static over time).
A typical example of a static categorical feature is a time series ID.
static_real_features (`torch.FloatTensor` of shape `(batch_size, number of static real features)`, *optional*):
Optional static real features which the model will add to the values of the time series.
Static real features are features which have the same value for all time steps (static over time).
A typical example of a static real feature is promotion information.
future_values (`torch.FloatTensor` of shape `(batch_size, prediction_length)` or `(batch_size, prediction_length, input_size)`, *optional*):
Future values of the time series, that serve as labels for the model. The `future_values` is what the
Transformer needs during training to learn to output, given the `past_values`.
The sequence length here is equal to `prediction_length`.
See the demo notebook and code snippets for details.
Optionally, during training any missing values need to be replaced with zeros and indicated via the
`future_observed_mask`.
For multivariate time series, the `input_size` > 1 dimension is required and corresponds to the number of
variates in the time series per time step.
future_time_features (`torch.FloatTensor` of shape `(batch_size, prediction_length, num_features)`):
Required time features for the prediction window, which the model internally will add to `future_values`.
These could be things like "month of year", "day of the month", etc. encoded as vectors (for instance as
Fourier features). These could also be so-called "age" features, which basically help the model know "at
which point in life" a time-series is. Age features have small values for distant past time steps and
increase monotonically the more we approach the current time step. Holiday features are also a good example
of time features.
These features serve as the "positional encodings" of the inputs. So contrary to a model like BERT, where
the position encodings are learned from scratch internally as parameters of the model, the Time Series
Transformer requires to provide additional time features. The Time Series Transformer only learns
additional embeddings for `static_categorical_features`.
Additional dynamic real covariates can be concatenated to this tensor, with the caveat that these features
must but known at prediction time.
The `num_features` here is equal to `config.`num_time_features` + `config.num_dynamic_real_features`.
future_observed_mask (`torch.BoolTensor` of shape `(batch_size, sequence_length)` or `(batch_size, sequence_length, input_size)`, *optional*):
Boolean mask to indicate which `future_values` were observed and which were missing. Mask values selected
in `[0, 1]`:
- 1 for values that are **observed**,
- 0 for values that are **missing** (i.e. NaNs that were replaced by zeros).
This mask is used to filter out missing values for the final loss calculation.
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on certain token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
decoder_attention_mask (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
Mask to avoid performing attention on certain token indices. By default, a causal mask will be used, to
make sure the model can only look at previous inputs in order to predict the future.
head_mask (`torch.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
decoder_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the attention modules in the decoder. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the cross-attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
encoder_outputs (`tuple(tuple(torch.FloatTensor)`, *optional*):
Tuple consists of `last_hidden_state`, `hidden_states` (*optional*) and `attentions` (*optional*)
`last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)` (*optional*) is a sequence of
hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
`(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
`(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
`decoder_input_ids` of shape `(batch_size, sequence_length)`.
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
model's internal embedding lookup matrix.
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
`past_key_values`).
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
class InformerEncoder(InformerPreTrainedModel):
"""
Informer encoder consisting of *config.encoder_layers* self attention layers with distillation layers. Each
attention layer is an [`InformerEncoderLayer`].
Args:
config: InformerConfig
"""
def __init__(self, config: InformerConfig):
super().__init__(config)
self.dropout = config.dropout
self.layerdrop = config.encoder_layerdrop
self.gradient_checkpointing = False
if config.prediction_length is None:
raise ValueError("The `prediction_length` config needs to be specified.")
self.value_embedding = InformerValueEmbedding(feature_size=config.feature_size, d_model=config.d_model)
self.embed_positions = InformerSinusoidalPositionalEmbedding(
config.context_length + config.prediction_length, config.d_model
)
self.layers = nn.ModuleList([InformerEncoderLayer(config) for _ in range(config.encoder_layers)])
self.layernorm_embedding = nn.LayerNorm(config.d_model)
if config.distil:
self.conv_layers = nn.ModuleList(
[InformerConvLayer(config.d_model) for _ in range(config.encoder_layers - 1)]
)
self.conv_layers.append(None)
else:
self.conv_layers = [None] * config.encoder_layers
# Initialize weights and apply final processing
self.post_init()
def forward(
self,
attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, BaseModelOutput]:
r"""
Args:
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
head_mask (`torch.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
This is useful if you want more control over how to convert `input_ids` indices into associated vectors
than the model's internal embedding lookup matrix.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
for more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
hidden_states = self.value_embedding(inputs_embeds)
embed_pos = self.embed_positions(inputs_embeds.size())
hidden_states = self.layernorm_embedding(hidden_states + embed_pos)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
# expand attention_mask
if attention_mask is not None:
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
attention_mask = _prepare_4d_attention_mask(attention_mask, inputs_embeds.dtype)
encoder_states = () if output_hidden_states else None
all_attentions = () if output_attentions else None
# check if head_mask has a correct number of layers specified if desired
if head_mask is not None:
if head_mask.size()[0] != (len(self.layers)):
raise ValueError(
f"The head_mask should be specified for {len(self.layers)} layers, but it is for"
f" {head_mask.size()[0]}."
)
for idx, (encoder_layer, conv_layer) in enumerate(zip(self.layers, self.conv_layers)):
if output_hidden_states:
encoder_states = encoder_states + (hidden_states,)
# add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
to_drop = False
if self.training:
dropout_probability = torch.rand([])
if dropout_probability < self.layerdrop: # skip the layer
to_drop = True
if to_drop:
layer_outputs = (None, None)
else:
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
encoder_layer.__call__,
hidden_states,
attention_mask,
(head_mask[idx] if head_mask is not None else None),
output_attentions,
)
if conv_layer is not None:
output = self._gradient_checkpointing_func(conv_layer, layer_outputs[0])
layer_outputs = (output,) + layer_outputs[1:]
else:
layer_outputs = encoder_layer(
hidden_states,
attention_mask,
layer_head_mask=(head_mask[idx] if head_mask is not None else None),
output_attentions=output_attentions,
)
if conv_layer is not None:
output = conv_layer(layer_outputs[0])
layer_outputs = (output,) + layer_outputs[1:]
hidden_states = layer_outputs[0]
if output_attentions:
all_attentions = all_attentions + (layer_outputs[1],)
if output_hidden_states:
encoder_states = encoder_states + (hidden_states,)
if not return_dict:
return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None)
return BaseModelOutput(
last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions
)
# Copied from transformers.models.time_series_transformer.modeling_time_series_transformer.TimeSeriesTransformerDecoder with TimeSeriesTransformer->Informer,TimeSeriesTransformerConfig->InformerConfig,time-series-transformer->informer,Transformer->Informer,TimeSeries->Informer
class InformerDecoder(InformerPreTrainedModel):
"""
Informer decoder consisting of *config.decoder_layers* layers. Each layer is a
[`InformerDecoderLayer`]
Args:
config: InformerConfig
"""
def __init__(self, config: InformerConfig):
super().__init__(config)
self.dropout = config.dropout
self.layerdrop = config.decoder_layerdrop
if config.prediction_length is None:
raise ValueError("The `prediction_length` config needs to be specified.")
self.value_embedding = InformerValueEmbedding(feature_size=config.feature_size, d_model=config.d_model)
self.embed_positions = InformerSinusoidalPositionalEmbedding(
config.context_length + config.prediction_length, config.d_model
)
self.layers = nn.ModuleList([InformerDecoderLayer(config) for _ in range(config.decoder_layers)])
self.layernorm_embedding = nn.LayerNorm(config.d_model)
self.gradient_checkpointing = False
# Initialize weights and apply final processing
self.post_init()
def forward(
self,
attention_mask: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
encoder_attention_mask: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.Tensor] = None,
cross_attn_head_mask: Optional[torch.Tensor] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, BaseModelOutputWithPastAndCrossAttentions]:
r"""
Args:
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, encoder_sequence_length, hidden_size)`, *optional*):
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention
of the decoder.
encoder_attention_mask (`torch.LongTensor` of shape `(batch_size, encoder_sequence_length)`, *optional*):
Mask to avoid performing cross-attention on padding tokens indices of encoder input_ids. Mask values
selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the cross-attention modules in the decoder to avoid performing
cross-attention on hidden heads. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of
shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the
cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those
that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of
all `decoder_input_ids` of shape `(batch_size, sequence_length)`.
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
This is useful if you want more control over how to convert `input_ids` indices into associated vectors
than the model's internal embedding lookup matrix.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
for more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
use_cache = use_cache if use_cache is not None else self.config.use_cache
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
input_shape = inputs_embeds.size()[:-1]
# past_key_values_length
past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0
attention_mask = _prepare_4d_causal_attention_mask(
attention_mask, input_shape, inputs_embeds, past_key_values_length
)
# expand encoder attention mask
if encoder_hidden_states is not None and encoder_attention_mask is not None:
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
encoder_attention_mask = _prepare_4d_attention_mask(
encoder_attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]
)
hidden_states = self.value_embedding(inputs_embeds)
embed_pos = self.embed_positions(inputs_embeds.size(), past_key_values_length=self.config.context_length)
hidden_states = self.layernorm_embedding(hidden_states + embed_pos)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
if self.gradient_checkpointing and self.training:
if use_cache:
logger.warning_once(
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
)
use_cache = False
# decoder layers
all_hidden_states = () if output_hidden_states else None
all_self_attns = () if output_attentions else None
all_cross_attentions = () if (output_attentions and encoder_hidden_states is not None) else None
next_decoder_cache = () if use_cache else None
# check if head_mask/cross_attn_head_mask has a correct number of layers specified if desired
for attn_mask, mask_name in zip([head_mask, cross_attn_head_mask], ["head_mask", "cross_attn_head_mask"]):
if attn_mask is not None:
if attn_mask.size()[0] != (len(self.layers)):
raise ValueError(
f"The `{mask_name}` should be specified for {len(self.layers)} layers, but it is for"
f" {head_mask.size()[0]}."
)
for idx, decoder_layer in enumerate(self.layers):
# add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
if output_hidden_states:
all_hidden_states += (hidden_states,)
if self.training:
dropout_probability = torch.rand([])
if dropout_probability < self.layerdrop:
continue
past_key_value = past_key_values[idx] if past_key_values is not None else None
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
decoder_layer.__call__,
hidden_states,
attention_mask,
encoder_hidden_states,
encoder_attention_mask,
head_mask[idx] if head_mask is not None else None,
cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None,
None,
output_attentions,
use_cache,
)
else:
layer_outputs = decoder_layer(
hidden_states,
attention_mask=attention_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
layer_head_mask=(head_mask[idx] if head_mask is not None else None),
cross_attn_layer_head_mask=(
cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None
),
past_key_value=past_key_value,
output_attentions=output_attentions,
use_cache=use_cache,
)
hidden_states = layer_outputs[0]
if use_cache:
next_decoder_cache += (layer_outputs[3 if output_attentions else 1],)
if output_attentions:
all_self_attns += (layer_outputs[1],)
if encoder_hidden_states is not None:
all_cross_attentions += (layer_outputs[2],)
# add hidden states from the last decoder layer
if output_hidden_states:
all_hidden_states += (hidden_states,)
next_cache = next_decoder_cache if use_cache else None
if not return_dict:
return tuple(
v
for v in [hidden_states, next_cache, all_hidden_states, all_self_attns, all_cross_attentions]
if v is not None
)
return BaseModelOutputWithPastAndCrossAttentions(
last_hidden_state=hidden_states,
past_key_values=next_cache,
hidden_states=all_hidden_states,
attentions=all_self_attns,
cross_attentions=all_cross_attentions,
)
@add_start_docstrings(
"The bare Informer Model outputting raw hidden-states without any specific head on top.",
INFORMER_START_DOCSTRING,
)
# Copied from transformers.models.time_series_transformer.modeling_time_series_transformer.TimeSeriesTransformerModel with TimeSeriesTransformer->Informer,TIME_SERIES_TRANSFORMER->INFORMER,time-series-transformer->informer,TimeSeries->Informer
class InformerModel(InformerPreTrainedModel):
def __init__(self, config: InformerConfig):
super().__init__(config)
if config.scaling == "mean" or config.scaling is True:
self.scaler = InformerMeanScaler(config)
elif config.scaling == "std":
self.scaler = InformerStdScaler(config)
else:
self.scaler = InformerNOPScaler(config)
if config.num_static_categorical_features > 0:
self.embedder = InformerFeatureEmbedder(
cardinalities=config.cardinality,
embedding_dims=config.embedding_dimension,
)
# transformer encoder-decoder and mask initializer
self.encoder = InformerEncoder(config)
self.decoder = InformerDecoder(config)
# Initialize weights and apply final processing
self.post_init()
@property
def _past_length(self) -> int:
return self.config.context_length + max(self.config.lags_sequence)
def get_lagged_subsequences(
self, sequence: torch.Tensor, subsequences_length: int, shift: int = 0
) -> torch.Tensor:
"""
Returns lagged subsequences of a given sequence. Returns a tensor of shape (N, S, C, I),
where S = subsequences_length and I = len(indices), containing lagged subsequences. Specifically, lagged[i,
j, :, k] = sequence[i, -indices[k]-S+j, :].
Args:
sequence: Tensor
The sequence from which lagged subsequences should be extracted. Shape: (N, T, C).
subsequences_length : int
Length of the subsequences to be extracted.
shift: int
Shift the lags by this amount back.
"""
sequence_length = sequence.shape[1]
indices = [lag - shift for lag in self.config.lags_sequence]
if max(indices) + subsequences_length > sequence_length:
raise ValueError(
f"lags cannot go further than history length, found lag {max(indices)} "
f"while history length is only {sequence_length}"
)
lagged_values = []
for lag_index in indices:
begin_index = -lag_index - subsequences_length
end_index = -lag_index if lag_index > 0 else None
lagged_values.append(sequence[:, begin_index:end_index, ...])
return torch.stack(lagged_values, dim=-1)
def create_network_inputs(
self,
past_values: torch.Tensor,
past_time_features: torch.Tensor,
static_categorical_features: Optional[torch.Tensor] = None,
static_real_features: Optional[torch.Tensor] = None,
past_observed_mask: Optional[torch.Tensor] = None,
future_values: Optional[torch.Tensor] = None,
future_time_features: Optional[torch.Tensor] = None,
):
# time feature
time_feat = (
torch.cat(
(
past_time_features[:, self._past_length - self.config.context_length :, ...],
future_time_features,
),
dim=1,
)
if future_values is not None
else past_time_features[:, self._past_length - self.config.context_length :, ...]
)
# target
if past_observed_mask is None:
past_observed_mask = torch.ones_like(past_values)
context = past_values[:, -self.config.context_length :]
observed_context = past_observed_mask[:, -self.config.context_length :]
_, loc, scale = self.scaler(context, observed_context)
inputs = (
(torch.cat((past_values, future_values), dim=1) - loc) / scale
if future_values is not None
else (past_values - loc) / scale
)
# static features
log_abs_loc = loc.abs().log1p() if self.config.input_size == 1 else loc.squeeze(1).abs().log1p()
log_scale = scale.log() if self.config.input_size == 1 else scale.squeeze(1).log()
static_feat = torch.cat((log_abs_loc, log_scale), dim=1)
if static_real_features is not None:
static_feat = torch.cat((static_real_features, static_feat), dim=1)
if static_categorical_features is not None:
embedded_cat = self.embedder(static_categorical_features)
static_feat = torch.cat((embedded_cat, static_feat), dim=1)
expanded_static_feat = static_feat.unsqueeze(1).expand(-1, time_feat.shape[1], -1)
# all features
features = torch.cat((expanded_static_feat, time_feat), dim=-1)
# lagged features
subsequences_length = (
self.config.context_length + self.config.prediction_length
if future_values is not None
else self.config.context_length
)
lagged_sequence = self.get_lagged_subsequences(sequence=inputs, subsequences_length=subsequences_length)
lags_shape = lagged_sequence.shape
reshaped_lagged_sequence = lagged_sequence.reshape(lags_shape[0], lags_shape[1], -1)
if reshaped_lagged_sequence.shape[1] != time_feat.shape[1]:
raise ValueError(
f"input length {reshaped_lagged_sequence.shape[1]} and time feature lengths {time_feat.shape[1]} does not match"
)
# transformer inputs
transformer_inputs = torch.cat((reshaped_lagged_sequence, features), dim=-1)
return transformer_inputs, loc, scale, static_feat
def get_encoder(self):
return self.encoder
def get_decoder(self):
return self.decoder
@add_start_docstrings_to_model_forward(INFORMER_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=Seq2SeqTSModelOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
past_values: torch.Tensor,
past_time_features: torch.Tensor,
past_observed_mask: torch.Tensor,
static_categorical_features: Optional[torch.Tensor] = None,
static_real_features: Optional[torch.Tensor] = None,
future_values: Optional[torch.Tensor] = None,
future_time_features: Optional[torch.Tensor] = None,
decoder_attention_mask: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.Tensor] = None,
decoder_head_mask: Optional[torch.Tensor] = None,
cross_attn_head_mask: Optional[torch.Tensor] = None,
encoder_outputs: Optional[List[torch.FloatTensor]] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
output_hidden_states: Optional[bool] = None,
output_attentions: Optional[bool] = None,
use_cache: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Seq2SeqTSModelOutput, Tuple]:
r"""
Returns:
Examples:
```python
>>> from huggingface_hub import hf_hub_download
>>> import torch
>>> from transformers import InformerModel
>>> file = hf_hub_download(
... repo_id="hf-internal-testing/tourism-monthly-batch", filename="train-batch.pt", repo_type="dataset"
... )
>>> batch = torch.load(file)
>>> model = InformerModel.from_pretrained("huggingface/informer-tourism-monthly")
>>> # during training, one provides both past and future values
>>> # as well as possible additional features
>>> outputs = model(
... past_values=batch["past_values"],
... past_time_features=batch["past_time_features"],
... past_observed_mask=batch["past_observed_mask"],
... static_categorical_features=batch["static_categorical_features"],
... static_real_features=batch["static_real_features"],
... future_values=batch["future_values"],
... future_time_features=batch["future_time_features"],
... )
>>> last_hidden_state = outputs.last_hidden_state
```"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
use_cache = use_cache if use_cache is not None else self.config.use_cache
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
transformer_inputs, loc, scale, static_feat = self.create_network_inputs(
past_values=past_values,
past_time_features=past_time_features,
past_observed_mask=past_observed_mask,
static_categorical_features=static_categorical_features,
static_real_features=static_real_features,
future_values=future_values,
future_time_features=future_time_features,
)
if encoder_outputs is None:
enc_input = transformer_inputs[:, : self.config.context_length, ...]
encoder_outputs = self.encoder(
inputs_embeds=enc_input,
head_mask=head_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
# If the user passed a tuple for encoder_outputs, we wrap it in a BaseModelOutput when return_dict=True
elif return_dict and not isinstance(encoder_outputs, BaseModelOutput):
encoder_outputs = BaseModelOutput(
last_hidden_state=encoder_outputs[0],
hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None,
attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None,
)
dec_input = transformer_inputs[:, self.config.context_length :, ...]
decoder_outputs = self.decoder(
inputs_embeds=dec_input,
attention_mask=decoder_attention_mask,
encoder_hidden_states=encoder_outputs[0],
head_mask=decoder_head_mask,
cross_attn_head_mask=cross_attn_head_mask,
past_key_values=past_key_values,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
if not return_dict:
return decoder_outputs + encoder_outputs + (loc, scale, static_feat)
return Seq2SeqTSModelOutput(
last_hidden_state=decoder_outputs.last_hidden_state,
past_key_values=decoder_outputs.past_key_values,
decoder_hidden_states=decoder_outputs.hidden_states,
decoder_attentions=decoder_outputs.attentions,
cross_attentions=decoder_outputs.cross_attentions,
encoder_last_hidden_state=encoder_outputs.last_hidden_state,
encoder_hidden_states=encoder_outputs.hidden_states,
encoder_attentions=encoder_outputs.attentions,
loc=loc,
scale=scale,
static_features=static_feat,
)
@add_start_docstrings(
"The Informer Model with a distribution head on top for time-series forecasting.",
INFORMER_START_DOCSTRING,
)
# Copied from transformers.models.time_series_transformer.modeling_time_series_transformer.TimeSeriesTransformerForPrediction with TimeSeriesTransformer->Informer,TIME_SERIES_TRANSFORMER->INFORMER,time-series-transformer->informer
class InformerForPrediction(InformerPreTrainedModel):
def __init__(self, config: InformerConfig):
super().__init__(config)
self.model = InformerModel(config)
if config.distribution_output == "student_t":
self.distribution_output = StudentTOutput(dim=config.input_size)
elif config.distribution_output == "normal":
self.distribution_output = NormalOutput(dim=config.input_size)
elif config.distribution_output == "negative_binomial":
self.distribution_output = NegativeBinomialOutput(dim=config.input_size)
else:
raise ValueError(f"Unknown distribution output {config.distribution_output}")
self.parameter_projection = self.distribution_output.get_parameter_projection(self.model.config.d_model)
self.target_shape = self.distribution_output.event_shape
if config.loss == "nll":
self.loss = nll
else:
raise ValueError(f"Unknown loss function {config.loss}")
# Initialize weights of distribution_output and apply final processing
self.post_init()
def output_params(self, dec_output):
return self.parameter_projection(dec_output)
def get_encoder(self):
return self.model.get_encoder()
def get_decoder(self):
return self.model.get_decoder()
@torch.jit.ignore
def output_distribution(self, params, loc=None, scale=None, trailing_n=None) -> torch.distributions.Distribution:
sliced_params = params
if trailing_n is not None:
sliced_params = [p[:, -trailing_n:] for p in params]
return self.distribution_output.distribution(sliced_params, loc=loc, scale=scale)
@add_start_docstrings_to_model_forward(INFORMER_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=Seq2SeqTSModelOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
past_values: torch.Tensor,
past_time_features: torch.Tensor,
past_observed_mask: torch.Tensor,
static_categorical_features: Optional[torch.Tensor] = None,
static_real_features: Optional[torch.Tensor] = None,
future_values: Optional[torch.Tensor] = None,
future_time_features: Optional[torch.Tensor] = None,
future_observed_mask: Optional[torch.Tensor] = None,
decoder_attention_mask: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.Tensor] = None,
decoder_head_mask: Optional[torch.Tensor] = None,
cross_attn_head_mask: Optional[torch.Tensor] = None,
encoder_outputs: Optional[List[torch.FloatTensor]] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
output_hidden_states: Optional[bool] = None,
output_attentions: Optional[bool] = None,
use_cache: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Seq2SeqTSModelOutput, Tuple]:
r"""
Returns:
Examples:
```python
>>> from huggingface_hub import hf_hub_download
>>> import torch
>>> from transformers import InformerForPrediction
>>> file = hf_hub_download(
... repo_id="hf-internal-testing/tourism-monthly-batch", filename="train-batch.pt", repo_type="dataset"
... )
>>> batch = torch.load(file)
>>> model = InformerForPrediction.from_pretrained(
... "huggingface/informer-tourism-monthly"
... )
>>> # during training, one provides both past and future values
>>> # as well as possible additional features
>>> outputs = model(
... past_values=batch["past_values"],
... past_time_features=batch["past_time_features"],
... past_observed_mask=batch["past_observed_mask"],
... static_categorical_features=batch["static_categorical_features"],
... static_real_features=batch["static_real_features"],
... future_values=batch["future_values"],
... future_time_features=batch["future_time_features"],
... )
>>> loss = outputs.loss
>>> loss.backward()
>>> # during inference, one only provides past values
>>> # as well as possible additional features
>>> # the model autoregressively generates future values
>>> outputs = model.generate(
... past_values=batch["past_values"],
... past_time_features=batch["past_time_features"],
... past_observed_mask=batch["past_observed_mask"],
... static_categorical_features=batch["static_categorical_features"],
... static_real_features=batch["static_real_features"],
... future_time_features=batch["future_time_features"],
... )
>>> mean_prediction = outputs.sequences.mean(dim=1)
```"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if future_values is not None:
use_cache = False
outputs = self.model(
past_values=past_values,
past_time_features=past_time_features,
past_observed_mask=past_observed_mask,
static_categorical_features=static_categorical_features,
static_real_features=static_real_features,
future_values=future_values,
future_time_features=future_time_features,
decoder_attention_mask=decoder_attention_mask,
head_mask=head_mask,
decoder_head_mask=decoder_head_mask,
cross_attn_head_mask=cross_attn_head_mask,
encoder_outputs=encoder_outputs,
past_key_values=past_key_values,
output_hidden_states=output_hidden_states,
output_attentions=output_attentions,
use_cache=use_cache,
return_dict=return_dict,
)
prediction_loss = None
params = None
if future_values is not None:
params = self.output_params(outputs[0]) # outputs.last_hidden_state
# loc is 3rd last and scale is 2nd last output
distribution = self.output_distribution(params, loc=outputs[-3], scale=outputs[-2])
loss = self.loss(distribution, future_values)
if future_observed_mask is None:
future_observed_mask = torch.ones_like(future_values)
if len(self.target_shape) == 0:
loss_weights = future_observed_mask
else:
loss_weights, _ = future_observed_mask.min(dim=-1, keepdim=False)
prediction_loss = weighted_average(loss, weights=loss_weights)
if not return_dict:
outputs = ((params,) + outputs[1:]) if params is not None else outputs[1:]
return ((prediction_loss,) + outputs) if prediction_loss is not None else outputs
return Seq2SeqTSPredictionOutput(
loss=prediction_loss,
params=params,
past_key_values=outputs.past_key_values,
decoder_hidden_states=outputs.decoder_hidden_states,
decoder_attentions=outputs.decoder_attentions,
cross_attentions=outputs.cross_attentions,
encoder_last_hidden_state=outputs.encoder_last_hidden_state,
encoder_hidden_states=outputs.encoder_hidden_states,
encoder_attentions=outputs.encoder_attentions,
loc=outputs.loc,
scale=outputs.scale,
static_features=outputs.static_features,
)
@torch.no_grad()
def generate(
self,
past_values: torch.Tensor,
past_time_features: torch.Tensor,
future_time_features: torch.Tensor,
past_observed_mask: Optional[torch.Tensor] = None,
static_categorical_features: Optional[torch.Tensor] = None,
static_real_features: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
) -> SampleTSPredictionOutput:
r"""
Greedily generate sequences of sample predictions from a model with a probability distribution head.
Parameters:
past_values (`torch.FloatTensor` of shape `(batch_size, sequence_length)` or `(batch_size, sequence_length, input_size)`):
Past values of the time series, that serve as context in order to predict the future. The sequence size
of this tensor must be larger than the `context_length` of the model, since the model will use the
larger size to construct lag features, i.e. additional values from the past which are added in order to
serve as "extra context".
The `sequence_length` here is equal to `config.context_length` + `max(config.lags_sequence)`, which if
no `lags_sequence` is configured, is equal to `config.context_length` + 7 (as by default, the largest
look-back index in `config.lags_sequence` is 7). The property `_past_length` returns the actual length
of the past.
The `past_values` is what the Transformer encoder gets as input (with optional additional features,
such as `static_categorical_features`, `static_real_features`, `past_time_features` and lags).
Optionally, missing values need to be replaced with zeros and indicated via the `past_observed_mask`.
For multivariate time series, the `input_size` > 1 dimension is required and corresponds to the number
of variates in the time series per time step.
past_time_features (`torch.FloatTensor` of shape `(batch_size, sequence_length, num_features)`):
Required time features, which the model internally will add to `past_values`. These could be things
like "month of year", "day of the month", etc. encoded as vectors (for instance as Fourier features).
These could also be so-called "age" features, which basically help the model know "at which point in
life" a time-series is. Age features have small values for distant past time steps and increase
monotonically the more we approach the current time step. Holiday features are also a good example of
time features.
These features serve as the "positional encodings" of the inputs. So contrary to a model like BERT,
where the position encodings are learned from scratch internally as parameters of the model, the Time
Series Transformer requires to provide additional time features. The Time Series Transformer only
learns additional embeddings for `static_categorical_features`.
Additional dynamic real covariates can be concatenated to this tensor, with the caveat that these
features must but known at prediction time.
The `num_features` here is equal to `config.`num_time_features` + `config.num_dynamic_real_features`.
future_time_features (`torch.FloatTensor` of shape `(batch_size, prediction_length, num_features)`):
Required time features for the prediction window, which the model internally will add to sampled
predictions. These could be things like "month of year", "day of the month", etc. encoded as vectors
(for instance as Fourier features). These could also be so-called "age" features, which basically help
the model know "at which point in life" a time-series is. Age features have small values for distant
past time steps and increase monotonically the more we approach the current time step. Holiday features
are also a good example of time features.
These features serve as the "positional encodings" of the inputs. So contrary to a model like BERT,
where the position encodings are learned from scratch internally as parameters of the model, the Time
Series Transformer requires to provide additional time features. The Time Series Transformer only
learns additional embeddings for `static_categorical_features`.
Additional dynamic real covariates can be concatenated to this tensor, with the caveat that these
features must but known at prediction time.
The `num_features` here is equal to `config.`num_time_features` + `config.num_dynamic_real_features`.
past_observed_mask (`torch.BoolTensor` of shape `(batch_size, sequence_length)` or `(batch_size, sequence_length, input_size)`, *optional*):
Boolean mask to indicate which `past_values` were observed and which were missing. Mask values selected
in `[0, 1]`:
- 1 for values that are **observed**,
- 0 for values that are **missing** (i.e. NaNs that were replaced by zeros).
static_categorical_features (`torch.LongTensor` of shape `(batch_size, number of static categorical features)`, *optional*):
Optional static categorical features for which the model will learn an embedding, which it will add to
the values of the time series.
Static categorical features are features which have the same value for all time steps (static over
time).
A typical example of a static categorical feature is a time series ID.
static_real_features (`torch.FloatTensor` of shape `(batch_size, number of static real features)`, *optional*):
Optional static real features which the model will add to the values of the time series.
Static real features are features which have the same value for all time steps (static over time).
A typical example of a static real feature is promotion information.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers.
Return:
[`SampleTSPredictionOutput`] where the outputs `sequences` tensor will have shape `(batch_size, number of
samples, prediction_length)` or `(batch_size, number of samples, prediction_length, input_size)` for
multivariate predictions.
"""
outputs = self(
static_categorical_features=static_categorical_features,
static_real_features=static_real_features,
past_time_features=past_time_features,
past_values=past_values,
past_observed_mask=past_observed_mask,
future_time_features=future_time_features,
future_values=None,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=True,
use_cache=True,
)
decoder = self.model.get_decoder()
enc_last_hidden = outputs.encoder_last_hidden_state
loc = outputs.loc
scale = outputs.scale
static_feat = outputs.static_features
num_parallel_samples = self.config.num_parallel_samples
repeated_loc = loc.repeat_interleave(repeats=num_parallel_samples, dim=0)
repeated_scale = scale.repeat_interleave(repeats=num_parallel_samples, dim=0)
repeated_past_values = (
past_values.repeat_interleave(repeats=num_parallel_samples, dim=0) - repeated_loc
) / repeated_scale
expanded_static_feat = static_feat.unsqueeze(1).expand(-1, future_time_features.shape[1], -1)
features = torch.cat((expanded_static_feat, future_time_features), dim=-1)
repeated_features = features.repeat_interleave(repeats=num_parallel_samples, dim=0)
repeated_enc_last_hidden = enc_last_hidden.repeat_interleave(repeats=num_parallel_samples, dim=0)
future_samples = []
# greedy decoding
for k in range(self.config.prediction_length):
lagged_sequence = self.model.get_lagged_subsequences(
sequence=repeated_past_values,
subsequences_length=1 + k,
shift=1,
)
lags_shape = lagged_sequence.shape
reshaped_lagged_sequence = lagged_sequence.reshape(lags_shape[0], lags_shape[1], -1)
decoder_input = torch.cat((reshaped_lagged_sequence, repeated_features[:, : k + 1]), dim=-1)
dec_output = decoder(inputs_embeds=decoder_input, encoder_hidden_states=repeated_enc_last_hidden)
dec_last_hidden = dec_output.last_hidden_state
params = self.parameter_projection(dec_last_hidden[:, -1:])
distr = self.output_distribution(params, loc=repeated_loc, scale=repeated_scale)
next_sample = distr.sample()
repeated_past_values = torch.cat(
(repeated_past_values, (next_sample - repeated_loc) / repeated_scale), dim=1
)
future_samples.append(next_sample)
concat_future_samples = torch.cat(future_samples, dim=1)
return SampleTSPredictionOutput(
sequences=concat_future_samples.reshape(
(-1, num_parallel_samples, self.config.prediction_length) + self.target_shape,
)
)
__all__ = ["InformerForPrediction", "InformerModel", "InformerPreTrainedModel"]
```
|
====================================================================================================================================
SOURCE CODE FILE: __init__.py
LINES: 1
SIZE: 1.02 KB
PATH: scripts\freecad_env\Lib\site-packages\transformers\models\instructblip\__init__.py
ENCODING: utf-8
```py
# Copyright 2024 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import _LazyModule
from ...utils.import_utils import define_import_structure
if TYPE_CHECKING:
from .configuration_instructblip import *
from .modeling_instructblip import *
from .processing_instructblip import *
else:
import sys
_file = globals()["__file__"]
sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)
```
|
======================================================================================================================================================
SOURCE CODE FILE: configuration_instructblip.py
LINES: 1
SIZE: 15.39 KB
PATH: scripts\freecad_env\Lib\site-packages\transformers\models\instructblip\configuration_instructblip.py
ENCODING: utf-8
```py
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""InstructBLIP model configuration"""
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING, AutoConfig
logger = logging.get_logger(__name__)
class InstructBlipVisionConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`InstructBlipVisionModel`]. It is used to
instantiate a InstructBLIP vision encoder according to the specified arguments, defining the model architecture.
Instantiating a configuration defaults will yield a similar configuration to that of the InstructBLIP
[Salesforce/instruct-blip-flan-t5](https://huggingface.co/Salesforce/instruct-blip-flan-t5) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
hidden_size (`int`, *optional*, defaults to 1408):
Dimensionality of the encoder layers and the pooler layer.
intermediate_size (`int`, *optional*, defaults to 6144):
Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
num_hidden_layers (`int`, *optional*, defaults to 39):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 16):
Number of attention heads for each attention layer in the Transformer encoder.
image_size (`int`, *optional*, defaults to 224):
The size (resolution) of each image.
patch_size (`int`, *optional*, defaults to 14):
The size (resolution) of each patch.
hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"selu"` and `"gelu_new"` `"gelu"` are supported. to 1e-5): The epsilon used by the layer
normalization layers.
layer_norm_eps (`float`, *optional*, defaults to 1e-06):
The epsilon used by the layer normalization layers.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
initializer_range (`float`, *optional*, defaults to 1e-10):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
qkv_bias (`bool`, *optional*, defaults to `True`):
Whether to add a bias to the queries and values in the self-attention layers.
Example:
```python
>>> from transformers import InstructBlipVisionConfig, InstructBlipVisionModel
>>> # Initializing a InstructBlipVisionConfig with Salesforce/instruct-blip-flan-t5 style configuration
>>> configuration = InstructBlipVisionConfig()
>>> # Initializing a InstructBlipVisionModel (with random weights) from the Salesforce/instruct-blip-flan-t5 style configuration
>>> model = InstructBlipVisionModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "instructblip_vision_model"
base_config_key = "vision_config"
def __init__(
self,
hidden_size=1408,
intermediate_size=6144,
num_hidden_layers=39,
num_attention_heads=16,
image_size=224,
patch_size=14,
hidden_act="gelu",
layer_norm_eps=1e-6,
attention_dropout=0.0,
initializer_range=1e-10,
qkv_bias=True,
**kwargs,
):
super().__init__(**kwargs)
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.patch_size = patch_size
self.image_size = image_size
self.initializer_range = initializer_range
self.attention_dropout = attention_dropout
self.layer_norm_eps = layer_norm_eps
self.hidden_act = hidden_act
self.qkv_bias = qkv_bias
class InstructBlipQFormerConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`InstructBlipQFormerModel`]. It is used to
instantiate a InstructBLIP Querying Transformer (Q-Former) model according to the specified arguments, defining the
model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of
the InstructBLIP [Salesforce/instruct-blip-flan-t5](https://huggingface.co/Salesforce/instruct-blip-flan-t5)
architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs.
Read the documentation from [`PretrainedConfig`] for more information.
Note that [`InstructBlipQFormerModel`] is very similar to [`BertLMHeadModel`] with interleaved cross-attention.
Args:
vocab_size (`int`, *optional*, defaults to 30522):
Vocabulary size of the Q-Former model. Defines the number of different tokens that can be represented by
the `inputs_ids` passed when calling the model.
hidden_size (`int`, *optional*, defaults to 768):
Dimensionality of the encoder layers and the pooler layer.
num_hidden_layers (`int`, *optional*, defaults to 12):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 12):
Number of attention heads for each attention layer in the Transformer encoder.
intermediate_size (`int`, *optional*, defaults to 3072):
Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
hidden_act (`str` or `Callable`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"silu"` and `"gelu_new"` are supported.
hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
The dropout ratio for the attention probabilities.
max_position_embeddings (`int`, *optional*, defaults to 512):
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (`float`, *optional*, defaults to 1e-12):
The epsilon used by the layer normalization layers.
pad_token_id (`int`, *optional*, defaults to 0):
Token id used for padding sequences.
position_embedding_type (`str`, *optional*, defaults to `"absolute"`):
Type of position embedding. Choose one of `"absolute"`, `"relative_key"`, `"relative_key_query"`. For
positional embeddings use `"absolute"`. For more information on `"relative_key"`, please refer to
[Self-Attention with Relative Position Representations (Shaw et al.)](https://arxiv.org/abs/1803.02155).
For more information on `"relative_key_query"`, please refer to *Method 4* in [Improve Transformer Models
with Better Relative Position Embeddings (Huang et al.)](https://arxiv.org/abs/2009.13658).
cross_attention_frequency (`int`, *optional*, defaults to 2):
The frequency of adding cross-attention to the Transformer layers.
encoder_hidden_size (`int`, *optional*, defaults to 1408):
The hidden size of the hidden states for cross-attention.
Examples:
```python
>>> from transformers import InstructBlipQFormerConfig, InstructBlipQFormerModel
>>> # Initializing a InstructBLIP Salesforce/instruct-blip-flan-t5 style configuration
>>> configuration = InstructBlipQFormerConfig()
>>> # Initializing a model (with random weights) from the Salesforce/instruct-blip-flan-t5 style configuration
>>> model = InstructBlipQFormerModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "instructblip_qformer"
base_config_key = "qformer_config"
def __init__(
self,
vocab_size=30522,
hidden_size=768,
num_hidden_layers=12,
num_attention_heads=12,
intermediate_size=3072,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
initializer_range=0.02,
layer_norm_eps=1e-12,
pad_token_id=0,
position_embedding_type="absolute",
cross_attention_frequency=2,
encoder_hidden_size=1408,
**kwargs,
):
super().__init__(pad_token_id=pad_token_id, **kwargs)
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.hidden_act = hidden_act
self.intermediate_size = intermediate_size
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.initializer_range = initializer_range
self.layer_norm_eps = layer_norm_eps
self.position_embedding_type = position_embedding_type
self.cross_attention_frequency = cross_attention_frequency
self.encoder_hidden_size = encoder_hidden_size
class InstructBlipConfig(PretrainedConfig):
r"""
[`InstructBlipConfig`] is the configuration class to store the configuration of a
[`InstructBlipForConditionalGeneration`]. It is used to instantiate a InstructBLIP model according to the specified
arguments, defining the vision model, Q-Former model and language model configs. Instantiating a configuration with
the defaults will yield a similar configuration to that of the InstructBLIP
[Salesforce/instruct-blip-flan-t5](https://huggingface.co/Salesforce/instruct-blip-flan-t5) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vision_config (`dict`, *optional*):
Dictionary of configuration options used to initialize [`InstructBlipVisionConfig`].
qformer_config (`dict`, *optional*):
Dictionary of configuration options used to initialize [`InstructBlipQFormerConfig`].
text_config (`dict`, *optional*):
Dictionary of configuration options used to initialize any [`PretrainedConfig`].
num_query_tokens (`int`, *optional*, defaults to 32):
The number of query tokens passed through the Transformer.
image_token_index (`int`, *optional*):
Token index of special image token.
kwargs (*optional*):
Dictionary of keyword arguments.
Example:
```python
>>> from transformers import (
... InstructBlipVisionConfig,
... InstructBlipQFormerConfig,
... OPTConfig,
... InstructBlipConfig,
... InstructBlipForConditionalGeneration,
... )
>>> # Initializing a InstructBlipConfig with Salesforce/instruct-blip-flan-t5 style configuration
>>> configuration = InstructBlipConfig()
>>> # Initializing a InstructBlipForConditionalGeneration (with random weights) from the Salesforce/instruct-blip-flan-t5 style configuration
>>> model = InstructBlipForConditionalGeneration(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
>>> # We can also initialize a InstructBlipConfig from a InstructBlipVisionConfig, InstructBlipQFormerConfig and any PretrainedConfig
>>> # Initializing InstructBLIP vision, InstructBLIP Q-Former and language model configurations
>>> vision_config = InstructBlipVisionConfig()
>>> qformer_config = InstructBlipQFormerConfig()
>>> text_config = OPTConfig()
>>> config = InstructBlipConfig.from_text_vision_configs(vision_config, qformer_config, text_config)
```"""
model_type = "instructblip"
sub_configs = {
"text_config": AutoConfig,
"qformer_config": InstructBlipQFormerConfig,
"vision_config": InstructBlipVisionConfig,
}
def __init__(
self,
vision_config=None,
qformer_config=None,
text_config=None,
num_query_tokens=32,
image_token_index=None,
**kwargs,
):
super().__init__(**kwargs)
if vision_config is None:
vision_config = {}
logger.info("vision_config is None. initializing the InstructBlipVisionConfig with default values.")
if qformer_config is None:
qformer_config = {}
logger.info("qformer_config is None. Initializing the InstructBlipQFormerConfig with default values.")
if text_config is None:
text_config = {}
logger.info("text_config is None. Initializing the text config with default values (`OPTConfig`).")
self.vision_config = InstructBlipVisionConfig(**vision_config)
self.qformer_config = InstructBlipQFormerConfig(**qformer_config)
text_model_type = text_config["model_type"] if "model_type" in text_config else "opt"
self.text_config = CONFIG_MAPPING[text_model_type](**text_config)
self.num_query_tokens = num_query_tokens
self.image_token_index = image_token_index
self.qformer_config.encoder_hidden_size = self.vision_config.hidden_size
self.use_decoder_only_language_model = self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
self.initializer_factor = 1.0
self.initializer_range = 0.02
@classmethod
def from_vision_qformer_text_configs(
cls,
vision_config: InstructBlipVisionConfig,
qformer_config: InstructBlipQFormerConfig,
text_config: PretrainedConfig,
**kwargs,
):
r"""
Instantiate a [`InstructBlipConfig`] (or a derived class) from a InstructBLIP vision model, Q-Former and
language model configurations.
Returns:
[`InstructBlipConfig`]: An instance of a configuration object
"""
return cls(
vision_config=vision_config.to_dict(),
qformer_config=qformer_config.to_dict(),
text_config=text_config.to_dict(),
**kwargs,
)
__all__ = ["InstructBlipConfig", "InstructBlipQFormerConfig", "InstructBlipVisionConfig"]
```
|
=================================================================================================================================================
SOURCE CODE FILE: modeling_instructblip.py
LINES: 1
SIZE: 74.18 KB
PATH: scripts\freecad_env\Lib\site-packages\transformers\models\instructblip\modeling_instructblip.py
ENCODING: utf-8
```py
# coding=utf-8
# Copyright 2023 The Salesforce Authors and The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""PyTorch InstructBLIP model."""
import math
from dataclasses import dataclass
from typing import Any, Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import CrossEntropyLoss
from ...activations import ACT2FN
from ...generation import GenerationMixin
from ...modeling_outputs import (
BaseModelOutput,
BaseModelOutputWithPastAndCrossAttentions,
BaseModelOutputWithPooling,
BaseModelOutputWithPoolingAndCrossAttentions,
)
from ...modeling_utils import PreTrainedModel
from ...pytorch_utils import apply_chunking_to_forward, find_pruneable_heads_and_indices, prune_linear_layer
from ...utils import (
ModelOutput,
add_start_docstrings,
add_start_docstrings_to_model_forward,
logging,
replace_return_docstrings,
torch_int,
)
from ..auto import AutoModelForCausalLM, AutoModelForSeq2SeqLM
from .configuration_instructblip import InstructBlipConfig, InstructBlipQFormerConfig, InstructBlipVisionConfig
logger = logging.get_logger(__name__)
_CHECKPOINT_FOR_DOC = "Salesforce/instructblip-flan-t5-xl"
@dataclass
# Copied from transformers.models.blip_2.modeling_blip_2.Blip2ForConditionalGenerationModelOutput with Blip2->InstructBlip
class InstructBlipForConditionalGenerationModelOutput(ModelOutput):
"""
Class defining the outputs of [`InstructBlipForConditionalGeneration`].
Args:
loss (`torch.FloatTensor`, *optional*, returned when `labels` is provided, `torch.FloatTensor` of shape `(1,)`):
Language modeling loss from the language model.
logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
Prediction scores of the language modeling head of the language model.
vision_outputs (`BaseModelOutputWithPooling`):
Outputs of the vision encoder.
qformer_outputs (`BaseModelOutputWithPoolingAndCrossAttentions`):
Outputs of the Q-Former (Querying Transformer).
language_model_outputs (`CausalLMOutputWithPast` or `Seq2SeqLMOutput`):
Outputs of the language model.
"""
loss: Optional[Tuple[torch.FloatTensor]] = None
logits: Optional[Tuple[torch.FloatTensor]] = None
vision_outputs: Optional[torch.FloatTensor] = None
qformer_outputs: Optional[Tuple[torch.FloatTensor]] = None
language_model_outputs: Optional[Tuple[torch.FloatTensor]] = None
def to_tuple(self) -> Tuple[Any]:
return tuple(
self[k]
if k not in ["vision_outputs", "qformer_outputs", "language_model_outputs"]
else getattr(self, k).to_tuple()
for k in self.keys()
)
# Copied from transformers.models.blip.modeling_blip.BlipVisionEmbeddings with Blip->InstructBlip
class InstructBlipVisionEmbeddings(nn.Module):
def __init__(self, config: InstructBlipVisionConfig):
super().__init__()
self.config = config
self.embed_dim = config.hidden_size
self.image_size = config.image_size
self.patch_size = config.patch_size
self.class_embedding = nn.Parameter(torch.randn(1, 1, self.embed_dim))
self.patch_embedding = nn.Conv2d(
in_channels=3, out_channels=self.embed_dim, kernel_size=self.patch_size, stride=self.patch_size
)
self.num_patches = (self.image_size // self.patch_size) ** 2
self.num_positions = self.num_patches + 1
self.position_embedding = nn.Parameter(torch.randn(1, self.num_positions, self.embed_dim))
def interpolate_pos_encoding(self, embeddings: torch.Tensor, height: int, width: int) -> torch.Tensor:
"""
This method allows to interpolate the pre-trained position encodings, to be able to use the model on higher resolution
images. This method is also adapted to support torch.jit tracing.
Adapted from:
- https://github.com/facebookresearch/dino/blob/de9ee3df6cf39fac952ab558447af1fa1365362a/vision_transformer.py#L174-L194, and
- https://github.com/facebookresearch/dinov2/blob/e1277af2ba9496fbadf7aec6eba56e8d882d1e35/dinov2/models/vision_transformer.py#L179-L211
"""
num_patches = embeddings.shape[1] - 1
num_positions = self.position_embedding.shape[1] - 1
# always interpolate when tracing to ensure the exported model works for dynamic input shapes
if not torch.jit.is_tracing() and num_patches == num_positions and height == width:
return self.position_embedding
class_pos_embed = self.position_embedding[:, :1]
patch_pos_embed = self.position_embedding[:, 1:]
dim = embeddings.shape[-1]
new_height = height // self.patch_size
new_width = width // self.patch_size
sqrt_num_positions = torch_int(num_positions**0.5)
patch_pos_embed = patch_pos_embed.reshape(1, sqrt_num_positions, sqrt_num_positions, dim)
patch_pos_embed = patch_pos_embed.permute(0, 3, 1, 2)
patch_pos_embed = nn.functional.interpolate(
patch_pos_embed,
size=(new_height, new_width),
mode="bicubic",
align_corners=False,
)
patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim)
return torch.cat((class_pos_embed, patch_pos_embed), dim=1)
def forward(self, pixel_values: torch.FloatTensor, interpolate_pos_encoding: bool = False) -> torch.Tensor:
batch_size, _, height, width = pixel_values.shape
target_dtype = self.patch_embedding.weight.dtype
patch_embeds = self.patch_embedding(pixel_values.to(dtype=target_dtype)) # shape = [*, width, grid, grid]
patch_embeds = patch_embeds.flatten(2).transpose(1, 2)
class_embeds = self.class_embedding.expand(batch_size, 1, -1).to(target_dtype)
embeddings = torch.cat([class_embeds, patch_embeds], dim=1)
if interpolate_pos_encoding:
position_embedding = self.interpolate_pos_encoding(embeddings, height, width)
else:
position_embedding = self.position_embedding
embeddings = embeddings + position_embedding[:, : embeddings.size(1), :].to(target_dtype)
return embeddings
# Copied from transformers.models.blip_2.modeling_blip_2.Blip2Attention with Blip2->InstructBlip
class InstructBlipAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(self, config):
super().__init__()
self.config = config
self.embed_dim = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_heads != self.embed_dim:
raise ValueError(
f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:"
f" {self.num_heads})."
)
self.scale = self.head_dim**-0.5
self.dropout = nn.Dropout(config.attention_dropout)
# small tweak here compared to CLIP, no bias here
self.qkv = nn.Linear(self.embed_dim, 3 * self.embed_dim, bias=False)
if config.qkv_bias:
q_bias = nn.Parameter(torch.zeros(self.embed_dim))
v_bias = nn.Parameter(torch.zeros(self.embed_dim))
else:
q_bias = None
v_bias = None
if q_bias is not None:
qkv_bias = torch.cat((q_bias, torch.zeros_like(v_bias, requires_grad=False), v_bias))
self.qkv.bias = nn.Parameter(qkv_bias)
self.projection = nn.Linear(self.embed_dim, self.embed_dim)
def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
def forward(
self,
hidden_states: torch.Tensor,
head_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = False,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
"""Input shape: Batch x Time x Channel"""
bsz, tgt_len, embed_dim = hidden_states.size()
mixed_qkv = self.qkv(hidden_states)
mixed_qkv = mixed_qkv.reshape(bsz, tgt_len, 3, self.num_heads, embed_dim // self.num_heads).permute(
2, 0, 3, 1, 4
)
query_states, key_states, value_states = mixed_qkv[0], mixed_qkv[1], mixed_qkv[2]
# Take the dot product between "query" and "key" to get the raw attention scores.
attention_scores = torch.matmul(query_states, key_states.transpose(-1, -2))
attention_scores = attention_scores * self.scale
# Normalize the attention scores to probabilities.
attention_probs = nn.functional.softmax(attention_scores, dim=-1)
# This is actually dropping out entire tokens to attend to, which might
# seem a bit unusual, but is taken from the original Transformer paper.
attention_probs = self.dropout(attention_probs)
# Mask heads if we want to
if head_mask is not None:
attention_probs = attention_probs * head_mask
context_layer = torch.matmul(attention_probs, value_states).permute(0, 2, 1, 3)
new_context_layer_shape = context_layer.size()[:-2] + (self.embed_dim,)
context_layer = context_layer.reshape(new_context_layer_shape)
output = self.projection(context_layer)
outputs = (output, attention_probs) if output_attentions else (output, None)
return outputs
# Copied from transformers.models.blip.modeling_blip.BlipMLP
class InstructBlipMLP(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.activation_fn = ACT2FN[config.hidden_act]
self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size)
self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size)
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidden_states = self.fc1(hidden_states)
hidden_states = self.activation_fn(hidden_states)
hidden_states = self.fc2(hidden_states)
return hidden_states
# Copied from transformers.models.blip.modeling_blip.BlipEncoderLayer with Blip->InstructBlip
class InstructBlipEncoderLayer(nn.Module):
def __init__(self, config: InstructBlipConfig):
super().__init__()
self.embed_dim = config.hidden_size
self.self_attn = InstructBlipAttention(config)
self.layer_norm1 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
self.mlp = InstructBlipMLP(config)
self.layer_norm2 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: torch.Tensor,
output_attentions: Optional[bool] = False,
) -> Tuple[torch.FloatTensor]:
"""
Args:
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
attention_mask (`torch.FloatTensor`): attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
`(config.encoder_attention_heads,)`.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
"""
residual = hidden_states
hidden_states = self.layer_norm1(hidden_states)
hidden_states, attn_weights = self.self_attn(
hidden_states=hidden_states,
head_mask=attention_mask,
output_attentions=output_attentions,
)
hidden_states = hidden_states + residual
residual = hidden_states
hidden_states = self.layer_norm2(hidden_states)
hidden_states = self.mlp(hidden_states)
hidden_states = hidden_states + residual
outputs = (hidden_states,)
if output_attentions:
outputs += (attn_weights,)
return outputs
class InstructBlipPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = InstructBlipConfig
base_model_prefix = "blip"
supports_gradient_checkpointing = True
_no_split_modules = [
"InstructBlipQFormerEmbeddings",
"InstructBlipAttention",
"InstructBlipQFormerMultiHeadAttention",
"InstructBlipQFormerSelfOutput",
]
# Copied from transformers.models.blip_2.modeling_blip_2.Blip2PreTrainedModel._init_weights with Blip2->InstructBlip
def _init_weights(self, module):
"""Initialize the weights"""
factor = self.config.initializer_range
if isinstance(module, nn.Conv2d) or isinstance(module, nn.Embedding) or isinstance(module, nn.Linear):
module.weight.data.normal_(mean=0.0, std=factor)
if hasattr(module, "bias") and module.bias is not None:
module.bias.data.zero_()
if isinstance(module, InstructBlipVisionEmbeddings):
if hasattr(self.config, "vision_config") and not isinstance(self.config, InstructBlipVisionConfig):
factor = self.config.vision_config.initializer_range
nn.init.trunc_normal_(module.position_embedding, mean=0.0, std=factor)
nn.init.trunc_normal_(module.class_embedding, mean=0.0, std=factor)
elif isinstance(module, nn.LayerNorm):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
elif isinstance(module, nn.Linear) and module.bias is not None:
module.bias.data.zero_()
INSTRUCTBLIP_START_DOCSTRING = r"""
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.
Parameters:
config ([`InstructBlipConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
INSTRUCTBLIP_VISION_INPUTS_DOCSTRING = r"""
Args:
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Pixel values can be obtained using [`InstructBlipProcessor`]. See
[`InstructBlipProcessor.__call__`] for details.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
interpolate_pos_encoding (`bool`, *optional*, defaults to `False`):
Whether to interpolate the pre-trained position encodings.
"""
INSTRUCTBLIP_INPUTS_DOCSTRING = r"""
Args:
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Pixel values can be obtained using [`InstructBlipProcessor`]. See
[`InstructBlipProcessor.__call__`] for details.
qformer_input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Indices of input sequence tokens in the vocabulary of the Q-Former. Input tokens can optionally be provided
to serve as text prompt, which the Q-Former model will encode.
Indices can be obtained using [`InstructBlipProcessor`]. See [`InstructBlipProcessor.__call__`] for
details.
[What are input IDs?](../glossary#input-ids)
qformer_attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Indices of input sequence tokens in the vocabulary of the language model. Input tokens can optionally be
provided to serve as text prompt, which the language model can continue.
Indices can be obtained using [`InstructBlipProcessor`]. See [`InstructBlipProcessor.__call__`] for
details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
Indices of decoder input sequence tokens in the vocabulary of the language model. Only relevant in case an
encoder-decoder language model (like T5) is used.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details. [What are decoder input IDs?](../glossary#decoder-input-ids)
decoder_attention_mask (`torch.BoolTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
be used by default.
Only relevant in case an encoder-decoder language model (like T5) is used.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
interpolate_pos_encoding (`bool`, *optional*, defaults to `False`):
Whether to interpolate the pre-trained position encodings.
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
`past_key_values`).
"""
# Copied from transformers.models.blip.modeling_blip.BlipEncoder with Blip->InstructBlip
class InstructBlipEncoder(nn.Module):
"""
Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a
[`InstructBlipEncoderLayer`].
Args:
config (`InstructBlipConfig`):
The corresponding vision configuration for the `InstructBlipEncoder`.
"""
def __init__(self, config: InstructBlipConfig):
super().__init__()
self.config = config
self.layers = nn.ModuleList([InstructBlipEncoderLayer(config) for _ in range(config.num_hidden_layers)])
self.gradient_checkpointing = False
def forward(
self,
inputs_embeds,
attention_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, BaseModelOutput]:
r"""
Args:
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Embedded representation of the inputs. Should be float, not int tokens.
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
for more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
encoder_states = () if output_hidden_states else None
all_attentions = () if output_attentions else None
hidden_states = inputs_embeds
for idx, encoder_layer in enumerate(self.layers):
if output_hidden_states:
encoder_states = encoder_states + (hidden_states,)
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
encoder_layer.__call__,
hidden_states,
attention_mask,
output_attentions,
)
else:
layer_outputs = encoder_layer(
hidden_states,
attention_mask,
output_attentions=output_attentions,
)
hidden_states = layer_outputs[0]
if output_attentions:
all_attentions = all_attentions + (layer_outputs[1],)
if output_hidden_states:
encoder_states = encoder_states + (hidden_states,)
if not return_dict:
return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None)
return BaseModelOutput(
last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions
)
# Copied from transformers.models.blip.modeling_blip.BlipVisionModel with Blip->InstructBlip, BLIP->INSTRUCTBLIP
class InstructBlipVisionModel(InstructBlipPreTrainedModel):
main_input_name = "pixel_values"
config_class = InstructBlipVisionConfig
def __init__(self, config: InstructBlipVisionConfig):
super().__init__(config)
self.config = config
embed_dim = config.hidden_size
self.embeddings = InstructBlipVisionEmbeddings(config)
self.encoder = InstructBlipEncoder(config)
self.post_layernorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
self.post_init()
@add_start_docstrings_to_model_forward(INSTRUCTBLIP_VISION_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=InstructBlipVisionConfig)
def forward(
self,
pixel_values: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
interpolate_pos_encoding: bool = False,
) -> Union[Tuple, BaseModelOutputWithPooling]:
r"""
Returns:
"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if pixel_values is None:
raise ValueError("You have to specify pixel_values")
hidden_states = self.embeddings(pixel_values, interpolate_pos_encoding=interpolate_pos_encoding)
encoder_outputs = self.encoder(
inputs_embeds=hidden_states,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
last_hidden_state = encoder_outputs[0]
last_hidden_state = self.post_layernorm(last_hidden_state)
pooled_output = last_hidden_state[:, 0, :]
pooled_output = self.post_layernorm(pooled_output)
if not return_dict:
return (last_hidden_state, pooled_output) + encoder_outputs[1:]
return BaseModelOutputWithPooling(
last_hidden_state=last_hidden_state,
pooler_output=pooled_output,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
)
def get_input_embeddings(self):
return self.embeddings
class InstructBlipQFormerMultiHeadAttention(nn.Module):
def __init__(self, config, is_cross_attention=False):
super().__init__()
self.config = config
if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
raise ValueError(
"The hidden size (%d) is not a multiple of the number of attention heads (%d)"
% (config.hidden_size, config.num_attention_heads)
)
self.num_attention_heads = config.num_attention_heads
self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
self.all_head_size = self.num_attention_heads * self.attention_head_size
self.query = nn.Linear(config.hidden_size, self.all_head_size)
if is_cross_attention:
self.key = nn.Linear(config.encoder_hidden_size, self.all_head_size)
self.value = nn.Linear(config.encoder_hidden_size, self.all_head_size)
else:
self.key = nn.Linear(config.hidden_size, self.all_head_size)
self.value = nn.Linear(config.hidden_size, self.all_head_size)
self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
self.max_position_embeddings = config.max_position_embeddings
self.distance_embedding = nn.Embedding(2 * config.max_position_embeddings - 1, self.attention_head_size)
self.save_attention = False
def save_attn_gradients(self, attn_gradients):
self.attn_gradients = attn_gradients
def get_attn_gradients(self):
return self.attn_gradients
def save_attention_map(self, attention_map):
self.attention_map = attention_map
def get_attention_map(self):
return self.attention_map
def transpose_for_scores(self, x):
new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
x = x.view(*new_x_shape)
return x.permute(0, 2, 1, 3)
def forward(
self,
hidden_states,
attention_mask=None,
head_mask=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
past_key_value=None,
output_attentions=False,
):
# If this is instantiated as a cross-attention module, the keys
# and values come from an encoder; the attention mask needs to be
# such that the encoder's padding tokens are not attended to.
is_cross_attention = encoder_hidden_states is not None
if is_cross_attention:
key_layer = self.transpose_for_scores(self.key(encoder_hidden_states))
value_layer = self.transpose_for_scores(self.value(encoder_hidden_states))
attention_mask = encoder_attention_mask
elif past_key_value is not None:
key_layer = self.transpose_for_scores(self.key(hidden_states))
value_layer = self.transpose_for_scores(self.value(hidden_states))
key_layer = torch.cat([past_key_value[0], key_layer], dim=2)
value_layer = torch.cat([past_key_value[1], value_layer], dim=2)
else:
key_layer = self.transpose_for_scores(self.key(hidden_states))
value_layer = self.transpose_for_scores(self.value(hidden_states))
mixed_query_layer = self.query(hidden_states)
query_layer = self.transpose_for_scores(mixed_query_layer)
past_key_value = (key_layer, value_layer)
# Take the dot product between "query" and "key" to get the raw attention scores.
attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
seq_length = hidden_states.size()[1]
position_ids_l = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(-1, 1)
position_ids_r = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(1, -1)
distance = position_ids_l - position_ids_r
positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1)
positional_embedding = positional_embedding.to(dtype=query_layer.dtype) # fp16 compatibility
if self.position_embedding_type == "relative_key":
relative_position_scores = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
attention_scores = attention_scores + relative_position_scores
elif self.position_embedding_type == "relative_key_query":
relative_position_scores_query = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
relative_position_scores_key = torch.einsum("bhrd,lrd->bhlr", key_layer, positional_embedding)
attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key
attention_scores = attention_scores / math.sqrt(self.attention_head_size)
attention_scores_dtype = attention_scores.dtype
if attention_mask is not None:
# Apply the attention mask is (precomputed for all layers in BertModel forward() function)
attention_scores = attention_scores + attention_mask
# Normalize the attention scores to probabilities.
attention_probs = nn.Softmax(dim=-1)(attention_scores).to(attention_scores_dtype)
if is_cross_attention and self.save_attention:
self.save_attention_map(attention_probs)
attention_probs.register_hook(self.save_attn_gradients)
# This is actually dropping out entire tokens to attend to, which might
# seem a bit unusual, but is taken from the original Transformer paper.
attention_probs_dropped = self.dropout(attention_probs)
# Mask heads if we want to
if head_mask is not None:
attention_probs_dropped = attention_probs_dropped * head_mask
context_layer = torch.matmul(attention_probs_dropped, value_layer)
context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
context_layer = context_layer.view(*new_context_layer_shape)
outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
outputs = outputs + (past_key_value,)
return outputs
# Copied from transformers.models.bert.modeling_bert.BertSelfOutput with Bert->InstructBlipQFormer
class InstructBlipQFormerSelfOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
hidden_states = self.dense(hidden_states)
hidden_states = self.dropout(hidden_states)
hidden_states = self.LayerNorm(hidden_states + input_tensor)
return hidden_states
# Copied from transformers.models.blip_2.modeling_blip_2.Blip2QFormerAttention with Blip2->InstructBlip
class InstructBlipQFormerAttention(nn.Module):
def __init__(self, config, is_cross_attention=False):
super().__init__()
self.attention = InstructBlipQFormerMultiHeadAttention(config, is_cross_attention)
self.output = InstructBlipQFormerSelfOutput(config)
self.pruned_heads = set()
def prune_heads(self, heads):
if len(heads) == 0:
return
heads, index = find_pruneable_heads_and_indices(
heads, self.attention.num_attention_heads, self.attention.attention_head_size, self.pruned_heads
)
# Prune linear layers
self.attention.query = prune_linear_layer(self.attention.query, index)
self.attention.key = prune_linear_layer(self.attention.key, index)
self.attention.value = prune_linear_layer(self.attention.value, index)
self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
# Update hyper params and store pruned heads
self.attention.num_attention_heads = self.attention.num_attention_heads - len(heads)
self.attention.all_head_size = self.attention.attention_head_size * self.attention.num_attention_heads
self.pruned_heads = self.pruned_heads.union(heads)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
encoder_attention_mask: Optional[torch.FloatTensor] = None,
past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
output_attentions: Optional[bool] = False,
) -> Tuple[torch.Tensor]:
self_outputs = self.attention(
hidden_states,
attention_mask,
head_mask,
encoder_hidden_states,
encoder_attention_mask,
past_key_value,
output_attentions,
)
attention_output = self.output(self_outputs[0], hidden_states)
outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them
return outputs
# Copied from transformers.models.bert.modeling_bert.BertIntermediate with Bert->InstructBlipQFormer
class InstructBlipQFormerIntermediate(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
if isinstance(config.hidden_act, str):
self.intermediate_act_fn = ACT2FN[config.hidden_act]
else:
self.intermediate_act_fn = config.hidden_act
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidden_states = self.dense(hidden_states)
hidden_states = self.intermediate_act_fn(hidden_states)
return hidden_states
# Copied from transformers.models.bert.modeling_bert.BertOutput with Bert->InstructBlipQFormer
class InstructBlipQFormerOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
hidden_states = self.dense(hidden_states)
hidden_states = self.dropout(hidden_states)
hidden_states = self.LayerNorm(hidden_states + input_tensor)
return hidden_states
class InstructBlipQFormerLayer(nn.Module):
def __init__(self, config, layer_idx):
super().__init__()
self.chunk_size_feed_forward = config.chunk_size_feed_forward
self.seq_len_dim = 1
self.attention = InstructBlipQFormerAttention(config)
self.layer_idx = layer_idx
if layer_idx % config.cross_attention_frequency == 0:
self.crossattention = InstructBlipQFormerAttention(config, is_cross_attention=True)
self.has_cross_attention = True
else:
self.has_cross_attention = False
self.intermediate = InstructBlipQFormerIntermediate(config)
self.output = InstructBlipQFormerOutput(config)
self.intermediate_query = InstructBlipQFormerIntermediate(config)
self.output_query = InstructBlipQFormerOutput(config)
def forward(
self,
hidden_states,
attention_mask=None,
head_mask=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
past_key_value=None,
output_attentions=False,
query_length=0,
):
# decoder uni-directional self-attention cached key/values tuple is at positions 1,2
self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
self_attention_outputs = self.attention(
hidden_states,
attention_mask,
head_mask,
output_attentions=output_attentions,
past_key_value=self_attn_past_key_value,
)
attention_output = self_attention_outputs[0]
outputs = self_attention_outputs[1:-1]
present_key_value = self_attention_outputs[-1]
if query_length > 0:
query_attention_output = attention_output[:, :query_length, :]
if self.has_cross_attention:
if encoder_hidden_states is None:
raise ValueError("encoder_hidden_states must be given for cross-attention layers")
cross_attention_outputs = self.crossattention(
query_attention_output,
attention_mask,
head_mask,
encoder_hidden_states,
encoder_attention_mask,
output_attentions=output_attentions,
)
query_attention_output = cross_attention_outputs[0]
# add cross attentions if we output attention weights
outputs = outputs + cross_attention_outputs[1:-1]
layer_output = apply_chunking_to_forward(
self.feed_forward_chunk_query,
self.chunk_size_feed_forward,
self.seq_len_dim,
query_attention_output,
)
if attention_output.shape[1] > query_length:
layer_output_text = apply_chunking_to_forward(
self.feed_forward_chunk,
self.chunk_size_feed_forward,
self.seq_len_dim,
attention_output[:, query_length:, :],
)
layer_output = torch.cat([layer_output, layer_output_text], dim=1)
else:
layer_output = apply_chunking_to_forward(
self.feed_forward_chunk,
self.chunk_size_feed_forward,
self.seq_len_dim,
attention_output,
)
outputs = (layer_output,) + outputs
outputs = outputs + (present_key_value,)
return outputs
def feed_forward_chunk(self, attention_output):
intermediate_output = self.intermediate(attention_output)
layer_output = self.output(intermediate_output, attention_output)
return layer_output
def feed_forward_chunk_query(self, attention_output):
intermediate_output = self.intermediate_query(attention_output)
layer_output = self.output_query(intermediate_output, attention_output)
return layer_output
# Copied from transformers.models.blip_2.modeling_blip_2.Blip2QFormerEncoder with Blip2->InstructBlip
class InstructBlipQFormerEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.layer = nn.ModuleList(
[InstructBlipQFormerLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
)
self.gradient_checkpointing = False
def forward(
self,
hidden_states,
attention_mask=None,
head_mask=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
past_key_values=None,
use_cache=None,
output_attentions=False,
output_hidden_states=False,
return_dict=True,
query_length=0,
):
all_hidden_states = () if output_hidden_states else None
all_self_attentions = () if output_attentions else None
all_cross_attentions = () if output_attentions else None
next_decoder_cache = () if use_cache else None
for i in range(self.config.num_hidden_layers):
layer_module = self.layer[i]
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
layer_head_mask = head_mask[i] if head_mask is not None else None
past_key_value = past_key_values[i] if past_key_values is not None else None
if getattr(self.config, "gradient_checkpointing", False) and self.training:
if use_cache:
logger.warning(
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
)
use_cache = False
layer_outputs = self._gradient_checkpointing_func(
layer_module.__call__,
hidden_states,
attention_mask,
layer_head_mask,
encoder_hidden_states,
encoder_attention_mask,
)
else:
layer_outputs = layer_module(
hidden_states,
attention_mask,
layer_head_mask,
encoder_hidden_states,
encoder_attention_mask,
past_key_value,
output_attentions,
query_length,
)
hidden_states = layer_outputs[0]
if use_cache:
next_decoder_cache += (layer_outputs[-1],)
if output_attentions:
all_self_attentions = all_self_attentions + (layer_outputs[1],)
if layer_module.has_cross_attention:
all_cross_attentions = all_cross_attentions + (layer_outputs[2],)
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if not return_dict:
return tuple(
v
for v in [
hidden_states,
next_decoder_cache,
all_hidden_states,
all_self_attentions,
all_cross_attentions,
]
if v is not None
)
return BaseModelOutputWithPastAndCrossAttentions(
last_hidden_state=hidden_states,
past_key_values=next_decoder_cache,
hidden_states=all_hidden_states,
attentions=all_self_attentions,
cross_attentions=all_cross_attentions,
)
class InstructBlipQFormerEmbeddings(nn.Module):
"""Construct the embeddings from word and position embeddings."""
def __init__(self, config):
super().__init__()
self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
# position_ids (1, len position emb) is contiguous in memory and exported when serialized
self.register_buffer(
"position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False
)
self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
self.config = config
def forward(
self,
input_ids=None,
position_ids=None,
query_embeds=None,
past_key_values_length=0,
):
if input_ids is not None:
seq_length = input_ids.size()[1]
else:
seq_length = 0
if position_ids is None:
position_ids = self.position_ids[:, past_key_values_length : seq_length + past_key_values_length].clone()
if input_ids is not None:
embeddings = self.word_embeddings(input_ids)
if self.position_embedding_type == "absolute":
position_embeddings = self.position_embeddings(position_ids.to(embeddings.device))
embeddings = embeddings + position_embeddings
if query_embeds is not None:
embeddings = torch.cat((query_embeds, embeddings), dim=1)
else:
embeddings = query_embeds
embeddings = embeddings.to(self.layernorm.weight.dtype)
embeddings = self.layernorm(embeddings)
embeddings = self.dropout(embeddings)
return embeddings
class InstructBlipQFormerModel(InstructBlipPreTrainedModel):
"""
Querying Transformer (Q-Former), used in InstructBLIP. Slightly modified from BLIP-2 as it also takes the
instruction as input.
"""
def __init__(self, config: InstructBlipQFormerConfig):
super().__init__(config)
self.config = config
self.embeddings = InstructBlipQFormerEmbeddings(config)
self.encoder = InstructBlipQFormerEncoder(config)
self.post_init()
def get_input_embeddings(self):
return self.embeddings.word_embeddings
def set_input_embeddings(self, value):
self.embeddings.word_embeddings = value
def _prune_heads(self, heads_to_prune):
"""
Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
class PreTrainedModel
"""
for layer, heads in heads_to_prune.items():
self.encoder.layer[layer].attention.prune_heads(heads)
def get_extended_attention_mask(
self,
attention_mask: torch.Tensor,
input_shape: Tuple[int],
device: torch.device,
has_query: bool = False,
) -> torch.Tensor:
"""
Makes broadcastable attention and causal masks so that future and masked tokens are ignored.
Arguments:
attention_mask (`torch.Tensor`):
Mask with ones indicating tokens to attend to, zeros for tokens to ignore.
input_shape (`Tuple[int]`):
The shape of the input to the model.
device: (`torch.device`):
The device of the input to the model.
Returns:
`torch.Tensor` The extended attention mask, with a the same dtype as `attention_mask.dtype`.
"""
# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
# ourselves in which case we just need to make it broadcastable to all heads.
if attention_mask.dim() == 3:
extended_attention_mask = attention_mask[:, None, :, :]
elif attention_mask.dim() == 2:
# Provided a padding mask of dimensions [batch_size, seq_length]
# - the model is an encoder, so make the mask broadcastable to [batch_size, num_heads, seq_length, seq_length]
extended_attention_mask = attention_mask[:, None, None, :]
else:
raise ValueError(
f"Wrong shape for input_ids (shape {input_shape}) or attention_mask (shape {attention_mask.shape})",
)
# Since attention_mask is 1.0 for positions we want to attend and 0.0 for
# masked positions, this operation will create a tensor which is 0.0 for
# positions we want to attend and -10000.0 for masked positions.
# Since we are adding it to the raw scores before the softmax, this is
# effectively the same as removing these entirely.
extended_attention_mask = extended_attention_mask.to(dtype=self.dtype) # fp16 compatibility
extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0
return extended_attention_mask
def forward(
self,
input_ids: torch.LongTensor,
attention_mask: Optional[torch.FloatTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
query_embeds: Optional[torch.Tensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
encoder_attention_mask: Optional[torch.FloatTensor] = None,
past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple[torch.FloatTensor], BaseModelOutputWithPoolingAndCrossAttentions]:
r"""
encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
the model is configured as a decoder.
encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of:
shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): Contains precomputed key and
value hidden states of the attention blocks. Can be used to speed up decoding. If `past_key_values` are
used, the user can optionally input only the last `decoder_input_ids` (those that don't have their past key
value states given to this model) of shape `(batch_size, 1)` instead of all `decoder_input_ids` of shape
`(batch_size, sequence_length)`.
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
`past_key_values`).
"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if input_ids is None and query_embeds is None:
raise ValueError("You have to specify query_embeds when input_ids is None")
# past_key_values_length
past_key_values_length = (
past_key_values[0][0].shape[2] - self.config.query_length if past_key_values is not None else 0
)
query_length = query_embeds.shape[1] if query_embeds is not None else 0
embedding_output = self.embeddings(
input_ids=input_ids,
position_ids=position_ids,
query_embeds=query_embeds,
past_key_values_length=past_key_values_length,
)
input_shape = embedding_output.size()[:-1]
batch_size, seq_length = input_shape
device = embedding_output.device
if attention_mask is None:
attention_mask = torch.ones(((batch_size, seq_length + past_key_values_length)), device=device)
# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
# ourselves in which case we just need to make it broadcastable to all heads.
extended_attention_mask = self.get_extended_attention_mask(attention_mask, input_shape, device)
# If a 2D or 3D attention mask is provided for the cross-attention
# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
if encoder_hidden_states is not None:
if isinstance(encoder_hidden_states, list):
encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states[0].size()
else:
encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size()
encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
if isinstance(encoder_attention_mask, list):
encoder_extended_attention_mask = [self.invert_attention_mask(mask) for mask in encoder_attention_mask]
elif encoder_attention_mask is None:
encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device)
encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
else:
encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
else:
encoder_extended_attention_mask = None
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x n_heads x N x N
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
encoder_outputs = self.encoder(
embedding_output,
attention_mask=extended_attention_mask,
head_mask=head_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_extended_attention_mask,
past_key_values=past_key_values,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
query_length=query_length,
)
sequence_output = encoder_outputs[0]
pooled_output = sequence_output[:, 0, :]
if not return_dict:
return (sequence_output, pooled_output) + encoder_outputs[1:]
return BaseModelOutputWithPoolingAndCrossAttentions(
last_hidden_state=sequence_output,
pooler_output=pooled_output,
past_key_values=encoder_outputs.past_key_values,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
cross_attentions=encoder_outputs.cross_attentions,
)
@add_start_docstrings(
"""
InstructBLIP Model for generating text given an image and an optional text prompt. The model consists of a vision
encoder, Querying Transformer (Q-Former) and a language model.
One can optionally pass `input_ids` to the model, which serve as a text prompt, to make the language model continue
the prompt. Otherwise, the language model starts generating text from the [BOS] (beginning-of-sequence) token.
""",
INSTRUCTBLIP_START_DOCSTRING,
)
class InstructBlipForConditionalGeneration(InstructBlipPreTrainedModel, GenerationMixin):
config_class = InstructBlipConfig
main_input_name = "pixel_values"
_supports_cache_class = True
_supports_static_cache = True
_supports_quantized_cache = False # not all LM bacbones support (e.g. T5)
_keep_in_fp32_modules = ["query_tokens"] # TODO @ArthurZucker I don't know why this is required for FP8
def __init__(self, config: InstructBlipConfig):
super().__init__(config)
self.vision_model = InstructBlipVisionModel(config.vision_config)
self.query_tokens = nn.Parameter(torch.zeros(1, config.num_query_tokens, config.qformer_config.hidden_size))
self.qformer = InstructBlipQFormerModel(config.qformer_config)
self.language_projection = nn.Linear(config.qformer_config.hidden_size, config.text_config.hidden_size)
if config.use_decoder_only_language_model:
language_model = AutoModelForCausalLM.from_config(config.text_config)
else:
language_model = AutoModelForSeq2SeqLM.from_config(config.text_config)
if language_model._no_split_modules is not None:
self._no_split_modules.extend(language_model._no_split_modules)
if language_model._keep_in_fp32_modules is not None:
self._keep_in_fp32_modules.extend(language_model._keep_in_fp32_modules)
self.language_model = language_model
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.language_model.get_input_embeddings()
def set_input_embeddings(self, value):
self.language_model.set_input_embeddings(value)
def set_output_embeddings(self, new_embeddings):
self.language_model.set_output_embeddings(new_embeddings)
def get_output_embeddings(self) -> nn.Module:
return self.language_model.get_output_embeddings()
def get_encoder(self):
return self.language_model.get_encoder()
def get_decoder(self):
return self.language_model.get_decoder()
def _tie_weights(self):
if not self.config.use_decoder_only_language_model:
self.language_model.encoder.embed_tokens = self.language_model.shared
self.language_model.decoder.embed_tokens = self.language_model.shared
def _preprocess_accelerate(self):
r"""
Some pre-processing hacks to make the model `accelerate` compatible. Check
https://github.com/huggingface/transformers/pull/21707 for more details.
"""
hf_device_map = self.hf_device_map
if len(hf_device_map) > 1 and "language_model" not in hf_device_map and torch.cuda.device_count() > 1:
# warn users about unexpected behavior when using multi-GPU + InstructBLIP + `accelerate`.
logger.warning(
"The `language_model` is not in the `hf_device_map` dictionary and you are running your script"
" in a multi-GPU environment. this may lead to unexpected behavior when using `accelerate`."
" Please pass a `device_map` that contains `language_model` to remove this warning."
" Please refer to https://github.com/huggingface/blog/blob/main/accelerate-large-models.md for"
" more details on creating a `device_map` for large models.",
)
if hasattr(self.language_model, "_hf_hook"):
self.language_model._hf_hook.io_same_device = True # For `generate` compatibility
@add_start_docstrings_to_model_forward(INSTRUCTBLIP_INPUTS_DOCSTRING)
@replace_return_docstrings(
output_type=InstructBlipForConditionalGenerationModelOutput, config_class=InstructBlipVisionConfig
)
def forward(
self,
pixel_values: torch.FloatTensor,
qformer_input_ids: torch.FloatTensor,
qformer_attention_mask: Optional[torch.LongTensor] = None,
input_ids: Optional[torch.FloatTensor] = None,
attention_mask: Optional[torch.LongTensor] = None,
decoder_input_ids: Optional[torch.LongTensor] = None,
decoder_attention_mask: Optional[torch.LongTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
labels: Optional[torch.LongTensor] = None,
return_dict: Optional[bool] = None,
interpolate_pos_encoding: bool = False,
use_cache: Optional[bool] = None,
) -> Union[Tuple, InstructBlipForConditionalGenerationModelOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the language modeling loss. Indices should be in `[-100, 0, ..., config.vocab_size -
1]`. All labels set to `-100` are ignored (masked), the loss is only computed for labels in `[0, ...,
config.vocab_size]`
Returns:
Examples:
```python
>>> from transformers import InstructBlipProcessor, InstructBlipForConditionalGeneration
>>> import torch
>>> from PIL import Image
>>> import requests
>>> model = InstructBlipForConditionalGeneration.from_pretrained("Salesforce/instructblip-vicuna-7b")
>>> processor = InstructBlipProcessor.from_pretrained("Salesforce/instructblip-vicuna-7b")
>>> device = "cuda" if torch.cuda.is_available() else "cpu"
>>> model.to(device) # doctest: +IGNORE_RESULT
>>> url = "https://raw.githubusercontent.com/salesforce/LAVIS/main/docs/_static/Confusing-Pictures.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw).convert("RGB")
>>> prompt = "What is unusual about this image?"
>>> inputs = processor(images=image, text=prompt, return_tensors="pt").to(device)
>>> outputs = model.generate(
... **inputs,
... do_sample=False,
... num_beams=5,
... max_length=256,
... min_length=1,
... top_p=0.9,
... repetition_penalty=1.5,
... length_penalty=1.0,
... temperature=1,
... )
>>> generated_text = processor.batch_decode(outputs, skip_special_tokens=True)[0].strip()
>>> print(generated_text)
The unusual aspect of this image is that a man is ironing clothes on the back of a yellow SUV, which is parked in the middle of a busy city street. This is an unconventional approach to ironing clothes, as it requires the man to balance himself and his ironing equipment on top of the vehicle while navigating through traffic. Additionally, the presence of taxis and other vehicles in the scene further emphasizes the unusual nature of this situation.
```"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
# step 1: forward the images through the vision encoder,
# to get image embeddings of shape (batch_size, seq_len, hidden_size)
vision_outputs = self.vision_model(
pixel_values=pixel_values,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
interpolate_pos_encoding=interpolate_pos_encoding,
)
image_embeds = vision_outputs[0]
# step 2: forward the query tokens through the QFormer, using the image embeddings for cross-attention
image_attention_mask = torch.ones(image_embeds.size()[:-1], dtype=torch.long, device=image_embeds.device)
# difference with BLIP-2 here: we also feed the instruction prompt to the Q-Former
query_tokens = self.query_tokens.expand(image_embeds.shape[0], -1, -1)
query_attention_mask = torch.ones(query_tokens.size()[:-1], dtype=torch.long, device=image_embeds.device)
if qformer_attention_mask is None:
qformer_attention_mask = torch.ones_like(qformer_input_ids)
qformer_attention_mask = torch.cat([query_attention_mask, qformer_attention_mask], dim=1)
query_outputs = self.qformer(
input_ids=qformer_input_ids,
attention_mask=qformer_attention_mask,
query_embeds=query_tokens,
encoder_hidden_states=image_embeds,
encoder_attention_mask=image_attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
query_output = query_outputs[0][:, : query_tokens.size(1), :]
# step 3: use the language model, conditioned on the query outputs and the prompt
language_model_inputs = self.language_projection(query_output)
language_model_attention_mask = torch.ones(
language_model_inputs.size()[:-1], dtype=torch.long, device=language_model_inputs.device
)
inputs_embeds = self.language_model.get_input_embeddings()(input_ids)
if attention_mask is None:
attention_mask = torch.ones_like(input_ids)
# if the model already has "image_token_index" then the input is expanded to account for image embeds
# otherwise we expand manually by concatenating
if getattr(self.config, "image_token_index", None) is not None:
special_image_mask = (input_ids == self.config.image_token_index).unsqueeze(-1).expand_as(inputs_embeds)
inputs_embeds[special_image_mask] = language_model_inputs.flatten()
else:
logger.warning_once(
"Expanding inputs for image tokens in InstructBLIP should be done in processing. "
"Please follow instruction here (https://gist.github.com/zucchini-nlp/e9f20b054fa322f84ac9311d9ab67042) to update your InstructBLIP model. "
"Using processors without these attributes in the config is deprecated and will throw an error in v4.50."
)
inputs_embeds = torch.cat([language_model_inputs, inputs_embeds.to(language_model_inputs.device)], dim=1)
attention_mask = torch.cat(
[language_model_attention_mask, attention_mask.to(language_model_attention_mask.device)], dim=1
)
if self.config.use_decoder_only_language_model:
outputs = self.language_model(
inputs_embeds=inputs_embeds,
attention_mask=attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
use_cache=use_cache,
)
logits = outputs.logits if return_dict else outputs[0]
loss = None
# we compute the loss here since we need to take into account the sequence length of the query embeds
if labels is not None:
labels = labels.to(logits.device)
logits = logits[:, -labels.size(1) :, :]
# Shift so that tokens < n predict n
shift_logits = logits[..., :-1, :].contiguous()
shift_labels = labels[..., 1:].contiguous().to(logits.device)
# Flatten the tokens
loss_fct = CrossEntropyLoss(reduction="mean")
loss = loss_fct(shift_logits.view(-1, self.config.text_config.vocab_size), shift_labels.view(-1))
else:
outputs = self.language_model(
inputs_embeds=inputs_embeds,
attention_mask=attention_mask,
decoder_input_ids=decoder_input_ids,
decoder_attention_mask=decoder_attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
labels=labels,
use_cache=use_cache,
)
loss = outputs.loss if return_dict else outputs[0]
logits = outputs.logits if return_dict else outputs[1]
if not return_dict:
output = (logits, vision_outputs, query_outputs, outputs)
return ((loss,) + output) if loss is not None else output
return InstructBlipForConditionalGenerationModelOutput(
loss=loss,
logits=logits,
vision_outputs=vision_outputs,
qformer_outputs=query_outputs,
language_model_outputs=outputs,
)
@torch.no_grad()
def generate(
self,
pixel_values: torch.FloatTensor,
qformer_input_ids: Optional[torch.LongTensor] = None,
qformer_attention_mask: Optional[torch.LongTensor] = None,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.LongTensor] = None,
interpolate_pos_encoding: bool = False,
**generate_kwargs,
) -> torch.LongTensor:
"""
Overrides `generate` function to be able to use the model as a conditional generator.
Args:
pixel_values (`torch.FloatTensor` of shape (batch_size, num_channels, height, width)):
Input images to be processed.
qformer_input_ids (`torch.LongTensor` of shape (batch_size, sequence_length), *optional*):
The sequence used as a prompt to be fed to the Q-Former module.
qformer_attention_mask (`torch.LongTensor` of shape (batch_size, sequence_length), *optional*):
Mask to avoid performing attention on padding token indices.
input_ids (`torch.LongTensor` of shape (batch_size, sequence_length), *optional*):
The sequence used as a prompt for the generation.
attention_mask (`torch.LongTensor` of shape (batch_size, sequence_length), *optional*):
Mask to avoid performing attention on padding token indices.
interpolate_pos_encoding (`bool`, *optional*, defaults to `False`):
Whether to interpolate the positional encoding of the image embeddings.
Returns:
captions (list): A list of strings of length batch_size * num_captions.
"""
if hasattr(self, "hf_device_map"):
# preprocess for `accelerate`
self._preprocess_accelerate()
batch_size = pixel_values.shape[0]
image_embeds = self.vision_model(
pixel_values,
return_dict=True,
interpolate_pos_encoding=interpolate_pos_encoding,
).last_hidden_state
image_attention_mask = torch.ones(image_embeds.size()[:-1], dtype=torch.long, device=image_embeds.device)
query_tokens = self.query_tokens.expand(image_embeds.shape[0], -1, -1)
query_attention_mask = torch.ones(query_tokens.size()[:-1], dtype=torch.long, device=image_embeds.device)
if qformer_attention_mask is None:
qformer_attention_mask = torch.ones_like(qformer_input_ids)
qformer_attention_mask = torch.cat([query_attention_mask, qformer_attention_mask], dim=1)
query_outputs = self.qformer(
input_ids=qformer_input_ids,
attention_mask=qformer_attention_mask,
query_embeds=query_tokens,
encoder_hidden_states=image_embeds,
encoder_attention_mask=image_attention_mask,
return_dict=True,
)
query_output = query_outputs.last_hidden_state[:, : query_tokens.size(1), :]
language_model_inputs = self.language_projection(query_output)
language_attention_mask = torch.ones(
language_model_inputs.size()[:-1], dtype=torch.long, device=language_model_inputs.device
)
if input_ids is None:
start_tokens = [self.config.text_config.bos_token_id]
if getattr(self.config, "image_token_index", None) is not None:
start_tokens = [self.config.image_token_index] * self.config.num_query_tokens + start_tokens
input_ids = torch.tensor([start_tokens], dtype=torch.long, device=image_embeds.device)
input_ids = input_ids.repeat(batch_size, 1)
if attention_mask is None:
attention_mask = torch.ones_like(input_ids)
inputs_embeds = self.get_input_embeddings()(input_ids)
# if the model already has "image_token_index" then the input is expanded to account for image embeds
# otherwise we expand manually by concatenating
if getattr(self.config, "image_token_index", None) is not None:
special_image_mask = (input_ids == self.config.image_token_index).unsqueeze(-1).expand_as(inputs_embeds)
inputs_embeds[special_image_mask] = language_model_inputs.flatten().to(inputs_embeds.device)
else:
logger.warning_once(
"Expanding inputs for image tokens in InstructBLIP should be done in processing. "
"Please follow instruction here (https://gist.github.com/zucchini-nlp/e9f20b054fa322f84ac9311d9ab67042) to update your InstructBLIP model. "
"Using processors without these attributes in the config is deprecated and will throw an error in v4.50."
)
inputs_embeds = torch.cat([language_model_inputs, inputs_embeds.to(language_model_inputs.device)], dim=1)
attention_mask = torch.cat(
[language_attention_mask, attention_mask.to(language_attention_mask.device)], dim=1
)
# add image_embeds length to max_length, so that the final max_length in counted only on token embeds
# -1 is to account for the prepended BOS after `generate.`
if not self.language_model.config.is_encoder_decoder:
generate_kwargs["max_length"] = (
generate_kwargs.get("max_length", 20) + language_model_inputs.shape[1] - 1
)
generate_kwargs["min_length"] = generate_kwargs.get("min_length", 0) + language_model_inputs.shape[1]
inputs = {"inputs_embeds": inputs_embeds, "attention_mask": attention_mask}
if not self.language_model.config.is_encoder_decoder:
inputs["input_ids"] = input_ids
outputs = self.language_model.generate(**inputs, **generate_kwargs)
return outputs
__all__ = [
"InstructBlipQFormerModel",
"InstructBlipPreTrainedModel",
"InstructBlipForConditionalGeneration",
"InstructBlipVisionModel",
]
```
|
===================================================================================================================================================
SOURCE CODE FILE: processing_instructblip.py
LINES: 1
SIZE: 10.22 KB
PATH: scripts\freecad_env\Lib\site-packages\transformers\models\instructblip\processing_instructblip.py
ENCODING: utf-8
```py
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Processor class for InstructBLIP. Largely copy of Blip2Processor with addition of a tokenizer for the Q-Former.
"""
import os
from typing import List, Union
from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessingKwargs, ProcessorMixin, Unpack
from ...tokenization_utils_base import (
AddedToken,
BatchEncoding,
PreTokenizedInput,
TextInput,
)
from ...utils import logging
from ..auto import AutoTokenizer
logger = logging.get_logger(__name__)
class InstructBlipProcessorKwargs(ProcessingKwargs, total=False):
_defaults = {
"text_kwargs": {
"add_special_tokens": True,
"padding": False,
"stride": 0,
"return_overflowing_tokens": False,
"return_special_tokens_mask": False,
"return_offsets_mapping": False,
"return_token_type_ids": False,
"return_length": False,
"verbose": True,
},
"images_kwargs": {},
}
class InstructBlipProcessor(ProcessorMixin):
r"""
Constructs an InstructBLIP processor which wraps a BLIP image processor and a LLaMa/T5 tokenizer into a single
processor.
[`InstructBlipProcessor`] offers all the functionalities of [`BlipImageProcessor`] and [`AutoTokenizer`]. See the
docstring of [`~BlipProcessor.__call__`] and [`~BlipProcessor.decode`] for more information.
Args:
image_processor (`BlipImageProcessor`):
An instance of [`BlipImageProcessor`]. The image processor is a required input.
tokenizer (`AutoTokenizer`):
An instance of ['PreTrainedTokenizer`]. The tokenizer is a required input.
qformer_tokenizer (`AutoTokenizer`):
An instance of ['PreTrainedTokenizer`]. The Q-Former tokenizer is a required input.
num_query_tokens (`int`, *optional*):"
Number of tokens used by the Qformer as queries, should be same as in model's config.
"""
attributes = ["image_processor", "tokenizer", "qformer_tokenizer"]
valid_kwargs = ["num_query_tokens"]
image_processor_class = ("BlipImageProcessor", "BlipImageProcessorFast")
tokenizer_class = "AutoTokenizer"
qformer_tokenizer_class = "AutoTokenizer"
def __init__(self, image_processor, tokenizer, qformer_tokenizer, num_query_tokens=None, **kwargs):
if not hasattr(tokenizer, "image_token"):
self.image_token = AddedToken("<image>", normalized=False, special=True)
tokenizer.add_tokens([self.image_token], special_tokens=True)
else:
self.image_token = tokenizer.image_token
self.num_query_tokens = num_query_tokens
super().__init__(image_processor, tokenizer, qformer_tokenizer)
def __call__(
self,
images: ImageInput = None,
text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,
audio=None,
videos=None,
**kwargs: Unpack[InstructBlipProcessorKwargs],
) -> BatchFeature:
"""
This method uses [`BlipImageProcessor.__call__`] method to prepare image(s) for the model, and
[`BertTokenizerFast.__call__`] to prepare text for the model.
Please refer to the docstring of the above two methods for more information.
Args:
images (`ImageInput`):
The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch
tensor. Both channels-first and channels-last formats are supported.
text (`TextInput`, `PreTokenizedInput`, `List[TextInput]`, `List[PreTokenizedInput]`):
The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
(pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
`is_split_into_words=True` (to lift the ambiguity with a batch of sequences).
"""
if images is None and text is None:
raise ValueError("You have to specify at least images or text.")
output_kwargs = self._merge_kwargs(
InstructBlipProcessorKwargs,
tokenizer_init_kwargs=self.tokenizer.init_kwargs,
**kwargs,
)
encoding = BatchFeature()
if text is not None:
if isinstance(text, str):
text = [text]
elif not isinstance(text, list) and not isinstance(text[0], str):
raise ValueError("Invalid input text. Please provide a string, or a list of strings")
# we have to concatenate lists - so we keep track of return_tensors here
return_tensors = output_kwargs["text_kwargs"].pop("return_tensors", None)
_text_encoding = self.tokenizer(text, **output_kwargs["text_kwargs"], return_tensors=None)
output_kwargs["text_kwargs"]["return_tensors"] = return_tensors
# if we know how many query tokens, expand text inside processor. We need this hacky manipulation
# because BLIP expects image tokens to be at the beginning even before BOS token
if self.num_query_tokens is not None and images is not None:
text_encoding = {}
image_tokens = self.image_token.content * self.num_query_tokens
image_token_encoding = self.tokenizer(
[image_tokens] * len(text), add_special_tokens=False, return_tensors=None
)
for k in _text_encoding:
text_encoding[k] = [
img_encoding + txt_encoding
for img_encoding, txt_encoding in zip(image_token_encoding[k], _text_encoding[k])
]
else:
text_encoding = _text_encoding
if images is not None:
logger.warning_once(
"Expanding inputs for image tokens in InstructBLIP should be done in processing. "
"Please follow instruction here (https://gist.github.com/zucchini-nlp/e9f20b054fa322f84ac9311d9ab67042) to update your InstructBLIP model. "
"Using processors without these attributes in the config is deprecated and will throw an error in v4.50."
)
# cast to desired return tensors type after concatenating
text_encoding = BatchEncoding(text_encoding, tensor_type=return_tensors)
encoding.update(text_encoding)
qformer_text_encoding = self.qformer_tokenizer(text, **output_kwargs["text_kwargs"])
encoding["qformer_input_ids"] = qformer_text_encoding.pop("input_ids")
encoding["qformer_attention_mask"] = qformer_text_encoding.pop("attention_mask")
if images is not None:
image_encoding = self.image_processor(images, **output_kwargs["images_kwargs"])
encoding.update(image_encoding)
return encoding
# Copied from transformers.models.blip.processing_blip.BlipProcessor.batch_decode with BertTokenizerFast->PreTrainedTokenizer
def batch_decode(self, *args, **kwargs):
"""
This method forwards all its arguments to PreTrainedTokenizer's [`~PreTrainedTokenizer.batch_decode`]. Please
refer to the docstring of this method for more information.
"""
return self.tokenizer.batch_decode(*args, **kwargs)
# Copied from transformers.models.blip.processing_blip.BlipProcessor.decode with BertTokenizerFast->PreTrainedTokenizer
def decode(self, *args, **kwargs):
"""
This method forwards all its arguments to PreTrainedTokenizer's [`~PreTrainedTokenizer.decode`]. Please refer to
the docstring of this method for more information.
"""
return self.tokenizer.decode(*args, **kwargs)
@property
# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
def model_input_names(self):
tokenizer_input_names = self.tokenizer.model_input_names
image_processor_input_names = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))
# overwrite to save the Q-Former tokenizer in a separate folder
def save_pretrained(self, save_directory, **kwargs):
if os.path.isfile(save_directory):
raise ValueError(f"Provided path ({save_directory}) should be a directory, not a file")
os.makedirs(save_directory, exist_ok=True)
qformer_tokenizer_path = os.path.join(save_directory, "qformer_tokenizer")
self.qformer_tokenizer.save_pretrained(qformer_tokenizer_path)
# We modify the attributes so that only the tokenizer and image processor are saved in the main folder
qformer_present = "qformer_tokenizer" in self.attributes
if qformer_present:
self.attributes.remove("qformer_tokenizer")
outputs = super().save_pretrained(save_directory, **kwargs)
if qformer_present:
self.attributes += ["qformer_tokenizer"]
return outputs
# overwrite to load the Q-Former tokenizer from a separate folder
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path, **kwargs):
processor = super().from_pretrained(pretrained_model_name_or_path, **kwargs)
# if return_unused_kwargs a tuple is returned where the second element is 'unused_kwargs'
if isinstance(processor, tuple):
processor = processor[0]
qformer_tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path, subfolder="qformer_tokenizer")
processor.qformer_tokenizer = qformer_tokenizer
return processor
__all__ = ["InstructBlipProcessor"]
```
|
=========================================================================================================================================
SOURCE CODE FILE: __init__.py
LINES: 1
SIZE: 2.62 KB
PATH: scripts\freecad_env\Lib\site-packages\transformers\models\instructblipvideo\__init__.py
ENCODING: utf-8
```py
# Copyright 2024 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_import_structure = {
"configuration_instructblipvideo": [
"InstructBlipVideoConfig",
"InstructBlipVideoQFormerConfig",
"InstructBlipVideoVisionConfig",
],
"processing_instructblipvideo": ["InstructBlipVideoProcessor"],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["image_processing_instructblipvideo"] = ["InstructBlipVideoImageProcessor"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_instructblipvideo"] = [
"InstructBlipVideoQFormerModel",
"InstructBlipVideoPreTrainedModel",
"InstructBlipVideoForConditionalGeneration",
"InstructBlipVideoVisionModel",
]
if TYPE_CHECKING:
from .configuration_instructblipvideo import (
InstructBlipVideoConfig,
InstructBlipVideoQFormerConfig,
InstructBlipVideoVisionConfig,
)
from .processing_instructblipvideo import InstructBlipVideoProcessor
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_instructblipvideo import InstructBlipVideoImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_instructblipvideo import (
InstructBlipVideoForConditionalGeneration,
InstructBlipVideoPreTrainedModel,
InstructBlipVideoQFormerModel,
InstructBlipVideoVisionModel,
)
else:
import sys
sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
```
|
================================================================================================================================================================
SOURCE CODE FILE: configuration_instructblipvideo.py
LINES: 1
SIZE: 16.37 KB
PATH: scripts\freecad_env\Lib\site-packages\transformers\models\instructblipvideo\configuration_instructblipvideo.py
ENCODING: utf-8
```py
# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
# This file was automatically generated from src/transformers/models/instructblipvideo/modular_instructblipvideo.py.
# Do NOT edit this file manually as any edits will be overwritten by the generation of
# the file from the modular. If any change should be done, please apply the change to the
# modular_instructblipvideo.py file directly. One of our CI enforces this.
# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
# coding=utf-8
# Copyright 2024 HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING, AutoConfig
logger = logging.get_logger(__name__)
class InstructBlipVideoVisionConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`InstructBlipVideoVisionModel`]. It is used to
instantiate a InstructBlipVideo vision encoder according to the specified arguments, defining the model architecture.
Instantiating a configuration defaults will yield a similar configuration to that of the InstructBlipVideo
[Salesforce/instruct-blip-flan-t5](https://huggingface.co/Salesforce/instruct-blip-flan-t5) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
hidden_size (`int`, *optional*, defaults to 1408):
Dimensionality of the encoder layers and the pooler layer.
intermediate_size (`int`, *optional*, defaults to 6144):
Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
num_hidden_layers (`int`, *optional*, defaults to 39):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 16):
Number of attention heads for each attention layer in the Transformer encoder.
image_size (`int`, *optional*, defaults to 224):
The size (resolution) of each image.
patch_size (`int`, *optional*, defaults to 14):
The size (resolution) of each patch.
hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"selu"` and `"gelu_new"` `"gelu"` are supported. to 1e-5): The epsilon used by the layer
normalization layers.
layer_norm_eps (`float`, *optional*, defaults to 1e-06):
The epsilon used by the layer normalization layers.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
initializer_range (`float`, *optional*, defaults to 1e-10):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
qkv_bias (`bool`, *optional*, defaults to `True`):
Whether to add a bias to the queries and values in the self-attention layers.
Example:
```python
>>> from transformers import InstructBlipVideoVisionConfig, InstructBlipVideoVisionModel
>>> # Initializing a InstructBlipVideoVisionConfig with Salesforce/instruct-blip-flan-t5 style configuration
>>> configuration = InstructBlipVideoVisionConfig()
>>> # Initializing a InstructBlipVideoVisionModel (with random weights) from the Salesforce/instruct-blip-flan-t5 style configuration
>>> model = InstructBlipVideoVisionModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "instructblipvideo_vision_model"
base_config_key = "vision_config"
def __init__(
self,
hidden_size=1408,
intermediate_size=6144,
num_hidden_layers=39,
num_attention_heads=16,
image_size=224,
patch_size=14,
hidden_act="gelu",
layer_norm_eps=1e-6,
attention_dropout=0.0,
initializer_range=1e-10,
qkv_bias=True,
**kwargs,
):
super().__init__(**kwargs)
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.patch_size = patch_size
self.image_size = image_size
self.initializer_range = initializer_range
self.attention_dropout = attention_dropout
self.layer_norm_eps = layer_norm_eps
self.hidden_act = hidden_act
self.qkv_bias = qkv_bias
class InstructBlipVideoQFormerConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`InstructBlipVideoQFormerModel`]. It is used to
instantiate a InstructBlipVideo Querying Transformer (Q-Former) model according to the specified arguments, defining the
model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of
the InstructBlipVideo [Salesforce/instruct-blip-flan-t5](https://huggingface.co/Salesforce/instruct-blip-flan-t5)
architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs.
Read the documentation from [`PretrainedConfig`] for more information.
Note that [`InstructBlipVideoQFormerModel`] is very similar to [`BertLMHeadModel`] with interleaved cross-attention.
Args:
vocab_size (`int`, *optional*, defaults to 30522):
Vocabulary size of the Q-Former model. Defines the number of different tokens that can be represented by
the `inputs_ids` passed when calling the model.
hidden_size (`int`, *optional*, defaults to 768):
Dimensionality of the encoder layers and the pooler layer.
num_hidden_layers (`int`, *optional*, defaults to 12):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 12):
Number of attention heads for each attention layer in the Transformer encoder.
intermediate_size (`int`, *optional*, defaults to 3072):
Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
hidden_act (`str` or `Callable`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"silu"` and `"gelu_new"` are supported.
hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
The dropout ratio for the attention probabilities.
max_position_embeddings (`int`, *optional*, defaults to 512):
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (`float`, *optional*, defaults to 1e-12):
The epsilon used by the layer normalization layers.
pad_token_id (`int`, *optional*, defaults to 0):
Token id used for padding sequences.
position_embedding_type (`str`, *optional*, defaults to `"absolute"`):
Type of position embedding. Choose one of `"absolute"`, `"relative_key"`, `"relative_key_query"`. For
positional embeddings use `"absolute"`. For more information on `"relative_key"`, please refer to
[Self-Attention with Relative Position Representations (Shaw et al.)](https://arxiv.org/abs/1803.02155).
For more information on `"relative_key_query"`, please refer to *Method 4* in [Improve Transformer Models
with Better Relative Position Embeddings (Huang et al.)](https://arxiv.org/abs/2009.13658).
cross_attention_frequency (`int`, *optional*, defaults to 2):
The frequency of adding cross-attention to the Transformer layers.
encoder_hidden_size (`int`, *optional*, defaults to 1408):
The hidden size of the hidden states for cross-attention.
Examples:
```python
>>> from transformers import InstructBlipVideoQFormerConfig, InstructBlipVideoQFormerModel
>>> # Initializing a InstructBlipVideo Salesforce/instruct-blip-flan-t5 style configuration
>>> configuration = InstructBlipVideoQFormerConfig()
>>> # Initializing a model (with random weights) from the Salesforce/instruct-blip-flan-t5 style configuration
>>> model = InstructBlipVideoQFormerModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "instructblipvideo_qformer"
base_config_key = "qformer_config"
def __init__(
self,
vocab_size=30522,
hidden_size=768,
num_hidden_layers=12,
num_attention_heads=12,
intermediate_size=3072,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
initializer_range=0.02,
layer_norm_eps=1e-12,
pad_token_id=0,
position_embedding_type="absolute",
cross_attention_frequency=2,
encoder_hidden_size=1408,
**kwargs,
):
super().__init__(pad_token_id=pad_token_id, **kwargs)
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.hidden_act = hidden_act
self.intermediate_size = intermediate_size
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.initializer_range = initializer_range
self.layer_norm_eps = layer_norm_eps
self.position_embedding_type = position_embedding_type
self.cross_attention_frequency = cross_attention_frequency
self.encoder_hidden_size = encoder_hidden_size
class InstructBlipVideoConfig(PretrainedConfig):
r"""
[`InstructBlipVideoConfig`] is the configuration class to store the configuration of a
[`InstructBlipVideoForConditionalGeneration`]. It is used to instantiate a Instructblipvideo model according to the specified
arguments, defining the vision model, Q-Former model and language model configs. Instantiating a configuration with
the defaults will yield a similar configuration to that of the Instructblipvideo
[Salesforce/instruct-blip-flan-t5](https://huggingface.co/Salesforce/instruct-blip-flan-t5) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vision_config (`dict`, *optional*):
Dictionary of configuration options used to initialize [`InstructBlipVideoVisionConfig`].
qformer_config (`dict`, *optional*):
Dictionary of configuration options used to initialize [`InstructBlipVideoQFormerConfig`].
text_config (`dict`, *optional*):
Dictionary of configuration options used to initialize any [`PretrainedConfig`].
num_query_tokens (`int`, *optional*, defaults to 32):
The number of query tokens passed through the Transformer.
video_token_index (`int`, *optional*):
Token index of special video token.
kwargs (*optional*):
Dictionary of keyword arguments.
Example:
```python
>>> from transformers import (
... InstructBlipVideoVisionConfig,
... InstructBlipVideoQFormerConfig,
... OPTConfig,
... InstructBlipVideoConfig,
... InstructBlipVideoForConditionalGeneration,
... )
>>> # Initializing a InstructBlipVideoConfig with Salesforce/instruct-blip-flan-t5 style configuration
>>> configuration = InstructBlipVideoConfig()
>>> # Initializing a InstructBlipVideoForConditionalGeneration (with random weights) from the Salesforce/instruct-blip-flan-t5 style configuration
>>> model = InstructBlipVideoForConditionalGeneration(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
>>> # We can also initialize a InstructBlipVideoConfig from a InstructBlipVideoVisionConfig, InstructBlipVideoQFormerConfig and any PretrainedConfig
>>> # Initializing Instructblipvideo vision, Instructblipvideo Q-Former and language model configurations
>>> vision_config = InstructBlipVideoVisionConfig()
>>> qformer_config = InstructBlipVideoQFormerConfig()
>>> text_config = OPTConfig()
>>> config = InstructBlipVideoConfig.from_text_vision_configs(vision_config, qformer_config, text_config)
```"""
model_type = "instructblipvideo"
sub_configs = {
"text_config": AutoConfig,
"qformer_config": InstructBlipVideoQFormerConfig,
"vision_config": InstructBlipVideoVisionConfig,
}
def __init__(
self,
vision_config=None,
qformer_config=None,
text_config=None,
num_query_tokens=32,
video_token_index=None,
**kwargs,
):
super().__init__(**kwargs)
if vision_config is None:
vision_config = {}
logger.info("vision_config is None. initializing the InstructBlipVideoVisionConfig with default values.")
if qformer_config is None:
qformer_config = {}
logger.info("qformer_config is None. Initializing the InstructBlipVideoQFormerConfig with default values.")
if text_config is None:
text_config = {}
logger.info("text_config is None. Initializing the text config with default values (`OPTConfig`).")
self.vision_config = InstructBlipVideoVisionConfig(**vision_config)
self.qformer_config = InstructBlipVideoQFormerConfig(**qformer_config)
text_model_type = text_config["model_type"] if "model_type" in text_config else "opt"
self.text_config = CONFIG_MAPPING[text_model_type](**text_config)
self.num_query_tokens = num_query_tokens
self.video_token_index = video_token_index
self.qformer_config.encoder_hidden_size = self.vision_config.hidden_size
self.use_decoder_only_language_model = self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
self.initializer_factor = 1.0
self.initializer_range = 0.02
@classmethod
def from_vision_qformer_text_configs(
cls,
vision_config: InstructBlipVideoVisionConfig,
qformer_config: InstructBlipVideoQFormerConfig,
text_config: PretrainedConfig,
**kwargs,
):
r"""
Instantiate a [`InstructBlipVideoConfig`] (or a derived class) from a InstructBlipVideo vision model, Q-Former and
language model configurations.
Returns:
[`InstructBlipVideoConfig`]: An instance of a configuration object
"""
return cls(
vision_config=vision_config.to_dict(),
qformer_config=qformer_config.to_dict(),
text_config=text_config.to_dict(),
**kwargs,
)
```
|
===================================================================================================================================================================
SOURCE CODE FILE: image_processing_instructblipvideo.py
LINES: 1
SIZE: 16.24 KB
PATH: scripts\freecad_env\Lib\site-packages\transformers\models\instructblipvideo\image_processing_instructblipvideo.py
ENCODING: utf-8
```py
# coding=utf-8
# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Image processor class for InstructBLIPVideo. Largely copy of Blip2Processor with addition of a video processing abilities
"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import convert_to_rgb, resize, to_channel_dimension_format
from ...image_utils import (
OPENAI_CLIP_MEAN,
OPENAI_CLIP_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
VideoInput,
infer_channel_dimension_format,
is_scaled_image,
make_batched_videos,
to_numpy_array,
valid_images,
validate_preprocess_arguments,
)
from ...utils import TensorType, filter_out_non_signature_kwargs, logging
logger = logging.get_logger(__name__)
# Copied from transformers.models.blip.image_processing_blip.BlipImageProcessor with Blip->InstructBlipVideo, BLIP->InstructBLIPVideo
class InstructBlipVideoImageProcessor(BaseImageProcessor):
r"""
Constructs a InstructBLIPVideo image processor.
Args:
do_resize (`bool`, *optional*, defaults to `True`):
Whether to resize the image's (height, width) dimensions to the specified `size`. Can be overridden by the
`do_resize` parameter in the `preprocess` method.
size (`dict`, *optional*, defaults to `{"height": 384, "width": 384}`):
Size of the output image after resizing. Can be overridden by the `size` parameter in the `preprocess`
method.
resample (`PILImageResampling`, *optional*, defaults to `Resampling.BICUBIC`):
Resampling filter to use if resizing the image. Only has an effect if `do_resize` is set to `True`. Can be
overridden by the `resample` parameter in the `preprocess` method.
do_rescale (`bool`, *optional*, defaults to `True`):
Whether to rescale the image by the specified scale `rescale_factor`. Can be overridden by the
`do_rescale` parameter in the `preprocess` method.
rescale_factor (`int` or `float`, *optional*, defaults to `1/255`):
Scale factor to use if rescaling the image. Only has an effect if `do_rescale` is set to `True`. Can be
overridden by the `rescale_factor` parameter in the `preprocess` method.
do_normalize (`bool`, *optional*, defaults to `True`):
Whether to normalize the image. Can be overridden by the `do_normalize` parameter in the `preprocess`
method. Can be overridden by the `do_normalize` parameter in the `preprocess` method.
image_mean (`float` or `List[float]`, *optional*, defaults to `IMAGENET_STANDARD_MEAN`):
Mean to use if normalizing the image. This is a float or list of floats the length of the number of
channels in the image. Can be overridden by the `image_mean` parameter in the `preprocess` method. Can be
overridden by the `image_mean` parameter in the `preprocess` method.
image_std (`float` or `List[float]`, *optional*, defaults to `IMAGENET_STANDARD_STD`):
Standard deviation to use if normalizing the image. This is a float or list of floats the length of the
number of channels in the image. Can be overridden by the `image_std` parameter in the `preprocess` method.
Can be overridden by the `image_std` parameter in the `preprocess` method.
do_convert_rgb (`bool`, *optional*, defaults to `True`):
Whether to convert the image to RGB.
"""
model_input_names = ["pixel_values"]
def __init__(
self,
do_resize: bool = True,
size: Dict[str, int] = None,
resample: PILImageResampling = PILImageResampling.BICUBIC,
do_rescale: bool = True,
rescale_factor: Union[int, float] = 1 / 255,
do_normalize: bool = True,
image_mean: Optional[Union[float, List[float]]] = None,
image_std: Optional[Union[float, List[float]]] = None,
do_convert_rgb: bool = True,
**kwargs,
) -> None:
super().__init__(**kwargs)
size = size if size is not None else {"height": 384, "width": 384}
size = get_size_dict(size, default_to_square=True)
self.do_resize = do_resize
self.size = size
self.resample = resample
self.do_rescale = do_rescale
self.rescale_factor = rescale_factor
self.do_normalize = do_normalize
self.image_mean = image_mean if image_mean is not None else OPENAI_CLIP_MEAN
self.image_std = image_std if image_std is not None else OPENAI_CLIP_STD
self.do_convert_rgb = do_convert_rgb
# Copied from transformers.models.vit.image_processing_vit.ViTImageProcessor.resize with PILImageResampling.BILINEAR->PILImageResampling.BICUBIC
def resize(
self,
image: np.ndarray,
size: Dict[str, int],
resample: PILImageResampling = PILImageResampling.BICUBIC,
data_format: Optional[Union[str, ChannelDimension]] = None,
input_data_format: Optional[Union[str, ChannelDimension]] = None,
**kwargs,
) -> np.ndarray:
"""
Resize an image to `(size["height"], size["width"])`.
Args:
image (`np.ndarray`):
Image to resize.
size (`Dict[str, int]`):
Dictionary in the format `{"height": int, "width": int}` specifying the size of the output image.
resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BICUBIC`):
`PILImageResampling` filter to use when resizing the image e.g. `PILImageResampling.BICUBIC`.
data_format (`ChannelDimension` or `str`, *optional*):
The channel dimension format for the output image. If unset, the channel dimension format of the input
image is used. Can be one of:
- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
- `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
input_data_format (`ChannelDimension` or `str`, *optional*):
The channel dimension format for the input image. If unset, the channel dimension format is inferred
from the input image. Can be one of:
- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
- `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
Returns:
`np.ndarray`: The resized image.
"""
size = get_size_dict(size)
if "height" not in size or "width" not in size:
raise ValueError(f"The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}")
output_size = (size["height"], size["width"])
return resize(
image,
size=output_size,
resample=resample,
data_format=data_format,
input_data_format=input_data_format,
**kwargs,
)
# Ignore copy
@filter_out_non_signature_kwargs()
def preprocess(
self,
images: VideoInput = None,
do_resize: Optional[bool] = None,
size: Optional[Dict[str, int]] = None,
resample: PILImageResampling = None,
do_rescale: Optional[bool] = None,
rescale_factor: Optional[float] = None,
do_normalize: Optional[bool] = None,
image_mean: Optional[Union[float, List[float]]] = None,
image_std: Optional[Union[float, List[float]]] = None,
return_tensors: Optional[Union[str, TensorType]] = None,
do_convert_rgb: Optional[bool] = None,
data_format: ChannelDimension = ChannelDimension.FIRST,
input_data_format: Optional[Union[str, ChannelDimension]] = None,
) -> BatchFeature:
"""
Preprocess a video or batch of images/videos.
Args:
videos (`VideoInput`):
Video frames to preprocess. Expects a single or batch of videos as a list of frames with pixel values
ranging from 0 to 255. If passing in video with pixel values between 0 and 1, set `do_rescale=False`.
do_resize (`bool`, *optional*, defaults to `self.do_resize`):
Whether to resize the video.
size (`Dict[str, int]`, *optional*, defaults to `self.size`):
Controls the size of the video after `resize`. The shortest edge of the image is resized to
`size["shortest_edge"]` whilst preserving the aspect ratio. If the longest edge of this resized image
is > `int(size["shortest_edge"] * (1333 / 800))`, then the image is resized again to make the longest
edge equal to `int(size["shortest_edge"] * (1333 / 800))`.
resample (`PILImageResampling`, *optional*, defaults to `self.resample`):
Resampling filter to use if resizing the video. Only has an effect if `do_resize` is set to `True`.
do_rescale (`bool`, *optional*, defaults to `self.do_rescale`):
Whether to rescale the video values between [0 - 1].
rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`):
Rescale factor to rescale the video by if `do_rescale` is set to `True`.
do_normalize (`bool`, *optional*, defaults to `self.do_normalize`):
Whether to normalize the video.
image_mean (`float` or `List[float]`, *optional*, defaults to `self.image_mean`):
Image mean to normalize the video by if `do_normalize` is set to `True`.
image_std (`float` or `List[float]`, *optional*, defaults to `self.image_std`):
Image standard deviation to normalize the video by if `do_normalize` is set to `True`.
do_convert_rgb (`bool`, *optional*, defaults to `self.do_convert_rgb`):
Whether to convert the image to RGB.
return_tensors (`str` or `TensorType`, *optional*):
The type of tensors to return. Can be one of:
- Unset: Return a list of `np.ndarray`.
- `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`.
- `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
- `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`.
- `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`.
data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`):
The channel dimension format for the output image. Can be one of:
- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
- Unset: Use the channel dimension format of the input image.
input_data_format (`ChannelDimension` or `str`, *optional*):
The channel dimension format for the input image. If unset, the channel dimension format is inferred
from the input image. Can be one of:
- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
- `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
"""
do_resize = do_resize if do_resize is not None else self.do_resize
resample = resample if resample is not None else self.resample
do_rescale = do_rescale if do_rescale is not None else self.do_rescale
rescale_factor = rescale_factor if rescale_factor is not None else self.rescale_factor
do_normalize = do_normalize if do_normalize is not None else self.do_normalize
image_mean = image_mean if image_mean is not None else self.image_mean
image_std = image_std if image_std is not None else self.image_std
do_convert_rgb = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
size = size if size is not None else self.size
size = get_size_dict(size, default_to_square=False)
videos = make_batched_videos(images)
validate_preprocess_arguments(
do_rescale=do_rescale,
rescale_factor=rescale_factor,
do_normalize=do_normalize,
image_mean=image_mean,
image_std=image_std,
do_resize=do_resize,
size=size,
resample=resample,
)
if not valid_images(videos):
raise ValueError(
"Invalid input type. Must be of type PIL.Image.Image, numpy.ndarray, "
"torch.Tensor, tf.Tensor or jax.ndarray."
)
pixel_values = [
[
self._preprocess_image(
image=frame,
do_resize=do_resize,
size=size,
resample=resample,
do_rescale=do_rescale,
rescale_factor=rescale_factor,
do_normalize=do_normalize,
image_mean=image_mean,
image_std=image_std,
do_convert_rgb=do_convert_rgb,
data_format=data_format,
input_data_format=input_data_format,
)
for frame in video
]
for video in videos
]
encoded_outputs = BatchFeature(data={"pixel_values": pixel_values}, tensor_type=return_tensors)
return encoded_outputs
# Ignore copy
def _preprocess_image(
self,
image: ImageInput = None,
do_resize: Optional[bool] = None,
size: Optional[Dict[str, int]] = None,
resample: PILImageResampling = None,
do_rescale: Optional[bool] = None,
rescale_factor: Optional[float] = None,
do_normalize: Optional[bool] = None,
image_mean: Optional[Union[float, List[float]]] = None,
image_std: Optional[Union[float, List[float]]] = None,
do_convert_rgb: Optional[bool] = None,
data_format: ChannelDimension = ChannelDimension.FIRST,
input_data_format: Optional[Union[str, ChannelDimension]] = None,
) -> np.ndarray:
# PIL RGBA images are converted to RGB
if do_convert_rgb:
image = convert_to_rgb(image)
# All transformations expect numpy arrays.
image = to_numpy_array(image)
if do_rescale and is_scaled_image(image):
logger.warning_once(
"It looks like you are trying to rescale already rescaled video frames. If the input"
" images have pixel values between 0 and 1, set `do_rescale=False` to avoid rescaling them again."
)
if input_data_format is None:
# We assume that all images have the same channel dimension format.
input_data_format = infer_channel_dimension_format(image)
if do_resize:
image = self.resize(image=image, size=size, resample=resample, input_data_format=input_data_format)
if do_rescale:
image = self.rescale(image=image, scale=rescale_factor, input_data_format=input_data_format)
if do_normalize:
image = self.normalize(image=image, mean=image_mean, std=image_std, input_data_format=input_data_format)
image = to_channel_dimension_format(image, data_format, input_channel_dim=input_data_format)
return image
```
|
===========================================================================================================================================================
SOURCE CODE FILE: modeling_instructblipvideo.py
LINES: 1
SIZE: 75.83 KB
PATH: scripts\freecad_env\Lib\site-packages\transformers\models\instructblipvideo\modeling_instructblipvideo.py
ENCODING: utf-8
```py
# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
# This file was automatically generated from src/transformers/models/instructblipvideo/modular_instructblipvideo.py.
# Do NOT edit this file manually as any edits will be overwritten by the generation of
# the file from the modular. If any change should be done, please apply the change to the
# modular_instructblipvideo.py file directly. One of our CI enforces this.
# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
# coding=utf-8
# Copyright 2024 HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import math
from dataclasses import dataclass
from typing import Any, Optional, Tuple, Union
import torch
from torch import nn
from torch.nn import CrossEntropyLoss
from ...activations import ACT2FN
from ...generation import GenerationMixin
from ...modeling_outputs import (
BaseModelOutput,
BaseModelOutputWithPastAndCrossAttentions,
BaseModelOutputWithPooling,
BaseModelOutputWithPoolingAndCrossAttentions,
)
from ...modeling_utils import PreTrainedModel
from ...pytorch_utils import apply_chunking_to_forward, find_pruneable_heads_and_indices, prune_linear_layer
from ...utils import (
ModelOutput,
add_start_docstrings,
add_start_docstrings_to_model_forward,
logging,
replace_return_docstrings,
torch_int,
)
from ..auto import AutoModelForCausalLM, AutoModelForSeq2SeqLM
from .configuration_instructblipvideo import (
InstructBlipVideoConfig,
InstructBlipVideoQFormerConfig,
InstructBlipVideoVisionConfig,
)
logger = logging.get_logger(__name__)
@dataclass
class InstructBlipVideoForConditionalGenerationModelOutput(ModelOutput):
"""
Class defining the outputs of [`InstructBlipVideoForConditionalGeneration`].
Args:
loss (`torch.FloatTensor`, *optional*, returned when `labels` is provided, `torch.FloatTensor` of shape `(1,)`):
Language modeling loss from the language model.
logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
Prediction scores of the language modeling head of the language model.
vision_outputs (`BaseModelOutputWithPooling`):
Outputs of the vision encoder.
qformer_outputs (`BaseModelOutputWithPoolingAndCrossAttentions`):
Outputs of the Q-Former (Querying Transformer).
language_model_outputs (`CausalLMOutputWithPast` or `Seq2SeqLMOutput`):
Outputs of the language model.
"""
loss: Optional[Tuple[torch.FloatTensor]] = None
logits: Optional[Tuple[torch.FloatTensor]] = None
vision_outputs: Optional[torch.FloatTensor] = None
qformer_outputs: Optional[Tuple[torch.FloatTensor]] = None
language_model_outputs: Optional[Tuple[torch.FloatTensor]] = None
def to_tuple(self) -> Tuple[Any]:
return tuple(
self[k]
if k not in ["vision_outputs", "qformer_outputs", "language_model_outputs"]
else getattr(self, k).to_tuple()
for k in self.keys()
)
class InstructBlipVideoVisionEmbeddings(nn.Module):
def __init__(self, config: InstructBlipVideoVisionConfig):
super().__init__()
self.config = config
self.embed_dim = config.hidden_size
self.image_size = config.image_size
self.patch_size = config.patch_size
self.class_embedding = nn.Parameter(torch.randn(1, 1, self.embed_dim))
self.patch_embedding = nn.Conv2d(
in_channels=3, out_channels=self.embed_dim, kernel_size=self.patch_size, stride=self.patch_size
)
self.num_patches = (self.image_size // self.patch_size) ** 2
self.num_positions = self.num_patches + 1
self.position_embedding = nn.Parameter(torch.randn(1, self.num_positions, self.embed_dim))
def interpolate_pos_encoding(self, embeddings: torch.Tensor, height: int, width: int) -> torch.Tensor:
"""
This method allows to interpolate the pre-trained position encodings, to be able to use the model on higher resolution
images. This method is also adapted to support torch.jit tracing.
Adapted from:
- https://github.com/facebookresearch/dino/blob/de9ee3df6cf39fac952ab558447af1fa1365362a/vision_transformer.py#L174-L194, and
- https://github.com/facebookresearch/dinov2/blob/e1277af2ba9496fbadf7aec6eba56e8d882d1e35/dinov2/models/vision_transformer.py#L179-L211
"""
num_patches = embeddings.shape[1] - 1
num_positions = self.position_embedding.shape[1] - 1
# always interpolate when tracing to ensure the exported model works for dynamic input shapes
if not torch.jit.is_tracing() and num_patches == num_positions and height == width:
return self.position_embedding
class_pos_embed = self.position_embedding[:, :1]
patch_pos_embed = self.position_embedding[:, 1:]
dim = embeddings.shape[-1]
new_height = height // self.patch_size
new_width = width // self.patch_size
sqrt_num_positions = torch_int(num_positions**0.5)
patch_pos_embed = patch_pos_embed.reshape(1, sqrt_num_positions, sqrt_num_positions, dim)
patch_pos_embed = patch_pos_embed.permute(0, 3, 1, 2)
patch_pos_embed = nn.functional.interpolate(
patch_pos_embed,
size=(new_height, new_width),
mode="bicubic",
align_corners=False,
)
patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim)
return torch.cat((class_pos_embed, patch_pos_embed), dim=1)
def forward(self, pixel_values: torch.FloatTensor, interpolate_pos_encoding: bool = False) -> torch.Tensor:
batch_size, _, height, width = pixel_values.shape
target_dtype = self.patch_embedding.weight.dtype
patch_embeds = self.patch_embedding(pixel_values.to(dtype=target_dtype)) # shape = [*, width, grid, grid]
patch_embeds = patch_embeds.flatten(2).transpose(1, 2)
class_embeds = self.class_embedding.expand(batch_size, 1, -1).to(target_dtype)
embeddings = torch.cat([class_embeds, patch_embeds], dim=1)
if interpolate_pos_encoding:
position_embedding = self.interpolate_pos_encoding(embeddings, height, width)
else:
position_embedding = self.position_embedding
embeddings = embeddings + position_embedding[:, : embeddings.size(1), :].to(target_dtype)
return embeddings
class InstructBlipVideoAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(self, config):
super().__init__()
self.config = config
self.embed_dim = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_heads != self.embed_dim:
raise ValueError(
f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:"
f" {self.num_heads})."
)
self.scale = self.head_dim**-0.5
self.dropout = nn.Dropout(config.attention_dropout)
# small tweak here compared to CLIP, no bias here
self.qkv = nn.Linear(self.embed_dim, 3 * self.embed_dim, bias=False)
if config.qkv_bias:
q_bias = nn.Parameter(torch.zeros(self.embed_dim))
v_bias = nn.Parameter(torch.zeros(self.embed_dim))
else:
q_bias = None
v_bias = None
if q_bias is not None:
qkv_bias = torch.cat((q_bias, torch.zeros_like(v_bias, requires_grad=False), v_bias))
self.qkv.bias = nn.Parameter(qkv_bias)
self.projection = nn.Linear(self.embed_dim, self.embed_dim)
def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
def forward(
self,
hidden_states: torch.Tensor,
head_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = False,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
"""Input shape: Batch x Time x Channel"""
bsz, tgt_len, embed_dim = hidden_states.size()
mixed_qkv = self.qkv(hidden_states)
mixed_qkv = mixed_qkv.reshape(bsz, tgt_len, 3, self.num_heads, embed_dim // self.num_heads).permute(
2, 0, 3, 1, 4
)
query_states, key_states, value_states = mixed_qkv[0], mixed_qkv[1], mixed_qkv[2]
# Take the dot product between "query" and "key" to get the raw attention scores.
attention_scores = torch.matmul(query_states, key_states.transpose(-1, -2))
attention_scores = attention_scores * self.scale
# Normalize the attention scores to probabilities.
attention_probs = nn.functional.softmax(attention_scores, dim=-1)
# This is actually dropping out entire tokens to attend to, which might
# seem a bit unusual, but is taken from the original Transformer paper.
attention_probs = self.dropout(attention_probs)
# Mask heads if we want to
if head_mask is not None:
attention_probs = attention_probs * head_mask
context_layer = torch.matmul(attention_probs, value_states).permute(0, 2, 1, 3)
new_context_layer_shape = context_layer.size()[:-2] + (self.embed_dim,)
context_layer = context_layer.reshape(new_context_layer_shape)
output = self.projection(context_layer)
outputs = (output, attention_probs) if output_attentions else (output, None)
return outputs
class InstructBlipVideoMLP(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.activation_fn = ACT2FN[config.hidden_act]
self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size)
self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size)
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidden_states = self.fc1(hidden_states)
hidden_states = self.activation_fn(hidden_states)
hidden_states = self.fc2(hidden_states)
return hidden_states
class InstructBlipVideoEncoderLayer(nn.Module):
def __init__(self, config: InstructBlipVideoConfig):
super().__init__()
self.embed_dim = config.hidden_size
self.self_attn = InstructBlipVideoAttention(config)
self.layer_norm1 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
self.mlp = InstructBlipVideoMLP(config)
self.layer_norm2 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: torch.Tensor,
output_attentions: Optional[bool] = False,
) -> Tuple[torch.FloatTensor]:
"""
Args:
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
attention_mask (`torch.FloatTensor`): attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
`(config.encoder_attention_heads,)`.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
"""
residual = hidden_states
hidden_states = self.layer_norm1(hidden_states)
hidden_states, attn_weights = self.self_attn(
hidden_states=hidden_states,
head_mask=attention_mask,
output_attentions=output_attentions,
)
hidden_states = hidden_states + residual
residual = hidden_states
hidden_states = self.layer_norm2(hidden_states)
hidden_states = self.mlp(hidden_states)
hidden_states = hidden_states + residual
outputs = (hidden_states,)
if output_attentions:
outputs += (attn_weights,)
return outputs
class InstructBlipVideoPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = InstructBlipVideoConfig
base_model_prefix = "blip"
supports_gradient_checkpointing = True
_no_split_modules = [
"InstructBlipVideoQFormerEmbeddings",
"InstructBlipVideoAttention",
"InstructBlipVideoQFormerMultiHeadAttention",
"InstructBlipVideoQFormerSelfOutput",
]
def _init_weights(self, module):
"""Initialize the weights"""
factor = self.config.initializer_range
if isinstance(module, nn.Conv2d) or isinstance(module, nn.Embedding) or isinstance(module, nn.Linear):
module.weight.data.normal_(mean=0.0, std=factor)
if hasattr(module, "bias") and module.bias is not None:
module.bias.data.zero_()
if isinstance(module, InstructBlipVideoVisionEmbeddings):
if hasattr(self.config, "vision_config") and not isinstance(self.config, InstructBlipVideoVisionConfig):
factor = self.config.vision_config.initializer_range
nn.init.trunc_normal_(module.position_embedding, mean=0.0, std=factor)
nn.init.trunc_normal_(module.class_embedding, mean=0.0, std=factor)
elif isinstance(module, nn.LayerNorm):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
elif isinstance(module, nn.Linear) and module.bias is not None:
module.bias.data.zero_()
class InstructBlipVideoEncoder(nn.Module):
"""
Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a
[`InstructBlipVideoEncoderLayer`].
Args:
config (`InstructBlipVideoConfig`):
The corresponding vision configuration for the `InstructBlipVideoEncoder`.
"""
def __init__(self, config: InstructBlipVideoConfig):
super().__init__()
self.config = config
self.layers = nn.ModuleList([InstructBlipVideoEncoderLayer(config) for _ in range(config.num_hidden_layers)])
self.gradient_checkpointing = False
def forward(
self,
inputs_embeds,
attention_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, BaseModelOutput]:
r"""
Args:
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Embedded representation of the inputs. Should be float, not int tokens.
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
for more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
encoder_states = () if output_hidden_states else None
all_attentions = () if output_attentions else None
hidden_states = inputs_embeds
for idx, encoder_layer in enumerate(self.layers):
if output_hidden_states:
encoder_states = encoder_states + (hidden_states,)
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
encoder_layer.__call__,
hidden_states,
attention_mask,
output_attentions,
)
else:
layer_outputs = encoder_layer(
hidden_states,
attention_mask,
output_attentions=output_attentions,
)
hidden_states = layer_outputs[0]
if output_attentions:
all_attentions = all_attentions + (layer_outputs[1],)
if output_hidden_states:
encoder_states = encoder_states + (hidden_states,)
if not return_dict:
return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None)
return BaseModelOutput(
last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions
)
INSTRUCTBLIPVIDEO_VISION_INPUTS_DOCSTRING = r"""
Args:
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Pixel values can be obtained using [`InstructBlipVideoProcessor`]. See
[`InstructBlipVideoProcessor.__call__`] for details.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
interpolate_pos_encoding (`bool`, *optional*, defaults to `False`):
Whether to interpolate the pre-trained position encodings.
"""
class InstructBlipVideoVisionModel(InstructBlipVideoPreTrainedModel):
main_input_name = "pixel_values"
config_class = InstructBlipVideoVisionConfig
def __init__(self, config: InstructBlipVideoVisionConfig):
super().__init__(config)
self.config = config
embed_dim = config.hidden_size
self.embeddings = InstructBlipVideoVisionEmbeddings(config)
self.encoder = InstructBlipVideoEncoder(config)
self.post_layernorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
self.post_init()
@add_start_docstrings_to_model_forward(INSTRUCTBLIPVIDEO_VISION_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=InstructBlipVideoVisionConfig)
def forward(
self,
pixel_values: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
interpolate_pos_encoding: bool = False,
) -> Union[Tuple, BaseModelOutputWithPooling]:
r"""
Returns:
"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if pixel_values is None:
raise ValueError("You have to specify pixel_values")
hidden_states = self.embeddings(pixel_values, interpolate_pos_encoding=interpolate_pos_encoding)
encoder_outputs = self.encoder(
inputs_embeds=hidden_states,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
last_hidden_state = encoder_outputs[0]
last_hidden_state = self.post_layernorm(last_hidden_state)
pooled_output = last_hidden_state[:, 0, :]
pooled_output = self.post_layernorm(pooled_output)
if not return_dict:
return (last_hidden_state, pooled_output) + encoder_outputs[1:]
return BaseModelOutputWithPooling(
last_hidden_state=last_hidden_state,
pooler_output=pooled_output,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
)
def get_input_embeddings(self):
return self.embeddings
class InstructBlipVideoQFormerMultiHeadAttention(nn.Module):
def __init__(self, config, is_cross_attention=False):
super().__init__()
self.config = config
if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
raise ValueError(
"The hidden size (%d) is not a multiple of the number of attention heads (%d)"
% (config.hidden_size, config.num_attention_heads)
)
self.num_attention_heads = config.num_attention_heads
self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
self.all_head_size = self.num_attention_heads * self.attention_head_size
self.query = nn.Linear(config.hidden_size, self.all_head_size)
if is_cross_attention:
self.key = nn.Linear(config.encoder_hidden_size, self.all_head_size)
self.value = nn.Linear(config.encoder_hidden_size, self.all_head_size)
else:
self.key = nn.Linear(config.hidden_size, self.all_head_size)
self.value = nn.Linear(config.hidden_size, self.all_head_size)
self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
self.max_position_embeddings = config.max_position_embeddings
self.distance_embedding = nn.Embedding(2 * config.max_position_embeddings - 1, self.attention_head_size)
self.save_attention = False
def save_attn_gradients(self, attn_gradients):
self.attn_gradients = attn_gradients
def get_attn_gradients(self):
return self.attn_gradients
def save_attention_map(self, attention_map):
self.attention_map = attention_map
def get_attention_map(self):
return self.attention_map
def transpose_for_scores(self, x):
new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
x = x.view(*new_x_shape)
return x.permute(0, 2, 1, 3)
def forward(
self,
hidden_states,
attention_mask=None,
head_mask=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
past_key_value=None,
output_attentions=False,
):
# If this is instantiated as a cross-attention module, the keys
# and values come from an encoder; the attention mask needs to be
# such that the encoder's padding tokens are not attended to.
is_cross_attention = encoder_hidden_states is not None
if is_cross_attention:
key_layer = self.transpose_for_scores(self.key(encoder_hidden_states))
value_layer = self.transpose_for_scores(self.value(encoder_hidden_states))
attention_mask = encoder_attention_mask
elif past_key_value is not None:
key_layer = self.transpose_for_scores(self.key(hidden_states))
value_layer = self.transpose_for_scores(self.value(hidden_states))
key_layer = torch.cat([past_key_value[0], key_layer], dim=2)
value_layer = torch.cat([past_key_value[1], value_layer], dim=2)
else:
key_layer = self.transpose_for_scores(self.key(hidden_states))
value_layer = self.transpose_for_scores(self.value(hidden_states))
mixed_query_layer = self.query(hidden_states)
query_layer = self.transpose_for_scores(mixed_query_layer)
past_key_value = (key_layer, value_layer)
# Take the dot product between "query" and "key" to get the raw attention scores.
attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
seq_length = hidden_states.size()[1]
position_ids_l = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(-1, 1)
position_ids_r = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(1, -1)
distance = position_ids_l - position_ids_r
positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1)
positional_embedding = positional_embedding.to(dtype=query_layer.dtype) # fp16 compatibility
if self.position_embedding_type == "relative_key":
relative_position_scores = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
attention_scores = attention_scores + relative_position_scores
elif self.position_embedding_type == "relative_key_query":
relative_position_scores_query = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
relative_position_scores_key = torch.einsum("bhrd,lrd->bhlr", key_layer, positional_embedding)
attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key
attention_scores = attention_scores / math.sqrt(self.attention_head_size)
attention_scores_dtype = attention_scores.dtype
if attention_mask is not None:
# Apply the attention mask is (precomputed for all layers in BertModel forward() function)
attention_scores = attention_scores + attention_mask
# Normalize the attention scores to probabilities.
attention_probs = nn.Softmax(dim=-1)(attention_scores).to(attention_scores_dtype)
if is_cross_attention and self.save_attention:
self.save_attention_map(attention_probs)
attention_probs.register_hook(self.save_attn_gradients)
# This is actually dropping out entire tokens to attend to, which might
# seem a bit unusual, but is taken from the original Transformer paper.
attention_probs_dropped = self.dropout(attention_probs)
# Mask heads if we want to
if head_mask is not None:
attention_probs_dropped = attention_probs_dropped * head_mask
context_layer = torch.matmul(attention_probs_dropped, value_layer)
context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
context_layer = context_layer.view(*new_context_layer_shape)
outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
outputs = outputs + (past_key_value,)
return outputs
class InstructBlipVideoQFormerSelfOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
hidden_states = self.dense(hidden_states)
hidden_states = self.dropout(hidden_states)
hidden_states = self.LayerNorm(hidden_states + input_tensor)
return hidden_states
class InstructBlipVideoQFormerAttention(nn.Module):
def __init__(self, config, is_cross_attention=False):
super().__init__()
self.attention = InstructBlipVideoQFormerMultiHeadAttention(config, is_cross_attention)
self.output = InstructBlipVideoQFormerSelfOutput(config)
self.pruned_heads = set()
def prune_heads(self, heads):
if len(heads) == 0:
return
heads, index = find_pruneable_heads_and_indices(
heads, self.attention.num_attention_heads, self.attention.attention_head_size, self.pruned_heads
)
# Prune linear layers
self.attention.query = prune_linear_layer(self.attention.query, index)
self.attention.key = prune_linear_layer(self.attention.key, index)
self.attention.value = prune_linear_layer(self.attention.value, index)
self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
# Update hyper params and store pruned heads
self.attention.num_attention_heads = self.attention.num_attention_heads - len(heads)
self.attention.all_head_size = self.attention.attention_head_size * self.attention.num_attention_heads
self.pruned_heads = self.pruned_heads.union(heads)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
encoder_attention_mask: Optional[torch.FloatTensor] = None,
past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
output_attentions: Optional[bool] = False,
) -> Tuple[torch.Tensor]:
self_outputs = self.attention(
hidden_states,
attention_mask,
head_mask,
encoder_hidden_states,
encoder_attention_mask,
past_key_value,
output_attentions,
)
attention_output = self.output(self_outputs[0], hidden_states)
outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them
return outputs
class InstructBlipVideoQFormerIntermediate(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
if isinstance(config.hidden_act, str):
self.intermediate_act_fn = ACT2FN[config.hidden_act]
else:
self.intermediate_act_fn = config.hidden_act
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidden_states = self.dense(hidden_states)
hidden_states = self.intermediate_act_fn(hidden_states)
return hidden_states
class InstructBlipVideoQFormerOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
hidden_states = self.dense(hidden_states)
hidden_states = self.dropout(hidden_states)
hidden_states = self.LayerNorm(hidden_states + input_tensor)
return hidden_states
class InstructBlipVideoQFormerLayer(nn.Module):
def __init__(self, config, layer_idx):
super().__init__()
self.chunk_size_feed_forward = config.chunk_size_feed_forward
self.seq_len_dim = 1
self.attention = InstructBlipVideoQFormerAttention(config)
self.layer_idx = layer_idx
if layer_idx % config.cross_attention_frequency == 0:
self.crossattention = InstructBlipVideoQFormerAttention(config, is_cross_attention=True)
self.has_cross_attention = True
else:
self.has_cross_attention = False
self.intermediate = InstructBlipVideoQFormerIntermediate(config)
self.output = InstructBlipVideoQFormerOutput(config)
self.intermediate_query = InstructBlipVideoQFormerIntermediate(config)
self.output_query = InstructBlipVideoQFormerOutput(config)
def forward(
self,
hidden_states,
attention_mask=None,
head_mask=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
past_key_value=None,
output_attentions=False,
query_length=0,
):
# decoder uni-directional self-attention cached key/values tuple is at positions 1,2
self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
self_attention_outputs = self.attention(
hidden_states,
attention_mask,
head_mask,
output_attentions=output_attentions,
past_key_value=self_attn_past_key_value,
)
attention_output = self_attention_outputs[0]
outputs = self_attention_outputs[1:-1]
present_key_value = self_attention_outputs[-1]
if query_length > 0:
query_attention_output = attention_output[:, :query_length, :]
if self.has_cross_attention:
if encoder_hidden_states is None:
raise ValueError("encoder_hidden_states must be given for cross-attention layers")
cross_attention_outputs = self.crossattention(
query_attention_output,
attention_mask,
head_mask,
encoder_hidden_states,
encoder_attention_mask,
output_attentions=output_attentions,
)
query_attention_output = cross_attention_outputs[0]
# add cross attentions if we output attention weights
outputs = outputs + cross_attention_outputs[1:-1]
layer_output = apply_chunking_to_forward(
self.feed_forward_chunk_query,
self.chunk_size_feed_forward,
self.seq_len_dim,
query_attention_output,
)
if attention_output.shape[1] > query_length:
layer_output_text = apply_chunking_to_forward(
self.feed_forward_chunk,
self.chunk_size_feed_forward,
self.seq_len_dim,
attention_output[:, query_length:, :],
)
layer_output = torch.cat([layer_output, layer_output_text], dim=1)
else:
layer_output = apply_chunking_to_forward(
self.feed_forward_chunk,
self.chunk_size_feed_forward,
self.seq_len_dim,
attention_output,
)
outputs = (layer_output,) + outputs
outputs = outputs + (present_key_value,)
return outputs
def feed_forward_chunk(self, attention_output):
intermediate_output = self.intermediate(attention_output)
layer_output = self.output(intermediate_output, attention_output)
return layer_output
def feed_forward_chunk_query(self, attention_output):
intermediate_output = self.intermediate_query(attention_output)
layer_output = self.output_query(intermediate_output, attention_output)
return layer_output
class InstructBlipVideoQFormerEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.layer = nn.ModuleList(
[InstructBlipVideoQFormerLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
)
self.gradient_checkpointing = False
def forward(
self,
hidden_states,
attention_mask=None,
head_mask=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
past_key_values=None,
use_cache=None,
output_attentions=False,
output_hidden_states=False,
return_dict=True,
query_length=0,
):
all_hidden_states = () if output_hidden_states else None
all_self_attentions = () if output_attentions else None
all_cross_attentions = () if output_attentions else None
next_decoder_cache = () if use_cache else None
for i in range(self.config.num_hidden_layers):
layer_module = self.layer[i]
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
layer_head_mask = head_mask[i] if head_mask is not None else None
past_key_value = past_key_values[i] if past_key_values is not None else None
if getattr(self.config, "gradient_checkpointing", False) and self.training:
if use_cache:
logger.warning(
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
)
use_cache = False
layer_outputs = self._gradient_checkpointing_func(
layer_module.__call__,
hidden_states,
attention_mask,
layer_head_mask,
encoder_hidden_states,
encoder_attention_mask,
)
else:
layer_outputs = layer_module(
hidden_states,
attention_mask,
layer_head_mask,
encoder_hidden_states,
encoder_attention_mask,
past_key_value,
output_attentions,
query_length,
)
hidden_states = layer_outputs[0]
if use_cache:
next_decoder_cache += (layer_outputs[-1],)
if output_attentions:
all_self_attentions = all_self_attentions + (layer_outputs[1],)
if layer_module.has_cross_attention:
all_cross_attentions = all_cross_attentions + (layer_outputs[2],)
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if not return_dict:
return tuple(
v
for v in [
hidden_states,
next_decoder_cache,
all_hidden_states,
all_self_attentions,
all_cross_attentions,
]
if v is not None
)
return BaseModelOutputWithPastAndCrossAttentions(
last_hidden_state=hidden_states,
past_key_values=next_decoder_cache,
hidden_states=all_hidden_states,
attentions=all_self_attentions,
cross_attentions=all_cross_attentions,
)
class InstructBlipVideoQFormerEmbeddings(nn.Module):
"""Construct the embeddings from word and position embeddings."""
def __init__(self, config):
super().__init__()
self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
# position_ids (1, len position emb) is contiguous in memory and exported when serialized
self.register_buffer(
"position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False
)
self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
self.config = config
def forward(
self,
input_ids=None,
position_ids=None,
query_embeds=None,
past_key_values_length=0,
):
if input_ids is not None:
seq_length = input_ids.size()[1]
else:
seq_length = 0
if position_ids is None:
position_ids = self.position_ids[:, past_key_values_length : seq_length + past_key_values_length].clone()
if input_ids is not None:
embeddings = self.word_embeddings(input_ids)
if self.position_embedding_type == "absolute":
position_embeddings = self.position_embeddings(position_ids.to(embeddings.device))
embeddings = embeddings + position_embeddings
if query_embeds is not None:
embeddings = torch.cat((query_embeds, embeddings), dim=1)
else:
embeddings = query_embeds
embeddings = embeddings.to(self.layernorm.weight.dtype)
embeddings = self.layernorm(embeddings)
embeddings = self.dropout(embeddings)
return embeddings
class InstructBlipVideoQFormerModel(InstructBlipVideoPreTrainedModel):
"""
Querying Transformer (Q-Former), used in InstructBlipVideo. Slightly modified from BLIP-2 as it also takes the
instruction as input.
"""
def __init__(self, config: InstructBlipVideoQFormerConfig):
super().__init__(config)
self.config = config
self.embeddings = InstructBlipVideoQFormerEmbeddings(config)
self.encoder = InstructBlipVideoQFormerEncoder(config)
self.post_init()
def get_input_embeddings(self):
return self.embeddings.word_embeddings
def set_input_embeddings(self, value):
self.embeddings.word_embeddings = value
def _prune_heads(self, heads_to_prune):
"""
Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
class PreTrainedModel
"""
for layer, heads in heads_to_prune.items():
self.encoder.layer[layer].attention.prune_heads(heads)
def get_extended_attention_mask(
self,
attention_mask: torch.Tensor,
input_shape: Tuple[int],
device: torch.device,
has_query: bool = False,
) -> torch.Tensor:
"""
Makes broadcastable attention and causal masks so that future and masked tokens are ignored.
Arguments:
attention_mask (`torch.Tensor`):
Mask with ones indicating tokens to attend to, zeros for tokens to ignore.
input_shape (`Tuple[int]`):
The shape of the input to the model.
device: (`torch.device`):
The device of the input to the model.
Returns:
`torch.Tensor` The extended attention mask, with a the same dtype as `attention_mask.dtype`.
"""
# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
# ourselves in which case we just need to make it broadcastable to all heads.
if attention_mask.dim() == 3:
extended_attention_mask = attention_mask[:, None, :, :]
elif attention_mask.dim() == 2:
# Provided a padding mask of dimensions [batch_size, seq_length]
# - the model is an encoder, so make the mask broadcastable to [batch_size, num_heads, seq_length, seq_length]
extended_attention_mask = attention_mask[:, None, None, :]
else:
raise ValueError(
f"Wrong shape for input_ids (shape {input_shape}) or attention_mask (shape {attention_mask.shape})",
)
# Since attention_mask is 1.0 for positions we want to attend and 0.0 for
# masked positions, this operation will create a tensor which is 0.0 for
# positions we want to attend and -10000.0 for masked positions.
# Since we are adding it to the raw scores before the softmax, this is
# effectively the same as removing these entirely.
extended_attention_mask = extended_attention_mask.to(dtype=self.dtype) # fp16 compatibility
extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0
return extended_attention_mask
def forward(
self,
input_ids: torch.LongTensor,
attention_mask: Optional[torch.FloatTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
query_embeds: Optional[torch.Tensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
encoder_attention_mask: Optional[torch.FloatTensor] = None,
past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple[torch.FloatTensor], BaseModelOutputWithPoolingAndCrossAttentions]:
r"""
encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
the model is configured as a decoder.
encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of:
shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): Contains precomputed key and
value hidden states of the attention blocks. Can be used to speed up decoding. If `past_key_values` are
used, the user can optionally input only the last `decoder_input_ids` (those that don't have their past key
value states given to this model) of shape `(batch_size, 1)` instead of all `decoder_input_ids` of shape
`(batch_size, sequence_length)`.
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
`past_key_values`).
"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if input_ids is None and query_embeds is None:
raise ValueError("You have to specify query_embeds when input_ids is None")
# past_key_values_length
past_key_values_length = (
past_key_values[0][0].shape[2] - self.config.query_length if past_key_values is not None else 0
)
query_length = query_embeds.shape[1] if query_embeds is not None else 0
embedding_output = self.embeddings(
input_ids=input_ids,
position_ids=position_ids,
query_embeds=query_embeds,
past_key_values_length=past_key_values_length,
)
input_shape = embedding_output.size()[:-1]
batch_size, seq_length = input_shape
device = embedding_output.device
if attention_mask is None:
attention_mask = torch.ones(((batch_size, seq_length + past_key_values_length)), device=device)
# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
# ourselves in which case we just need to make it broadcastable to all heads.
extended_attention_mask = self.get_extended_attention_mask(attention_mask, input_shape, device)
# If a 2D or 3D attention mask is provided for the cross-attention
# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
if encoder_hidden_states is not None:
if isinstance(encoder_hidden_states, list):
encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states[0].size()
else:
encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size()
encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
if isinstance(encoder_attention_mask, list):
encoder_extended_attention_mask = [self.invert_attention_mask(mask) for mask in encoder_attention_mask]
elif encoder_attention_mask is None:
encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device)
encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
else:
encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
else:
encoder_extended_attention_mask = None
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x n_heads x N x N
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
encoder_outputs = self.encoder(
embedding_output,
attention_mask=extended_attention_mask,
head_mask=head_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_extended_attention_mask,
past_key_values=past_key_values,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
query_length=query_length,
)
sequence_output = encoder_outputs[0]
pooled_output = sequence_output[:, 0, :]
if not return_dict:
return (sequence_output, pooled_output) + encoder_outputs[1:]
return BaseModelOutputWithPoolingAndCrossAttentions(
last_hidden_state=sequence_output,
pooler_output=pooled_output,
past_key_values=encoder_outputs.past_key_values,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
cross_attentions=encoder_outputs.cross_attentions,
)
INSTRUCTBLIPVIDEO_START_DOCSTRING = r"""
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.
Parameters:
config ([`InstructBlipVideoConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
INSTRUCTBLIPVIDEO_INPUTS_DOCSTRING = r"""
Args:
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Pixel values can be obtained using [`InstructBlipVideoProcessor`]. See
[`InstructBlipVideoProcessor.__call__`] for details.
qformer_input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Indices of input sequence tokens in the vocabulary of the Q-Former. Input tokens can optionally be provided
to serve as text prompt, which the Q-Former model will encode.
Indices can be obtained using [`InstructBlipVideoProcessor`]. See [`InstructBlipVideoProcessor.__call__`] for
details.
[What are input IDs?](../glossary#input-ids)
qformer_attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Indices of input sequence tokens in the vocabulary of the language model. Input tokens can optionally be
provided to serve as text prompt, which the language model can continue.
Indices can be obtained using [`InstructBlipVideoProcessor`]. See [`InstructBlipVideoProcessor.__call__`] for
details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
Indices of decoder input sequence tokens in the vocabulary of the language model. Only relevant in case an
encoder-decoder language model (like T5) is used.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details. [What are decoder input IDs?](../glossary#decoder-input-ids)
decoder_attention_mask (`torch.BoolTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
be used by default.
Only relevant in case an encoder-decoder language model (like T5) is used.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
interpolate_pos_encoding (`bool`, *optional*, defaults to `False`):
Whether to interpolate the pre-trained position encodings.
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
`past_key_values`).
"""
@add_start_docstrings(
"""
InstructBlipVideo Model for generating text given an image and an optional text prompt. The model consists of a vision
encoder, Querying Transformer (Q-Former) and a language model.
One can optionally pass `input_ids` to the model, which serve as a text prompt, to make the language model continue
the prompt. Otherwise, the language model starts generating text from the [BOS] (beginning-of-sequence) token.
""",
INSTRUCTBLIPVIDEO_START_DOCSTRING,
)
class InstructBlipVideoForConditionalGeneration(InstructBlipVideoPreTrainedModel, GenerationMixin):
config_class = InstructBlipVideoConfig
main_input_name = "pixel_values"
_supports_cache_class = True
_supports_static_cache = True
_supports_quantized_cache = False # not all LM bacbones support (e.g. T5)
_keep_in_fp32_modules = ["query_tokens"] # TODO @ArthurZucker I don't know why this is required for FP8
def __init__(self, config: InstructBlipVideoConfig):
super().__init__(config)
self.vision_model = InstructBlipVideoVisionModel(config.vision_config)
self.query_tokens = nn.Parameter(torch.zeros(1, config.num_query_tokens, config.qformer_config.hidden_size))
self.qformer = InstructBlipVideoQFormerModel(config.qformer_config)
self.language_projection = nn.Linear(config.qformer_config.hidden_size, config.text_config.hidden_size)
if config.use_decoder_only_language_model:
language_model = AutoModelForCausalLM.from_config(config.text_config)
else:
language_model = AutoModelForSeq2SeqLM.from_config(config.text_config)
if language_model._no_split_modules is not None:
self._no_split_modules.extend(language_model._no_split_modules)
if language_model._keep_in_fp32_modules is not None:
self._keep_in_fp32_modules.extend(language_model._keep_in_fp32_modules)
self.language_model = language_model
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.language_model.get_input_embeddings()
def set_input_embeddings(self, value):
self.language_model.set_input_embeddings(value)
def set_output_embeddings(self, new_embeddings):
self.language_model.set_output_embeddings(new_embeddings)
def get_output_embeddings(self) -> nn.Module:
return self.language_model.get_output_embeddings()
def get_encoder(self):
return self.language_model.get_encoder()
def get_decoder(self):
return self.language_model.get_decoder()
def _tie_weights(self):
if not self.config.use_decoder_only_language_model:
self.language_model.encoder.embed_tokens = self.language_model.shared
self.language_model.decoder.embed_tokens = self.language_model.shared
def _preprocess_accelerate(self):
r"""
Some pre-processing hacks to make the model `accelerate` compatible. Check
https://github.com/huggingface/transformers/pull/21707 for more details.
"""
hf_device_map = self.hf_device_map
if len(hf_device_map) > 1 and "language_model" not in hf_device_map and torch.cuda.device_count() > 1:
# warn users about unexpected behavior when using multi-GPU + InstructBlipVideo + `accelerate`.
logger.warning(
"The `language_model` is not in the `hf_device_map` dictionary and you are running your script"
" in a multi-GPU environment. this may lead to unexpected behavior when using `accelerate`."
" Please pass a `device_map` that contains `language_model` to remove this warning."
" Please refer to https://github.com/huggingface/blog/blob/main/accelerate-large-models.md for"
" more details on creating a `device_map` for large models.",
)
if hasattr(self.language_model, "_hf_hook"):
self.language_model._hf_hook.io_same_device = True # For `generate` compatibility
@add_start_docstrings_to_model_forward(INSTRUCTBLIPVIDEO_INPUTS_DOCSTRING)
@replace_return_docstrings(
output_type=InstructBlipVideoForConditionalGenerationModelOutput, config_class=InstructBlipVideoVisionConfig
)
def forward(
self,
pixel_values: torch.FloatTensor,
qformer_input_ids: torch.FloatTensor,
qformer_attention_mask: Optional[torch.LongTensor] = None,
input_ids: Optional[torch.FloatTensor] = None,
attention_mask: Optional[torch.LongTensor] = None,
decoder_input_ids: Optional[torch.LongTensor] = None,
decoder_attention_mask: Optional[torch.LongTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
labels: Optional[torch.LongTensor] = None,
return_dict: Optional[bool] = None,
interpolate_pos_encoding: bool = False,
use_cache: Optional[bool] = None,
) -> Union[Tuple, InstructBlipVideoForConditionalGenerationModelOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the language modeling loss. Indices should be in `[-100, 0, ..., config.vocab_size -
1]`. All labels set to `-100` are ignored (masked), the loss is only computed for labels in `[0, ...,
config.vocab_size]`
Returns:
Examples:
```python
>>> from transformers import InstructBlipVideoProcessor, InstructBlipVideoForConditionalGeneration
>>> import torch
>>> from huggingface_hub import hf_hub_download
>>> import av
>>> import numpy as np
>>> def read_video_pyav(container, indices):
... '''
... Decode the video with PyAV decoder.
... Args:
... container (`av.container.input.InputContainer`): PyAV container.
... indices (`List[int]`): List of frame indices to decode.
... Returns:
... result (np.ndarray): np array of decoded frames of shape (num_frames, height, width, 3).
... '''
... frames = []
... container.seek(0)
... start_index = indices[0]
... end_index = indices[-1]
... for i, frame in enumerate(container.decode(video=0)):
... if i > end_index:
... break
... if i >= start_index and i in indices:
... frames.append(frame)
... return np.stack([x.to_ndarray(format="rgb24") for x in frames])
>>> model = InstructBlipVideoForConditionalGeneration.from_pretrained("Salesforce/instructblip-vicuna-7b", device_map="auto")
>>> processor = InstructBlipVideoProcessor.from_pretrained("Salesforce/instructblip-vicuna-7b")
>>> file_path = hf_hub_download(
... repo_id="nielsr/video-demo", filename="eating_spaghetti.mp4", repo_type="dataset"
... )
>>> container = av.open(file_path)
>>> # sample uniformly 4 frames from the videWhy is this video funny?o
>>> total_frames = container.streams.video[0].frames
>>> indices = np.arange(0, total_frames, total_frames / 4).astype(int)
>>> clip = read_video_pyav(container, indices)
>>> prompt = "What is happening in the video?"
>>> inputs = processor(text=prompt, images=clip, return_tensors="pt").to(model.device)
>>> outputs = model.generate(
... **inputs,
... do_sample=False,
... num_beams=5,
... max_length=256,
... repetition_penalty=1.5,
... length_penalty=1.0,
... )
>>> generated_text = processor.batch_decode(outputs, skip_special_tokens=True)[0].strip()
>>> print(generated_text)
"A person is eating a bowl of pasta, and they are using a fork to eat it. The person is sitting at a table, and the plate of pasta is on the table in front"
```"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
# step 1: forward the images through the vision encoder,
# we process in a batched way, later unbatch it back (video has frames=4 always)
batch_size, frames, channel, height, width = pixel_values.shape
pixel_values = pixel_values.reshape(batch_size * frames, channel, height, width)
vision_outputs = self.vision_model(
pixel_values=pixel_values,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
interpolate_pos_encoding=interpolate_pos_encoding,
)
image_embeds = vision_outputs[0]
# step 2: forward the query tokens through the QFormer, using the image embeddings for cross-attention
image_attention_mask = torch.ones(image_embeds.size()[:-1], dtype=torch.long, device=image_embeds.device)
# difference with BLIP-2 here: we also feed the instruction prompt to the Q-Former
query_tokens = self.query_tokens.expand(image_embeds.shape[0], -1, -1)
query_attention_mask = torch.ones(query_tokens.size()[:-1], dtype=torch.long, device=image_embeds.device)
if qformer_attention_mask is None:
qformer_attention_mask = torch.ones_like(qformer_input_ids)
qformer_input_ids = qformer_input_ids.repeat_interleave(frames, dim=0)
qformer_attention_mask = qformer_attention_mask.repeat_interleave(frames, dim=0)
qformer_attention_mask = torch.cat([query_attention_mask, qformer_attention_mask], dim=1)
query_outputs = self.qformer(
input_ids=qformer_input_ids,
attention_mask=qformer_attention_mask,
query_embeds=query_tokens,
encoder_hidden_states=image_embeds,
encoder_attention_mask=image_attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
query_output = query_outputs[0][:, : query_tokens.size(1), :]
# step 3: use the language model, conditioned on the query outputs and the prompt
language_model_inputs = self.language_projection(query_output)
# unbatch inputs back, each video-frame gets `num_query_tokens` seq length
language_model_inputs = language_model_inputs.reshape(batch_size, self.config.num_query_tokens * frames, -1)
language_model_attention_mask = torch.ones(
language_model_inputs.size()[:-1], dtype=torch.long, device=language_model_inputs.device
)
inputs_embeds = self.language_model.get_input_embeddings()(input_ids)
if attention_mask is None:
attention_mask = torch.ones_like(input_ids)
# if the model already has "video_token_index" then the input is expanded to account for image embeds
# otherwise we expand manually by concatenating
if getattr(self.config, "video_token_index", None) is not None:
special_image_mask = (input_ids == self.config.video_token_index).unsqueeze(-1).expand_as(inputs_embeds)
inputs_embeds[special_image_mask] = language_model_inputs.flatten().to(inputs_embeds.device)
else:
logger.warning_once(
"Expanding inputs for video tokens in InstructBLIPVideo should be done in processing. "
"Please follow instruction here (https://gist.github.com/zucchini-nlp/65f22892b054dc0d68228af56fbeaac2) to update your InstructBLIPVideo model. "
"Using processors without these attributes in the config is deprecated and will throw an error in v4.47."
)
inputs_embeds = torch.cat([language_model_inputs, inputs_embeds.to(language_model_inputs.device)], dim=1)
attention_mask = torch.cat(
[language_model_attention_mask, attention_mask.to(language_model_attention_mask.device)], dim=1
)
if self.config.use_decoder_only_language_model:
outputs = self.language_model(
inputs_embeds=inputs_embeds,
attention_mask=attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
use_cache=use_cache,
)
logits = outputs.logits if return_dict else outputs[0]
loss = None
# we compute the loss here since we need to take into account the sequence length of the query embeds
if labels is not None:
labels = labels.to(logits.device)
logits = logits[:, -labels.size(1) :, :]
# Shift so that tokens < n predict n
shift_logits = logits[..., :-1, :].contiguous()
shift_labels = labels[..., 1:].contiguous().to(logits.device)
# Flatten the tokens
loss_fct = CrossEntropyLoss(reduction="mean")
loss = loss_fct(shift_logits.view(-1, self.config.text_config.vocab_size), shift_labels.view(-1))
else:
outputs = self.language_model(
inputs_embeds=inputs_embeds,
attention_mask=attention_mask,
decoder_input_ids=decoder_input_ids,
decoder_attention_mask=decoder_attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
labels=labels,
use_cache=use_cache,
)
loss = outputs.loss if return_dict else outputs[0]
logits = outputs.logits if return_dict else outputs[1]
if not return_dict:
output = (logits, vision_outputs, query_outputs, outputs)
return ((loss,) + output) if loss is not None else output
return InstructBlipVideoForConditionalGenerationModelOutput(
loss=loss,
logits=logits,
vision_outputs=vision_outputs,
qformer_outputs=query_outputs,
language_model_outputs=outputs,
)
@torch.no_grad()
def generate(
self,
pixel_values: torch.FloatTensor,
qformer_input_ids: Optional[torch.LongTensor] = None,
qformer_attention_mask: Optional[torch.LongTensor] = None,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.LongTensor] = None,
interpolate_pos_encoding: bool = False,
**generate_kwargs,
) -> torch.LongTensor:
r"""
Overrides `generate` function to be able to use the model as a conditional generator.
Args:
pixel_values (`torch.FloatTensor` of shape (batch_size, num_channels, height, width) or
(batch_size, num_frames, num_channels, height, width)): Input images or videos to be processed.
qformer_input_ids (`torch.LongTensor` of shape (batch_size, sequence_length), *optional*):
The sequence used as a prompt to be fed to the Q-Former module.
qformer_attention_mask (`torch.LongTensor` of shape (batch_size, sequence_length), *optional*):
Mask to avoid performing attention on padding token indices.
input_ids (`torch.LongTensor` of shape (batch_size, sequence_length), *optional*):
The sequence used as a prompt for the generation.
attention_mask (`torch.LongTensor` of shape (batch_size, sequence_length), *optional*):
Mask to avoid performing attention on padding token indices.
interpolate_pos_encoding (`bool`, *optional*, defaults to `False`):
Whether to interpolate the positional encoding of the image embeddings.
Returns:
captions (list): A list of strings of length batch_size * num_captions.
"""
if hasattr(self, "hf_device_map"):
# preprocess for `accelerate`
self._preprocess_accelerate()
# we process in a batched way, later unbatch it back (video has frames=4)
batch_size, frames, channel, height, width = pixel_values.shape
pixel_values = pixel_values.reshape(batch_size * frames, channel, height, width)
image_embeds = self.vision_model(
pixel_values,
return_dict=True,
interpolate_pos_encoding=interpolate_pos_encoding,
).last_hidden_state
image_attention_mask = torch.ones(image_embeds.size()[:-1], dtype=torch.long, device=image_embeds.device)
query_tokens = self.query_tokens.expand(image_embeds.shape[0], -1, -1)
query_attention_mask = torch.ones(query_tokens.size()[:-1], dtype=torch.long, device=image_embeds.device)
if qformer_attention_mask is None:
qformer_attention_mask = torch.ones_like(qformer_input_ids)
qformer_input_ids = qformer_input_ids.repeat_interleave(frames, dim=0)
qformer_attention_mask = qformer_attention_mask.repeat_interleave(frames, dim=0)
qformer_attention_mask = torch.cat([query_attention_mask, qformer_attention_mask], dim=1)
query_outputs = self.qformer(
input_ids=qformer_input_ids,
attention_mask=qformer_attention_mask,
query_embeds=query_tokens,
encoder_hidden_states=image_embeds,
encoder_attention_mask=image_attention_mask,
return_dict=True,
)
query_output = query_outputs.last_hidden_state[:, : query_tokens.size(1), :]
language_model_inputs = self.language_projection(query_output)
# unbatch the embeddings back by moving frames to seq-len
language_model_inputs = language_model_inputs.reshape(batch_size, self.config.num_query_tokens * frames, -1)
language_attention_mask = torch.ones(
language_model_inputs.size()[:-1], dtype=torch.long, device=language_model_inputs.device
)
if input_ids is None:
start_tokens = [self.config.text_config.bos_token_id]
if getattr(self.config, "video_token_index", None) is not None:
start_tokens = [self.config.video_token_index] * self.config.num_query_tokens * 4 + start_tokens
input_ids = torch.tensor([start_tokens], dtype=torch.long, device=image_embeds.device)
input_ids = input_ids.repeat(batch_size, 1)
if attention_mask is None:
attention_mask = torch.ones_like(input_ids)
inputs_embeds = self.get_input_embeddings()(input_ids)
# if the model already has "video_token_index" then the input is expanded to account for image embeds
# otherwise we expand manually by concatenating
if getattr(self.config, "video_token_index", None) is not None:
special_image_mask = (input_ids == self.config.video_token_index).unsqueeze(-1).expand_as(inputs_embeds)
inputs_embeds[special_image_mask] = language_model_inputs.flatten().to(inputs_embeds.device)
else:
logger.warning_once(
"Expanding inputs for video tokens in InstructBLIPVideo should be done in processing. "
"Please follow instruction here (https://gist.github.com/zucchini-nlp/65f22892b054dc0d68228af56fbeaac2) to update your InstructBLIPVideo model. "
"Using processors without these attributes in the config is deprecated and will throw an error in v4.47."
)
inputs_embeds = torch.cat([language_model_inputs, inputs_embeds.to(language_model_inputs.device)], dim=1)
attention_mask = torch.cat(
[language_attention_mask, attention_mask.to(language_attention_mask.device)], dim=1
)
# add image_embeds length to max_length, so that the final max_length in counted only on token embeds
# -1 is to account for the prepended BOS after `generate.`
if not self.language_model.config.is_encoder_decoder:
generate_kwargs["max_length"] = (
generate_kwargs.get("max_length", 20) + language_model_inputs.shape[1] - 1
)
generate_kwargs["min_length"] = generate_kwargs.get("min_length", 0) + language_model_inputs.shape[1]
inputs = {"inputs_embeds": inputs_embeds, "attention_mask": attention_mask}
if not self.language_model.config.is_encoder_decoder:
inputs["input_ids"] = input_ids
outputs = self.language_model.generate(**inputs, **generate_kwargs)
return outputs
```
|
==========================================================================================================================================================
SOURCE CODE FILE: modular_instructblipvideo.py
LINES: 1
SIZE: 22.47 KB
PATH: scripts\freecad_env\Lib\site-packages\transformers\models\instructblipvideo\modular_instructblipvideo.py
ENCODING: utf-8
```py
# coding=utf-8
# Copyright 2024 HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch.nn import CrossEntropyLoss
from transformers.models.instructblip.configuration_instructblip import (
InstructBlipQFormerConfig,
InstructBlipVisionConfig,
)
from transformers.models.instructblip.modeling_instructblip import (
InstructBlipForConditionalGeneration,
InstructBlipForConditionalGenerationModelOutput,
)
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING, AutoConfig
logger = logging.get_logger(__name__)
class InstructBlipVideoVisionConfig(InstructBlipVisionConfig):
pass
class InstructBlipVideoQFormerConfig(InstructBlipQFormerConfig):
pass
class InstructBlipVideoConfig(PretrainedConfig):
r"""
[`InstructBlipVideoConfig`] is the configuration class to store the configuration of a
[`InstructBlipVideoForConditionalGeneration`]. It is used to instantiate a Instructblipvideo model according to the specified
arguments, defining the vision model, Q-Former model and language model configs. Instantiating a configuration with
the defaults will yield a similar configuration to that of the Instructblipvideo
[Salesforce/instruct-blip-flan-t5](https://huggingface.co/Salesforce/instruct-blip-flan-t5) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vision_config (`dict`, *optional*):
Dictionary of configuration options used to initialize [`InstructBlipVideoVisionConfig`].
qformer_config (`dict`, *optional*):
Dictionary of configuration options used to initialize [`InstructBlipVideoQFormerConfig`].
text_config (`dict`, *optional*):
Dictionary of configuration options used to initialize any [`PretrainedConfig`].
num_query_tokens (`int`, *optional*, defaults to 32):
The number of query tokens passed through the Transformer.
video_token_index (`int`, *optional*):
Token index of special video token.
kwargs (*optional*):
Dictionary of keyword arguments.
Example:
```python
>>> from transformers import (
... InstructBlipVideoVisionConfig,
... InstructBlipVideoQFormerConfig,
... OPTConfig,
... InstructBlipVideoConfig,
... InstructBlipVideoForConditionalGeneration,
... )
>>> # Initializing a InstructBlipVideoConfig with Salesforce/instruct-blip-flan-t5 style configuration
>>> configuration = InstructBlipVideoConfig()
>>> # Initializing a InstructBlipVideoForConditionalGeneration (with random weights) from the Salesforce/instruct-blip-flan-t5 style configuration
>>> model = InstructBlipVideoForConditionalGeneration(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
>>> # We can also initialize a InstructBlipVideoConfig from a InstructBlipVideoVisionConfig, InstructBlipVideoQFormerConfig and any PretrainedConfig
>>> # Initializing Instructblipvideo vision, Instructblipvideo Q-Former and language model configurations
>>> vision_config = InstructBlipVideoVisionConfig()
>>> qformer_config = InstructBlipVideoQFormerConfig()
>>> text_config = OPTConfig()
>>> config = InstructBlipVideoConfig.from_text_vision_configs(vision_config, qformer_config, text_config)
```"""
model_type = "instructblipvideo"
sub_configs = {
"text_config": AutoConfig,
"qformer_config": InstructBlipVideoQFormerConfig,
"vision_config": InstructBlipVideoVisionConfig,
}
def __init__(
self,
vision_config=None,
qformer_config=None,
text_config=None,
num_query_tokens=32,
video_token_index=None,
**kwargs,
):
super().__init__(**kwargs)
if vision_config is None:
vision_config = {}
logger.info("vision_config is None. initializing the InstructBlipVideoVisionConfig with default values.")
if qformer_config is None:
qformer_config = {}
logger.info("qformer_config is None. Initializing the InstructBlipVideoQFormerConfig with default values.")
if text_config is None:
text_config = {}
logger.info("text_config is None. Initializing the text config with default values (`OPTConfig`).")
self.vision_config = InstructBlipVideoVisionConfig(**vision_config)
self.qformer_config = InstructBlipVideoQFormerConfig(**qformer_config)
text_model_type = text_config["model_type"] if "model_type" in text_config else "opt"
self.text_config = CONFIG_MAPPING[text_model_type](**text_config)
self.num_query_tokens = num_query_tokens
self.video_token_index = video_token_index
self.qformer_config.encoder_hidden_size = self.vision_config.hidden_size
self.use_decoder_only_language_model = self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
self.initializer_factor = 1.0
self.initializer_range = 0.02
@classmethod
def from_vision_qformer_text_configs(
cls,
vision_config: InstructBlipVideoVisionConfig,
qformer_config: InstructBlipVideoQFormerConfig,
text_config: PretrainedConfig,
**kwargs,
):
r"""
Instantiate a [`InstructBlipVideoConfig`] (or a derived class) from a InstructBlipVideo vision model, Q-Former and
language model configurations.
Returns:
[`InstructBlipVideoConfig`]: An instance of a configuration object
"""
return cls(
vision_config=vision_config.to_dict(),
qformer_config=qformer_config.to_dict(),
text_config=text_config.to_dict(),
**kwargs,
)
@dataclass
class InstructBlipVideoForConditionalGenerationModelOutput(InstructBlipForConditionalGenerationModelOutput):
pass
class InstructBlipVideoForConditionalGeneration(InstructBlipForConditionalGeneration):
def forward(
self,
pixel_values: torch.FloatTensor,
qformer_input_ids: torch.FloatTensor,
qformer_attention_mask: Optional[torch.LongTensor] = None,
input_ids: Optional[torch.FloatTensor] = None,
attention_mask: Optional[torch.LongTensor] = None,
decoder_input_ids: Optional[torch.LongTensor] = None,
decoder_attention_mask: Optional[torch.LongTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
labels: Optional[torch.LongTensor] = None,
return_dict: Optional[bool] = None,
interpolate_pos_encoding: bool = False,
use_cache: Optional[bool] = None,
) -> Union[Tuple, InstructBlipVideoForConditionalGenerationModelOutput]:
r"""
```python
>>> from transformers import InstructBlipVideoProcessor, InstructBlipVideoForConditionalGeneration
>>> import torch
>>> from huggingface_hub import hf_hub_download
>>> import av
>>> import numpy as np
>>> def read_video_pyav(container, indices):
... '''
... Decode the video with PyAV decoder.
... Args:
... container (`av.container.input.InputContainer`): PyAV container.
... indices (`List[int]`): List of frame indices to decode.
... Returns:
... result (np.ndarray): np array of decoded frames of shape (num_frames, height, width, 3).
... '''
... frames = []
... container.seek(0)
... start_index = indices[0]
... end_index = indices[-1]
... for i, frame in enumerate(container.decode(video=0)):
... if i > end_index:
... break
... if i >= start_index and i in indices:
... frames.append(frame)
... return np.stack([x.to_ndarray(format="rgb24") for x in frames])
>>> model = InstructBlipVideoForConditionalGeneration.from_pretrained("Salesforce/instructblip-vicuna-7b", device_map="auto")
>>> processor = InstructBlipVideoProcessor.from_pretrained("Salesforce/instructblip-vicuna-7b")
>>> file_path = hf_hub_download(
... repo_id="nielsr/video-demo", filename="eating_spaghetti.mp4", repo_type="dataset"
... )
>>> container = av.open(file_path)
>>> # sample uniformly 4 frames from the videWhy is this video funny?o
>>> total_frames = container.streams.video[0].frames
>>> indices = np.arange(0, total_frames, total_frames / 4).astype(int)
>>> clip = read_video_pyav(container, indices)
>>> prompt = "What is happening in the video?"
>>> inputs = processor(text=prompt, images=clip, return_tensors="pt").to(model.device)
>>> outputs = model.generate(
... **inputs,
... do_sample=False,
... num_beams=5,
... max_length=256,
... repetition_penalty=1.5,
... length_penalty=1.0,
... )
>>> generated_text = processor.batch_decode(outputs, skip_special_tokens=True)[0].strip()
>>> print(generated_text)
"A person is eating a bowl of pasta, and they are using a fork to eat it. The person is sitting at a table, and the plate of pasta is on the table in front"
```"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
# step 1: forward the images through the vision encoder,
# we process in a batched way, later unbatch it back (video has frames=4 always)
batch_size, frames, channel, height, width = pixel_values.shape
pixel_values = pixel_values.reshape(batch_size * frames, channel, height, width)
vision_outputs = self.vision_model(
pixel_values=pixel_values,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
interpolate_pos_encoding=interpolate_pos_encoding,
)
image_embeds = vision_outputs[0]
# step 2: forward the query tokens through the QFormer, using the image embeddings for cross-attention
image_attention_mask = torch.ones(image_embeds.size()[:-1], dtype=torch.long, device=image_embeds.device)
# difference with BLIP-2 here: we also feed the instruction prompt to the Q-Former
query_tokens = self.query_tokens.expand(image_embeds.shape[0], -1, -1)
query_attention_mask = torch.ones(query_tokens.size()[:-1], dtype=torch.long, device=image_embeds.device)
if qformer_attention_mask is None:
qformer_attention_mask = torch.ones_like(qformer_input_ids)
qformer_input_ids = qformer_input_ids.repeat_interleave(frames, dim=0)
qformer_attention_mask = qformer_attention_mask.repeat_interleave(frames, dim=0)
qformer_attention_mask = torch.cat([query_attention_mask, qformer_attention_mask], dim=1)
query_outputs = self.qformer(
input_ids=qformer_input_ids,
attention_mask=qformer_attention_mask,
query_embeds=query_tokens,
encoder_hidden_states=image_embeds,
encoder_attention_mask=image_attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
query_output = query_outputs[0][:, : query_tokens.size(1), :]
# step 3: use the language model, conditioned on the query outputs and the prompt
language_model_inputs = self.language_projection(query_output)
# unbatch inputs back, each video-frame gets `num_query_tokens` seq length
language_model_inputs = language_model_inputs.reshape(batch_size, self.config.num_query_tokens * frames, -1)
language_model_attention_mask = torch.ones(
language_model_inputs.size()[:-1], dtype=torch.long, device=language_model_inputs.device
)
inputs_embeds = self.language_model.get_input_embeddings()(input_ids)
if attention_mask is None:
attention_mask = torch.ones_like(input_ids)
# if the model already has "video_token_index" then the input is expanded to account for image embeds
# otherwise we expand manually by concatenating
if getattr(self.config, "video_token_index", None) is not None:
special_image_mask = (input_ids == self.config.video_token_index).unsqueeze(-1).expand_as(inputs_embeds)
inputs_embeds[special_image_mask] = language_model_inputs.flatten().to(inputs_embeds.device)
else:
logger.warning_once(
"Expanding inputs for video tokens in InstructBLIPVideo should be done in processing. "
"Please follow instruction here (https://gist.github.com/zucchini-nlp/65f22892b054dc0d68228af56fbeaac2) to update your InstructBLIPVideo model. "
"Using processors without these attributes in the config is deprecated and will throw an error in v4.47."
)
inputs_embeds = torch.cat([language_model_inputs, inputs_embeds.to(language_model_inputs.device)], dim=1)
attention_mask = torch.cat(
[language_model_attention_mask, attention_mask.to(language_model_attention_mask.device)], dim=1
)
if self.config.use_decoder_only_language_model:
outputs = self.language_model(
inputs_embeds=inputs_embeds,
attention_mask=attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
use_cache=use_cache,
)
logits = outputs.logits if return_dict else outputs[0]
loss = None
# we compute the loss here since we need to take into account the sequence length of the query embeds
if labels is not None:
labels = labels.to(logits.device)
logits = logits[:, -labels.size(1) :, :]
# Shift so that tokens < n predict n
shift_logits = logits[..., :-1, :].contiguous()
shift_labels = labels[..., 1:].contiguous().to(logits.device)
# Flatten the tokens
loss_fct = CrossEntropyLoss(reduction="mean")
loss = loss_fct(shift_logits.view(-1, self.config.text_config.vocab_size), shift_labels.view(-1))
else:
outputs = self.language_model(
inputs_embeds=inputs_embeds,
attention_mask=attention_mask,
decoder_input_ids=decoder_input_ids,
decoder_attention_mask=decoder_attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
labels=labels,
use_cache=use_cache,
)
loss = outputs.loss if return_dict else outputs[0]
logits = outputs.logits if return_dict else outputs[1]
if not return_dict:
output = (logits, vision_outputs, query_outputs, outputs)
return ((loss,) + output) if loss is not None else output
return InstructBlipVideoForConditionalGenerationModelOutput(
loss=loss,
logits=logits,
vision_outputs=vision_outputs,
qformer_outputs=query_outputs,
language_model_outputs=outputs,
)
@torch.no_grad()
def generate(
self,
pixel_values: torch.FloatTensor,
qformer_input_ids: Optional[torch.LongTensor] = None,
qformer_attention_mask: Optional[torch.LongTensor] = None,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.LongTensor] = None,
interpolate_pos_encoding: bool = False,
**generate_kwargs,
) -> torch.LongTensor:
r"""
Overrides `generate` function to be able to use the model as a conditional generator.
Args:
pixel_values (`torch.FloatTensor` of shape (batch_size, num_channels, height, width) or
(batch_size, num_frames, num_channels, height, width)): Input images or videos to be processed.
qformer_input_ids (`torch.LongTensor` of shape (batch_size, sequence_length), *optional*):
The sequence used as a prompt to be fed to the Q-Former module.
qformer_attention_mask (`torch.LongTensor` of shape (batch_size, sequence_length), *optional*):
Mask to avoid performing attention on padding token indices.
input_ids (`torch.LongTensor` of shape (batch_size, sequence_length), *optional*):
The sequence used as a prompt for the generation.
attention_mask (`torch.LongTensor` of shape (batch_size, sequence_length), *optional*):
Mask to avoid performing attention on padding token indices.
interpolate_pos_encoding (`bool`, *optional*, defaults to `False`):
Whether to interpolate the positional encoding of the image embeddings.
Returns:
captions (list): A list of strings of length batch_size * num_captions.
"""
if hasattr(self, "hf_device_map"):
# preprocess for `accelerate`
self._preprocess_accelerate()
# we process in a batched way, later unbatch it back (video has frames=4)
batch_size, frames, channel, height, width = pixel_values.shape
pixel_values = pixel_values.reshape(batch_size * frames, channel, height, width)
image_embeds = self.vision_model(
pixel_values,
return_dict=True,
interpolate_pos_encoding=interpolate_pos_encoding,
).last_hidden_state
image_attention_mask = torch.ones(image_embeds.size()[:-1], dtype=torch.long, device=image_embeds.device)
query_tokens = self.query_tokens.expand(image_embeds.shape[0], -1, -1)
query_attention_mask = torch.ones(query_tokens.size()[:-1], dtype=torch.long, device=image_embeds.device)
if qformer_attention_mask is None:
qformer_attention_mask = torch.ones_like(qformer_input_ids)
qformer_input_ids = qformer_input_ids.repeat_interleave(frames, dim=0)
qformer_attention_mask = qformer_attention_mask.repeat_interleave(frames, dim=0)
qformer_attention_mask = torch.cat([query_attention_mask, qformer_attention_mask], dim=1)
query_outputs = self.qformer(
input_ids=qformer_input_ids,
attention_mask=qformer_attention_mask,
query_embeds=query_tokens,
encoder_hidden_states=image_embeds,
encoder_attention_mask=image_attention_mask,
return_dict=True,
)
query_output = query_outputs.last_hidden_state[:, : query_tokens.size(1), :]
language_model_inputs = self.language_projection(query_output)
# unbatch the embeddings back by moving frames to seq-len
language_model_inputs = language_model_inputs.reshape(batch_size, self.config.num_query_tokens * frames, -1)
language_attention_mask = torch.ones(
language_model_inputs.size()[:-1], dtype=torch.long, device=language_model_inputs.device
)
if input_ids is None:
start_tokens = [self.config.text_config.bos_token_id]
if getattr(self.config, "video_token_index", None) is not None:
start_tokens = [self.config.video_token_index] * self.config.num_query_tokens * 4 + start_tokens
input_ids = torch.tensor([start_tokens], dtype=torch.long, device=image_embeds.device)
input_ids = input_ids.repeat(batch_size, 1)
if attention_mask is None:
attention_mask = torch.ones_like(input_ids)
inputs_embeds = self.get_input_embeddings()(input_ids)
# if the model already has "video_token_index" then the input is expanded to account for image embeds
# otherwise we expand manually by concatenating
if getattr(self.config, "video_token_index", None) is not None:
special_image_mask = (input_ids == self.config.video_token_index).unsqueeze(-1).expand_as(inputs_embeds)
inputs_embeds[special_image_mask] = language_model_inputs.flatten().to(inputs_embeds.device)
else:
logger.warning_once(
"Expanding inputs for video tokens in InstructBLIPVideo should be done in processing. "
"Please follow instruction here (https://gist.github.com/zucchini-nlp/65f22892b054dc0d68228af56fbeaac2) to update your InstructBLIPVideo model. "
"Using processors without these attributes in the config is deprecated and will throw an error in v4.47."
)
inputs_embeds = torch.cat([language_model_inputs, inputs_embeds.to(language_model_inputs.device)], dim=1)
attention_mask = torch.cat(
[language_attention_mask, attention_mask.to(language_attention_mask.device)], dim=1
)
# add image_embeds length to max_length, so that the final max_length in counted only on token embeds
# -1 is to account for the prepended BOS after `generate.`
if not self.language_model.config.is_encoder_decoder:
generate_kwargs["max_length"] = (
generate_kwargs.get("max_length", 20) + language_model_inputs.shape[1] - 1
)
generate_kwargs["min_length"] = generate_kwargs.get("min_length", 0) + language_model_inputs.shape[1]
inputs = {"inputs_embeds": inputs_embeds, "attention_mask": attention_mask}
if not self.language_model.config.is_encoder_decoder:
inputs["input_ids"] = input_ids
outputs = self.language_model.generate(**inputs, **generate_kwargs)
return outputs
```
|
=============================================================================================================================================================
SOURCE CODE FILE: processing_instructblipvideo.py
LINES: 1
SIZE: 10.85 KB
PATH: scripts\freecad_env\Lib\site-packages\transformers\models\instructblipvideo\processing_instructblipvideo.py
ENCODING: utf-8
```py
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Processor class for InstructBLIP. Largely copy of Blip2Processor with addition of a tokenizer for the Q-Former.
"""
import os
from typing import List, Optional, Union
from ...image_processing_utils import BatchFeature
from ...image_utils import VideoInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import (
AddedToken,
BatchEncoding,
PaddingStrategy,
PreTokenizedInput,
TextInput,
TruncationStrategy,
)
from ...utils import TensorType, logging
from ..auto import AutoTokenizer
logger = logging.get_logger(__name__)
class InstructBlipVideoProcessor(ProcessorMixin):
r"""
Constructs an InstructBLIPVideo processor which wraps a InstructBLIP image processor and a LLaMa/T5 tokenizer into a single
processor.
[`InstructBlipVideoProcessor`] offers all the functionalities of [`InstructBlipVideoImageProcessor`] and [`AutoTokenizer`]. See the
docstring of [`~InstructBlipVideoProcessor.__call__`] and [`~InstructBlipVideoProcessor.decode`] for more information.
Args:
image_processor (`InstructBlipVideoImageProcessor`):
An instance of [`InstructBlipVideoImageProcessor`]. The image processor is a required input.
tokenizer (`AutoTokenizer`):
An instance of ['PreTrainedTokenizer`]. The tokenizer is a required input.
qformer_tokenizer (`AutoTokenizer`):
An instance of ['PreTrainedTokenizer`]. The Q-Former tokenizer is a required input.
num_query_tokens (`int`, *optional*):
Number of tokens used by the Qformer as queries, should be same as in model's config.
"""
attributes = ["image_processor", "tokenizer", "qformer_tokenizer"]
valid_kwargs = ["num_query_tokens"]
image_processor_class = "InstructBlipVideoImageProcessor"
tokenizer_class = "AutoTokenizer"
qformer_tokenizer_class = "AutoTokenizer"
def __init__(self, image_processor, tokenizer, qformer_tokenizer, num_query_tokens=None, **kwargs):
if not hasattr(tokenizer, "video_token"):
self.video_token = AddedToken("<video>", normalized=False, special=True)
tokenizer.add_tokens([self.video_token], special_tokens=True)
else:
self.video_token = tokenizer.video_token
self.num_query_tokens = num_query_tokens
super().__init__(image_processor, tokenizer, qformer_tokenizer)
def __call__(
self,
images: VideoInput = None,
text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,
add_special_tokens: bool = True,
padding: Union[bool, str, PaddingStrategy] = False,
truncation: Union[bool, str, TruncationStrategy] = None,
max_length: Optional[int] = None,
stride: int = 0,
pad_to_multiple_of: Optional[int] = None,
return_attention_mask: Optional[bool] = None,
return_overflowing_tokens: bool = False,
return_special_tokens_mask: bool = False,
return_offsets_mapping: bool = False,
return_token_type_ids: bool = False,
return_length: bool = False,
verbose: bool = True,
return_tensors: Optional[Union[str, TensorType]] = None,
**kwargs,
) -> BatchFeature:
"""
This method uses [`InstructBlipVideoImageProcessor.__call__`] method to prepare image(s) or video(s) for the model, and
[`BertTokenizerFast.__call__`] to prepare text for the model.
Please refer to the docstring of the above two methods for more information.
"""
if images is None and text is None:
raise ValueError("You have to specify at least one of images or text.")
encoding = BatchFeature()
if text is not None:
if isinstance(text, str):
text = [text]
elif not isinstance(text, list) and not isinstance(text[0], str):
raise ValueError("Invalid input text. Please provide a string, or a list of strings")
_text_encoding = self.tokenizer(
text=text,
add_special_tokens=add_special_tokens,
padding=padding,
truncation=truncation,
max_length=max_length,
stride=stride,
pad_to_multiple_of=pad_to_multiple_of,
return_attention_mask=return_attention_mask,
return_overflowing_tokens=return_overflowing_tokens,
return_special_tokens_mask=return_special_tokens_mask,
return_offsets_mapping=return_offsets_mapping,
return_token_type_ids=return_token_type_ids,
return_length=return_length,
verbose=verbose,
return_tensors=None, # required to concatenate below
**kwargs,
)
# if we know how many query tokens, expand text inside processor. We need this hacky manipulation
# because BLIP expects image tokens to be at the beginning even before BOS token
if self.num_query_tokens is not None and images is not None:
text_encoding = {}
video_tokens = (
self.video_token.content * self.num_query_tokens * 4
) # InstrucBLIP works with 4 frames only
video_token_encoding = self.tokenizer(
[video_tokens] * len(text), add_special_tokens=False, return_tensors=None
)
for k in _text_encoding:
text_encoding[k] = [
img_encoding + txt_encoding
for img_encoding, txt_encoding in zip(video_token_encoding[k], _text_encoding[k])
]
else:
text_encoding = _text_encoding
if images is not None:
logger.warning_once(
"Expanding inputs for video tokens in InstructBLIPVideo should be done in processing. "
"Please follow instruction here (https://gist.github.com/zucchini-nlp/65f22892b054dc0d68228af56fbeaac2) to update your InstructBLIPVideo model. "
"Using processors without these attributes in the config is deprecated and will throw an error in v4.47."
)
# cast to desired return tensors type after concatenating
text_encoding = BatchEncoding(text_encoding, tensor_type=return_tensors)
encoding.update(text_encoding)
qformer_text_encoding = self.qformer_tokenizer(
text=text,
add_special_tokens=add_special_tokens,
padding=padding,
truncation=truncation,
max_length=max_length,
stride=stride,
pad_to_multiple_of=pad_to_multiple_of,
return_attention_mask=return_attention_mask,
return_overflowing_tokens=return_overflowing_tokens,
return_special_tokens_mask=return_special_tokens_mask,
return_offsets_mapping=return_offsets_mapping,
return_token_type_ids=return_token_type_ids,
return_length=return_length,
verbose=verbose,
return_tensors=return_tensors,
**kwargs,
)
encoding["qformer_input_ids"] = qformer_text_encoding.pop("input_ids")
encoding["qformer_attention_mask"] = qformer_text_encoding.pop("attention_mask")
if images is not None:
image_encoding = self.image_processor(images, return_tensors=return_tensors)
encoding.update(image_encoding)
return encoding
# Copied from transformers.models.blip.processing_blip.BlipProcessor.batch_decode with BertTokenizerFast->PreTrainedTokenizer
def batch_decode(self, *args, **kwargs):
"""
This method forwards all its arguments to PreTrainedTokenizer's [`~PreTrainedTokenizer.batch_decode`]. Please
refer to the docstring of this method for more information.
"""
return self.tokenizer.batch_decode(*args, **kwargs)
# Copied from transformers.models.blip.processing_blip.BlipProcessor.decode with BertTokenizerFast->PreTrainedTokenizer
def decode(self, *args, **kwargs):
"""
This method forwards all its arguments to PreTrainedTokenizer's [`~PreTrainedTokenizer.decode`]. Please refer to
the docstring of this method for more information.
"""
return self.tokenizer.decode(*args, **kwargs)
@property
# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
def model_input_names(self):
tokenizer_input_names = self.tokenizer.model_input_names
image_processor_input_names = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))
# overwrite to save the Q-Former tokenizer in a separate folder
def save_pretrained(self, save_directory, **kwargs):
if os.path.isfile(save_directory):
raise ValueError(f"Provided path ({save_directory}) should be a directory, not a file")
os.makedirs(save_directory, exist_ok=True)
qformer_tokenizer_path = os.path.join(save_directory, "qformer_tokenizer")
self.qformer_tokenizer.save_pretrained(qformer_tokenizer_path)
# We modify the attributes so that only the tokenizer and image processor are saved in the main folder
qformer_present = "qformer_tokenizer" in self.attributes
if qformer_present:
self.attributes.remove("qformer_tokenizer")
outputs = super().save_pretrained(save_directory, **kwargs)
if qformer_present:
self.attributes += ["qformer_tokenizer"]
return outputs
# overwrite to load the Q-Former tokenizer from a separate folder
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path, **kwargs):
processor = super().from_pretrained(pretrained_model_name_or_path, **kwargs)
# if return_unused_kwargs a tuple is returned where the second element is 'unused_kwargs'
if isinstance(processor, tuple):
processor = processor[0]
qformer_tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path, subfolder="qformer_tokenizer")
processor.qformer_tokenizer = qformer_tokenizer
return processor
```
|
=============================================================================================================================
SOURCE CODE FILE: __init__.py
LINES: 1
SIZE: 0.97 KB
PATH: scripts\freecad_env\Lib\site-packages\transformers\models\jamba\__init__.py
ENCODING: utf-8
```py
# Copyright 2024 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import _LazyModule
from ...utils.import_utils import define_import_structure
if TYPE_CHECKING:
from .configuration_jamba import *
from .modeling_jamba import *
else:
import sys
_file = globals()["__file__"]
sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)
```
|
========================================================================================================================================
SOURCE CODE FILE: configuration_jamba.py
LINES: 1
SIZE: 11.46 KB
PATH: scripts\freecad_env\Lib\site-packages\transformers\models\jamba\configuration_jamba.py
ENCODING: utf-8
```py
# coding=utf-8
# Copyright 2024 AI21 Labs Ltd. and the HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Jamba model configuration"""
import math
from ...configuration_utils import PretrainedConfig
from ...utils import logging
logger = logging.get_logger(__name__)
class JambaConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`JambaModel`]. It is used to instantiate a
Jamba model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a similar configuration to that of the Jamba-v0.1 model.
[ai21labs/Jamba-v0.1](https://huggingface.co/ai21labs/Jamba-v0.1)
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 65536):
Vocabulary size of the Jamba model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`JambaModel`]
tie_word_embeddings (`bool`, *optional*, defaults to `False`):
Whether the model's input and output word embeddings should be tied. Note that this is only relevant if the
model has a output word embedding layer.
hidden_size (`int`, *optional*, defaults to 4096):
Dimension of the hidden representations.
intermediate_size (`int`, *optional*, defaults to 14336):
Dimension of the MLP representations.
num_hidden_layers (`int`, *optional*, defaults to 32):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 32):
Number of attention heads for each attention layer in the Transformer encoder.
num_key_value_heads (`int`, *optional*, defaults to 8):
This is the number of key_value heads that should be used to implement Grouped Query Attention. If
`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
`num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
by meanpooling all the original heads within that group. For more details checkout [this
paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to `8`.
hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
The non-linear activation function (function or string) in the decoder.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
rms_norm_eps (`float`, *optional*, defaults to 1e-06):
The epsilon used by the rms normalization layers.
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models). Only
relevant if `config.is_decoder=True`.
num_logits_to_keep (`int` or `None`, *optional*, defaults to 1):
Number of prompt logits to calculate during generation. If `None`, all logits will be calculated. If an
integer value, only last `num_logits_to_keep` logits will be calculated. Default is 1 because only the
logits of the last prompt token are needed for generation. For long sequences, the logits for the entire
sequence may use a lot of memory so, setting `num_logits_to_keep=1` will reduce memory footprint
significantly.
output_router_logits (`bool`, *optional*, defaults to `False`):
Whether or not the router logits should be returned by the model. Enabling this will also
allow the model to output the auxiliary loss. See [here]() for more details
router_aux_loss_coef (`float`, *optional*, defaults to 0.001):
The aux loss factor for the total loss.
pad_token_id (`int`, *optional*, defaults to 0):
The id of the padding token.
bos_token_id (`int`, *optional*, defaults to 1):
The id of the "beginning-of-sequence" token.
eos_token_id (`int`, *optional*, defaults to 2):
The id of the "end-of-sequence" token.
sliding_window (`int`, *optional*):
Sliding window attention window size. If not specified, will default to `None`.
max_position_embeddings (`int`, *optional*, defaults to 262144):
This value doesn't have any real effect. The maximum sequence length that this model is intended to be
used with. It can be used with longer sequences, but performance may degrade.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
num_experts_per_tok (`int`, *optional*, defaults to 2):
The number of experts to root per-token, can be also interpreted as the `top-p` routing
parameter
num_experts (`int`, *optional*, defaults to 16):
Number of experts per Sparse MLP layer.
expert_layer_period (`int`, *optional*, defaults to 2):
Once in this many layers, we will have an expert layer
expert_layer_offset (`int`, *optional*, defaults to 1):
The first layer index that contains an expert mlp layer
attn_layer_period (`int`, *optional*, defaults to 8):
Once in this many layers, we will have a vanilla attention layer
attn_layer_offset (`int`, *optional*, defaults to 4):
The first layer index that contains a vanilla attention mlp layer
use_mamba_kernels (`bool`, *optional*, defaults to `True`):
Flag indicating whether or not to use the fast mamba kernels. These are available only if `mamba-ssm` and
`causal-conv1d` are installed, and the mamba modules are running on a CUDA device. Raises ValueError if
`True` and kernels are not available
mamba_d_state (`int`, *optional*, defaults to 16):
The dimension the mamba state space latents
mamba_d_conv (`int`, *optional*, defaults to 4):
The size of the mamba convolution kernel
mamba_expand (`int`, *optional*, defaults to 2):
Expanding factor (relative to hidden_size) used to determine the mamba intermediate size
mamba_dt_rank (`Union[int,str]`, *optional*, defaults to `"auto"`):
Rank of the mamba discretization projection matrix. `"auto"` means that it will default to `math.ceil(self.hidden_size / 16)`
mamba_conv_bias (`bool`, *optional*, defaults to `True`):
Flag indicating whether or not to use bias in the convolution layer of the mamba mixer block.
mamba_proj_bias (`bool`, *optional*, defaults to `False`):
Flag indicating whether or not to use bias in the input and output projections (["in_proj", "out_proj"]) of the mamba mixer block
"""
model_type = "jamba"
keys_to_ignore_at_inference = ["past_key_values"]
def __init__(
self,
vocab_size=65536,
tie_word_embeddings=False,
hidden_size=4096,
intermediate_size=14336,
num_hidden_layers=32,
num_attention_heads=32,
num_key_value_heads=8,
hidden_act="silu",
initializer_range=0.02,
rms_norm_eps=1e-6,
use_cache=True,
num_logits_to_keep=1,
output_router_logits=False,
router_aux_loss_coef=0.001,
pad_token_id=0,
bos_token_id=1,
eos_token_id=2,
sliding_window=None,
max_position_embeddings=262144,
attention_dropout=0.0,
num_experts_per_tok=2,
num_experts=16,
expert_layer_period=2,
expert_layer_offset=1,
attn_layer_period=8,
attn_layer_offset=4,
use_mamba_kernels=True,
mamba_d_state=16,
mamba_d_conv=4,
mamba_expand=2,
mamba_dt_rank="auto",
mamba_conv_bias=True,
mamba_proj_bias=False,
**kwargs,
):
self.vocab_size = vocab_size
self.tie_word_embeddings = tie_word_embeddings
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.sliding_window = sliding_window
self.max_position_embeddings = max_position_embeddings
self.attention_dropout = attention_dropout
# for backward compatibility
if num_key_value_heads is None:
num_key_value_heads = num_attention_heads
self.num_key_value_heads = num_key_value_heads
self.hidden_act = hidden_act
self.initializer_range = initializer_range
self.rms_norm_eps = rms_norm_eps
self.use_cache = use_cache
self.num_logits_to_keep = num_logits_to_keep
self.output_router_logits = output_router_logits
self.router_aux_loss_coef = router_aux_loss_coef
self.num_experts_per_tok = num_experts_per_tok
self.num_experts = num_experts
self.expert_layer_period = expert_layer_period
self.expert_layer_offset = expert_layer_offset
self.attn_layer_period = attn_layer_period
self.attn_layer_offset = attn_layer_offset
self._check_supported_offset("attention", self.attn_layer_period, self.attn_layer_offset)
self._check_supported_offset("expert", self.expert_layer_period, self.expert_layer_offset)
self.use_mamba_kernels = use_mamba_kernels
self.mamba_d_state = mamba_d_state
self.mamba_d_conv = mamba_d_conv
self.mamba_expand = mamba_expand
self.mamba_dt_rank = math.ceil(self.hidden_size / 16) if mamba_dt_rank == "auto" else mamba_dt_rank
self.mamba_conv_bias = mamba_conv_bias
self.mamba_proj_bias = mamba_proj_bias
super().__init__(
pad_token_id=pad_token_id,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
tie_word_embeddings=tie_word_embeddings,
**kwargs,
)
@property
def layers_block_type(self):
return [
"attention" if i % self.attn_layer_period == self.attn_layer_offset else "mamba"
for i in range(self.num_hidden_layers)
]
@property
def layers_num_experts(self):
return [
self.num_experts if i % self.expert_layer_period == self.expert_layer_offset else 1
for i in range(self.num_hidden_layers)
]
def _check_supported_offset(self, property_: str, period: int, offset: int):
if offset >= period:
raise ValueError(
f"{property_} layer offset ({offset}) must be smaller than {property_} layer period ({period})"
)
__all__ = ["JambaConfig"]
```
|
===================================================================================================================================
SOURCE CODE FILE: modeling_jamba.py
LINES: 2
SIZE: 78.58 KB
PATH: scripts\freecad_env\Lib\site-packages\transformers\models\jamba\modeling_jamba.py
ENCODING: utf-8
```py
# coding=utf-8
# Copyright 2024 AI21 Labs Ltd. and the HuggingFace Inc. team. All rights reserved.
#
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
# and OPT implementations in this library. It has been modified from its
# original forms to accommodate minor architectural differences compared
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""PyTorch Jamba model."""
import math
from typing import Any, Dict, Optional, Tuple, Union
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
from torch import nn
from ...activations import ACT2FN
from ...cache_utils import Cache, DynamicCache # we need __iter__ and __len__ of pkv
from ...generation import GenerationMixin
from ...modeling_attn_mask_utils import (
AttentionMaskConverter,
)
from ...modeling_flash_attention_utils import flash_attn_supports_top_left_mask, is_flash_attn_available
from ...modeling_outputs import (
MoeCausalLMOutputWithPast,
MoeModelOutputWithPast,
SequenceClassifierOutputWithPast,
)
from ...modeling_utils import PreTrainedModel
from ...utils import (
add_start_docstrings,
add_start_docstrings_to_model_forward,
can_return_tuple,
logging,
replace_return_docstrings,
)
from ...utils.deprecation import deprecate_kwarg
from ...utils.import_utils import (
is_causal_conv1d_available,
is_mamba_ssm_available,
)
from .configuration_jamba import JambaConfig
if is_flash_attn_available():
from ...modeling_flash_attention_utils import _flash_attention_forward
if is_mamba_ssm_available():
from mamba_ssm.ops.selective_scan_interface import mamba_inner_fn, selective_scan_fn
from mamba_ssm.ops.triton.selective_state_update import selective_state_update
else:
selective_state_update, selective_scan_fn, mamba_inner_fn = None, None, None
if is_causal_conv1d_available():
from causal_conv1d import causal_conv1d_fn, causal_conv1d_update
else:
causal_conv1d_update, causal_conv1d_fn = None, None
is_fast_path_available = all(
(selective_state_update, selective_scan_fn, causal_conv1d_fn, causal_conv1d_update, mamba_inner_fn)
)
logger = logging.get_logger(__name__)
_CONFIG_FOR_DOC = "JambaConfig"
# Copied from transformers.models.mixtral.modeling_mixtral.load_balancing_loss_func with gate->router
def load_balancing_loss_func(
router_logits: Union[torch.Tensor, Tuple[torch.Tensor], None],
num_experts: Optional[int] = None,
top_k=2,
attention_mask: Optional[torch.Tensor] = None,
) -> Union[torch.Tensor, int]:
r"""
Computes auxiliary load balancing loss as in Switch Transformer - implemented in Pytorch.
See Switch Transformer (https://arxiv.org/abs/2101.03961) for more details. This function implements the loss
function presented in equations (4) - (6) of the paper. It aims at penalizing cases where the routing between
experts is too unbalanced.
Args:
router_logits:
Logits from the `router`, should be a tuple of model.config.num_hidden_layers tensors of
shape [batch_size X sequence_length, num_experts].
num_experts:
Number of experts
top_k:
The number of experts to route per-token, can be also interpreted as the `top-k` routing
parameter.
attention_mask (`torch.Tensor`, *optional*):
The attention_mask used in forward function
shape [batch_size X sequence_length] if not None.
Returns:
The auxiliary loss.
"""
if router_logits is None or not isinstance(router_logits, tuple):
return 0
if isinstance(router_logits, tuple):
compute_device = router_logits[0].device
concatenated_router_logits = torch.cat(
[layer_router.to(compute_device) for layer_router in router_logits], dim=0
)
routing_weights = torch.nn.functional.softmax(concatenated_router_logits, dim=-1)
_, selected_experts = torch.topk(routing_weights, top_k, dim=-1)
expert_mask = torch.nn.functional.one_hot(selected_experts, num_experts)
if attention_mask is None:
# Compute the percentage of tokens routed to each experts
tokens_per_expert = torch.mean(expert_mask.float(), dim=0)
# Compute the average probability of routing to these experts
router_prob_per_expert = torch.mean(routing_weights, dim=0)
else:
batch_size, sequence_length = attention_mask.shape
num_hidden_layers = concatenated_router_logits.shape[0] // (batch_size * sequence_length)
# Compute the mask that masks all padding tokens as 0 with the same shape of expert_mask
expert_attention_mask = (
attention_mask[None, :, :, None, None]
.expand((num_hidden_layers, batch_size, sequence_length, top_k, num_experts))
.reshape(-1, top_k, num_experts)
.to(compute_device)
)
# Compute the percentage of tokens routed to each experts
tokens_per_expert = torch.sum(expert_mask.float() * expert_attention_mask, dim=0) / torch.sum(
expert_attention_mask, dim=0
)
# Compute the mask that masks all padding tokens as 0 with the same shape of tokens_per_expert
router_per_expert_attention_mask = (
attention_mask[None, :, :, None]
.expand((num_hidden_layers, batch_size, sequence_length, num_experts))
.reshape(-1, num_experts)
.to(compute_device)
)
# Compute the average probability of routing to these experts
router_prob_per_expert = torch.sum(routing_weights * router_per_expert_attention_mask, dim=0) / torch.sum(
router_per_expert_attention_mask, dim=0
)
overall_loss = torch.sum(tokens_per_expert * router_prob_per_expert.unsqueeze(0))
return overall_loss * num_experts
# Copied from transformers.models.llama.modeling_llama.LlamaRMSNorm with Llama->Jamba
class JambaRMSNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-6):
"""
JambaRMSNorm is equivalent to T5LayerNorm
"""
super().__init__()
self.weight = nn.Parameter(torch.ones(hidden_size))
self.variance_epsilon = eps
def forward(self, hidden_states):
input_dtype = hidden_states.dtype
hidden_states = hidden_states.to(torch.float32)
variance = hidden_states.pow(2).mean(-1, keepdim=True)
hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
return self.weight * hidden_states.to(input_dtype)
def extra_repr(self):
return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}"
# Copied from transformers.models.llama.modeling_llama.repeat_kv
def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
"""
This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
"""
batch, num_key_value_heads, slen, head_dim = hidden_states.shape
if n_rep == 1:
return hidden_states
hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
class HybridMambaAttentionDynamicCache(DynamicCache):
"""
A dynamic cache that can handle both the attention cache (which has a seq_len dimension) and the mamba cache
(which has a constant shape regardless of seq_len).
This cache has two sets of lists of tensors: `key_cache` and `value_cache` for attention cache and `conv_states`
and `ssm_states` for mamba cache. Each of these lists has `num_layers` tensors. The expected shape for each tensor
For attention layers, `key_cache` and `value_cache` have a shape of `(batch_size, num_heads, seq_len, head_dim)`,
while `conv_states` and `ssm_states` have a shape of `(batch_size, 0)` (empty tensors).
For mamba layers, `key_cache` and `value_cache` have a shape of `(batch_size, 0)` (empty tensors),
while `conv_states` represents the convolution state and has a shape of `(batch_size, d_inner, d_conv)`,
and `ssm_states` represents the ssm state and has a shape of `(batch_size, d_inner, d_state)`.
"""
def __init__(self, config, batch_size, dtype=torch.float16, device=None):
super().__init__()
self.dtype = dtype
self.layers_block_type = config.layers_block_type
self.has_previous_state = False # only used by mamba
intermediate_size = config.mamba_expand * config.hidden_size
ssm_state_size = config.mamba_d_state
conv_kernel_size = config.mamba_d_conv
self.conv_states = []
self.ssm_states = []
self.transformer_layers = []
for i in range(config.num_hidden_layers):
if self.layers_block_type[i] == "mamba":
self.conv_states += [
torch.zeros(batch_size, intermediate_size, conv_kernel_size, device=device, dtype=dtype)
]
self.ssm_states += [
torch.zeros(batch_size, intermediate_size, ssm_state_size, device=device, dtype=dtype)
]
else:
self.conv_states += [torch.tensor([[]] * batch_size, device=device)]
self.ssm_states += [torch.tensor([[]] * batch_size, device=device)]
self.transformer_layers.append(i)
self.key_cache = [torch.tensor([[]] * batch_size, device=device) for _ in range(config.num_hidden_layers)]
self.value_cache = [torch.tensor([[]] * batch_size, device=device) for _ in range(config.num_hidden_layers)]
def update(
self,
key_states: torch.Tensor,
value_states: torch.Tensor,
layer_idx: int,
cache_kwargs: Optional[Dict[str, Any]] = None,
) -> Tuple[torch.Tensor, torch.Tensor]:
# Update the cache
if self.key_cache[layer_idx].shape[-1] == 0:
self.key_cache[layer_idx] = key_states
self.value_cache[layer_idx] = value_states
else:
self.key_cache[layer_idx] = torch.cat([self.key_cache[layer_idx], key_states], dim=2)
self.value_cache[layer_idx] = torch.cat([self.value_cache[layer_idx], value_states], dim=2)
return self.key_cache[layer_idx], self.value_cache[layer_idx]
def reorder_cache(self, beam_idx: torch.LongTensor):
"""Reorders the cache for beam search, given the selected beam indices."""
for layer_idx in range(len(self.key_cache)):
device = self.key_cache[layer_idx].device
self.key_cache[layer_idx] = self.key_cache[layer_idx].index_select(0, beam_idx.to(device))
device = self.value_cache[layer_idx].device
self.value_cache[layer_idx] = self.value_cache[layer_idx].index_select(0, beam_idx.to(device))
device = self.conv_states[layer_idx].device
self.conv_states[layer_idx] = self.conv_states[layer_idx].index_select(0, beam_idx.to(device))
device = self.ssm_states[layer_idx].device
self.ssm_states[layer_idx] = self.ssm_states[layer_idx].index_select(0, beam_idx.to(device))
def get_seq_length(self, layer_idx: Optional[int] = 0) -> int:
"""Returns the sequence length of the cached states. A layer index can be optionally passed."""
# take any layer that contains cache and not empty tensor
layer_idx = self.transformer_layers[0] if layer_idx not in self.transformer_layers else layer_idx
if len(self.key_cache) <= layer_idx:
return 0
return self.key_cache[layer_idx].shape[-2]
def to_legacy_cache(self) -> Tuple[Tuple[torch.Tensor], Tuple[torch.Tensor]]:
raise NotImplementedError("HybridMambaAttentionDynamicCache does not have a legacy cache equivalent.")
@classmethod
def from_legacy_cache(cls, past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None) -> "DynamicCache":
raise NotImplementedError("HybridMambaAttentionDynamicCache does not have a legacy cache equivalent.")
# Adapted from transformers.models.mistral.modeling_mistral.MistralAttention with Mistral->Jamba
class JambaAttention(nn.Module):
"""
Multi-headed attention from 'Attention Is All You Need' paper. Modified to use sliding window attention: Longformer
and "Generating Long Sequences with Sparse Transformers".
"""
def __init__(self, config: JambaConfig, layer_idx: Optional[int] = None):
super().__init__()
self.config = config
self.layer_idx = layer_idx
if layer_idx is None:
logger.warning_once(
f"Instantiating {self.__class__.__name__} without passing a `layer_idx` is not recommended and will "
"lead to errors during the forward call if caching is used. Please make sure to provide a `layer_idx` "
"when creating this class."
)
self.hidden_size = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = self.hidden_size // self.num_heads
self.num_key_value_heads = config.num_key_value_heads
self.num_key_value_groups = self.num_heads // self.num_key_value_heads
self.is_causal = True
self.attention_dropout = config.attention_dropout
if (self.head_dim * self.num_heads) != self.hidden_size:
raise ValueError(
f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
f" and `num_heads`: {self.num_heads})."
)
self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[HybridMambaAttentionDynamicCache] = None,
output_attentions: bool = False,
use_cache: bool = False,
cache_position: Optional[torch.LongTensor] = None,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
bsz, q_len, _ = hidden_states.size()
query_states = self.q_proj(hidden_states)
key_states = self.k_proj(hidden_states)
value_states = self.v_proj(hidden_states)
query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
if past_key_value is not None:
key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx)
# repeat k/v heads if n_kv_heads < n_heads
key_states = repeat_kv(key_states, self.num_key_value_groups)
value_states = repeat_kv(value_states, self.num_key_value_groups)
attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
if attention_mask is not None: # no matter the length, we just slice it
causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
attn_weights = attn_weights + causal_mask
# upcast attention to fp32
attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training)
attn_output = torch.matmul(attn_weights, value_states)
if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
raise ValueError(
f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
f" {attn_output.size()}"
)
attn_output = attn_output.transpose(1, 2).contiguous()
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
attn_output = self.o_proj(attn_output)
if not output_attentions:
attn_weights = None
return attn_output, attn_weights, past_key_value
# Adapted from transformers.models.mistral.modeling_mistral.MistralFlashAttention2 with Mistral->Jamba
class JambaFlashAttention2(JambaAttention):
"""
Jamba flash attention module. This module inherits from `JambaAttention` as the weights of the module stays
untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
flash attention and deal with padding tokens in case the input contains any of them.
"""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
# TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
# flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignment, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
# Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left).
self._flash_attn_uses_top_left_mask = flash_attn_supports_top_left_mask()
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[HybridMambaAttentionDynamicCache] = None,
output_attentions: bool = False,
use_cache: bool = False,
cache_position: Optional[torch.LongTensor] = None,
**kwargs,
):
bsz, q_len, _ = hidden_states.size()
query_states = self.q_proj(hidden_states)
key_states = self.k_proj(hidden_states)
value_states = self.v_proj(hidden_states)
# Flash attention requires the input to have the shape
# batch_size x seq_length x head_dim x hidden_dim
# therefore we just need to keep the original shape
query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim)
key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
if past_key_value is not None:
key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx)
# repeat k/v heads if n_kv_heads < n_heads
key_states = repeat_kv(key_states, self.num_key_value_groups)
value_states = repeat_kv(value_states, self.num_key_value_groups)
dropout_rate = 0.0 if not self.training else self.attention_dropout
# In PEFT, usually we cast the layer norms in float32 for training stability reasons
# therefore the input hidden states gets silently casted in float32. Hence, we need
# cast them back in float16 just to be sure everything works as expected.
input_dtype = query_states.dtype
if input_dtype == torch.float32:
if torch.is_autocast_enabled():
target_dtype = torch.get_autocast_gpu_dtype()
# Handle the case where the model is quantized
elif hasattr(self.config, "_pre_quantization_dtype"):
target_dtype = self.config._pre_quantization_dtype
else:
target_dtype = self.q_proj.weight.dtype
logger.warning_once(
f"The input hidden states seems to be silently casted in float32, this might be related to"
f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
f" {target_dtype}."
)
query_states = query_states.to(target_dtype)
key_states = key_states.to(target_dtype)
value_states = value_states.to(target_dtype)
# Reashape to the expected shape for Flash Attention
key_states = key_states.transpose(1, 2)
value_states = value_states.transpose(1, 2)
attn_output = _flash_attention_forward(
query_states,
key_states,
value_states,
attention_mask,
q_len,
dropout=dropout_rate,
sliding_window=getattr(self.config, "sliding_window", None),
is_causal=self.is_causal,
use_top_left_mask=self._flash_attn_uses_top_left_mask,
)
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous()
attn_output = self.o_proj(attn_output)
if not output_attentions:
attn_weights = None
return attn_output, attn_weights, past_key_value
# Adapted from transformers.models.mistral.modeling_mistral.MistralSdpaAttention with Mistral->Jamba
class JambaSdpaAttention(JambaAttention):
"""
Jamba attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
`JambaAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to
SDPA API.
"""
# Adapted from JambaAttention.forward
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[HybridMambaAttentionDynamicCache] = None,
output_attentions: bool = False,
use_cache: bool = False,
cache_position: Optional[torch.LongTensor] = None,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
if output_attentions:
# TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"` once this is implemented.
logger.warning_once(
"JambaModel is using JambaSdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to the manual attention implementation, "
'but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
)
return super().forward(
hidden_states=hidden_states,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_value=past_key_value,
output_attentions=output_attentions,
use_cache=use_cache,
)
bsz, q_len, _ = hidden_states.size()
query_states = self.q_proj(hidden_states)
key_states = self.k_proj(hidden_states)
value_states = self.v_proj(hidden_states)
query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
if past_key_value is not None:
key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx)
key_states = repeat_kv(key_states, self.num_key_value_groups)
value_states = repeat_kv(value_states, self.num_key_value_groups)
causal_mask = attention_mask
if attention_mask is not None:
causal_mask = causal_mask[:, :, :, : key_states.shape[-2]]
# SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with custom attn_mask,
# Reference: https://github.com/pytorch/pytorch/issues/112577.
if query_states.device.type == "cuda" and attention_mask is not None:
query_states = query_states.contiguous()
key_states = key_states.contiguous()
value_states = value_states.contiguous()
# We dispatch to SDPA's Flash Attention or Efficient kernels via this `is_causal` if statement instead of an inline conditional assignment
# in SDPA to support both torch.compile's dynamic shapes and full graph options. An inline conditional prevents dynamic shapes from compiling.
# The q_len > 1 is necessary to match with AttentionMaskConverter.to_causal_4d that does not create a causal mask in case q_len == 1.
is_causal = True if self.is_causal and causal_mask is None and q_len > 1 else False
attn_output = torch.nn.functional.scaled_dot_product_attention(
query_states,
key_states,
value_states,
attn_mask=causal_mask,
dropout_p=self.attention_dropout if self.training else 0.0,
is_causal=is_causal,
)
attn_output = attn_output.transpose(1, 2).contiguous()
attn_output = attn_output.view(bsz, q_len, self.hidden_size)
attn_output = self.o_proj(attn_output)
return attn_output, None, past_key_value
JAMBA_ATTENTION_CLASSES = {
"eager": JambaAttention,
"flash_attention_2": JambaFlashAttention2,
"sdpa": JambaSdpaAttention,
}
# Adapted from transformers.models.mamba.modeling_mamba.MambaMixer
class JambaMambaMixer(nn.Module):
"""
Compute ∆, A, B, C, and D the state space parameters and compute the `contextualized_states`.
A, D are input independent (see Mamba paper [1] Section 3.5.2 "Interpretation of A" for why A isn't selective)
∆, B, C are input-dependent (this is a key difference between Mamba and the linear time invariant S4,
and is why Mamba is called **selective** state spaces)
"""
def __init__(self, config: JambaConfig, layer_idx):
super().__init__()
self.config = config
self.layer_idx = layer_idx
self.hidden_size = config.hidden_size
self.ssm_state_size = config.mamba_d_state
self.conv_kernel_size = config.mamba_d_conv
self.intermediate_size = config.mamba_expand * config.hidden_size
self.time_step_rank = config.mamba_dt_rank
self.use_conv_bias = config.mamba_conv_bias
self.use_bias = config.mamba_proj_bias
self.conv1d = nn.Conv1d(
in_channels=self.intermediate_size,
out_channels=self.intermediate_size,
bias=self.use_conv_bias,
kernel_size=self.conv_kernel_size,
groups=self.intermediate_size,
padding=self.conv_kernel_size - 1,
)
self.activation = config.hidden_act
self.act = ACT2FN[config.hidden_act]
self.use_fast_kernels = config.use_mamba_kernels
# projection of the input hidden states
self.in_proj = nn.Linear(self.hidden_size, self.intermediate_size * 2, bias=self.use_bias)
# selective projection used to make dt, B and C input dependant
self.x_proj = nn.Linear(self.intermediate_size, self.time_step_rank + self.ssm_state_size * 2, bias=False)
# time step projection (discretization)
self.dt_proj = nn.Linear(self.time_step_rank, self.intermediate_size, bias=True)
# S4D real initialization. These are not discretized!
# The core is to load them, compute the discrete states, then write the updated state. Keeps the memory bounded
A = torch.arange(1, self.ssm_state_size + 1)[None, :]
A = A.expand(self.intermediate_size, -1).contiguous()
self.A_log = nn.Parameter(torch.log(A))
self.D = nn.Parameter(torch.ones(self.intermediate_size))
self.out_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=self.use_bias)
self.dt_layernorm = JambaRMSNorm(self.time_step_rank, eps=config.rms_norm_eps)
self.b_layernorm = JambaRMSNorm(self.ssm_state_size, eps=config.rms_norm_eps)
self.c_layernorm = JambaRMSNorm(self.ssm_state_size, eps=config.rms_norm_eps)
if not is_fast_path_available:
logger.warning_once(
"The fast path is not available because on of `(selective_state_update, selective_scan_fn, causal_conv1d_fn, causal_conv1d_update, mamba_inner_fn)`"
" is None. To install follow https://github.com/state-spaces/mamba/#installation and"
" https://github.com/Dao-AILab/causal-conv1d. If you want to use the naive implementation, set `use_mamba_kernels=False` in the model config"
)
def cuda_kernels_forward(
self,
hidden_states: torch.Tensor,
cache_params: HybridMambaAttentionDynamicCache = None,
attention_mask: Optional[torch.LongTensor] = None,
):
batch_size, seq_len, _ = hidden_states.shape
use_precomputed_states = (
cache_params is not None
and cache_params.has_previous_state
and seq_len == 1
and cache_params.conv_states[self.layer_idx].shape[0]
== cache_params.ssm_states[self.layer_idx].shape[0]
== batch_size
)
# 1. Gated MLP's linear projection
projected_states = self.in_proj(hidden_states).transpose(1, 2)
# We can't use `mamba_inner_fn` even if in training and without cache params because we have the
# inner layernorms which isn't supported by this fused kernel
hidden_states, gate = projected_states.chunk(2, dim=1)
if attention_mask is not None:
hidden_states = hidden_states * attention_mask.unsqueeze(1)
# 2. Convolution sequence transformation
conv_weights = self.conv1d.weight.view(self.conv1d.weight.size(0), self.conv1d.weight.size(2))
if use_precomputed_states:
hidden_states = causal_conv1d_update(
hidden_states.squeeze(-1),
cache_params.conv_states[self.layer_idx],
conv_weights,
self.conv1d.bias,
self.activation,
)
hidden_states = hidden_states.unsqueeze(-1)
else:
if cache_params is not None:
conv_states = nn.functional.pad(hidden_states, (self.conv_kernel_size - hidden_states.shape[-1], 0))
cache_params.conv_states[self.layer_idx].copy_(conv_states)
hidden_states = causal_conv1d_fn(hidden_states, conv_weights, self.conv1d.bias, activation=self.activation)
if attention_mask is not None:
hidden_states = hidden_states * attention_mask.unsqueeze(1)
# 3. State Space Model sequence transformation
# 3.a. input varying initialization of time_step, B and C
ssm_parameters = self.x_proj(hidden_states.transpose(1, 2))
time_step, B, C = torch.split(
ssm_parameters, [self.time_step_rank, self.ssm_state_size, self.ssm_state_size], dim=-1
)
time_step = self.dt_layernorm(time_step)
B = self.b_layernorm(B)
C = self.c_layernorm(C)
# Here we need to apply dt_proj without the bias, as the bias is added in the selective scan kernel.
# This is a hack to apply dt_proj while still using the forward pass of `torch.nn.Linear`, which is needed
# in order to make quantization work. Quantization code replaces `torch.nn.Linear` layers with quantized
# linear layers, and requires to call the forward pass directly.
# Quantized model can't work with the original code:
# ```discrete_time_step = self.dt_proj.weight @ time_step.transpose(1, 2)```
time_proj_bias = self.dt_proj.bias.data
with torch.no_grad():
self.dt_proj.bias.data = torch.zeros_like(self.dt_proj.bias.data)
discrete_time_step = self.dt_proj(time_step).transpose(1, 2)
with torch.no_grad():
self.dt_proj.bias.data = time_proj_bias
A = -torch.exp(self.A_log.float())
# 3.c perform the recurrence y ← SSM(A, B, C)(x)
time_proj_bias = time_proj_bias.float() if time_proj_bias is not None else None
if use_precomputed_states:
scan_outputs = selective_state_update(
cache_params.ssm_states[self.layer_idx],
hidden_states[..., 0],
discrete_time_step[..., 0],
A,
B[:, 0],
C[:, 0],
self.D,
gate[..., 0],
time_proj_bias,
dt_softplus=True,
).unsqueeze(-1)
else:
scan_outputs, ssm_state = selective_scan_fn(
hidden_states,
discrete_time_step,
A,
B.transpose(1, 2),
C.transpose(1, 2),
self.D.float(),
gate,
time_proj_bias,
delta_softplus=True,
return_last_state=True,
)
if ssm_state is not None and cache_params is not None:
cache_params.ssm_states[self.layer_idx].copy_(ssm_state)
# 4. Final linear projection
contextualized_states = self.out_proj(scan_outputs.transpose(1, 2))
return contextualized_states
# fmt: off
def slow_forward(self, input_states, cache_params: HybridMambaAttentionDynamicCache = None, attention_mask: Optional[torch.LongTensor] = None):
batch_size, seq_len, _ = input_states.shape
dtype = input_states.dtype
# 1. Gated MLP's linear projection
projected_states = self.in_proj(input_states).transpose(1, 2) # [batch, 2 * intermediate_size, seq_len]
hidden_states, gate = projected_states.chunk(2, dim=1)
if attention_mask is not None:
hidden_states = hidden_states * attention_mask.unsqueeze(1)
use_cache = isinstance(cache_params, HybridMambaAttentionDynamicCache)
# 2. Convolution sequence transformation
if use_cache and cache_params.ssm_states[self.layer_idx].shape[0] == batch_size:
if self.training:
# In training mode, we don't want to perform in-place operations on ssm_state so we can compute the backwards pass
ssm_state = cache_params.ssm_states[self.layer_idx].clone()
else:
ssm_state = cache_params.ssm_states[self.layer_idx]
ssm_state = ssm_state.to(hidden_states.device)
if cache_params.has_previous_state and seq_len == 1 and \
cache_params.conv_states[self.layer_idx].shape[0] == batch_size:
conv_state = cache_params.conv_states[self.layer_idx] # [batch, intermediate_size, conv_kernel_size]
conv_state = torch.roll(conv_state, shifts=-1, dims=-1)
conv_state[:, :, -1] = hidden_states[:, :, 0]
cache_params.conv_states[self.layer_idx] = conv_state
hidden_states = torch.sum(conv_state * self.conv1d.weight[:, 0, :], dim=-1)
if self.use_conv_bias:
hidden_states += self.conv1d.bias
hidden_states = self.act(hidden_states).to(dtype).unsqueeze(-1) # [batch, intermediate_size, 1] : decoding
else:
conv_state = nn.functional.pad(
hidden_states,
(self.conv_kernel_size - hidden_states.shape[-1], 0)
)
cache_params.conv_states[self.layer_idx] = conv_state
hidden_states = self.act(self.conv1d(hidden_states)[..., :seq_len]) # [batch, intermediate_size, seq_len]
else:
ssm_state = torch.zeros(
(batch_size, self.intermediate_size, self.ssm_state_size),
device=hidden_states.device, dtype=dtype
)
hidden_states = self.act(self.conv1d(hidden_states)[..., :seq_len]) # [batch, intermediate_size, seq_len]
if attention_mask is not None:
hidden_states = hidden_states * attention_mask.unsqueeze(1)
# 3. State Space Model sequence transformation
# 3.a. Selection: [batch, seq_len, self.time_step_rank + self.ssm_state_size * 2]
ssm_parameters = self.x_proj(hidden_states.transpose(1, 2))
time_step, B, C = torch.split(
ssm_parameters, [self.time_step_rank, self.ssm_state_size, self.ssm_state_size], dim=-1
)
time_step = self.dt_layernorm(time_step)
B = self.b_layernorm(B)
C = self.c_layernorm(C)
discrete_time_step = self.dt_proj(time_step) # [batch, seq_len, intermediate_size]
discrete_time_step = nn.functional.softplus(discrete_time_step).transpose(1, 2) # [batch, intermediate_size, seq_len]
# 3.b. Discretization: B and C to [batch, seq_len, intermediate_size, ssm_state_size] (SRAM)
A = -torch.exp(self.A_log.float()) # [intermediate_size, ssm_state_size]
discrete_A = torch.exp(A[None, :, None, :] * discrete_time_step[:, :, :, None]) # [batch, intermediate_size, seq_len, ssm_state_size]
discrete_B = discrete_time_step[:, :, :, None] * B[:, None, :, :].float() # [batch, intermediate_size, seq_len, ssm_state_size]
deltaB_u = discrete_B * hidden_states[:, :, :, None].float()
# 3.c perform the recurrence y ← SSM(A, B, C)(x)
scan_outputs = []
for i in range(seq_len):
ssm_state = discrete_A[:, :, i, :] * ssm_state + deltaB_u[:, :, i, :] # [batch, intermediate_size, ssm_state]
scan_output = torch.matmul(ssm_state.to(dtype), C[:, i, :].unsqueeze(-1)) # [batch, intermediate_size, 1]
scan_outputs.append(scan_output[:, :, 0])
scan_output = torch.stack(scan_outputs, dim=-1) # [batch, intermediate_size, seq_len]
scan_output = scan_output + (hidden_states * self.D[None, :, None])
scan_output = (scan_output * self.act(gate))
if use_cache:
cache_params.ssm_states[self.layer_idx] = ssm_state
# 4. Final linear projection
contextualized_states = self.out_proj(scan_output.transpose(1, 2)) # [batch, seq_len, hidden_size]
return contextualized_states
# fmt: on
def forward(
self,
hidden_states,
cache_params: HybridMambaAttentionDynamicCache = None,
attention_mask: Optional[torch.LongTensor] = None,
):
if self.use_fast_kernels:
if not is_fast_path_available or "cuda" not in self.x_proj.weight.device.type:
raise ValueError(
"Fast Mamba kernels are not available. Make sure to they are installed and that the mamba module is on a CUDA device"
)
return self.cuda_kernels_forward(hidden_states, cache_params, attention_mask)
return self.slow_forward(hidden_states, cache_params, attention_mask)
# Copied from transformers.models.mistral.modeling_mistral.MistralMLP with Mistral->Jamba
class JambaMLP(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.hidden_size = config.hidden_size
self.intermediate_size = config.intermediate_size
self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
self.act_fn = ACT2FN[config.hidden_act]
def forward(self, x):
down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
return down_proj
# Adapted from transformers.models.mixtral.modeling_mixtral.MixtralSparseMoeBlock with Mistral->Jamba
class JambaSparseMoeBlock(nn.Module):
"""
This implementation is
strictly equivalent to standard MoE with full capacity (no
dropped tokens). It's faster since it formulates MoE operations
in terms of block-sparse operations to accommodate imbalanced
assignments of tokens to experts, whereas standard MoE either
(1) drop tokens at the cost of reduced performance or (2) set
capacity factor to number of experts and thus waste computation
and memory on padding.
"""
def __init__(self, config: JambaConfig):
super().__init__()
self.hidden_dim = config.hidden_size
self.ffn_dim = config.intermediate_size
self.num_experts = config.num_experts
self.top_k = config.num_experts_per_tok
self.router = nn.Linear(self.hidden_dim, self.num_experts, bias=False)
self.experts = nn.ModuleList([JambaMLP(config) for _ in range(self.num_experts)])
def forward(self, hidden_states: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
""" """
batch_size, sequence_length, hidden_dim = hidden_states.shape
hidden_states = hidden_states.view(-1, hidden_dim)
# router_logits: (batch * sequence_length, n_experts)
router_logits = self.router(hidden_states)
routing_weights = F.softmax(router_logits, dim=1, dtype=torch.float)
routing_weights, selected_experts = torch.topk(routing_weights, self.top_k, dim=-1)
# we cast back to the input dtype
routing_weights = routing_weights.to(hidden_states.dtype)
final_hidden_states = torch.zeros(
(batch_size * sequence_length, hidden_dim), dtype=hidden_states.dtype, device=hidden_states.device
)
# One hot encode the selected experts to create an expert mask
# this will be used to easily index which expert is going to be sollicitated
expert_mask = torch.nn.functional.one_hot(selected_experts, num_classes=self.num_experts).permute(2, 1, 0)
# Loop over all available experts in the model and perform the computation on each expert
for expert_idx in range(self.num_experts):
expert_layer = self.experts[expert_idx]
idx, top_x = torch.where(expert_mask[expert_idx])
if top_x.shape[0] == 0:
continue
# Index the correct hidden states and compute the expert hidden state for
# the current expert. We need to make sure to multiply the output hidden
# states by `routing_weights` on the corresponding tokens (top-1 and top-2)
current_state = hidden_states[None, top_x].reshape(-1, hidden_dim)
current_hidden_states = expert_layer(current_state) * routing_weights[top_x, idx, None]
# However `index_add_` only support torch tensors for indexing so we'll use
# the `top_x` tensor here.
final_hidden_states.index_add_(0, top_x, current_hidden_states.to(hidden_states.dtype))
final_hidden_states = final_hidden_states.reshape(batch_size, sequence_length, hidden_dim)
return final_hidden_states, router_logits
class JambaAttentionDecoderLayer(nn.Module):
def __init__(self, config: JambaConfig, layer_idx: int):
super().__init__()
num_experts = config.layers_num_experts[layer_idx]
self.self_attn = JAMBA_ATTENTION_CLASSES[config._attn_implementation](config, layer_idx)
ffn_layer_class = JambaSparseMoeBlock if num_experts > 1 else JambaMLP
self.feed_forward = ffn_layer_class(config)
self.input_layernorm = JambaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.pre_ff_layernorm = JambaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[HybridMambaAttentionDynamicCache] = None,
output_attentions: Optional[bool] = False,
output_router_logits: Optional[bool] = False,
use_cache: Optional[bool] = False,
cache_position: Optional[torch.LongTensor] = None,
) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
"""
Args:
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
`(batch, sequence_length)` where padding elements are indicated by 0.
past_key_value (`HybridMambaAttentionDynamicCache`, *optional*): cached past key and value projection states
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
output_router_logits (`bool`, *optional*):
Whether or not to return the logits of all the routers. They are useful for computing the router loss, and
should not be returned during inference.
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
(see `past_key_values`).
cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
Indices depicting the position of the input sequence tokens in the sequence.
"""
residual = hidden_states
hidden_states = self.input_layernorm(hidden_states)
hidden_states, self_attn_weights, present_key_value = self.self_attn(
hidden_states=hidden_states,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_value=past_key_value,
output_attentions=output_attentions,
use_cache=use_cache,
cache_position=cache_position,
)
# residual connection after attention
hidden_states = residual + hidden_states
# feed-forward (experts/MLP)
residual = hidden_states
hidden_states = self.pre_ff_layernorm(hidden_states)
ff_outputs = self.feed_forward(hidden_states)
if isinstance(ff_outputs, tuple):
hidden_states, router_logits = ff_outputs
else:
hidden_states, router_logits = ff_outputs, None
hidden_states = residual + hidden_states
outputs = (hidden_states,)
if output_attentions:
outputs += (self_attn_weights,)
if use_cache:
outputs += (present_key_value,)
if output_router_logits:
outputs += (router_logits,)
return outputs
class JambaMambaDecoderLayer(nn.Module):
def __init__(self, config: JambaConfig, layer_idx: int):
super().__init__()
num_experts = config.layers_num_experts[layer_idx]
self.mamba = JambaMambaMixer(config=config, layer_idx=layer_idx)
ffn_layer_class = JambaSparseMoeBlock if num_experts > 1 else JambaMLP
self.feed_forward = ffn_layer_class(config)
self.input_layernorm = JambaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.pre_ff_layernorm = JambaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[HybridMambaAttentionDynamicCache] = None,
output_attentions: Optional[bool] = False,
output_router_logits: Optional[bool] = False,
use_cache: Optional[bool] = False,
cache_position: Optional[torch.LongTensor] = None,
) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
"""
Args:
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
`(batch, sequence_length)` where padding elements are indicated by 0.
past_key_value (`HybridMambaAttentionDynamicCache`, *optional*): cached past key and value projection states
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
output_router_logits (`bool`, *optional*):
Whether or not to return the logits of all the routers. They are useful for computing the router loss, and
should not be returned during inference.
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
(see `past_key_values`).
cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
Indices depicting the position of the input sequence tokens in the sequence.
"""
residual = hidden_states
hidden_states = self.input_layernorm(hidden_states)
hidden_states = self.mamba(
hidden_states=hidden_states,
cache_params=past_key_value,
attention_mask=attention_mask,
)
self_attn_weights = None
# residual connection after mamba
hidden_states = residual + hidden_states
# feed-forward (experts/MLP)
residual = hidden_states
hidden_states = self.pre_ff_layernorm(hidden_states)
ff_outputs = self.feed_forward(hidden_states)
if isinstance(ff_outputs, tuple):
hidden_states, router_logits = ff_outputs
else:
hidden_states, router_logits = ff_outputs, None
hidden_states = residual + hidden_states
outputs = (hidden_states,)
if output_attentions:
outputs += (self_attn_weights,)
if use_cache:
outputs += (past_key_value,)
if output_router_logits:
outputs += (router_logits,)
return outputs
JAMBA_START_DOCSTRING = r"""
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.
Parameters:
config ([`JambaConfig`]):
Model configuration class with all the parameters of the model. Initializing with a config file does not
load the weights associated with the model, only the configuration. Check out the
[`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
@add_start_docstrings(
"The bare Jamba Model outputting raw hidden-states without any specific head on top.",
JAMBA_START_DOCSTRING,
)
class JambaPreTrainedModel(PreTrainedModel):
config_class = JambaConfig
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = ["JambaAttentionDecoderLayer", "JambaMambaDecoderLayer"]
_skip_keys_device_placement = "past_key_values"
_supports_flash_attn_2 = True
_supports_sdpa = True
_supports_cache_class = True # Note: only supports HybridMambaAttentionDynamicCache
_is_stateful = True
def _init_weights(self, module):
std = self.config.initializer_range
if isinstance(module, (nn.Linear, nn.Conv1d)):
module.weight.data.normal_(mean=0.0, std=std)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.Embedding):
module.weight.data.normal_(mean=0.0, std=std)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
JAMBA_INPUTS_DOCSTRING = r"""
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
it.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
If `past_key_values` is used, optionally only the last `input_ids` have to be input (see
`past_key_values`).
If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
information on the default strategy.
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
config.n_positions - 1]`.
[What are position IDs?](../glossary#position-ids)
past_key_values (`HybridMambaAttentionDynamicCache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
A HybridMambaAttentionDynamicCache object containing pre-computed hidden-states (keys and values in the
self-attention blocks and convolution and ssm states in the mamba blocks) that can be used (see
`past_key_values` input) to speed up sequential decoding.
Key and value cache tensors have shape `(batch_size, num_heads, seq_len, head_dim)`.
Convolution and ssm states tensors have shape `(batch_size, d_inner, d_conv)` and
`(batch_size, d_inner, d_state)` respectively.
See the `HybridMambaAttentionDynamicCache` class for more details.
If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that
don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
`input_ids` of shape `(batch_size, sequence_length)`.
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
model's internal embedding lookup matrix.
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
`past_key_values`).
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
output_router_logits (`bool`, *optional*):
Whether or not to return the logits of all the routers. They are useful for computing the router loss, and
should not be returned during inference.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
Indices depicting the position of the input sequence tokens in the sequence. Contrarily to `position_ids`,
this tensor is not affected by padding. It is used to update the cache in the correct position and to infer
the complete sequence length.
"""
ALL_DECODER_LAYER_TYPES = {"attention": JambaAttentionDecoderLayer, "mamba": JambaMambaDecoderLayer}
@add_start_docstrings(
"The bare Jamba Model outputting raw hidden-states without any specific head on top.",
JAMBA_START_DOCSTRING,
)
# Adapted from transformers.models.mistral.modeling_mistral.MistralModel with MISTRAL->JAMBA, Mistral->Jamba
class JambaModel(JambaPreTrainedModel):
"""
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`JambaDecoderLayer`]
Args:
config: JambaConfig
"""
def __init__(self, config: JambaConfig):
super().__init__(config)
self.padding_idx = config.pad_token_id
self.vocab_size = config.vocab_size
self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
decoder_layers = []
for i in range(config.num_hidden_layers):
layer_class = ALL_DECODER_LAYER_TYPES[config.layers_block_type[i]]
decoder_layers.append(layer_class(config, layer_idx=i))
self.layers = nn.ModuleList(decoder_layers)
self._attn_implementation = config._attn_implementation
self.final_layernorm = JambaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.gradient_checkpointing = False
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.embed_tokens
def set_input_embeddings(self, value):
self.embed_tokens = value
@can_return_tuple
@add_start_docstrings_to_model_forward(JAMBA_INPUTS_DOCSTRING)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[HybridMambaAttentionDynamicCache] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
output_router_logits: Optional[bool] = None,
cache_position: Optional[torch.LongTensor] = None,
) -> MoeModelOutputWithPast:
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_router_logits = (
output_router_logits if output_router_logits is not None else self.config.output_router_logits
)
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
use_cache = use_cache if use_cache is not None else self.config.use_cache
if (input_ids is None) ^ (inputs_embeds is not None):
raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
if self.gradient_checkpointing and self.training and use_cache:
logger.warning_once(
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`."
)
use_cache = False
if inputs_embeds is None:
inputs_embeds = self.embed_tokens(input_ids)
hidden_states = inputs_embeds
if use_cache and past_key_values is None:
logger.warning_once(
"Jamba requires an initialized `HybridMambaAttentionDynamicCache` to return a cache. None was "
"provided, so no cache will be returned."
)
if cache_position is None:
cache_position = torch.arange(hidden_states.shape[1], device=hidden_states.device)
if position_ids is None:
position_ids = cache_position.unsqueeze(0)
causal_mask = self._update_causal_mask(attention_mask, inputs_embeds, cache_position)
mamba_mask = self._update_mamba_mask(attention_mask, cache_position)
all_hidden_states = () if output_hidden_states else None
all_self_attns = () if output_attentions else None
all_router_logits = () if output_router_logits else None
for decoder_layer in self.layers:
# Depending on the layer type we opt for 2D base attention mask (Mamba) or 4D causal mask (Attention)
layer_mask = mamba_mask if isinstance(decoder_layer, JambaMambaDecoderLayer) else causal_mask
if output_hidden_states:
all_hidden_states += (hidden_states,)
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
decoder_layer.__call__,
hidden_states,
layer_mask,
position_ids,
past_key_values,
output_attentions,
output_router_logits,
use_cache,
cache_position,
)
else:
layer_outputs = decoder_layer(
hidden_states,
attention_mask=layer_mask,
position_ids=position_ids,
past_key_value=past_key_values,
output_attentions=output_attentions,
output_router_logits=output_router_logits,
use_cache=use_cache,
cache_position=cache_position,
)
hidden_states = layer_outputs[0]
if output_attentions:
if layer_outputs[1] is not None:
# append attentions only of attention layers. Mamba layers return `None` as the attention weights
all_self_attns += (layer_outputs[1],)
if output_router_logits:
if layer_outputs[-1] is not None:
# append router logits only of expert layers. Regular MLP layers return `None` as the router logits
all_router_logits += (layer_outputs[-1],)
hidden_states = self.final_layernorm(hidden_states)
# add hidden states from the last decoder layer
if output_hidden_states:
all_hidden_states += (hidden_states,)
if past_key_values and not past_key_values.has_previous_state:
past_key_values.has_previous_state = True
next_cache = None if not use_cache else past_key_values
return MoeModelOutputWithPast(
last_hidden_state=hidden_states,
past_key_values=next_cache,
hidden_states=all_hidden_states,
attentions=all_self_attns,
router_logits=all_router_logits,
)
def _update_causal_mask(self, attention_mask, input_tensor, cache_position):
if self.config._attn_implementation == "flash_attention_2":
if attention_mask is not None and 0.0 in attention_mask:
return attention_mask
return None
dtype, device = input_tensor.dtype, input_tensor.device
min_dtype = torch.finfo(dtype).min
sequence_length = input_tensor.shape[1]
target_length = cache_position[-1] + 1
causal_mask = torch.full((sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=device)
if sequence_length != 1:
causal_mask = torch.triu(causal_mask, diagonal=1)
causal_mask *= torch.arange(target_length, device=device) > cache_position.reshape(-1, 1)
causal_mask = causal_mask[None, None, :, :].expand(input_tensor.shape[0], 1, -1, -1)
if attention_mask is not None:
causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit
if attention_mask.dim() == 2:
mask_length = attention_mask.shape[-1]
padding_mask = causal_mask[..., :mask_length].eq(0.0) * attention_mask[:, None, None, :].eq(0.0)
causal_mask[..., :mask_length] = causal_mask[..., :mask_length].masked_fill(padding_mask, min_dtype)
if (
self.config._attn_implementation == "sdpa"
and attention_mask is not None
and attention_mask.device.type in ["cuda", "xpu"]
):
# Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when
# using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path.
# Details: https://github.com/pytorch/pytorch/issues/110213
causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype)
return causal_mask
def _update_mamba_mask(self, attention_mask, cache_position):
"""
No need for zeroing states when
1. Cached forward
2. Attending to all inputs
"""
mamba_mask = attention_mask
if cache_position[0] > 0 or (attention_mask is not None and torch.all(attention_mask == 1)):
mamba_mask = None
return mamba_mask
# Adapted from transformers.models.mixtral.modeling_mixtral.MixtralForCausalLM with MIXTRAL->JAMBA, Mixtral->Jamba
class JambaForCausalLM(JambaPreTrainedModel, GenerationMixin):
_tied_weights_keys = ["lm_head.weight"]
def __init__(self, config: JambaConfig):
super().__init__(config)
self.model = JambaModel(config)
self.vocab_size = config.vocab_size
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
self.router_aux_loss_coef = config.router_aux_loss_coef
self.num_experts = config.num_experts
self.num_experts_per_tok = config.num_experts_per_tok
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.model.embed_tokens
def set_input_embeddings(self, value):
self.model.embed_tokens = value
def get_output_embeddings(self):
return self.lm_head
def set_output_embeddings(self, new_embeddings):
self.lm_head = new_embeddings
def set_decoder(self, decoder):
self.model = decoder
def get_decoder(self):
return self.model
@can_return_tuple
@deprecate_kwarg("num_logits_to_keep", version="4.50", new_name="logits_to_keep")
@add_start_docstrings_to_model_forward(JAMBA_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=MoeCausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[HybridMambaAttentionDynamicCache] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
output_router_logits: Optional[bool] = None,
cache_position: Optional[torch.LongTensor] = None,
logits_to_keep: Union[int, torch.Tensor] = 0,
**loss_kwargs,
) -> MoeCausalLMOutputWithPast:
r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
logits_to_keep (`int` or `torch.Tensor`, *optional*):
If an `int`, compute logits for the last `logits_to_keep` tokens. If `0`, calculate logits for all
`input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that
token can save memory, which becomes pretty significant for long sequences or large vocabulary size.
If a `torch.Tensor`, must be 1D corresponding to the indices to keep in the sequence length dimension.
This is useful when using packed tensor format (single dimension for batch and sequence length).
Returns:
Example:
```python
>>> from transformers import AutoTokenizer, JambaForCausalLM
>>> model = JambaForCausalLM.from_pretrained("ai21labs/Jamba-v0.1")
>>> tokenizer = AutoTokenizer.from_pretrained("ai21labs/Jamba-v0.1")
>>> prompt = "Hey, are you conscious? Can you talk to me?"
>>> inputs = tokenizer(prompt, return_tensors="pt")
>>> # Generate
>>> generate_ids = model.generate(inputs.input_ids, max_length=30)
>>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
"Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
```"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_router_logits = (
output_router_logits if output_router_logits is not None else self.config.output_router_logits
)
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
outputs: MoeModelOutputWithPast = self.model(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
output_router_logits=output_router_logits,
cache_position=cache_position,
)
hidden_states = outputs.last_hidden_state
slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
logits = self.lm_head(hidden_states[:, slice_indices, :])
loss = None
if labels is not None:
loss = self.loss_function(logits, labels, self.vocab_size, **loss_kwargs)
aux_loss = None
if output_router_logits:
aux_loss = load_balancing_loss_func(
outputs.router_logits,
self.num_experts,
self.num_experts_per_tok,
attention_mask,
)
if labels is not None:
loss += self.router_aux_loss_coef * aux_loss.to(loss.device) # make sure to reside in the same device
return MoeCausalLMOutputWithPast(
loss=loss,
aux_loss=aux_loss,
logits=logits,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
router_logits=outputs.router_logits,
)
def prepare_inputs_for_generation(
self,
input_ids,
past_key_values=None,
attention_mask=None,
inputs_embeds=None,
output_router_logits=False,
cache_position=None,
position_ids=None,
use_cache=True,
**kwargs,
):
# Overwitten -- has a unique cache type, `HybridMambaAttentionDynamicCache`
empty_past_kv = past_key_values is None
# If we have cache: let's slice `input_ids` through `cache_position`, to keep only the unprocessed tokens
# Exception 1: when passing input_embeds, input_ids may be missing entries
# Exception 2: some generation methods do special slicing of input_ids, so we don't need to do it here
# Exception 3: with synced GPUs cache_position may go out of bounds, but we only want dummy token in that case.
# (we can't check exception 3 while compiling)
if not empty_past_kv:
if (
inputs_embeds is not None # Exception 1
or cache_position[-1] >= input_ids.shape[1] # Exception 3
):
input_ids = input_ids[:, -cache_position.shape[0] :]
elif input_ids.shape[1] != cache_position.shape[0]: # Default case (the "else", a no op, is Exception 2)
input_ids = input_ids[:, cache_position]
else:
past_key_values = HybridMambaAttentionDynamicCache(
self.config, input_ids.shape[0], self.dtype, device=self.device
)
if attention_mask is not None and position_ids is None:
# create position_ids on the fly for batch generation
position_ids = attention_mask.long().cumsum(-1) - 1
position_ids.masked_fill_(attention_mask == 0, 1)
if not empty_past_kv:
position_ids = position_ids[:, -input_ids.shape[1] :]
# if `inputs_embeds` are passed, we only want to use them in the 1st generation step
if inputs_embeds is not None and empty_past_kv:
model_inputs = {"inputs_embeds": inputs_embeds}
else:
model_inputs = {"input_ids": input_ids.contiguous()} # `contiguous()` needed for compilation use cases
model_inputs.update(
{
"position_ids": position_ids,
"past_key_values": past_key_values,
"use_cache": use_cache,
"attention_mask": attention_mask,
"output_router_logits": output_router_logits,
"logits_to_keep": self.config.num_logits_to_keep,
"cache_position": cache_position,
}
)
return model_inputs
@add_start_docstrings(
"""
The Jamba Model with a sequence classification head on top (linear layer).
[`JambaForSequenceClassification`] uses the last token in order to do the classification, as other causal models
(e.g. GPT-2) do.
Since it does classification on the last token, it requires to know the position of the last token. If a
`pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
each row of the batch).
""",
JAMBA_START_DOCSTRING,
)
# Copied from transformers.models.mixtral.modeling_mixtral.MixtralForSequenceClassification with Mixtral->Jamba, MIXTRAL->JAMBA, BaseModelOutputWithPast->MoeModelOutputWithPast
class JambaForSequenceClassification(JambaPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.model = JambaModel(config)
self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.model.embed_tokens
def set_input_embeddings(self, value):
self.model.embed_tokens = value
@can_return_tuple
@add_start_docstrings_to_model_forward(JAMBA_INPUTS_DOCSTRING)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Cache] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
) -> SequenceClassifierOutputWithPast:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
"""
transformer_outputs: MoeModelOutputWithPast = self.model(
input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
)
hidden_states = transformer_outputs.last_hidden_state
logits = self.score(hidden_states)
if input_ids is not None:
batch_size = input_ids.shape[0]
else:
batch_size = inputs_embeds.shape[0]
if self.config.pad_token_id is None and batch_size != 1:
raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
if self.config.pad_token_id is None:
last_non_pad_token = -1
elif input_ids is not None:
# To handle both left- and right- padding, we take the rightmost token that is not equal to pad_token_id
non_pad_mask = (input_ids != self.config.pad_token_id).to(logits.device, torch.int32)
token_indices = torch.arange(input_ids.shape[-1], device=logits.device, dtype=torch.int32)
last_non_pad_token = (token_indices * non_pad_mask).argmax(-1)
else:
last_non_pad_token = -1
logger.warning_once(
f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be "
"unexpected if using padding tokens in conjunction with `inputs_embeds.`"
)
pooled_logits = logits[torch.arange(batch_size, device=logits.device), last_non_pad_token]
loss = None
if labels is not None:
loss = self.loss_function(logits=logits, labels=labels, pooled_logits=pooled_logits, config=self.config)
return SequenceClassifierOutputWithPast(
loss=loss,
logits=pooled_logits,
past_key_values=transformer_outputs.past_key_values,
hidden_states=transformer_outputs.hidden_states,
attentions=transformer_outputs.attentions,
)
__all__ = ["JambaForCausalLM", "JambaForSequenceClassification", "JambaModel", "JambaPreTrainedModel"]
```
|
==============================================================================================================================
SOURCE CODE FILE: __init__.py
LINES: 1
SIZE: 0.97 KB
PATH: scripts\freecad_env\Lib\site-packages\transformers\models\jetmoe\__init__.py
ENCODING: utf-8
```py
# Copyright 2024 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import _LazyModule
from ...utils.import_utils import define_import_structure
if TYPE_CHECKING:
from .configuration_jetmoe import *
from .modeling_jetmoe import *
else:
import sys
_file = globals()["__file__"]
sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)
```
|
==========================================================================================================================================
SOURCE CODE FILE: configuration_jetmoe.py
LINES: 1
SIZE: 6.64 KB
PATH: scripts\freecad_env\Lib\site-packages\transformers\models\jetmoe\configuration_jetmoe.py
ENCODING: utf-8
```py
# coding=utf-8
# Copyright 2024 JetMoe AI and the HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""JetMoe model configuration"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
logger = logging.get_logger(__name__)
class JetMoeConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`JetMoeModel`]. It is used to instantiate a
JetMoe model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a configuration of the JetMoe-4B.
[jetmoe/jetmoe-8b](https://huggingface.co/jetmoe/jetmoe-8b)
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 32000):
Vocabulary size of the JetMoe model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`JetMoeModel`]
hidden_size (`int`, *optional*, defaults to 2048):
Dimension of the hidden representations.
num_hidden_layers (`int`, *optional*, defaults to 12):
Number of hidden layers in the Transformer encoder.
num_key_value_heads (`int`, *optional*, defaults to 16):
Number of attention heads for each key and value in the Transformer encoder.
kv_channels (`int`, *optional*, defaults to 128):
Defines the number of channels for the key and value tensors.
intermediate_size (`int`, *optional*, defaults to 5632):
Dimension of the MLP representations.
max_position_embeddings (`int`, *optional*, defaults to 4096):
The maximum sequence length that this model might ever be used with. JetMoe's attention allows sequence of
up to 4096 tokens.
activation_function (`string`, *optional*, defaults to `"silu"`):
Defines the activation function for MLP experts.
num_local_experts (`int`, *optional*, defaults to 8):
Defines the number of experts in the MoE and MoA.
num_experts_per_tok (`int, *optional*, defaults to 2):
The number of experts to route per-token and for MoE and MoA.
output_router_logits (`bool`, *optional*, defaults to `False`):
Whether or not the router logits should be returned by the model. Enabeling this will also
allow the model to output the auxiliary loss.
aux_loss_coef (`float`, *optional*, defaults to 0.01):
The coefficient for the auxiliary loss.
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models). Only
relevant if `config.is_decoder=True`.
bos_token_id (`int`, *optional*, defaults to 1):
The id of the "beginning-of-sequence" token.
eos_token_id (`int`, *optional*, defaults to 2):
The id of the "end-of-sequence" token.
tie_word_embeddings (`bool`, *optional*, defaults to `True`):
Whether the model's input and output word embeddings should be tied.
rope_theta (`float`, *optional*, defaults to 10000.0):
The base period of the RoPE embeddings.
rms_norm_eps (`float`, *optional*, defaults to 1e-06):
The epsilon used by the rms normalization layers.
initializer_range (`float`, *optional*, defaults to 0.01):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
```python
>>> from transformers import JetMoeModel, JetMoeConfig
>>> # Initializing a JetMoe 4B style configuration
>>> configuration = JetMoeConfig()
>>> # Initializing a model from the JetMoe 4B style configuration
>>> model = JetMoeModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "jetmoe"
keys_to_ignore_at_inference = ["past_key_values"]
def __init__(
self,
vocab_size=32000,
hidden_size=2048,
num_hidden_layers=12,
num_key_value_heads=16,
kv_channels=128,
intermediate_size=5632,
max_position_embeddings=4096,
activation_function="silu",
num_local_experts=8,
num_experts_per_tok=2,
output_router_logits=False,
aux_loss_coef=0.01,
use_cache=True,
bos_token_id=1,
eos_token_id=2,
tie_word_embeddings=True,
rope_theta=10000.0,
rms_norm_eps=1e-6,
initializer_range=0.01,
attention_dropout=0.0,
**kwargs,
):
if num_experts_per_tok > num_local_experts:
raise ValueError("`num_experts_per_tok` must be less than or equal to `num_local_experts`")
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_key_value_heads * num_experts_per_tok
self.num_key_value_heads = num_key_value_heads
self.kv_channels = kv_channels
self.intermediate_size = intermediate_size
self.max_position_embeddings = max_position_embeddings
self.activation_function = activation_function
self.num_local_experts = num_local_experts
self.num_experts_per_tok = num_experts_per_tok
self.output_router_logits = output_router_logits
self.aux_loss_coef = aux_loss_coef
self.use_cache = use_cache
self.initializer_range = initializer_range
self.attention_dropout = attention_dropout
self.bos_token_id = bos_token_id
self.eos_token_id = eos_token_id
self.rope_theta = rope_theta
self.rms_norm_eps = rms_norm_eps
super().__init__(
bos_token_id=bos_token_id, eos_token_id=eos_token_id, tie_word_embeddings=tie_word_embeddings, **kwargs
)
__all__ = ["JetMoeConfig"]
```
|
=====================================================================================================================================
SOURCE CODE FILE: modeling_jetmoe.py
LINES: 1
SIZE: 64.96 KB
PATH: scripts\freecad_env\Lib\site-packages\transformers\models\jetmoe\modeling_jetmoe.py
ENCODING: utf-8
```py
# coding=utf-8
# Copyright 2024 JetMoe AI and the HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""PyTorch JetMoe model."""
import math
from typing import List, Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import functional as F
from ...activations import ACT2FN
from ...cache_utils import Cache, DynamicCache, StaticCache
from ...generation import GenerationMixin
from ...modeling_attn_mask_utils import AttentionMaskConverter
from ...modeling_flash_attention_utils import flash_attn_supports_top_left_mask, is_flash_attn_available
from ...modeling_outputs import (
MoeCausalLMOutputWithPast,
MoeModelOutputWithPast,
SequenceClassifierOutputWithPast,
)
from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
from ...modeling_utils import PreTrainedModel
from ...utils import (
add_start_docstrings,
add_start_docstrings_to_model_forward,
can_return_tuple,
is_torch_flex_attn_available,
logging,
replace_return_docstrings,
)
from ...utils.deprecation import deprecate_kwarg
from .configuration_jetmoe import JetMoeConfig
if is_torch_flex_attn_available():
from torch.nn.attention.flex_attention import BlockMask
from ...integrations.flex_attention import make_flex_block_causal_mask
if is_flash_attn_available():
from ...modeling_flash_attention_utils import _flash_attention_forward
logger = logging.get_logger(__name__)
_CHECKPOINT_FOR_DOC = "jetmoe"
_CONFIG_FOR_DOC = "JetMoeConfig"
# Copied from transformers.models.mixtral.modeling_mixtral.load_balancing_loss_func
def load_balancing_loss_func(
gate_logits: Union[torch.Tensor, Tuple[torch.Tensor], None],
num_experts: Optional[int] = None,
top_k=2,
attention_mask: Optional[torch.Tensor] = None,
) -> Union[torch.Tensor, int]:
r"""
Computes auxiliary load balancing loss as in Switch Transformer - implemented in Pytorch.
See Switch Transformer (https://arxiv.org/abs/2101.03961) for more details. This function implements the loss
function presented in equations (4) - (6) of the paper. It aims at penalizing cases where the routing between
experts is too unbalanced.
Args:
gate_logits:
Logits from the `gate`, should be a tuple of model.config.num_hidden_layers tensors of
shape [batch_size X sequence_length, num_experts].
num_experts:
Number of experts
top_k:
The number of experts to route per-token, can be also interpreted as the `top-k` routing
parameter.
attention_mask (`torch.Tensor`, *optional*):
The attention_mask used in forward function
shape [batch_size X sequence_length] if not None.
Returns:
The auxiliary loss.
"""
if gate_logits is None or not isinstance(gate_logits, tuple):
return 0
if isinstance(gate_logits, tuple):
compute_device = gate_logits[0].device
concatenated_gate_logits = torch.cat([layer_gate.to(compute_device) for layer_gate in gate_logits], dim=0)
routing_weights = torch.nn.functional.softmax(concatenated_gate_logits, dim=-1)
_, selected_experts = torch.topk(routing_weights, top_k, dim=-1)
expert_mask = torch.nn.functional.one_hot(selected_experts, num_experts)
if attention_mask is None:
# Compute the percentage of tokens routed to each experts
tokens_per_expert = torch.mean(expert_mask.float(), dim=0)
# Compute the average probability of routing to these experts
router_prob_per_expert = torch.mean(routing_weights, dim=0)
else:
batch_size, sequence_length = attention_mask.shape
num_hidden_layers = concatenated_gate_logits.shape[0] // (batch_size * sequence_length)
# Compute the mask that masks all padding tokens as 0 with the same shape of expert_mask
expert_attention_mask = (
attention_mask[None, :, :, None, None]
.expand((num_hidden_layers, batch_size, sequence_length, top_k, num_experts))
.reshape(-1, top_k, num_experts)
.to(compute_device)
)
# Compute the percentage of tokens routed to each experts
tokens_per_expert = torch.sum(expert_mask.float() * expert_attention_mask, dim=0) / torch.sum(
expert_attention_mask, dim=0
)
# Compute the mask that masks all padding tokens as 0 with the same shape of tokens_per_expert
router_per_expert_attention_mask = (
attention_mask[None, :, :, None]
.expand((num_hidden_layers, batch_size, sequence_length, num_experts))
.reshape(-1, num_experts)
.to(compute_device)
)
# Compute the average probability of routing to these experts
router_prob_per_expert = torch.sum(routing_weights * router_per_expert_attention_mask, dim=0) / torch.sum(
router_per_expert_attention_mask, dim=0
)
overall_loss = torch.sum(tokens_per_expert * router_prob_per_expert.unsqueeze(0))
return overall_loss * num_experts
class JetMoeParallelExperts(nn.Module):
def __init__(self, num_experts: int, input_size: int, output_size: int) -> None:
"""
Initialize the JetMoeParallelExperts module.
The experts weights are stored in [num_experts, output_size, input_size] format. Such that it's comptible with
many MoE libraries, such as [Megablock](https://github.com/databricks/megablocks) and
[ScatterMoE](https://github.com/shawntan/scattermoe), as well as the
[MoE kernel](https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/layers/fused_moe/fused_moe.py)
used in vllm.
Args:
num_experts (int):
Number of experts.
input_size (int):
Size of the input.
output_size (int):
Size of the output.
"""
super().__init__()
self.weight = nn.Parameter(torch.empty(num_experts, output_size, input_size))
self.num_experts = num_experts
self.input_size = input_size
self.output_size = output_size
def forward(self, inputs, expert_size):
"""
Forward pass of the JetMoeParallelExperts module.
Args:
inputs (Tensor):
Input tensor.
expert_size:
Expert size information.
Returns:
Tensor: Output tensor.
"""
input_list = inputs.split(expert_size, dim=0)
output_list = []
for i in range(self.num_experts):
output_list.append(F.linear(input_list[i], self.weight[i]))
results = torch.cat(output_list, dim=0)
return results
class JetMoeTopKGating(nn.Module):
def __init__(self, input_size: int, num_experts: int, top_k: int):
"""
Initialize the top-k gating mechanism.
Args:
input_size (`int`):
Size of the input.
num_experts (`int`):
Number of experts.
top_k (`int`):
Number of top experts to select.
"""
super().__init__()
self.num_experts = num_experts
self.input_size = input_size
self.top_k = top_k
self.layer = nn.Linear(input_size, num_experts, bias=False)
def forward(self, hidden_states):
# compute the top_k routing decision
logits = self.layer(hidden_states).float() # [batch_size x seq_len, num_experts]
top_k_logits, top_k_indices = logits.topk(self.top_k, dim=1) # [num_tokens, top_k]
top_k_gates = torch.softmax(top_k_logits, dim=1).type_as(hidden_states) # [num_tokens, top_k]
# compute number of input given to each expert
zeros = torch.zeros(
[top_k_gates.size(0), self.num_experts], dtype=top_k_gates.dtype, device=top_k_gates.device
) # [num_tokens, num_experts]
gates = zeros.scatter(1, top_k_indices, 1) # [num_tokens, num_experts]
expert_size = gates.long().sum(0) # [num_experts,]
# (This cause torch.compile to fail with `torch._dynamo.exc.Unsupported: Backend compiler failed with a fake tensor exception at`)
# (and `DataDependentOutputException`)
expert_size = expert_size.tolist()
# sort and group input tokens according to expert assignment
top_k_experts = top_k_indices.flatten() # [num_tokens * top_k]
_, index_sorted_experts = top_k_experts.sort(0) # [num_tokens * top_k]
batch_index = index_sorted_experts.div(self.top_k, rounding_mode="trunc") # [num_tokens * top_k]
# gather the gate values for grouped input tokens
top_k_gates = top_k_gates.flatten() # [num_tokens * top_k]
batch_gates = top_k_gates[index_sorted_experts] # [num_tokens * top_k]
return index_sorted_experts, batch_index, batch_gates, expert_size, logits
class JetMoeMoE(nn.Module):
"""
A Sparsely gated mixture of experts layer with 1-layer Feed-Forward networks as experts.
Args:
config:
Configuration object with model hyperparameters.
"""
def __init__(self, config: JetMoeConfig):
super(JetMoeMoE, self).__init__()
self.input_size = config.hidden_size
self.hidden_size = config.intermediate_size
self.activation = ACT2FN[config.activation_function]
self.bias = torch.nn.Parameter(torch.empty(self.input_size))
self.input_linear = JetMoeParallelExperts(config.num_local_experts, self.input_size, self.hidden_size * 2)
self.output_linear = JetMoeParallelExperts(config.num_local_experts, self.hidden_size, self.input_size)
self.router = JetMoeTopKGating(
input_size=self.input_size,
num_experts=config.num_local_experts,
top_k=config.num_experts_per_tok,
)
def forward(self, layer_input):
"""
Forward pass of the mixture of experts layer.
Args:
layer_input (Tensor):
Input tensor.
Returns:
Tensor:
Output tensor.
Tensor:
Router logits.
"""
bsz, length, emb_size = layer_input.size()
layer_input = layer_input.reshape(-1, emb_size)
_, batch_index, batch_gates, expert_size, router_logits = self.router(layer_input)
expert_inputs = layer_input[batch_index]
hidden_states = self.input_linear(expert_inputs, expert_size)
chunked_hidden_states = hidden_states.chunk(2, dim=-1)
hidden_states = self.activation(chunked_hidden_states[0]) * chunked_hidden_states[1]
expert_outputs = self.output_linear(hidden_states, expert_size)
expert_outputs = expert_outputs * batch_gates[:, None]
zeros = torch.zeros((bsz * length, self.input_size), dtype=expert_outputs.dtype, device=expert_outputs.device)
layer_output = zeros.index_add(0, batch_index, expert_outputs)
layer_output = layer_output.view(bsz, length, self.input_size)
layer_output = layer_output + self.bias
return layer_output, router_logits
class JetMoeMoA(nn.Module):
"""
A Sparsely gated mixture of attention layer with pairs of query- and output-projections as experts.
Args:
config:
Configuration object with model hyperparameters.
"""
def __init__(self, config: JetMoeConfig):
super(JetMoeMoA, self).__init__()
self.num_experts = config.num_local_experts
self.input_size = config.hidden_size
self.hidden_size = config.kv_channels * config.num_key_value_heads
self.top_k = config.num_experts_per_tok
self.bias = torch.nn.Parameter(torch.empty(self.input_size))
self.input_linear = JetMoeParallelExperts(self.num_experts, self.input_size, self.hidden_size)
self.output_linear = JetMoeParallelExperts(self.num_experts, self.hidden_size, self.input_size)
self.router = JetMoeTopKGating(
input_size=self.input_size,
num_experts=self.num_experts,
top_k=self.top_k,
)
def map(self, layer_input):
"""
Map inputs to attention experts according to routing decision and compute query projection inside each experts.
"""
# Compute gating topology
bsz, length, emb_size = layer_input.size()
layer_input = layer_input.reshape(-1, emb_size) # [bsz * length, emb_size]
index_sorted_experts, batch_index, batch_gates, expert_size, router_logits = self.router(layer_input)
topo_info = (index_sorted_experts, batch_index, batch_gates, expert_size)
# Group inputs according to topology and compute query projection
expert_inputs = layer_input[batch_index] # [bsz * length * top_k, emb_size]
expert_outputs = self.input_linear(expert_inputs, expert_size) # [bsz * length * top_k, hidden_size]
# Ungroup queries back to original order
zeros = torch.zeros(
(bsz * length * self.top_k, self.hidden_size), dtype=expert_outputs.dtype, device=expert_outputs.device
)
layer_output = zeros.index_add(0, index_sorted_experts, expert_outputs)
layer_output = layer_output.view(bsz, length, self.top_k, -1) # [bsz, length, top_k, hidden_size]
return layer_output, router_logits, topo_info
def reduce(self, layer_input, topo_info):
"""
Compute output projection inside each attention experts and merge the outputs of different experts.
"""
bsz, length, k, hidden_size = layer_input.size()
layer_input = layer_input.reshape(-1, hidden_size) # [bsz * length * k, hidden_size]
index_sorted_experts, batch_index, batch_gates, expert_size = topo_info
# Group inputs according to topology and compute output projection
expert_inputs = layer_input[index_sorted_experts] # [bsz * length * top_k, hidden_size]
expert_outputs = self.output_linear(expert_inputs, expert_size) # [bsz * length * top_k, emb_size]
# Apply gates to attention expert outputs
expert_outputs = expert_outputs * batch_gates[:, None]
# Ungroup and merge outputs to original order
zeros = torch.zeros((bsz * length, self.input_size), dtype=expert_outputs.dtype, device=expert_outputs.device)
layer_output = zeros.index_add(0, batch_index, expert_outputs)
layer_output = layer_output.view(bsz, length, self.input_size)
layer_output = layer_output + self.bias
return layer_output
def forward(self, layer_input):
raise NotImplementedError("This module doesn't support call and forward.")
# Copied from transformers.models.llama.modeling_llama.LlamaRMSNorm with Llama->JetMoe
class JetMoeRMSNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-6):
"""
JetMoeRMSNorm is equivalent to T5LayerNorm
"""
super().__init__()
self.weight = nn.Parameter(torch.ones(hidden_size))
self.variance_epsilon = eps
def forward(self, hidden_states):
input_dtype = hidden_states.dtype
hidden_states = hidden_states.to(torch.float32)
variance = hidden_states.pow(2).mean(-1, keepdim=True)
hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
return self.weight * hidden_states.to(input_dtype)
def extra_repr(self):
return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}"
# Copied from transformers.models.gemma.modeling_gemma.GemmaRotaryEmbedding with Gemma->JetMoe
class JetMoeRotaryEmbedding(nn.Module):
def __init__(self, config: JetMoeConfig, device=None):
super().__init__()
# BC: "rope_type" was originally "type"
if hasattr(config, "rope_scaling") and config.rope_scaling is not None:
self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type"))
else:
self.rope_type = "default"
self.max_seq_len_cached = config.max_position_embeddings
self.original_max_seq_len = config.max_position_embeddings
self.config = config
self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device)
self.register_buffer("inv_freq", inv_freq, persistent=False)
self.original_inv_freq = self.inv_freq
@torch.no_grad()
@dynamic_rope_update # power user: used with advanced RoPE types (e.g. dynamic rope)
def forward(self, x, position_ids):
inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device)
position_ids_expanded = position_ids[:, None, :].float()
device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
with torch.autocast(device_type=device_type, enabled=False): # Force float32
freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
emb = torch.cat((freqs, freqs), dim=-1)
cos = emb.cos() * self.attention_scaling
sin = emb.sin() * self.attention_scaling
return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
# Copied from transformers.models.llama.modeling_llama.rotate_half
def rotate_half(x):
"""Rotates half the hidden dims of the input."""
x1 = x[..., : x.shape[-1] // 2]
x2 = x[..., x.shape[-1] // 2 :]
return torch.cat((-x2, x1), dim=-1)
# Copied from transformers.models.llama.modeling_llama.apply_rotary_pos_emb
def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
"""Applies Rotary Position Embedding to the query and key tensors.
Args:
q (`torch.Tensor`): The query tensor.
k (`torch.Tensor`): The key tensor.
cos (`torch.Tensor`): The cosine part of the rotary embedding.
sin (`torch.Tensor`): The sine part of the rotary embedding.
position_ids (`torch.Tensor`, *optional*):
Deprecated and unused.
unsqueeze_dim (`int`, *optional*, defaults to 1):
The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
Returns:
`tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
"""
cos = cos.unsqueeze(unsqueeze_dim)
sin = sin.unsqueeze(unsqueeze_dim)
q_embed = (q * cos) + (rotate_half(q) * sin)
k_embed = (k * cos) + (rotate_half(k) * sin)
return q_embed, k_embed
class JetMoeAttention(nn.Module):
"""
Multi-headed attention from 'Attention Is All You Need' paper.
"""
def __init__(self, config: JetMoeConfig, layer_idx: Optional[int] = None):
"""
Initialize the JetMoeAttention module.
Args:
config:
Configuration object with model hyperparameters.
layer_idx:
Index of the layer in the model.
"""
super().__init__()
self.config = config
self.layer_idx = layer_idx
self.is_causal = True
if layer_idx is None:
logger.warning_once(
f"Instantiating {self.__class__.__name__} without passing a `layer_idx` is not recommended and will "
"lead to errors during the forward call if caching is used. Please make sure to provide a `layer_idx` "
"when creating this class."
)
self.top_k = config.num_experts_per_tok
self.attention_dropout = config.attention_dropout
self.kv_projection_size = config.kv_channels * config.num_key_value_heads
self.num_key_value_heads = config.num_key_value_heads
self.num_heads = config.num_attention_heads
self.head_dim = config.kv_channels
self.experts = JetMoeMoA(config)
self.kv_proj = torch.nn.Linear(config.hidden_size, self.kv_projection_size * 2, bias=False)
self.rotary_emb = JetMoeRotaryEmbedding(config)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Cache] = None,
output_attentions: bool = False,
use_cache: bool = False,
cache_position: Optional[torch.LongTensor] = None,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
bsz, q_len, _ = hidden_states.size()
query_states, router_logits, topo_info = self.experts.map(hidden_states)
key_states, value_states = self.kv_proj(hidden_states).chunk(2, dim=-1)
query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
cos, sin = self.rotary_emb(value_states, position_ids)
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
if past_key_value is not None:
# sin and cos are specific to RoPE models; cache_position needed for the static cache
cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
# repeat k/v heads for top-k attention experts
key_states = key_states.repeat(1, self.top_k, 1, 1)
value_states = value_states.repeat(1, self.top_k, 1, 1)
attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
if attention_mask is not None: # no matter the length, we just slice it
causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
attn_weights = attn_weights + causal_mask
# upcast attention to fp32
attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training)
attn_output = torch.matmul(attn_weights, value_states)
if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
raise ValueError(
f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
f" {attn_output.size()}"
)
attn_output = attn_output.transpose(1, 2).contiguous()
attn_output = attn_output.reshape(bsz, q_len, self.top_k, self.kv_projection_size)
attn_output = self.experts.reduce(attn_output, topo_info)
attn_output = attn_output.view(bsz, q_len, -1)
if not output_attentions:
attn_weights = None
return attn_output, attn_weights, past_key_value, router_logits
class JetMoeSdpaAttention(JetMoeAttention):
"""
JetMoe attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
`JetMoeAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to
SDPA API.
"""
# Adapted from JetMoeAttention.forward
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Cache] = None,
output_attentions: bool = False,
use_cache: bool = False,
cache_position: Optional[torch.LongTensor] = None,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]], Optional[torch.Tensor]]:
if output_attentions:
# TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"` once this is implemented.
logger.warning_once(
"JetMoeModel is using JetMoeSdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to the manual attention implementation, "
'but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
)
return super().forward(
hidden_states=hidden_states,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_value=past_key_value,
output_attentions=output_attentions,
use_cache=use_cache,
cache_position=cache_position,
)
bsz, q_len, _ = hidden_states.size()
query_states, router_logits, topo_info = self.experts.map(hidden_states)
key_states, value_states = self.kv_proj(hidden_states).chunk(2, dim=-1)
query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
cos, sin = self.rotary_emb(value_states, position_ids)
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
if past_key_value is not None:
# sin and cos are specific to RoPE models; cache_position needed for the static cache
cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
# repeat k/v heads for top-k attention experts
key_states = key_states.repeat(1, self.top_k, 1, 1)
value_states = value_states.repeat(1, self.top_k, 1, 1)
causal_mask = attention_mask
if attention_mask is not None:
causal_mask = causal_mask[:, :, :, : key_states.shape[-2]]
# SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with custom attn_mask,
# Reference: https://github.com/pytorch/pytorch/issues/112577.
if query_states.device.type == "cuda" and causal_mask is not None:
query_states = query_states.contiguous()
key_states = key_states.contiguous()
value_states = value_states.contiguous()
# We dispatch to SDPA's Flash Attention or Efficient kernels via this `is_causal` if statement instead of an inline conditional assignment
# in SDPA to support both torch.compile's dynamic shapes and full graph options. An inline conditional prevents dynamic shapes from compiling.
is_causal = True if causal_mask is None and q_len > 1 else False
attn_output = torch.nn.functional.scaled_dot_product_attention(
query_states,
key_states,
value_states,
attn_mask=causal_mask,
dropout_p=self.attention_dropout if self.training else 0.0,
is_causal=is_causal,
)
attn_output = attn_output.transpose(1, 2).contiguous()
attn_output = attn_output.reshape(bsz, q_len, self.top_k, self.kv_projection_size)
attn_output = self.experts.reduce(attn_output, topo_info)
attn_output = attn_output.view(bsz, q_len, -1)
return attn_output, None, past_key_value, router_logits
class JetMoeFlashAttention2(JetMoeAttention):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
# TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
# flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignment, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
# Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left).
self._flash_attn_uses_top_left_mask = flash_attn_supports_top_left_mask()
def forward(
self,
hidden_states: Optional[torch.FloatTensor],
attention_mask: Optional[torch.FloatTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Cache] = None,
use_cache: Optional[bool] = False,
output_attentions: Optional[bool] = False,
cache_position: Optional[torch.LongTensor] = None,
) -> Union[
Tuple[torch.Tensor, Tuple[torch.Tensor]],
Optional[Tuple[torch.Tensor, Tuple[torch.Tensor], Tuple[torch.Tensor, ...]]],
]:
"""
Forward pass of the JetMoeAttention module.
Args:
hidden_states (Optional[torch.FloatTensor]): Input hidden states.
attention_mask (Optional[torch.FloatTensor]): Attention mask.
layer_past (Optional[Tuple[torch.Tensor]]): Past layer state.
use_cache (Optional[bool]): Whether to use cached states.
output_attentions (Optional[bool]): Whether to output attention weights.
cache_position (Optional[torch.LongTensor]): Position of the cache.
Returns:
Union[Tuple[torch.Tensor, Tuple[torch.Tensor]], Optional[Tuple[...]]]: Tuple containing outputs.
"""
output_attentions = False
bsz, q_len, hidden_size = hidden_states.size()
# calculate query, key, values
query_states, router_logits, topo_info = self.experts.map(hidden_states)
key_states, value_states = self.kv_proj(hidden_states).chunk(2, dim=-1)
query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
cos, sin = self.rotary_emb(value_states, position_ids)
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
if past_key_value is not None:
# sin and cos are specific to RoPE models; cache_position needed for the static cache
cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
# repeat k/v heads for top-k attention experts
key_states = key_states.repeat(1, self.top_k, 1, 1)
value_states = value_states.repeat(1, self.top_k, 1, 1)
# TODO: These transpose are quite inefficient but Flash Attention requires the layout [batch_size, sequence_length, num_heads, head_dim]. We would need to refactor the KV cache
# to be able to avoid many of these transpose/reshape/view.
query_states = query_states.transpose(1, 2)
key_states = key_states.transpose(1, 2)
value_states = value_states.transpose(1, 2)
dropout_rate = self.attention_dropout if self.training else 0.0
# In PEFT, usually we cast the layer norms in float32 for training stability reasons
# therefore the input hidden states gets silently casted in float32. Hence, we need
# cast them back in the correct dtype just to be sure everything works as expected.
# This might slowdown training & inference so it is recommended to not cast the LayerNorms
# in fp32. (LlamaRMSNorm handles it correctly)
input_dtype = query_states.dtype
if input_dtype == torch.float32:
if torch.is_autocast_enabled():
target_dtype = torch.get_autocast_gpu_dtype()
# Handle the case where the model is quantized
elif hasattr(self.config, "_pre_quantization_dtype"):
target_dtype = self.config._pre_quantization_dtype
else:
target_dtype = self.kv_proj.weight.dtype
logger.warning_once(
f"The input hidden states seems to be silently casted in float32, this might be related to"
f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
f" {target_dtype}."
)
query_states = query_states.to(target_dtype)
key_states = key_states.to(target_dtype)
value_states = value_states.to(target_dtype)
attn_output = _flash_attention_forward(
query_states,
key_states,
value_states,
attention_mask,
q_len,
dropout=dropout_rate,
use_top_left_mask=self._flash_attn_uses_top_left_mask,
is_causal=self.is_causal,
).to(input_dtype)
# output projection
attn_output = attn_output.reshape(bsz, q_len, self.top_k, self.kv_projection_size)
attn_output = self.experts.reduce(attn_output, topo_info)
attn_output = attn_output.view(bsz, q_len, hidden_size) # re-assemble all head outputs side by side
if not output_attentions:
attn_weights = None
return attn_output, attn_weights, past_key_value, router_logits
JETMOE_ATTENTION_CLASSES = {
"eager": JetMoeAttention,
"flash_attention_2": JetMoeFlashAttention2,
"sdpa": JetMoeSdpaAttention,
}
class JetMoeBlock(nn.Module):
def __init__(self, config: JetMoeConfig, layer_idx: Optional[int] = None):
"""
Initialize the JetMoeBlock module.
Args:
config:
Configuration object with model hyperparameters.
"""
super().__init__()
self.input_layernorm = JetMoeRMSNorm(config.hidden_size)
self.self_attention = JETMOE_ATTENTION_CLASSES[config._attn_implementation](config, layer_idx)
self.post_attention_layernorm = JetMoeRMSNorm(config.hidden_size)
self.mlp = JetMoeMoE(config)
def forward(
self,
hidden_states: Optional[torch.FloatTensor],
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
attention_mask: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = False,
output_router_logits: Optional[bool] = False,
use_cache: Optional[bool] = False,
cache_position: Optional[torch.LongTensor] = None,
) -> Union[Tuple[torch.Tensor], Optional[Tuple[torch.Tensor, Tuple[torch.FloatTensor, ...]]]]:
# Self Attention
attn_output, self_attn_weights, present_key_value, attn_router_logits = self.self_attention(
hidden_states=self.input_layernorm(hidden_states),
attention_mask=attention_mask,
position_ids=position_ids,
past_key_value=past_key_value,
output_attentions=output_attentions,
use_cache=use_cache,
cache_position=cache_position,
)
hidden_states = hidden_states + attn_output
x_mlp, mlp_router_logits = self.mlp(self.post_attention_layernorm(hidden_states))
hidden_states = hidden_states + x_mlp
outputs = (hidden_states,)
if output_attentions:
outputs += (self_attn_weights,)
if use_cache:
outputs += (present_key_value,)
if output_router_logits:
outputs += attn_router_logits, mlp_router_logits
return outputs
class JetMoePreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = JetMoeConfig
base_model_prefix = "transformer"
supports_gradient_checkpointing = False
_no_split_modules = ["JetMoeBlock"]
_skip_keys_device_placement = ["past_key_values"]
_supports_flash_attn_2 = True
_supports_sdpa = True
_supports_cache_class = True
def _init_weights(self, module):
"""Initialize the weights."""
if isinstance(module, (nn.Linear,)):
# Slightly different from Mesh Transformer JAX which uses truncated_normal for initialization
# cf https://github.com/pytorch/pytorch/pull/5617
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.Embedding):
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
elif isinstance(module, nn.LayerNorm):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
elif isinstance(module, JetMoeParallelExperts):
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
elif isinstance(module, JetMoeMoA):
module.bias.data.zero_()
elif isinstance(module, JetMoeMoE):
module.bias.data.zero_()
JETMOE_START_DOCSTRING = r"""
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`JetMoeConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
JETMOE_INPUTS_DOCSTRING = r"""
Args:
input_ids (`torch.LongTensor` of shape `({0})`):
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using [`AutoProcenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
position_ids (`torch.LongTensor` of shape `({0})`, *optional*):
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
config.n_positions - 1]`.
[What are position IDs?](../glossary#position-ids)
inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_dim)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert *input_ids* indices into associated vectors than the
model's internal embedding lookup matrix.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
output_router_logits (`bool`, *optional*):
Whether or not to return the logits of all the routers. They are useful for computing the router loss, and
should not be returned during inference.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
Indices depicting the position of the input sequence tokens in the sequence. Contrarily to `position_ids`,
this tensor is not affected by padding. It is used to update the cache in the correct position and to infer
the complete sequence length.
"""
@add_start_docstrings(
"The bare JetMoe Model outputting raw hidden-states without any specific head on top.",
JETMOE_START_DOCSTRING,
)
class JetMoeModel(JetMoePreTrainedModel):
"""
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`JetMoeBlock`]
Args:
config:
JetMoeConfig
"""
def __init__(self, config: JetMoeConfig):
super().__init__(config)
self.padding_idx = config.pad_token_id
self.vocab_size = config.vocab_size
self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
self.layers = nn.ModuleList([JetMoeBlock(config, layer_idx) for layer_idx in range(config.num_hidden_layers)])
self._attn_implementation = config._attn_implementation
self.norm = JetMoeRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.gradient_checkpointing = False
# Initialize weights and apply final processing
self.post_init()
# Copied from transformers.models.llama.modeling_llama.LlamaModel.get_input_embeddings
def get_input_embeddings(self):
return self.embed_tokens
# Copied from transformers.models.llama.modeling_llama.LlamaModel.set_input_embeddings
def set_input_embeddings(self, value):
self.embed_tokens = value
@can_return_tuple
@add_start_docstrings_to_model_forward(JETMOE_INPUTS_DOCSTRING)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
output_router_logits: Optional[bool] = None,
cache_position: Optional[torch.LongTensor] = None,
) -> MoeModelOutputWithPast:
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
output_router_logits = (
output_router_logits if output_router_logits is not None else self.config.output_router_logits
)
use_cache = use_cache if use_cache is not None else self.config.use_cache
if (input_ids is None) ^ (inputs_embeds is not None):
raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
if self.gradient_checkpointing and self.training and use_cache:
logger.warning_once(
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`."
)
use_cache = False
if inputs_embeds is None:
inputs_embeds = self.embed_tokens(input_ids)
# kept for BC (non `Cache` `past_key_values` inputs)
return_legacy_cache = False
if use_cache and not isinstance(past_key_values, Cache):
return_legacy_cache = True
if past_key_values is None:
past_key_values = DynamicCache()
else:
past_key_values = DynamicCache.from_legacy_cache(past_key_values)
logger.warning_once(
"We detected that you are passing `past_key_values` as a tuple of tuples. This is deprecated and "
"will be removed in v4.47. Please convert your cache or use an appropriate `Cache` class "
"(https://huggingface.co/docs/transformers/kv_cache#legacy-cache-format)"
)
if cache_position is None:
past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
cache_position = torch.arange(
past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
)
if position_ids is None:
position_ids = cache_position.unsqueeze(0)
if attention_mask is not None and self._attn_implementation == "flash_attention_2" and use_cache:
batch_size = inputs_embeds.shape[0]
is_padding_right = attention_mask[:, -1].sum().item() != batch_size
if is_padding_right:
raise ValueError(
"You are attempting to perform batched generation with padding_side='right'"
" this may lead to unexpected behaviour for Flash Attention version of JetMoe. Make sure to "
" call `tokenizer.padding_side = 'left'` before tokenizing the input. "
)
causal_mask = self._update_causal_mask(
attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions
)
hidden_states = inputs_embeds
# decoder layers
all_hidden_states = () if output_hidden_states else None
all_self_attns = () if output_attentions else None
all_router_logits = () if output_router_logits else None
next_decoder_cache = None
for decoder_layer in self.layers:
if output_hidden_states:
all_hidden_states += (hidden_states,)
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
decoder_layer.__call__,
hidden_states,
position_ids,
past_key_values,
causal_mask,
output_attentions,
output_router_logits,
use_cache,
use_reentrant=False,
)
else:
layer_outputs = decoder_layer(
hidden_states,
attention_mask=causal_mask,
position_ids=position_ids,
past_key_value=past_key_values,
output_attentions=output_attentions,
output_router_logits=output_router_logits,
use_cache=use_cache,
)
hidden_states = layer_outputs[0]
if use_cache:
next_decoder_cache = layer_outputs[2 if output_attentions else 1]
if output_attentions:
all_self_attns += (layer_outputs[1],)
if output_router_logits:
all_router_logits += (layer_outputs[-2], layer_outputs[-1])
hidden_states = self.norm(hidden_states)
# add hidden states from the last decoder layer
if output_hidden_states:
all_hidden_states += (hidden_states,)
next_cache = next_decoder_cache if use_cache else None
if return_legacy_cache:
next_cache = next_cache.to_legacy_cache()
return MoeModelOutputWithPast(
last_hidden_state=hidden_states,
past_key_values=next_cache,
hidden_states=all_hidden_states,
attentions=all_self_attns,
router_logits=all_router_logits,
)
# Copied from transformers.models.llama.modeling_llama.LlamaModel._update_causal_mask
def _update_causal_mask(
self,
attention_mask: torch.Tensor,
input_tensor: torch.Tensor,
cache_position: torch.Tensor,
past_key_values: Cache,
output_attentions: bool = False,
):
if self.config._attn_implementation == "flash_attention_2":
if attention_mask is not None and (attention_mask == 0.0).any():
return attention_mask
return None
if self.config._attn_implementation == "flex_attention":
if isinstance(attention_mask, torch.Tensor):
attention_mask = make_flex_block_causal_mask(attention_mask)
if isinstance(attention_mask, BlockMask):
return attention_mask
# For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in
# order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail
# to infer the attention mask.
past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
using_static_cache = isinstance(past_key_values, StaticCache)
# When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward
if self.config._attn_implementation == "sdpa" and not using_static_cache and not output_attentions:
if AttentionMaskConverter._ignore_causal_mask_sdpa(
attention_mask,
inputs_embeds=input_tensor,
past_key_values_length=past_seen_tokens,
is_training=self.training,
):
return None
dtype, device = input_tensor.dtype, input_tensor.device
sequence_length = input_tensor.shape[1]
if using_static_cache:
target_length = past_key_values.get_max_cache_shape()
else:
target_length = (
attention_mask.shape[-1]
if isinstance(attention_mask, torch.Tensor)
else past_seen_tokens + sequence_length + 1
)
# In case the provided `attention` mask is 2D, we generate a causal mask here (4D).
causal_mask = self._prepare_4d_causal_attention_mask_with_cache_position(
attention_mask,
sequence_length=sequence_length,
target_length=target_length,
dtype=dtype,
device=device,
cache_position=cache_position,
batch_size=input_tensor.shape[0],
)
if (
self.config._attn_implementation == "sdpa"
and attention_mask is not None
and attention_mask.device.type in ["cuda", "xpu"]
and not output_attentions
):
# Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when
# using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path.
# Details: https://github.com/pytorch/pytorch/issues/110213
min_dtype = torch.finfo(dtype).min
causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype)
return causal_mask
@staticmethod
# Copied from transformers.models.llama.modeling_llama.LlamaModel._prepare_4d_causal_attention_mask_with_cache_position
def _prepare_4d_causal_attention_mask_with_cache_position(
attention_mask: torch.Tensor,
sequence_length: int,
target_length: int,
dtype: torch.dtype,
device: torch.device,
cache_position: torch.Tensor,
batch_size: int,
**kwargs,
):
"""
Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape
`(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing.
Args:
attention_mask (`torch.Tensor`):
A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape
`(batch_size, 1, query_length, key_value_length)`.
sequence_length (`int`):
The sequence length being processed.
target_length (`int`):
The target length: when generating with static cache, the mask should be as long as the static cache,
to account for the 0 padding, the part of the cache that is not filled yet.
dtype (`torch.dtype`):
The dtype to use for the 4D attention mask.
device (`torch.device`):
The device to place the 4D attention mask on.
cache_position (`torch.Tensor`):
Indices depicting the position of the input sequence tokens in the sequence.
batch_size (`torch.Tensor`):
Batch size.
"""
if attention_mask is not None and attention_mask.dim() == 4:
# In this case we assume that the mask comes already in inverted form and requires no inversion or slicing.
causal_mask = attention_mask
else:
min_dtype = torch.finfo(dtype).min
causal_mask = torch.full(
(sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=device
)
if sequence_length != 1:
causal_mask = torch.triu(causal_mask, diagonal=1)
causal_mask *= torch.arange(target_length, device=device) > cache_position.reshape(-1, 1)
causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1)
if attention_mask is not None:
causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit
mask_length = attention_mask.shape[-1]
padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :].to(
causal_mask.device
)
padding_mask = padding_mask == 0
causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
padding_mask, min_dtype
)
return causal_mask
class JetMoeForCausalLM(JetMoePreTrainedModel, GenerationMixin):
_tied_weights_keys = ["lm_head.weight"]
def __init__(self, config):
super().__init__(config)
self.model = JetMoeModel(config)
self.vocab_size = config.vocab_size
self.aux_loss_coef = config.aux_loss_coef
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
self.tie_word_embeddings = config.tie_word_embeddings
# Initialize weights and apply final processing
self.post_init()
# Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.get_input_embeddings
def get_input_embeddings(self):
return self.model.embed_tokens
# Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.set_input_embeddings
def set_input_embeddings(self, value):
self.model.embed_tokens = value
# Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.get_output_embeddings
def get_output_embeddings(self):
return self.lm_head
# Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.set_output_embeddings
def set_output_embeddings(self, new_embeddings):
self.lm_head = new_embeddings
# Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.set_decoder
def set_decoder(self, decoder):
self.model = decoder
# Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.get_decoder
def get_decoder(self):
return self.model
@can_return_tuple
@deprecate_kwarg("num_logits_to_keep", version="4.50", new_name="logits_to_keep")
@add_start_docstrings_to_model_forward(JETMOE_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=MoeCausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
output_router_logits: Optional[bool] = None,
cache_position: Optional[torch.LongTensor] = None,
logits_to_keep: Union[int, torch.Tensor] = 0,
**kwargs,
) -> MoeCausalLMOutputWithPast:
r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
logits_to_keep (`int` or `torch.Tensor`, *optional*):
If an `int`, compute logits for the last `logits_to_keep` tokens. If `0`, calculate logits for all
`input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that
token can save memory, which becomes pretty significant for long sequences or large vocabulary size.
If a `torch.Tensor`, must be 1D corresponding to the indices to keep in the sequence length dimension.
This is useful when using packed tensor format (single dimension for batch and sequence length).
Returns:
"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
outputs: MoeModelOutputWithPast = self.model(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
cache_position=cache_position,
)
hidden_states = outputs.last_hidden_state
# Only compute necessary logits, and do not upcast them to float if we are not computing the loss
slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
logits = self.lm_head(hidden_states[:, slice_indices, :])
loss = None
if labels is not None:
# Upcast to float if we need to compute the loss to avoid potential precision issues
logits = logits.float()
# Shift so that tokens < n predict n
shift_logits = logits[..., :-1, :].contiguous()
shift_labels = labels[..., 1:].contiguous()
# Flatten the tokens
shift_logits = shift_logits.view(-1, self.config.vocab_size)
shift_labels = shift_labels.view(-1)
# Ensure tensors are on the same device
shift_labels = shift_labels.to(shift_logits.device)
loss = self.loss_function(
shift_logits,
shift_labels,
vocab_size=self.config.vocab_size,
**kwargs,
)
aux_loss = None
if output_router_logits:
aux_loss = load_balancing_loss_func(
outputs.router_logits,
self.num_experts,
self.num_experts_per_tok,
attention_mask,
)
if labels is not None:
loss += self.aux_loss_coef * aux_loss.to(loss.device) # make sure to reside in the same device
return MoeCausalLMOutputWithPast(
loss=loss,
aux_loss=aux_loss,
logits=logits,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
router_logits=outputs.router_logits,
)
@add_start_docstrings(
"""
The JetMoe Model transformer with a sequence classification head on top (linear layer).
[`JetMoeForSequenceClassification`] uses the last token in order to do the classification, as other causal models
(e.g. GPT-2) do.
Since it does classification on the last token, it requires to know the position of the last token. If a
`pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
each row of the batch).
""",
JETMOE_START_DOCSTRING,
)
# Copied from transformers.models.llama.modeling_llama.LlamaForSequenceClassification with Llama->JetMoe, LLAMA->JETMOE, BaseModelOutputWithPast->MoeModelOutputWithPast
class JetMoeForSequenceClassification(JetMoePreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.model = JetMoeModel(config)
self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.model.embed_tokens
def set_input_embeddings(self, value):
self.model.embed_tokens = value
@can_return_tuple
@add_start_docstrings_to_model_forward(JETMOE_INPUTS_DOCSTRING)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Cache] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
) -> SequenceClassifierOutputWithPast:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
"""
transformer_outputs: MoeModelOutputWithPast = self.model(
input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
)
hidden_states = transformer_outputs.last_hidden_state
logits = self.score(hidden_states)
if input_ids is not None:
batch_size = input_ids.shape[0]
else:
batch_size = inputs_embeds.shape[0]
if self.config.pad_token_id is None and batch_size != 1:
raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
if self.config.pad_token_id is None:
last_non_pad_token = -1
elif input_ids is not None:
# To handle both left- and right- padding, we take the rightmost token that is not equal to pad_token_id
non_pad_mask = (input_ids != self.config.pad_token_id).to(logits.device, torch.int32)
token_indices = torch.arange(input_ids.shape[-1], device=logits.device, dtype=torch.int32)
last_non_pad_token = (token_indices * non_pad_mask).argmax(-1)
else:
last_non_pad_token = -1
logger.warning_once(
f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be "
"unexpected if using padding tokens in conjunction with `inputs_embeds.`"
)
pooled_logits = logits[torch.arange(batch_size, device=logits.device), last_non_pad_token]
loss = None
if labels is not None:
loss = self.loss_function(logits=logits, labels=labels, pooled_logits=pooled_logits, config=self.config)
return SequenceClassifierOutputWithPast(
loss=loss,
logits=pooled_logits,
past_key_values=transformer_outputs.past_key_values,
hidden_states=transformer_outputs.hidden_states,
attentions=transformer_outputs.attentions,
)
__all__ = ["JetMoeForCausalLM", "JetMoeModel", "JetMoePreTrainedModel", "JetMoeForSequenceClassification"]
```
|
===============================================================================================================================
SOURCE CODE FILE: __init__.py
LINES: 1
SIZE: 1.01 KB
PATH: scripts\freecad_env\Lib\site-packages\transformers\models\kosmos2\__init__.py
ENCODING: utf-8
```py
# Copyright 2024 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import _LazyModule
from ...utils.import_utils import define_import_structure
if TYPE_CHECKING:
from .configuration_kosmos2 import *
from .modeling_kosmos2 import *
from .processing_kosmos2 import *
else:
import sys
_file = globals()["__file__"]
sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)
```
|
============================================================================================================================================
SOURCE CODE FILE: configuration_kosmos2.py
LINES: 1
SIZE: 11.60 KB
PATH: scripts\freecad_env\Lib\site-packages\transformers\models\kosmos2\configuration_kosmos2.py
ENCODING: utf-8
```py
# coding=utf-8
# Copyright 2023 Microsoft Research and The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""KOSMOS-2 model configuration"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
logger = logging.get_logger(__name__)
class Kosmos2TextConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`Kosmos2TextModel`]. It is used to instantiate a
KOSMOS-2 text decoder according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will yield a similar configuration to that of the text decoder of the KOSMOS-2
[microsoft/kosmos-2-patch14-224](https://huggingface.co/microsoft/kosmos-2-patch14-224) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 65037):
Vocabulary size of the Kosmos2 model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`Kosmos2Model`].
max_position_embeddings (`int`, *optional*, defaults to 2048):
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).
embed_dim (`int`, *optional*, defaults to 2048):
Dimensionality of the layers and the pooler layer.
layers (`int`, *optional*, defaults to 24):
Number of hidden layers in the Transformer encoder.
ffn_dim (`int`, *optional*, defaults to 8192):
Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
attention_heads (`int`, *optional*, defaults to 32):
Number of attention heads for each attention layer in the Transformer encoder.
activation_function (`str` or `function`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"silu"` and `"gelu_new"` are supported.
dropout (`float`, *optional*, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_dropout (`float`, *optional*, defaults to 0.1):
The dropout ratio for the attention probabilities.
activation_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for activations inside the fully connected layer.
layerdrop (`float`, *optional*, defaults to 0.0):
The LayerDrop probability for the decoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556)
for more details.
layer_norm_eps (`float`, *optional*, defaults to 1e-05):
The epsilon used by the layer normalization layers.
init_std (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
scale_embedding (`bool`, *optional*, defaults to `True`):
Scale embeddings by diving by sqrt(embed_dim).
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models).
pad_token_id (`int`, *optional*, defaults to 1):
Token id used for padding.
bos_token_id (`int`, *optional*, defaults to 0):
Token id used for beginning of string.
eos_token_id (`int`, *optional*, defaults to 2):
Token id used for end of string.
```"""
model_type = "kosmos_2_text_model"
base_config_key = "text_config"
keys_to_ignore_at_inference = ["past_key_values"]
attribute_map = {
"num_attention_heads": "attention_heads",
"hidden_size": "embed_dim",
"num_hidden_layers": "layers",
}
def __init__(
self,
vocab_size=65037,
max_position_embeddings=2048,
embed_dim=2048,
layers=24,
ffn_dim=8192,
attention_heads=32,
activation_function="gelu",
dropout=0.1,
attention_dropout=0.1,
activation_dropout=0.0,
layerdrop=0.0,
layer_norm_eps=1e-5,
init_std=0.02,
scale_embedding=True,
use_cache=True,
pad_token_id=1,
bos_token_id=0,
eos_token_id=2,
**kwargs,
):
super().__init__(
pad_token_id=pad_token_id,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
**kwargs,
)
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
self.embed_dim = embed_dim
self.layers = layers
self.ffn_dim = ffn_dim
self.attention_heads = attention_heads
self.activation_function = activation_function
self.dropout = dropout
self.attention_dropout = attention_dropout
self.activation_dropout = activation_dropout
self.layerdrop = layerdrop
self.layer_norm_eps = layer_norm_eps
self.init_std = init_std
self.scale_embedding = scale_embedding
self.use_cache = use_cache
class Kosmos2VisionConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`Kosmos2VisionModel`]. It is used to instantiate a
KOSMOS-2 vision encoder according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will yield a similar configuration to that of the vision encoder of the KOSMOS-2
[microsoft/kosmos-2-patch14-224](https://huggingface.co/microsoft/kosmos-2-patch14-224) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
hidden_size (`int`, *optional*, defaults to 1024):
Dimensionality of the encoder layers and the pooler layer.
intermediate_size (`int`, *optional*, defaults to 4096):
Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
num_hidden_layers (`int`, *optional*, defaults to 24):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 16):
Number of attention heads for each attention layer in the Transformer encoder.
num_channels (`int`, *optional*, defaults to 3):
The number of input channels.
image_size (`int`, *optional*, defaults to 224):
The size (resolution) of each image.
patch_size (`int`, *optional*, defaults to 14):
The size (resolution) of each patch.
hidden_act (`str` or `function`, *optional*, defaults to `"quick_gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"selu"` and `"gelu_new"` `"quick_gelu"` are supported.
layer_norm_eps (`float`, *optional*, defaults to 1e-05):
The epsilon used by the layer normalization layers.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
initializer_factor (`float`, *optional*, defaults to 1.0):
A factor for initializing all weight matrices (should be kept to 1, used internally for initialization
testing).
```"""
model_type = "kosmos_2_vision_model"
base_config_key = "vision_config"
def __init__(
self,
hidden_size=1024,
intermediate_size=4096,
num_hidden_layers=24,
num_attention_heads=16,
num_channels=3,
image_size=224,
patch_size=14,
hidden_act="quick_gelu",
layer_norm_eps=1e-5,
attention_dropout=0.0,
initializer_range=0.02,
initializer_factor=1.0,
**kwargs,
):
super().__init__(**kwargs)
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.num_channels = num_channels
self.patch_size = patch_size
self.image_size = image_size
self.initializer_range = initializer_range
self.initializer_factor = initializer_factor
self.attention_dropout = attention_dropout
self.layer_norm_eps = layer_norm_eps
self.hidden_act = hidden_act
class Kosmos2Config(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`Kosmos2Model`]. It is used to instantiate a
KOSMOS-2 model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a similar configuration to that of the KOSMOS-2
[microsoft/kosmos-2-patch14-224](https://huggingface.co/microsoft/kosmos-2-patch14-224) architecture.
Args:
text_config (`dict`, *optional*):
Dictionary of configuration options used to initialize [`Kosmos2TextConfig`].
vision_config (`dict`, *optional*):
Dictionary of configuration options used to initialize [`Kosmos2VisionConfig`].
latent_query_num (`int`, *optional*, defaults to 64):
The number of latent query tokens that represent the image features used in the text decoder component.
kwargs (*optional*):
Dictionary of keyword arguments.
Example:
```python
>>> from transformers import Kosmos2Config, Kosmos2Model
>>> # Initializing a Kosmos-2 kosmos-2-patch14-224 style configuration
>>> configuration = Kosmos2Config()
>>> # Initializing a model (with random weights) from the kosmos-2-patch14-224 style configuration
>>> model = Kosmos2Model(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "kosmos-2"
sub_configs = {"text_config": Kosmos2TextConfig, "vision_config": Kosmos2VisionConfig}
def __init__(
self,
text_config=None,
vision_config=None,
latent_query_num=64,
**kwargs,
):
super().__init__(**kwargs)
if text_config is None:
text_config = {}
logger.info("`text_config` is `None`. Initializing the `Kosmos2TextConfig` with default values.")
if vision_config is None:
vision_config = {}
logger.info("`vision_config` is `None`. Initializing the `Kosmos2VisionConfig` with default values.")
self.text_config = Kosmos2TextConfig(**text_config)
self.vision_config = Kosmos2VisionConfig(**vision_config)
self.latent_query_num = latent_query_num
__all__ = ["Kosmos2Config"]
```
|
=======================================================================================================================================
SOURCE CODE FILE: modeling_kosmos2.py
LINES: 1
SIZE: 95.67 KB
PATH: scripts\freecad_env\Lib\site-packages\transformers\models\kosmos2\modeling_kosmos2.py
ENCODING: utf-8
```py
# coding=utf-8
# Copyright 2023 Microsoft Research and The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""PyTorch KOSMOS-2 model."""
import math
from dataclasses import dataclass
from typing import Any, List, Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import CrossEntropyLoss
from ...activations import ACT2FN
from ...generation import GenerationMixin
from ...modeling_outputs import (
BaseModelOutput,
BaseModelOutputWithPastAndCrossAttentions,
BaseModelOutputWithPooling,
CausalLMOutputWithCrossAttentions,
)
from ...modeling_utils import PreTrainedModel
from ...utils import (
ModelOutput,
add_start_docstrings,
add_start_docstrings_to_model_forward,
logging,
replace_return_docstrings,
torch_int,
)
from .configuration_kosmos2 import Kosmos2Config, Kosmos2TextConfig, Kosmos2VisionConfig
logger = logging.get_logger(__name__)
_CONFIG_FOR_DOC = Kosmos2Config
def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
"""
Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
"""
bsz, src_len = mask.size()
tgt_len = tgt_len if tgt_len is not None else src_len
expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
inverted_mask = 1.0 - expanded_mask
return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)
def _make_causal_mask(
input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0
):
"""
Make causal mask used for bi-directional self-attention.
"""
bsz, tgt_len = input_ids_shape
mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min, device=device)
mask_cond = torch.arange(mask.size(-1), device=device)
mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
mask = mask.to(dtype)
if past_key_values_length > 0:
mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1)
return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)
# Copied from transformers.models.roberta.modeling_roberta.create_position_ids_from_input_ids
def create_position_ids_from_input_ids(input_ids, padding_idx, past_key_values_length=0):
"""
Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols
are ignored. This is modified from fairseq's `utils.make_positions`.
Args:
x: torch.Tensor x:
Returns: torch.Tensor
"""
# The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA.
mask = input_ids.ne(padding_idx).int()
incremental_indices = (torch.cumsum(mask, dim=1).type_as(mask) + past_key_values_length) * mask
return incremental_indices.long() + padding_idx
KOSMOS2_START_DOCSTRING = r"""
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.
Parameters:
config ([`Kosmos2Config`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
KOSMOS2_VISION_INPUTS_DOCSTRING = r"""
Args:
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
[`CLIPImageProcessor.__call__`] for details.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
interpolate_pos_encoding (`bool`, *optional*, defaults `False`):
Whether to interpolate the pre-trained position encodings.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
KOSMOS2_TEXT_INPUTS_DOCSTRING = r"""
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
it.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
image_embeds: (`torch.FloatTensor` of shape `(batch_size, latent_query_num, hidden_size)`, *optional*):
Sequence of hidden-states at the output of `Kosmos2ImageToTextProjection`.
image_embeds_position_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to indicate the location in a sequence to insert the image features . Mask values selected in `[0,
1]`:
- 1 for places where to put the image features,
- 0 for places that are not for image features (i.e. for text tokens).
encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
the model is configured as a decoder.
encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the cross-attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
`decoder_input_ids` of shape `(batch_size, sequence_length)`.
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
model's internal embedding lookup matrix.
position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
config.max_position_embeddings - 1]`.
[What are position IDs?](../glossary#position-ids)
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
`past_key_values`).
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
KOSMOS2_INPUTS_DOCSTRING = r"""
Args:
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
[`CLIPImageProcessor.__call__`] for details.
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
it.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
image_embeds_position_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to indicate the location in a sequence to insert the image features . Mask values selected in `[0,
1]`:
- 1 for places where to put the image features,
- 0 for places that are not for image features (i.e. for text tokens).
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
`decoder_input_ids` of shape `(batch_size, sequence_length)`.
image_embeds: (`torch.FloatTensor` of shape `(batch_size, latent_query_num, hidden_size)`, *optional*):
Sequence of hidden-states at the output of `Kosmos2ImageToTextProjection`.
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
model's internal embedding lookup matrix.
position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
config.max_position_embeddings - 1]`.
[What are position IDs?](../glossary#position-ids)
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
`past_key_values`).
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
interpolate_pos_encoding (`bool`, *optional*, defaults `False`):
Whether to interpolate the pre-trained position encodings.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
@dataclass
class Kosmos2ModelOutput(ModelOutput):
"""
Base class for text model's outputs that also contains a pooling of the last hidden states.
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the model.
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
image_embeds (`torch.FloatTensor` of shape `(batch_size, latent_query_num, hidden_size)`, *optional*):
Sequence of hidden-states at the output of `Kosmos2ImageToTextProjection`.
projection_attentions (`tuple(torch.FloatTensor)`, *optional*):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights given by `Kosmos2ImageToTextProjection`, after the attention softmax, used to compute
the weighted average in the self-attention heads.
vision_model_output(`BaseModelOutputWithPooling`, *optional*):
The output of the [`Kosmos2VisionModel`].
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
`(batch_size, num_heads, sequence_length, embed_size_per_head)`) and optionally if
`config.is_encoder_decoder=True` 2 additional tensors of shape `(batch_size, num_heads,
encoder_sequence_length, embed_size_per_head)`.
Contains pre-computed hidden-states (key and values in the self-attention blocks and optionally if
`config.is_encoder_decoder=True` in the cross-attention blocks) that can be used (see `past_key_values`
input) to speed up sequential decoding.
"""
last_hidden_state: Optional[torch.FloatTensor] = None
past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None
image_embeds: Optional[torch.FloatTensor] = None
projection_attentions: Optional[Tuple[torch.FloatTensor]] = None
vision_model_output: BaseModelOutputWithPooling = None
def to_tuple(self) -> Tuple[Any]:
return tuple(
self[k] if k not in ["text_model_output", "vision_model_output"] else getattr(self, k).to_tuple()
for k in self.keys()
)
@dataclass
class Kosmos2ForConditionalGenerationModelOutput(ModelOutput):
"""
Model output class for `Kosmos2ForConditionalGeneration`.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Language modeling loss (for next-token prediction).
logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
image_embeds (`torch.FloatTensor` of shape `(batch_size, latent_query_num, hidden_size)`, *optional*):
Sequence of hidden-states at the output of `Kosmos2ImageToTextProjection`.
projection_attentions (`tuple(torch.FloatTensor)`, *optional*):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights given by `Kosmos2ImageToTextProjection`, after the attention softmax, used to compute
the weighted average in the self-attention heads.
vision_model_output(`BaseModelOutputWithPooling`, *optional*):
The output of the [`Kosmos2VisionModel`].
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
`(batch_size, num_heads, sequence_length, embed_size_per_head)`) and optionally if
`config.is_encoder_decoder=True` 2 additional tensors of shape `(batch_size, num_heads,
encoder_sequence_length, embed_size_per_head)`.
Contains pre-computed hidden-states (key and values in the self-attention blocks and optionally if
`config.is_encoder_decoder=True` in the cross-attention blocks) that can be used (see `past_key_values`
input) to speed up sequential decoding.
"""
loss: Optional[torch.FloatTensor] = None
logits: Optional[torch.FloatTensor] = None
past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None
image_embeds: Optional[torch.FloatTensor] = None
projection_attentions: Optional[Tuple[torch.FloatTensor]] = None
vision_model_output: BaseModelOutputWithPooling = None
def to_tuple(self) -> Tuple[Any]:
return tuple(
self[k] if k not in ["text_model_output", "vision_model_output"] else getattr(self, k).to_tuple()
for k in self.keys()
)
# Copied from transformers.models.clip.modeling_clip.CLIPVisionEmbeddings with CLIP->Kosmos2
class Kosmos2VisionEmbeddings(nn.Module):
def __init__(self, config: Kosmos2VisionConfig):
super().__init__()
self.config = config
self.embed_dim = config.hidden_size
self.image_size = config.image_size
self.patch_size = config.patch_size
self.class_embedding = nn.Parameter(torch.randn(self.embed_dim))
self.patch_embedding = nn.Conv2d(
in_channels=config.num_channels,
out_channels=self.embed_dim,
kernel_size=self.patch_size,
stride=self.patch_size,
bias=False,
)
self.num_patches = (self.image_size // self.patch_size) ** 2
self.num_positions = self.num_patches + 1
self.position_embedding = nn.Embedding(self.num_positions, self.embed_dim)
self.register_buffer("position_ids", torch.arange(self.num_positions).expand((1, -1)), persistent=False)
def interpolate_pos_encoding(self, embeddings: torch.Tensor, height: int, width: int) -> torch.Tensor:
"""
This method allows to interpolate the pre-trained position encodings, to be able to use the model on higher resolution
images. This method is also adapted to support torch.jit tracing.
Adapted from:
- https://github.com/facebookresearch/dino/blob/de9ee3df6cf39fac952ab558447af1fa1365362a/vision_transformer.py#L174-L194, and
- https://github.com/facebookresearch/dinov2/blob/e1277af2ba9496fbadf7aec6eba56e8d882d1e35/dinov2/models/vision_transformer.py#L179-L211
"""
num_patches = embeddings.shape[1] - 1
position_embedding = self.position_embedding.weight.unsqueeze(0)
num_positions = position_embedding.shape[1] - 1
# always interpolate when tracing to ensure the exported model works for dynamic input shapes
if not torch.jit.is_tracing() and num_patches == num_positions and height == width:
return self.position_embedding(self.position_ids)
class_pos_embed = position_embedding[:, :1]
patch_pos_embed = position_embedding[:, 1:]
dim = embeddings.shape[-1]
new_height = height // self.patch_size
new_width = width // self.patch_size
sqrt_num_positions = torch_int(num_positions**0.5)
patch_pos_embed = patch_pos_embed.reshape(1, sqrt_num_positions, sqrt_num_positions, dim)
patch_pos_embed = patch_pos_embed.permute(0, 3, 1, 2)
patch_pos_embed = nn.functional.interpolate(
patch_pos_embed,
size=(new_height, new_width),
mode="bicubic",
align_corners=False,
)
patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim)
return torch.cat((class_pos_embed, patch_pos_embed), dim=1)
def forward(self, pixel_values: torch.FloatTensor, interpolate_pos_encoding=False) -> torch.Tensor:
batch_size, _, height, width = pixel_values.shape
if not interpolate_pos_encoding and (height != self.image_size or width != self.image_size):
raise ValueError(
f"Input image size ({height}*{width}) doesn't match model ({self.image_size}*{self.image_size})."
)
target_dtype = self.patch_embedding.weight.dtype
patch_embeds = self.patch_embedding(pixel_values.to(dtype=target_dtype)) # shape = [*, width, grid, grid]
patch_embeds = patch_embeds.flatten(2).transpose(1, 2)
class_embeds = self.class_embedding.expand(batch_size, 1, -1)
embeddings = torch.cat([class_embeds, patch_embeds], dim=1)
if interpolate_pos_encoding:
embeddings = embeddings + self.interpolate_pos_encoding(embeddings, height, width)
else:
embeddings = embeddings + self.position_embedding(self.position_ids)
return embeddings
# Copied from transformers.models.clip.modeling_clip.CLIPAttention with CLIP->Kosmos2Vision
class Kosmos2VisionAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(self, config):
super().__init__()
self.config = config
self.embed_dim = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_heads != self.embed_dim:
raise ValueError(
f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:"
f" {self.num_heads})."
)
self.scale = self.head_dim**-0.5
self.dropout = config.attention_dropout
self.k_proj = nn.Linear(self.embed_dim, self.embed_dim)
self.v_proj = nn.Linear(self.embed_dim, self.embed_dim)
self.q_proj = nn.Linear(self.embed_dim, self.embed_dim)
self.out_proj = nn.Linear(self.embed_dim, self.embed_dim)
def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
causal_attention_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = False,
) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
"""Input shape: Batch x Time x Channel"""
bsz, tgt_len, embed_dim = hidden_states.size()
# get query proj
query_states = self.q_proj(hidden_states) * self.scale
key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
proj_shape = (bsz * self.num_heads, -1, self.head_dim)
query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape)
key_states = key_states.view(*proj_shape)
value_states = value_states.view(*proj_shape)
src_len = key_states.size(1)
attn_weights = torch.bmm(query_states, key_states.transpose(1, 2))
if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len):
raise ValueError(
f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is"
f" {attn_weights.size()}"
)
# apply the causal_attention_mask first
if causal_attention_mask is not None:
if causal_attention_mask.size() != (bsz, 1, tgt_len, src_len):
raise ValueError(
f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is"
f" {causal_attention_mask.size()}"
)
attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + causal_attention_mask
attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
if attention_mask is not None:
if attention_mask.size() != (bsz, 1, tgt_len, src_len):
raise ValueError(
f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}"
)
attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask
attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
attn_weights = nn.functional.softmax(attn_weights, dim=-1)
if output_attentions:
# this operation is a bit awkward, but it's required to
# make sure that attn_weights keeps its gradient.
# In order to do so, attn_weights have to reshaped
# twice and have to be reused in the following
attn_weights_reshaped = attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
attn_weights = attn_weights_reshaped.view(bsz * self.num_heads, tgt_len, src_len)
else:
attn_weights_reshaped = None
attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)
attn_output = torch.bmm(attn_probs, value_states)
if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim):
raise ValueError(
f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is"
f" {attn_output.size()}"
)
attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim)
attn_output = attn_output.transpose(1, 2)
attn_output = attn_output.reshape(bsz, tgt_len, embed_dim)
attn_output = self.out_proj(attn_output)
return attn_output, attn_weights_reshaped
# Copied from transformers.models.clip.modeling_clip.CLIPMLP with CLIP->Kosmos2Vision
class Kosmos2VisionMLP(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.activation_fn = ACT2FN[config.hidden_act]
self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size)
self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size)
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidden_states = self.fc1(hidden_states)
hidden_states = self.activation_fn(hidden_states)
hidden_states = self.fc2(hidden_states)
return hidden_states
# Copied from transformers.models.altclip.modeling_altclip.AltCLIPEncoderLayer with AltCLIP->Kosmos2Vision
class Kosmos2VisionEncoderLayer(nn.Module):
def __init__(self, config: Kosmos2VisionConfig):
super().__init__()
self.embed_dim = config.hidden_size
self.self_attn = Kosmos2VisionAttention(config)
self.layer_norm1 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
self.mlp = Kosmos2VisionMLP(config)
self.layer_norm2 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: torch.Tensor,
causal_attention_mask: torch.Tensor,
output_attentions: Optional[bool] = False,
) -> Tuple[torch.FloatTensor]:
"""
Args:
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
attention_mask (`torch.FloatTensor`): attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
`(config.encoder_attention_heads,)`.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
"""
residual = hidden_states
hidden_states = self.layer_norm1(hidden_states)
hidden_states, attn_weights = self.self_attn(
hidden_states=hidden_states,
attention_mask=attention_mask,
causal_attention_mask=causal_attention_mask,
output_attentions=output_attentions,
)
hidden_states = residual + hidden_states
residual = hidden_states
hidden_states = self.layer_norm2(hidden_states)
hidden_states = self.mlp(hidden_states)
hidden_states = residual + hidden_states
outputs = (hidden_states,)
if output_attentions:
outputs += (attn_weights,)
return outputs
# Copied from transformers.models.altclip.modeling_altclip.AltCLIPEncoder with AltCLIP->Kosmos2Vision
class Kosmos2VisionEncoder(nn.Module):
"""
Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a
[`Kosmos2VisionEncoderLayer`].
Args:
config: Kosmos2VisionConfig
"""
def __init__(self, config: Kosmos2VisionConfig):
super().__init__()
self.config = config
self.layers = nn.ModuleList([Kosmos2VisionEncoderLayer(config) for _ in range(config.num_hidden_layers)])
self.gradient_checkpointing = False
def forward(
self,
inputs_embeds,
attention_mask: Optional[torch.Tensor] = None,
causal_attention_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, BaseModelOutput]:
r"""
Args:
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
This is useful if you want more control over how to convert `input_ids` indices into associated vectors
than the model's internal embedding lookup matrix.
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
causal_attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Causal mask for the text model. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
for more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
encoder_states = () if output_hidden_states else None
all_attentions = () if output_attentions else None
hidden_states = inputs_embeds
for idx, encoder_layer in enumerate(self.layers):
if output_hidden_states:
encoder_states = encoder_states + (hidden_states,)
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
encoder_layer.__call__,
hidden_states,
attention_mask,
causal_attention_mask,
output_attentions,
)
else:
layer_outputs = encoder_layer(
hidden_states,
attention_mask,
causal_attention_mask,
output_attentions=output_attentions,
)
hidden_states = layer_outputs[0]
if output_attentions:
all_attentions = all_attentions + (layer_outputs[1],)
if output_hidden_states:
encoder_states = encoder_states + (hidden_states,)
if not return_dict:
return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None)
return BaseModelOutput(
last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions
)
# Similar to `transformers.models.clip.modeling_clip.CLIPVisionTransformer` but without docstring for `forward`
class Kosmos2VisionTransformer(nn.Module):
# Copied from transformers.models.altclip.modeling_altclip.AltCLIPVisionTransformer.__init__ with AltCLIPVision->Kosmos2Vision,ALTCLIP_VISION->KOSMOS2_VISION,AltCLIP->Kosmos2Vision
def __init__(self, config: Kosmos2VisionConfig):
super().__init__()
self.config = config
embed_dim = config.hidden_size
self.embeddings = Kosmos2VisionEmbeddings(config)
self.pre_layrnorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
self.encoder = Kosmos2VisionEncoder(config)
self.post_layernorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
def forward(
self,
pixel_values: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
interpolate_pos_encoding: bool = False,
return_dict: Optional[bool] = None,
) -> Union[Tuple, BaseModelOutputWithPooling]:
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if pixel_values is None:
raise ValueError("You have to specify pixel_values")
hidden_states = self.embeddings(pixel_values, interpolate_pos_encoding=interpolate_pos_encoding)
hidden_states = self.pre_layrnorm(hidden_states)
encoder_outputs = self.encoder(
inputs_embeds=hidden_states,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
last_hidden_state = encoder_outputs[0]
pooled_output = last_hidden_state[:, 0, :]
pooled_output = self.post_layernorm(pooled_output)
if not return_dict:
return (last_hidden_state, pooled_output) + encoder_outputs[1:]
return BaseModelOutputWithPooling(
last_hidden_state=last_hidden_state,
pooler_output=pooled_output,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
)
# Similar to `transformers.models.m2m_100.modeling_m2m_100.M2M100SinusoidalPositionalEmbedding` but allowing to pass `position_ids`
class Kosmos2TextSinusoidalPositionalEmbedding(nn.Module):
"""This module produces sinusoidal positional embeddings of any length."""
# Copied from transformers.models.m2m_100.modeling_m2m_100.M2M100SinusoidalPositionalEmbedding.__init__
def __init__(self, num_positions: int, embedding_dim: int, padding_idx: Optional[int] = None):
super().__init__()
self.offset = 2
self.embedding_dim = embedding_dim
self.padding_idx = padding_idx
self.make_weights(num_positions + self.offset, embedding_dim, padding_idx)
# Copied from transformers.models.m2m_100.modeling_m2m_100.M2M100SinusoidalPositionalEmbedding.make_weights
def make_weights(self, num_embeddings: int, embedding_dim: int, padding_idx: Optional[int] = None):
emb_weights = self.get_embedding(num_embeddings, embedding_dim, padding_idx)
if hasattr(self, "weights"):
# in forward put the weights on the correct dtype and device of the param
emb_weights = emb_weights.to(dtype=self.weights.dtype, device=self.weights.device)
self.register_buffer("weights", emb_weights, persistent=False)
@staticmethod
# Copied from transformers.models.m2m_100.modeling_m2m_100.M2M100SinusoidalPositionalEmbedding.get_embedding
def get_embedding(num_embeddings: int, embedding_dim: int, padding_idx: Optional[int] = None):
"""
Build sinusoidal embeddings.
This matches the implementation in tensor2tensor, but differs slightly from the description in Section 3.5 of
"Attention Is All You Need".
"""
half_dim = embedding_dim // 2
emb = math.log(10000) / (half_dim - 1)
emb = torch.exp(torch.arange(half_dim, dtype=torch.int64).float() * -emb)
emb = torch.arange(num_embeddings, dtype=torch.int64).float().unsqueeze(1) * emb.unsqueeze(0)
emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1).view(num_embeddings, -1)
if embedding_dim % 2 == 1:
# zero pad
emb = torch.cat([emb, torch.zeros(num_embeddings, 1)], dim=1)
if padding_idx is not None:
emb[padding_idx, :] = 0
return emb.to(torch.get_default_dtype())
@torch.no_grad()
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
past_key_values_length: int = 0,
position_ids: Optional[torch.Tensor] = None,
):
if input_ids is not None:
bsz, seq_len = input_ids.size()
if position_ids is None:
# Create the position ids from the input token ids. Any padded tokens remain padded.
position_ids = create_position_ids_from_input_ids(
input_ids, self.padding_idx, past_key_values_length
).to(input_ids.device)
else:
bsz, seq_len = inputs_embeds.size()[:-1]
if position_ids is None:
position_ids = self.create_position_ids_from_inputs_embeds(inputs_embeds, past_key_values_length)
# expand embeddings if needed
max_pos = self.padding_idx + 1 + seq_len + past_key_values_length
if max_pos > self.weights.size(0):
self.make_weights(max_pos + self.offset, self.embedding_dim, self.padding_idx)
return self.weights.index_select(0, position_ids.view(-1)).view(bsz, seq_len, self.weights.shape[-1]).detach()
# Copied from transformers.models.m2m_100.modeling_m2m_100.M2M100SinusoidalPositionalEmbedding.create_position_ids_from_inputs_embeds
def create_position_ids_from_inputs_embeds(self, inputs_embeds, past_key_values_length):
"""
We are provided embeddings directly. We cannot infer which are padded so just generate sequential position ids.
Args:
inputs_embeds: torch.Tensor
Returns: torch.Tensor
"""
input_shape = inputs_embeds.size()[:-1]
sequence_length = input_shape[1]
position_ids = torch.arange(
self.padding_idx + 1, sequence_length + self.padding_idx + 1, dtype=torch.long, device=inputs_embeds.device
)
return position_ids.unsqueeze(0).expand(input_shape).contiguous() + past_key_values_length
class KosmosTextAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
# Similar to transformers.models.bart.modeling_bart.BartAttention.__init__ except an additional `inner_attn_ln`.
def __init__(
self,
config,
embed_dim: int,
num_heads: int,
dropout: float = 0.0,
is_decoder: bool = False,
add_inner_attn_layernorm: bool = False,
bias: bool = True,
):
super().__init__()
self.embed_dim = embed_dim
self.num_heads = num_heads
self.dropout = dropout
self.head_dim = embed_dim // num_heads
if (self.head_dim * num_heads) != self.embed_dim:
raise ValueError(
f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}"
f" and `num_heads`: {num_heads})."
)
self.scaling = self.head_dim**-0.5
self.is_decoder = is_decoder
self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
# End opy
self.inner_attn_ln = None
if add_inner_attn_layernorm:
self.inner_attn_ln = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
def _shape(self, projection: torch.Tensor) -> torch.Tensor:
new_projection_shape = projection.size()[:-1] + (self.num_heads, self.head_dim)
# move heads to 2nd position (B, T, H * D) -> (B, T, H, D) -> (B, H, T, D)
new_projection = projection.view(new_projection_shape).permute(0, 2, 1, 3)
return new_projection
def forward(
self,
hidden_states: torch.Tensor,
encoder_hidden_states: Optional[torch.Tensor] = None,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
attention_mask: Optional[torch.Tensor] = None,
layer_head_mask: Optional[torch.Tensor] = None,
output_attentions: bool = False,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
"""Input shape: Batch x Time x Channel"""
# if key_value_states are provided this layer is used as a cross-attention layer
# for the decoder
is_cross_attention = encoder_hidden_states is not None
batch_size, seq_length = hidden_states.shape[:2]
# use encoder_hidden_states if cross attention
current_states = encoder_hidden_states if encoder_hidden_states is not None else hidden_states
# checking that the `sequence_length` of the `past_key_value` is the same as the he provided
# `encoder_hidden_states` to support prefix tuning
if is_cross_attention and past_key_value and past_key_value[0].shape[2] == current_states.shape[1]:
# reuse k,v, cross_attentions
key_states = past_key_value[0]
value_states = past_key_value[1]
else:
key_states = self._shape(self.k_proj(current_states))
value_states = self._shape(self.v_proj(current_states))
if past_key_value is not None and not is_cross_attention:
# reuse k, v, self_attention
key_states = torch.cat([past_key_value[0], key_states], dim=2)
value_states = torch.cat([past_key_value[1], value_states], dim=2)
query_states = self._shape(self.q_proj(hidden_states) * self.scaling)
attn_weights = torch.matmul(query_states, key_states.transpose(-1, -2))
if self.is_decoder:
# if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states.
# Further calls to cross_attention layer can then reuse all cross-attention
# key/value_states (first "if" case)
# if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of
# all previous decoder key/value_states. Further calls to uni-directional self-attention
# can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
# if encoder bi-directional self-attention `past_key_value` is always `None`
past_key_value = (key_states, value_states)
src_len = key_states.size(2)
if attention_mask is not None:
if attention_mask.size() != (batch_size, 1, seq_length, src_len):
raise ValueError(
f"Attention mask should be of size {(batch_size, 1, seq_length, src_len)}, but is {attention_mask.size()}"
)
attn_weights = attn_weights + attention_mask
attn_weights = nn.functional.softmax(attn_weights, dim=-1)
# Mask heads if we want to
if layer_head_mask is not None:
attn_weights = attn_weights * layer_head_mask
attn_weights = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)
# attn_output = torch.bmm(attn_probs, value_states) ?
context_states = torch.matmul(attn_weights, value_states)
# attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim) ?
context_states = context_states.permute(0, 2, 1, 3).contiguous().view(batch_size, seq_length, -1)
if self.inner_attn_ln is not None:
context_states = self.inner_attn_ln(context_states)
attn_output = self.out_proj(context_states)
return attn_output, attn_weights, past_key_value
class Kosmos2TextFFN(nn.Module):
def __init__(self, config: Kosmos2TextConfig):
super().__init__()
self.dropout = config.dropout
self.activation_fn = ACT2FN[config.activation_function]
self.activation_dropout = config.activation_dropout
self.fc1 = nn.Linear(config.embed_dim, config.ffn_dim)
self.fc2 = nn.Linear(config.ffn_dim, config.embed_dim)
self.ffn_layernorm = nn.LayerNorm(config.ffn_dim, eps=config.layer_norm_eps)
def forward(self, hidden_states):
hidden_states = self.activation_fn(self.fc1(hidden_states))
hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)
hidden_states = self.ffn_layernorm(hidden_states)
hidden_states = self.fc2(hidden_states)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
return hidden_states
class Kosmos2TextBlock(nn.Module):
def __init__(self, config: Kosmos2TextConfig):
super().__init__()
self.embed_dim = config.embed_dim
self.self_attn = KosmosTextAttention(
config,
embed_dim=self.embed_dim,
num_heads=config.attention_heads,
dropout=config.attention_dropout,
is_decoder=True,
add_inner_attn_layernorm=True,
)
self.dropout = config.dropout
self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
if config.add_cross_attention:
self.encoder_attn = KosmosTextAttention(
config,
embed_dim=self.embed_dim,
num_heads=config.attention_heads,
dropout=config.attention_dropout,
is_decoder=True,
add_inner_attn_layernorm=False,
)
self.encoder_attn_layer_norm = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
self.ffn = Kosmos2TextFFN(config)
self.final_layer_norm = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
encoder_attention_mask: Optional[torch.Tensor] = None,
layer_head_mask: Optional[torch.Tensor] = None,
cross_attn_layer_head_mask: Optional[torch.Tensor] = None,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
output_attentions: Optional[bool] = False,
use_cache: Optional[bool] = True,
) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
residual = hidden_states
# Self Attention
# decoder uni-directional self-attention cached key/values tuple is at positions 1,2
self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
hidden_states = self.self_attn_layer_norm(hidden_states)
# add present self-attn cache to positions 1,2 of present_key_value tuple
hidden_states, self_attn_weights, present_key_value = self.self_attn(
hidden_states=hidden_states,
past_key_value=self_attn_past_key_value,
attention_mask=attention_mask,
layer_head_mask=layer_head_mask,
output_attentions=output_attentions,
)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = residual + hidden_states
# Cross-Attention Block
cross_attn_present_key_value = None
cross_attn_weights = None
if encoder_hidden_states is not None:
if not hasattr(self, "encoder_attn"):
raise ValueError(
f"If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention layers"
" by setting `config.add_cross_attention=True`"
)
residual = hidden_states
hidden_states = self.encoder_attn_layer_norm(hidden_states)
# cross_attn cached key/values tuple is at positions 3,4 of present_key_value tuple
cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None
hidden_states, cross_attn_weights, cross_attn_present_key_value = self.encoder_attn(
hidden_states=hidden_states,
encoder_hidden_states=encoder_hidden_states,
attention_mask=encoder_attention_mask,
layer_head_mask=cross_attn_layer_head_mask,
past_key_value=cross_attn_past_key_value,
output_attentions=output_attentions,
)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = residual + hidden_states
# add cross-attn to positions 3,4 of present_key_value tuple
present_key_value = present_key_value + cross_attn_present_key_value
# Fully Connected
residual = hidden_states
hidden_states = self.final_layer_norm(hidden_states)
# FFN
hidden_states = self.ffn(hidden_states)
hidden_states = residual + hidden_states
outputs = (hidden_states,)
if output_attentions:
outputs += (self_attn_weights, cross_attn_weights)
if use_cache:
outputs += (present_key_value,)
return outputs
class Kosmos2TextTransformer(nn.Module):
"""
Transformer decoder consisting of `config.layers` layers. Each layer is a [`Kosmos2TextBlock`].
Args:
config: Kosmos2TextConfig
"""
def __init__(self, config: Kosmos2TextConfig):
super().__init__()
self.config = config
self.dropout = config.dropout
self.layerdrop = config.layerdrop
self.embed_scale = math.sqrt(config.embed_dim) if config.scale_embedding else 1.0
self.embed_tokens = nn.Embedding(config.vocab_size, config.embed_dim, padding_idx=config.pad_token_id)
self.embed_positions = Kosmos2TextSinusoidalPositionalEmbedding(
num_positions=config.max_position_embeddings,
embedding_dim=config.embed_dim,
padding_idx=config.pad_token_id,
)
self.layers = nn.ModuleList([Kosmos2TextBlock(config) for _ in range(config.layers)])
self.layer_norm = nn.LayerNorm(config.embed_dim, config.layer_norm_eps)
self.gradient_checkpointing = False
def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length):
# create causal mask
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
combined_attention_mask = None
if input_shape[-1] > 1:
combined_attention_mask = _make_causal_mask(
input_shape,
inputs_embeds.dtype,
device=inputs_embeds.device,
past_key_values_length=past_key_values_length,
)
if attention_mask is not None:
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to(
inputs_embeds.device
)
combined_attention_mask = (
expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask
)
return combined_attention_mask
def forward_embedding(
self,
input_ids,
inputs_embeds: Optional[torch.Tensor] = None,
image_embeds: Optional[torch.Tensor] = None,
img_input_mask: Optional[torch.Tensor] = None,
past_key_values_length: int = 0,
position_ids: Optional[torch.Tensor] = None,
):
# The argument `inputs_embeds` should be the one without being multiplied by `self.embed_scale`.
if inputs_embeds is None:
inputs_embeds = self.embed_tokens(input_ids)
if image_embeds is not None:
inputs_embeds[img_input_mask.to(dtype=torch.bool)] = image_embeds.to(inputs_embeds.device).view(
-1, image_embeds.size(-1)
)
inputs_embeds = inputs_embeds * self.embed_scale
# embed positions
positions = self.embed_positions(
input_ids=input_ids,
inputs_embeds=inputs_embeds,
past_key_values_length=past_key_values_length,
position_ids=position_ids,
)
positions = positions.to(inputs_embeds.device)
hidden_states = inputs_embeds + positions
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
return hidden_states
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
image_embeds: Optional[torch.Tensor] = None,
image_embeds_position_mask: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
encoder_attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
cross_attn_head_mask: Optional[torch.Tensor] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
inputs_embeds: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, BaseModelOutputWithPastAndCrossAttentions]:
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
use_cache = use_cache if use_cache is not None else self.config.use_cache
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
elif input_ids is not None:
input_shape = input_ids.shape
input_ids = input_ids.view(-1, input_shape[-1])
elif inputs_embeds is not None:
input_shape = inputs_embeds.size()[:-1]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
# past_key_values_length
past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0
# We don't need img info. when `past_key_values_length` > 0
if past_key_values_length > 0:
image_embeds = None
image_embeds_position_mask = None
hidden_states = self.forward_embedding(
input_ids=input_ids,
inputs_embeds=inputs_embeds,
image_embeds=image_embeds,
img_input_mask=image_embeds_position_mask,
past_key_values_length=past_key_values_length,
position_ids=position_ids,
)
attention_mask = self._prepare_decoder_attention_mask(
attention_mask, input_shape, hidden_states, past_key_values_length
)
# expand encoder attention mask
if encoder_hidden_states is not None and encoder_attention_mask is not None:
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
encoder_attention_mask = _expand_mask(encoder_attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1])
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
if self.gradient_checkpointing and self.training:
if use_cache:
logger.warning_once(
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
)
use_cache = False
# decoder layers
all_hidden_states = () if output_hidden_states else None
all_self_attns = () if output_attentions else None
all_cross_attentions = () if (output_attentions and encoder_hidden_states is not None) else None
present_key_value_states = () if use_cache else None
# check if head_mask/cross_attn_head_mask has a correct number of layers specified if desired
for attn_mask, mask_name in zip([head_mask, cross_attn_head_mask], ["head_mask", "cross_attn_head_mask"]):
if attn_mask is not None:
if attn_mask.size()[0] != (len(self.layers)):
raise ValueError(
f"The `{mask_name}` should be specified for {len(self.layers)} layers, but it is for"
f" {head_mask.size()[0]}."
)
for idx, decoder_layer in enumerate(self.layers):
# add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
if output_hidden_states:
all_hidden_states += (hidden_states,)
if self.training:
dropout_probability = torch.rand([])
if dropout_probability < self.layerdrop:
continue
past_key_value = past_key_values[idx] if past_key_values is not None else None
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
decoder_layer.__call__,
hidden_states,
attention_mask,
encoder_hidden_states,
encoder_attention_mask,
head_mask[idx] if head_mask is not None else None,
cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None,
None,
output_attentions,
use_cache,
)
else:
layer_outputs = decoder_layer(
hidden_states,
attention_mask=attention_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
layer_head_mask=(head_mask[idx] if head_mask is not None else None),
cross_attn_layer_head_mask=(
cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None
),
past_key_value=past_key_value,
output_attentions=output_attentions,
use_cache=use_cache,
)
hidden_states = layer_outputs[0]
if use_cache:
present_key_value_states += (layer_outputs[3 if output_attentions else 1],)
if output_attentions:
all_self_attns += (layer_outputs[1],)
if encoder_hidden_states is not None:
all_cross_attentions += (layer_outputs[2],)
# add final layer norm
hidden_states = self.layer_norm(hidden_states)
# add hidden states from the last decoder layer
if output_hidden_states:
all_hidden_states += (hidden_states,)
if not return_dict:
return tuple(
v
for v in [
hidden_states,
present_key_value_states,
all_hidden_states,
all_self_attns,
all_cross_attentions,
]
if v is not None
)
return BaseModelOutputWithPastAndCrossAttentions(
last_hidden_state=hidden_states,
past_key_values=present_key_value_states,
hidden_states=all_hidden_states,
attentions=all_self_attns,
cross_attentions=all_cross_attentions,
)
class Kosmos2PreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = Kosmos2Config
supports_gradient_checkpointing = True
_no_split_modules = ["Kosmos2VisionEncoderLayer", "Kosmos2TextBlock"]
def _init_weights(self, module):
"""Initialize the weights"""
if isinstance(self, Kosmos2VisionModel):
factor = self.config.initializer_factor
elif isinstance(self, (Kosmos2Model, Kosmos2ForConditionalGeneration)):
factor = self.config.vision_config.initializer_factor
if isinstance(self, (Kosmos2TextModel, Kosmos2TextForCausalLM)):
std = self.config.init_std
elif isinstance(self, (Kosmos2Model, Kosmos2ForConditionalGeneration)):
std = self.config.text_config.init_std
if isinstance(module, Kosmos2VisionEmbeddings):
nn.init.normal_(module.class_embedding, mean=0.0, std=module.embed_dim**-0.5 * factor)
nn.init.normal_(module.patch_embedding.weight, std=module.config.initializer_range * factor)
nn.init.normal_(module.position_embedding.weight, std=module.config.initializer_range * factor)
elif isinstance(module, Kosmos2VisionAttention):
in_proj_std = (module.embed_dim**-0.5) * ((2 * module.config.num_hidden_layers) ** -0.5) * factor
out_proj_std = (module.embed_dim**-0.5) * factor
nn.init.normal_(module.q_proj.weight, std=in_proj_std)
nn.init.normal_(module.k_proj.weight, std=in_proj_std)
nn.init.normal_(module.v_proj.weight, std=in_proj_std)
nn.init.normal_(module.out_proj.weight, std=out_proj_std)
if module.q_proj.bias is not None:
module.q_proj.bias.data.zero_()
if module.k_proj.bias is not None:
module.k_proj.bias.data.zero_()
if module.v_proj.bias is not None:
module.v_proj.bias.data.zero_()
if module.out_proj.bias is not None:
module.out_proj.bias.data.zero_()
elif isinstance(module, Kosmos2VisionMLP):
in_proj_std = (module.config.hidden_size**-0.5) * ((2 * module.config.num_hidden_layers) ** -0.5) * factor
fc_std = (2 * module.config.hidden_size) ** -0.5 * factor
nn.init.normal_(module.fc1.weight, std=fc_std)
nn.init.normal_(module.fc2.weight, std=in_proj_std)
if module.fc1.bias is not None:
module.fc1.bias.data.zero_()
if module.fc2.bias is not None:
module.fc2.bias.data.zero_()
elif isinstance(module, Kosmos2VisionEncoderLayer):
module.layer_norm1.bias.data.zero_()
module.layer_norm1.weight.data.fill_(1.0)
module.layer_norm2.bias.data.zero_()
module.layer_norm2.weight.data.fill_(1.0)
elif isinstance(module, Kosmos2VisionTransformer):
module.pre_layrnorm.bias.data.zero_()
module.pre_layrnorm.weight.data.fill_(1.0)
module.post_layernorm.bias.data.zero_()
module.post_layernorm.weight.data.fill_(1.0)
elif isinstance(module, KosmosTextAttention):
nn.init.normal_(module.q_proj.weight, std=std)
nn.init.normal_(module.k_proj.weight, std=std)
nn.init.normal_(module.v_proj.weight, std=std)
nn.init.normal_(module.out_proj.weight, std=std)
if module.q_proj.bias is not None:
module.q_proj.bias.data.zero_()
if module.k_proj.bias is not None:
module.k_proj.bias.data.zero_()
if module.v_proj.bias is not None:
module.v_proj.bias.data.zero_()
if module.out_proj.bias is not None:
module.out_proj.bias.data.zero_()
elif isinstance(module, Kosmos2TextFFN):
nn.init.normal_(module.fc1.weight, std=std)
nn.init.normal_(module.fc2.weight, std=std)
if module.fc1.bias is not None:
module.fc1.bias.data.zero_()
if module.fc2.bias is not None:
module.fc2.bias.data.zero_()
elif isinstance(module, Kosmos2TextForCausalLM):
nn.init.normal_(module.lm_head.weight, std=std)
if module.lm_head.bias is not None:
module.lm_head.bias.data.zero_()
elif isinstance(module, Kosmos2ImageToTextProjection):
nn.init.normal_(module.dense.weight, std=std)
if module.dense.bias is not None:
module.dense.bias.data.zero_()
elif isinstance(module, Kosmos2TextTransformer):
module.embed_tokens.weight.data.normal_(mean=0.0, std=std)
if module.embed_tokens.padding_idx is not None:
module.embed_tokens.weight.data[module.embed_tokens.padding_idx].zero_()
class Kosmos2VisionModel(Kosmos2PreTrainedModel):
config_class = Kosmos2VisionConfig
main_input_name = "pixel_values"
# Copied from transformers.models.clip.modeling_clip.CLIPVisionModel.__init__ with CLIP_VISION->KOSMOS2_VISION,CLIP->Kosmos2,self.vision_model->self.model
def __init__(self, config: Kosmos2VisionConfig):
super().__init__(config)
self.model = Kosmos2VisionTransformer(config)
# Initialize weights and apply final processing
self.post_init()
# Copied from transformers.models.clip.modeling_clip.CLIPVisionModel.get_input_embeddings with CLIP_VISION->KOSMOS2_VISION,CLIP->Kosmos2,self.vision_model->self.model
def get_input_embeddings(self) -> nn.Module:
return self.model.embeddings.patch_embedding
@add_start_docstrings_to_model_forward(KOSMOS2_VISION_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=Kosmos2VisionConfig)
def forward(
self,
pixel_values: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
interpolate_pos_encoding: bool = False,
return_dict: Optional[bool] = None,
) -> Union[Tuple, BaseModelOutputWithPooling]:
r"""
Returns:
"""
return self.model(
pixel_values=pixel_values,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
interpolate_pos_encoding=interpolate_pos_encoding,
return_dict=return_dict,
)
class Kosmos2TextModel(Kosmos2PreTrainedModel):
config_class = Kosmos2TextConfig
def __init__(self, config: Kosmos2TextConfig):
super().__init__(config)
self.model = Kosmos2TextTransformer(config)
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self) -> nn.Module:
return self.model.embed_tokens
def set_input_embeddings(self, value):
self.model.embed_tokens = value
@add_start_docstrings_to_model_forward(KOSMOS2_TEXT_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=BaseModelOutputWithPastAndCrossAttentions, config_class=Kosmos2TextConfig)
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
image_embeds: Optional[torch.Tensor] = None,
image_embeds_position_mask: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
encoder_attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
cross_attn_head_mask: Optional[torch.Tensor] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
inputs_embeds: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, BaseModelOutputWithPastAndCrossAttentions]:
r"""
Returns:
"""
return self.model(
input_ids=input_ids,
attention_mask=attention_mask,
image_embeds=image_embeds,
image_embeds_position_mask=image_embeds_position_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
head_mask=head_mask,
cross_attn_head_mask=cross_attn_head_mask,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
position_ids=position_ids,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
@add_start_docstrings(
"""
The text model from KOSMOS-2 with a language modeling head on top (linear layer with weights tied to the input
embeddings).
""",
KOSMOS2_START_DOCSTRING,
)
class Kosmos2TextForCausalLM(Kosmos2PreTrainedModel, GenerationMixin):
config_class = Kosmos2TextConfig
_tied_weights_keys = ["lm_head.weight"]
def __init__(self, config: Kosmos2TextConfig):
super().__init__(config)
self.model = Kosmos2TextTransformer(config)
self.lm_head = nn.Linear(in_features=config.embed_dim, out_features=config.vocab_size, bias=False)
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self) -> nn.Module:
return self.model.embed_tokens
def set_input_embeddings(self, value):
self.model.embed_tokens = value
def get_output_embeddings(self) -> nn.Module:
return self.lm_head
def set_output_embeddings(self, new_embeddings):
self.lm_head = new_embeddings
@add_start_docstrings_to_model_forward(KOSMOS2_TEXT_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=CausalLMOutputWithCrossAttentions, config_class=Kosmos2TextConfig)
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
image_embeds: Optional[torch.Tensor] = None,
image_embeds_position_mask: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
encoder_attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
cross_attn_head_mask: Optional[torch.Tensor] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
inputs_embeds: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, CausalLMOutputWithCrossAttentions]:
r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in
`[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are
ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`
Returns:
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if labels is not None:
if use_cache:
logger.warning("The `use_cache` argument is changed to `False` since `labels` is provided.")
use_cache = False
outputs = self.model(
input_ids=input_ids,
attention_mask=attention_mask,
image_embeds=image_embeds,
image_embeds_position_mask=image_embeds_position_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
head_mask=head_mask,
cross_attn_head_mask=cross_attn_head_mask,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
position_ids=position_ids,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
lm_logits = self.lm_head(outputs[0])
loss = None
if labels is not None:
# move labels to correct device to enable model parallelism
labels = labels.to(lm_logits.device)
# Shift so that tokens < n predict n
shift_logits = lm_logits[..., :-1, :].contiguous()
shift_labels = labels[..., 1:].contiguous()
batch_size, seq_length, vocab_size = shift_logits.shape
# Flatten the tokens
loss_fct = CrossEntropyLoss()
loss = loss_fct(
shift_logits.view(batch_size * seq_length, vocab_size), shift_labels.view(batch_size * seq_length)
)
if not return_dict:
output = (lm_logits,) + outputs[1:]
return (loss,) + output if loss is not None else output
return CausalLMOutputWithCrossAttentions(
loss=loss,
logits=lm_logits,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
cross_attentions=outputs.cross_attentions,
)
def prepare_inputs_for_generation(
self,
input_ids,
image_embeds=None,
image_embeds_position_mask=None,
past_key_values=None,
attention_mask=None,
use_cache=None,
cache_position=None,
**model_kwargs,
):
# Overwritten -- in specific circumstances we don't want to forward image inputs to the model
# Kosmos2 has offset for position ids, so we need to create them correctly
position_ids = create_position_ids_from_input_ids(
input_ids,
padding_idx=self.config.pad_token_id,
past_key_values_length=0,
)
if past_key_values is not None:
image_embeds = None
image_embeds_position_mask = None
# appending `False` to `image_embeds_position_mask` (because `input_ids` grows during generation)
elif image_embeds_position_mask is not None:
batch_size, seq_len = input_ids.size()
mask_len = image_embeds_position_mask.size()[-1]
image_embeds_position_mask = torch.cat(
(
image_embeds_position_mask,
torch.zeros(size=(batch_size, seq_len - mask_len), dtype=torch.bool, device=input_ids.device),
),
dim=1,
)
model_inputs = super().prepare_inputs_for_generation(
input_ids,
past_key_values=past_key_values,
attention_mask=attention_mask,
image_embeds=image_embeds,
image_embeds_position_mask=image_embeds_position_mask,
use_cache=use_cache,
position_ids=position_ids,
cache_position=cache_position,
**model_kwargs,
)
return model_inputs
@staticmethod
# Copied from transformers.models.umt5.modeling_umt5.UMT5ForConditionalGeneration._reorder_cache
def _reorder_cache(past_key_values, beam_idx):
reordered_past = ()
for layer_past in past_key_values:
reordered_past += (
tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
)
return reordered_past
class Kosmos2ImageToTextProjection(nn.Module):
"""The layer that transforms the image model's output to part of the text model's input (namely, image features)"""
def __init__(self, config: Kosmos2Config):
super().__init__()
self.dense = nn.Linear(config.vision_config.hidden_size, config.text_config.embed_dim)
self.latent_query = nn.Parameter(torch.randn(config.latent_query_num, config.text_config.embed_dim))
self.x_attn = KosmosTextAttention(
config.text_config,
config.text_config.embed_dim,
config.text_config.attention_heads,
dropout=config.text_config.attention_dropout,
is_decoder=False,
add_inner_attn_layernorm=False,
)
def forward(self, features):
hidden_states = self.dense(features)
# shape = [batch, latent_query_num, h_dim]
latent_query = self.latent_query.unsqueeze(0).expand(hidden_states.size(0), -1, -1)
key_value_states = torch.cat([hidden_states, latent_query], dim=1)
hidden_states, attn_weights, _ = self.x_attn(
hidden_states=latent_query,
encoder_hidden_states=key_value_states,
past_key_value=None,
attention_mask=None,
output_attentions=None,
)
return hidden_states, attn_weights
@add_start_docstrings(
"""
KOSMOS-2 Model for generating text and image features. The model consists of a vision encoder and a language model.
""",
KOSMOS2_START_DOCSTRING,
)
class Kosmos2Model(Kosmos2PreTrainedModel):
config_class = Kosmos2Config
main_input_name = "pixel_values"
def __init__(self, config: Kosmos2Config):
super().__init__(config)
self.text_model = Kosmos2TextModel(config.text_config)
self.vision_model = Kosmos2VisionModel(config.vision_config)
self.image_to_text_projection = Kosmos2ImageToTextProjection(config)
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self) -> nn.Module:
return self.text_model.model.embed_tokens
def set_input_embeddings(self, value):
self.text_model.model.embed_tokens = value
@add_start_docstrings_to_model_forward(KOSMOS2_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=Kosmos2ModelOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
pixel_values: Optional[torch.Tensor] = None,
input_ids: Optional[torch.Tensor] = None,
image_embeds_position_mask: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
image_embeds: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
interpolate_pos_encoding: bool = False,
return_dict: Optional[bool] = None,
) -> Union[Tuple, Kosmos2ModelOutput]:
r"""
Returns:
Examples:
```python
>>> from PIL import Image
>>> import requests
>>> from transformers import AutoProcessor, Kosmos2Model
>>> model = Kosmos2Model.from_pretrained("microsoft/kosmos-2-patch14-224")
>>> processor = AutoProcessor.from_pretrained("microsoft/kosmos-2-patch14-224")
>>> url = "https://huggingface.co/microsoft/kosmos-2-patch14-224/resolve/main/snowman.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> text = (
... "<grounding> An image of<phrase> a snowman</phrase><object><patch_index_0044><patch_index_0863>"
... "</object> warming himself by<phrase> a fire</phrase><object><patch_index_0005><patch_index_0911>"
... "</object>"
... )
>>> inputs = processor(text=text, images=image, return_tensors="pt", add_eos_token=True)
>>> last_hidden_state = model(
... pixel_values=inputs["pixel_values"],
... input_ids=inputs["input_ids"],
... attention_mask=inputs["attention_mask"],
... image_embeds_position_mask=inputs["image_embeds_position_mask"],
... ).last_hidden_state
>>> list(last_hidden_state.shape)
[1, 91, 2048]
```"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
vision_model_output = None
projection_attentions = None
if image_embeds is None:
if pixel_values is None:
raise ValueError("You have to specify either `pixel_values` or `image_embeds`.")
vision_model_output = self.vision_model(
pixel_values=pixel_values,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
interpolate_pos_encoding=interpolate_pos_encoding,
return_dict=return_dict,
)
# The whole `last_hidden_state` through `post_layernorm` instead of just `pooled_output`.
image_embeds = self.vision_model.model.post_layernorm(vision_model_output[0])
# normalized features
image_embeds = nn.functional.normalize(image_embeds, dim=-1)
image_embeds, projection_attentions = self.image_to_text_projection(image_embeds)
outputs = self.text_model(
input_ids=input_ids,
attention_mask=attention_mask,
image_embeds=image_embeds,
image_embeds_position_mask=image_embeds_position_mask,
head_mask=head_mask,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
position_ids=position_ids,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
if not return_dict:
outputs = outputs + (image_embeds, projection_attentions, vision_model_output)
return tuple(output for output in outputs if output is not None)
return Kosmos2ModelOutput(
last_hidden_state=outputs.last_hidden_state,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
image_embeds=image_embeds,
projection_attentions=projection_attentions,
vision_model_output=vision_model_output,
)
@add_start_docstrings(
"""
KOSMOS-2 Model for generating text and bounding boxes given an image. The model consists of a vision encoder and a
language model.
""",
KOSMOS2_START_DOCSTRING,
)
class Kosmos2ForConditionalGeneration(Kosmos2PreTrainedModel, GenerationMixin):
config_class = Kosmos2Config
main_input_name = "pixel_values"
_tied_weights_keys = ["text_model.lm_head.weight"]
def __init__(self, config: Kosmos2Config):
super().__init__(config)
self.text_model = Kosmos2TextForCausalLM(config.text_config)
self.vision_model = Kosmos2VisionModel(config.vision_config)
self.image_to_text_projection = Kosmos2ImageToTextProjection(config)
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self) -> nn.Module:
return self.text_model.model.embed_tokens
def set_input_embeddings(self, value):
self.text_model.model.embed_tokens = value
def get_output_embeddings(self) -> nn.Module:
return self.text_model.get_output_embeddings()
def set_output_embeddings(self, new_embeddings):
self.text_model.set_output_embeddings(new_embeddings)
@add_start_docstrings_to_model_forward(KOSMOS2_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=Kosmos2ForConditionalGenerationModelOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
pixel_values: Optional[torch.Tensor] = None,
input_ids: Optional[torch.Tensor] = None,
image_embeds_position_mask: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
image_embeds: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, Kosmos2ForConditionalGenerationModelOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in
`[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are
ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`
Returns:
Examples:
```python
>>> from PIL import Image
>>> import requests
>>> from transformers import AutoProcessor, Kosmos2ForConditionalGeneration
>>> model = Kosmos2ForConditionalGeneration.from_pretrained("microsoft/kosmos-2-patch14-224")
>>> processor = AutoProcessor.from_pretrained("microsoft/kosmos-2-patch14-224")
>>> url = "https://huggingface.co/microsoft/kosmos-2-patch14-224/resolve/main/snowman.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> prompt = "<grounding> An image of"
>>> inputs = processor(text=prompt, images=image, return_tensors="pt")
>>> generated_ids = model.generate(
... pixel_values=inputs["pixel_values"],
... input_ids=inputs["input_ids"],
... attention_mask=inputs["attention_mask"],
... image_embeds=None,
... image_embeds_position_mask=inputs["image_embeds_position_mask"],
... use_cache=True,
... max_new_tokens=64,
... )
>>> generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
>>> processed_text = processor.post_process_generation(generated_text, cleanup_and_extract=False)
>>> processed_text
'<grounding> An image of<phrase> a snowman</phrase><object><patch_index_0044><patch_index_0863></object> warming himself by<phrase> a fire</phrase><object><patch_index_0005><patch_index_0911></object>.'
>>> caption, entities = processor.post_process_generation(generated_text)
>>> caption
'An image of a snowman warming himself by a fire.'
>>> entities
[('a snowman', (12, 21), [(0.390625, 0.046875, 0.984375, 0.828125)]), ('a fire', (41, 47), [(0.171875, 0.015625, 0.484375, 0.890625)])]
```"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
vision_model_output = None
projection_attentions = None
if image_embeds is None:
if pixel_values is None:
raise ValueError("You have to specify either `pixel_values` or `image_embeds`.")
vision_model_output = self.vision_model(
pixel_values=pixel_values,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
# The whole `last_hidden_state` through `post_layernorm` instead of just `pooled_output`.
image_embeds = self.vision_model.model.post_layernorm(vision_model_output[0])
# normalized features
image_embeds = nn.functional.normalize(image_embeds, dim=-1)
image_embeds, projection_attentions = self.image_to_text_projection(image_embeds)
lm_outputs = self.text_model(
input_ids=input_ids,
attention_mask=attention_mask,
image_embeds=image_embeds,
image_embeds_position_mask=image_embeds_position_mask,
head_mask=head_mask,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
position_ids=position_ids,
labels=labels,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
if not return_dict:
outputs = lm_outputs + (image_embeds, projection_attentions, vision_model_output)
return tuple(output for output in outputs if output is not None)
return Kosmos2ForConditionalGenerationModelOutput(
loss=lm_outputs.loss,
logits=lm_outputs.logits,
past_key_values=lm_outputs.past_key_values,
hidden_states=lm_outputs.hidden_states,
attentions=lm_outputs.attentions,
image_embeds=image_embeds,
projection_attentions=projection_attentions,
vision_model_output=vision_model_output,
)
def generate(
self,
pixel_values: Optional[torch.Tensor] = None,
image_embeds_position_mask: Optional[torch.Tensor] = None,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
image_embeds: Optional[torch.Tensor] = None,
**kwargs,
):
# in order to allow `inputs` argument (as in `GenerationMixin`)
inputs = kwargs.pop("inputs", None)
if pixel_values is not None and inputs is not None:
raise ValueError(
f"`inputs`: {inputs} were passed alongside `pixel_values` which is not allowed."
f"Make sure to either pass `inputs` or pixel_values=..."
)
if pixel_values is None and inputs is not None:
pixel_values = inputs
if image_embeds is None:
vision_model_output = self.vision_model(pixel_values)
# The whole `last_hidden_state` through `post_layernorm` instead of just `pooled_output`.
image_embeds = self.vision_model.model.post_layernorm(vision_model_output[0])
# normalized features
image_embeds = nn.functional.normalize(image_embeds, dim=-1)
image_embeds, projection_attentions = self.image_to_text_projection(image_embeds)
output = self.text_model.generate(
input_ids=input_ids,
attention_mask=attention_mask,
image_embeds=image_embeds,
image_embeds_position_mask=image_embeds_position_mask,
**kwargs,
)
return output
__all__ = ["Kosmos2ForConditionalGeneration", "Kosmos2Model", "Kosmos2PreTrainedModel"]
```
|
=========================================================================================================================================
SOURCE CODE FILE: processing_kosmos2.py
LINES: 1
SIZE: 31.15 KB
PATH: scripts\freecad_env\Lib\site-packages\transformers\models\kosmos2\processing_kosmos2.py
ENCODING: utf-8
```py
# coding=utf-8
# Copyright 2023 Microsoft Research and The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Processor class for KOSMOS-2."""
import copy
import math
import re
from typing import List, Optional, Tuple, Union
from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput, is_batched
from ...processing_utils import ImagesKwargs, ProcessingKwargs, ProcessorMixin, TextKwargs, Unpack
from ...tokenization_utils import AddedToken
from ...tokenization_utils_base import BatchEncoding, TextInput
BboxInput = Union[
List[Tuple[int, int]],
List[Tuple[float, float, float, float]],
List[List[Tuple[int, int]]],
List[List[Tuple[float, float, float]]],
]
class Kosmos2ImagesKwargs(ImagesKwargs, total=False):
bboxes: Optional[List[float]]
num_image_tokens: Optional[int]
first_image_token_id: Optional[int]
class Kosmos2TextKwargs(TextKwargs, total=False):
add_eos_token: Optional[bool]
class Kosmos2ProcessorKwargs(ProcessingKwargs, total=False):
text_kwargs: Kosmos2TextKwargs
images_kwargs: Kosmos2ImagesKwargs
_defaults = {
"text_kwargs": {
"add_special_tokens": True,
"padding": False,
"stride": 0,
"return_overflowing_tokens": False,
"return_special_tokens_mask": False,
"return_offsets_mapping": False,
"return_token_type_ids": False,
"verbose": True,
"add_eos_token": False,
},
"images_kwargs": {
"num_image_tokens": 64,
},
}
class Kosmos2Processor(ProcessorMixin):
r"""
Constructs an KOSMOS-2 processor which wraps a KOSMOS-2 image processor and a KOSMOS-2 tokenizer into a single
processor.
[`Kosmos2Processor`] offers all the functionalities of [`CLIPImageProcessor`] and some functionalities of
[`XLMRobertaTokenizerFast`]. See the docstring of [`~Kosmos2Processor.__call__`] and [`~Kosmos2Processor.decode`]
for more information.
Args:
image_processor (`CLIPImageProcessor`):
An instance of [`CLIPImageProcessor`]. The image processor is a required input.
tokenizer (`XLMRobertaTokenizerFast`):
An instance of ['XLMRobertaTokenizerFast`]. The tokenizer is a required input.
num_patch_index_tokens (`int`, *optional*, defaults to 1024):
The number of tokens that represent patch indices.
"""
attributes = ["image_processor", "tokenizer"]
valid_kwargs = ["num_patch_index_tokens"]
image_processor_class = ("CLIPImageProcessor", "CLIPImageProcessorFast")
tokenizer_class = "AutoTokenizer"
def __init__(self, image_processor, tokenizer, num_patch_index_tokens=1024, *kwargs):
tokenizer.return_token_type_ids = False
self.eod_token = "</doc>"
self.boi_token = "<image>"
self.eoi_token = "</image>"
self.eoc_token = "</chunk>"
self.eol_token = "</line>"
self.bop_token = "<phrase>"
self.eop_token = "</phrase>"
self.boo_token = "<object>"
self.eoo_token = "</object>"
self.dom_token = "</delimiter_of_multi_objects/>"
self.grd_token = "<grounding>"
self.tag_tokens = [
self.eod_token,
self.boi_token,
self.eoi_token,
self.eoc_token,
self.eol_token,
self.bop_token,
self.eop_token,
self.boo_token,
self.eoo_token,
self.dom_token,
self.grd_token,
]
self.num_patch_index_tokens = num_patch_index_tokens
patch_index_tokens = [f"<patch_index_{str(x).zfill(4)}>" for x in range(self.num_patch_index_tokens)]
tokens_to_add = []
for token in self.tag_tokens + patch_index_tokens:
tokens_to_add.append(AddedToken(token, lstrip=True, rstrip=False, normalized=False))
tokenizer.add_tokens(tokens_to_add)
super().__init__(image_processor, tokenizer)
def __call__(
self,
images: ImageInput = None,
text: Union[TextInput, List[TextInput]] = None,
audio=None,
videos=None,
**kwargs: Unpack[Kosmos2ProcessorKwargs],
) -> BatchFeature:
"""
This method uses [`CLIPImageProcessor.__call__`] method to prepare image(s) for the model, and
[`XLMRobertaTokenizerFast.__call__`] to prepare text for the model.
Please refer to the docstring of the above two methods for more information.
The rest of this documentation shows the arguments specific to `Kosmos2Processor`.
Args:
bboxes (`Union[List[Tuple[int]], List[Tuple[float]], List[List[Tuple[int]]], List[List[Tuple[float]]]]`, *optional*):
The bounding bboxes associated to `texts`.
num_image_tokens (`int`, *optional* defaults to 64):
The number of (consecutive) places that are used to mark the placeholders to store image information.
This should be the same as `latent_query_num` in the instance of `Kosmos2Config` you are using.
first_image_token_id (`int`, *optional*):
The token id that will be used for the first place of the subsequence that is reserved to store image
information. If unset, will default to `self.tokenizer.unk_token_id + 1`.
add_eos_token (`bool`, defaults to `False`):
Whether or not to include `EOS` token id in the encoding when `add_special_tokens=True`.
"""
if images is None and text is None:
raise ValueError("You have to specify either images or text.")
output_kwargs = self._merge_kwargs(
Kosmos2ProcessorKwargs,
tokenizer_init_kwargs=self.tokenizer.init_kwargs,
**kwargs,
)
bboxes = output_kwargs["images_kwargs"].pop("bboxes", None)
num_image_tokens = output_kwargs["images_kwargs"].pop("num_image_tokens", 64)
first_image_token_id = output_kwargs["images_kwargs"].pop("first_image_token_id", None)
add_eos_token = output_kwargs["text_kwargs"].pop("add_eos_token", False)
add_special_tokens = output_kwargs["text_kwargs"]["add_special_tokens"]
padding = output_kwargs["text_kwargs"]["padding"]
return_tensors = output_kwargs["text_kwargs"].setdefault("return_tensors", None)
encoding = BatchFeature()
if images is not None:
image_encoding = self.image_processor(images, **output_kwargs["images_kwargs"])
encoding.update(image_encoding)
if text is not None:
text = self.preprocess_examples(text, images, bboxes, num_image_tokens=num_image_tokens)
if add_special_tokens and not add_eos_token:
if isinstance(text, str):
text = f"{self.tokenizer.bos_token}{text}"
elif isinstance(text, list):
text = [f"{self.tokenizer.bos_token}{s}" for s in text]
output_kwargs["text_kwargs"]["add_special_tokens"] = (
output_kwargs["text_kwargs"]["add_special_tokens"] and add_eos_token
)
output_kwargs["text_kwargs"]["padding"] = padding if images is None else False
output_kwargs["text_kwargs"]["return_tensors"] = return_tensors if images is None else None
text_encoding = self.tokenizer(text=text, **output_kwargs["text_kwargs"])
encoding.update(text_encoding)
output_kwargs["text_kwargs"]["add_special_tokens"] = add_special_tokens
output_kwargs["text_kwargs"]["padding"] = padding
output_kwargs["text_kwargs"]["return_tensors"] = return_tensors
if text is not None and images is not None:
# Use the id of the first token after <unk>
if first_image_token_id is None:
first_image_token_id = self.tokenizer.unk_token_id + 1
# To see if we need one more `0` (for `<s>`) at the beginning of `image_embeds_position_mask`.
with_bos = add_special_tokens
# The first (actual) `<image>` token is always at the 1st or 2nd place (after `<s>` if any). Here we look
# for the second `<image>` token (which indicate the first image token).
start_index = int(with_bos) + 1
# Add `image_embeds_position_mask`: the leading and trailing `0` are for `boi` and `eoi` tokens. The `1` indicates
# the places of image tokens.
image_token_ids = list(range(first_image_token_id, first_image_token_id + num_image_tokens))
base_image_embeds_position_mask = [0] + [1] * num_image_tokens + [0]
# loop over `encoding["input_ids"]`
input_ids = []
image_embeds_position_mask = []
all_input_ids = encoding["input_ids"]
# not batched -> (changed to) batch of size 1
if isinstance(text, str):
all_input_ids = [all_input_ids]
encoding["attention_mask"] = [encoding["attention_mask"]]
for text_ids in all_input_ids:
# change the ids for the fake `<image>` tokens in `input_ids`
text_ids = text_ids[:start_index] + image_token_ids + text_ids[start_index + num_image_tokens :]
input_ids.append(text_ids)
mask = copy.copy(base_image_embeds_position_mask)
if with_bos:
# for `<s>`
mask = [0] + mask
# trailing part (which are not related to the image)
mask += [0] * (len(text_ids) - len(mask))
image_embeds_position_mask.append(mask)
if isinstance(text, list):
sorted_length = sorted(
[(idx, len(x)) for idx, x in enumerate(text_encoding.input_ids)], key=lambda x: x[-1]
)
_, min_len_not_padded = sorted_length[0]
idx, _ = sorted_length[-1]
output_kwargs["text_kwargs"]["add_special_tokens"] = (
output_kwargs["text_kwargs"]["add_special_tokens"] and add_eos_token
)
output_kwargs["text_kwargs"]["return_tensors"] = None
text_encoding = self.tokenizer(text=[text[idx]], **output_kwargs["text_kwargs"])
max_len_padded = len(text_encoding.input_ids[0])
if min_len_not_padded != max_len_padded:
if self.tokenizer.padding_side == "right":
input_ids = [x + [self.tokenizer.pad_token_id] * (max_len_padded - len(x)) for x in input_ids]
image_embeds_position_mask = [
x + [0] * (max_len_padded - len(x)) for x in image_embeds_position_mask
]
encoding["attention_mask"] = [
x + [0] * (max_len_padded - len(x)) for x in encoding["attention_mask"]
]
elif self.tokenizer.padding_side == "left":
input_ids = [[self.tokenizer.pad_token_id] * (max_len_padded - len(x)) + x for x in input_ids]
image_embeds_position_mask = [
[0] * (max_len_padded - len(x)) + x for x in image_embeds_position_mask
]
encoding["attention_mask"] = [
[0] * (max_len_padded - len(x)) + x for x in encoding["attention_mask"]
]
# un-batch if necessary
if isinstance(text, str) and return_tensors is None:
input_ids = input_ids[0]
encoding["attention_mask"] = encoding["attention_mask"][0]
image_embeds_position_mask = image_embeds_position_mask[0]
# update (with the target tensor type if specified)
encoding.update(
BatchEncoding(
data={
"input_ids": input_ids,
"attention_mask": encoding["attention_mask"],
"image_embeds_position_mask": image_embeds_position_mask,
},
tensor_type=return_tensors,
)
)
return encoding
def _check_bboxes_for_single_text(self, bboxes):
"""
Check `bboxes` for a single text example. It could be
- `None`: no bounding box associated to a text.
- A list with each element being the bounding boxes associated to one `<phrase> ... </phrase>` pair found
in a text. This could be:
- `None`: no bounding box associated to a `<phrase> ... </phrase>` pair.
- A tuple of 2 integers: A single bounding box specified by patch indices.
- A tuple of 4 float point number: A single bounding box specified by (normalized) coordinates.
- A list containing the above 2 tuple types: Multiple bounding boxes for a
`<phrase> ... </phrase>` pair.
"""
if bboxes is None:
return
elif not isinstance(bboxes, list):
raise ValueError("`bboxes` (for a single text example) should be `None` or a list.")
# `bbox` is the bounding boxes for a single <phrase> </phrase> pair
for bbox in bboxes:
if bbox is None:
continue
elif not isinstance(bbox, list):
bbox = [bbox]
for element in bbox:
if not isinstance(element, tuple) or not (
(len(element) == 2 and all(isinstance(x, int) for x in element))
or (len(element) == 4 and all(isinstance(x, float) for x in element))
):
raise ValueError(
"Each element in `bboxes` (for a single text example) should be either `None`, a tuple containing "
"2 integers or 4 float point numbers, or a list containing such tuples. Also "
"make sure the arguments `texts` and `bboxes` passed to `preprocess_text` are both in "
"batches or both for a single example."
)
def _preprocess_single_example(self, text, image, bboxes, img_info_tokens):
text = text.strip()
if image is not None:
# Add `<image> ... (fake) image tokens ... </image>`
text = f"{img_info_tokens} {text}"
# Add `<object> <patch_idx_xxxx> <patch_idx_yyy> </object>` after `<phrase> phrase text </phrase>`
text = self._insert_patch_index_tokens(text, bboxes)
return text
def preprocess_examples(
self,
texts: Union[TextInput, List[TextInput]],
images: ImageInput = None,
bboxes: BboxInput = None,
num_image_tokens: Optional[int] = 64,
) -> Union[str, List[str]]:
"""Add image and bounding box information to `texts` as image and patch index tokens.
Args:
texts (`Union[TextInput, List[TextInput]]`): The texts to be processed.
images (`ImageInput`, *optional*): The images associated to `texts`.
bboxes (`Union[List[Tuple[int]], List[Tuple[float]], List[List[Tuple[int]]], List[List[Tuple[float]]]]`, *optional*):
The bounding bboxes associated to `texts`.
num_image_tokens (`int`, *optional*, defaults to 64):
The number of image tokens (used as latent queries). This should corresponds to the `latent_query_num`
attribute in `Kosmos2Config`.
Returns:
`Union[TextInput, List[TextInput]]`: The processed texts with image and patch index tokens.
"""
# These are fake `<image>` tokens enclosed between (the actual) `<image>` token and `</image>`.
img_tokens = [self.boi_token] * num_image_tokens
img_info_tokens = " ".join([self.boi_token] + img_tokens + [self.eoi_token])
# make batch to simplify processing logic
batched = True
if isinstance(texts, str):
batched = False
texts = [texts]
if images is None:
images = [None] * len(texts)
elif not is_batched(images):
images = [images]
if len(texts) != len(images):
raise ValueError(
f"The number of examples in `texts` and `images` should be the same. Got {len(texts)} v.s. {len(images)} instead."
)
if not batched:
self._check_bboxes_for_single_text(bboxes)
bboxes = [bboxes]
elif bboxes is not None:
if not isinstance(bboxes, list):
raise ValueError("`bboxes` should be `None` or a list (as a batch) when `texts` is passed as a batch.")
for x in bboxes:
self._check_bboxes_for_single_text(x)
else:
bboxes = [None] * len(texts)
if len(bboxes) != len(texts):
raise ValueError(
f"The number of examples in `texts` and `bboxes` should be the same. Got {len(texts)} v.s. {len(bboxes)} instead."
)
result = [
self._preprocess_single_example(text, image, bbox, img_info_tokens)
for text, image, bbox in zip(texts, images, bboxes)
]
# un-batch if necessary
if not batched:
result = result[0]
return result
# Copied from transformers.models.blip.processing_blip.BlipProcessor.batch_decode with BertTokenizerFast->PreTrainedTokenizer
def batch_decode(self, *args, **kwargs):
"""
This method forwards all its arguments to PreTrainedTokenizer's [`~PreTrainedTokenizer.batch_decode`]. Please
refer to the docstring of this method for more information.
"""
return self.tokenizer.batch_decode(*args, **kwargs)
# Copied from transformers.models.blip.processing_blip.BlipProcessor.decode with BertTokenizerFast->PreTrainedTokenizer
def decode(self, *args, **kwargs):
"""
This method forwards all its arguments to PreTrainedTokenizer's [`~PreTrainedTokenizer.decode`]. Please refer to
the docstring of this method for more information.
"""
return self.tokenizer.decode(*args, **kwargs)
def post_process_generation(self, text, cleanup_and_extract=True):
caption = text.split(self.eoi_token)[-1]
if cleanup_and_extract:
return clean_text_and_extract_entities_with_bboxes(caption)
return caption
def post_process_image_text_to_text(self, generated_outputs, skip_special_tokens=True, **kwargs):
"""
Post-process the output of the model to decode the text.
Args:
generated_outputs (`torch.Tensor` or `np.ndarray`):
The output of the model `generate` function. The output is expected to be a tensor of shape `(batch_size, sequence_length)`
or `(sequence_length,)`.
skip_special_tokens (`bool`, *optional*, defaults to `True`):
Whether or not to remove special tokens in the output. Argument passed to the tokenizer's `batch_decode` method.
**kwargs:
Additional arguments to be passed to the tokenizer's `batch_decode method`.
Returns:
`List[str]`: The decoded text.
"""
generated_texts = self.batch_decode(generated_outputs, skip_special_tokens=skip_special_tokens, **kwargs)
return [self.post_process_generation(text, cleanup_and_extract=False) for text in generated_texts]
@property
# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
def model_input_names(self):
tokenizer_input_names = self.tokenizer.model_input_names
image_processor_input_names = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))
def _insert_patch_index_tokens(self, text: str, bboxes: Union[List[Tuple[int]], List[Tuple[float]]]) -> str:
if bboxes is None or len(bboxes) == 0:
return text
matched_phrases = list(re.finditer(r"<phrase>.+?</phrase>", string=text))
if len(matched_phrases) != len(bboxes):
raise ValueError(
f"The number of elements in `bboxes` should be the same as the number of `<phrase> ... </phrase>` pairs in `text`. Got {len(matched_phrases)} v.s. {len(bboxes)} instead."
)
# insert object's patch index tokens
# the found `<phrase> ... </phrase>` pairs.
curr_pos = 0
buffer = []
for matched, bbox in zip(matched_phrases, bboxes):
_, end = matched.span()
buffer.append(text[curr_pos:end])
curr_pos = end
# A phrase without bbox
if bbox is None:
continue
# A phrase with a single bbox
if isinstance(bbox, tuple):
bbox = [bbox]
patch_index_strings = []
# A phrase could have multiple bboxes
if not all(box is not None for box in bbox):
raise ValueError(
"The multiple bounding boxes for a single phrase should not contain any `None` value."
)
for box in bbox:
patch_index_1, patch_index_2 = self._convert_bbox_to_patch_index_tokens(box)
patch_index_strings.append(f"{patch_index_1} {patch_index_2}")
# `bbox` being an empty list
if len(patch_index_strings) == 0:
continue
position_str = " </delimiter_of_multi_objects/> ".join(patch_index_strings)
buffer.append(f"<object> {position_str} </object>")
# remaining
if curr_pos < len(text):
buffer.append(text[curr_pos:])
text = "".join(buffer)
return text
def _convert_bbox_to_patch_index_tokens(
self, bbox: Union[Tuple[int, int], Tuple[float, float, float, float]]
) -> Tuple[str, str]:
# already computed patch indices
if len(bbox) == 2:
idx_1, idx_2 = bbox
# bbox specified with (normalized) coordinates
else:
# use `self.tokenizer` to get `num_patches_per_side`
num_patches_per_side = int(math.sqrt(self.num_patch_index_tokens))
idx_1, idx_2 = coordinate_to_patch_index(bbox, num_patches_per_side)
token_1 = f"<patch_index_{str(idx_1).zfill(4)}>"
token_2 = f"<patch_index_{str(idx_2).zfill(4)}>"
return token_1, token_2
def coordinate_to_patch_index(bbox: Tuple[float, float, float, float], num_patches_per_side: int) -> Tuple[int, int]:
"""Convert a bounding box to a pair of patch indices.
Args:
bbox (`Tuple[float, float, float, float]`):
The 4 coordinates of the bounding box, with the format being (x1, y1, x2, y2) specifying the upper-left and
lower-right corners of the box. It should have x2 > x1 and y2 > y1.
num_patches_per_side (`int`): the number of patches along each side.
Returns:
`Tuple[int, int]`: A pair of patch indices representing the upper-left patch and lower-right patch.
"""
(x1, y1, x2, y2) = bbox
if not (x2 > x1 and y2 > y1):
raise ValueError("The coordinates in `bbox` should be `(x1, y1, x2, y2)` with `x2 > x1` and `y2 > y1`.")
ul_x = math.floor(x1 * num_patches_per_side)
ul_y = math.floor(y1 * num_patches_per_side)
lr_x = math.ceil(x2 * num_patches_per_side - 1)
lr_y = math.ceil(y2 * num_patches_per_side - 1)
ul_idx = ul_y * num_patches_per_side + ul_x
lr_idx = lr_y * num_patches_per_side + lr_x
return ul_idx, lr_idx
# copied from https://github.com/microsoft/unilm/blob/97e4923e97d3ee10b57e97013556e3fd0d207a9b/kosmos-2/demo/decode_string.py#L35C1-L75C38
# (with format modifications)
def patch_index_to_coordinate(ul_idx: int, lr_idx: int, num_patches_per_side: int):
"""
Given a grid of length `num_patches_per_side` and the indices of the upper-left and lower-right corners of a
bounding box, returns the normalized coordinates of the bounding box, in the form (x1, y1, x2, y2).
Args:
ul_idx (`int`): the index of the grid cell that corresponds to the upper-left corner of the bounding box.
lr_idx (`int`): the index of the grid cell that corresponds to the lower-right corner of the bounding box.
num_patches_per_side (`int`): the number of patches along each side.
Returns:
`Tuple[float]`: the normalized coordinates of the bounding box, in the form (x1, y1, x2, y2).
"""
# Compute the size of each cell in the grid
cell_size = 1.0 / num_patches_per_side
# Compute the x and y indices of the upper-left and lower-right corners of the bounding box
ul_x = ul_idx % num_patches_per_side
ul_y = ul_idx // num_patches_per_side
lr_x = lr_idx % num_patches_per_side
lr_y = lr_idx // num_patches_per_side
# Compute the normalized coordinates of the bounding box
if ul_idx == lr_idx:
x1 = ul_x * cell_size
y1 = ul_y * cell_size
x2 = lr_x * cell_size + cell_size
y2 = lr_y * cell_size + cell_size
elif ul_x == lr_x or ul_y == lr_y:
x1 = ul_x * cell_size
y1 = ul_y * cell_size
x2 = lr_x * cell_size + cell_size
y2 = lr_y * cell_size + cell_size
else:
x1 = ul_x * cell_size + cell_size / 2
y1 = ul_y * cell_size + cell_size / 2
x2 = lr_x * cell_size + cell_size / 2
y2 = lr_y * cell_size + cell_size / 2
return x1, y1, x2, y2
# copied from https://github.com/microsoft/unilm/blob/97e4923e97d3ee10b57e97013556e3fd0d207a9b/kosmos-2/demo/decode_string.py#L4-L33
# (with format modifications)
def extract_entities_with_patch_indices(text):
"""Extract entities contained in `text`. The bounding bboxes is given in the form of patch indices.
This functioin is only intended to be used within `clean_text_and_extract_entities_with_bboxes` where further
processing happens, including converting to normalized coordinates and whitespace character cleaning up.
Examples:
```python
>>> text = "<grounding> An image of<phrase> a snowman</phrase><object><patch_index_0044><patch_index_0863></object> warming himself by<phrase> a fire</phrase><object><patch_index_0005><patch_index_0911></object>."
>>> entities = extract_entities_with_patch_indices(text)
>>> entities
[(' a snowman', (31, 41), [(44, 863)]), (' a fire', (130, 137), [(5, 911)])]
```"""
# The regular expression pattern for matching the required formats
pattern = r"(?:(<phrase>([^<]+)</phrase>))?<object>((?:<patch_index_\d+><patch_index_\d+></delimiter_of_multi_objects/>)*<patch_index_\d+><patch_index_\d+>)</object>"
# Find all matches in the given string
matches = re.finditer(pattern, text)
# Initialize an empty list to store the valid patch_index combinations
entities_with_patch_indices = []
for match in matches:
# span of a `phrase` that is between <phrase> and </phrase>
span = match.span(2)
phrase_tag, phrase, match_content = match.groups()
if not phrase_tag:
phrase = None
# We take the starting position of `<object>`
span = (match.span(0)[0], match.span(0)[0])
# Split the match_content by the delimiter to get individual patch_index pairs
patch_index_pairs = match_content.split("</delimiter_of_multi_objects/>")
entity_bboxes = []
for pair in patch_index_pairs:
# Extract the xxxx and yyyy values from the patch_index pair
x = re.search(r"<patch_index_(\d+)>", pair)
y = re.search(r"<patch_index_(\d+)>", pair[1:])
if x and y:
if phrase:
entity_bboxes.append((int(x.group(1)), int(y.group(1))))
else:
entity_bboxes.append((int(x.group(1)), int(y.group(1))))
if phrase:
entities_with_patch_indices.append((phrase, span, entity_bboxes))
else:
for bbox in entity_bboxes:
# fake entity name
entity = f"<patch_index_{bbox[0]}><patch_index_{bbox[1]}>"
entities_with_patch_indices.append((entity, span, [bbox]))
return entities_with_patch_indices
def adjust_entity_positions(entity, text):
"""Adjust the positions of the entities in `text` to be relative to the text with special fields removed."""
entity_name, (start, end) = entity
# computed the length of strings with special fields (tag tokens, patch index tokens, etc.) removed
adjusted_start = len(re.sub("<.*?>", "", text[:start]))
adjusted_end = len(re.sub("<.*?>", "", text[:end]))
adjusted_entity = (entity_name, (adjusted_start, adjusted_end))
return adjusted_entity
def _cleanup_spaces(text, entities):
"""Remove the spaces around the text and the entities in it."""
new_text = text.strip()
leading_spaces = len(text) - len(text.lstrip())
new_entities = []
for entity_name, (start, end), bboxes in entities:
entity_name_leading_spaces = len(entity_name) - len(entity_name.lstrip())
entity_name_trailing_spaces = len(entity_name) - len(entity_name.rstrip())
start = start - leading_spaces + entity_name_leading_spaces
end = end - leading_spaces - entity_name_trailing_spaces
entity_name = entity_name.strip()
new_entities.append((entity_name, (start, end), bboxes))
return new_text, new_entities
# copied from https://github.com/microsoft/unilm/blob/97e4923e97d3ee10b57e97013556e3fd0d207a9b/kosmos-2/demo/decode_string.py#L77-L87
# (with format modifications)
def clean_text_and_extract_entities_with_bboxes(text, num_patches_per_side=32):
"""Remove the tag tokens from `text`, extract entities in it with some cleaning up of white characters.
Examples:
```python
>>> text = "<grounding> An image of<phrase> a snowman</phrase><object><patch_index_0044><patch_index_0863></object> warming himself by<phrase> a fire</phrase><object><patch_index_0005><patch_index_0911></object>."
>>> clean_text, entities = clean_text_and_extract_entities_with_bboxes(text)
>>> clean_text
'An image of a snowman warming himself by a fire.'
>>> entities
[('a snowman', (12, 21), [(0.390625, 0.046875, 0.984375, 0.828125)]), ('a fire', (41, 47), [(0.171875, 0.015625, 0.484375, 0.890625)])]
```"""
# remove special fields (tag tokens, patch index tokens, etc.)
processed_text = re.sub("<.*?>", "", text)
entities_with_patch_indices = extract_entities_with_patch_indices(text)
entities = []
for item in entities_with_patch_indices:
entity, bboxes = item[0:2], item[2]
adjusted_entity = adjust_entity_positions(entity, text)
bboxes_in_coords = [patch_index_to_coordinate(bbox[0], bbox[1], num_patches_per_side) for bbox in bboxes]
entities.append(adjusted_entity + (bboxes_in_coords,))
return _cleanup_spaces(processed_text, entities)
__all__ = ["Kosmos2Processor"]
```
|
================================================================================================================================
SOURCE CODE FILE: __init__.py
LINES: 1
SIZE: 1.10 KB
PATH: scripts\freecad_env\Lib\site-packages\transformers\models\layoutlm\__init__.py
ENCODING: utf-8
```py
# Copyright 2024 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import _LazyModule
from ...utils.import_utils import define_import_structure
if TYPE_CHECKING:
from .configuration_layoutlm import *
from .modeling_layoutlm import *
from .modeling_tf_layoutlm import *
from .tokenization_layoutlm import *
from .tokenization_layoutlm_fast import *
else:
import sys
_file = globals()["__file__"]
sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)
```
|
==============================================================================================================================================
SOURCE CODE FILE: configuration_layoutlm.py
LINES: 1
SIZE: 8.92 KB
PATH: scripts\freecad_env\Lib\site-packages\transformers\models\layoutlm\configuration_layoutlm.py
ENCODING: utf-8
```py
# coding=utf-8
# Copyright 2010, The Microsoft Research Asia LayoutLM Team authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""LayoutLM model configuration"""
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PretrainedConfig, PreTrainedTokenizer
from ...onnx import OnnxConfig, PatchingSpec
from ...utils import TensorType, is_torch_available, logging
logger = logging.get_logger(__name__)
class LayoutLMConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`LayoutLMModel`]. It is used to instantiate a
LayoutLM model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a similar configuration to that of the LayoutLM
[microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) architecture.
Configuration objects inherit from [`BertConfig`] and can be used to control the model outputs. Read the
documentation from [`BertConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 30522):
Vocabulary size of the LayoutLM model. Defines the different tokens that can be represented by the
*inputs_ids* passed to the forward method of [`LayoutLMModel`].
hidden_size (`int`, *optional*, defaults to 768):
Dimensionality of the encoder layers and the pooler layer.
num_hidden_layers (`int`, *optional*, defaults to 12):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 12):
Number of attention heads for each attention layer in the Transformer encoder.
intermediate_size (`int`, *optional*, defaults to 3072):
Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"silu"` and `"gelu_new"` are supported.
hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
The dropout ratio for the attention probabilities.
max_position_embeddings (`int`, *optional*, defaults to 512):
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).
type_vocab_size (`int`, *optional*, defaults to 2):
The vocabulary size of the `token_type_ids` passed into [`LayoutLMModel`].
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (`float`, *optional*, defaults to 1e-12):
The epsilon used by the layer normalization layers.
pad_token_id (`int`, *optional*, defaults to 0):
The value used to pad input_ids.
position_embedding_type (`str`, *optional*, defaults to `"absolute"`):
Type of position embedding. Choose one of `"absolute"`, `"relative_key"`, `"relative_key_query"`. For
positional embeddings use `"absolute"`. For more information on `"relative_key"`, please refer to
[Self-Attention with Relative Position Representations (Shaw et al.)](https://arxiv.org/abs/1803.02155).
For more information on `"relative_key_query"`, please refer to *Method 4* in [Improve Transformer Models
with Better Relative Position Embeddings (Huang et al.)](https://arxiv.org/abs/2009.13658).
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models). Only
relevant if `config.is_decoder=True`.
max_2d_position_embeddings (`int`, *optional*, defaults to 1024):
The maximum value that the 2D position embedding might ever used. Typically set this to something large
just in case (e.g., 1024).
Examples:
```python
>>> from transformers import LayoutLMConfig, LayoutLMModel
>>> # Initializing a LayoutLM configuration
>>> configuration = LayoutLMConfig()
>>> # Initializing a model (with random weights) from the configuration
>>> model = LayoutLMModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "layoutlm"
def __init__(
self,
vocab_size=30522,
hidden_size=768,
num_hidden_layers=12,
num_attention_heads=12,
intermediate_size=3072,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
type_vocab_size=2,
initializer_range=0.02,
layer_norm_eps=1e-12,
pad_token_id=0,
position_embedding_type="absolute",
use_cache=True,
max_2d_position_embeddings=1024,
**kwargs,
):
super().__init__(pad_token_id=pad_token_id, **kwargs)
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.hidden_act = hidden_act
self.intermediate_size = intermediate_size
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.type_vocab_size = type_vocab_size
self.initializer_range = initializer_range
self.layer_norm_eps = layer_norm_eps
self.position_embedding_type = position_embedding_type
self.use_cache = use_cache
self.max_2d_position_embeddings = max_2d_position_embeddings
class LayoutLMOnnxConfig(OnnxConfig):
def __init__(
self,
config: PretrainedConfig,
task: str = "default",
patching_specs: List[PatchingSpec] = None,
):
super().__init__(config, task=task, patching_specs=patching_specs)
self.max_2d_positions = config.max_2d_position_embeddings - 1
@property
def inputs(self) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("input_ids", {0: "batch", 1: "sequence"}),
("bbox", {0: "batch", 1: "sequence"}),
("attention_mask", {0: "batch", 1: "sequence"}),
("token_type_ids", {0: "batch", 1: "sequence"}),
]
)
def generate_dummy_inputs(
self,
tokenizer: PreTrainedTokenizer,
batch_size: int = -1,
seq_length: int = -1,
is_pair: bool = False,
framework: Optional[TensorType] = None,
) -> Mapping[str, Any]:
"""
Generate inputs to provide to the ONNX exporter for the specific framework
Args:
tokenizer: The tokenizer associated with this model configuration
batch_size: The batch size (int) to export the model for (-1 means dynamic axis)
seq_length: The sequence length (int) to export the model for (-1 means dynamic axis)
is_pair: Indicate if the input is a pair (sentence 1, sentence 2)
framework: The framework (optional) the tokenizer will generate tensor for
Returns:
Mapping[str, Tensor] holding the kwargs to provide to the model's forward function
"""
input_dict = super().generate_dummy_inputs(
tokenizer, batch_size=batch_size, seq_length=seq_length, is_pair=is_pair, framework=framework
)
# Generate a dummy bbox
box = [48, 84, 73, 128]
if not framework == TensorType.PYTORCH:
raise NotImplementedError("Exporting LayoutLM to ONNX is currently only supported for PyTorch.")
if not is_torch_available():
raise ValueError("Cannot generate dummy inputs without PyTorch installed.")
import torch
batch_size, seq_length = input_dict["input_ids"].shape
input_dict["bbox"] = torch.tensor([*[box] * seq_length]).tile(batch_size, 1, 1)
return input_dict
__all__ = ["LayoutLMConfig", "LayoutLMOnnxConfig"]
```
|
=========================================================================================================================================
SOURCE CODE FILE: modeling_layoutlm.py
LINES: 1
SIZE: 59.88 KB
PATH: scripts\freecad_env\Lib\site-packages\transformers\models\layoutlm\modeling_layoutlm.py
ENCODING: utf-8
```py
# coding=utf-8
# Copyright 2018 The Microsoft Research Asia LayoutLM Team Authors and the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""PyTorch LayoutLM model."""
import math
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACT2FN
from ...modeling_outputs import (
BaseModelOutputWithPastAndCrossAttentions,
BaseModelOutputWithPoolingAndCrossAttentions,
MaskedLMOutput,
QuestionAnsweringModelOutput,
SequenceClassifierOutput,
TokenClassifierOutput,
)
from ...modeling_utils import PreTrainedModel
from ...pytorch_utils import apply_chunking_to_forward, find_pruneable_heads_and_indices, prune_linear_layer
from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
from .configuration_layoutlm import LayoutLMConfig
logger = logging.get_logger(__name__)
_CONFIG_FOR_DOC = "LayoutLMConfig"
_CHECKPOINT_FOR_DOC = "microsoft/layoutlm-base-uncased"
LayoutLMLayerNorm = nn.LayerNorm
class LayoutLMEmbeddings(nn.Module):
"""Construct the embeddings from word, position and token_type embeddings."""
def __init__(self, config):
super(LayoutLMEmbeddings, self).__init__()
self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
self.x_position_embeddings = nn.Embedding(config.max_2d_position_embeddings, config.hidden_size)
self.y_position_embeddings = nn.Embedding(config.max_2d_position_embeddings, config.hidden_size)
self.h_position_embeddings = nn.Embedding(config.max_2d_position_embeddings, config.hidden_size)
self.w_position_embeddings = nn.Embedding(config.max_2d_position_embeddings, config.hidden_size)
self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)
self.LayerNorm = LayoutLMLayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.register_buffer(
"position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False
)
def forward(
self,
input_ids=None,
bbox=None,
token_type_ids=None,
position_ids=None,
inputs_embeds=None,
):
if input_ids is not None:
input_shape = input_ids.size()
else:
input_shape = inputs_embeds.size()[:-1]
seq_length = input_shape[1]
device = input_ids.device if input_ids is not None else inputs_embeds.device
if position_ids is None:
position_ids = self.position_ids[:, :seq_length]
if token_type_ids is None:
token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
if inputs_embeds is None:
inputs_embeds = self.word_embeddings(input_ids)
words_embeddings = inputs_embeds
position_embeddings = self.position_embeddings(position_ids)
try:
left_position_embeddings = self.x_position_embeddings(bbox[:, :, 0])
upper_position_embeddings = self.y_position_embeddings(bbox[:, :, 1])
right_position_embeddings = self.x_position_embeddings(bbox[:, :, 2])
lower_position_embeddings = self.y_position_embeddings(bbox[:, :, 3])
except IndexError as e:
raise IndexError("The `bbox`coordinate values should be within 0-1000 range.") from e
h_position_embeddings = self.h_position_embeddings(bbox[:, :, 3] - bbox[:, :, 1])
w_position_embeddings = self.w_position_embeddings(bbox[:, :, 2] - bbox[:, :, 0])
token_type_embeddings = self.token_type_embeddings(token_type_ids)
embeddings = (
words_embeddings
+ position_embeddings
+ left_position_embeddings
+ upper_position_embeddings
+ right_position_embeddings
+ lower_position_embeddings
+ h_position_embeddings
+ w_position_embeddings
+ token_type_embeddings
)
embeddings = self.LayerNorm(embeddings)
embeddings = self.dropout(embeddings)
return embeddings
# Copied from transformers.models.bert.modeling_bert.BertSelfAttention with Bert->LayoutLM
class LayoutLMSelfAttention(nn.Module):
def __init__(self, config, position_embedding_type=None):
super().__init__()
if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
raise ValueError(
f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention "
f"heads ({config.num_attention_heads})"
)
self.num_attention_heads = config.num_attention_heads
self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
self.all_head_size = self.num_attention_heads * self.attention_head_size
self.query = nn.Linear(config.hidden_size, self.all_head_size)
self.key = nn.Linear(config.hidden_size, self.all_head_size)
self.value = nn.Linear(config.hidden_size, self.all_head_size)
self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
self.position_embedding_type = position_embedding_type or getattr(
config, "position_embedding_type", "absolute"
)
if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
self.max_position_embeddings = config.max_position_embeddings
self.distance_embedding = nn.Embedding(2 * config.max_position_embeddings - 1, self.attention_head_size)
self.is_decoder = config.is_decoder
def transpose_for_scores(self, x: torch.Tensor) -> torch.Tensor:
new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
x = x.view(new_x_shape)
return x.permute(0, 2, 1, 3)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
encoder_attention_mask: Optional[torch.FloatTensor] = None,
past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
output_attentions: Optional[bool] = False,
) -> Tuple[torch.Tensor]:
mixed_query_layer = self.query(hidden_states)
# If this is instantiated as a cross-attention module, the keys
# and values come from an encoder; the attention mask needs to be
# such that the encoder's padding tokens are not attended to.
is_cross_attention = encoder_hidden_states is not None
if is_cross_attention and past_key_value is not None:
# reuse k,v, cross_attentions
key_layer = past_key_value[0]
value_layer = past_key_value[1]
attention_mask = encoder_attention_mask
elif is_cross_attention:
key_layer = self.transpose_for_scores(self.key(encoder_hidden_states))
value_layer = self.transpose_for_scores(self.value(encoder_hidden_states))
attention_mask = encoder_attention_mask
elif past_key_value is not None:
key_layer = self.transpose_for_scores(self.key(hidden_states))
value_layer = self.transpose_for_scores(self.value(hidden_states))
key_layer = torch.cat([past_key_value[0], key_layer], dim=2)
value_layer = torch.cat([past_key_value[1], value_layer], dim=2)
else:
key_layer = self.transpose_for_scores(self.key(hidden_states))
value_layer = self.transpose_for_scores(self.value(hidden_states))
query_layer = self.transpose_for_scores(mixed_query_layer)
use_cache = past_key_value is not None
if self.is_decoder:
# if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states.
# Further calls to cross_attention layer can then reuse all cross-attention
# key/value_states (first "if" case)
# if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of
# all previous decoder key/value_states. Further calls to uni-directional self-attention
# can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
# if encoder bi-directional self-attention `past_key_value` is always `None`
past_key_value = (key_layer, value_layer)
# Take the dot product between "query" and "key" to get the raw attention scores.
attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
query_length, key_length = query_layer.shape[2], key_layer.shape[2]
if use_cache:
position_ids_l = torch.tensor(key_length - 1, dtype=torch.long, device=hidden_states.device).view(
-1, 1
)
else:
position_ids_l = torch.arange(query_length, dtype=torch.long, device=hidden_states.device).view(-1, 1)
position_ids_r = torch.arange(key_length, dtype=torch.long, device=hidden_states.device).view(1, -1)
distance = position_ids_l - position_ids_r
positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1)
positional_embedding = positional_embedding.to(dtype=query_layer.dtype) # fp16 compatibility
if self.position_embedding_type == "relative_key":
relative_position_scores = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
attention_scores = attention_scores + relative_position_scores
elif self.position_embedding_type == "relative_key_query":
relative_position_scores_query = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
relative_position_scores_key = torch.einsum("bhrd,lrd->bhlr", key_layer, positional_embedding)
attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key
attention_scores = attention_scores / math.sqrt(self.attention_head_size)
if attention_mask is not None:
# Apply the attention mask is (precomputed for all layers in LayoutLMModel forward() function)
attention_scores = attention_scores + attention_mask
# Normalize the attention scores to probabilities.
attention_probs = nn.functional.softmax(attention_scores, dim=-1)
# This is actually dropping out entire tokens to attend to, which might
# seem a bit unusual, but is taken from the original Transformer paper.
attention_probs = self.dropout(attention_probs)
# Mask heads if we want to
if head_mask is not None:
attention_probs = attention_probs * head_mask
context_layer = torch.matmul(attention_probs, value_layer)
context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
context_layer = context_layer.view(new_context_layer_shape)
outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
if self.is_decoder:
outputs = outputs + (past_key_value,)
return outputs
# Copied from transformers.models.bert.modeling_bert.BertSelfOutput with Bert->LayoutLM
class LayoutLMSelfOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
hidden_states = self.dense(hidden_states)
hidden_states = self.dropout(hidden_states)
hidden_states = self.LayerNorm(hidden_states + input_tensor)
return hidden_states
LAYOUTLM_SELF_ATTENTION_CLASSES = {
"eager": LayoutLMSelfAttention,
}
# Copied from transformers.models.bert.modeling_bert.BertAttention with Bert->LayoutLM,BERT->LAYOUTLM
class LayoutLMAttention(nn.Module):
def __init__(self, config, position_embedding_type=None):
super().__init__()
self.self = LAYOUTLM_SELF_ATTENTION_CLASSES[config._attn_implementation](
config, position_embedding_type=position_embedding_type
)
self.output = LayoutLMSelfOutput(config)
self.pruned_heads = set()
def prune_heads(self, heads):
if len(heads) == 0:
return
heads, index = find_pruneable_heads_and_indices(
heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads
)
# Prune linear layers
self.self.query = prune_linear_layer(self.self.query, index)
self.self.key = prune_linear_layer(self.self.key, index)
self.self.value = prune_linear_layer(self.self.value, index)
self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
# Update hyper params and store pruned heads
self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads
self.pruned_heads = self.pruned_heads.union(heads)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
encoder_attention_mask: Optional[torch.FloatTensor] = None,
past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
output_attentions: Optional[bool] = False,
) -> Tuple[torch.Tensor]:
self_outputs = self.self(
hidden_states,
attention_mask,
head_mask,
encoder_hidden_states,
encoder_attention_mask,
past_key_value,
output_attentions,
)
attention_output = self.output(self_outputs[0], hidden_states)
outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them
return outputs
# Copied from transformers.models.bert.modeling_bert.BertIntermediate
class LayoutLMIntermediate(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
if isinstance(config.hidden_act, str):
self.intermediate_act_fn = ACT2FN[config.hidden_act]
else:
self.intermediate_act_fn = config.hidden_act
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidden_states = self.dense(hidden_states)
hidden_states = self.intermediate_act_fn(hidden_states)
return hidden_states
# Copied from transformers.models.bert.modeling_bert.BertOutput with Bert->LayoutLM
class LayoutLMOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
hidden_states = self.dense(hidden_states)
hidden_states = self.dropout(hidden_states)
hidden_states = self.LayerNorm(hidden_states + input_tensor)
return hidden_states
# Copied from transformers.models.bert.modeling_bert.BertLayer with Bert->LayoutLM
class LayoutLMLayer(nn.Module):
def __init__(self, config):
super().__init__()
self.chunk_size_feed_forward = config.chunk_size_feed_forward
self.seq_len_dim = 1
self.attention = LayoutLMAttention(config)
self.is_decoder = config.is_decoder
self.add_cross_attention = config.add_cross_attention
if self.add_cross_attention:
if not self.is_decoder:
raise ValueError(f"{self} should be used as a decoder model if cross attention is added")
self.crossattention = LayoutLMAttention(config, position_embedding_type="absolute")
self.intermediate = LayoutLMIntermediate(config)
self.output = LayoutLMOutput(config)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
encoder_attention_mask: Optional[torch.FloatTensor] = None,
past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
output_attentions: Optional[bool] = False,
) -> Tuple[torch.Tensor]:
# decoder uni-directional self-attention cached key/values tuple is at positions 1,2
self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
self_attention_outputs = self.attention(
hidden_states,
attention_mask,
head_mask,
output_attentions=output_attentions,
past_key_value=self_attn_past_key_value,
)
attention_output = self_attention_outputs[0]
# if decoder, the last output is tuple of self-attn cache
if self.is_decoder:
outputs = self_attention_outputs[1:-1]
present_key_value = self_attention_outputs[-1]
else:
outputs = self_attention_outputs[1:] # add self attentions if we output attention weights
cross_attn_present_key_value = None
if self.is_decoder and encoder_hidden_states is not None:
if not hasattr(self, "crossattention"):
raise ValueError(
f"If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention layers"
" by setting `config.add_cross_attention=True`"
)
# cross_attn cached key/values tuple is at positions 3,4 of past_key_value tuple
cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None
cross_attention_outputs = self.crossattention(
attention_output,
attention_mask,
head_mask,
encoder_hidden_states,
encoder_attention_mask,
cross_attn_past_key_value,
output_attentions,
)
attention_output = cross_attention_outputs[0]
outputs = outputs + cross_attention_outputs[1:-1] # add cross attentions if we output attention weights
# add cross-attn cache to positions 3,4 of present_key_value tuple
cross_attn_present_key_value = cross_attention_outputs[-1]
present_key_value = present_key_value + cross_attn_present_key_value
layer_output = apply_chunking_to_forward(
self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output
)
outputs = (layer_output,) + outputs
# if decoder, return the attn key/values as the last output
if self.is_decoder:
outputs = outputs + (present_key_value,)
return outputs
def feed_forward_chunk(self, attention_output):
intermediate_output = self.intermediate(attention_output)
layer_output = self.output(intermediate_output, attention_output)
return layer_output
# Copied from transformers.models.bert.modeling_bert.BertEncoder with Bert->LayoutLM
class LayoutLMEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.layer = nn.ModuleList([LayoutLMLayer(config) for _ in range(config.num_hidden_layers)])
self.gradient_checkpointing = False
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
encoder_attention_mask: Optional[torch.FloatTensor] = None,
past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = False,
output_hidden_states: Optional[bool] = False,
return_dict: Optional[bool] = True,
) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPastAndCrossAttentions]:
all_hidden_states = () if output_hidden_states else None
all_self_attentions = () if output_attentions else None
all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None
if self.gradient_checkpointing and self.training:
if use_cache:
logger.warning_once(
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
)
use_cache = False
next_decoder_cache = () if use_cache else None
for i, layer_module in enumerate(self.layer):
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
layer_head_mask = head_mask[i] if head_mask is not None else None
past_key_value = past_key_values[i] if past_key_values is not None else None
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
layer_module.__call__,
hidden_states,
attention_mask,
layer_head_mask,
encoder_hidden_states,
encoder_attention_mask,
past_key_value,
output_attentions,
)
else:
layer_outputs = layer_module(
hidden_states,
attention_mask,
layer_head_mask,
encoder_hidden_states,
encoder_attention_mask,
past_key_value,
output_attentions,
)
hidden_states = layer_outputs[0]
if use_cache:
next_decoder_cache += (layer_outputs[-1],)
if output_attentions:
all_self_attentions = all_self_attentions + (layer_outputs[1],)
if self.config.add_cross_attention:
all_cross_attentions = all_cross_attentions + (layer_outputs[2],)
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if not return_dict:
return tuple(
v
for v in [
hidden_states,
next_decoder_cache,
all_hidden_states,
all_self_attentions,
all_cross_attentions,
]
if v is not None
)
return BaseModelOutputWithPastAndCrossAttentions(
last_hidden_state=hidden_states,
past_key_values=next_decoder_cache,
hidden_states=all_hidden_states,
attentions=all_self_attentions,
cross_attentions=all_cross_attentions,
)
# Copied from transformers.models.bert.modeling_bert.BertPooler
class LayoutLMPooler(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.activation = nn.Tanh()
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
# We "pool" the model by simply taking the hidden state corresponding
# to the first token.
first_token_tensor = hidden_states[:, 0]
pooled_output = self.dense(first_token_tensor)
pooled_output = self.activation(pooled_output)
return pooled_output
# Copied from transformers.models.bert.modeling_bert.BertPredictionHeadTransform with Bert->LayoutLM
class LayoutLMPredictionHeadTransform(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
if isinstance(config.hidden_act, str):
self.transform_act_fn = ACT2FN[config.hidden_act]
else:
self.transform_act_fn = config.hidden_act
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidden_states = self.dense(hidden_states)
hidden_states = self.transform_act_fn(hidden_states)
hidden_states = self.LayerNorm(hidden_states)
return hidden_states
# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->LayoutLM
class LayoutLMLMPredictionHead(nn.Module):
def __init__(self, config):
super().__init__()
self.transform = LayoutLMPredictionHeadTransform(config)
# The output weights are the same as the input embeddings, but there is
# an output-only bias for each token.
self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
self.bias = nn.Parameter(torch.zeros(config.vocab_size))
# Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
self.decoder.bias = self.bias
def _tie_weights(self):
self.decoder.bias = self.bias
def forward(self, hidden_states):
hidden_states = self.transform(hidden_states)
hidden_states = self.decoder(hidden_states)
return hidden_states
# Copied from transformers.models.bert.modeling_bert.BertOnlyMLMHead with Bert->LayoutLM
class LayoutLMOnlyMLMHead(nn.Module):
def __init__(self, config):
super().__init__()
self.predictions = LayoutLMLMPredictionHead(config)
def forward(self, sequence_output: torch.Tensor) -> torch.Tensor:
prediction_scores = self.predictions(sequence_output)
return prediction_scores
class LayoutLMPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = LayoutLMConfig
base_model_prefix = "layoutlm"
supports_gradient_checkpointing = True
def _init_weights(self, module):
"""Initialize the weights"""
if isinstance(module, nn.Linear):
# Slightly different from the TF version which uses truncated_normal for initialization
# cf https://github.com/pytorch/pytorch/pull/5617
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.Embedding):
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
elif isinstance(module, LayoutLMLayerNorm):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
elif isinstance(module, LayoutLMLMPredictionHead):
module.bias.data.zero_()
LAYOUTLM_START_DOCSTRING = r"""
The LayoutLM model was proposed in [LayoutLM: Pre-training of Text and Layout for Document Image
Understanding](https://arxiv.org/abs/1912.13318) by Yiheng Xu, Minghao Li, Lei Cui, Shaohan Huang, Furu Wei and
Ming Zhou.
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`LayoutLMConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
LAYOUTLM_INPUTS_DOCSTRING = r"""
Args:
input_ids (`torch.LongTensor` of shape `({0})`):
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
bbox (`torch.LongTensor` of shape `({0}, 4)`, *optional*):
Bounding boxes of each input sequence tokens. Selected in the range `[0,
config.max_2d_position_embeddings-1]`. Each bounding box should be a normalized version in (x0, y0, x1, y1)
format, where (x0, y0) corresponds to the position of the upper left corner in the bounding box, and (x1,
y1) represents the position of the lower right corner. See [Overview](#Overview) for normalization.
attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: `1` for
tokens that are NOT MASKED, `0` for MASKED tokens.
[What are attention masks?](../glossary#attention-mask)
token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*):
Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
1]`: `0` corresponds to a *sentence A* token, `1` corresponds to a *sentence B* token
[What are token type IDs?](../glossary#token-type-ids)
position_ids (`torch.LongTensor` of shape `({0})`, *optional*):
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
config.max_position_embeddings - 1]`.
[What are position IDs?](../glossary#position-ids)
head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: `1`
indicates the head is **not masked**, `0` indicates the head is **masked**.
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert *input_ids* indices into associated vectors than the
model's internal embedding lookup matrix.
output_attentions (`bool`, *optional*):
If set to `True`, the attentions tensors of all attention layers are returned. See `attentions` under
returned tensors for more detail.
output_hidden_states (`bool`, *optional*):
If set to `True`, the hidden states of all layers are returned. See `hidden_states` under returned tensors
for more detail.
return_dict (`bool`, *optional*):
If set to `True`, the model will return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
@add_start_docstrings(
"The bare LayoutLM Model transformer outputting raw hidden-states without any specific head on top.",
LAYOUTLM_START_DOCSTRING,
)
class LayoutLMModel(LayoutLMPreTrainedModel):
def __init__(self, config):
super(LayoutLMModel, self).__init__(config)
self.config = config
self.embeddings = LayoutLMEmbeddings(config)
self.encoder = LayoutLMEncoder(config)
self.pooler = LayoutLMPooler(config)
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.embeddings.word_embeddings
def set_input_embeddings(self, value):
self.embeddings.word_embeddings = value
def _prune_heads(self, heads_to_prune):
"""
Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
class PreTrainedModel
"""
for layer, heads in heads_to_prune.items():
self.encoder.layer[layer].attention.prune_heads(heads)
@add_start_docstrings_to_model_forward(LAYOUTLM_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=BaseModelOutputWithPoolingAndCrossAttentions, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
bbox: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
encoder_attention_mask: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, BaseModelOutputWithPoolingAndCrossAttentions]:
r"""
Returns:
Examples:
```python
>>> from transformers import AutoTokenizer, LayoutLMModel
>>> import torch
>>> tokenizer = AutoTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")
>>> model = LayoutLMModel.from_pretrained("microsoft/layoutlm-base-uncased")
>>> words = ["Hello", "world"]
>>> normalized_word_boxes = [637, 773, 693, 782], [698, 773, 733, 782]
>>> token_boxes = []
>>> for word, box in zip(words, normalized_word_boxes):
... word_tokens = tokenizer.tokenize(word)
... token_boxes.extend([box] * len(word_tokens))
>>> # add bounding boxes of cls + sep tokens
>>> token_boxes = [[0, 0, 0, 0]] + token_boxes + [[1000, 1000, 1000, 1000]]
>>> encoding = tokenizer(" ".join(words), return_tensors="pt")
>>> input_ids = encoding["input_ids"]
>>> attention_mask = encoding["attention_mask"]
>>> token_type_ids = encoding["token_type_ids"]
>>> bbox = torch.tensor([token_boxes])
>>> outputs = model(
... input_ids=input_ids, bbox=bbox, attention_mask=attention_mask, token_type_ids=token_type_ids
... )
>>> last_hidden_states = outputs.last_hidden_state
```"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
elif input_ids is not None:
self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
input_shape = input_ids.size()
elif inputs_embeds is not None:
input_shape = inputs_embeds.size()[:-1]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
device = input_ids.device if input_ids is not None else inputs_embeds.device
if attention_mask is None:
attention_mask = torch.ones(input_shape, device=device)
if token_type_ids is None:
token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
if bbox is None:
bbox = torch.zeros(input_shape + (4,), dtype=torch.long, device=device)
extended_attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)
extended_attention_mask = extended_attention_mask.to(dtype=self.dtype)
extended_attention_mask = (1.0 - extended_attention_mask) * torch.finfo(self.dtype).min
if head_mask is not None:
if head_mask.dim() == 1:
head_mask = head_mask.unsqueeze(0).unsqueeze(0).unsqueeze(-1).unsqueeze(-1)
head_mask = head_mask.expand(self.config.num_hidden_layers, -1, -1, -1, -1)
elif head_mask.dim() == 2:
head_mask = head_mask.unsqueeze(1).unsqueeze(-1).unsqueeze(-1)
head_mask = head_mask.to(dtype=next(self.parameters()).dtype)
else:
head_mask = [None] * self.config.num_hidden_layers
embedding_output = self.embeddings(
input_ids=input_ids,
bbox=bbox,
position_ids=position_ids,
token_type_ids=token_type_ids,
inputs_embeds=inputs_embeds,
)
encoder_outputs = self.encoder(
embedding_output,
extended_attention_mask,
head_mask=head_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = encoder_outputs[0]
pooled_output = self.pooler(sequence_output)
if not return_dict:
return (sequence_output, pooled_output) + encoder_outputs[1:]
return BaseModelOutputWithPoolingAndCrossAttentions(
last_hidden_state=sequence_output,
pooler_output=pooled_output,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
cross_attentions=encoder_outputs.cross_attentions,
)
@add_start_docstrings("""LayoutLM Model with a `language modeling` head on top.""", LAYOUTLM_START_DOCSTRING)
class LayoutLMForMaskedLM(LayoutLMPreTrainedModel):
_tied_weights_keys = ["cls.predictions.decoder.bias", "cls.predictions.decoder.weight"]
def __init__(self, config):
super().__init__(config)
self.layoutlm = LayoutLMModel(config)
self.cls = LayoutLMOnlyMLMHead(config)
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.layoutlm.embeddings.word_embeddings
def get_output_embeddings(self):
return self.cls.predictions.decoder
def set_output_embeddings(self, new_embeddings):
self.cls.predictions.decoder = new_embeddings
self.cls.predictions.bias = new_embeddings.bias
@add_start_docstrings_to_model_forward(LAYOUTLM_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=MaskedLMOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
bbox: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
encoder_attention_mask: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, MaskedLMOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the
loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`
Returns:
Examples:
```python
>>> from transformers import AutoTokenizer, LayoutLMForMaskedLM
>>> import torch
>>> tokenizer = AutoTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")
>>> model = LayoutLMForMaskedLM.from_pretrained("microsoft/layoutlm-base-uncased")
>>> words = ["Hello", "[MASK]"]
>>> normalized_word_boxes = [637, 773, 693, 782], [698, 773, 733, 782]
>>> token_boxes = []
>>> for word, box in zip(words, normalized_word_boxes):
... word_tokens = tokenizer.tokenize(word)
... token_boxes.extend([box] * len(word_tokens))
>>> # add bounding boxes of cls + sep tokens
>>> token_boxes = [[0, 0, 0, 0]] + token_boxes + [[1000, 1000, 1000, 1000]]
>>> encoding = tokenizer(" ".join(words), return_tensors="pt")
>>> input_ids = encoding["input_ids"]
>>> attention_mask = encoding["attention_mask"]
>>> token_type_ids = encoding["token_type_ids"]
>>> bbox = torch.tensor([token_boxes])
>>> labels = tokenizer("Hello world", return_tensors="pt")["input_ids"]
>>> outputs = model(
... input_ids=input_ids,
... bbox=bbox,
... attention_mask=attention_mask,
... token_type_ids=token_type_ids,
... labels=labels,
... )
>>> loss = outputs.loss
```"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.layoutlm(
input_ids,
bbox,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = outputs[0]
prediction_scores = self.cls(sequence_output)
masked_lm_loss = None
if labels is not None:
loss_fct = CrossEntropyLoss()
masked_lm_loss = loss_fct(
prediction_scores.view(-1, self.config.vocab_size),
labels.view(-1),
)
if not return_dict:
output = (prediction_scores,) + outputs[2:]
return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
return MaskedLMOutput(
loss=masked_lm_loss,
logits=prediction_scores,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
@add_start_docstrings(
"""
LayoutLM Model with a sequence classification head on top (a linear layer on top of the pooled output) e.g. for
document image classification tasks such as the [RVL-CDIP](https://www.cs.cmu.edu/~aharley/rvl-cdip/) dataset.
""",
LAYOUTLM_START_DOCSTRING,
)
class LayoutLMForSequenceClassification(LayoutLMPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.layoutlm = LayoutLMModel(config)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.classifier = nn.Linear(config.hidden_size, config.num_labels)
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.layoutlm.embeddings.word_embeddings
@add_start_docstrings_to_model_forward(LAYOUTLM_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=SequenceClassifierOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
bbox: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, SequenceClassifierOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
Returns:
Examples:
```python
>>> from transformers import AutoTokenizer, LayoutLMForSequenceClassification
>>> import torch
>>> tokenizer = AutoTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")
>>> model = LayoutLMForSequenceClassification.from_pretrained("microsoft/layoutlm-base-uncased")
>>> words = ["Hello", "world"]
>>> normalized_word_boxes = [637, 773, 693, 782], [698, 773, 733, 782]
>>> token_boxes = []
>>> for word, box in zip(words, normalized_word_boxes):
... word_tokens = tokenizer.tokenize(word)
... token_boxes.extend([box] * len(word_tokens))
>>> # add bounding boxes of cls + sep tokens
>>> token_boxes = [[0, 0, 0, 0]] + token_boxes + [[1000, 1000, 1000, 1000]]
>>> encoding = tokenizer(" ".join(words), return_tensors="pt")
>>> input_ids = encoding["input_ids"]
>>> attention_mask = encoding["attention_mask"]
>>> token_type_ids = encoding["token_type_ids"]
>>> bbox = torch.tensor([token_boxes])
>>> sequence_label = torch.tensor([1])
>>> outputs = model(
... input_ids=input_ids,
... bbox=bbox,
... attention_mask=attention_mask,
... token_type_ids=token_type_ids,
... labels=sequence_label,
... )
>>> loss = outputs.loss
>>> logits = outputs.logits
```"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.layoutlm(
input_ids=input_ids,
bbox=bbox,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
pooled_output = outputs[1]
pooled_output = self.dropout(pooled_output)
logits = self.classifier(pooled_output)
loss = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
self.config.problem_type = "regression"
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
self.config.problem_type = "single_label_classification"
else:
self.config.problem_type = "multi_label_classification"
if self.config.problem_type == "regression":
loss_fct = MSELoss()
if self.num_labels == 1:
loss = loss_fct(logits.squeeze(), labels.squeeze())
else:
loss = loss_fct(logits, labels)
elif self.config.problem_type == "single_label_classification":
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
elif self.config.problem_type == "multi_label_classification":
loss_fct = BCEWithLogitsLoss()
loss = loss_fct(logits, labels)
if not return_dict:
output = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return SequenceClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
@add_start_docstrings(
"""
LayoutLM Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for
sequence labeling (information extraction) tasks such as the [FUNSD](https://guillaumejaume.github.io/FUNSD/)
dataset and the [SROIE](https://rrc.cvc.uab.es/?ch=13) dataset.
""",
LAYOUTLM_START_DOCSTRING,
)
class LayoutLMForTokenClassification(LayoutLMPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.layoutlm = LayoutLMModel(config)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.classifier = nn.Linear(config.hidden_size, config.num_labels)
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.layoutlm.embeddings.word_embeddings
@add_start_docstrings_to_model_forward(LAYOUTLM_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=TokenClassifierOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
bbox: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, TokenClassifierOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
Returns:
Examples:
```python
>>> from transformers import AutoTokenizer, LayoutLMForTokenClassification
>>> import torch
>>> tokenizer = AutoTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")
>>> model = LayoutLMForTokenClassification.from_pretrained("microsoft/layoutlm-base-uncased")
>>> words = ["Hello", "world"]
>>> normalized_word_boxes = [637, 773, 693, 782], [698, 773, 733, 782]
>>> token_boxes = []
>>> for word, box in zip(words, normalized_word_boxes):
... word_tokens = tokenizer.tokenize(word)
... token_boxes.extend([box] * len(word_tokens))
>>> # add bounding boxes of cls + sep tokens
>>> token_boxes = [[0, 0, 0, 0]] + token_boxes + [[1000, 1000, 1000, 1000]]
>>> encoding = tokenizer(" ".join(words), return_tensors="pt")
>>> input_ids = encoding["input_ids"]
>>> attention_mask = encoding["attention_mask"]
>>> token_type_ids = encoding["token_type_ids"]
>>> bbox = torch.tensor([token_boxes])
>>> token_labels = torch.tensor([1, 1, 0, 0]).unsqueeze(0) # batch size of 1
>>> outputs = model(
... input_ids=input_ids,
... bbox=bbox,
... attention_mask=attention_mask,
... token_type_ids=token_type_ids,
... labels=token_labels,
... )
>>> loss = outputs.loss
>>> logits = outputs.logits
```"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.layoutlm(
input_ids=input_ids,
bbox=bbox,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = outputs[0]
sequence_output = self.dropout(sequence_output)
logits = self.classifier(sequence_output)
loss = None
if labels is not None:
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
if not return_dict:
output = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return TokenClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
@add_start_docstrings(
"""
LayoutLM Model with a span classification head on top for extractive question-answering tasks such as
[DocVQA](https://rrc.cvc.uab.es/?ch=17) (a linear layer on top of the final hidden-states output to compute `span
start logits` and `span end logits`).
""",
LAYOUTLM_START_DOCSTRING,
)
class LayoutLMForQuestionAnswering(LayoutLMPreTrainedModel):
def __init__(self, config, has_visual_segment_embedding=True):
super().__init__(config)
self.num_labels = config.num_labels
self.layoutlm = LayoutLMModel(config)
self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.layoutlm.embeddings.word_embeddings
@replace_return_docstrings(output_type=QuestionAnsweringModelOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
bbox: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
start_positions: Optional[torch.LongTensor] = None,
end_positions: Optional[torch.LongTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, QuestionAnsweringModelOutput]:
r"""
start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for position (index) of the start of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
are not taken into account for computing the loss.
end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for position (index) of the end of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
are not taken into account for computing the loss.
Returns:
Example:
In the example below, we prepare a question + context pair for the LayoutLM model. It will give us a prediction
of what it thinks the answer is (the span of the answer within the texts parsed from the image).
```python
>>> from transformers import AutoTokenizer, LayoutLMForQuestionAnswering
>>> from datasets import load_dataset
>>> import torch
>>> tokenizer = AutoTokenizer.from_pretrained("impira/layoutlm-document-qa", add_prefix_space=True)
>>> model = LayoutLMForQuestionAnswering.from_pretrained("impira/layoutlm-document-qa", revision="1e3ebac")
>>> dataset = load_dataset("nielsr/funsd", split="train", trust_remote_code=True)
>>> example = dataset[0]
>>> question = "what's his name?"
>>> words = example["words"]
>>> boxes = example["bboxes"]
>>> encoding = tokenizer(
... question.split(), words, is_split_into_words=True, return_token_type_ids=True, return_tensors="pt"
... )
>>> bbox = []
>>> for i, s, w in zip(encoding.input_ids[0], encoding.sequence_ids(0), encoding.word_ids(0)):
... if s == 1:
... bbox.append(boxes[w])
... elif i == tokenizer.sep_token_id:
... bbox.append([1000] * 4)
... else:
... bbox.append([0] * 4)
>>> encoding["bbox"] = torch.tensor([bbox])
>>> word_ids = encoding.word_ids(0)
>>> outputs = model(**encoding)
>>> loss = outputs.loss
>>> start_scores = outputs.start_logits
>>> end_scores = outputs.end_logits
>>> start, end = word_ids[start_scores.argmax(-1)], word_ids[end_scores.argmax(-1)]
>>> print(" ".join(words[start : end + 1]))
M. Hamann P. Harper, P. Martinez
```"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.layoutlm(
input_ids=input_ids,
bbox=bbox,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = outputs[0]
logits = self.qa_outputs(sequence_output)
start_logits, end_logits = logits.split(1, dim=-1)
start_logits = start_logits.squeeze(-1).contiguous()
end_logits = end_logits.squeeze(-1).contiguous()
total_loss = None
if start_positions is not None and end_positions is not None:
# If we are on multi-GPU, split add a dimension
if len(start_positions.size()) > 1:
start_positions = start_positions.squeeze(-1)
if len(end_positions.size()) > 1:
end_positions = end_positions.squeeze(-1)
# sometimes the start/end positions are outside our model inputs, we ignore these terms
ignored_index = start_logits.size(1)
start_positions = start_positions.clamp(0, ignored_index)
end_positions = end_positions.clamp(0, ignored_index)
loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
start_loss = loss_fct(start_logits, start_positions)
end_loss = loss_fct(end_logits, end_positions)
total_loss = (start_loss + end_loss) / 2
if not return_dict:
output = (start_logits, end_logits) + outputs[2:]
return ((total_loss,) + output) if total_loss is not None else output
return QuestionAnsweringModelOutput(
loss=total_loss,
start_logits=start_logits,
end_logits=end_logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
__all__ = [
"LayoutLMForMaskedLM",
"LayoutLMForSequenceClassification",
"LayoutLMForTokenClassification",
"LayoutLMForQuestionAnswering",
"LayoutLMModel",
"LayoutLMPreTrainedModel",
]
```
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