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# 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.
"""Blip model configuration"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
logger = logging.get_logger(__name__)
class BlipTextConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`BlipTextModel`]. It is used to instantiate a BLIP
text 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 `BlipText` used by the [base
architectures](https://huggingface.co/Salesforce/blip-vqa-base).
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 30524):
Vocabulary size of the `Blip` text model. Defines the number of different tokens that can be represented by
the `inputs_ids` passed when calling [`BlipModel`].
hidden_size (`int`, *optional*, defaults to 768):
Dimensionality of the encoder layers and the pooler layer.
encoder_hidden_size (`int`, *optional*, defaults to 768):
Dimensionality of the encoder layers from the vision model.
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 8):
Number of attention heads for each attention layer in the Transformer encoder.
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).
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.
layer_norm_eps (`float`, *optional*, defaults to 1e-12):
The epsilon used by the layer normalization layers.
hidden_dropout_prob (`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.
bos_token_id (`int`, *optional*, defaults to 30522):
The id of the `beginning-of-sequence` token.
eos_token_id (`int`, *optional*, defaults to 2):
The id of the `end-of-sequence` token.
pad_token_id (`int`, *optional*, defaults to 0):
The id of the `padding` token.
sep_token_id (`int`, *optional*, defaults to 102):
The id of the `separator` token.
is_decoder (`bool`, *optional*, defaults to `True`):
Whether the model is used as a decoder.
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models).
label_smoothing (float, *optional*):
A float in [0.0, 1.0]. Specifies the amount of smoothing when computing the loss, where 0.0 means no smoothing. The targets
become a mixture of the original ground truth and a uniform distribution as described in
`Rethinking the Inception Architecture for Computer Vision <https://arxiv.org/abs/1512.00567>`__. Default: :math:`0.0`.
Example:
```python
>>> from transformers import BlipTextConfig, BlipTextModel
>>> # Initializing a BlipTextConfig with Salesforce/blip-vqa-base style configuration
>>> configuration = BlipTextConfig()
>>> # Initializing a BlipTextModel (with random weights) from the Salesforce/blip-vqa-base style configuration
>>> model = BlipTextModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "blip_text_model"
base_config_key = "text_config"
def __init__(
self,
vocab_size=30524,
hidden_size=768,
encoder_hidden_size=768,
intermediate_size=3072,
projection_dim=768,
num_hidden_layers=12,
num_attention_heads=8,
max_position_embeddings=512,
hidden_act="gelu",
layer_norm_eps=1e-12,
hidden_dropout_prob=0.0,
attention_probs_dropout_prob=0.0,
initializer_range=0.02,
bos_token_id=30522,
eos_token_id=2,
pad_token_id=0,
sep_token_id=102,
is_decoder=True,
use_cache=True,
label_smoothing=0.0,
**kwargs,
):
super().__init__(
pad_token_id=pad_token_id,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
sep_token_id=sep_token_id,
**kwargs,
)
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.encoder_hidden_size = encoder_hidden_size
self.intermediate_size = intermediate_size
self.projection_dim = projection_dim
self.hidden_dropout_prob = hidden_dropout_prob
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.attention_probs_dropout_prob = attention_probs_dropout_prob
self.is_decoder = is_decoder
self.use_cache = use_cache
self.label_smoothing = label_smoothing
class BlipVisionConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`BlipVisionModel`]. It is used to instantiate a
BLIP vision model according to the specified arguments, defining the model architecture. Instantiating a
configuration defaults will yield a similar configuration to that of the Blip-base
[Salesforce/blip-vqa-base](https://huggingface.co/Salesforce/blip-vqa-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:
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.
image_size (`int`, *optional*, defaults to 384):
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"` `"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.
initializer_range (`float`, *optional*, defaults to 1e-10):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
Example:
```python
>>> from transformers import BlipVisionConfig, BlipVisionModel
>>> # Initializing a BlipVisionConfig with Salesforce/blip-vqa-base style configuration
>>> configuration = BlipVisionConfig()
>>> # Initializing a BlipVisionModel (with random weights) from the Salesforce/blip-vqa-base style configuration
>>> model = BlipVisionModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "blip_vision_model"
base_config_key = "vision_config"
def __init__(
self,
hidden_size=768,
intermediate_size=3072,
projection_dim=512,
num_hidden_layers=12,
num_attention_heads=12,
image_size=384,
patch_size=16,
hidden_act="gelu",
layer_norm_eps=1e-5,
attention_dropout=0.0,
initializer_range=1e-10,
**kwargs,
):
super().__init__(**kwargs)
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.projection_dim = projection_dim
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
class BlipConfig(PretrainedConfig):
r"""
[`BlipConfig`] is the configuration class to store the configuration of a [`BlipModel`]. It is used to instantiate
a BLIP 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 BLIP-base
[Salesforce/blip-vqa-base](https://huggingface.co/Salesforce/blip-vqa-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:
text_config (`dict`, *optional*):
Dictionary of configuration options used to initialize [`BlipTextConfig`].
vision_config (`dict`, *optional*):
Dictionary of configuration options used to initialize [`BlipVisionConfig`].
projection_dim (`int`, *optional*, defaults to 512):
Dimensionality 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 BLIP implementation.
image_text_hidden_size (`int`, *optional*, defaults to 256):
Dimensionality of the hidden state of the image-text fusion layer.
label_smoothing (float, optional, *optional*, defaults to 0.0):
A float in [0.0, 1.0]. Specifies the amount of smoothing when computing the loss, where 0.0 means no smoothing. The targets
become a mixture of the original ground truth and a uniform distribution as described in
`Rethinking the Inception Architecture for Computer Vision <https://arxiv.org/abs/1512.00567>`__. Default: :math:`0.0`.
kwargs (*optional*):
Dictionary of keyword arguments.
Example:
```python
>>> from transformers import BlipConfig, BlipModel
>>> # Initializing a BlipConfig with Salesforce/blip-vqa-base style configuration
>>> configuration = BlipConfig()
>>> # Initializing a BlipPModel (with random weights) from the Salesforce/blip-vqa-base style configuration
>>> model = BlipModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
>>> # We can also initialize a BlipConfig from a BlipTextConfig and a BlipVisionConfig
>>> # Initializing a BLIPText and BLIPVision configuration
>>> config_text = BlipTextConfig()
>>> config_vision = BlipVisionConfig()
>>> config = BlipConfig.from_text_vision_configs(config_text, config_vision)
```"""
model_type = "blip"
sub_configs = {"text_config": BlipTextConfig, "vision_config": BlipVisionConfig}
def __init__(
self,
text_config=None,
vision_config=None,
projection_dim=512,
logit_scale_init_value=2.6592,
image_text_hidden_size=256,
label_smoothing=0.0,
**kwargs,
):
super().__init__(**kwargs)
if text_config is None:
text_config = {}
logger.info("`text_config` is `None`. Initializing the `BlipTextConfig` with default values.")
if vision_config is None:
vision_config = {}
logger.info("`vision_config` is `None`. Initializing the `BlipVisionConfig` with default values.")
self.text_config = BlipTextConfig(**text_config)
self.vision_config = BlipVisionConfig(**vision_config)
self.text_config.encoder_hidden_size = self.vision_config.hidden_size
self.projection_dim = projection_dim
self.logit_scale_init_value = logit_scale_init_value
self.initializer_factor = 1.0
self.initializer_range = 0.02
self.image_text_hidden_size = image_text_hidden_size
self.label_smoothing = label_smoothing
@classmethod
def from_text_vision_configs(cls, text_config: BlipTextConfig, vision_config: BlipVisionConfig, **kwargs):
r"""
Instantiate a [`BlipConfig`] (or a derived class) from blip text model configuration and blip vision model
configuration.
Returns:
[`BlipConfig`]: An instance of a configuration object
"""
return cls(text_config=text_config.to_dict(), vision_config=vision_config.to_dict(), **kwargs)
__all__ = ["BlipConfig", "BlipTextConfig", "BlipVisionConfig"]
| transformers/src/transformers/models/blip/configuration_blip.py/0 | {
"file_path": "transformers/src/transformers/models/blip/configuration_blip.py",
"repo_id": "transformers",
"token_count": 5580
} |
# 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.
"""Convert BigScience BLOOM checkpoint."""
import argparse
import json
import os
import re
import torch
from transformers import BloomConfig, BloomModel
from transformers.file_utils import CONFIG_NAME, WEIGHTS_NAME
from transformers.utils import logging
logging.set_verbosity_info()
WEIGHTS_TO_AVERAGE_ENDSWITH = [
"word_embeddings_layernorm.weight",
"word_embeddings_layernorm.bias",
"input_layernorm.weight",
"input_layernorm.bias",
"post_attention_layernorm.weight",
"post_attention_layernorm.bias",
"self_attention.dense.bias",
"mlp.dense_4h_to_h.bias",
"ln_f.weight",
"ln_f.bias",
]
WEIGHTS_WITH_ROW_PARALLELISM_CONTAIN = [
"mlp.dense_4h_to_h.weight",
"self_attention.dense.weight",
]
def layer_name_mapping(key, file):
"""Convert Megatron-DeepSpeed TP/PP weights mapping in transformers PP only"""
# Handle first and last layers
layer_rename_map = {
"word_embeddings.weight": "word_embeddings.weight",
"word_embeddings.norm.weight": "word_embeddings_layernorm.weight",
"word_embeddings.norm.bias": "word_embeddings_layernorm.bias",
"weight": "ln_f.weight",
"bias": "ln_f.bias",
}
if key in layer_rename_map:
return layer_rename_map[key]
# Handle transformer blocks
layer_number = int(re.match(r".*layer_(\d*).*", file)[1])
layer_number -= 3
return f"h.{layer_number}." + key
def get_dtype_size(dtype):
if dtype == torch.bool:
return 1 / 8
bit_search = re.search(r"[^\d](\d+)$", str(dtype))
if bit_search is None:
raise ValueError(f"`dtype` is not a valid dtype: {dtype}.")
bit_size = int(bit_search.groups()[0])
return bit_size // 8
def convert_bloom_checkpoint_to_pytorch(
bloom_checkpoint_path, bloom_config_file, pytorch_dump_folder_path, shard_model, pretraining_tp
):
# Construct model
if bloom_config_file == "":
config = BloomConfig()
else:
config = BloomConfig.from_json_file(bloom_config_file)
if shard_model:
file_names = os.listdir(bloom_checkpoint_path)
file_names = sorted(filter(lambda s: s.startswith("layer") and "model_00" in s, file_names))
index_dict = {"weight_map": {}, "metadata": {}}
total_size = 0
missing_keys = None
config = BloomConfig()
for j, file in enumerate(file_names):
print("Processing file: {}".format(file))
tensors = None
for i in range(pretraining_tp):
# load all TP files
f_name = file.replace("model_00", f"model_0{i}")
temp = torch.load(os.path.join(bloom_checkpoint_path, f_name), map_location="cpu")
# Rename keys in the transformers names
keys = list(temp.keys())
for key in keys:
temp[layer_name_mapping(key, file)] = temp.pop(key)
if tensors is None:
tensors = temp
else:
for key in tensors.keys():
if any(key.endswith(end) for end in WEIGHTS_TO_AVERAGE_ENDSWITH):
# We average (sum and then divide) some weights accross TP ranks (see https://github.com/bigscience-workshop/Megatron-DeepSpeed/blob/olruwase/sync_layer_norms/megatron/training.py#L425)
tensors[key] += temp[key]
else:
# Some weights are RowParallelLinear in Megatron-Deepspeed, others are ColumnParallel
cat_dim = 1 if any(text in key for text in WEIGHTS_WITH_ROW_PARALLELISM_CONTAIN) else 0
# We concatenate these weights accross TP ranks
tensors[key] = torch.cat([tensors[key], temp[key]], dim=cat_dim)
# Divide by the number of TP the weights we want to average
for key in tensors.keys():
if any(key.endswith(end) for end in WEIGHTS_TO_AVERAGE_ENDSWITH):
tensors[key] = tensors[key] / pretraining_tp
torch.save(
tensors,
os.path.join(
pytorch_dump_folder_path,
"pytorch_model_{}-of-{}.bin".format(str(j + 1).zfill(5), str(len(file_names)).zfill(5)),
),
)
for key in tensors.keys():
value = tensors[key]
total_size += value.numel() * get_dtype_size(value.dtype)
if key not in index_dict["weight_map"]:
index_dict["weight_map"][key] = "pytorch_model_{}-of-{}.bin".format(
str(j + 1).zfill(5), str(len(file_names)).zfill(5)
)
config = BloomConfig()
pytorch_config_dump_path = pytorch_dump_folder_path + "/" + CONFIG_NAME
index_dict["metadata"]["total_size"] = total_size
with open(pytorch_config_dump_path, "w", encoding="utf-8") as f:
f.write(config.to_json_string())
with open(os.path.join(pytorch_dump_folder_path, WEIGHTS_NAME + ".index.json"), "w", encoding="utf-8") as f:
json_config = json.dumps(index_dict, indent=2, sort_keys=True) + "\n"
f.write(json_config)
else:
model = BloomModel(config)
file_names = os.listdir(bloom_checkpoint_path)
file_names = sorted(filter(lambda s: s.startswith("layer") and "model_00" in s, file_names))
missing_keys = None
for i, file in enumerate(file_names):
tensors = None
for i in range(pretraining_tp):
# load all TP files
f_name = file.replace("model_00", f"model_0{i}")
temp = torch.load(os.path.join(bloom_checkpoint_path, f_name), map_location="cpu")
# Rename keys in the transformers names
keys = list(temp.keys())
for key in keys:
temp[layer_name_mapping(key, file)] = temp.pop(key)
if tensors is None:
tensors = temp
else:
for key in tensors.keys():
# We average (sum and then divide) some weights accross TP ranks (see https://github.com/bigscience-workshop/Megatron-DeepSpeed/blob/olruwase/sync_layer_norms/megatron/training.py#L425)
if any(key.endswith(end) for end in WEIGHTS_TO_AVERAGE_ENDSWITH):
tensors[key] += temp[key]
else:
# Some weights are RowParallelLinear in Megatron-Deepspeed, others are ColumnParallel
cat_dim = 1 if any(text in key for text in WEIGHTS_WITH_ROW_PARALLELISM_CONTAIN) else 0
# We concatenate these weights accross TP ranks
tensors[key] = torch.cat([tensors[key], temp[key]], dim=cat_dim)
# Divide by the number of TP the weights we want to average
for key in tensors.keys():
if any(key.endswith(end) for end in WEIGHTS_TO_AVERAGE_ENDSWITH):
tensors[key] = tensors[key] / pretraining_tp
other_keys = model.load_state_dict(tensors, strict=False)
assert not other_keys.unexpected_keys, f"The keys {other_keys.unexpected_keys} are unexpected"
if missing_keys is None:
missing_keys = set(other_keys.missing_keys)
else:
missing_keys = missing_keys.intersection(set(other_keys.missing_keys))
assert not missing_keys, f"The keys {missing_keys} are missing"
# Save pytorch-model
os.makedirs(pytorch_dump_folder_path, exist_ok=True)
pytorch_weights_dump_path = pytorch_dump_folder_path + "/" + WEIGHTS_NAME
pytorch_config_dump_path = pytorch_dump_folder_path + "/" + CONFIG_NAME
print(f"Save PyTorch model to {pytorch_weights_dump_path} with dtype {config.torch_dtype}")
if config.torch_dtype is not None:
model = model.to(config.torch_dtype)
torch.save(model.state_dict(), pytorch_weights_dump_path)
print(f"Save configuration file to {pytorch_config_dump_path}")
with open(pytorch_config_dump_path, "w", encoding="utf-8") as f:
f.write(config.to_json_string())
if __name__ == "__main__":
parser = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--bloom_checkpoint_path",
default=None,
type=str,
required=True,
help="Path to the Megatron-LM checkpoint path.",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
parser.add_argument(
"--bloom_config_file",
default="",
type=str,
help=(
"An optional config json file corresponding to the pre-trained model. \n"
"This specifies the model architecture."
),
)
parser.add_argument(
"--shard_model",
action="store_true",
help="An optional setting to shard the output model \nThis enables sharding the converted checkpoint",
)
parser.add_argument(
"--pretraining_tp",
default=4,
type=int,
help="Pretraining TP rank that has been used when training the model in Megatron-LM \n",
)
args = parser.parse_args()
convert_bloom_checkpoint_to_pytorch(
args.bloom_checkpoint_path,
args.bloom_config_file,
args.pytorch_dump_folder_path,
args.shard_model,
args.pretraining_tp,
)
| transformers/src/transformers/models/bloom/convert_bloom_original_checkpoint_to_pytorch.py/0 | {
"file_path": "transformers/src/transformers/models/bloom/convert_bloom_original_checkpoint_to_pytorch.py",
"repo_id": "transformers",
"token_count": 4802
} |
# coding=utf-8
# Copyright 2021 T5 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.
"""Tokenization class for model ByT5."""
import warnings
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
logger = logging.get_logger(__name__)
class ByT5Tokenizer(PreTrainedTokenizer):
"""
Construct a ByT5 tokenizer. ByT5 simply uses raw bytes utf-8 encoding.
This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
this superclass for more information regarding those methods.
Args:
eos_token (`str`, *optional*, defaults to `"</s>"`):
The end of sequence token.
<Tip>
When building a sequence using special tokens, this is not the token that is used for the end of sequence.
The token used is the `sep_token`.
</Tip>
unk_token (`str`, *optional*, defaults to `"<unk>"`):
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.
pad_token (`str`, *optional*, defaults to `"<pad>"`):
The token used for padding, for example when batching sequences of different lengths.
extra_ids (`int`, *optional*, defaults to 125):
Add a number of extra ids added to the end of the vocabulary for use as sentinels. These tokens are
accessible as "<extra_id_{%d}>" where "{%d}" is a number between 0 and extra_ids-1. Extra tokens are
indexed from the end of the vocabulary up to beginning ("<extra_id_0>" is the last token in the vocabulary
like in ByT5 preprocessing see
[here](https://github.com/google-research/text-to-text-transfer-transformer/blob/9fd7b14a769417be33bc6c850f9598764913c833/t5/data/preprocessors.py#L2117)).
additional_special_tokens (`List[str]`, *optional*):
Additional special tokens used by the tokenizer.
"""
model_input_names = ["input_ids", "attention_mask"]
def __init__(
self,
eos_token="</s>",
unk_token="<unk>",
pad_token="<pad>",
extra_ids=125,
additional_special_tokens=None,
**kwargs,
) -> None:
# Add extra_ids to the special token list
if extra_ids > 0 and additional_special_tokens is None:
additional_special_tokens = [f"<extra_id_{i}>" for i in range(extra_ids)]
elif extra_ids > 0 and additional_special_tokens is not None and len(additional_special_tokens) > 0:
# Check that we have the right number of extra_id special tokens
extra_tokens = len(set(filter(lambda x: bool("extra_id" in str(x)), additional_special_tokens)))
if extra_tokens != extra_ids:
raise ValueError(
f"Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"
" provided to ByT5Tokenizer. In this case the additional_special_tokens must include the"
" extra_ids tokens"
)
pad_token = AddedToken(pad_token, lstrip=True, rstrip=True) if isinstance(pad_token, str) else pad_token
# we force left and right stripping for backward compatibility. The byt5tests depend on this.
eos_token = AddedToken(eos_token, lstrip=True, rstrip=True) if isinstance(eos_token, str) else eos_token
unk_token = AddedToken(unk_token, lstrip=True, rstrip=True) if isinstance(unk_token, str) else unk_token
# unk token needs to be in the vocab with correct index
self._added_tokens_decoder = {0: pad_token, 1: eos_token, 2: unk_token}
self.offset = len(self._added_tokens_decoder)
self._utf_vocab_size = 2**8 # utf is 8 bits
super().__init__(
eos_token=eos_token,
unk_token=unk_token,
pad_token=pad_token,
extra_ids=0,
additional_special_tokens=additional_special_tokens, # TODO extra ids are not used :sweatywmile:
**kwargs,
)
@property
def vocab_size(self):
return self._utf_vocab_size
def get_vocab(self):
vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size + self.offset)}
vocab.update(self.added_tokens_encoder)
return vocab
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
)
# normal case: some special tokens
if token_ids_1 is None:
return ([0] * len(token_ids_0)) + [1]
return ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1]
def _add_eos_if_not_present(self, token_ids: List[int]) -> List[int]:
"""Do not add eos again if user already added it."""
if len(token_ids) > 0 and token_ids[-1] == self.eos_token_id:
warnings.warn(
f"This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated"
" eos tokens being added."
)
return token_ids
else:
return token_ids + [self.eos_token_id]
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. ByT5 does not
make use of token type ids, therefore a list of zeros is returned.
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 zeros.
"""
eos = [self.eos_token_id]
if token_ids_1 is None:
return len(token_ids_0 + eos) * [0]
return len(token_ids_0 + eos + token_ids_1 + eos) * [0]
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. A sequence has the following format:
- single sequence: `X </s>`
- pair of sequences: `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.
"""
token_ids_0 = self._add_eos_if_not_present(token_ids_0)
if token_ids_1 is None:
return token_ids_0
else:
token_ids_1 = self._add_eos_if_not_present(token_ids_1)
return token_ids_0 + token_ids_1
def _tokenize(self, text: str) -> List[str]:
"""Take as input a string and return a list of strings (tokens) for words/sub-words"""
tokens = [chr(i) for i in text.encode("utf-8")]
return tokens
def _convert_token_to_id(self, token):
"""Converts a token (str) in an id using the vocab."""
if len(token) != 1:
token_id = None
else:
token_id = ord(token) + self.offset
return token_id
def _convert_id_to_token(self, index):
"""Converts an index (integer) in a token (str) using the vocab."""
token = chr(index - self.offset)
return token
def convert_tokens_to_string(self, tokens):
"""Converts a sequence of tokens (string) in a single string."""
bstring = b""
for token in tokens:
if token in self.added_tokens_decoder:
tok_string = self.added_tokens_decoder[token].encode("utf-8")
elif token in self.added_tokens_encoder:
tok_string = token.encode("utf-8")
else:
tok_string = bytes([ord(token)])
bstring += tok_string
string = bstring.decode("utf-8", errors="ignore")
return string
# ByT5Tokenizer has no vocab file
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
return ()
__all__ = ["ByT5Tokenizer"]
| transformers/src/transformers/models/byt5/tokenization_byt5.py/0 | {
"file_path": "transformers/src/transformers/models/byt5/tokenization_byt5.py",
"repo_id": "transformers",
"token_count": 4292
} |
# coding=utf-8
# Copyright 2024 Meta Inc. 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 Chameleon model."""
import math
from functools import cached_property
from typing import 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_flash_attention_utils import _flash_attention_forward
from ...modeling_outputs import (
BaseModelOutputWithPast,
CausalLMOutputWithPast,
)
from ...modeling_utils import PreTrainedModel
from ...pytorch_utils import ALL_LAYERNORM_LAYERS
from ...utils import (
add_code_sample_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
is_flash_attn_2_available,
is_flash_attn_greater_or_equal_2_10,
is_torchdynamo_compiling,
logging,
replace_return_docstrings,
)
from .configuration_chameleon import ChameleonConfig, ChameleonVQVAEConfig
if is_flash_attn_2_available():
from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input # noqa
logger = logging.get_logger(__name__)
_CONFIG_FOR_DOC = "ChameleonConfig"
_CHECKPOINT_FOR_DOC = "meta/chameleon-7b"
_EXPECTED_OUTPUT_SHAPE = [1, 7, 4096]
_SEQ_CLASS_EXPECTED_LOSS = 1.03
_SEQ_CLASS_EXPECTED_OUTPUT = "'LABEL_0'"
# Copied from transformers.models.llama.modeling_llama.LlamaRMSNorm with Llama->Chameleon
class ChameleonRMSNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-6):
"""
ChameleonRMSNorm 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(ChameleonRMSNorm)
# copied from transformers.models.llama.modeling_llama.LlamaRotaryEmbedding with Llama->Chameleon
# TODO(joao): add me back asap :)
class ChameleonRotaryEmbedding(nn.Module):
def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
super().__init__()
self.scaling_factor = scaling_factor
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).float().to(device) / self.dim))
self.register_buffer("inv_freq", inv_freq, persistent=False)
# For BC we register cos and sin cached
self.max_seq_len_cached = max_position_embeddings
@torch.no_grad()
def forward(self, x, position_ids):
# x: [bs, num_attention_heads, seq_len, head_size]
inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1)
position_ids_expanded = position_ids[:, None, :].float()
# Force float32 since bfloat16 loses precision on long contexts
# See https://github.com/huggingface/transformers/pull/29285
device_type = x.device.type
device_type = device_type if isinstance(device_type, str) and device_type != "mps" else "cpu"
with torch.autocast(device_type=device_type, enabled=False):
freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
emb = torch.cat((freqs, freqs), dim=-1)
cos = emb.cos()
sin = emb.sin()
return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
class ChameleonLinearScalingRotaryEmbedding(ChameleonRotaryEmbedding):
"""ChameleonRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""
def forward(self, x, position_ids):
# difference to the original RoPE: a scaling factor is aplied to the position ids
position_ids = position_ids.float() / self.scaling_factor
cos, sin = super().forward(x, position_ids)
return cos, sin
class ChameleonDynamicNTKScalingRotaryEmbedding(ChameleonRotaryEmbedding):
"""ChameleonRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla"""
def forward(self, x, position_ids):
# difference to the original RoPE: inv_freq is recomputed when the sequence length > original length
seq_len = torch.max(position_ids) + 1
if seq_len > self.max_position_embeddings:
base = self.base * (
(self.scaling_factor * seq_len / self.max_position_embeddings) - (self.scaling_factor - 1)
) ** (self.dim / (self.dim - 2))
inv_freq = 1.0 / (
base ** (torch.arange(0, self.dim, 2, dtype=torch.int64).float().to(x.device) / self.dim)
)
self.register_buffer("inv_freq", inv_freq, persistent=False) # TODO joao: this may break with compilation
cos, sin = super().forward(x, position_ids)
return cos, sin
# 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
# Copied from transformers.models.llama.modeling_llama.LlamaMLP with Llama->Chameleon
class ChameleonMLP(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]
# Ignore copy
def forward(self, x):
down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
return down_proj
class ChameleonLayerNorm(nn.LayerNorm):
"""
LayerNorm but computes stats only over the last dim because Chameleon applies gamma and beta
from each shard separately to each head, instead of reducing. We can apply each head's own
gamma/beta by repeat-interleaving weights from each shard, but the stats have to be computed
in the last dimension. This module applies gamma/beta manually to fulfill this requirement.
"""
def __init__(self, hidden_size, *args, **kwargs):
super().__init__(hidden_size, *args, **kwargs)
self.normalized_shape = (hidden_size[-1],)
def forward(self, hidden_states):
hidden_states = F.layer_norm(hidden_states, self.normalized_shape, None, None, eps=1e-5)
hidden_states = hidden_states * self.weight + self.bias
return hidden_states
# 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 ChameleonAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(self, config: ChameleonConfig, 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.max_position_embeddings = config.max_position_embeddings
self.rope_theta = config.rope_theta
self.is_causal = True
self.model_parallel_size = config.model_parallel_size
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)
self.q_norm = ChameleonLayerNorm((self.num_heads, self.head_dim))
self.k_norm = ChameleonLayerNorm((self.num_key_value_heads, self.head_dim))
self._init_rope()
# copied from transformers.models.llama.modeling_llama.LlamaAttention._init_rope with Llama->Chameleon
# TODO(joao): add me back asap :)
def _init_rope(self):
if self.config.rope_scaling is None:
self.rotary_emb = ChameleonRotaryEmbedding(
self.head_dim,
max_position_embeddings=self.max_position_embeddings,
base=self.rope_theta,
)
else:
scaling_type = self.config.rope_scaling["type"]
scaling_factor = self.config.rope_scaling["factor"]
if scaling_type == "linear":
self.rotary_emb = ChameleonLinearScalingRotaryEmbedding(
self.head_dim,
max_position_embeddings=self.max_position_embeddings,
scaling_factor=scaling_factor,
base=self.rope_theta,
)
elif scaling_type == "dynamic":
self.rotary_emb = ChameleonDynamicNTKScalingRotaryEmbedding(
self.head_dim,
max_position_embeddings=self.max_position_embeddings,
scaling_factor=scaling_factor,
base=self.rope_theta,
)
else:
raise ValueError(f"Unknown RoPE scaling type {scaling_type}")
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,
**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.reshape(-1, self.num_heads, self.head_dim)
query_states = self.q_norm(query_states)
key_states = key_states.reshape(-1, self.num_key_value_heads, self.head_dim)
key_states = self.k_norm(key_states)
query_states = query_states.reshape(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
key_states = key_states.reshape(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; position_ids 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)) / 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
# NO LONGER EXIST copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2 with Llama->Chameleon
# TODO(joao): add me back asap :)
class ChameleonFlashAttention2(ChameleonAttention):
"""
Chameleon flash attention module. This module inherits from `ChameleonAttention` 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 alignement, 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 = not is_flash_attn_greater_or_equal_2_10()
# Ignore copy
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,
**kwargs,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
if isinstance(past_key_value, StaticCache):
raise ValueError(
"`static` cache implementation is not compatible with `attn_implementation==flash_attention_2` "
"make sure to use `sdpa` in the mean time, and open an issue at https://github.com/huggingface/transformers"
)
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)
query_states = query_states.reshape(-1, self.num_heads, self.head_dim)
query_states = self.q_norm(query_states)
key_states = key_states.reshape(-1, self.num_key_value_heads, self.head_dim)
key_states = self.k_norm(key_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 = 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; position_ids 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. (ChameleonRMSNorm 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=dropout_rate,
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
class ChameleonSdpaAttention(ChameleonAttention):
"""
Chameleon attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
`ChameleonAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to
SDPA API.
"""
# Adapted from ChameleonAttention.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]]]:
if output_attentions:
# TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"` once this is implemented.
logger.warning_once(
"ChameleonModel is using ChameleonSdpaAttention, 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 = self.q_proj(hidden_states)
key_states = self.k_proj(hidden_states)
value_states = self.v_proj(hidden_states)
query_states = query_states.reshape(-1, self.num_heads, self.head_dim)
query_states = self.q_norm(query_states)
key_states = key_states.reshape(-1, self.num_key_value_heads, self.head_dim)
key_states = self.k_norm(key_states)
query_states = query_states.reshape(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
key_states = key_states.reshape(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, None)
if past_key_value is not None:
# sin and cos are specific to RoPE models; position_ids 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 and cache_position 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.view(bsz, q_len, self.hidden_size)
attn_output = self.o_proj(attn_output)
return attn_output, None, past_key_value
CHAMELEON_ATTENTION_CLASSES = {
"eager": ChameleonAttention,
"flash_attention_2": ChameleonFlashAttention2,
"sdpa": ChameleonSdpaAttention,
}
# copied from transformers.models.llama.modeling_llama.LlamaDecoderLayer with Llama->Chameleon, LLAMA->CHAMELEON
# TODO(joao): add me back asap :)
class ChameleonDecoderLayer(nn.Module):
def __init__(self, config: ChameleonConfig, layer_idx: int):
super().__init__()
self.hidden_size = config.hidden_size
self.self_attn = CHAMELEON_ATTENTION_CLASSES[config._attn_implementation](config=config, layer_idx=layer_idx)
self.mlp = ChameleonMLP(config)
self.input_layernorm = ChameleonRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.post_attention_layernorm = ChameleonRMSNorm(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,
**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
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,
**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,)
if use_cache:
outputs += (present_key_value,)
return outputs
class ChameleonSwinDecoderLayer(nn.Module):
def __init__(self, config: ChameleonConfig, layer_idx: int):
super().__init__()
self.hidden_size = config.hidden_size
self.self_attn = CHAMELEON_ATTENTION_CLASSES[config._attn_implementation](config=config, layer_idx=layer_idx)
self.mlp = ChameleonMLP(config)
self.input_layernorm = ChameleonRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.post_attention_layernorm = ChameleonRMSNorm(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,
**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.
position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Indices of positions of each input sequence tokens in the position embeddings
past_key_value (`Tuple(torch.FloatTensor)`, *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.
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
# 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,
**kwargs,
)
hidden_states = self.input_layernorm(hidden_states)
hidden_states = residual + hidden_states
# Fully Connected
residual = hidden_states
hidden_states = self.mlp(hidden_states)
hidden_states = self.post_attention_layernorm(hidden_states)
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 ChameleonVQVAEVectorQuantizer(nn.Module):
"""
A module for vector quantization using learned embedding vectors.
This module implements the quantization process similar to te one described in
the VQ-VAE (Vector Quantized Variational AutoEncoder) paper. It quantizes continuous
input vectors into discrete codebook vectors, which are learned during training.
Current implementation improves over previous ones by avoiding costly matrix multiplications
and allowing for post-hoc remapping of indices.
"""
def __init__(self, config):
super().__init__()
self.num_embeddings = config.num_embeddings
self.embedding_dim = config.embed_dim
self.beta = getattr(config, "beta", 0.25)
self.embedding = nn.Embedding(self.num_embeddings, self.embedding_dim)
self.re_embed = self.num_embeddings
def forward(self, hidden_state: torch.Tensor):
hidden_state = hidden_state.permute(0, 2, 3, 1).contiguous()
hidden_state_flattened = hidden_state.view(-1, self.embedding_dim)
# distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
distances = (
torch.sum(hidden_state_flattened**2, dim=1, keepdim=True)
+ torch.sum(self.embedding.weight**2, dim=1)
- 2 * torch.einsum("bd,dn->bn", hidden_state_flattened, self.embedding.weight.transpose(0, 1))
)
min_encoding_indices = torch.argmin(distances, dim=1)
hidden_state_quant = self.embedding(min_encoding_indices).view(hidden_state.shape)
# compute loss for embedding
loss = torch.mean((hidden_state_quant.detach() - hidden_state) ** 2) + self.beta * torch.mean(
(hidden_state_quant - hidden_state.detach()) ** 2
)
# preserve gradients
hidden_state_quant = hidden_state + (hidden_state_quant - hidden_state).detach()
# reshape back to match original input shape
hidden_state_quant = hidden_state_quant.permute(0, 3, 1, 2).contiguous()
return hidden_state_quant, loss, min_encoding_indices
class ChameleonVQVAEEncoderConvDownsample(nn.Module):
def __init__(self, in_channels):
super().__init__()
self.conv = nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=2, padding=0)
def forward(self, hidden_states):
# no asymmetric padding in torch conv, must do it ourselves
hidden_states = F.pad(hidden_states, pad=(0, 1, 0, 1), mode="constant", value=0)
hidden_states = self.conv(hidden_states)
return hidden_states
class ChameleonVQVAEEncoderResnetBlock(nn.Module):
def __init__(
self,
config,
in_channels,
out_channels=None,
conv_shortcut=False,
):
super().__init__()
self.in_channels = in_channels
self.out_channels = in_channels if out_channels is None else out_channels
self.use_conv_shortcut = conv_shortcut
self.norm1 = torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)
self.conv1 = torch.nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1)
self.norm2 = torch.nn.GroupNorm(num_groups=32, num_channels=out_channels, eps=1e-6, affine=True)
self.dropout = torch.nn.Dropout(config.dropout)
self.conv2 = torch.nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1)
if self.in_channels != self.out_channels:
if self.use_conv_shortcut:
self.conv_shortcut = torch.nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1)
else:
self.nin_shortcut = torch.nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0)
def forward(self, hidden_states):
residual = hidden_states
hidden_states = self.norm1(hidden_states)
hidden_states *= torch.sigmoid(hidden_states)
hidden_states = self.conv1(hidden_states)
hidden_states = self.norm2(hidden_states)
hidden_states *= torch.sigmoid(hidden_states)
hidden_states = self.dropout(hidden_states)
hidden_states = self.conv2(hidden_states)
if self.in_channels != self.out_channels:
if self.use_conv_shortcut:
residual = self.conv_shortcut(residual)
else:
residual = self.nin_shortcut(residual)
return residual + hidden_states
class ChameleonVQVAEEncoderAttnBlock(nn.Module):
def __init__(self, in_channels):
super().__init__()
self.in_channels = in_channels
self.norm = torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)
self.q = torch.nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0)
self.k = torch.nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0)
self.v = torch.nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0)
self.proj_out = torch.nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0)
def forward(self, hidden_states):
residual = hidden_states
hidden_states = self.norm(hidden_states)
query_states = self.q(hidden_states)
key_states = self.k(hidden_states)
value_states = self.v(hidden_states)
# compute attention
batch_size, channels, height, width = query_states.shape
query_states = query_states.reshape(batch_size, channels, height * width).permute(0, 2, 1)
key_states = key_states.reshape(batch_size, channels, height * width)
attn_weights = torch.bmm(query_states, key_states)
attn_weights = attn_weights * (int(channels) ** (-0.5))
attn_weights = F.softmax(attn_weights, dim=2)
# attend to values
value_states = value_states.reshape(batch_size, channels, height * width)
attn_weights = attn_weights.permute(0, 2, 1)
attn_output = torch.bmm(value_states, attn_weights).reshape(batch_size, channels, height, width)
attn_output = self.proj_out(attn_output)
return residual + attn_output
class ChameleonVQVAEEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.num_resolutions = len(config.channel_multiplier)
self.num_res_blocks = config.num_res_blocks
base_channels = config.base_channels
resolution = config.resolution
in_channels = config.in_channels
double_latent = config.double_latent
latent_channels = config.latent_channels
channel_multiplier = config.channel_multiplier
self.conv_in = torch.nn.Conv2d(in_channels, base_channels, kernel_size=3, stride=1, padding=1)
curr_res = resolution
in_channel_multiplier = (1,) + tuple(channel_multiplier)
self.in_channel_multiplier = in_channel_multiplier
self.down = nn.ModuleList()
for i_level in range(self.num_resolutions):
block = nn.ModuleList()
attn = nn.ModuleList()
block_in = base_channels * in_channel_multiplier[i_level]
block_out = base_channels * channel_multiplier[i_level]
for i_block in range(self.num_res_blocks):
block.append(
ChameleonVQVAEEncoderResnetBlock(
config=config,
in_channels=block_in,
out_channels=block_out,
)
)
block_in = block_out
if (
config.attn_resolutions is not None
and curr_res in config.attn_resolutions
and config.attn_type == "vanilla"
):
attn.append(ChameleonVQVAEEncoderAttnBlock(block_in))
down = nn.Module()
down.block = block
down.attn = attn
if i_level != self.num_resolutions - 1:
down.downsample = ChameleonVQVAEEncoderConvDownsample(block_in)
curr_res = curr_res // 2
self.down.append(down)
self.mid = nn.Module()
self.mid.block_1 = ChameleonVQVAEEncoderResnetBlock(
config=config,
in_channels=block_in,
out_channels=block_in,
)
self.mid.attn_1 = ChameleonVQVAEEncoderAttnBlock(block_in) if config.attn_type == "vanilla" else nn.Identity()
self.mid.block_2 = ChameleonVQVAEEncoderResnetBlock(
config=config,
in_channels=block_in,
out_channels=block_in,
)
self.norm_out = torch.nn.GroupNorm(num_groups=32, num_channels=block_in, eps=1e-6, affine=True)
self.conv_out = torch.nn.Conv2d(
block_in,
2 * latent_channels if double_latent else latent_channels,
kernel_size=3,
stride=1,
padding=1,
)
def forward(self, pixel_values: torch.LongTensor):
# downsampling
hidden_states = [self.conv_in(pixel_values)]
for i_level in range(self.num_resolutions):
for i_block in range(self.num_res_blocks):
hidden_state = self.down[i_level].block[i_block](
hidden_states[-1],
)
if len(self.down[i_level].attn) > 0:
hidden_state = self.down[i_level].attn[i_block](hidden_state)
hidden_states.append(hidden_state)
if i_level != self.num_resolutions - 1:
hidden_states.append(self.down[i_level].downsample(hidden_states[-1]))
# middle
last_hidden_state = hidden_states[-1]
last_hidden_state = self.mid.block_1(last_hidden_state)
last_hidden_state = self.mid.attn_1(last_hidden_state)
last_hidden_state = self.mid.block_2(last_hidden_state)
# end
last_hidden_state = self.norm_out(last_hidden_state)
last_hidden_state *= torch.sigmoid(last_hidden_state)
last_hidden_state = self.conv_out(last_hidden_state)
return last_hidden_state
class ChameleonImageVocabularyMapping:
"""
A class for mapping discrete image tokens from VQGAN to BPE tokens.
"""
def __init__(self, vocab_map):
self.vocab_map = vocab_map
self.image_token_id = vocab_map.get("<image>")
@cached_property
def val2name(self):
return {v: k for k, v in self.vocab_map.items()}
@cached_property
def image_tokens(self):
return sorted([val for name, val in self.vocab_map.items() if name.startswith("IMGIMG")])
@cached_property
def bpe2img(self):
img_tkn_chr_mapping = {chr(ord("A") + i): str(i) for i in range(10)}
def remap(old_name: str) -> str:
return "".join(img_tkn_chr_mapping.get(c, c) for c in old_name[len("IMGIMG") : -1])
return {tok: int(remap(self.val2name[tok])) for tok in self.image_tokens}
@cached_property
def img2bpe(self):
return {v: k for k, v in self.bpe2img.items()}
@cached_property
def bpe2img_search_tensors(self):
return torch.tensor(sorted(self.bpe2img.keys())), torch.tensor(sorted(self.bpe2img.values()))
@cached_property
def img2bpe_mapping_tensor(self):
mapping = torch.zeros(max(self.img2bpe.keys()) + 1, dtype=torch.int)
for k, v in self.img2bpe.items():
mapping[k] = v
return mapping
def convert_img2bpe(self, img_batch: torch.Tensor) -> torch.Tensor:
device = img_batch.device
img_tokens = self.img2bpe_mapping_tensor[img_batch.to("cpu")]
return img_tokens.to(device)
CHAMELEON_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 ([`ChameleonConfig`]):
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 chameleon Model outputting raw hidden-states without any specific head on top.",
CHAMELEON_START_DOCSTRING,
)
class ChameleonPreTrainedModel(PreTrainedModel):
config_class = ChameleonConfig
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = ["ChameleonDecoderLayer", "ChameleonSwinDecoderLayer"]
_skip_keys_device_placement = ["past_key_values", "causal_mask"]
_supports_flash_attn_2 = True
_supports_sdpa = True
_supports_quantized_cache = True
_supports_cache_class = True
_supports_static_cache = True
_supports_param_buffer_assignment = False
def _init_weights(self, module):
std = self.config.initializer_range
if isinstance(module, ChameleonVQVAE):
module.apply(module._init_weights)
elif 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_()
CHAMELEON_VQ_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 ([`ChameleonVQVAEConfig`]):
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 VQ-VAE model used in Chameleon for encoding/decoding images into discrete tokens.
This model follows the "Make-a-scene: Scene-based text-to-image generation with human priors" paper from
[ Oran Gafni, Adam Polyak, Oron Ashual, Shelly Sheynin, Devi Parikh, and Yaniv Taigman](https://arxiv.org/abs/2203.13131).
""",
CHAMELEON_VQ_START_DOCSTRING,
)
class ChameleonVQVAE(ChameleonPreTrainedModel):
config_class = ChameleonVQVAEConfig
_no_split_modules = ["ChameleonVQVAEVectorQuantizer"]
def _init_weights(self, module):
std = self.config.initializer_range
if isinstance(module, nn.Embedding):
module.weight.data.normal_(mean=0.0, std=std)
elif isinstance(module, nn.GroupNorm):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
elif 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_()
def __init__(self, config: ChameleonVQVAEConfig):
super().__init__(config)
self.encoder = ChameleonVQVAEEncoder(config)
self.quantize = ChameleonVQVAEVectorQuantizer(config)
self.quant_conv = torch.nn.Conv2d(config.latent_channels, config.embed_dim, 1)
self.post_quant_conv = torch.nn.Conv2d(config.embed_dim, config.latent_channels, 1)
self.eval() # Chameleon's VQ model is frozen
def encode(self, pixel_values: torch.LongTensor):
hidden_states = self.encoder(pixel_values)
hidden_states = self.quant_conv(hidden_states)
quant, emb_loss, indices = self.quantize(hidden_states)
return quant, emb_loss, indices
CHAMELEON_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)
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 [`ChameleonImageProcessor.__call__`] for details.
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`.
Should always be a [`~cache_utils.Cache`] instance and the model will output the same cache instance.
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 chameleon Model outputting raw hidden-states without any specific head on top.",
CHAMELEON_START_DOCSTRING,
)
class ChameleonModel(ChameleonPreTrainedModel):
"""
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`ChameleonDecoderLayer`]
Args:
config: ChameleonConfig
"""
def __init__(self, config: ChameleonConfig):
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.vocabulary_mapping = ChameleonImageVocabularyMapping(config.vocabulary_map)
decoder_layer = ChameleonDecoderLayer if not self.config.swin_norm else ChameleonSwinDecoderLayer
self.layers = nn.ModuleList(
[decoder_layer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
)
self.norm = ChameleonRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.vqmodel = ChameleonVQVAE._from_config(config.vq_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
def get_image_tokens(self, pixel_values: torch.FloatTensor):
"""
Tokenizes images into discrete tokens with VQGAN module. Converts
obtained image tokens into BPE tokens and wraps with "boi" and "eoi"
special tokens.
Args:
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)):
The tensors corresponding to the input images.
"""
batch_size = pixel_values.shape[0]
_, _, image_toks = self.vqmodel.encode(pixel_values)
bpe_toks = self.vocabulary_mapping.convert_img2bpe(image_toks)
bpe_toks = bpe_toks.view(batch_size, -1)
return bpe_toks
@add_start_docstrings_to_model_forward(CHAMELEON_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=BaseModelOutputWithPast,
config_class=_CONFIG_FOR_DOC,
expected_output=_EXPECTED_OUTPUT_SHAPE,
)
def forward(
self,
input_ids: torch.LongTensor = None,
pixel_values: torch.FloatTensor = 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,
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 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 (input_ids is None) ^ (inputs_embeds is not None):
raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
if pixel_values is not None and inputs_embeds is not None:
raise ValueError(
"You cannot specify both pixel_values and inputs_embeds at the same time, and must specify either one"
)
if pixel_values is not None:
image_tokens = self.get_image_tokens(pixel_values)
n_image_tokens_in_text = (input_ids == self.vocabulary_mapping.image_token_id).sum().item()
n_image_features = image_tokens.shape[0] * image_tokens.shape[1]
if n_image_tokens_in_text != n_image_features:
raise ValueError(
f"Image features and image tokens do not match: tokens: {n_image_tokens_in_text}, features {n_image_features}"
)
special_image_mask = input_ids == self.vocabulary_mapping.image_token_id
image_tokens = image_tokens.to(input_ids.device, input_ids.dtype)
input_ids = input_ids.masked_scatter(special_image_mask, image_tokens)
if inputs_embeds is None:
inputs_embeds = self.embed_tokens(input_ids)
# torch.jit.trace() doesn't support cache objects in the output
if use_cache and past_key_values is None and not torch.jit.is_tracing():
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
)
# embed positions
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 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,
)
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,
)
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 = None
if use_cache:
next_cache = next_decoder_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 BaseModelOutputWithPast(
last_hidden_state=hidden_states,
past_key_values=next_cache,
hidden_states=all_hidden_states,
attentions=all_self_attns,
)
# 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,
):
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
# 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.LlamaPreTrainedModel._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 plcae 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, :]
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(
"Chameleon Model with a head on top used for outputting logits for next token prediction.",
CHAMELEON_START_DOCSTRING,
)
class ChameleonForConditionalGeneration(ChameleonPreTrainedModel, GenerationMixin):
_tied_weights_keys = ["lm_head.weight"]
def __init__(self, config):
super().__init__(config)
self.model = ChameleonModel(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
@add_start_docstrings_to_model_forward(CHAMELEON_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: torch.LongTensor = None,
pixel_values: torch.FloatTensor = 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,
return_dict: Optional[bool] = None,
cache_position: Optional[torch.LongTensor] = None,
) -> Union[Tuple, CausalLMOutputWithPast]:
r"""
Args:
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 ChameleonProcessor, ChameleonForConditionalGeneration
>>> import torch
>>> import requests
>>> from PIL import Image
>>> model = ChameleonForConditionalGeneration.from_pretrained("facebook/chameleon-7b", torch_dtype=torch.bfloat16)
>>> processor = ChameleonProcessor.from_pretrained("facebook/chameleon-7b")
>>> prompt = "I used to know a lot about constellations when I was younger, but as I grew older, I forgot most of what I knew. These are the only two constellations that I really remember now.<image><image>I would like for you to tell me about 3 more constellations and give me a little bit of history about the constellation."
>>> image = Image.open(requests.get("https://nineplanets.org/wp-content/uploads/2020/12/the-big-dipper-1.jpg", stream=True).raw)
>>> image_2 = Image.open(requests.get("https://www.kxan.com/wp-content/uploads/sites/40/2020/10/ORION.jpg", stream=True).raw)
>>> inputs = processor(images=[image, image_2], text=prompt, return_tensors="pt").to(model.device, torch.bfloat16)
>>> generated_ids = model.generate(**inputs, max_new_tokens=100, do_sample=False)
>>> processor.batch_decode(generated_ids, skip_special_tokens=True)[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
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
outputs = self.model(
input_ids=input_ids,
pixel_values=pixel_values,
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,
return_dict=return_dict,
cache_position=cache_position,
)
hidden_states = outputs[0]
logits = self.lm_head(hidden_states)
# Disallow image tokens which does not include special begin-image and end-image tokens
image_tokens = self.model.vocabulary_mapping.image_tokens
logits[:, :, image_tokens] = torch.finfo(logits.dtype).min
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
loss_fct = CrossEntropyLoss()
shift_logits = shift_logits.view(-1, self.config.vocab_size)
shift_labels = shift_labels.view(-1)
# Enable model parallelism
shift_labels = shift_labels.to(shift_logits.device)
loss = loss_fct(shift_logits, shift_labels)
if not return_dict:
output = (logits,) + outputs[1:]
return (loss,) + output if loss is not None else output
return CausalLMOutputWithPast(
loss=loss,
logits=logits,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
def prepare_inputs_for_generation(
self,
input_ids,
pixel_values=None,
past_key_values=None,
attention_mask=None,
inputs_embeds=None,
cache_position=None,
position_ids=None,
use_cache=True,
**kwargs,
):
# Overwritten -- in specific circumstances we don't want to forward image inputs to the model
# 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)
# Exception 4: If input_embeds are passed then slice it through `cache_position`, to keep only the unprocessed tokens and
# generate the first token for each sequence. Later use the generated Input ids for continuation.
if past_key_values is not None:
if inputs_embeds is not None and input_ids.shape[1] == 0: # Exception 4
inputs_embeds = inputs_embeds[:, -cache_position.shape[0] :]
elif (
inputs_embeds is not None # Exception 1
or (is_torchdynamo_compiling() 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]
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 past_key_values:
if inputs_embeds is not None and input_ids.shape[1] == 0:
position_ids = position_ids[:, -inputs_embeds.shape[1] :]
else:
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 len(cache_position) == inputs_embeds.shape[1]:
model_inputs = {"inputs_embeds": inputs_embeds}
else:
model_inputs = {"input_ids": input_ids.contiguous()} # `contiguous()` needed for compilation use cases
if cache_position[0] == 0:
# If we're in cached decoding stage, pixel values should be `None` because input ids do not contain special image token anymore
# Otherwise we need pixel values to be passed to model
model_inputs["pixel_values"] = pixel_values
model_inputs.update(
{
"position_ids": position_ids,
"cache_position": cache_position,
"past_key_values": past_key_values,
"use_cache": use_cache,
"attention_mask": attention_mask,
}
)
return model_inputs
__all__ = ["ChameleonForConditionalGeneration", "ChameleonModel", "ChameleonPreTrainedModel", "ChameleonVQVAE"]
| transformers/src/transformers/models/chameleon/modeling_chameleon.py/0 | {
"file_path": "transformers/src/transformers/models/chameleon/modeling_chameleon.py",
"repo_id": "transformers",
"token_count": 33617
} |
# 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.
"""CLIP model configuration"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
if TYPE_CHECKING:
from ...processing_utils import ProcessorMixin
from ...utils import TensorType
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
logger = logging.get_logger(__name__)
class CLIPTextConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`CLIPTextModel`]. It is used to instantiate a CLIP
text 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 text encoder of the CLIP
[openai/clip-vit-base-patch32](https://huggingface.co/openai/clip-vit-base-patch32) 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 CLIP text model. Defines the number of different tokens that can be represented by
the `inputs_ids` passed when calling [`CLIPModel`].
hidden_size (`int`, *optional*, defaults to 512):
Dimensionality of the encoder layers and the pooler layer.
intermediate_size (`int`, *optional*, defaults to 2048):
Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
projection_dim (`int`, *optional*, defaults to 512):
Dimensionality of text and vision projection layers.
num_hidden_layers (`int`, *optional*, defaults to 12):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 8):
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-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).
pad_token_id (`int`, *optional*, defaults to 1):
Padding token id.
bos_token_id (`int`, *optional*, defaults to 49406):
Beginning of stream token id.
eos_token_id (`int`, *optional*, defaults to 49407):
End of stream token id.
Example:
```python
>>> from transformers import CLIPTextConfig, CLIPTextModel
>>> # Initializing a CLIPTextConfig with openai/clip-vit-base-patch32 style configuration
>>> configuration = CLIPTextConfig()
>>> # Initializing a CLIPTextModel (with random weights) from the openai/clip-vit-base-patch32 style configuration
>>> model = CLIPTextModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "clip_text_model"
base_config_key = "text_config"
def __init__(
self,
vocab_size=49408,
hidden_size=512,
intermediate_size=2048,
projection_dim=512,
num_hidden_layers=12,
num_attention_heads=8,
max_position_embeddings=77,
hidden_act="quick_gelu",
layer_norm_eps=1e-5,
attention_dropout=0.0,
initializer_range=0.02,
initializer_factor=1.0,
# This differs from `CLIPTokenizer`'s default and from openai/clip
# See https://github.com/huggingface/transformers/pull/24773#issuecomment-1632287538
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.projection_dim = projection_dim
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 CLIPVisionConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`CLIPVisionModel`]. It is used to instantiate a
CLIP 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 CLIP
[openai/clip-vit-base-patch32](https://huggingface.co/openai/clip-vit-base-patch32) 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.
intermediate_size (`int`, *optional*, defaults to 3072):
Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
projection_dim (`int`, *optional*, defaults to 512):
Dimensionality of text and vision projection layers.
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):
The number of input channels.
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 `"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).
Example:
```python
>>> from transformers import CLIPVisionConfig, CLIPVisionModel
>>> # Initializing a CLIPVisionConfig with openai/clip-vit-base-patch32 style configuration
>>> configuration = CLIPVisionConfig()
>>> # Initializing a CLIPVisionModel (with random weights) from the openai/clip-vit-base-patch32 style configuration
>>> model = CLIPVisionModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "clip_vision_model"
base_config_key = "vision_config"
def __init__(
self,
hidden_size=768,
intermediate_size=3072,
projection_dim=512,
num_hidden_layers=12,
num_attention_heads=12,
num_channels=3,
image_size=224,
patch_size=32,
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.projection_dim = projection_dim
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 CLIPConfig(PretrainedConfig):
r"""
[`CLIPConfig`] is the configuration class to store the configuration of a [`CLIPModel`]. It is used to instantiate
a CLIP 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 CLIP
[openai/clip-vit-base-patch32](https://huggingface.co/openai/clip-vit-base-patch32) 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 [`CLIPTextConfig`].
vision_config (`dict`, *optional*):
Dictionary of configuration options used to initialize [`CLIPVisionConfig`].
projection_dim (`int`, *optional*, defaults to 512):
Dimensionality 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 CLIP implementation.
kwargs (*optional*):
Dictionary of keyword arguments.
Example:
```python
>>> from transformers import CLIPConfig, CLIPModel
>>> # Initializing a CLIPConfig with openai/clip-vit-base-patch32 style configuration
>>> configuration = CLIPConfig()
>>> # Initializing a CLIPModel (with random weights) from the openai/clip-vit-base-patch32 style configuration
>>> model = CLIPModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
>>> # We can also initialize a CLIPConfig from a CLIPTextConfig and a CLIPVisionConfig
>>> from transformers import CLIPTextConfig, CLIPVisionConfig
>>> # Initializing a CLIPText and CLIPVision configuration
>>> config_text = CLIPTextConfig()
>>> config_vision = CLIPVisionConfig()
>>> config = CLIPConfig.from_text_vision_configs(config_text, config_vision)
```"""
model_type = "clip"
sub_configs = {"text_config": CLIPTextConfig, "vision_config": CLIPVisionConfig}
def __init__(
self, text_config=None, vision_config=None, projection_dim=512, 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 = CLIPTextConfig(**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 `CLIPTextConfig`. The "
f'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 = CLIPVisionConfig(**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 `CLIPVisionConfig`. "
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 `CLIPTextConfig` with default values.")
if vision_config is None:
vision_config = {}
logger.info("`vision_config` is `None`. initializing the `CLIPVisionConfig` with default values.")
self.text_config = CLIPTextConfig(**text_config)
self.vision_config = CLIPVisionConfig(**vision_config)
self.projection_dim = projection_dim
self.logit_scale_init_value = logit_scale_init_value
self.initializer_factor = 1.0
@classmethod
def from_text_vision_configs(cls, text_config: CLIPTextConfig, vision_config: CLIPVisionConfig, **kwargs):
r"""
Instantiate a [`CLIPConfig`] (or a derived class) from clip text model configuration and clip vision model
configuration.
Returns:
[`CLIPConfig`]: An instance of a configuration object
"""
return cls(text_config=text_config.to_dict(), vision_config=vision_config.to_dict(), **kwargs)
class CLIPOnnxConfig(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__ = ["CLIPConfig", "CLIPOnnxConfig", "CLIPTextConfig", "CLIPVisionConfig"]
| transformers/src/transformers/models/clip/configuration_clip.py/0 | {
"file_path": "transformers/src/transformers/models/clip/configuration_clip.py",
"repo_id": "transformers",
"token_count": 7637
} |
# 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.
"""Conditional DETR model configuration"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import verify_backbone_config_arguments
from ..auto import CONFIG_MAPPING
logger = logging.get_logger(__name__)
class ConditionalDetrConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`ConditionalDetrModel`]. It is used to instantiate
a Conditional DETR 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 Conditional DETR
[microsoft/conditional-detr-resnet-50](https://huggingface.co/microsoft/conditional-detr-resnet-50) 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_timm_backbone (`bool`, *optional*, defaults to `True`):
Whether or not to use the `timm` library for the backbone. If set to `False`, will use the [`AutoBackbone`]
API.
backbone_config (`PretrainedConfig` or `dict`, *optional*):
The configuration of the backbone model. Only used in case `use_timm_backbone` is set to `False` in which
case it will default to `ResNetConfig()`.
num_channels (`int`, *optional*, defaults to 3):
The number of input channels.
num_queries (`int`, *optional*, defaults to 100):
Number of object queries, i.e. detection slots. This is the maximal number of objects
[`ConditionalDetrModel`] can detect in a single image. For COCO, we recommend 100 queries.
d_model (`int`, *optional*, defaults to 256):
This parameter is a general dimension parameter, defining dimensions for components such as the encoder layer and projection parameters in the decoder layer, among others.
encoder_layers (`int`, *optional*, defaults to 6):
Number of encoder layers.
decoder_layers (`int`, *optional*, defaults to 6):
Number of decoder layers.
encoder_attention_heads (`int`, *optional*, defaults to 8):
Number of attention heads for each attention layer in the Transformer encoder.
decoder_attention_heads (`int`, *optional*, defaults to 8):
Number of attention heads for each attention layer in the Transformer decoder.
decoder_ffn_dim (`int`, *optional*, defaults to 2048):
Dimension of the "intermediate" (often named feed-forward) layer in decoder.
encoder_ffn_dim (`int`, *optional*, defaults to 2048):
Dimension of the "intermediate" (often named feed-forward) layer in decoder.
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.
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.
init_std (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
init_xavier_std (`float`, *optional*, defaults to 1):
The scaling factor used for the Xavier initialization gain in the HM Attention map module.
encoder_layerdrop (`float`, *optional*, defaults to 0.0):
The LayerDrop probability for the encoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556)
for more details.
decoder_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.
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"`.
backbone (`str`, *optional*, defaults to `"resnet50"`):
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 `True`):
Whether to use pretrained weights for the backbone.
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.
dilation (`bool`, *optional*, defaults to `False`):
Whether to replace stride with dilation in the last convolutional block (DC5). Only supported when
`use_timm_backbone` = `True`.
class_cost (`float`, *optional*, defaults to 1):
Relative weight of the classification error in the Hungarian matching cost.
bbox_cost (`float`, *optional*, defaults to 5):
Relative weight of the L1 error of the bounding box coordinates in the Hungarian matching cost.
giou_cost (`float`, *optional*, defaults to 2):
Relative weight of the generalized IoU loss of the bounding box in the Hungarian matching cost.
mask_loss_coefficient (`float`, *optional*, defaults to 1):
Relative weight of the Focal loss in the panoptic segmentation loss.
dice_loss_coefficient (`float`, *optional*, defaults to 1):
Relative weight of the DICE/F-1 loss in the panoptic segmentation loss.
bbox_loss_coefficient (`float`, *optional*, defaults to 5):
Relative weight of the L1 bounding box loss in the object detection loss.
giou_loss_coefficient (`float`, *optional*, defaults to 2):
Relative weight of the generalized IoU loss in the object detection loss.
eos_coefficient (`float`, *optional*, defaults to 0.1):
Relative classification weight of the 'no-object' class in the object detection loss.
focal_alpha (`float`, *optional*, defaults to 0.25):
Alpha parameter in the focal loss.
Examples:
```python
>>> from transformers import ConditionalDetrConfig, ConditionalDetrModel
>>> # Initializing a Conditional DETR microsoft/conditional-detr-resnet-50 style configuration
>>> configuration = ConditionalDetrConfig()
>>> # Initializing a model (with random weights) from the microsoft/conditional-detr-resnet-50 style configuration
>>> model = ConditionalDetrModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "conditional_detr"
keys_to_ignore_at_inference = ["past_key_values"]
attribute_map = {
"hidden_size": "d_model",
"num_attention_heads": "encoder_attention_heads",
}
def __init__(
self,
use_timm_backbone=True,
backbone_config=None,
num_channels=3,
num_queries=300,
encoder_layers=6,
encoder_ffn_dim=2048,
encoder_attention_heads=8,
decoder_layers=6,
decoder_ffn_dim=2048,
decoder_attention_heads=8,
encoder_layerdrop=0.0,
decoder_layerdrop=0.0,
is_encoder_decoder=True,
activation_function="relu",
d_model=256,
dropout=0.1,
attention_dropout=0.0,
activation_dropout=0.0,
init_std=0.02,
init_xavier_std=1.0,
auxiliary_loss=False,
position_embedding_type="sine",
backbone="resnet50",
use_pretrained_backbone=True,
backbone_kwargs=None,
dilation=False,
class_cost=2,
bbox_cost=5,
giou_cost=2,
mask_loss_coefficient=1,
dice_loss_coefficient=1,
cls_loss_coefficient=2,
bbox_loss_coefficient=5,
giou_loss_coefficient=2,
focal_alpha=0.25,
**kwargs,
):
# We default to values which were previously hard-coded in the model. This enables configurability of the config
# while keeping the default behavior the same.
if use_timm_backbone and backbone_kwargs is None:
backbone_kwargs = {}
if dilation:
backbone_kwargs["output_stride"] = 16
backbone_kwargs["out_indices"] = [1, 2, 3, 4]
backbone_kwargs["in_chans"] = num_channels
# Backwards compatibility
elif not use_timm_backbone and backbone in (None, "resnet50"):
if backbone_config is None:
logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.")
backbone_config = CONFIG_MAPPING["resnet"](out_features=["stage4"])
elif isinstance(backbone_config, dict):
backbone_model_type = backbone_config.get("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,
)
self.use_timm_backbone = use_timm_backbone
self.backbone_config = backbone_config
self.num_channels = num_channels
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.init_std = init_std
self.init_xavier_std = init_xavier_std
self.encoder_layerdrop = encoder_layerdrop
self.decoder_layerdrop = decoder_layerdrop
self.num_hidden_layers = encoder_layers
self.auxiliary_loss = auxiliary_loss
self.position_embedding_type = position_embedding_type
self.backbone = backbone
self.use_pretrained_backbone = use_pretrained_backbone
self.backbone_kwargs = backbone_kwargs
self.dilation = dilation
# Hungarian matcher
self.class_cost = class_cost
self.bbox_cost = bbox_cost
self.giou_cost = giou_cost
# Loss coefficients
self.mask_loss_coefficient = mask_loss_coefficient
self.dice_loss_coefficient = dice_loss_coefficient
self.cls_loss_coefficient = cls_loss_coefficient
self.bbox_loss_coefficient = bbox_loss_coefficient
self.giou_loss_coefficient = giou_loss_coefficient
self.focal_alpha = focal_alpha
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
class ConditionalDetrOnnxConfig(OnnxConfig):
torch_onnx_minimum_version = version.parse("1.11")
@property
def inputs(self) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
("pixel_mask", {0: "batch"}),
]
)
@property
def atol_for_validation(self) -> float:
return 1e-5
@property
def default_onnx_opset(self) -> int:
return 12
__all__ = ["ConditionalDetrConfig", "ConditionalDetrOnnxConfig"]
| transformers/src/transformers/models/conditional_detr/configuration_conditional_detr.py/0 | {
"file_path": "transformers/src/transformers/models/conditional_detr/configuration_conditional_detr.py",
"repo_id": "transformers",
"token_count": 5243
} |
# coding=utf-8
# Copyright 2024 Descript 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.
import argparse
import fnmatch
import re
import torch
from transformers import (
DacConfig,
DacFeatureExtractor,
DacModel,
logging,
)
# checkpoints downloaded using:
# pip install descript-audio-codec
# python3 -m dac download # downloads the default 44kHz variant
# python3 -m dac download --model_type 44khz # downloads the 44kHz variant
# python3 -m dac download --model_type 24khz # downloads the 24kHz variant
# python3 -m dac download --model_type 16khz # downloads the 16kHz variant
# More informations: https://github.com/descriptinc/descript-audio-codec/tree/main
logging.set_verbosity_info()
logger = logging.get_logger("transformers.models.dac")
def match_pattern(string, pattern):
# Split the pattern into parts
pattern_parts = pattern.split(".")
string_parts = string.split(".")
pattern_block_count = string_block_count = 0
for part in pattern_parts:
if part.startswith("block"):
pattern_block_count += 1
for part in string_parts:
if part.startswith("block"):
string_block_count += 1
return fnmatch.fnmatch(string, pattern) and string_block_count == pattern_block_count
TOP_LEVEL_KEYS = []
IGNORE_KEYS = []
MAPPING_ENCODER = {
"encoder.block.0": ["encoder.conv1"],
"encoder.block.5": ["encoder.snake1"],
"encoder.block.6": ["encoder.conv2"],
"encoder.block.*.block.*.block.0".replace("*", r"\d+"): ["encoder.block", "res_unit", "snake1"],
"encoder.block.*.block.*.block.1".replace("*", r"\d+"): ["encoder.block", "res_unit", "conv1"],
"encoder.block.*.block.*.block.2".replace("*", r"\d+"): ["encoder.block", "res_unit", "snake2"],
"encoder.block.*.block.*.block.3".replace("*", r"\d+"): ["encoder.block", "res_unit", "conv2"],
"encoder.block.*.block.3".replace("*", r"\d+"): ["encoder.block", "snake1"],
"encoder.block.*.block.4".replace("*", r"\d+"): ["encoder.block", "conv1"],
}
MAPPING_QUANTIZER = {
"quantizer.quantizers.*": ["quantizer.quantizers.*"],
}
MAPPING_DECODER = {
"decoder.model.0": ["decoder.conv1"],
"decoder.model.5": ["decoder.snake1"],
"decoder.model.6": ["decoder.conv2"],
"decoder.model.*.block.0".replace("*", r"\d+"): ["decoder.block", "snake1"],
"decoder.model.*.block.1".replace("*", r"\d+"): ["decoder.block", "conv_t1"],
"decoder.model.*.block.*.block.0".replace("*", r"\d+"): ["decoder.block", "res_unit", "snake1"],
"decoder.model.*.block.*.block.1".replace("*", r"\d+"): ["decoder.block", "res_unit", "conv1"],
"decoder.model.*.block.*.block.2".replace("*", r"\d+"): ["decoder.block", "res_unit", "snake2"],
"decoder.model.*.block.*.block.3".replace("*", r"\d+"): ["decoder.block", "res_unit", "conv2"],
}
MAPPING = {
**MAPPING_ENCODER,
**MAPPING_QUANTIZER,
**MAPPING_DECODER,
}
def set_recursively(hf_pointer, key, value, full_name, weight_type):
for attribute in key.split("."):
hf_pointer = getattr(hf_pointer, attribute)
if weight_type is not None:
hf_shape = getattr(hf_pointer, weight_type).shape
else:
hf_shape = hf_pointer.shape
if hf_shape != value.shape:
raise ValueError(
f"Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"
f" {value.shape} for {full_name}"
)
if weight_type == "weight":
hf_pointer.weight.data = value
elif weight_type == "weight_g":
hf_pointer.weight_g.data = value
elif weight_type == "weight_v":
hf_pointer.weight_v.data = value
elif weight_type == "bias":
hf_pointer.bias.data = value
elif weight_type == "alpha":
hf_pointer.alpha.data = value
logger.info(f"{key + ('.' + weight_type if weight_type is not None else '')} was initialized from {full_name}.")
def should_ignore(name, ignore_keys):
for key in ignore_keys:
if key.endswith(".*"):
if name.startswith(key[:-1]):
return True
elif ".*." in key:
prefix, suffix = key.split(".*.")
if prefix in name and suffix in name:
return True
elif key in name:
return True
return False
def recursively_load_weights(orig_dict, hf_model, model_name):
unused_weights = []
if model_name not in ["dac_16khz", "dac_24khz", "dac_44khz"]:
raise ValueError(f"Unsupported model: {model_name}")
for name, value in orig_dict.items():
is_used = False
for key, mapped_key in MAPPING.items():
regex = re.compile(key)
if regex.search(name):
if len(mapped_key) == 1:
if mapped_key[0][0] == "q":
mapped_key = ".".join(name.split(".")[:-1])
else:
mapped_key = mapped_key[0]
elif len(mapped_key) == 3:
integers = re.findall(r"\b\d+\b", name)
if mapped_key[0][0] == "d":
mapped_key = "{}.{}.{}{}.{}".format(
mapped_key[0],
str(int(integers[0]) - 1),
mapped_key[1],
str(int(integers[1]) - 1),
mapped_key[2],
)
else:
mapped_key = "{}.{}.{}{}.{}".format(
mapped_key[0],
str(int(integers[0]) - 1),
mapped_key[1],
str(int(integers[1]) + 1),
mapped_key[2],
)
elif len(mapped_key) == 2:
integers = re.findall(r"\b\d+\b", name)
mapped_key = "{}.{}.{}".format(mapped_key[0], str(int(integers[0]) - 1), mapped_key[1])
is_used = True
if "weight_g" in name:
weight_type = "weight_g"
elif "weight_v" in name:
weight_type = "weight_v"
elif "bias" in name:
weight_type = "bias"
elif "alpha" in name:
weight_type = "alpha"
elif "weight" in name:
weight_type = "weight"
set_recursively(hf_model, mapped_key, value, name, weight_type)
if not is_used:
unused_weights.append(name)
print(list(set(unused_weights)))
logger.warning(f"Unused weights: {unused_weights}")
@torch.no_grad()
def convert_checkpoint(
model_name,
checkpoint_path,
pytorch_dump_folder_path,
sample_rate=16000,
repo_id=None,
):
model_dict = torch.load(checkpoint_path, "cpu")
config = DacConfig()
metadata = model_dict["metadata"]["kwargs"]
config.encoder_hidden_size = metadata["encoder_dim"]
config.downsampling_ratios = metadata["encoder_rates"]
config.codebook_size = metadata["codebook_size"]
config.n_codebooks = metadata["n_codebooks"]
config.codebook_dim = metadata["codebook_dim"]
config.decoder_hidden_size = metadata["decoder_dim"]
config.upsampling_ratios = metadata["decoder_rates"]
config.quantizer_dropout = float(metadata["quantizer_dropout"])
config.sampling_rate = sample_rate
model = DacModel(config)
feature_extractor = DacFeatureExtractor()
feature_extractor.sampling_rate = sample_rate
original_checkpoint = model_dict["state_dict"]
model.apply_weight_norm()
recursively_load_weights(original_checkpoint, model, model_name)
model.remove_weight_norm()
model.save_pretrained(pytorch_dump_folder_path)
if repo_id:
print("Pushing to the hub...")
feature_extractor.push_to_hub(repo_id)
model.push_to_hub(repo_id)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--model",
default="dac_44khz",
type=str,
help="The model to convert. Should be one of 'dac_16khz', 'dac_24khz', 'dac_44khz'.",
)
parser.add_argument("--checkpoint_path", required=True, default=None, type=str, help="Path to original checkpoint")
parser.add_argument(
"--pytorch_dump_folder_path", required=True, default=None, type=str, help="Path to the output PyTorch model."
)
parser.add_argument(
"--push_to_hub", default=None, type=str, help="Where to upload the converted model on the 🤗 hub."
)
parser.add_argument("--sample_rate", default=None, type=str, help="Sample rate used by DacFeatureExtractor")
args = parser.parse_args()
convert_checkpoint(
args.model, args.checkpoint_path, args.pytorch_dump_folder_path, args.sample_rate, args.push_to_hub
)
| transformers/src/transformers/models/dac/convert_dac_checkpoint.py/0 | {
"file_path": "transformers/src/transformers/models/dac/convert_dac_checkpoint.py",
"repo_id": "transformers",
"token_count": 4288
} |
# coding=utf-8
# Copyright 2024 Databricks Mosaic 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 DBRX model."""
import math
from typing import Any, Optional, Tuple, Union
import torch
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_outputs import MoeCausalLMOutputWithPast, MoeModelOutputWithPast
from ...modeling_utils import PreTrainedModel
from ...utils import (
add_start_docstrings,
add_start_docstrings_to_model_forward,
is_flash_attn_2_available,
is_flash_attn_greater_or_equal_2_10,
logging,
replace_return_docstrings,
)
from ...utils.deprecation import deprecate_kwarg
from .configuration_dbrx import DbrxConfig
if is_flash_attn_2_available():
from ...modeling_flash_attention_utils import _flash_attention_forward
logger = logging.get_logger(__name__)
_CONFIG_FOR_DOC = "DbrxConfig"
class DbrxRotaryEmbedding(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).float() / self.dim))
self.register_buffer("inv_freq", tensor=inv_freq, persistent=False)
@torch.no_grad()
def forward(self, x, position_ids, seq_len=None):
# x: [bs, num_attention_heads, seq_len, head_size]
self.inv_freq.to(x.device)
inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1)
position_ids_expanded = position_ids[:, None, :].float()
# Force float32 since bfloat16 loses precision on long contexts
# See https://github.com/huggingface/transformers/pull/29285
device_type = x.device.type
device_type = device_type if isinstance(device_type, str) and device_type != "mps" else "cpu"
with torch.autocast(device_type=device_type, enabled=False):
freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
emb = torch.cat((freqs, freqs), dim=-1)
cos = emb.cos()
sin = emb.sin()
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
# 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)
def load_balancing_loss_func(
gate_logits: torch.Tensor,
num_experts: int,
top_k: int,
attention_mask: Optional[torch.Tensor],
) -> torch.Tensor:
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 (Union[`torch.Tensor`, Tuple[torch.Tensor]):
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 (`int`):
Number of experts.
top_k (`int`):
The number of experts each token is routed to.
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 torch.tensor(0.0)
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 DbrxAttention(nn.Module):
"""Multi-head self attention."""
def __init__(self, config: DbrxConfig, block_idx: Optional[int] = None):
super().__init__()
self.config = config
self.hidden_size = config.d_model
self.num_heads = config.n_heads
self.head_dim = self.hidden_size // self.num_heads
self.max_position_embeddings = config.max_seq_len
self.block_idx = block_idx
if block_idx is None:
logger.warning_once(
f"Instantiating {self.__class__.__name__} without passing a `block_idx` is not recommended and will "
+ "lead to errors during the forward call if caching is used. Please make sure to provide a `block_idx` "
+ "when creating this class."
)
attn_config = config.attn_config
self.attn_pdrop = attn_config.attn_pdrop
self.clip_qkv = attn_config.clip_qkv
self.num_key_value_heads = attn_config.kv_n_heads
self.num_key_value_groups = self.num_heads // self.num_key_value_heads
self.rope_theta = attn_config.rope_theta
self.is_causal = True
self.Wqkv = nn.Linear(
self.hidden_size, self.hidden_size + 2 * self.num_key_value_heads * self.head_dim, bias=False
)
self.out_proj = nn.Linear(self.hidden_size, self.hidden_size, bias=False)
self.rotary_emb = DbrxRotaryEmbedding(
self.head_dim,
max_position_embeddings=self.max_position_embeddings,
base=self.rope_theta,
)
def forward(
self,
hidden_states: torch.Tensor,
position_ids: torch.LongTensor,
attention_mask: Optional[torch.Tensor] = None,
past_key_value: Optional[Cache] = None,
output_attentions: bool = False,
use_cache: bool = False,
cache_position: Optional[torch.LongTensor] = None,
**kwargs: Any,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Cache]]:
bsz, q_len, _ = hidden_states.size()
qkv_states = self.Wqkv(hidden_states)
min_val = -self.clip_qkv if self.clip_qkv is not None else None
max_val = self.clip_qkv
qkv_states = qkv_states.clamp(min=min_val, max=max_val)
query_states, key_states, value_states = qkv_states.split(
[
self.hidden_size,
self.num_key_value_heads * self.head_dim,
self.num_key_value_heads * self.head_dim,
],
dim=2,
)
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; position_ids 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.block_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)) / 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.attn_pdrop, 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.out_proj(attn_output)
if not output_attentions:
attn_weights = None
return attn_output, attn_weights, past_key_value
class DbrxFlashAttention2(DbrxAttention):
"""Dbrx flash attention module.
This module inherits from `DbrxAttention` as the weights of the module stays
untouched. The only required change would be on the forward pass where it
calls the public API of flash attention.
"""
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 alignement, 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 = not is_flash_attn_greater_or_equal_2_10()
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,
**kwargs: Any,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
if isinstance(past_key_value, StaticCache):
raise ValueError(
"`static` cache implementation is not compatible with `attn_implementation==flash_attention_2` "
"make sure to use `sdpa` in the mean time, and open an issue at https://github.com/huggingface/transformers"
)
logger.info("Implicitly setting `output_attentions` to False as it is not supported in Flash Attention.")
output_attentions = False
bsz, q_len, _ = hidden_states.size()
qkv_states = self.Wqkv(hidden_states)
if self.clip_qkv is not None:
qkv_states = qkv_states.clamp(min=-self.clip_qkv, max=self.clip_qkv)
query_states, key_states, value_states = qkv_states.split(
[
self.hidden_size,
self.num_key_value_heads * self.head_dim,
self.num_key_value_heads * self.head_dim,
],
dim=2,
)
# 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 = 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.block_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.attn_pdrop 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 = query_states.dtype
logger.warning_once(
"The input hidden states seems to be silently casted in float32, this might be "
+ "related to the fact you have upcasted embedding or layer norm layers in "
+ f"float32. We will cast back the input in {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,
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.out_proj(attn_output)
if not output_attentions:
attn_weights = None
return attn_output, attn_weights, past_key_value
class DbrxSdpaAttention(DbrxAttention):
"""
Dbrx attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
`DbrxAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to
SDPA API.
"""
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]]]:
if output_attentions:
# TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"` once this is implemented.
logger.warning_once(
"DbrxModel is using DbrxSdpaAttention, 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()
qkv_states = self.Wqkv(hidden_states)
if self.clip_qkv is not None:
qkv_states = qkv_states.clamp(min=-self.clip_qkv, max=self.clip_qkv)
query_states, key_states, value_states = qkv_states.split(
[
self.hidden_size,
self.num_key_value_heads * self.head_dim,
self.num_key_value_heads * self.head_dim,
],
dim=2,
)
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, seq_len=None)
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, None)
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.block_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.attn_pdrop 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, -1)
attn_output = self.out_proj(attn_output)
return attn_output, None, past_key_value
DBRX_ATTENTION_CLASSES = {
"eager": DbrxAttention,
"flash_attention_2": DbrxFlashAttention2,
"sdpa": DbrxSdpaAttention,
}
class DbrxNormAttentionNorm(nn.Module):
def __init__(self, config: DbrxConfig, block_idx: Optional[int] = None):
super().__init__()
self.block_idx = block_idx
self.resid_pdrop = config.resid_pdrop
self.norm_1 = nn.LayerNorm(config.d_model, bias=False)
self.attn = DBRX_ATTENTION_CLASSES[config._attn_implementation](
config=config,
block_idx=block_idx,
)
self.norm_2 = nn.LayerNorm(config.d_model, bias=False)
def forward(
self,
hidden_states: torch.Tensor,
position_ids: torch.LongTensor,
attention_mask: Optional[torch.Tensor] = None,
past_key_value: Optional[Cache] = None,
output_attentions: bool = False,
use_cache: bool = False,
cache_position: Optional[torch.LongTensor] = None,
**kwargs: Any,
) -> Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor], Optional[Cache]]:
residual_states = hidden_states
hidden_states = self.norm_1(hidden_states).to(hidden_states.dtype)
hidden_states, attn_weights, past_key_value = 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,
**kwargs,
)
hidden_states = nn.functional.dropout(hidden_states, p=self.resid_pdrop, training=self.training)
hidden_states = hidden_states + residual_states
residual_states = hidden_states
hidden_states = self.norm_2(hidden_states).to(hidden_states.dtype)
return residual_states, hidden_states, attn_weights, past_key_value
class DbrxRouter(nn.Module):
def __init__(
self,
hidden_size: int,
moe_num_experts: int,
moe_top_k: int,
moe_jitter_eps: Optional[float],
moe_normalize_expert_weights: Optional[float],
):
super().__init__()
self.hidden_size = hidden_size
self.moe_num_experts = moe_num_experts
self.moe_top_k = moe_top_k
self.moe_jitter_eps = moe_jitter_eps
self.moe_normalize_expert_weights = moe_normalize_expert_weights
self.layer = nn.Linear(self.hidden_size, self.moe_num_experts, bias=False)
def forward(self, hidden_states: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor, torch.LongTensor]:
if self.training and self.moe_jitter_eps is not None:
hidden_states *= torch.empty_like(hidden_states).uniform_(
1.0 - self.moe_jitter_eps, 1.0 + self.moe_jitter_eps
)
hidden_states = hidden_states.view(-1, hidden_states.shape[-1])
weights = self.layer(hidden_states).softmax(dim=-1, dtype=torch.float32)
top_weights, top_experts = torch.topk(weights, self.moe_top_k, dim=-1)
top_weights_scale = (
torch.norm(top_weights, p=self.moe_normalize_expert_weights, dim=-1, keepdim=True)
if self.moe_normalize_expert_weights is not None
else 1.0
)
top_weights = top_weights / top_weights_scale
weights = weights.to(hidden_states.dtype)
top_weights = top_weights.to(hidden_states.dtype)
return weights, top_weights, top_experts
class DbrxExpertGLU(nn.Module):
def __init__(self, hidden_size: int, ffn_hidden_size: int, moe_num_experts: int, ffn_act_fn: dict):
super().__init__()
self.hidden_size = hidden_size
self.ffn_hidden_size = ffn_hidden_size
self.moe_num_experts = moe_num_experts
self.w1 = nn.Parameter(torch.empty(moe_num_experts * ffn_hidden_size, hidden_size))
self.v1 = nn.Parameter(torch.empty(moe_num_experts * ffn_hidden_size, hidden_size))
self.w2 = nn.Parameter(torch.empty(moe_num_experts * ffn_hidden_size, hidden_size))
act_fn_name = ffn_act_fn.get("name", "silu")
self.activation_fn = ACT2FN[act_fn_name]
def forward(
self, x: torch.Tensor, expert_w1: torch.Tensor, expert_v1: torch.Tensor, expert_w2: torch.Tensor
) -> torch.Tensor:
gate_proj = x.matmul(expert_w1.t())
up_proj = x.matmul(expert_v1.t())
gate_proj = self.activation_fn(gate_proj)
intermediate_states = gate_proj * up_proj
down_proj = intermediate_states.matmul(expert_w2)
return down_proj
class DbrxExperts(nn.Module):
def __init__(self, hidden_size: int, ffn_hidden_size: int, moe_num_experts: int, ffn_act_fn: dict):
super().__init__()
self.moe_num_experts = moe_num_experts
self.mlp = DbrxExpertGLU(
hidden_size=hidden_size,
ffn_hidden_size=ffn_hidden_size,
moe_num_experts=moe_num_experts,
ffn_act_fn=ffn_act_fn,
)
def forward(
self, x: torch.Tensor, weights: torch.Tensor, top_weights: torch.Tensor, top_experts: torch.LongTensor
) -> torch.Tensor:
bsz, q_len, hidden_size = x.shape
x = x.view(-1, hidden_size)
out = torch.zeros_like(x)
expert_mask = nn.functional.one_hot(top_experts, num_classes=self.moe_num_experts).permute(2, 1, 0)
# Chunk experts at once to avoid storing full parameter multiple times in autograd
w1_chunked = self.mlp.w1.view(self.mlp.moe_num_experts, self.mlp.ffn_hidden_size, self.mlp.hidden_size).chunk(
self.moe_num_experts, dim=0
)
v1_chunked = self.mlp.v1.view(self.mlp.moe_num_experts, self.mlp.ffn_hidden_size, self.mlp.hidden_size).chunk(
self.moe_num_experts, dim=0
)
w2_chunked = self.mlp.w2.view(self.mlp.moe_num_experts, self.mlp.ffn_hidden_size, self.mlp.hidden_size).chunk(
self.moe_num_experts, dim=0
)
w1_chunked = [w1.squeeze(dim=0) for w1 in w1_chunked]
v1_chunked = [v1.squeeze(dim=0) for v1 in v1_chunked]
w2_chunked = [w2.squeeze(dim=0) for w2 in w2_chunked]
for expert_idx in range(0, self.moe_num_experts):
# (This cause torch.compile to fail with `torch._dynamo.exc.Unsupported: dynamic shape operator: aten.nonzero.default`)
# (set torch._dynamo.config.capture_dynamic_output_shape_ops = True may help but not tested)
topk_idx, token_idx = torch.where(expert_mask[expert_idx])
if token_idx.shape[0] == 0:
continue
token_list = token_idx
topk_list = topk_idx
expert_tokens = x[None, token_list].reshape(-1, hidden_size)
expert_out = (
self.mlp(expert_tokens, w1_chunked[expert_idx], v1_chunked[expert_idx], w2_chunked[expert_idx])
* top_weights[token_list, topk_list, None]
)
out.index_add_(0, token_idx, expert_out)
out = out.reshape(bsz, q_len, hidden_size)
return out
class DbrxFFN(nn.Module):
def __init__(self, config: DbrxConfig):
super().__init__()
ffn_config = config.ffn_config
self.router = DbrxRouter(
hidden_size=config.d_model,
moe_num_experts=ffn_config.moe_num_experts,
moe_top_k=ffn_config.moe_top_k,
moe_jitter_eps=ffn_config.moe_jitter_eps,
moe_normalize_expert_weights=ffn_config.moe_normalize_expert_weights,
)
self.experts = DbrxExperts(
hidden_size=config.d_model,
ffn_hidden_size=ffn_config.ffn_hidden_size,
moe_num_experts=ffn_config.moe_num_experts,
ffn_act_fn=ffn_config.ffn_act_fn,
)
def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
weights, top_weights, top_experts = self.router(x)
out = self.experts(x, weights, top_weights, top_experts)
return out, weights
class DbrxBlock(nn.Module):
def __init__(self, config: DbrxConfig, block_idx: int):
super().__init__()
self.hidden_size = config.d_model
self.resid_pdrop = config.resid_pdrop
self.block_idx = block_idx
self.norm_attn_norm = DbrxNormAttentionNorm(
config=config,
block_idx=block_idx,
)
self.ffn = DbrxFFN(config=config)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: torch.LongTensor = None,
past_key_value: Optional[Cache] = None,
output_attentions: Optional[bool] = False,
output_router_logits: Optional[bool] = False,
use_cache: Optional[bool] = False,
cache_position: Optional[torch.LongTensor] = None,
**kwargs: Any,
) -> Union[
Tuple[torch.Tensor],
Tuple[torch.Tensor, Optional[torch.Tensor]],
Tuple[torch.Tensor, Optional[Cache]],
Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Cache]],
Tuple[torch.Tensor, Optional[torch.Tensor], Optional[torch.Tensor]],
Tuple[torch.Tensor, Optional[Cache], Optional[torch.Tensor]],
Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Cache], Optional[torch.Tensor]],
]:
"""Forward function for DbrxBlock.
Args:
hidden_states (`torch.Tensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
position_ids (`torch.LongTensor`): position ids of shape `(batch, seq_len)`
attention_mask (`torch.Tensor`, *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.
past_key_value (`Tuple(torch.Tensor)`, *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 router logits.
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`, *optional*): position ids of the cache
"""
# Norm + Attention + Norm
resid_states, hidden_states, self_attn_weights, present_key_value = self.norm_attn_norm(
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,
**kwargs,
)
# Fully Connected
hidden_states, router_logits = self.ffn(hidden_states)
hidden_states = nn.functional.dropout(hidden_states, p=self.resid_pdrop, training=self.training)
hidden_states = resid_states + 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
DBRX_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 ([`DbrxConfig`]):
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 DBRX Model outputting raw hidden-states without any specific head on top.",
DBRX_START_DOCSTRING,
)
class DbrxPreTrainedModel(PreTrainedModel):
config_class = DbrxConfig
base_model_prefix = "transformer"
supports_gradient_checkpointing = True
_no_split_modules = ["DbrxBlock"]
_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: nn.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.weight.data.fill_(1.0)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, DbrxExpertGLU):
module.w1.data.normal_(mean=0.0, std=std)
module.v1.data.normal_(mean=0.0, std=std)
module.w2.data.normal_(mean=0.0, std=std)
DBRX_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 (`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)`.
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.
"""
@add_start_docstrings(
"The bare DBRX Model outputting raw hidden-states without any specific head on top.",
DBRX_START_DOCSTRING,
)
class DbrxModel(DbrxPreTrainedModel):
"""Transformer decoder consisting of *config.num_hidden_layers*. Each layer is a [`DbrxBlock`] layer.
Args:
config ([`DbrxConfig`]): Model configuration class with all 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.
"""
def __init__(self, config: DbrxConfig):
super().__init__(config)
self.padding_idx = config.pad_token_id
self.vocab_size = config.vocab_size
self.emb_pdrop = config.emb_pdrop
self.wte = nn.Embedding(config.vocab_size, config.d_model, self.padding_idx)
self.blocks = nn.ModuleList([DbrxBlock(config, block_idx) for block_idx in range(config.n_layers)])
self.norm_f = nn.LayerNorm(config.d_model, bias=False)
self.gradient_checkpointing = False
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self) -> nn.Embedding:
return self.wte
def set_input_embeddings(self, value: nn.Embedding):
self.wte = value
@add_start_docstrings_to_model_forward(DBRX_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.Tensor] = 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, # NOOP kwargs, for now
) -> Union[Tuple, 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
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.wte(input_ids)
inputs_embeds = nn.functional.dropout(inputs_embeds, p=self.emb_pdrop, training=self.training)
# 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)
causal_mask = self._update_causal_mask(
attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions
)
# embed positions
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 block in self.blocks:
if output_hidden_states:
all_hidden_states += (hidden_states,)
if self.gradient_checkpointing and self.training:
block_outputs = self._gradient_checkpointing_func(
block.__call__,
hidden_states,
causal_mask,
position_ids,
past_key_values,
output_attentions,
output_router_logits,
use_cache,
cache_position,
)
else:
block_outputs = block(
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,
cache_position=cache_position,
)
hidden_states = block_outputs[0]
if use_cache:
next_decoder_cache = block_outputs[2 if output_attentions else 1]
if output_attentions:
all_self_attns += (block_outputs[1],)
if output_router_logits:
all_router_logits += (block_outputs[-1],)
hidden_states = self.norm_f(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, all_router_logits]
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,
):
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
# 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.LlamaPreTrainedModel._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 plcae 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, :]
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 DBRX Model transformer for causal language modeling.", DBRX_START_DOCSTRING)
class DbrxForCausalLM(DbrxPreTrainedModel, GenerationMixin):
def __init__(self, config: DbrxConfig):
super().__init__(config)
self.transformer = DbrxModel(config)
self.vocab_size = config.vocab_size
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
self.moe_loss_weight = config.ffn_config.moe_loss_weight
self.num_experts = config.ffn_config.moe_num_experts
self.num_experts_per_tok = config.ffn_config.moe_top_k
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self) -> nn.Embedding:
return self.transformer.get_input_embeddings()
def set_input_embeddings(self, value: nn.Embedding):
self.transformer.set_input_embeddings(value)
def get_output_embeddings(self) -> nn.Linear:
return self.lm_head
def set_output_embeddings(self, new_embeddings: nn.Linear):
self.lm_head = new_embeddings
def set_decoder(self, decoder: DbrxModel):
self.transformer = decoder
def get_decoder(self) -> DbrxModel:
return self.transformer
@deprecate_kwarg("num_logits_to_keep", version="4.50", new_name="logits_to_keep")
@add_start_docstrings_to_model_forward(DBRX_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[Cache] = None,
inputs_embeds: 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,
output_router_logits: Optional[bool] = None,
return_dict: Optional[bool] = None,
cache_position: Optional[torch.LongTensor] = None,
logits_to_keep: Union[int, torch.Tensor] = 0,
**kwargs,
) -> Union[Tuple, MoeCausalLMOutputWithPast]:
r"""Forward function for causal language modeling.
Args:
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, DbrxForCausalLM
>> model = DbrxForCausalLM.from_pretrained("databricks/dbrx-instruct")
>> tokenizer = AutoTokenizer.from_pretrained("databricks/dbrx-instruct")
>> 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
)
output_router_logits = (
output_router_logits if output_router_logits is not None else self.config.output_router_logits
)
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.transformer(
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]
# No upscaling to float was ever done for Dbrx
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,
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 and loss is not None:
loss += self.moe_loss_weight * 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,
)
__all__ = ["DbrxForCausalLM", "DbrxModel", "DbrxPreTrainedModel"]
| transformers/src/transformers/models/dbrx/modeling_dbrx.py/0 | {
"file_path": "transformers/src/transformers/models/dbrx/modeling_dbrx.py",
"repo_id": "transformers",
"token_count": 27613
} |
# coding=utf-8
# Copyright 2022 Facebook AI Research (FAIR) 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 DeiT model."""
from __future__ import annotations
import collections.abc
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,
TFImageClassifierOutput,
TFMaskedImageModelingOutput,
)
from ...modeling_tf_utils import (
TFPreTrainedModel,
TFSequenceClassificationLoss,
get_initializer,
keras,
keras_serializable,
unpack_inputs,
)
from ...tf_utils import shape_list, stable_softmax
from ...utils import (
ModelOutput,
add_code_sample_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
logging,
replace_return_docstrings,
)
from .configuration_deit import DeiTConfig
logger = logging.get_logger(__name__)
# General docstring
_CONFIG_FOR_DOC = "DeiTConfig"
# Base docstring
_CHECKPOINT_FOR_DOC = "facebook/deit-base-distilled-patch16-224"
_EXPECTED_OUTPUT_SHAPE = [1, 198, 768]
# Image classification docstring
_IMAGE_CLASS_CHECKPOINT = "facebook/deit-base-distilled-patch16-224"
_IMAGE_CLASS_EXPECTED_OUTPUT = "tabby, tabby cat"
@dataclass
class TFDeiTForImageClassificationWithTeacherOutput(ModelOutput):
"""
Output type of [`DeiTForImageClassificationWithTeacher`].
Args:
logits (`tf.Tensor` of shape `(batch_size, config.num_labels)`):
Prediction scores as the average of the cls_logits and distillation logits.
cls_logits (`tf.Tensor` of shape `(batch_size, config.num_labels)`):
Prediction scores of the classification head (i.e. the linear layer on top of the final hidden state of the
class token).
distillation_logits (`tf.Tensor` of shape `(batch_size, config.num_labels)`):
Prediction scores of the distillation head (i.e. the linear layer on top of the final hidden state of the
distillation token).
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 + 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(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.
"""
logits: tf.Tensor = None
cls_logits: tf.Tensor = None
distillation_logits: tf.Tensor = None
hidden_states: Tuple[tf.Tensor] | None = None
attentions: Tuple[tf.Tensor] | None = None
class TFDeiTEmbeddings(keras.layers.Layer):
"""
Construct the CLS token, distillation token, position and patch embeddings. Optionally, also the mask token.
"""
def __init__(self, config: DeiTConfig, use_mask_token: bool = False, **kwargs) -> None:
super().__init__(**kwargs)
self.config = config
self.use_mask_token = use_mask_token
self.patch_embeddings = TFDeiTPatchEmbeddings(config=config, name="patch_embeddings")
self.dropout = keras.layers.Dropout(config.hidden_dropout_prob, name="dropout")
def build(self, input_shape=None):
self.cls_token = self.add_weight(
shape=(1, 1, self.config.hidden_size),
initializer=keras.initializers.zeros(),
trainable=True,
name="cls_token",
)
self.distillation_token = self.add_weight(
shape=(1, 1, self.config.hidden_size),
initializer=keras.initializers.zeros(),
trainable=True,
name="distillation_token",
)
self.mask_token = None
if self.use_mask_token:
self.mask_token = self.add_weight(
shape=(1, 1, self.config.hidden_size),
initializer=keras.initializers.zeros(),
trainable=True,
name="mask_token",
)
num_patches = self.patch_embeddings.num_patches
self.position_embeddings = self.add_weight(
shape=(1, num_patches + 2, self.config.hidden_size),
initializer=keras.initializers.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)
def interpolate_pos_encoding(self, embeddings: tf.Tensor, height: int, width: int) -> tf.Tensor:
num_patches = embeddings.shape[1] - 2
num_positions = self.position_embeddings.shape[1] - 2
if num_patches == num_positions and height == width:
return self.position_embeddings
class_pos_embed = self.position_embeddings[:, 0, :]
dist_pos_embed = self.position_embeddings[:, 1, :]
patch_pos_embed = self.position_embeddings[:, 2:, :]
dim = embeddings.shape[-1]
h0 = height // self.config.patch_size
w0 = 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
h0, w0 = h0 + 0.1, w0 + 0.1
patch_pos_embed = tf.reshape(
patch_pos_embed, (1, int(math.sqrt(num_positions)), int(math.sqrt(num_positions)), dim)
)
patch_pos_embed = tf.image.resize(patch_pos_embed, size=(int(h0), int(w0)), method="bicubic")
patch_pos_embed = tf.transpose(patch_pos_embed, perm=[0, 2, 3, 1])
patch_pos_embed = tf.reshape(patch_pos_embed, (1, -1, dim))
return tf.concat(
[tf.expand_dims(class_pos_embed, axis=0), tf.expand_dims(dist_pos_embed, axis=0), patch_pos_embed], axis=1
)
def call(
self,
pixel_values: tf.Tensor,
bool_masked_pos: tf.Tensor | None = None,
training: bool = False,
interpolate_pos_encoding: bool = False,
) -> tf.Tensor:
_, height, width, _ = pixel_values.shape
embeddings = self.patch_embeddings(pixel_values)
batch_size, seq_length, _ = shape_list(embeddings)
if bool_masked_pos is not None:
mask_tokens = tf.tile(self.mask_token, [batch_size, seq_length, 1])
# replace the masked visual tokens by mask_tokens
mask = tf.expand_dims(bool_masked_pos, axis=-1)
mask = tf.cast(mask, dtype=mask_tokens.dtype)
embeddings = embeddings * (1.0 - mask) + mask_tokens * mask
cls_tokens = tf.repeat(self.cls_token, repeats=batch_size, axis=0)
distillation_tokens = tf.repeat(self.distillation_token, repeats=batch_size, axis=0)
embeddings = tf.concat((cls_tokens, distillation_tokens, embeddings), axis=1)
position_embedding = self.position_embeddings
if interpolate_pos_encoding:
position_embedding = self.interpolate_pos_encoding(embeddings, height, width)
embeddings = embeddings + position_embedding
embeddings = self.dropout(embeddings, training=training)
return embeddings
class TFDeiTPatchEmbeddings(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: DeiTConfig, **kwargs) -> None:
super().__init__(**kwargs)
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 = keras.layers.Conv2D(
hidden_size, kernel_size=patch_size, strides=patch_size, name="projection"
)
def call(self, pixel_values: tf.Tensor) -> tf.Tensor:
batch_size, height, width, num_channels = 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."
)
x = self.projection(pixel_values)
batch_size, height, width, num_channels = shape_list(x)
x = tf.reshape(x, (batch_size, height * width, num_channels))
return x
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])
# Copied from transformers.models.vit.modeling_tf_vit.TFViTSelfAttention with ViT->DeiT
class TFDeiTSelfAttention(keras.layers.Layer):
def __init__(self, config: DeiTConfig, **kwargs):
super().__init__(**kwargs)
if config.hidden_size % config.num_attention_heads != 0:
raise ValueError(
f"The hidden size ({config.hidden_size}) is not a multiple of the number "
f"of attention 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.sqrt_att_head_size = math.sqrt(self.attention_head_size)
self.query = keras.layers.Dense(
units=self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="query"
)
self.key = keras.layers.Dense(
units=self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="key"
)
self.value = keras.layers.Dense(
units=self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="value"
)
self.dropout = keras.layers.Dropout(rate=config.attention_probs_dropout_prob)
self.config = config
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,
head_mask: tf.Tensor,
output_attentions: bool,
training: bool = False,
) -> Tuple[tf.Tensor]:
batch_size = shape_list(hidden_states)[0]
mixed_query_layer = self.query(inputs=hidden_states)
mixed_key_layer = self.key(inputs=hidden_states)
mixed_value_layer = self.value(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)
# 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, training=training)
# Mask heads if we want to
if head_mask is not None:
attention_probs = tf.multiply(attention_probs, head_mask)
attention_output = tf.matmul(attention_probs, value_layer)
attention_output = tf.transpose(attention_output, perm=[0, 2, 1, 3])
# (batch_size, seq_len_q, all_head_size)
attention_output = tf.reshape(tensor=attention_output, shape=(batch_size, -1, self.all_head_size))
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, "query", None) is not None:
with tf.name_scope(self.query.name):
self.query.build([None, None, self.config.hidden_size])
if getattr(self, "key", None) is not None:
with tf.name_scope(self.key.name):
self.key.build([None, None, self.config.hidden_size])
if getattr(self, "value", None) is not None:
with tf.name_scope(self.value.name):
self.value.build([None, None, self.config.hidden_size])
# Copied from transformers.models.vit.modeling_tf_vit.TFViTSelfOutput with ViT->DeiT
class TFDeiTSelfOutput(keras.layers.Layer):
"""
The residual connection is defined in TFDeiTLayer instead of here (as is the case with other models), due to the
layernorm applied before each block.
"""
def __init__(self, config: DeiTConfig, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
)
self.dropout = keras.layers.Dropout(rate=config.hidden_dropout_prob)
self.config = config
def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool = False) -> tf.Tensor:
hidden_states = self.dense(inputs=hidden_states)
hidden_states = self.dropout(inputs=hidden_states, training=training)
return hidden_states
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "dense", None) is not None:
with tf.name_scope(self.dense.name):
self.dense.build([None, None, self.config.hidden_size])
# Copied from transformers.models.vit.modeling_tf_vit.TFViTAttention with ViT->DeiT
class TFDeiTAttention(keras.layers.Layer):
def __init__(self, config: DeiTConfig, **kwargs):
super().__init__(**kwargs)
self.self_attention = TFDeiTSelfAttention(config, name="attention")
self.dense_output = TFDeiTSelfOutput(config, name="output")
def prune_heads(self, heads):
raise NotImplementedError
def call(
self,
input_tensor: tf.Tensor,
head_mask: tf.Tensor,
output_attentions: bool,
training: bool = False,
) -> Tuple[tf.Tensor]:
self_outputs = self.self_attention(
hidden_states=input_tensor, head_mask=head_mask, output_attentions=output_attentions, training=training
)
attention_output = self.dense_output(
hidden_states=self_outputs[0], input_tensor=input_tensor, training=training
)
outputs = (attention_output,) + self_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_attention", None) is not None:
with tf.name_scope(self.self_attention.name):
self.self_attention.build(None)
if getattr(self, "dense_output", None) is not None:
with tf.name_scope(self.dense_output.name):
self.dense_output.build(None)
# Copied from transformers.models.vit.modeling_tf_vit.TFViTIntermediate with ViT->DeiT
class TFDeiTIntermediate(keras.layers.Layer):
def __init__(self, config: DeiTConfig, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
units=config.intermediate_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
)
if isinstance(config.hidden_act, str):
self.intermediate_act_fn = get_tf_activation(config.hidden_act)
else:
self.intermediate_act_fn = config.hidden_act
self.config = config
def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
hidden_states = self.dense(inputs=hidden_states)
hidden_states = self.intermediate_act_fn(hidden_states)
return hidden_states
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "dense", None) is not None:
with tf.name_scope(self.dense.name):
self.dense.build([None, None, self.config.hidden_size])
# Copied from transformers.models.vit.modeling_tf_vit.TFViTOutput with ViT->DeiT
class TFDeiTOutput(keras.layers.Layer):
def __init__(self, config: DeiTConfig, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
)
self.dropout = keras.layers.Dropout(rate=config.hidden_dropout_prob)
self.config = config
def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool = False) -> tf.Tensor:
hidden_states = self.dense(inputs=hidden_states)
hidden_states = self.dropout(inputs=hidden_states, training=training)
hidden_states = hidden_states + input_tensor
return hidden_states
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "dense", None) is not None:
with tf.name_scope(self.dense.name):
self.dense.build([None, None, self.config.intermediate_size])
class TFDeiTLayer(keras.layers.Layer):
"""This corresponds to the Block class in the timm implementation."""
def __init__(self, config: DeiTConfig, **kwargs):
super().__init__(**kwargs)
self.attention = TFDeiTAttention(config, name="attention")
self.intermediate = TFDeiTIntermediate(config, name="intermediate")
self.deit_output = TFDeiTOutput(config, name="output")
self.layernorm_before = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layernorm_before")
self.layernorm_after = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layernorm_after")
self.config = config
def call(
self,
hidden_states: tf.Tensor,
head_mask: tf.Tensor,
output_attentions: bool,
training: bool = False,
) -> Tuple[tf.Tensor]:
attention_outputs = self.attention(
# in DeiT, layernorm is applied before self-attention
input_tensor=self.layernorm_before(inputs=hidden_states, training=training),
head_mask=head_mask,
output_attentions=output_attentions,
training=training,
)
attention_output = attention_outputs[0]
# first residual connection
hidden_states = attention_output + hidden_states
# in DeiT, layernorm is also applied after self-attention
layer_output = self.layernorm_after(inputs=hidden_states, training=training)
intermediate_output = self.intermediate(hidden_states=layer_output, training=training)
# second residual connection is done here
layer_output = self.deit_output(
hidden_states=intermediate_output, input_tensor=hidden_states, training=training
)
outputs = (layer_output,) + 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, "attention", None) is not None:
with tf.name_scope(self.attention.name):
self.attention.build(None)
if getattr(self, "intermediate", None) is not None:
with tf.name_scope(self.intermediate.name):
self.intermediate.build(None)
if getattr(self, "deit_output", None) is not None:
with tf.name_scope(self.deit_output.name):
self.deit_output.build(None)
if getattr(self, "layernorm_before", None) is not None:
with tf.name_scope(self.layernorm_before.name):
self.layernorm_before.build([None, None, self.config.hidden_size])
if getattr(self, "layernorm_after", None) is not None:
with tf.name_scope(self.layernorm_after.name):
self.layernorm_after.build([None, None, self.config.hidden_size])
# Copied from transformers.models.vit.modeling_tf_vit.TFViTEncoder with ViT->DeiT
class TFDeiTEncoder(keras.layers.Layer):
def __init__(self, config: DeiTConfig, **kwargs):
super().__init__(**kwargs)
self.layer = [TFDeiTLayer(config, name=f"layer_._{i}") for i in range(config.num_hidden_layers)]
def call(
self,
hidden_states: tf.Tensor,
head_mask: tf.Tensor,
output_attentions: bool,
output_hidden_states: bool,
return_dict: bool,
training: bool = False,
) -> Union[TFBaseModelOutput, Tuple[tf.Tensor]]:
all_hidden_states = () if output_hidden_states else None
all_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_outputs = layer_module(
hidden_states=hidden_states,
head_mask=head_mask[i],
output_attentions=output_attentions,
training=training,
)
hidden_states = layer_outputs[0]
if output_attentions:
all_attentions = all_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_attentions] if v is not None)
return TFBaseModelOutput(
last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_attentions
)
def build(self, input_shape=None):
if self.built:
return
self.built = True
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 TFDeiTMainLayer(keras.layers.Layer):
config_class = DeiTConfig
def __init__(
self, config: DeiTConfig, add_pooling_layer: bool = True, use_mask_token: bool = False, **kwargs
) -> None:
super().__init__(**kwargs)
self.config = config
self.embeddings = TFDeiTEmbeddings(config, use_mask_token=use_mask_token, name="embeddings")
self.encoder = TFDeiTEncoder(config, name="encoder")
self.layernorm = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layernorm")
self.pooler = TFDeiTPooler(config, name="pooler") if add_pooling_layer else None
def get_input_embeddings(self) -> TFDeiTPatchEmbeddings:
return self.embeddings.patch_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} See base
class PreTrainedModel
"""
raise NotImplementedError
def get_head_mask(self, head_mask):
if head_mask is not None:
raise NotImplementedError
else:
head_mask = [None] * self.config.num_hidden_layers
return head_mask
@unpack_inputs
def call(
self,
pixel_values: tf.Tensor | None = None,
bool_masked_pos: tf.Tensor | None = None,
head_mask: tf.Tensor | None = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
interpolate_pos_encoding: bool = False,
training: bool = False,
) -> Union[TFBaseModelOutputWithPooling, 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
)
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")
# TF 2.0 image layers can't use NCHW format when running on CPU.
# (batch_size, num_channels, height, width) -> (batch_size, height, width, num_channels)
pixel_values = tf.transpose(pixel_values, (0, 2, 3, 1))
# 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)
embedding_output = self.embeddings(
pixel_values,
bool_masked_pos=bool_masked_pos,
training=training,
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,
training=training,
)
sequence_output = encoder_outputs[0]
sequence_output = self.layernorm(sequence_output, training=training)
pooled_output = self.pooler(sequence_output, training=training) 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 TFBaseModelOutputWithPooling(
last_hidden_state=sequence_output,
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.config.hidden_size])
if getattr(self, "pooler", None) is not None:
with tf.name_scope(self.pooler.name):
self.pooler.build(None)
# Copied from transformers.models.vit.modeling_tf_vit.TFViTPreTrainedModel with ViT->DeiT all-casing
class TFDeiTPreTrainedModel(TFPreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = DeiTConfig
base_model_prefix = "deit"
main_input_name = "pixel_values"
DEIT_START_DOCSTRING = r"""
This model is a TensorFlow
[keras.layers.Layer](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer). Use it as a regular
TensorFlow Module and refer to the TensorFlow documentation for all matter related to general usage and behavior.
Parameters:
config ([`DeiTConfig`]): 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.
"""
DEIT_INPUTS_DOCSTRING = r"""
Args:
pixel_values (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
[`DeiTImageProcessor.__call__`] for details.
head_mask (`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**.
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 to `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.
"""
@add_start_docstrings(
"The bare DeiT Model transformer outputting raw hidden-states without any specific head on top.",
DEIT_START_DOCSTRING,
)
class TFDeiTModel(TFDeiTPreTrainedModel):
def __init__(
self, config: DeiTConfig, add_pooling_layer: bool = True, use_mask_token: bool = False, **kwargs
) -> None:
super().__init__(config, **kwargs)
self.deit = TFDeiTMainLayer(
config, add_pooling_layer=add_pooling_layer, use_mask_token=use_mask_token, name="deit"
)
@unpack_inputs
@add_start_docstrings_to_model_forward(DEIT_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TFBaseModelOutputWithPooling,
config_class=_CONFIG_FOR_DOC,
modality="vision",
expected_output=_EXPECTED_OUTPUT_SHAPE,
)
def call(
self,
pixel_values: tf.Tensor | None = None,
bool_masked_pos: tf.Tensor | None = None,
head_mask: tf.Tensor | None = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
interpolate_pos_encoding: bool = False,
training: bool = False,
) -> Union[Tuple, TFBaseModelOutputWithPooling]:
outputs = self.deit(
pixel_values=pixel_values,
bool_masked_pos=bool_masked_pos,
head_mask=head_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
interpolate_pos_encoding=interpolate_pos_encoding,
training=training,
)
return outputs
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "deit", None) is not None:
with tf.name_scope(self.deit.name):
self.deit.build(None)
# Copied from transformers.models.vit.modeling_tf_vit.TFViTPooler with ViT->DeiT
class TFDeiTPooler(keras.layers.Layer):
def __init__(self, config: DeiTConfig, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
units=config.hidden_size,
kernel_initializer=get_initializer(config.initializer_range),
activation="tanh",
name="dense",
)
self.config = config
def call(self, hidden_states: tf.Tensor) -> tf.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(inputs=first_token_tensor)
return pooled_output
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "dense", None) is not None:
with tf.name_scope(self.dense.name):
self.dense.build([None, None, self.config.hidden_size])
class TFDeitPixelShuffle(keras.layers.Layer):
"""TF layer implementation of torch.nn.PixelShuffle"""
def __init__(self, upscale_factor: int, **kwargs) -> None:
super().__init__(**kwargs)
if not isinstance(upscale_factor, int) or upscale_factor < 2:
raise ValueError(f"upscale_factor must be an integer value >= 2 got {upscale_factor}")
self.upscale_factor = upscale_factor
def call(self, x: tf.Tensor) -> tf.Tensor:
hidden_states = x
batch_size, _, _, num_input_channels = shape_list(hidden_states)
block_size_squared = self.upscale_factor**2
output_depth = int(num_input_channels / block_size_squared)
# When the number of output channels >= 2, PyTorch's PixelShuffle and
# TF's depth_to_space differ in their output as the order of channels selected for combining
# is a permutation of the other c.f.
# https://stackoverflow.com/questions/68272502/tf-depth-to-space-not-same-as-torchs-pixelshuffle-when-output-channels-1
permutation = tf.constant(
[[i + j * block_size_squared for i in range(block_size_squared) for j in range(output_depth)]]
)
hidden_states = tf.gather(params=hidden_states, indices=tf.tile(permutation, [batch_size, 1]), batch_dims=-1)
hidden_states = tf.nn.depth_to_space(hidden_states, block_size=self.upscale_factor, data_format="NHWC")
return hidden_states
class TFDeitDecoder(keras.layers.Layer):
def __init__(self, config: DeiTConfig, **kwargs) -> None:
super().__init__(**kwargs)
self.conv2d = keras.layers.Conv2D(
filters=config.encoder_stride**2 * config.num_channels, kernel_size=1, name="0"
)
self.pixel_shuffle = TFDeitPixelShuffle(config.encoder_stride, name="1")
self.config = config
def call(self, inputs: tf.Tensor, training: bool = False) -> tf.Tensor:
hidden_states = inputs
hidden_states = self.conv2d(hidden_states)
hidden_states = self.pixel_shuffle(hidden_states)
return hidden_states
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "conv2d", None) is not None:
with tf.name_scope(self.conv2d.name):
self.conv2d.build([None, None, None, self.config.hidden_size])
if getattr(self, "pixel_shuffle", None) is not None:
with tf.name_scope(self.pixel_shuffle.name):
self.pixel_shuffle.build(None)
@add_start_docstrings(
"DeiT Model with a decoder on top for masked image modeling, as proposed in"
" [SimMIM](https://arxiv.org/abs/2111.09886).",
DEIT_START_DOCSTRING,
)
class TFDeiTForMaskedImageModeling(TFDeiTPreTrainedModel):
def __init__(self, config: DeiTConfig) -> None:
super().__init__(config)
self.deit = TFDeiTMainLayer(config, add_pooling_layer=False, use_mask_token=True, name="deit")
self.decoder = TFDeitDecoder(config, name="decoder")
@unpack_inputs
@add_start_docstrings_to_model_forward(DEIT_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=TFMaskedImageModelingOutput, config_class=_CONFIG_FOR_DOC)
def call(
self,
pixel_values: tf.Tensor | None = None,
bool_masked_pos: tf.Tensor | None = None,
head_mask: tf.Tensor | None = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
interpolate_pos_encoding: bool = False,
training: bool = False,
) -> Union[tuple, TFMaskedImageModelingOutput]:
r"""
bool_masked_pos (`tf.Tensor` of type bool and shape `(batch_size, num_patches)`):
Boolean masked positions. Indicates which patches are masked (1) and which aren't (0).
Returns:
Examples:
```python
>>> from transformers import AutoImageProcessor, TFDeiTForMaskedImageModeling
>>> import tensorflow as tf
>>> 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/deit-base-distilled-patch16-224")
>>> model = TFDeiTForMaskedImageModeling.from_pretrained("facebook/deit-base-distilled-patch16-224")
>>> num_patches = (model.config.image_size // model.config.patch_size) ** 2
>>> pixel_values = image_processor(images=image, return_tensors="tf").pixel_values
>>> # create random boolean mask of shape (batch_size, num_patches)
>>> bool_masked_pos = tf.cast(tf.random.uniform((1, num_patches), minval=0, maxval=2, dtype=tf.int32), tf.bool)
>>> outputs = model(pixel_values, bool_masked_pos=bool_masked_pos)
>>> loss, reconstructed_pixel_values = outputs.loss, outputs.reconstruction
>>> list(reconstructed_pixel_values.shape)
[1, 3, 224, 224]
```"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.deit(
pixel_values,
bool_masked_pos=bool_masked_pos,
head_mask=head_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
interpolate_pos_encoding=interpolate_pos_encoding,
training=training,
)
sequence_output = outputs[0]
# Reshape to (batch_size, num_channels, height, width)
sequence_output = sequence_output[:, 1:-1]
batch_size, sequence_length, num_channels = shape_list(sequence_output)
height = width = int(sequence_length**0.5)
sequence_output = tf.reshape(sequence_output, (batch_size, height, width, num_channels))
# Reconstruct pixel values
reconstructed_pixel_values = self.decoder(sequence_output, training=training)
# TF 2.0 image layers can't use NCHW format when running on CPU, so intermediate layers use NHWC,
# including the decoder. We transpose to compute the loss against the pixel values
# (batch_size, height, width, num_channels) -> (batch_size, num_channels, height, width)
reconstructed_pixel_values = tf.transpose(reconstructed_pixel_values, (0, 3, 1, 2))
masked_im_loss = None
if bool_masked_pos is not None:
size = self.config.image_size // self.config.patch_size
bool_masked_pos = tf.reshape(bool_masked_pos, (-1, size, size))
mask = tf.repeat(bool_masked_pos, self.config.patch_size, 1)
mask = tf.repeat(mask, self.config.patch_size, 2)
mask = tf.expand_dims(mask, 1)
mask = tf.cast(mask, tf.float32)
reconstruction_loss = keras.losses.mean_absolute_error(
# Swap axes as metric calculation reduces over the final dimension
tf.transpose(pixel_values, (1, 2, 3, 0)),
tf.transpose(reconstructed_pixel_values, (1, 2, 3, 0)),
)
reconstruction_loss = tf.expand_dims(reconstruction_loss, 0)
total_loss = tf.reduce_sum(reconstruction_loss * mask)
num_masked_pixels = (tf.reduce_sum(mask) + 1e-5) * self.config.num_channels
masked_im_loss = total_loss / num_masked_pixels
masked_im_loss = tf.reshape(masked_im_loss, (1,))
if not return_dict:
output = (reconstructed_pixel_values,) + outputs[1:]
return ((masked_im_loss,) + output) if masked_im_loss is not None else output
return TFMaskedImageModelingOutput(
loss=masked_im_loss,
reconstruction=reconstructed_pixel_values,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "deit", None) is not None:
with tf.name_scope(self.deit.name):
self.deit.build(None)
if getattr(self, "decoder", None) is not None:
with tf.name_scope(self.decoder.name):
self.decoder.build(None)
@add_start_docstrings(
"""
DeiT Model transformer with an image classification head on top (a linear layer on top of the final hidden state of
the [CLS] token) e.g. for ImageNet.
""",
DEIT_START_DOCSTRING,
)
class TFDeiTForImageClassification(TFDeiTPreTrainedModel, TFSequenceClassificationLoss):
def __init__(self, config: DeiTConfig):
super().__init__(config)
self.num_labels = config.num_labels
self.deit = TFDeiTMainLayer(config, add_pooling_layer=False, name="deit")
# Classifier head
self.classifier = (
keras.layers.Dense(config.num_labels, name="classifier")
if config.num_labels > 0
else keras.layers.Activation("linear", name="classifier")
)
self.config = config
@unpack_inputs
@add_start_docstrings_to_model_forward(DEIT_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=TFImageClassifierOutput, config_class=_CONFIG_FOR_DOC)
def call(
self,
pixel_values: tf.Tensor | None = None,
head_mask: tf.Tensor | None = None,
labels: tf.Tensor | None = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
interpolate_pos_encoding: bool = False,
training: bool = False,
) -> Union[tf.Tensor, TFImageClassifierOutput]:
r"""
labels (`tf.Tensor` 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).
Returns:
Examples:
```python
>>> from transformers import AutoImageProcessor, TFDeiTForImageClassification
>>> import tensorflow as tf
>>> from PIL import Image
>>> import requests
>>> keras.utils.set_random_seed(3) # doctest: +IGNORE_RESULT
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> # note: we are loading a TFDeiTForImageClassificationWithTeacher from the hub here,
>>> # so the head will be randomly initialized, hence the predictions will be random
>>> image_processor = AutoImageProcessor.from_pretrained("facebook/deit-base-distilled-patch16-224")
>>> model = TFDeiTForImageClassification.from_pretrained("facebook/deit-base-distilled-patch16-224")
>>> inputs = image_processor(images=image, return_tensors="tf")
>>> outputs = model(**inputs)
>>> logits = outputs.logits
>>> # model predicts one of the 1000 ImageNet classes
>>> predicted_class_idx = tf.math.argmax(logits, axis=-1)[0]
>>> print("Predicted class:", model.config.id2label[int(predicted_class_idx)])
Predicted class: little blue heron, Egretta caerulea
```"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.deit(
pixel_values,
head_mask=head_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
interpolate_pos_encoding=interpolate_pos_encoding,
training=training,
)
sequence_output = outputs[0]
logits = self.classifier(sequence_output[:, 0, :])
# we don't use the distillation token
loss = None if labels is None else self.hf_compute_loss(labels, logits)
if not return_dict:
output = (logits,) + outputs[1:]
return ((loss,) + output) if loss is not None else output
return TFImageClassifierOutput(
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, "deit", None) is not None:
with tf.name_scope(self.deit.name):
self.deit.build(None)
if getattr(self, "classifier", None) is not None:
with tf.name_scope(self.classifier.name):
self.classifier.build([None, None, self.config.hidden_size])
@add_start_docstrings(
"""
DeiT Model transformer with image classification heads on top (a linear layer on top of the final hidden state of
the [CLS] token and a linear layer on top of the final hidden state of the distillation token) e.g. for ImageNet.
.. warning::
This model supports inference-only. Fine-tuning with distillation (i.e. with a teacher) is not yet
supported.
""",
DEIT_START_DOCSTRING,
)
class TFDeiTForImageClassificationWithTeacher(TFDeiTPreTrainedModel):
def __init__(self, config: DeiTConfig) -> None:
super().__init__(config)
self.num_labels = config.num_labels
self.deit = TFDeiTMainLayer(config, add_pooling_layer=False, name="deit")
# Classifier heads
self.cls_classifier = (
keras.layers.Dense(config.num_labels, name="cls_classifier")
if config.num_labels > 0
else keras.layers.Activation("linear", name="cls_classifier")
)
self.distillation_classifier = (
keras.layers.Dense(config.num_labels, name="distillation_classifier")
if config.num_labels > 0
else keras.layers.Activation("linear", name="distillation_classifier")
)
self.config = config
@unpack_inputs
@add_start_docstrings_to_model_forward(DEIT_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT,
output_type=TFDeiTForImageClassificationWithTeacherOutput,
config_class=_CONFIG_FOR_DOC,
expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT,
)
def call(
self,
pixel_values: tf.Tensor | None = None,
head_mask: tf.Tensor | None = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
interpolate_pos_encoding: bool = False,
training: bool = False,
) -> Union[tuple, TFDeiTForImageClassificationWithTeacherOutput]:
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.deit(
pixel_values,
head_mask=head_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
interpolate_pos_encoding=interpolate_pos_encoding,
training=training,
)
sequence_output = outputs[0]
cls_logits = self.cls_classifier(sequence_output[:, 0, :])
distillation_logits = self.distillation_classifier(sequence_output[:, 1, :])
# during inference, return the average of both classifier predictions
logits = (cls_logits + distillation_logits) / 2
if not return_dict:
output = (logits, cls_logits, distillation_logits) + outputs[1:]
return output
return TFDeiTForImageClassificationWithTeacherOutput(
logits=logits,
cls_logits=cls_logits,
distillation_logits=distillation_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, "deit", None) is not None:
with tf.name_scope(self.deit.name):
self.deit.build(None)
if getattr(self, "cls_classifier", None) is not None:
with tf.name_scope(self.cls_classifier.name):
self.cls_classifier.build([None, None, self.config.hidden_size])
if getattr(self, "distillation_classifier", None) is not None:
with tf.name_scope(self.distillation_classifier.name):
self.distillation_classifier.build([None, None, self.config.hidden_size])
__all__ = [
"TFDeiTForImageClassification",
"TFDeiTForImageClassificationWithTeacher",
"TFDeiTForMaskedImageModeling",
"TFDeiTModel",
"TFDeiTPreTrainedModel",
]
| transformers/src/transformers/models/deit/modeling_tf_deit.py/0 | {
"file_path": "transformers/src/transformers/models/deit/modeling_tf_deit.py",
"repo_id": "transformers",
"token_count": 22225
} |
# Copyright 2023 The HuggingFace and Baidu 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
# rely on isort to merge the imports
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_torch_available
_import_structure = {
"configuration_ernie_m": ["ErnieMConfig"],
}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["tokenization_ernie_m"] = ["ErnieMTokenizer"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_ernie_m"] = [
"ErnieMForMultipleChoice",
"ErnieMForQuestionAnswering",
"ErnieMForSequenceClassification",
"ErnieMForTokenClassification",
"ErnieMModel",
"ErnieMPreTrainedModel",
"ErnieMForInformationExtraction",
]
if TYPE_CHECKING:
from .configuration_ernie_m import ErnieMConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_ernie_m import ErnieMTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_ernie_m import (
ErnieMForInformationExtraction,
ErnieMForMultipleChoice,
ErnieMForQuestionAnswering,
ErnieMForSequenceClassification,
ErnieMForTokenClassification,
ErnieMModel,
ErnieMPreTrainedModel,
)
else:
import sys
sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| transformers/src/transformers/models/deprecated/ernie_m/__init__.py/0 | {
"file_path": "transformers/src/transformers/models/deprecated/ernie_m/__init__.py",
"repo_id": "transformers",
"token_count": 920
} |
# 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.
"""Convert Jukebox checkpoints"""
import argparse
import json
import os
from pathlib import Path
import requests
import torch
from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging
logging.set_verbosity_info()
logger = logging.get_logger(__name__)
PREFIX = "https://openaipublic.azureedge.net/jukebox/models/"
MODEL_MAPPING = {
"jukebox-1b-lyrics": [
"5b/vqvae.pth.tar",
"5b/prior_level_0.pth.tar",
"5b/prior_level_1.pth.tar",
"1b_lyrics/prior_level_2.pth.tar",
],
"jukebox-5b-lyrics": [
"5b/vqvae.pth.tar",
"5b/prior_level_0.pth.tar",
"5b/prior_level_1.pth.tar",
"5b_lyrics/prior_level_2.pth.tar",
],
}
def replace_key(key):
if key.endswith(".model.1.bias") and len(key.split(".")) > 10:
key = key.replace(".model.1.bias", ".conv1d_1.bias")
elif key.endswith(".model.1.weight") and len(key.split(".")) > 10:
key = key.replace(".model.1.weight", ".conv1d_1.weight")
elif key.endswith(".model.3.bias") and len(key.split(".")) > 10:
key = key.replace(".model.3.bias", ".conv1d_2.bias")
elif key.endswith(".model.3.weight") and len(key.split(".")) > 10:
key = key.replace(".model.3.weight", ".conv1d_2.weight")
if "conditioner_blocks.0." in key:
key = key.replace("conditioner_blocks.0", "conditioner_blocks")
if "prime_prior" in key:
key = key.replace("prime_prior", "encoder")
if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
key = key.replace(".emb.", ".")
if key.endswith("k"): # replace vqvae.X.k with vqvae.X.codebook
return key.replace(".k", ".codebook")
if "y_emb." in key:
return key.replace("y_emb.", "metadata_embedding.")
if "x_emb.emb." in key:
key = key.replace("0.x_emb.emb", "embed_tokens")
if "prime_state_ln" in key:
return key.replace("prime_state_ln", "encoder.final_layer_norm")
if ".ln" in key:
return key.replace(".ln", ".layer_norm")
if "_ln" in key:
return key.replace("_ln", "_layer_norm")
if "prime_state_proj" in key:
return key.replace("prime_state_proj", "encoder.proj_in")
if "prime_x_out" in key:
return key.replace("prime_x_out", "encoder.lm_head")
if "prior.x_out" in key:
return key.replace("x_out", "fc_proj_out")
if "x_emb" in key:
return key.replace("x_emb", "embed_tokens")
return key
def fix_jukebox_keys(state_dict, model_state_dict, key_prefix, mapping):
new_dict = {}
import re
re_encoder_block_conv_in = re.compile(r"encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)")
re_encoder_block_resnet = re.compile(
r"encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"
)
re_encoder_block_proj_out = re.compile(r"encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)")
re_decoder_block_conv_out = re.compile(r"decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)")
re_decoder_block_resnet = re.compile(
r"decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"
)
re_decoder_block_proj_in = re.compile(r"decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)")
re_prior_cond_conv_out = re.compile(r"conditioner_blocks.(\d*).cond.model.(\d*).(\d).(bias|weight)")
re_prior_cond_resnet = re.compile(
r"conditioner_blocks.(\d*).cond.model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"
)
re_prior_cond_proj_in = re.compile(r"conditioner_blocks.(\d*).cond.model.(\d*).(bias|weight)")
for original_key, value in state_dict.items():
# rename vqvae.encoder keys
if re_encoder_block_conv_in.fullmatch(original_key):
regex_match = re_encoder_block_conv_in.match(original_key)
groups = regex_match.groups()
block_index = int(groups[2]) * 2 + int(groups[3])
re_new_key = f"encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.{groups[-1]}"
key = re_encoder_block_conv_in.sub(re_new_key, original_key)
elif re_encoder_block_resnet.fullmatch(original_key):
regex_match = re_encoder_block_resnet.match(original_key)
groups = regex_match.groups()
block_index = int(groups[2]) * 2 + int(groups[3])
conv_index = {"1": 1, "3": 2}[groups[-2]]
prefix = f"encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}."
resnet_block = f"resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"
re_new_key = prefix + resnet_block
key = re_encoder_block_resnet.sub(re_new_key, original_key)
elif re_encoder_block_proj_out.fullmatch(original_key):
regex_match = re_encoder_block_proj_out.match(original_key)
groups = regex_match.groups()
re_new_key = f"encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}"
key = re_encoder_block_proj_out.sub(re_new_key, original_key)
# rename vqvae.decoder keys
elif re_decoder_block_conv_out.fullmatch(original_key):
regex_match = re_decoder_block_conv_out.match(original_key)
groups = regex_match.groups()
block_index = int(groups[2]) * 2 + int(groups[3]) - 2
re_new_key = f"decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}.{groups[-1]}"
key = re_decoder_block_conv_out.sub(re_new_key, original_key)
elif re_decoder_block_resnet.fullmatch(original_key):
regex_match = re_decoder_block_resnet.match(original_key)
groups = regex_match.groups()
block_index = int(groups[2]) * 2 + int(groups[3]) - 2
conv_index = {"1": 1, "3": 2}[groups[-2]]
prefix = f"decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}."
resnet_block = f"resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"
re_new_key = prefix + resnet_block
key = re_decoder_block_resnet.sub(re_new_key, original_key)
elif re_decoder_block_proj_in.fullmatch(original_key):
regex_match = re_decoder_block_proj_in.match(original_key)
groups = regex_match.groups()
re_new_key = f"decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}"
key = re_decoder_block_proj_in.sub(re_new_key, original_key)
# rename prior cond.model to upsampler.upsample_block and resnet
elif re_prior_cond_conv_out.fullmatch(original_key):
regex_match = re_prior_cond_conv_out.match(original_key)
groups = regex_match.groups()
block_index = int(groups[1]) * 2 + int(groups[2]) - 2
re_new_key = f"conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}"
key = re_prior_cond_conv_out.sub(re_new_key, original_key)
elif re_prior_cond_resnet.fullmatch(original_key):
regex_match = re_prior_cond_resnet.match(original_key)
groups = regex_match.groups()
block_index = int(groups[1]) * 2 + int(groups[2]) - 2
conv_index = {"1": 1, "3": 2}[groups[-2]]
prefix = f"conditioner_blocks.upsampler.upsample_block.{block_index}."
resnet_block = f"resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"
re_new_key = prefix + resnet_block
key = re_prior_cond_resnet.sub(re_new_key, original_key)
elif re_prior_cond_proj_in.fullmatch(original_key):
regex_match = re_prior_cond_proj_in.match(original_key)
groups = regex_match.groups()
re_new_key = f"conditioner_blocks.upsampler.proj_in.{groups[-1]}"
key = re_prior_cond_proj_in.sub(re_new_key, original_key)
# keep original key
else:
key = original_key
key = replace_key(key)
if f"{key_prefix}.{key}" not in model_state_dict or key is None:
print(f"failed converting {original_key} to {key}, does not match")
# handle missmatched shape
elif value.shape != model_state_dict[f"{key_prefix}.{key}"].shape:
val = model_state_dict[f"{key_prefix}.{key}"]
print(f"{original_key}-> {key} : \nshape {val.shape} and { value.shape}, do not match")
key = original_key
mapping[key] = original_key
new_dict[key] = value
return new_dict
@torch.no_grad()
def convert_openai_checkpoint(model_name=None, pytorch_dump_folder_path=None):
"""
Copy/paste/tweak model's weights to our Jukebox structure.
"""
for file in MODEL_MAPPING[model_name]:
if not os.path.isfile(f"{pytorch_dump_folder_path}/{file.split('/')[-1]}"):
r = requests.get(f"{PREFIX}{file}", allow_redirects=True)
os.makedirs(f"{pytorch_dump_folder_path}/", exist_ok=True)
open(f"{pytorch_dump_folder_path}/{file.split('/')[-1]}", "wb").write(r.content)
model_to_convert = MODEL_MAPPING[model_name.split("/")[-1]]
config = JukeboxConfig.from_pretrained(model_name)
model = JukeboxModel(config)
weight_dict = []
mapping = {}
for i, dict_name in enumerate(model_to_convert):
old_dic = torch.load(f"{pytorch_dump_folder_path}/{dict_name.split('/')[-1]}")["model"]
new_dic = {}
for k in old_dic.keys():
if k.endswith(".b"):
new_dic[k.replace("b", "bias")] = old_dic[k]
elif k.endswith(".w"):
new_dic[k.replace("w", "weight")] = old_dic[k]
elif "level_2" not in dict_name and "cond.model." in k:
new_dic[k.replace(".blocks.", ".model.")] = old_dic[k]
else:
new_dic[k] = old_dic[k]
key_prefix = "vqvae" if i == 0 else f"priors.{3 - i}"
new_dic = fix_jukebox_keys(new_dic, model.state_dict(), key_prefix, mapping)
weight_dict.append(new_dic)
vqvae_state_dict = weight_dict.pop(0)
model.vqvae.load_state_dict(vqvae_state_dict)
for i in range(len(weight_dict)):
model.priors[i].load_state_dict(weight_dict[2 - i])
Path(pytorch_dump_folder_path).mkdir(exist_ok=True)
with open(f"{pytorch_dump_folder_path}/mapping.json", "w") as txtfile:
json.dump(mapping, txtfile)
print(f"Saving model {model_name} to {pytorch_dump_folder_path}")
model.save_pretrained(pytorch_dump_folder_path)
return weight_dict
if __name__ == "__main__":
parser = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--model_name",
default="jukebox-5b-lyrics",
type=str,
help="Name of the model you'd like to convert.",
)
parser.add_argument(
"--pytorch_dump_folder_path",
default="jukebox-5b-lyrics-converted",
type=str,
help="Path to the output PyTorch model directory.",
)
args = parser.parse_args()
convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)
| transformers/src/transformers/models/deprecated/jukebox/convert_jukebox.py/0 | {
"file_path": "transformers/src/transformers/models/deprecated/jukebox/convert_jukebox.py",
"repo_id": "transformers",
"token_count": 5498
} |
# coding=utf-8
# Copyright 2022 The REALM 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.
"""Fast Tokenization classes for REALM."""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ....tokenization_utils_base import BatchEncoding
from ....tokenization_utils_fast import PreTrainedTokenizerFast
from ....utils import PaddingStrategy, logging
from .tokenization_realm import RealmTokenizer
logger = logging.get_logger(__name__)
VOCAB_FILES_NAMES = {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}
class RealmTokenizerFast(PreTrainedTokenizerFast):
r"""
Construct a "fast" REALM tokenizer (backed by HuggingFace's *tokenizers* library). Based on WordPiece.
[`RealmTokenizerFast`] is identical to [`BertTokenizerFast`] and runs end-to-end tokenization: punctuation
splitting and wordpiece.
This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should
refer to this superclass for more information regarding those methods.
Args:
vocab_file (`str`):
File containing the vocabulary.
do_lower_case (`bool`, *optional*, defaults to `True`):
Whether or not to lowercase the input when tokenizing.
unk_token (`str`, *optional*, defaults to `"[UNK]"`):
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.
sep_token (`str`, *optional*, defaults to `"[SEP]"`):
The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
sequence classification or for a text and a question for question answering. It is also used as the last
token of a sequence built with special tokens.
pad_token (`str`, *optional*, defaults to `"[PAD]"`):
The token used for padding, for example when batching sequences of different lengths.
cls_token (`str`, *optional*, defaults to `"[CLS]"`):
The classifier token which is used when doing sequence classification (classification of the whole sequence
instead of per-token classification). It is the first token of the sequence when built with special tokens.
mask_token (`str`, *optional*, defaults to `"[MASK]"`):
The token used for masking values. This is the token used when training this model with masked language
modeling. This is the token which the model will try to predict.
clean_text (`bool`, *optional*, defaults to `True`):
Whether or not to clean the text before tokenization by removing any control characters and replacing all
whitespaces by the classic one.
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).
wordpieces_prefix (`str`, *optional*, defaults to `"##"`):
The prefix for subwords.
"""
vocab_files_names = VOCAB_FILES_NAMES
slow_tokenizer_class = RealmTokenizer
def __init__(
self,
vocab_file=None,
tokenizer_file=None,
do_lower_case=True,
unk_token="[UNK]",
sep_token="[SEP]",
pad_token="[PAD]",
cls_token="[CLS]",
mask_token="[MASK]",
tokenize_chinese_chars=True,
strip_accents=None,
**kwargs,
):
super().__init__(
vocab_file,
tokenizer_file=tokenizer_file,
do_lower_case=do_lower_case,
unk_token=unk_token,
sep_token=sep_token,
pad_token=pad_token,
cls_token=cls_token,
mask_token=mask_token,
tokenize_chinese_chars=tokenize_chinese_chars,
strip_accents=strip_accents,
**kwargs,
)
normalizer_state = json.loads(self.backend_tokenizer.normalizer.__getstate__())
if (
normalizer_state.get("lowercase", do_lower_case) != do_lower_case
or normalizer_state.get("strip_accents", strip_accents) != strip_accents
or normalizer_state.get("handle_chinese_chars", tokenize_chinese_chars) != tokenize_chinese_chars
):
normalizer_class = getattr(normalizers, normalizer_state.pop("type"))
normalizer_state["lowercase"] = do_lower_case
normalizer_state["strip_accents"] = strip_accents
normalizer_state["handle_chinese_chars"] = tokenize_chinese_chars
self.backend_tokenizer.normalizer = normalizer_class(**normalizer_state)
self.do_lower_case = do_lower_case
def batch_encode_candidates(self, text, **kwargs):
r"""
Encode a batch of text or text pair. This method is similar to regular __call__ method but has the following
differences:
1. Handle additional num_candidate axis. (batch_size, num_candidates, text)
2. Always pad the sequences to *max_length*.
3. Must specify *max_length* in order to stack packs of candidates into a batch.
- single sequence: `[CLS] X [SEP]`
- pair of sequences: `[CLS] A [SEP] B [SEP]`
Args:
text (`List[List[str]]`):
The batch of sequences to be encoded. Each sequence must be in this format: (batch_size,
num_candidates, text).
text_pair (`List[List[str]]`, *optional*):
The batch of sequences to be encoded. Each sequence must be in this format: (batch_size,
num_candidates, text).
**kwargs:
Keyword arguments of the __call__ method.
Returns:
[`BatchEncoding`]: Encoded text or text pair.
Example:
```python
>>> from transformers import RealmTokenizerFast
>>> # batch_size = 2, num_candidates = 2
>>> text = [["Hello world!", "Nice to meet you!"], ["The cute cat.", "The adorable dog."]]
>>> tokenizer = RealmTokenizerFast.from_pretrained("google/realm-cc-news-pretrained-encoder")
>>> tokenized_text = tokenizer.batch_encode_candidates(text, max_length=10, return_tensors="pt")
```"""
# Always using a fixed sequence length to encode in order to stack candidates into a batch.
kwargs["padding"] = PaddingStrategy.MAX_LENGTH
batch_text = text
batch_text_pair = kwargs.pop("text_pair", None)
return_tensors = kwargs.pop("return_tensors", None)
output_data = {
"input_ids": [],
"attention_mask": [],
"token_type_ids": [],
}
for idx, candidate_text in enumerate(batch_text):
if batch_text_pair is not None:
candidate_text_pair = batch_text_pair[idx]
else:
candidate_text_pair = None
encoded_candidates = super().__call__(candidate_text, candidate_text_pair, return_tensors=None, **kwargs)
encoded_input_ids = encoded_candidates.get("input_ids")
encoded_attention_mask = encoded_candidates.get("attention_mask")
encoded_token_type_ids = encoded_candidates.get("token_type_ids")
if encoded_input_ids is not None:
output_data["input_ids"].append(encoded_input_ids)
if encoded_attention_mask is not None:
output_data["attention_mask"].append(encoded_attention_mask)
if encoded_token_type_ids is not None:
output_data["token_type_ids"].append(encoded_token_type_ids)
output_data = {key: item for key, item in output_data.items() if len(item) != 0}
return BatchEncoding(output_data, tensor_type=return_tensors)
def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
"""
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. A REALM sequence has the following format:
- single sequence: `[CLS] X [SEP]`
- pair of sequences: `[CLS] A [SEP] B [SEP]`
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.
"""
output = [self.cls_token_id] + token_ids_0 + [self.sep_token_id]
if token_ids_1 is not None:
output += token_ids_1 + [self.sep_token_id]
return output
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. A REALM 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]
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)
| transformers/src/transformers/models/deprecated/realm/tokenization_realm_fast.py/0 | {
"file_path": "transformers/src/transformers/models/deprecated/realm/tokenization_realm_fast.py",
"repo_id": "transformers",
"token_count": 4469
} |
# coding=utf-8
# Copyright 2022 The Trajectory Transformers paper 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.
"""PyTorch TrajectoryTransformer model."""
import math
import os
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import functional as F
from ....modeling_utils import PreTrainedModel
from ....utils import (
ModelOutput,
add_start_docstrings,
add_start_docstrings_to_model_forward,
logging,
replace_return_docstrings,
)
from .configuration_trajectory_transformer import TrajectoryTransformerConfig
logger = logging.get_logger(__name__)
_CHECKPOINT_FOR_DOC = "CarlCochet/trajectory-transformer-halfcheetah-medium-v2"
_CONFIG_FOR_DOC = "TrajectoryTransformerConfig"
def load_tf_weights_in_trajectory_transformer(model, config, tf_checkpoint_path):
"""Load tf checkpoints in a pytorch model."""
try:
import re
import numpy as np
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(tf_checkpoint_path)
logger.info(f"Converting TensorFlow checkpoint from {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(f"Loading TF weight {name} with shape {shape}")
array = tf.train.load_variable(tf_path, name)
names.append(name)
arrays.append(array)
for name, array in zip(names, arrays):
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
):
logger.info(f"Skipping {'/'.join(name)}")
continue
pointer = model
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] == "kernel" or scope_names[0] == "gamma":
pointer = getattr(pointer, "weight")
elif scope_names[0] == "output_bias" or scope_names[0] == "beta":
pointer = getattr(pointer, "bias")
elif scope_names[0] == "output_weights":
pointer = getattr(pointer, "weight")
elif scope_names[0] == "squad":
pointer = getattr(pointer, "classifier")
else:
try:
pointer = getattr(pointer, scope_names[0])
except AttributeError:
logger.info(f"Skipping {'/'.join(name)}")
continue
if len(scope_names) >= 2:
num = int(scope_names[1])
pointer = pointer[num]
if m_name[-11:] == "_embeddings":
pointer = getattr(pointer, "weight")
elif m_name == "kernel":
array = np.transpose(array)
try:
if pointer.shape != array.shape:
raise ValueError(f"Pointer shape {pointer.shape} and array shape {array.shape} mismatched")
except AssertionError as e:
e.args += (pointer.shape, array.shape)
raise
logger.info(f"Initialize PyTorch weight {name}")
pointer.data = torch.from_numpy(array)
return model
@dataclass
class TrajectoryTransformerOutput(ModelOutput):
"""
Base class for model's outputs that also contains a pooling of the last hidden states.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Language modeling loss.
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.Tensor]]`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
Tuple of length `config.n_layers`, containing tuples of tensors of shape `(batch_size, num_heads,
sequence_length, embed_size_per_head)`). Contains pre-computed hidden-states (key and values in the
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 + 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)`. GPT2Attentions weights after the attention softmax, used to compute the weighted average
in the self-attention heads.
"""
loss: Optional[torch.FloatTensor] = None
logits: 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
class TrajectoryTransformerPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = TrajectoryTransformerConfig
load_tf_weights = load_tf_weights_in_trajectory_transformer
base_model_prefix = "trajectory_transformer"
main_input_name = "trajectories"
supports_gradient_checkpointing = True
def _init_weights(self, module):
if isinstance(module, (nn.Linear, nn.Embedding)):
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if isinstance(module, nn.Linear) and 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, EinLinear):
for i in range(module.n_models):
nn.init.kaiming_uniform_(module.weight[i], a=math.sqrt(5) / self.config.kaiming_initializer_range)
if module.bias is not None:
fan_in, _ = nn.init._calculate_fan_in_and_fan_out(module.weight[i])
bound = (1 / math.sqrt(fan_in)) * self.config.initializer_range
nn.init.uniform_(module.bias[i], -bound, bound)
TRAJECTORY_TRANSFORMER_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 ([`TrajectoryTransformerConfig`]): 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.
"""
TRAJECTORY_TRANSFORMER_INPUTS_DOCSTRING = r"""
Args:
trajectories (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Batch of trajectories, where a trajectory is a sequence of states, actions and rewards.
past_key_values (`Tuple[Tuple[torch.Tensor]]` of length `config.n_layers`, *optional*):
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.
targets (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Desired targets used to compute the loss.
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)
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 EinLinear(nn.Module):
def __init__(self, n_models, in_features, out_features, bias):
super().__init__()
self.n_models = n_models
self.out_features = out_features
self.in_features = in_features
self.weight = nn.Parameter(torch.Tensor(n_models, out_features, in_features))
if bias:
self.bias = nn.Parameter(torch.Tensor(n_models, out_features))
else:
self.register_parameter("bias", None)
def reset_parameters(self):
for i in range(self.n_models):
nn.init.kaiming_uniform_(self.weight[i], a=math.sqrt(5))
if self.bias is not None:
fan_in, _ = nn.init._calculate_fan_in_and_fan_out(self.weight[i])
bound = 1 / math.sqrt(fan_in)
nn.init.uniform_(self.bias[i], -bound, bound)
def forward(self, input):
"""
Args:
input (`torch.FloatTensor` of shape `(B, n_models, input_dim)`):
The input to the layer.
"""
# [ batch_size x n_models x output_dim ]
output = torch.einsum("eoi,bei->beo", self.weight, input)
if self.bias is not None:
raise RuntimeError()
return output
class CausalSelfAttention(nn.Module):
def __init__(self, config):
super().__init__()
if config.n_embd % config.n_head != 0:
raise ValueError(f"n_head ({config.n_head}) should be a divisor of n_embd ({config.n_embd})")
# key, query, value projections for all heads
self.key = nn.Linear(config.n_embd, config.n_embd)
self.query = nn.Linear(config.n_embd, config.n_embd)
self.value = nn.Linear(config.n_embd, config.n_embd)
# regularization
self.attn_drop = nn.Dropout(config.attn_pdrop)
self.resid_drop = nn.Dropout(config.resid_pdrop)
# output projection
self.proj = nn.Linear(config.n_embd, config.n_embd)
# causal mask to ensure that attention is only applied to the left in the input sequence
self.register_buffer(
"mask",
torch.tril(torch.ones(config.block_size, config.block_size)).view(
1, 1, config.block_size, config.block_size
),
persistent=False,
)
# mask previous value estimates
joined_dim = config.observation_dim + config.action_dim + 2
self.mask.squeeze()[:, joined_dim - 1 :: joined_dim] = 0
self.n_head = config.n_head
def forward(
self,
hidden_states: Optional[Tuple[torch.FloatTensor]],
layer_past: Optional[Tuple[torch.Tensor]] = None,
use_cache: Optional[bool] = False,
output_attentions: Optional[bool] = False,
):
batch_size, sequence_length, embedding_dim = hidden_states.size()
# calculate query, key, values for all heads in batch and move head forward to be the batch dim
# [ batch_size x n_heads x sequence_length x head_dim ]
key = (
self.key(hidden_states)
.view(batch_size, sequence_length, self.n_head, embedding_dim // self.n_head)
.transpose(1, 2)
)
query = (
self.query(hidden_states)
.view(batch_size, sequence_length, self.n_head, embedding_dim // self.n_head)
.transpose(1, 2)
)
value = (
self.value(hidden_states)
.view(batch_size, sequence_length, self.n_head, embedding_dim // self.n_head)
.transpose(1, 2)
)
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
# causal self-attention
# [ batch_size x n_heads x sequence_length x sequence_length ]
attn_weights = (torch.matmul(query, key.transpose(-2, -1))) * (1.0 / math.sqrt(key.size(-1)))
attn_weights = attn_weights.masked_fill(
self.mask[:, :, :sequence_length, :sequence_length] == 0, torch.finfo(attn_weights.dtype).min
)
attn_weights = F.softmax(attn_weights, dim=-1)
self._attn_map = attn_weights.clone()
attn_weights = self.attn_drop(attn_weights)
output = torch.matmul(attn_weights, value)
# [ batch_size x sequence_length x embedding_dim ]
# re-assemble all head outputs side by side
output = output.transpose(1, 2).contiguous().view(batch_size, sequence_length, embedding_dim)
# output projection
output = self.resid_drop(self.proj(output))
outputs = (output, present)
if output_attentions:
outputs += (attn_weights,)
return outputs
class Block(nn.Module):
def __init__(self, config):
super().__init__()
self.ln1 = nn.LayerNorm(config.n_embd)
self.ln2 = nn.LayerNorm(config.n_embd)
self.attn = CausalSelfAttention(config)
# MLP
self.l1 = nn.Linear(config.n_embd, 4 * config.n_embd)
self.act = nn.GELU()
self.l2 = nn.Linear(4 * config.n_embd, config.n_embd)
self.drop = nn.Dropout(config.resid_pdrop)
def forward(
self,
hidden_states: Optional[Tuple[torch.FloatTensor]],
layer_past: Optional[Tuple[torch.Tensor]] = None,
use_cache: Optional[bool] = False,
output_attentions: Optional[bool] = False,
):
residual = hidden_states
hidden_states = self.ln1(hidden_states)
attn_outputs = self.attn(
hidden_states, layer_past=layer_past, use_cache=use_cache, output_attentions=output_attentions
)
attn_output = attn_outputs[0]
outputs = attn_outputs[1:]
hidden_states = attn_output + residual
residual = hidden_states
hidden_states = self.ln2(hidden_states)
hidden_states = self.l1(hidden_states)
hidden_states = self.act(hidden_states)
hidden_states = self.l2(hidden_states)
hidden_states = residual + self.drop(hidden_states)
if use_cache:
outputs = (hidden_states,) + outputs
else:
outputs = (hidden_states,) + outputs[1:]
return outputs
@add_start_docstrings(
"The bare TrajectoryTransformer Model transformer outputting raw hidden-states without any specific head on top.",
TRAJECTORY_TRANSFORMER_START_DOCSTRING,
)
class TrajectoryTransformerModel(TrajectoryTransformerPreTrainedModel):
"""the full GPT language model, with a context size of block_size"""
def __init__(self, config):
super().__init__(config)
# input embedding stem (+1 for stop token)
self.tok_emb = nn.Embedding(config.vocab_size * config.transition_dim + 1, config.n_embd)
self.pos_emb = nn.Parameter(torch.zeros(1, config.block_size, config.n_embd))
self.drop = nn.Dropout(config.embd_pdrop)
# transformer
self.blocks = nn.ModuleList([Block(config) for _ in range(config.n_layer)])
# decoder head
self.ln_f = nn.LayerNorm(config.n_embd)
self.head = EinLinear(config.transition_dim, config.n_embd, config.vocab_size + 1, bias=False)
self.vocab_size = config.vocab_size
self.stop_token = config.vocab_size * config.transition_dim
self.block_size = config.block_size
self.observation_dim = config.observation_dim
self.action_dim = config.action_dim
self.transition_dim = config.transition_dim
self.embedding_dim = config.n_embd
self.action_weight = config.action_weight
self.reward_weight = config.reward_weight
self.value_weight = config.value_weight
self.gradient_checkpointing = False
self.post_init()
def get_block_size(self):
return self.block_size
def offset_tokens(self, trajectories):
_, sequence_length = trajectories.shape
n_states = int(np.ceil(sequence_length / self.transition_dim))
offsets = torch.arange(self.transition_dim) * self.vocab_size
offsets = offsets.repeat(n_states).to(trajectories.device)
offset_trajectories = trajectories + offsets[:sequence_length]
offset_trajectories[trajectories == self.vocab_size] = self.stop_token
return offset_trajectories
def pad_to_full_observation(self, hidden_states):
batch_size, sequence_length, _ = hidden_states.shape
n_pad = (self.transition_dim - sequence_length % self.transition_dim) % self.transition_dim
padding = torch.zeros(batch_size, n_pad, self.embedding_dim, device=hidden_states.device)
# [ batch_size x padded_sequence_length' x embedding_dim ]
hidden_states_pad = torch.cat([hidden_states, padding], dim=1)
hidden_states_pad = hidden_states_pad.view(-1, self.transition_dim, self.embedding_dim)
return hidden_states_pad, n_pad
@add_start_docstrings_to_model_forward(
TRAJECTORY_TRANSFORMER_INPUTS_DOCSTRING.format("batch_size, sequence_length")
)
@replace_return_docstrings(output_type=TrajectoryTransformerOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
trajectories: Optional[torch.LongTensor] = None,
past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
targets: Optional[torch.FloatTensor] = None,
attention_mask: 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[torch.Tensor], TrajectoryTransformerOutput]:
r"""
Returns:
Examples:
```python
>>> from transformers import TrajectoryTransformerModel
>>> import torch
>>> model = TrajectoryTransformerModel.from_pretrained(
... "CarlCochet/trajectory-transformer-halfcheetah-medium-v2"
... )
>>> model.to(device)
>>> model.eval()
>>> observations_dim, action_dim, batch_size = 17, 6, 256
>>> seq_length = observations_dim + action_dim + 1
>>> trajectories = torch.LongTensor([np.random.permutation(self.seq_length) for _ in range(batch_size)]).to(
... device
... )
>>> targets = torch.LongTensor([np.random.permutation(self.seq_length) for _ in range(batch_size)]).to(device)
>>> outputs = model(
... trajectories,
... targets=targets,
... use_cache=True,
... output_attentions=True,
... output_hidden_states=True,
... return_dict=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
)
if past_key_values is None:
past_key_values = tuple([None] * len(self.blocks))
batch_size, sequence_length = trajectories.size()
if sequence_length > self.block_size:
raise ValueError("Cannot forward, model block size is exhausted.")
offset_trajectories = self.offset_tokens(trajectories)
# [ batch_size x sequence_length x embedding_dim ]
# forward the GPT model
token_embeddings = self.tok_emb(offset_trajectories) # each index maps to a (learnable) vector
position_embeddings = self.pos_emb[:, :sequence_length, :] # each position maps to a (learnable) vector
hidden_states = self.drop(token_embeddings + position_embeddings)
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_hidden_states = () if output_hidden_states else None
for i, (block, layer_past) in enumerate(zip(self.blocks, past_key_values)):
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,
layer_past,
use_cache,
output_attentions,
)
else:
outputs = block(hidden_states, layer_past, use_cache, 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],)
# [ batch_size x sequence_length x embedding_dim ]
hidden_state = self.ln_f(hidden_states)
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
hidden_states_pad, n_pad = self.pad_to_full_observation(hidden_state)
logits = self.head(hidden_states_pad)
logits = logits.reshape(batch_size, sequence_length + n_pad, self.vocab_size + 1)
logits = logits[:, :sequence_length]
# if we are given some desired targets also calculate the loss
if targets is not None:
loss = F.cross_entropy(logits.reshape(-1, logits.size(-1)), targets.view(-1), reduction="none")
if self.action_weight != 1 or self.reward_weight != 1 or self.value_weight != 1:
# make weights
n_states = int(np.ceil(sequence_length / self.transition_dim))
weights = torch.cat(
[
torch.ones(self.observation_dim, device=trajectories.device),
torch.ones(self.action_dim, device=trajectories.device) * self.action_weight,
torch.ones(1, device=trajectories.device) * self.reward_weight,
torch.ones(1, device=trajectories.device) * self.value_weight,
]
)
weights = weights.repeat(n_states)
weights = weights[1:].repeat(batch_size, 1)
loss = loss * weights.view(-1)
loss = (loss * attention_mask.view(-1)).mean()
else:
loss = None
if not return_dict:
return tuple(v for v in [loss, logits, presents, all_hidden_states, all_self_attentions] if v is not None)
return TrajectoryTransformerOutput(
loss=loss,
logits=logits,
past_key_values=presents,
hidden_states=all_hidden_states,
attentions=all_self_attentions,
)
| transformers/src/transformers/models/deprecated/trajectory_transformer/modeling_trajectory_transformer.py/0 | {
"file_path": "transformers/src/transformers/models/deprecated/trajectory_transformer/modeling_trajectory_transformer.py",
"repo_id": "transformers",
"token_count": 11006
} |
# 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.
"""VAN model configuration"""
from ....configuration_utils import PretrainedConfig
from ....utils import logging
logger = logging.get_logger(__name__)
class VanConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`VanModel`]. It is used to instantiate a VAN 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 VAN
[Visual-Attention-Network/van-base](https://huggingface.co/Visual-Attention-Network/van-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:
image_size (`int`, *optional*, defaults to 224):
The size (resolution) of each image.
num_channels (`int`, *optional*, defaults to 3):
The number of input channels.
patch_sizes (`List[int]`, *optional*, defaults to `[7, 3, 3, 3]`):
Patch size to use in each stage's embedding layer.
strides (`List[int]`, *optional*, defaults to `[4, 2, 2, 2]`):
Stride size to use in each stage's embedding layer to downsample the input.
hidden_sizes (`List[int]`, *optional*, defaults to `[64, 128, 320, 512]`):
Dimensionality (hidden size) at each stage.
depths (`List[int]`, *optional*, defaults to `[3, 3, 12, 3]`):
Depth (number of layers) for each stage.
mlp_ratios (`List[int]`, *optional*, defaults to `[8, 8, 4, 4]`):
The expansion ratio for mlp layer at each stage.
hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in each layer. 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.
layer_norm_eps (`float`, *optional*, defaults to 1e-06):
The epsilon used by the layer normalization layers.
layer_scale_init_value (`float`, *optional*, defaults to 0.01):
The initial value for layer scaling.
drop_path_rate (`float`, *optional*, defaults to 0.0):
The dropout probability for stochastic depth.
dropout_rate (`float`, *optional*, defaults to 0.0):
The dropout probability for dropout.
Example:
```python
>>> from transformers import VanModel, VanConfig
>>> # Initializing a VAN van-base style configuration
>>> configuration = VanConfig()
>>> # Initializing a model from the van-base style configuration
>>> model = VanModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "van"
def __init__(
self,
image_size=224,
num_channels=3,
patch_sizes=[7, 3, 3, 3],
strides=[4, 2, 2, 2],
hidden_sizes=[64, 128, 320, 512],
depths=[3, 3, 12, 3],
mlp_ratios=[8, 8, 4, 4],
hidden_act="gelu",
initializer_range=0.02,
layer_norm_eps=1e-6,
layer_scale_init_value=1e-2,
drop_path_rate=0.0,
dropout_rate=0.0,
**kwargs,
):
super().__init__(**kwargs)
self.image_size = image_size
self.num_channels = num_channels
self.patch_sizes = patch_sizes
self.strides = strides
self.hidden_sizes = hidden_sizes
self.depths = depths
self.mlp_ratios = mlp_ratios
self.hidden_act = hidden_act
self.initializer_range = initializer_range
self.layer_norm_eps = layer_norm_eps
self.layer_scale_init_value = layer_scale_init_value
self.drop_path_rate = drop_path_rate
self.dropout_rate = dropout_rate
| transformers/src/transformers/models/deprecated/van/configuration_van.py/0 | {
"file_path": "transformers/src/transformers/models/deprecated/van/configuration_van.py",
"repo_id": "transformers",
"token_count": 1771
} |
# coding=utf-8
# Copyright 2022 SHI Labs 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 Dilated Neighborhood Attention Transformer model."""
import math
from dataclasses import dataclass
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 BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...pytorch_utils import find_pruneable_heads_and_indices, prune_linear_layer
from ...utils import (
ModelOutput,
OptionalDependencyNotAvailable,
add_code_sample_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
is_natten_available,
logging,
replace_return_docstrings,
requires_backends,
)
from ...utils.backbone_utils import BackboneMixin
from .configuration_dinat import DinatConfig
if is_natten_available():
from natten.functional import natten2dav, natten2dqkrpb
else:
def natten2dqkrpb(*args, **kwargs):
raise OptionalDependencyNotAvailable()
def natten2dav(*args, **kwargs):
raise OptionalDependencyNotAvailable()
logger = logging.get_logger(__name__)
# General docstring
_CONFIG_FOR_DOC = "DinatConfig"
# Base docstring
_CHECKPOINT_FOR_DOC = "shi-labs/dinat-mini-in1k-224"
_EXPECTED_OUTPUT_SHAPE = [1, 7, 7, 512]
# Image classification docstring
_IMAGE_CLASS_CHECKPOINT = "shi-labs/dinat-mini-in1k-224"
_IMAGE_CLASS_EXPECTED_OUTPUT = "tabby, tabby cat"
# drop_path and DinatDropPath are from the timm library.
@dataclass
class DinatEncoderOutput(ModelOutput):
"""
Dinat 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)`.
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, hidden_size, height, width)`.
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: 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 DinatModelOutput(ModelOutput):
"""
Dinat 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.
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)`.
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, hidden_size, height, width)`.
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: torch.FloatTensor = None
pooler_output: 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 DinatImageClassifierOutput(ModelOutput):
"""
Dinat outputs for image classification.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Classification (or regression if config.num_labels==1) loss.
logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`):
Classification (or regression if config.num_labels==1) scores (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 + one for the output of each stage) 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 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, hidden_size, height, width)`.
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: torch.FloatTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
reshaped_hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
class DinatEmbeddings(nn.Module):
"""
Construct the patch and position embeddings.
"""
def __init__(self, config):
super().__init__()
self.patch_embeddings = DinatPatchEmbeddings(config)
self.norm = nn.LayerNorm(config.embed_dim)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, pixel_values: Optional[torch.FloatTensor]) -> Tuple[torch.Tensor]:
embeddings = self.patch_embeddings(pixel_values)
embeddings = self.norm(embeddings)
embeddings = self.dropout(embeddings)
return embeddings
class DinatPatchEmbeddings(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, height, width, hidden_size)` to be consumed by a
Transformer.
"""
def __init__(self, config):
super().__init__()
patch_size = config.patch_size
num_channels, hidden_size = config.num_channels, config.embed_dim
self.num_channels = num_channels
if patch_size == 4:
pass
else:
# TODO: Support arbitrary patch sizes.
raise ValueError("Dinat only supports patch size of 4 at the moment.")
self.projection = nn.Sequential(
nn.Conv2d(self.num_channels, hidden_size // 2, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)),
nn.Conv2d(hidden_size // 2, hidden_size, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)),
)
def forward(self, pixel_values: Optional[torch.FloatTensor]) -> torch.Tensor:
_, 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."
)
embeddings = self.projection(pixel_values)
embeddings = embeddings.permute(0, 2, 3, 1)
return embeddings
class DinatDownsampler(nn.Module):
"""
Convolutional Downsampling Layer.
Args:
dim (`int`):
Number of input channels.
norm_layer (`nn.Module`, *optional*, defaults to `nn.LayerNorm`):
Normalization layer class.
"""
def __init__(self, dim: int, norm_layer: nn.Module = nn.LayerNorm) -> None:
super().__init__()
self.dim = dim
self.reduction = nn.Conv2d(dim, 2 * dim, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
self.norm = norm_layer(2 * dim)
def forward(self, input_feature: torch.Tensor) -> torch.Tensor:
input_feature = self.reduction(input_feature.permute(0, 3, 1, 2)).permute(0, 2, 3, 1)
input_feature = self.norm(input_feature)
return input_feature
# 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->Dinat
class DinatDropPath(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 NeighborhoodAttention(nn.Module):
def __init__(self, config, dim, num_heads, kernel_size, dilation):
super().__init__()
if dim % num_heads != 0:
raise ValueError(
f"The hidden size ({dim}) is not a multiple of the number of attention heads ({num_heads})"
)
self.num_attention_heads = num_heads
self.attention_head_size = int(dim / num_heads)
self.all_head_size = self.num_attention_heads * self.attention_head_size
self.kernel_size = kernel_size
self.dilation = dilation
# rpb is learnable relative positional biases; same concept is used Swin.
self.rpb = nn.Parameter(torch.zeros(num_heads, (2 * self.kernel_size - 1), (2 * self.kernel_size - 1)))
self.query = nn.Linear(self.all_head_size, self.all_head_size, bias=config.qkv_bias)
self.key = nn.Linear(self.all_head_size, self.all_head_size, bias=config.qkv_bias)
self.value = nn.Linear(self.all_head_size, self.all_head_size, bias=config.qkv_bias)
self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
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, 3, 1, 2, 4)
def forward(
self,
hidden_states: torch.Tensor,
output_attentions: Optional[bool] = False,
) -> Tuple[torch.Tensor]:
query_layer = self.transpose_for_scores(self.query(hidden_states))
key_layer = self.transpose_for_scores(self.key(hidden_states))
value_layer = self.transpose_for_scores(self.value(hidden_states))
# Apply the scale factor before computing attention weights. It's usually more efficient because
# attention weights are typically a bigger tensor compared to query.
# It gives identical results because scalars are commutable in matrix multiplication.
query_layer = query_layer / math.sqrt(self.attention_head_size)
# Compute NA between "query" and "key" to get the raw attention scores, and add relative positional biases.
attention_scores = natten2dqkrpb(query_layer, key_layer, self.rpb, self.kernel_size, self.dilation)
# 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 = natten2dav(attention_probs, value_layer, self.kernel_size, self.dilation)
context_layer = context_layer.permute(0, 2, 3, 1, 4).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,)
return outputs
class NeighborhoodAttentionOutput(nn.Module):
def __init__(self, config, dim):
super().__init__()
self.dense = nn.Linear(dim, dim)
self.dropout = nn.Dropout(config.attention_probs_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 NeighborhoodAttentionModule(nn.Module):
def __init__(self, config, dim, num_heads, kernel_size, dilation):
super().__init__()
self.self = NeighborhoodAttention(config, dim, num_heads, kernel_size, dilation)
self.output = NeighborhoodAttentionOutput(config, dim)
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,
output_attentions: Optional[bool] = False,
) -> Tuple[torch.Tensor]:
self_outputs = self.self(hidden_states, 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 DinatIntermediate(nn.Module):
def __init__(self, config, dim):
super().__init__()
self.dense = nn.Linear(dim, int(config.mlp_ratio * dim))
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 DinatOutput(nn.Module):
def __init__(self, config, dim):
super().__init__()
self.dense = nn.Linear(int(config.mlp_ratio * dim), dim)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidden_states = self.dense(hidden_states)
hidden_states = self.dropout(hidden_states)
return hidden_states
class DinatLayer(nn.Module):
def __init__(self, config, dim, num_heads, dilation, drop_path_rate=0.0):
super().__init__()
self.chunk_size_feed_forward = config.chunk_size_feed_forward
self.kernel_size = config.kernel_size
self.dilation = dilation
self.window_size = self.kernel_size * self.dilation
self.layernorm_before = nn.LayerNorm(dim, eps=config.layer_norm_eps)
self.attention = NeighborhoodAttentionModule(
config, dim, num_heads, kernel_size=self.kernel_size, dilation=self.dilation
)
self.drop_path = DinatDropPath(drop_path_rate) if drop_path_rate > 0.0 else nn.Identity()
self.layernorm_after = nn.LayerNorm(dim, eps=config.layer_norm_eps)
self.intermediate = DinatIntermediate(config, dim)
self.output = DinatOutput(config, dim)
self.layer_scale_parameters = (
nn.Parameter(config.layer_scale_init_value * torch.ones((2, dim)), requires_grad=True)
if config.layer_scale_init_value > 0
else None
)
def maybe_pad(self, hidden_states, height, width):
window_size = self.window_size
pad_values = (0, 0, 0, 0, 0, 0)
if height < window_size or width < window_size:
pad_l = pad_t = 0
pad_r = max(0, window_size - width)
pad_b = max(0, window_size - height)
pad_values = (0, 0, pad_l, pad_r, pad_t, pad_b)
hidden_states = nn.functional.pad(hidden_states, pad_values)
return hidden_states, pad_values
def forward(
self,
hidden_states: torch.Tensor,
output_attentions: Optional[bool] = False,
) -> Tuple[torch.Tensor, torch.Tensor]:
batch_size, height, width, channels = hidden_states.size()
shortcut = hidden_states
hidden_states = self.layernorm_before(hidden_states)
# pad hidden_states if they are smaller than kernel size x dilation
hidden_states, pad_values = self.maybe_pad(hidden_states, height, width)
_, height_pad, width_pad, _ = hidden_states.shape
attention_outputs = self.attention(hidden_states, output_attentions=output_attentions)
attention_output = attention_outputs[0]
was_padded = pad_values[3] > 0 or pad_values[5] > 0
if was_padded:
attention_output = attention_output[:, :height, :width, :].contiguous()
if self.layer_scale_parameters is not None:
attention_output = self.layer_scale_parameters[0] * attention_output
hidden_states = shortcut + self.drop_path(attention_output)
layer_output = self.layernorm_after(hidden_states)
layer_output = self.output(self.intermediate(layer_output))
if self.layer_scale_parameters is not None:
layer_output = self.layer_scale_parameters[1] * layer_output
layer_output = hidden_states + self.drop_path(layer_output)
layer_outputs = (layer_output, attention_outputs[1]) if output_attentions else (layer_output,)
return layer_outputs
class DinatStage(nn.Module):
def __init__(self, config, dim, depth, num_heads, dilations, drop_path_rate, downsample):
super().__init__()
self.config = config
self.dim = dim
self.layers = nn.ModuleList(
[
DinatLayer(
config=config,
dim=dim,
num_heads=num_heads,
dilation=dilations[i],
drop_path_rate=drop_path_rate[i],
)
for i in range(depth)
]
)
# patch merging layer
if downsample is not None:
self.downsample = downsample(dim=dim, norm_layer=nn.LayerNorm)
else:
self.downsample = None
self.pointing = False
def forward(
self,
hidden_states: torch.Tensor,
output_attentions: Optional[bool] = False,
) -> Tuple[torch.Tensor]:
_, height, width, _ = hidden_states.size()
for i, layer_module in enumerate(self.layers):
layer_outputs = layer_module(hidden_states, output_attentions)
hidden_states = layer_outputs[0]
hidden_states_before_downsampling = hidden_states
if self.downsample is not None:
hidden_states = self.downsample(hidden_states_before_downsampling)
stage_outputs = (hidden_states, hidden_states_before_downsampling)
if output_attentions:
stage_outputs += layer_outputs[1:]
return stage_outputs
class DinatEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.num_levels = len(config.depths)
self.config = config
dpr = [x.item() for x in torch.linspace(0, config.drop_path_rate, sum(config.depths))]
self.levels = nn.ModuleList(
[
DinatStage(
config=config,
dim=int(config.embed_dim * 2**i_layer),
depth=config.depths[i_layer],
num_heads=config.num_heads[i_layer],
dilations=config.dilations[i_layer],
drop_path_rate=dpr[sum(config.depths[:i_layer]) : sum(config.depths[: i_layer + 1])],
downsample=DinatDownsampler if (i_layer < self.num_levels - 1) else None,
)
for i_layer in range(self.num_levels)
]
)
def forward(
self,
hidden_states: torch.Tensor,
output_attentions: Optional[bool] = False,
output_hidden_states: Optional[bool] = False,
output_hidden_states_before_downsampling: Optional[bool] = False,
return_dict: Optional[bool] = True,
) -> Union[Tuple, DinatEncoderOutput]:
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:
# rearrange b h w c -> b c h w
reshaped_hidden_state = hidden_states.permute(0, 3, 1, 2)
all_hidden_states += (hidden_states,)
all_reshaped_hidden_states += (reshaped_hidden_state,)
for i, layer_module in enumerate(self.levels):
layer_outputs = layer_module(hidden_states, output_attentions)
hidden_states = layer_outputs[0]
hidden_states_before_downsampling = layer_outputs[1]
if output_hidden_states and output_hidden_states_before_downsampling:
# rearrange b h w c -> b c h w
reshaped_hidden_state = hidden_states_before_downsampling.permute(0, 3, 1, 2)
all_hidden_states += (hidden_states_before_downsampling,)
all_reshaped_hidden_states += (reshaped_hidden_state,)
elif output_hidden_states and not output_hidden_states_before_downsampling:
# rearrange b h w c -> b c h w
reshaped_hidden_state = hidden_states.permute(0, 3, 1, 2)
all_hidden_states += (hidden_states,)
all_reshaped_hidden_states += (reshaped_hidden_state,)
if output_attentions:
all_self_attentions += layer_outputs[2:]
if not return_dict:
return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None)
return DinatEncoderOutput(
last_hidden_state=hidden_states,
hidden_states=all_hidden_states,
attentions=all_self_attentions,
reshaped_hidden_states=all_reshaped_hidden_states,
)
class DinatPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = DinatConfig
base_model_prefix = "dinat"
main_input_name = "pixel_values"
def _init_weights(self, module):
"""Initialize the weights"""
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=self.config.initializer_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)
DINAT_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 ([`DinatConfig`]): 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.
"""
DINAT_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 [`ViTImageProcessor.__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.
"""
@add_start_docstrings(
"The bare Dinat Model transformer outputting raw hidden-states without any specific head on top.",
DINAT_START_DOCSTRING,
)
class DinatModel(DinatPreTrainedModel):
def __init__(self, config, add_pooling_layer=True):
super().__init__(config)
requires_backends(self, ["natten"])
self.config = config
self.num_levels = len(config.depths)
self.num_features = int(config.embed_dim * 2 ** (self.num_levels - 1))
self.embeddings = DinatEmbeddings(config)
self.encoder = DinatEncoder(config)
self.layernorm = nn.LayerNorm(self.num_features, eps=config.layer_norm_eps)
self.pooler = nn.AdaptiveAvgPool1d(1) if add_pooling_layer else None
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.embeddings.patch_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} 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(DINAT_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=DinatModelOutput,
config_class=_CONFIG_FOR_DOC,
modality="vision",
expected_output=_EXPECTED_OUTPUT_SHAPE,
)
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, DinatModelOutput]:
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")
embedding_output = self.embeddings(pixel_values)
encoder_outputs = self.encoder(
embedding_output,
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 = None
if self.pooler is not None:
pooled_output = self.pooler(sequence_output.flatten(1, 2).transpose(1, 2))
pooled_output = torch.flatten(pooled_output, 1)
if not return_dict:
output = (sequence_output, pooled_output) + encoder_outputs[1:]
return output
return DinatModelOutput(
last_hidden_state=sequence_output,
pooler_output=pooled_output,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
reshaped_hidden_states=encoder_outputs.reshaped_hidden_states,
)
@add_start_docstrings(
"""
Dinat Model transformer with an image classification head on top (a linear layer on top of the final hidden state
of the [CLS] token) e.g. for ImageNet.
""",
DINAT_START_DOCSTRING,
)
class DinatForImageClassification(DinatPreTrainedModel):
def __init__(self, config):
super().__init__(config)
requires_backends(self, ["natten"])
self.num_labels = config.num_labels
self.dinat = DinatModel(config)
# Classifier head
self.classifier = (
nn.Linear(self.dinat.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(DINAT_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT,
output_type=DinatImageClassifierOutput,
config_class=_CONFIG_FOR_DOC,
expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT,
)
def forward(
self,
pixel_values: 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, DinatImageClassifierOutput]:
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.dinat(
pixel_values,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
pooled_output = outputs[1]
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 DinatImageClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
reshaped_hidden_states=outputs.reshaped_hidden_states,
)
@add_start_docstrings(
"NAT backbone, to be used with frameworks like DETR and MaskFormer.",
DINAT_START_DOCSTRING,
)
class DinatBackbone(DinatPreTrainedModel, BackboneMixin):
def __init__(self, config):
super().__init__(config)
super()._init_backbone(config)
requires_backends(self, ["natten"])
self.embeddings = DinatEmbeddings(config)
self.encoder = DinatEncoder(config)
self.num_features = [config.embed_dim] + [int(config.embed_dim * 2**i) for i in range(len(config.depths))]
# 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
@add_start_docstrings_to_model_forward(DINAT_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=BackboneOutput, config_class=_CONFIG_FOR_DOC)
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("shi-labs/nat-mini-in1k-224")
>>> model = AutoBackbone.from_pretrained(
... "shi-labs/nat-mini-in1k-224", 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, 512, 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,
output_attentions=output_attentions,
output_hidden_states=True,
output_hidden_states_before_downsampling=True,
return_dict=True,
)
hidden_states = outputs.reshaped_hidden_states
feature_maps = ()
for stage, hidden_state in zip(self.stage_names, hidden_states):
if stage in self.out_features:
batch_size, num_channels, height, width = hidden_state.shape
hidden_state = hidden_state.permute(0, 2, 3, 1).contiguous()
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.hidden_states,)
return output
return BackboneOutput(
feature_maps=feature_maps,
hidden_states=outputs.hidden_states if output_hidden_states else None,
attentions=outputs.attentions,
)
__all__ = ["DinatForImageClassification", "DinatModel", "DinatPreTrainedModel", "DinatBackbone"]
| transformers/src/transformers/models/dinat/modeling_dinat.py/0 | {
"file_path": "transformers/src/transformers/models/dinat/modeling_dinat.py",
"repo_id": "transformers",
"token_count": 16674
} |
# coding=utf-8
# Copyright 2018 The HuggingFace Inc. team, The Hugging Face 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.
"""Tokenization classes for DPR."""
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert import BertTokenizer
logger = logging.get_logger(__name__)
VOCAB_FILES_NAMES = {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}
class DPRContextEncoderTokenizer(BertTokenizer):
r"""
Construct a DPRContextEncoder tokenizer.
[`DPRContextEncoderTokenizer`] is identical to [`BertTokenizer`] and runs end-to-end tokenization: punctuation
splitting and wordpiece.
Refer to superclass [`BertTokenizer`] for usage examples and documentation concerning parameters.
"""
vocab_files_names = VOCAB_FILES_NAMES
class DPRQuestionEncoderTokenizer(BertTokenizer):
r"""
Constructs a DPRQuestionEncoder tokenizer.
[`DPRQuestionEncoderTokenizer`] is identical to [`BertTokenizer`] and runs end-to-end tokenization: punctuation
splitting and wordpiece.
Refer to superclass [`BertTokenizer`] for usage examples and documentation concerning parameters.
"""
vocab_files_names = VOCAB_FILES_NAMES
DPRSpanPrediction = collections.namedtuple(
"DPRSpanPrediction", ["span_score", "relevance_score", "doc_id", "start_index", "end_index", "text"]
)
DPRReaderOutput = collections.namedtuple("DPRReaderOutput", ["start_logits", "end_logits", "relevance_logits"])
CUSTOM_DPR_READER_DOCSTRING = r"""
Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.
It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),
using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`
with the format:
```
[CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>
```
Args:
questions (`str` or `List[str]`):
The questions to be encoded. You can specify one question for many passages. In this case, the question
will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in
`titles` or `texts`.
titles (`str` or `List[str]`):
The passages titles to be encoded. This can be a string or a list of strings if there are several passages.
texts (`str` or `List[str]`):
The passages texts to be encoded. This can be a string or a list of strings if there are several passages.
padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):
Activates and controls padding. Accepts the following values:
- `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single sequence
if provided).
- `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
acceptable input length for the model if that argument is not provided.
- `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different
lengths).
truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):
Activates and controls truncation. Accepts the following values:
- `True` or `'longest_first'`: Truncate to a maximum length specified with the argument `max_length` or to
the maximum acceptable input length for the model if that argument is not provided. This will truncate
token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch
of pairs) is provided.
- `'only_first'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum
acceptable input length for the model if that argument is not provided. This will only truncate the first
sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
- `'only_second'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum
acceptable input length for the model if that argument is not provided. This will only truncate the
second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
- `False` or `'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths
greater than the model maximum admissible input size).
max_length (`int`, *optional*):
Controls the maximum length to use by one of the truncation/padding parameters.
If left unset or set to `None`, this will use the predefined model maximum length if a maximum length
is required by one of the truncation/padding parameters. If the model has no specific maximum input
length (like XLNet) truncation/padding to a maximum length will be deactivated.
return_tensors (`str` or [`~utils.TensorType`], *optional*):
If set, will return tensors instead of list of python integers. Acceptable values are:
- `'tf'`: Return TensorFlow `tf.constant` objects.
- `'pt'`: Return PyTorch `torch.Tensor` objects.
- `'np'`: Return Numpy `np.ndarray` objects.
return_attention_mask (`bool`, *optional*):
Whether or not to return the attention mask. If not set, will return the attention mask according to the
specific tokenizer's default, defined by the `return_outputs` attribute.
[What are attention masks?](../glossary#attention-mask)
Returns:
`Dict[str, List[List[int]]]`: A dictionary with the following keys:
- `input_ids`: List of token ids to be fed to a model.
- `attention_mask`: List of indices specifying which tokens should be attended to by the model.
"""
@add_start_docstrings(CUSTOM_DPR_READER_DOCSTRING)
class CustomDPRReaderTokenizerMixin:
def __call__(
self,
questions,
titles: Optional[str] = None,
texts: Optional[str] = None,
padding: Union[bool, str] = False,
truncation: Union[bool, str] = False,
max_length: Optional[int] = None,
return_tensors: Optional[Union[str, TensorType]] = None,
return_attention_mask: Optional[bool] = None,
**kwargs,
) -> BatchEncoding:
if titles is None and texts is None:
return super().__call__(
questions,
padding=padding,
truncation=truncation,
max_length=max_length,
return_tensors=return_tensors,
return_attention_mask=return_attention_mask,
**kwargs,
)
elif titles is None or texts is None:
text_pair = titles if texts is None else texts
return super().__call__(
questions,
text_pair,
padding=padding,
truncation=truncation,
max_length=max_length,
return_tensors=return_tensors,
return_attention_mask=return_attention_mask,
**kwargs,
)
titles = titles if not isinstance(titles, str) else [titles]
texts = texts if not isinstance(texts, str) else [texts]
n_passages = len(titles)
questions = questions if not isinstance(questions, str) else [questions] * n_passages
if len(titles) != len(texts):
raise ValueError(
f"There should be as many titles than texts but got {len(titles)} titles and {len(texts)} texts."
)
encoded_question_and_titles = super().__call__(questions, titles, padding=False, truncation=False)["input_ids"]
encoded_texts = super().__call__(texts, add_special_tokens=False, padding=False, truncation=False)["input_ids"]
encoded_inputs = {
"input_ids": [
(encoded_question_and_title + encoded_text)[:max_length]
if max_length is not None and truncation
else encoded_question_and_title + encoded_text
for encoded_question_and_title, encoded_text in zip(encoded_question_and_titles, encoded_texts)
]
}
if return_attention_mask is not False:
attention_mask = []
for input_ids in encoded_inputs["input_ids"]:
attention_mask.append([int(input_id != self.pad_token_id) for input_id in input_ids])
encoded_inputs["attention_mask"] = attention_mask
return self.pad(encoded_inputs, padding=padding, max_length=max_length, return_tensors=return_tensors)
def decode_best_spans(
self,
reader_input: BatchEncoding,
reader_output: DPRReaderOutput,
num_spans: int = 16,
max_answer_length: int = 64,
num_spans_per_passage: int = 4,
) -> List[DPRSpanPrediction]:
"""
Get the span predictions for the extractive Q&A model.
Returns: *List* of *DPRReaderOutput* sorted by descending *(relevance_score, span_score)*. Each
*DPRReaderOutput* is a *Tuple* with:
- **span_score**: `float` that corresponds to the score given by the reader for this span compared to other
spans in the same passage. It corresponds to the sum of the start and end logits of the span.
- **relevance_score**: `float` that corresponds to the score of the each passage to answer the question,
compared to all the other passages. It corresponds to the output of the QA classifier of the DPRReader.
- **doc_id**: `int` the id of the passage. - **start_index**: `int` the start index of the span
(inclusive). - **end_index**: `int` the end index of the span (inclusive).
Examples:
```python
>>> from transformers import DPRReader, DPRReaderTokenizer
>>> tokenizer = DPRReaderTokenizer.from_pretrained("facebook/dpr-reader-single-nq-base")
>>> model = DPRReader.from_pretrained("facebook/dpr-reader-single-nq-base")
>>> encoded_inputs = tokenizer(
... questions=["What is love ?"],
... titles=["Haddaway"],
... texts=["'What Is Love' is a song recorded by the artist Haddaway"],
... return_tensors="pt",
... )
>>> outputs = model(**encoded_inputs)
>>> predicted_spans = tokenizer.decode_best_spans(encoded_inputs, outputs)
>>> print(predicted_spans[0].text) # best span
a song
```"""
input_ids = reader_input["input_ids"]
start_logits, end_logits, relevance_logits = reader_output[:3]
n_passages = len(relevance_logits)
sorted_docs = sorted(range(n_passages), reverse=True, key=relevance_logits.__getitem__)
nbest_spans_predictions: List[DPRReaderOutput] = []
for doc_id in sorted_docs:
sequence_ids = list(input_ids[doc_id])
# assuming question & title information is at the beginning of the sequence
passage_offset = sequence_ids.index(self.sep_token_id, 2) + 1 # second sep id
if sequence_ids[-1] == self.pad_token_id:
sequence_len = sequence_ids.index(self.pad_token_id)
else:
sequence_len = len(sequence_ids)
best_spans = self._get_best_spans(
start_logits=start_logits[doc_id][passage_offset:sequence_len],
end_logits=end_logits[doc_id][passage_offset:sequence_len],
max_answer_length=max_answer_length,
top_spans=num_spans_per_passage,
)
for start_index, end_index in best_spans:
start_index += passage_offset
end_index += passage_offset
nbest_spans_predictions.append(
DPRSpanPrediction(
span_score=start_logits[doc_id][start_index] + end_logits[doc_id][end_index],
relevance_score=relevance_logits[doc_id],
doc_id=doc_id,
start_index=start_index,
end_index=end_index,
text=self.decode(sequence_ids[start_index : end_index + 1]),
)
)
if len(nbest_spans_predictions) >= num_spans:
break
return nbest_spans_predictions[:num_spans]
def _get_best_spans(
self,
start_logits: List[int],
end_logits: List[int],
max_answer_length: int,
top_spans: int,
) -> List[DPRSpanPrediction]:
"""
Finds the best answer span for the extractive Q&A model for one passage. It returns the best span by descending
`span_score` order and keeping max `top_spans` spans. Spans longer that `max_answer_length` are ignored.
"""
scores = []
for start_index, start_score in enumerate(start_logits):
for answer_length, end_score in enumerate(end_logits[start_index : start_index + max_answer_length]):
scores.append(((start_index, start_index + answer_length), start_score + end_score))
scores = sorted(scores, key=lambda x: x[1], reverse=True)
chosen_span_intervals = []
for (start_index, end_index), score in scores:
if start_index > end_index:
raise ValueError(f"Wrong span indices: [{start_index}:{end_index}]")
length = end_index - start_index + 1
if length > max_answer_length:
raise ValueError(f"Span is too long: {length} > {max_answer_length}")
if any(
start_index <= prev_start_index <= prev_end_index <= end_index
or prev_start_index <= start_index <= end_index <= prev_end_index
for (prev_start_index, prev_end_index) in chosen_span_intervals
):
continue
chosen_span_intervals.append((start_index, end_index))
if len(chosen_span_intervals) == top_spans:
break
return chosen_span_intervals
@add_end_docstrings(CUSTOM_DPR_READER_DOCSTRING)
class DPRReaderTokenizer(CustomDPRReaderTokenizerMixin, BertTokenizer):
r"""
Construct a DPRReader tokenizer.
[`DPRReaderTokenizer`] is almost identical to [`BertTokenizer`] and runs end-to-end tokenization: punctuation
splitting and wordpiece. The difference is that is has three inputs strings: question, titles and texts that are
combined to be fed to the [`DPRReader`] model.
Refer to superclass [`BertTokenizer`] for usage examples and documentation concerning parameters.
"""
vocab_files_names = VOCAB_FILES_NAMES
model_input_names = ["input_ids", "attention_mask"]
__all__ = ["DPRContextEncoderTokenizer", "DPRQuestionEncoderTokenizer", "DPRReaderOutput", "DPRReaderTokenizer"]
| transformers/src/transformers/models/dpr/tokenization_dpr.py/0 | {
"file_path": "transformers/src/transformers/models/dpr/tokenization_dpr.py",
"repo_id": "transformers",
"token_count": 6460
} |
# coding=utf-8
# Copyright 2023 Google Research, Inc. 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 EfficientNet 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 (
BaseModelOutputWithNoAttention,
BaseModelOutputWithPoolingAndNoAttention,
ImageClassifierOutputWithNoAttention,
)
from ...modeling_utils import PreTrainedModel
from ...utils import (
add_code_sample_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
logging,
)
from .configuration_efficientnet import EfficientNetConfig
logger = logging.get_logger(__name__)
# General docstring
_CONFIG_FOR_DOC = "EfficientNetConfig"
# Base docstring
_CHECKPOINT_FOR_DOC = "google/efficientnet-b7"
_EXPECTED_OUTPUT_SHAPE = [1, 768, 7, 7]
# Image classification docstring
_IMAGE_CLASS_CHECKPOINT = "google/efficientnet-b7"
_IMAGE_CLASS_EXPECTED_OUTPUT = "tabby, tabby cat"
EFFICIENTNET_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 ([`EfficientNetConfig`]): 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.
"""
EFFICIENTNET_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
[`AutoImageProcessor.__call__`] for details.
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 round_filters(config: EfficientNetConfig, num_channels: int):
r"""
Round number of filters based on depth multiplier.
"""
divisor = config.depth_divisor
num_channels *= config.width_coefficient
new_dim = max(divisor, int(num_channels + divisor / 2) // divisor * divisor)
# Make sure that round down does not go down by more than 10%.
if new_dim < 0.9 * num_channels:
new_dim += divisor
return int(new_dim)
def correct_pad(kernel_size: Union[int, Tuple], adjust: bool = True):
r"""
Utility function to get the tuple padding value for the depthwise convolution.
Args:
kernel_size (`int` or `tuple`):
Kernel size of the convolution layers.
adjust (`bool`, *optional*, defaults to `True`):
Adjusts padding value to apply to right and bottom sides of the input.
"""
if isinstance(kernel_size, int):
kernel_size = (kernel_size, kernel_size)
correct = (kernel_size[0] // 2, kernel_size[1] // 2)
if adjust:
return (correct[1] - 1, correct[1], correct[0] - 1, correct[0])
else:
return (correct[1], correct[1], correct[0], correct[0])
class EfficientNetEmbeddings(nn.Module):
r"""
A module that corresponds to the stem module of the original work.
"""
def __init__(self, config: EfficientNetConfig):
super().__init__()
self.out_dim = round_filters(config, 32)
self.padding = nn.ZeroPad2d(padding=(0, 1, 0, 1))
self.convolution = nn.Conv2d(
config.num_channels, self.out_dim, kernel_size=3, stride=2, padding="valid", bias=False
)
self.batchnorm = nn.BatchNorm2d(self.out_dim, eps=config.batch_norm_eps, momentum=config.batch_norm_momentum)
self.activation = ACT2FN[config.hidden_act]
def forward(self, pixel_values: torch.Tensor) -> torch.Tensor:
features = self.padding(pixel_values)
features = self.convolution(features)
features = self.batchnorm(features)
features = self.activation(features)
return features
class EfficientNetDepthwiseConv2d(nn.Conv2d):
def __init__(
self,
in_channels,
depth_multiplier=1,
kernel_size=3,
stride=1,
padding=0,
dilation=1,
bias=True,
padding_mode="zeros",
):
out_channels = in_channels * depth_multiplier
super().__init__(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=kernel_size,
stride=stride,
padding=padding,
dilation=dilation,
groups=in_channels,
bias=bias,
padding_mode=padding_mode,
)
class EfficientNetExpansionLayer(nn.Module):
r"""
This corresponds to the expansion phase of each block in the original implementation.
"""
def __init__(self, config: EfficientNetConfig, in_dim: int, out_dim: int, stride: int):
super().__init__()
self.expand_conv = nn.Conv2d(
in_channels=in_dim,
out_channels=out_dim,
kernel_size=1,
padding="same",
bias=False,
)
self.expand_bn = nn.BatchNorm2d(num_features=out_dim, eps=config.batch_norm_eps)
self.expand_act = ACT2FN[config.hidden_act]
def forward(self, hidden_states: torch.FloatTensor) -> torch.Tensor:
# Expand phase
hidden_states = self.expand_conv(hidden_states)
hidden_states = self.expand_bn(hidden_states)
hidden_states = self.expand_act(hidden_states)
return hidden_states
class EfficientNetDepthwiseLayer(nn.Module):
r"""
This corresponds to the depthwise convolution phase of each block in the original implementation.
"""
def __init__(
self,
config: EfficientNetConfig,
in_dim: int,
stride: int,
kernel_size: int,
adjust_padding: bool,
):
super().__init__()
self.stride = stride
conv_pad = "valid" if self.stride == 2 else "same"
padding = correct_pad(kernel_size, adjust=adjust_padding)
self.depthwise_conv_pad = nn.ZeroPad2d(padding=padding)
self.depthwise_conv = EfficientNetDepthwiseConv2d(
in_dim, kernel_size=kernel_size, stride=stride, padding=conv_pad, bias=False
)
self.depthwise_norm = nn.BatchNorm2d(
num_features=in_dim, eps=config.batch_norm_eps, momentum=config.batch_norm_momentum
)
self.depthwise_act = ACT2FN[config.hidden_act]
def forward(self, hidden_states: torch.FloatTensor) -> torch.Tensor:
# Depthwise convolution
if self.stride == 2:
hidden_states = self.depthwise_conv_pad(hidden_states)
hidden_states = self.depthwise_conv(hidden_states)
hidden_states = self.depthwise_norm(hidden_states)
hidden_states = self.depthwise_act(hidden_states)
return hidden_states
class EfficientNetSqueezeExciteLayer(nn.Module):
r"""
This corresponds to the Squeeze and Excitement phase of each block in the original implementation.
"""
def __init__(self, config: EfficientNetConfig, in_dim: int, expand_dim: int, expand: bool = False):
super().__init__()
self.dim = expand_dim if expand else in_dim
self.dim_se = max(1, int(in_dim * config.squeeze_expansion_ratio))
self.squeeze = nn.AdaptiveAvgPool2d(output_size=1)
self.reduce = nn.Conv2d(
in_channels=self.dim,
out_channels=self.dim_se,
kernel_size=1,
padding="same",
)
self.expand = nn.Conv2d(
in_channels=self.dim_se,
out_channels=self.dim,
kernel_size=1,
padding="same",
)
self.act_reduce = ACT2FN[config.hidden_act]
self.act_expand = nn.Sigmoid()
def forward(self, hidden_states: torch.FloatTensor) -> torch.Tensor:
inputs = hidden_states
hidden_states = self.squeeze(hidden_states)
hidden_states = self.reduce(hidden_states)
hidden_states = self.act_reduce(hidden_states)
hidden_states = self.expand(hidden_states)
hidden_states = self.act_expand(hidden_states)
hidden_states = torch.mul(inputs, hidden_states)
return hidden_states
class EfficientNetFinalBlockLayer(nn.Module):
r"""
This corresponds to the final phase of each block in the original implementation.
"""
def __init__(
self, config: EfficientNetConfig, in_dim: int, out_dim: int, stride: int, drop_rate: float, id_skip: bool
):
super().__init__()
self.apply_dropout = stride == 1 and not id_skip
self.project_conv = nn.Conv2d(
in_channels=in_dim,
out_channels=out_dim,
kernel_size=1,
padding="same",
bias=False,
)
self.project_bn = nn.BatchNorm2d(
num_features=out_dim, eps=config.batch_norm_eps, momentum=config.batch_norm_momentum
)
self.dropout = nn.Dropout(p=drop_rate)
def forward(self, embeddings: torch.FloatTensor, hidden_states: torch.FloatTensor) -> torch.Tensor:
hidden_states = self.project_conv(hidden_states)
hidden_states = self.project_bn(hidden_states)
if self.apply_dropout:
hidden_states = self.dropout(hidden_states)
hidden_states = hidden_states + embeddings
return hidden_states
class EfficientNetBlock(nn.Module):
r"""
This corresponds to the expansion and depthwise convolution phase of each block in the original implementation.
Args:
config ([`EfficientNetConfig`]):
Model configuration class.
in_dim (`int`):
Number of input channels.
out_dim (`int`):
Number of output channels.
stride (`int`):
Stride size to be used in convolution layers.
expand_ratio (`int`):
Expand ratio to set the output dimensions for the expansion and squeeze-excite layers.
kernel_size (`int`):
Kernel size for the depthwise convolution layer.
drop_rate (`float`):
Dropout rate to be used in the final phase of each block.
id_skip (`bool`):
Whether to apply dropout and sum the final hidden states with the input embeddings during the final phase
of each block. Set to `True` for the first block of each stage.
adjust_padding (`bool`):
Whether to apply padding to only right and bottom side of the input kernel before the depthwise convolution
operation, set to `True` for inputs with odd input sizes.
"""
def __init__(
self,
config: EfficientNetConfig,
in_dim: int,
out_dim: int,
stride: int,
expand_ratio: int,
kernel_size: int,
drop_rate: float,
id_skip: bool,
adjust_padding: bool,
):
super().__init__()
self.expand_ratio = expand_ratio
self.expand = True if self.expand_ratio != 1 else False
expand_in_dim = in_dim * expand_ratio
if self.expand:
self.expansion = EfficientNetExpansionLayer(
config=config, in_dim=in_dim, out_dim=expand_in_dim, stride=stride
)
self.depthwise_conv = EfficientNetDepthwiseLayer(
config=config,
in_dim=expand_in_dim if self.expand else in_dim,
stride=stride,
kernel_size=kernel_size,
adjust_padding=adjust_padding,
)
self.squeeze_excite = EfficientNetSqueezeExciteLayer(
config=config, in_dim=in_dim, expand_dim=expand_in_dim, expand=self.expand
)
self.projection = EfficientNetFinalBlockLayer(
config=config,
in_dim=expand_in_dim if self.expand else in_dim,
out_dim=out_dim,
stride=stride,
drop_rate=drop_rate,
id_skip=id_skip,
)
def forward(self, hidden_states: torch.FloatTensor) -> torch.Tensor:
embeddings = hidden_states
# Expansion and depthwise convolution phase
if self.expand_ratio != 1:
hidden_states = self.expansion(hidden_states)
hidden_states = self.depthwise_conv(hidden_states)
# Squeeze and excite phase
hidden_states = self.squeeze_excite(hidden_states)
hidden_states = self.projection(embeddings, hidden_states)
return hidden_states
class EfficientNetEncoder(nn.Module):
r"""
Forward propogates the embeddings through each EfficientNet block.
Args:
config ([`EfficientNetConfig`]):
Model configuration class.
"""
def __init__(self, config: EfficientNetConfig):
super().__init__()
self.config = config
self.depth_coefficient = config.depth_coefficient
def round_repeats(repeats):
# Round number of block repeats based on depth multiplier.
return int(math.ceil(self.depth_coefficient * repeats))
num_base_blocks = len(config.in_channels)
num_blocks = sum(round_repeats(n) for n in config.num_block_repeats)
curr_block_num = 0
blocks = []
for i in range(num_base_blocks):
in_dim = round_filters(config, config.in_channels[i])
out_dim = round_filters(config, config.out_channels[i])
stride = config.strides[i]
kernel_size = config.kernel_sizes[i]
expand_ratio = config.expand_ratios[i]
for j in range(round_repeats(config.num_block_repeats[i])):
id_skip = True if j == 0 else False
stride = 1 if j > 0 else stride
in_dim = out_dim if j > 0 else in_dim
adjust_padding = False if curr_block_num in config.depthwise_padding else True
drop_rate = config.drop_connect_rate * curr_block_num / num_blocks
block = EfficientNetBlock(
config=config,
in_dim=in_dim,
out_dim=out_dim,
stride=stride,
kernel_size=kernel_size,
expand_ratio=expand_ratio,
drop_rate=drop_rate,
id_skip=id_skip,
adjust_padding=adjust_padding,
)
blocks.append(block)
curr_block_num += 1
self.blocks = nn.ModuleList(blocks)
self.top_conv = nn.Conv2d(
in_channels=out_dim,
out_channels=round_filters(config, 1280),
kernel_size=1,
padding="same",
bias=False,
)
self.top_bn = nn.BatchNorm2d(
num_features=config.hidden_dim, eps=config.batch_norm_eps, momentum=config.batch_norm_momentum
)
self.top_activation = ACT2FN[config.hidden_act]
def forward(
self,
hidden_states: torch.FloatTensor,
output_hidden_states: Optional[bool] = False,
return_dict: Optional[bool] = True,
) -> BaseModelOutputWithNoAttention:
all_hidden_states = (hidden_states,) if output_hidden_states else None
for block in self.blocks:
hidden_states = block(hidden_states)
if output_hidden_states:
all_hidden_states += (hidden_states,)
hidden_states = self.top_conv(hidden_states)
hidden_states = self.top_bn(hidden_states)
hidden_states = self.top_activation(hidden_states)
if not return_dict:
return tuple(v for v in [hidden_states, all_hidden_states] if v is not None)
return BaseModelOutputWithNoAttention(
last_hidden_state=hidden_states,
hidden_states=all_hidden_states,
)
class EfficientNetPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = EfficientNetConfig
base_model_prefix = "efficientnet"
main_input_name = "pixel_values"
_no_split_modules = []
def _init_weights(self, module):
"""Initialize the weights"""
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=self.config.initializer_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)
@add_start_docstrings(
"The bare EfficientNet model outputting raw features without any specific head on top.",
EFFICIENTNET_START_DOCSTRING,
)
class EfficientNetModel(EfficientNetPreTrainedModel):
def __init__(self, config: EfficientNetConfig):
super().__init__(config)
self.config = config
self.embeddings = EfficientNetEmbeddings(config)
self.encoder = EfficientNetEncoder(config)
# Final pooling layer
if config.pooling_type == "mean":
self.pooler = nn.AvgPool2d(config.hidden_dim, ceil_mode=True)
elif config.pooling_type == "max":
self.pooler = nn.MaxPool2d(config.hidden_dim, ceil_mode=True)
else:
raise ValueError(f"config.pooling must be one of ['mean', 'max'] got {config.pooling}")
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(EFFICIENTNET_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=BaseModelOutputWithPoolingAndNoAttention,
config_class=_CONFIG_FOR_DOC,
modality="vision",
expected_output=_EXPECTED_OUTPUT_SHAPE,
)
def forward(
self,
pixel_values: torch.FloatTensor = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, BaseModelOutputWithPoolingAndNoAttention]:
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")
embedding_output = self.embeddings(pixel_values)
encoder_outputs = self.encoder(
embedding_output,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
# Apply pooling
last_hidden_state = encoder_outputs[0]
pooled_output = self.pooler(last_hidden_state)
# Reshape (batch_size, 1280, 1 , 1) -> (batch_size, 1280)
pooled_output = pooled_output.reshape(pooled_output.shape[:2])
if not return_dict:
return (last_hidden_state, pooled_output) + encoder_outputs[1:]
return BaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=last_hidden_state,
pooler_output=pooled_output,
hidden_states=encoder_outputs.hidden_states,
)
@add_start_docstrings(
"""
EfficientNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g.
for ImageNet.
""",
EFFICIENTNET_START_DOCSTRING,
)
class EfficientNetForImageClassification(EfficientNetPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.config = config
self.efficientnet = EfficientNetModel(config)
# Classifier head
self.dropout = nn.Dropout(p=config.dropout_rate)
self.classifier = nn.Linear(config.hidden_dim, self.num_labels) if self.num_labels > 0 else nn.Identity()
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(EFFICIENTNET_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT,
output_type=ImageClassifierOutputWithNoAttention,
config_class=_CONFIG_FOR_DOC,
expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT,
)
def forward(
self,
pixel_values: torch.FloatTensor = None,
labels: Optional[torch.LongTensor] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, ImageClassifierOutputWithNoAttention]:
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.efficientnet(pixel_values, output_hidden_states=output_hidden_states, return_dict=return_dict)
pooled_output = outputs.pooler_output if return_dict else 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 ImageClassifierOutputWithNoAttention(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
)
__all__ = ["EfficientNetForImageClassification", "EfficientNetModel", "EfficientNetPreTrainedModel"]
| transformers/src/transformers/models/efficientnet/modeling_efficientnet.py/0 | {
"file_path": "transformers/src/transformers/models/efficientnet/modeling_efficientnet.py",
"repo_id": "transformers",
"token_count": 10382
} |
# coding=utf-8
# Copyright 2022 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 ESM model."""
import math
from typing import List, Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_outputs import (
BaseModelOutputWithPastAndCrossAttentions,
BaseModelOutputWithPoolingAndCrossAttentions,
MaskedLMOutput,
SequenceClassifierOutput,
TokenClassifierOutput,
)
from ...modeling_utils import PreTrainedModel, find_pruneable_heads_and_indices, prune_linear_layer
from ...utils import logging
from .configuration_esm import EsmConfig
logger = logging.get_logger(__name__)
_CHECKPOINT_FOR_DOC = "facebook/esm2_t6_8M_UR50D"
_CONFIG_FOR_DOC = "EsmConfig"
def rotate_half(x):
x1, x2 = x.chunk(2, dim=-1)
return torch.cat((-x2, x1), dim=-1)
def apply_rotary_pos_emb(x, cos, sin):
cos = cos[:, :, : x.shape[-2], :]
sin = sin[:, :, : x.shape[-2], :]
return (x * cos) + (rotate_half(x) * sin)
def gelu(x):
"""
This is the gelu implementation from the original ESM repo. Using F.gelu yields subtly wrong results.
"""
return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0)))
def symmetrize(x):
"Make layer symmetric in final two dimensions, used for contact prediction."
return x + x.transpose(-1, -2)
def average_product_correct(x):
"Perform average product correct, used for contact prediction."
a1 = x.sum(-1, keepdims=True)
a2 = x.sum(-2, keepdims=True)
a12 = x.sum((-1, -2), keepdims=True)
avg = a1 * a2
avg.div_(a12) # in-place to reduce memory
normalized = x - avg
return normalized
class RotaryEmbedding(torch.nn.Module):
"""
Rotary position embeddings based on those in
[RoFormer](https://huggingface.co/docs/transformers/model_doc/roformer). Query and keys are transformed by rotation
matrices which depend on their relative positions.
"""
def __init__(self, dim: int):
super().__init__()
# Generate and save the inverse frequency buffer (non trainable)
inv_freq = 1.0 / (10000 ** (torch.arange(0, dim, 2, dtype=torch.int64).float() / dim))
inv_freq = inv_freq
self.register_buffer("inv_freq", inv_freq)
self._seq_len_cached = None
self._cos_cached = None
self._sin_cached = None
def _update_cos_sin_tables(self, x, seq_dimension=2):
seq_len = x.shape[seq_dimension]
# Reset the tables if the sequence length has changed,
# or if we're on a new device (possibly due to tracing for instance)
if seq_len != self._seq_len_cached or self._cos_cached.device != x.device:
self._seq_len_cached = seq_len
t = torch.arange(x.shape[seq_dimension], device=x.device).type_as(self.inv_freq)
freqs = torch.outer(t, self.inv_freq)
emb = torch.cat((freqs, freqs), dim=-1).to(x.device)
self._cos_cached = emb.cos()[None, None, :, :]
self._sin_cached = emb.sin()[None, None, :, :]
return self._cos_cached, self._sin_cached
def forward(self, q: torch.Tensor, k: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
self._cos_cached, self._sin_cached = self._update_cos_sin_tables(k, seq_dimension=-2)
return (
apply_rotary_pos_emb(q, self._cos_cached, self._sin_cached),
apply_rotary_pos_emb(k, self._cos_cached, self._sin_cached),
)
class EsmContactPredictionHead(nn.Module):
"""Performs symmetrization, apc, and computes a logistic regression on the output features"""
def __init__(
self,
in_features: int,
bias=True,
eos_idx: int = 2,
):
super().__init__()
self.in_features = in_features
self.eos_idx = eos_idx
self.regression = nn.Linear(in_features, 1, bias)
self.activation = nn.Sigmoid()
def forward(self, tokens, attentions):
# remove eos token attentions
eos_mask = tokens.ne(self.eos_idx).to(attentions)
eos_mask = eos_mask.unsqueeze(1) * eos_mask.unsqueeze(2)
attentions = attentions * eos_mask[:, None, None, :, :]
attentions = attentions[..., :-1, :-1]
# remove cls token attentions
attentions = attentions[..., 1:, 1:]
batch_size, layers, heads, seqlen, _ = attentions.size()
attentions = attentions.view(batch_size, layers * heads, seqlen, seqlen)
# features: batch x channels x tokens x tokens (symmetric)
attentions = attentions.to(
self.regression.weight.device
) # attentions always float32, may need to convert to float16
attentions = average_product_correct(symmetrize(attentions))
attentions = attentions.permute(0, 2, 3, 1)
return self.activation(self.regression(attentions).squeeze(3))
class EsmEmbeddings(nn.Module):
"""
Same as BertEmbeddings with a tiny tweak for positional embeddings indexing.
"""
def __init__(self, config):
super().__init__()
self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
if config.emb_layer_norm_before:
self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
else:
self.layer_norm = None
self.dropout = nn.Dropout(config.hidden_dropout_prob)
# position_ids (1, len position emb) is contiguous in memory and exported when serialized
self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
self.register_buffer(
"position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False
)
self.padding_idx = config.pad_token_id
self.position_embeddings = nn.Embedding(
config.max_position_embeddings, config.hidden_size, padding_idx=self.padding_idx
)
self.token_dropout = config.token_dropout
self.mask_token_id = config.mask_token_id
def forward(
self, input_ids=None, attention_mask=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)
else:
position_ids = self.create_position_ids_from_inputs_embeds(inputs_embeds)
if inputs_embeds is None:
inputs_embeds = self.word_embeddings(input_ids)
# Note that if we want to support ESM-1 (not 1b!) in future then we need to support an
# embedding_scale factor here.
embeddings = inputs_embeds
# Matt: ESM has the option to handle masking in MLM in a slightly unusual way. If the token_dropout
# flag is False then it is handled in the same was as BERT/RoBERTa. If it is set to True, however,
# masked tokens are treated as if they were selected for input dropout and zeroed out.
# This "mask-dropout" is compensated for when masked tokens are not present, by scaling embeddings by
# a factor of (fraction of unmasked tokens during training) / (fraction of unmasked tokens in sample).
# This is analogous to the way that dropout layers scale down outputs during evaluation when not
# actually dropping out values (or, equivalently, scale up their un-dropped outputs in training).
if self.token_dropout:
embeddings = embeddings.masked_fill((input_ids == self.mask_token_id).unsqueeze(-1), 0.0)
mask_ratio_train = 0.15 * 0.8 # Hardcoded as the ratio used in all ESM model training runs
src_lengths = attention_mask.sum(-1)
mask_ratio_observed = (input_ids == self.mask_token_id).sum(-1).float() / src_lengths
embeddings = (embeddings * (1 - mask_ratio_train) / (1 - mask_ratio_observed)[:, None, None]).to(
embeddings.dtype
)
if self.position_embedding_type == "absolute":
position_embeddings = self.position_embeddings(position_ids)
embeddings = embeddings + position_embeddings
if self.layer_norm is not None:
embeddings = self.layer_norm(embeddings)
if attention_mask is not None:
embeddings = (embeddings * attention_mask.unsqueeze(-1)).to(embeddings.dtype)
# Matt: I think this line was copied incorrectly from BERT, disabling it for now.
# embeddings = self.dropout(embeddings)
return embeddings
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 EsmSelfAttention(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"
)
self.rotary_embeddings = None
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)
elif self.position_embedding_type == "rotary":
self.rotary_embeddings = RotaryEmbedding(dim=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)
# Matt: Our BERT model (which this code was derived from) scales attention logits down by sqrt(head_dim).
# ESM scales the query down by the same factor instead. Modulo numerical stability these are equivalent,
# but not when rotary embeddings get involved. Therefore, we scale the query here to match the original
# ESM code and fix rotary embeddings.
query_layer = query_layer * self.attention_head_size**-0.5
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)
if self.position_embedding_type == "rotary":
query_layer, key_layer = self.rotary_embeddings(query_layer, key_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
if attention_mask is not None:
# Apply the attention mask is (precomputed for all layers in EsmModel 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.to(value_layer.dtype), 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
class EsmSelfOutput(nn.Module):
def __init__(self, config):
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, input_tensor):
hidden_states = self.dense(hidden_states)
hidden_states = self.dropout(hidden_states)
hidden_states = hidden_states + input_tensor
return hidden_states
class EsmAttention(nn.Module):
def __init__(self, config):
super().__init__()
self.self = EsmSelfAttention(config)
self.output = EsmSelfOutput(config)
self.pruned_heads = set()
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
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,
attention_mask=None,
head_mask=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
past_key_value=None,
output_attentions=False,
):
hidden_states_ln = self.LayerNorm(hidden_states)
self_outputs = self.self(
hidden_states_ln,
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 EsmIntermediate(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidden_states = self.dense(hidden_states)
hidden_states = gelu(hidden_states)
return hidden_states
class EsmOutput(nn.Module):
def __init__(self, config):
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, input_tensor):
hidden_states = self.dense(hidden_states)
hidden_states = self.dropout(hidden_states)
hidden_states = hidden_states + input_tensor
return hidden_states
class EsmLayer(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 = EsmAttention(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 RuntimeError(f"{self} should be used as a decoder model if cross attention is added")
self.crossattention = EsmAttention(config)
self.intermediate = EsmIntermediate(config)
self.output = EsmOutput(config)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
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,
):
# 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 AttributeError(
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 = self.feed_forward_chunk(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):
attention_output_ln = self.LayerNorm(attention_output)
intermediate_output = self.intermediate(attention_output_ln)
layer_output = self.output(intermediate_output, attention_output)
return layer_output
class EsmEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.layer = nn.ModuleList([EsmLayer(config) for _ in range(config.num_hidden_layers)])
self.emb_layer_norm_after = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
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,
):
if self.gradient_checkpointing and self.training:
if use_cache:
logger.warning_once(
"`use_cache=True` is incompatible with `config.gradient_checkpointing=True`. Setting "
"`use_cache=False`..."
)
use_cache = False
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
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 = 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 self.emb_layer_norm_after:
hidden_states = self.emb_layer_norm_after(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,
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 EsmPooler(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
class EsmPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = EsmConfig
base_model_prefix = "esm"
supports_gradient_checkpointing = True
_no_split_modules = ["EsmLayer", "EsmFoldTriangularSelfAttentionBlock", "EsmEmbeddings"]
# Copied from transformers.models.bert.modeling_bert.BertPreTrainedModel._init_weights
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, nn.LayerNorm):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
ESM_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 ([`EsmConfig`]): 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.
"""
ESM_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)
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 [`~file_utils.ModelOutput`] instead of a plain tuple.
"""
@add_start_docstrings(
"The bare ESM Model transformer outputting raw hidden-states without any specific head on top.",
ESM_START_DOCSTRING,
)
class EsmModel(EsmPreTrainedModel):
"""
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.
To behave as an decoder the model needs to be initialized with the `is_decoder` argument of the configuration set
to `True`. To be used in a Seq2Seq model, the model needs to initialized with both `is_decoder` argument and
`add_cross_attention` set to `True`; an `encoder_hidden_states` is then expected as an input to the forward pass.
"""
def __init__(self, config, add_pooling_layer=True):
super().__init__(config)
self.config = config
self.embeddings = EsmEmbeddings(config)
self.encoder = EsmEncoder(config)
self.pooler = EsmPooler(config) if add_pooling_layer else None
self.contact_head = EsmContactPredictionHead(
in_features=config.num_hidden_layers * config.num_attention_heads, bias=True
)
# 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(ESM_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.Tensor] = None,
attention_mask: 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,
past_key_values: Optional[List[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.Tensor], 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 self.config.is_decoder:
use_cache = use_cache if use_cache is not None else self.config.use_cache
else:
use_cache = False
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
# past_key_values_length
past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0
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: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape)
# 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.is_decoder 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_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)
embedding_output = self.embeddings(
input_ids=input_ids,
position_ids=position_ids,
attention_mask=attention_mask,
inputs_embeds=inputs_embeds,
past_key_values_length=past_key_values_length,
)
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,
)
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,
)
def predict_contacts(self, tokens, attention_mask):
attns = self(tokens, attention_mask=attention_mask, return_dict=True, output_attentions=True).attentions
attns = torch.stack(attns, dim=1) # Matches the original model layout
# In the original model, attentions for padding tokens are completely zeroed out.
# This makes no difference most of the time because the other tokens won't attend to them,
# but it does for the contact prediction task, which takes attentions as input,
# so we have to mimic that here.
attns *= attention_mask.unsqueeze(1).unsqueeze(2).unsqueeze(3)
attns *= attention_mask.unsqueeze(1).unsqueeze(2).unsqueeze(4)
return self.contact_head(tokens, attns)
@add_start_docstrings("""ESM Model with a `language modeling` head on top.""", ESM_START_DOCSTRING)
class EsmForMaskedLM(EsmPreTrainedModel):
_tied_weights_keys = ["lm_head.decoder.weight"]
def __init__(self, config):
super().__init__(config)
if config.is_decoder:
logger.warning(
"If you want to use `EsmForMaskedLM` make sure `config.is_decoder=False` for "
"bi-directional self-attention."
)
self.esm = EsmModel(config, add_pooling_layer=False)
self.lm_head = EsmLMHead(config)
self.init_weights()
def get_output_embeddings(self):
return self.lm_head.decoder
def set_output_embeddings(self, new_embeddings):
self.lm_head.decoder = new_embeddings
@add_start_docstrings_to_model_forward(ESM_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.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
encoder_attention_mask: Optional[torch.Tensor] = None,
labels: Optional[torch.LongTensor] = 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]`
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.esm(
input_ids,
attention_mask=attention_mask,
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.lm_head(sequence_output)
masked_lm_loss = None
if labels is not None:
loss_fct = CrossEntropyLoss()
labels = labels.to(prediction_scores.device)
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,
)
def predict_contacts(self, tokens, attention_mask):
return self.esm.predict_contacts(tokens, attention_mask=attention_mask)
class EsmLMHead(nn.Module):
"""ESM 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, bias=False)
self.bias = nn.Parameter(torch.zeros(config.vocab_size))
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) + self.bias
return x
@add_start_docstrings(
"""
ESM Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled
output) e.g. for GLUE tasks.
""",
ESM_START_DOCSTRING,
)
class EsmForSequenceClassification(EsmPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.config = config
self.esm = EsmModel(config, add_pooling_layer=False)
self.classifier = EsmClassificationHead(config)
self.init_weights()
@add_start_docstrings_to_model_forward(ESM_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.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.Tensor] = 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).
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.esm(
input_ids,
attention_mask=attention_mask,
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:
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 SequenceClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
@add_start_docstrings(
"""
ESM 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.
""",
ESM_START_DOCSTRING,
)
class EsmForTokenClassification(EsmPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.esm = EsmModel(config, add_pooling_layer=False)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.classifier = nn.Linear(config.hidden_size, config.num_labels)
self.init_weights()
@add_start_docstrings_to_model_forward(ESM_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.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.Tensor] = 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]`.
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.esm(
input_ids,
attention_mask=attention_mask,
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()
labels = labels.to(logits.device)
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 EsmClassificationHead(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):
x = features[:, 0, :] # take <s> token (equiv. to [CLS])
x = self.dropout(x)
x = self.dense(x)
x = torch.tanh(x)
x = self.dropout(x)
x = self.out_proj(x)
return x
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
__all__ = [
"EsmForMaskedLM",
"EsmForSequenceClassification",
"EsmForTokenClassification",
"EsmModel",
"EsmPreTrainedModel",
]
| transformers/src/transformers/models/esm/modeling_esm.py/0 | {
"file_path": "transformers/src/transformers/models/esm/modeling_esm.py",
"repo_id": "transformers",
"token_count": 23697
} |
# coding=utf-8
# Copyright 2023 the Falcon authors and 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 Falcon model."""
import math
from typing import TYPE_CHECKING, Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, LayerNorm, MSELoss
from torch.nn import functional as F
from ...activations import get_activation
from ...cache_utils import Cache, DynamicCache, StaticCache
from ...generation import GenerationMixin
from ...modeling_attn_mask_utils import (
AttentionMaskConverter,
)
from ...modeling_outputs import (
BaseModelOutputWithPastAndCrossAttentions,
CausalLMOutputWithCrossAttentions,
QuestionAnsweringModelOutput,
SequenceClassifierOutputWithPast,
TokenClassifierOutput,
)
from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS
from ...modeling_utils import PreTrainedModel
from ...utils import (
add_code_sample_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
is_flash_attn_2_available,
is_flash_attn_greater_or_equal_2_10,
logging,
)
from ...utils.deprecation import deprecate_kwarg
from .configuration_falcon import FalconConfig
if TYPE_CHECKING:
from ...configuration_utils import PretrainedConfig
if is_flash_attn_2_available():
from ...modeling_flash_attention_utils import _flash_attention_forward
logger = logging.get_logger(__name__)
_CHECKPOINT_FOR_DOC = "Rocketknight1/falcon-rw-1b"
_CONFIG_FOR_DOC = "FalconConfig"
# NOTE(Hesslow): Unfortunately we did not fuse matmul and bias during training, this means that there's one additional quantization to bfloat16 between the operations.
# In order not to degrade the quality of our HF-port, we keep these characteristics in the final model.
class FalconLinear(nn.Linear):
def forward(self, input: torch.Tensor) -> torch.Tensor:
hidden_states = input @ self.weight.T
if self.bias is None:
return hidden_states
return hidden_states + self.bias
# 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
# Copied from transformers.models.llama.modeling_llama.LlamaRotaryEmbedding with Llama->Falcon
class FalconRotaryEmbedding(nn.Module):
def __init__(self, config: FalconConfig, 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
def _dynamic_frequency_update(self, position_ids, device):
"""
dynamic RoPE layers should recompute `inv_freq` in the following situations:
1 - growing beyond the cached sequence length (allow scaling)
2 - the current sequence length is in the original scale (avoid losing precision with small sequences)
"""
seq_len = torch.max(position_ids) + 1
if seq_len > self.max_seq_len_cached: # growth
inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device, seq_len=seq_len)
self.register_buffer("inv_freq", inv_freq, persistent=False) # TODO joao: may break with compilation
self.max_seq_len_cached = seq_len
if seq_len < self.original_max_seq_len and self.max_seq_len_cached > self.original_max_seq_len: # reset
# This .to() is needed if the model has been moved to a device after being initialized (because
# the buffer is automatically moved, but not the original copy)
self.original_inv_freq = self.original_inv_freq.to(device)
self.register_buffer("inv_freq", self.original_inv_freq, persistent=False)
self.max_seq_len_cached = self.original_max_seq_len
@torch.no_grad()
def forward(self, x, position_ids):
if "dynamic" in self.rope_type:
self._dynamic_frequency_update(position_ids, device=x.device)
# Core RoPE block
inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1)
position_ids_expanded = position_ids[:, None, :].float()
# Force float32 (see https://github.com/huggingface/transformers/pull/29285)
device_type = x.device.type
device_type = device_type if isinstance(device_type, str) and device_type != "mps" else "cpu"
with torch.autocast(device_type=device_type, enabled=False):
freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
emb = torch.cat((freqs, freqs), dim=-1)
cos = emb.cos()
sin = emb.sin()
# Advanced RoPE types (e.g. yarn) apply a post-processing scaling factor, equivalent to scaling attention
cos = cos * self.attention_scaling
sin = sin * self.attention_scaling
return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
def build_alibi_tensor(attention_mask: torch.Tensor, num_heads: int, dtype: torch.dtype) -> torch.Tensor:
batch_size, seq_length = attention_mask.shape
closest_power_of_2 = 2 ** math.floor(math.log2(num_heads))
base = torch.tensor(
2 ** (-(2 ** -(math.log2(closest_power_of_2) - 3))), device=attention_mask.device, dtype=torch.float32
)
powers = torch.arange(1, 1 + closest_power_of_2, device=attention_mask.device, dtype=torch.int32)
slopes = torch.pow(base, powers)
if closest_power_of_2 != num_heads:
extra_base = torch.tensor(
2 ** (-(2 ** -(math.log2(2 * closest_power_of_2) - 3))), device=attention_mask.device, dtype=torch.float32
)
num_remaining_heads = min(closest_power_of_2, num_heads - closest_power_of_2)
extra_powers = torch.arange(1, 1 + 2 * num_remaining_heads, 2, device=attention_mask.device, dtype=torch.int32)
slopes = torch.cat([slopes, torch.pow(extra_base, extra_powers)], dim=0)
# Note: alibi will added to the attention bias that will be applied to the query, key product of attention
# => therefore alibi will have to be of shape (batch_size, num_heads, query_length, key_length)
# => here we set (batch_size=1, num_heads=num_heads, query_length=1, key_length=max_length)
# => the query_length dimension will then be broadcasted correctly
# This is more or less identical to T5's relative position bias:
# https://github.com/huggingface/transformers/blob/f681437203baa7671de3174b0fa583c349d9d5e1/src/transformers/models/t5/modeling_t5.py#L527
arange_tensor = ((attention_mask.cumsum(dim=-1) - 1) * attention_mask)[:, None, :]
alibi = slopes[..., None].bfloat16() * arange_tensor
return alibi.reshape(batch_size * num_heads, 1, seq_length).to(dtype)
# Copied from transformers.models.bloom.modeling_bloom.dropout_add
def dropout_add(x: torch.Tensor, residual: torch.Tensor, prob: float, training: bool) -> torch.Tensor:
"""
Dropout add function
Args:
x (`torch.tensor`):
input tensor
residual (`torch.tensor`):
residual tensor
prob (`float`):
dropout probability
training (`bool`):
training mode
"""
out = F.dropout(x, p=prob, training=training)
out = residual + out
return out
class FalconAttention(nn.Module):
def __init__(self, config: FalconConfig, layer_idx=None):
super().__init__()
self.config = config
self.hidden_size = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = self.hidden_size // self.num_heads
self.split_size = self.hidden_size
self.hidden_dropout = config.hidden_dropout
self.max_position_embeddings = config.max_position_embeddings
self.rope_theta = config.rope_theta
self.is_causal = True
self._use_sdpa = config._attn_implementation == "sdpa"
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 * self.num_heads != self.hidden_size:
raise ValueError(
f"`hidden_size` must be divisible by num_heads (got `hidden_size`: {self.hidden_size} and `num_heads`:"
f" {self.num_heads})."
)
# Layer-wise attention scaling
self.inv_norm_factor = 1.0 / math.sqrt(self.head_dim)
self.beta = self.inv_norm_factor
if config.new_decoder_architecture:
qkv_out_dim = (config.num_kv_heads * 2 + config.num_attention_heads) * self.head_dim
elif config.multi_query:
qkv_out_dim = self.hidden_size + 2 * self.head_dim
else:
qkv_out_dim = 3 * self.hidden_size
self.query_key_value = FalconLinear(self.hidden_size, qkv_out_dim, bias=config.bias)
self.new_decoder_architecture = config.new_decoder_architecture
self.multi_query = config.multi_query
self.dense = FalconLinear(self.hidden_size, self.hidden_size, bias=config.bias)
self.attention_dropout = nn.Dropout(config.attention_dropout)
self.num_kv_heads = config.num_kv_heads if (self.new_decoder_architecture or not self.multi_query) else 1
# TODO (raushan): remove in v4.46 (RoPE is computed in the model, not in the decoder layers)
if config.rotary:
self.rotary_emb = FalconRotaryEmbedding(config=self.config)
def _split_heads(self, fused_qkv: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
"""
Split the last dimension into (num_heads, head_dim), results share same memory storage as `fused_qkv`
Args:
fused_qkv (`torch.tensor`): [batch_size, seq_length, num_heads * 3 * head_dim]
Returns:
query: [batch_size, seq_length, num_heads, head_dim] key: [batch_size, seq_length, num_heads, head_dim]
value: [batch_size, seq_length, num_heads, head_dim]
"""
if self.new_decoder_architecture:
batch, seq_len, _ = fused_qkv.shape
qkv = fused_qkv.view(batch, seq_len, -1, self.num_heads // self.num_kv_heads + 2, self.head_dim)
query = qkv[:, :, :, :-2]
key = qkv[:, :, :, [-2]]
value = qkv[:, :, :, [-1]]
key = torch.broadcast_to(key, query.shape)
value = torch.broadcast_to(value, query.shape)
query, key, value = [x.flatten(2, 3) for x in (query, key, value)]
return query, key, value
elif not self.multi_query:
batch_size, seq_length, three_times_hidden_size = fused_qkv.shape
fused_qkv = fused_qkv.view(batch_size, seq_length, self.num_heads, 3, self.head_dim)
return fused_qkv[..., 0, :], fused_qkv[..., 1, :], fused_qkv[..., 2, :]
else:
batch_size, seq_length, three_times_hidden_size = fused_qkv.shape
fused_qkv = fused_qkv.view(batch_size, seq_length, self.num_heads + 2, self.head_dim)
return fused_qkv[..., :-2, :], fused_qkv[..., [-2], :], fused_qkv[..., [-1], :]
# Copied from transformers.models.bloom.modeling_bloom.BloomAttention._merge_heads
def _merge_heads(self, x: torch.Tensor) -> torch.Tensor:
"""
Merge heads together over the last dimension
Args:
x (`torch.tensor`): [batch_size * num_heads, seq_length, head_dim]
Returns:
torch.tensor: [batch_size, seq_length, num_heads * head_dim]
"""
# What we want to achieve is:
# batch_size * num_heads, seq_length, head_dim -> batch_size, seq_length, num_heads * head_dim
batch_size_and_num_heads, seq_length, _ = x.shape
batch_size = batch_size_and_num_heads // self.num_heads
# First view to decompose the batch size
# batch_size * num_heads, seq_length, head_dim -> batch_size, num_heads, seq_length, head_dim
x = x.view(batch_size, self.num_heads, seq_length, self.head_dim)
# batch_size, num_heads, seq_length, head_dim -> batch_size, seq_length, num_heads, head_dim
x = x.permute(0, 2, 1, 3)
# batch_size, seq_length, num_heads, head_dim -> batch_size, seq_length, num_heads * head_dim
return x.reshape(batch_size, seq_length, self.num_heads * self.head_dim)
def forward(
self,
hidden_states: torch.Tensor,
alibi: Optional[torch.Tensor],
attention_mask: torch.Tensor,
position_ids: Optional[torch.LongTensor] = None,
layer_past: Optional[Cache] = None,
head_mask: Optional[torch.Tensor] = None,
use_cache: bool = False,
output_attentions: bool = False,
cache_position: Optional[torch.LongTensor] = None,
position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, # necessary, but kept here for BC
):
fused_qkv = self.query_key_value(hidden_states) # [batch_size, seq_length, 3 x hidden_size]
num_kv_heads = self.num_heads if self.new_decoder_architecture else self.num_kv_heads
# 3 x [batch_size, seq_length, num_heads, head_dim]
(query_layer, key_layer, value_layer) = self._split_heads(fused_qkv)
batch_size, query_length, _, _ = query_layer.shape
query_layer = query_layer.transpose(1, 2).reshape(batch_size, self.num_heads, query_length, self.head_dim)
key_layer = key_layer.transpose(1, 2).reshape(batch_size, num_kv_heads, query_length, self.head_dim)
value_layer = value_layer.transpose(1, 2).reshape(batch_size, num_kv_heads, query_length, self.head_dim)
if alibi is None:
cos, sin = position_embeddings
query_layer, key_layer = apply_rotary_pos_emb(query_layer, key_layer, cos, sin)
if layer_past is not None:
cache_kwargs = {"cache_position": cache_position}
if alibi is None:
cache_kwargs.update({"sin": sin, "cos": cos})
key_layer, value_layer = layer_past.update(key_layer, value_layer, self.layer_idx, cache_kwargs)
kv_length = key_layer.shape[-2]
if self._use_sdpa and query_layer.device.type == "cuda" and attention_mask is not None:
# For torch<=2.1.2, SDPA with memory-efficient backend is bugged with non-contiguous inputs with custom attn_mask,
# Reference: https://github.com/pytorch/pytorch/issues/112577.
query_layer = query_layer.contiguous()
key_layer = key_layer.contiguous()
value_layer = value_layer.contiguous()
if attention_mask is not None:
attention_mask = attention_mask[:, :, :, : key_layer.shape[-2]]
if alibi is None:
if self._use_sdpa and not output_attentions:
# We dispatch to SDPA's Flash Attention or Efficient kernels via this if statement instead of an
# inline conditional assignment to support both torch.compile's `dynamic=True` and `fullgraph=True`
# The query_length > 1 is necessary to match with AttentionMaskConverter.to_causal_4d that does not
# create a causal mask in case query_length == 1.
is_causal = True if self.is_causal and attention_mask is None and query_length > 1 else False
attn_output = torch.nn.functional.scaled_dot_product_attention(
query_layer,
key_layer,
value_layer,
attn_mask=attention_mask,
dropout_p=0.0,
is_causal=is_causal,
)
attention_scores = None
else:
attention_scores = query_layer @ key_layer.transpose(-1, -2)
attention_scores /= math.sqrt(self.head_dim)
attention_scores = F.softmax(attention_scores + attention_mask, dim=-1, dtype=hidden_states.dtype)
# It is unclear why neither dropout nor head_mask is applied here (while it is with alibi).
attn_output = attention_scores @ value_layer
attn_output = attn_output.view(batch_size, self.num_heads, query_length, self.head_dim)
attn_output = attn_output.permute(0, 2, 1, 3)
attn_output = attn_output.reshape(batch_size, query_length, self.num_heads * self.head_dim)
attn_output = self.dense(attn_output)
if output_attentions:
return attn_output, layer_past, attention_scores
else:
return attn_output, layer_past
else:
if self._use_sdpa and not output_attentions and head_mask is None:
# We dispatch to SDPA's Flash Attention or Efficient kernels via this if statement instead of an
# inline conditional assignment to support both torch.compile's `dynamic=True` and `fullgraph=True`
is_causal = True if self.is_causal and attention_mask is None and query_length > 1 else False
attn_output = torch.nn.functional.scaled_dot_product_attention(
query_layer,
key_layer,
value_layer,
attn_mask=attention_mask,
dropout_p=self.attention_dropout.p if self.training else 0.0,
is_causal=is_causal,
)
attn_output = attn_output.transpose(1, 2)
attn_output = attn_output.reshape(batch_size, query_length, self.num_heads * self.head_dim)
attn_output = self.dense(attn_output)
else:
matmul_result = query_layer @ key_layer.transpose(-1, -2)
# change view to [batch_size, num_heads, q_length, kv_length]
attention_scores = matmul_result.view(batch_size, self.num_heads, query_length, kv_length)
# cast attention scores to fp32, compute scaled softmax and cast back to initial dtype - [batch_size, num_heads, q_length, kv_length]
input_dtype = attention_scores.dtype
# `float16` has a minimum value of -65504.0, whereas `bfloat16` and `float32` have a minimum value of `-3.4e+38`
if input_dtype == torch.float16 or input_dtype == torch.bfloat16:
attention_scores = attention_scores.to(torch.float32)
attention_logits = attention_scores + alibi.view(batch_size, self.num_heads, 1, -1)
attention_logits *= self.inv_norm_factor
attention_probs = F.softmax(attention_logits + attention_mask, dim=-1, dtype=hidden_states.dtype)
# [batch_size, num_heads, q_length, kv_length]
attention_probs = self.attention_dropout(attention_probs)
if head_mask is not None:
attention_probs = attention_probs * head_mask
# change view [batch_size, num_heads, q_length, kv_length]
attention_probs_reshaped = attention_probs.view(batch_size, self.num_heads, query_length, kv_length)
# matmul: [batch_size * num_heads, q_length, head_dim]
attn_output = (attention_probs_reshaped @ value_layer).flatten(0, 1)
# change view [batch_size, q_length, num_heads * head_dim]
attn_output = self._merge_heads(attn_output)
attn_output = self.dense(attn_output)
if output_attentions:
return attn_output, layer_past, attention_probs
else:
return attn_output, layer_past
class FalconFlashAttention2(FalconAttention):
"""
Falcon flash attention module. This module inherits from `FalconAttention` 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 alignement, 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 = not is_flash_attn_greater_or_equal_2_10()
def forward(
self,
hidden_states: torch.Tensor,
alibi: Optional[torch.Tensor],
attention_mask: torch.Tensor,
position_ids: Optional[torch.LongTensor] = None,
layer_past: Optional[Cache] = None,
head_mask: Optional[torch.Tensor] = None,
use_cache: bool = False,
output_attentions: bool = False,
cache_position: Optional[torch.LongTensor] = None,
position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, # necessary, but kept here for BC
):
fused_qkv = self.query_key_value(hidden_states) # [batch_size, seq_length, 3 x hidden_size]
num_kv_heads = self.num_heads if self.new_decoder_architecture else self.num_kv_heads
# 3 x [batch_size, seq_length, num_heads, head_dim]
(query_layer, key_layer, value_layer) = self._split_heads(fused_qkv)
batch_size, query_length, _, _ = query_layer.shape
query_layer = query_layer.transpose(1, 2).reshape(batch_size, self.num_heads, query_length, self.head_dim)
key_layer = key_layer.transpose(1, 2).reshape(batch_size, num_kv_heads, query_length, self.head_dim)
value_layer = value_layer.transpose(1, 2).reshape(batch_size, num_kv_heads, query_length, self.head_dim)
if alibi is None:
cos, sin = position_embeddings
query_layer, key_layer = apply_rotary_pos_emb(query_layer, key_layer, cos, sin)
if layer_past is not None:
cache_kwargs = {"cache_position": cache_position}
if alibi is None:
cache_kwargs.update({"sin": sin, "cos": cos})
key_layer, value_layer = layer_past.update(key_layer, value_layer, 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_layer = query_layer.transpose(1, 2)
key_layer = key_layer.transpose(1, 2)
value_layer = value_layer.transpose(1, 2)
if alibi is not None:
raise ValueError("`alibi` is not supported when `use_flash_attn` is True")
attn_dropout = self.config.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 float16 just to be sure everything works as expected.
input_dtype = query_layer.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.query_key_value.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_layer = query_layer.to(target_dtype)
key_layer = key_layer.to(target_dtype)
value_layer = value_layer.to(target_dtype)
attn_output = _flash_attention_forward(
query_layer,
key_layer,
value_layer,
attention_mask,
query_length,
position_ids=position_ids,
dropout=attn_dropout,
is_causal=self.is_causal,
use_top_left_mask=self._flash_attn_uses_top_left_mask,
)
attn_weights = attn_output.reshape(batch_size, query_length, self.num_heads * self.head_dim)
attn_output = self.dense(attn_weights)
if not output_attentions:
attn_weights = None
return attn_output, layer_past, attn_weights
class FalconMLP(nn.Module):
def __init__(self, config: FalconConfig):
super().__init__()
hidden_size = config.hidden_size
self.dense_h_to_4h = FalconLinear(hidden_size, config.ffn_hidden_size, bias=config.bias)
self.act = get_activation(config.activation)
self.dense_4h_to_h = FalconLinear(config.ffn_hidden_size, hidden_size, bias=config.bias)
self.hidden_dropout = config.hidden_dropout
def forward(self, x: torch.Tensor) -> torch.Tensor:
x = self.act(self.dense_h_to_4h(x))
x = self.dense_4h_to_h(x)
return x
FALCON_ATTENTION_CLASSES = {
"eager": FalconAttention,
"sdpa": FalconAttention, # FalconAttention originally implemented both a forward with & without SDPA
"flash_attention_2": FalconFlashAttention2,
}
class FalconDecoderLayer(nn.Module):
def __init__(self, config: FalconConfig, layer_idx=None):
super().__init__()
hidden_size = config.hidden_size
self.num_heads = config.num_attention_heads
self.self_attention = FALCON_ATTENTION_CLASSES[config._attn_implementation](config, layer_idx)
self.mlp = FalconMLP(config)
self.hidden_dropout = config.hidden_dropout
self.config = config
if config.num_ln_in_parallel_attn is None and config.new_decoder_architecture:
config.num_ln_in_parallel_attn = 2
if not config.parallel_attn:
self.post_attention_layernorm = LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
self.input_layernorm = LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
else:
if config.num_ln_in_parallel_attn == 2:
# The layer norm before self-attention
self.ln_attn = LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
# The layer norm before the MLP
self.ln_mlp = LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
else:
self.input_layernorm = LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
def forward(
self,
hidden_states: torch.Tensor,
alibi: Optional[torch.Tensor],
attention_mask: torch.Tensor,
position_ids: Optional[torch.LongTensor] = None,
layer_past: Optional[Union[Cache, Tuple[torch.Tensor, torch.Tensor]]] = None,
head_mask: Optional[torch.Tensor] = None,
use_cache: bool = False,
output_attentions: bool = False,
cache_position: Optional[torch.LongTensor] = None,
position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, # necessary, but kept here for BC
**kwargs,
):
residual = hidden_states
if self.config.new_decoder_architecture and self.config.num_ln_in_parallel_attn == 2:
attention_layernorm_out = self.ln_attn(hidden_states)
mlp_layernorm_out = self.ln_mlp(hidden_states)
else:
attention_layernorm_out = self.input_layernorm(hidden_states)
# Self attention.
attn_outputs = self.self_attention(
attention_layernorm_out,
layer_past=layer_past,
attention_mask=attention_mask,
position_ids=position_ids,
alibi=alibi,
head_mask=head_mask,
use_cache=use_cache,
output_attentions=output_attentions,
cache_position=cache_position,
position_embeddings=position_embeddings,
)
attention_output = attn_outputs[0]
if not self.config.new_decoder_architecture:
if self.config.parallel_attn:
mlp_layernorm_out = attention_layernorm_out
else:
residual = dropout_add(
attention_output, residual, self.config.attention_dropout, training=self.training
)
mlp_layernorm_out = self.post_attention_layernorm(residual)
if (
self.config.new_decoder_architecture
and self.config.parallel_attn
and self.config.num_ln_in_parallel_attn == 1
):
mlp_layernorm_out = attention_layernorm_out
outputs = attn_outputs[1:]
# MLP.
mlp_output = self.mlp(mlp_layernorm_out)
if self.config.new_decoder_architecture or self.config.parallel_attn:
mlp_output += attention_output
output = dropout_add(mlp_output, residual, self.config.hidden_dropout, training=self.training)
if use_cache:
outputs = (output,) + outputs
else:
outputs = (output,) + outputs[1:]
return outputs # hidden_states, past_kv, attentions
FALCON_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 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 ([`FalconConfig`]): 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.
"""
FALCON_INPUTS_DOCSTRING = r"""
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, input_ids_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 [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-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, 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)`.
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)
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)
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 [`~file_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.
"""
class FalconPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = FalconConfig
base_model_prefix = "transformer"
supports_gradient_checkpointing = True
_no_split_modules = ["FalconDecoderLayer"]
_supports_flash_attn_2 = True
_supports_sdpa = True
_supports_cache_class = True
_supports_quantized_cache = True
_supports_static_cache = True
def __init__(self, *inputs, **kwargs):
super().__init__(*inputs, **kwargs)
def _init_weights(self, module: nn.Module):
"""Initialize the weights."""
if isinstance(module, nn.Linear) or isinstance(module, FalconLinear):
# 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, LayerNorm):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
# Adapted from transformers.modeling_utils.PreTrainedModel._check_and_enable_sdpa
@classmethod
def _check_and_enable_sdpa(cls, config, hard_check_only: bool = False) -> "PretrainedConfig":
_is_bettertransformer = getattr(cls, "use_bettertransformer", False)
if _is_bettertransformer:
return config
if not hard_check_only:
config._attn_implementation = "sdpa"
return config
@add_start_docstrings(
"The bare Falcon Model transformer outputting raw hidden-states without any specific head on top.",
FALCON_START_DOCSTRING,
)
class FalconModel(FalconPreTrainedModel):
def __init__(self, config: FalconConfig):
super().__init__(config)
self.embed_dim = config.hidden_size
self.num_heads = config.num_attention_heads
self.use_alibi = config.alibi
# Embedding + LN Embedding
self.word_embeddings = nn.Embedding(config.vocab_size, self.embed_dim)
# Transformer blocks
self.h = nn.ModuleList([FalconDecoderLayer(config, layer_idx=i) for i in range(config.num_hidden_layers)])
self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2"
self._use_sdpa = config._attn_implementation == "sdpa"
# Final Layer Norm
self.ln_f = LayerNorm(self.embed_dim, eps=config.layer_norm_epsilon)
self.rotary_emb = FalconRotaryEmbedding(config=config)
self.gradient_checkpointing = False
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.word_embeddings
def set_input_embeddings(self, new_embeddings: torch.Tensor):
self.word_embeddings = new_embeddings
@add_start_docstrings_to_model_forward(FALCON_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=BaseModelOutputWithPastAndCrossAttentions,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Union[Cache, Tuple[Tuple[torch.Tensor, torch.Tensor], ...]]] = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.LongTensor] = None,
inputs_embeds: 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,
) -> Union[Tuple[torch.Tensor, ...], 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 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.word_embeddings(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)"
)
# Compute alibi tensor: check build_alibi_tensor documentation
alibi = None
past_key_values_length = past_key_values.get_seq_length() if past_key_values is not None else 0
batch_size, seq_length, _ = inputs_embeds.shape
if self.use_alibi:
mask = (
torch.ones(
(batch_size, seq_length + past_key_values_length), device=inputs_embeds.device, dtype=torch.long
)
if attention_mask is None
else attention_mask
)
alibi = build_alibi_tensor(mask, self.num_heads, dtype=inputs_embeds.dtype)
if cache_position is None:
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, head_mask, alibi
)
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape batch_size x num_heads x N x N
# head_mask has shape n_layer x batch x num_heads x N x N
head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
hidden_states = inputs_embeds
# create position embeddings to be shared across the decoder layers
position_embeddings = self.rotary_emb(hidden_states, position_ids)
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):
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,
alibi,
causal_mask,
position_ids,
head_mask[i],
past_key_values,
use_cache,
output_attentions,
cache_position,
position_embeddings,
)
else:
outputs = block(
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,
alibi=alibi,
cache_position=cache_position,
position_embeddings=position_embeddings,
)
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],)
# Add last hidden state
hidden_states = self.ln_f(hidden_states)
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 BaseModelOutputWithPastAndCrossAttentions(
last_hidden_state=hidden_states,
past_key_values=next_cache,
hidden_states=all_hidden_states,
attentions=all_self_attentions,
)
def _update_causal_mask(
self,
attention_mask: torch.Tensor,
input_tensor: torch.Tensor,
cache_position: torch.Tensor,
past_key_values: Cache,
output_attentions: bool,
head_mask: torch.Tensor,
alibi: torch.Tensor,
):
# TODO: As of torch==2.2.0, the `attention_mask` passed to the model in `generate` is 2D and of dynamic length even when the static
# KV cache is used. This is an issue for torch.compile which then recaptures cudagraphs at each decode steps due to the dynamic shapes.
# (`recording cudagraph tree for symint key 13`, etc.), which is VERY slow. A workaround is `@torch.compiler.disable`, but this prevents using
# `fullgraph=True`. See more context in https://github.com/huggingface/transformers/pull/29114
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
# 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
and head_mask is None
and alibi is None
):
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
min_dtype = torch.finfo(dtype).min
batch_size, sequence_length, _ = input_tensor.shape
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
)
# 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],
)
# We take care to integrate alibi bias in the causal_mask here
if head_mask is None and alibi is not None:
alibi = alibi.reshape(batch_size, -1, *alibi.shape[1:])
causal_mask = torch.masked_fill(
alibi / math.sqrt(self.config.hidden_size // self.num_heads),
causal_mask < -1,
min_dtype,
)
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
causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype)
return causal_mask
@staticmethod
# Copied from transformers.models.llama.modeling_llama.LlamaPreTrainedModel._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 plcae 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, :]
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 Falcon Model transformer with a language modeling head on top (linear layer with weights tied to the input embeddings).",
FALCON_START_DOCSTRING,
)
class FalconForCausalLM(FalconPreTrainedModel, GenerationMixin):
_tied_weights_keys = ["lm_head.weight"]
def __init__(self, config: FalconConfig):
super().__init__(config)
self.transformer = FalconModel(config)
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
# 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: torch.Tensor):
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(FALCON_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=CausalLMOutputWithCrossAttentions,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Union[Cache, Tuple[Tuple[torch.Tensor, torch.Tensor], ...]]] = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = 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,
cache_position: Optional[torch.LongTensor] = None,
logits_to_keep: Union[int, torch.Tensor] = 0,
**kwargs,
) -> Union[Tuple[torch.Tensor], 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]`
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).
"""
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,
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]
slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
lm_logits = self.lm_head(hidden_states[:, slice_indices, :])
loss = None
if labels is not None:
loss = self.loss_function(
lm_logits,
labels,
vocab_size=self.config.vocab_size,
**kwargs,
)
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,
)
def _reorder_cache(
self, past: Tuple[Tuple[torch.Tensor, torch.Tensor], ...], beam_idx: torch.LongTensor
) -> Tuple[Tuple[torch.Tensor, 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.
Output shares the same memory storage as `past`.
"""
# Get a copy of `beam_idx` on all the devices where we need those indices.
device_to_beam_idx = {
past_state.device: beam_idx.to(past_state.device) for layer_past in past for past_state in layer_past
}
reordered_past = tuple(
(
layer_past[0].index_select(0, device_to_beam_idx[layer_past[0].device]),
layer_past[1].index_select(0, device_to_beam_idx[layer_past[0].device]),
)
for layer_past in past
)
return reordered_past
@add_start_docstrings(
"""
The Falcon Model transformer with a sequence classification head on top (linear layer).
[`FalconForSequenceClassification`] uses the last token in order to do the classification, as other causal models
(e.g. GPT-1) 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).
""",
FALCON_START_DOCSTRING,
)
class FalconForSequenceClassification(FalconPreTrainedModel):
def __init__(self, config: FalconConfig):
super().__init__(config)
self.num_labels = config.num_labels
self.transformer = FalconModel(config)
self.score = nn.Linear(config.hidden_size, config.num_labels, bias=False)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(FALCON_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=SequenceClassifierOutputWithPast,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
attention_mask: 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,
) -> Union[Tuple[torch.Tensor], 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,
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)
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:
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, labels)
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(
"""
Falcon 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.
""",
FALCON_START_DOCSTRING,
)
class FalconForTokenClassification(FalconPreTrainedModel):
def __init__(self, config: FalconConfig):
super().__init__(config)
self.num_labels = config.num_labels
self.transformer = FalconModel(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.classifier = nn.Linear(config.hidden_size, config.num_labels)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(FALCON_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,
past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
attention_mask: 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,
) -> Union[Tuple[torch.Tensor], 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).
"""
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,
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]
hidden_states = self.dropout(hidden_states)
logits = self.classifier(hidden_states)
loss = None
if labels is not None:
batch_size, seq_length = labels.shape
loss_fct = CrossEntropyLoss()
loss = loss_fct(
logits.view(batch_size * seq_length, self.num_labels), labels.view(batch_size * seq_length)
)
if not return_dict:
output = (logits,) + transformer_outputs[2:]
return ((loss,) + output) if loss is not None else output
return TokenClassifierOutput(
loss=loss,
logits=logits,
hidden_states=transformer_outputs.hidden_states,
attentions=transformer_outputs.attentions,
)
@add_start_docstrings(
"""
The Falcon 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`).
""",
FALCON_START_DOCSTRING,
)
class FalconForQuestionAnswering(FalconPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.transformer = FalconModel(config)
self.qa_outputs = nn.Linear(config.hidden_size, 2)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(FALCON_INPUTS_DOCSTRING)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = 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,
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__ = [
"FalconForCausalLM",
"FalconModel",
"FalconPreTrainedModel",
"FalconForSequenceClassification",
"FalconForTokenClassification",
"FalconForQuestionAnswering",
]
| transformers/src/transformers/models/falcon/modeling_falcon.py/0 | {
"file_path": "transformers/src/transformers/models/falcon/modeling_falcon.py",
"repo_id": "transformers",
"token_count": 32173
} |
# 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.
"""Convert FocalNet checkpoints from the original repository. URL: https://github.com/microsoft/FocalNet/tree/main"""
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision import transforms
from transformers import BitImageProcessor, FocalNetConfig, FocalNetForImageClassification
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling
def get_focalnet_config(model_name):
depths = [2, 2, 6, 2] if "tiny" in model_name else [2, 2, 18, 2]
use_conv_embed = True if "large" in model_name or "huge" in model_name else False
use_post_layernorm = True if "large" in model_name or "huge" in model_name else False
use_layerscale = True if "large" in model_name or "huge" in model_name else False
if "large" in model_name or "xlarge" in model_name or "huge" in model_name:
if "fl3" in model_name:
focal_levels = [3, 3, 3, 3]
focal_windows = [5, 5, 5, 5]
elif "fl4" in model_name:
focal_levels = [4, 4, 4, 4]
focal_windows = [3, 3, 3, 3]
if "tiny" in model_name or "small" in model_name or "base" in model_name:
focal_windows = [3, 3, 3, 3]
if "lrf" in model_name:
focal_levels = [3, 3, 3, 3]
else:
focal_levels = [2, 2, 2, 2]
if "tiny" in model_name:
embed_dim = 96
elif "small" in model_name:
embed_dim = 96
elif "base" in model_name:
embed_dim = 128
elif "large" in model_name:
embed_dim = 192
elif "xlarge" in model_name:
embed_dim = 256
elif "huge" in model_name:
embed_dim = 352
# set label information
repo_id = "huggingface/label-files"
if "large" in model_name or "huge" in model_name:
filename = "imagenet-22k-id2label.json"
else:
filename = "imagenet-1k-id2label.json"
id2label = json.load(open(hf_hub_download(repo_id, filename, repo_type="dataset"), "r"))
id2label = {int(k): v for k, v in id2label.items()}
label2id = {v: k for k, v in id2label.items()}
config = FocalNetConfig(
embed_dim=embed_dim,
depths=depths,
focal_levels=focal_levels,
focal_windows=focal_windows,
use_conv_embed=use_conv_embed,
id2label=id2label,
label2id=label2id,
use_post_layernorm=use_post_layernorm,
use_layerscale=use_layerscale,
)
return config
def rename_key(name):
if "patch_embed.proj" in name:
name = name.replace("patch_embed.proj", "embeddings.patch_embeddings.projection")
if "patch_embed.norm" in name:
name = name.replace("patch_embed.norm", "embeddings.norm")
if "layers" in name:
name = "encoder." + name
if "encoder.layers" in name:
name = name.replace("encoder.layers", "encoder.stages")
if "downsample.proj" in name:
name = name.replace("downsample.proj", "downsample.projection")
if "blocks" in name:
name = name.replace("blocks", "layers")
if "modulation.f.weight" in name or "modulation.f.bias" in name:
name = name.replace("modulation.f", "modulation.projection_in")
if "modulation.h.weight" in name or "modulation.h.bias" in name:
name = name.replace("modulation.h", "modulation.projection_context")
if "modulation.proj.weight" in name or "modulation.proj.bias" in name:
name = name.replace("modulation.proj", "modulation.projection_out")
if name == "norm.weight":
name = "layernorm.weight"
if name == "norm.bias":
name = "layernorm.bias"
if "head" in name:
name = name.replace("head", "classifier")
else:
name = "focalnet." + name
return name
def convert_focalnet_checkpoint(model_name, pytorch_dump_folder_path, push_to_hub=False):
# fmt: off
model_name_to_url = {
"focalnet-tiny": "https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_tiny_srf.pth",
"focalnet-tiny-lrf": "https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_tiny_lrf.pth",
"focalnet-small": "https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_small_srf.pth",
"focalnet-small-lrf": "https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_small_lrf.pth",
"focalnet-base": "https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_base_srf.pth",
"focalnet-base-lrf": "https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_base_lrf.pth",
"focalnet-large-lrf-fl3": "https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_large_lrf_384.pth",
"focalnet-large-lrf-fl4": "https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_large_lrf_384_fl4.pth",
"focalnet-xlarge-lrf-fl3": "https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_xlarge_lrf_384.pth",
"focalnet-xlarge-lrf-fl4": "https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_xlarge_lrf_384_fl4.pth",
}
# fmt: on
checkpoint_url = model_name_to_url[model_name]
print("Checkpoint URL: ", checkpoint_url)
state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")["model"]
# rename keys
for key in state_dict.copy().keys():
val = state_dict.pop(key)
state_dict[rename_key(key)] = val
config = get_focalnet_config(model_name)
model = FocalNetForImageClassification(config)
model.eval()
# load state dict
model.load_state_dict(state_dict)
# verify conversion
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
processor = BitImageProcessor(
do_resize=True,
size={"shortest_edge": 256},
resample=PILImageResampling.BILINEAR,
do_center_crop=True,
crop_size=224,
do_normalize=True,
image_mean=IMAGENET_DEFAULT_MEAN,
image_std=IMAGENET_DEFAULT_STD,
)
image = Image.open(requests.get(url, stream=True).raw)
inputs = processor(images=image, return_tensors="pt")
image_transforms = transforms.Compose(
[
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
]
)
original_pixel_values = image_transforms(image).unsqueeze(0)
# verify pixel_values
assert torch.allclose(inputs.pixel_values, original_pixel_values, atol=1e-4)
outputs = model(**inputs)
predicted_class_idx = outputs.logits.argmax(-1).item()
print("Predicted class:", model.config.id2label[predicted_class_idx])
print("First values of logits:", outputs.logits[0, :3])
if model_name == "focalnet-tiny":
expected_slice = torch.tensor([0.2166, -0.4368, 0.2191])
elif model_name == "focalnet-tiny-lrf":
expected_slice = torch.tensor([1.1669, 0.0125, -0.1695])
elif model_name == "focalnet-small":
expected_slice = torch.tensor([0.4917, -0.0430, 0.1341])
elif model_name == "focalnet-small-lrf":
expected_slice = torch.tensor([-0.2588, -0.5342, -0.2331])
elif model_name == "focalnet-base":
expected_slice = torch.tensor([-0.1655, -0.4090, -0.1730])
elif model_name == "focalnet-base-lrf":
expected_slice = torch.tensor([0.5306, -0.0483, -0.3928])
assert torch.allclose(outputs.logits[0, :3], expected_slice, atol=1e-4)
print("Looks ok!")
if pytorch_dump_folder_path is not None:
print(f"Saving model and processor of {model_name} to {pytorch_dump_folder_path}")
model.save_pretrained(pytorch_dump_folder_path)
processor.save_pretrained(pytorch_dump_folder_path)
if push_to_hub:
print(f"Pushing model and processor of {model_name} to the hub...")
model.push_to_hub(f"{model_name}")
processor.push_to_hub(f"{model_name}")
if __name__ == "__main__":
parser = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--model_name",
default="focalnet-tiny",
type=str,
help="Name of the FocalNet model you'd like to convert.",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
)
parser.add_argument(
"--push_to_hub",
action="store_true",
help="Whether to push the model and processor to the hub.",
)
args = parser.parse_args()
convert_focalnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| transformers/src/transformers/models/focalnet/convert_focalnet_to_hf_format.py/0 | {
"file_path": "transformers/src/transformers/models/focalnet/convert_focalnet_to_hf_format.py",
"repo_id": "transformers",
"token_count": 3999
} |
# 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.
import argparse
import os
import sys
import warnings
import flatdict
import torch
from transformers import FuyuConfig, FuyuForCausalLM, LlamaTokenizer
try:
from transformers import LlamaTokenizerFast
tokenizer_class = LlamaTokenizerFast
except ImportError as e:
warnings.warn(e)
warnings.warn(
"The converted tokenizer will be the `slow` tokenizer. To use the fast, update your `tokenizers` library and re-run the tokenizer conversion"
)
tokenizer_class = LlamaTokenizer
"""
Sample usage: # TODO fix clone links from persimmon to fuyu
```
git clone https://github.com/adept-ai-labs/adept-inference
wget https://axtkn4xl5cip.objectstorage.us-phoenix-1.oci.customer-oci.com/n/axtkn4xl5cip/b/adept-public-data/o/8b_base_model_release.tar
wget https://axtkn4xl5cip.objectstorage.us-phoenix-1.oci.customer-oci.com/n/axtkn4xl5cip/b/adept-public-data/o/8b_chat_model_release.tar
python src/transformers/models/fuyu/convert_fuyu_weights_to_hf.py --input_dir /path/to/downloaded/fuyu/weights/ --output_dir /output/path
```
Thereafter, models can be loaded via:
```py
from transformers import FuyuForCausalLM, FuyuTokenizer
model = FuyuForCausalLM.from_pretrained("/output/path")
tokenizer = FuyuTokenizer.from_pretrained("/output/path")
```
Important note: you need to be able to host the whole model in RAM to execute this script (even if the biggest versions
come in several checkpoints they each contain a part of each weight of the model, so we need to load them all in RAM).
"""
KEYS_TO_MODIFY_MAPPING = {
"self_attention": "self_attn",
"language_model.encoder": "language_model.model",
"word_embeddings_for_head": "language_model.lm_head",
"language_model.embedding.word_embeddings": "language_model.model.embed_tokens",
"vit_encoder.linear_encoder": "vision_embed_tokens",
}
KEYS_TO_REMOVE = {
"rotary_emb.inv_freq",
"image_patch_projection",
"image_patch_projection.weight",
"image_patch_projection.bias",
}
def rename_state_dict(state_dict):
model_state_dict = {}
for key, value in state_dict.items():
for key_to_modify, new_key in KEYS_TO_MODIFY_MAPPING.items():
if key_to_modify in key:
key = key.replace(key_to_modify, new_key)
# if KEYS_TO_REMOVE in key:
if key in KEYS_TO_REMOVE:
continue
model_state_dict[key] = value
return model_state_dict
def convert_fuyu_checkpoint(pytorch_dump_folder_path, ada_lib_path, pt_model_path, safe_serialization=False):
sys.path.insert(0, ada_lib_path)
model_state_dict_base = torch.load(pt_model_path, map_location="cpu")
state_dict = flatdict.FlatDict(model_state_dict_base["model"], ".")
state_dict = rename_state_dict(state_dict)
transformers_config = FuyuConfig()
model = FuyuForCausalLM(transformers_config).to(torch.bfloat16)
model.load_state_dict(state_dict)
model.save_pretrained(pytorch_dump_folder_path, safe_serialization=safe_serialization)
transformers_config.save_pretrained(pytorch_dump_folder_path)
def main():
parser = argparse.ArgumentParser()
parser.add_argument(
"--input_dir",
help="Location of Fuyu weights, which contains tokenizer.model and model folders",
)
parser.add_argument(
"--pt_model_path",
help="Location of Fuyu `model_optim_rng.pt`",
)
parser.add_argument(
"--output_dir",
help="Location to write HF model and tokenizer",
)
parser.add_argument(
"--ada_lib_path",
help="Location of original source code from adept to deserialize .pt checkpoint",
)
parser.add_argument("--safe_serialization", type=bool, help="Whether or not to save using `safetensors`.")
args = parser.parse_args()
spm_path = os.path.join(args.input_dir, "adept_vocab.model")
convert_fuyu_checkpoint(
pytorch_dump_folder_path=args.output_dir,
pt_model_path=args.pt_model_path,
safe_serialization=args.safe_serialization,
ada_lib_path=args.ada_lib_path,
)
tokenizer = tokenizer_class(spm_path, bos_token="|ENDOFTEXT|", eos_token="|ENDOFTEXT|")
tokenizer.save_pretrained(args.output_dir)
if __name__ == "__main__":
main()
| transformers/src/transformers/models/fuyu/convert_fuyu_model_weights_to_hf.py/0 | {
"file_path": "transformers/src/transformers/models/fuyu/convert_fuyu_model_weights_to_hf.py",
"repo_id": "transformers",
"token_count": 1851
} |
# coding=utf-8
# Copyright 2024 Google Inc. 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 Callable, Optional, Tuple, Union
import torch
import torch.nn as nn
import torch.utils.checkpoint
from ...activations import ACT2FN
from ...cache_utils import Cache, HybridCache
from ...configuration_utils import PretrainedConfig
from ...modeling_flash_attention_utils import FlashAttentionKwargs
from ...modeling_outputs import (
BaseModelOutputWithPast,
CausalLMOutputWithPast,
)
from ...modeling_utils import ALL_ATTENTION_FUNCTIONS
from ...processing_utils import Unpack
from ...utils import is_torchdynamo_compiling, logging
from ..gemma.modeling_gemma import (
GemmaAttention,
GemmaForCausalLM,
GemmaForSequenceClassification,
GemmaForTokenClassification,
GemmaMLP,
GemmaModel,
GemmaRMSNorm,
apply_rotary_pos_emb,
repeat_kv,
)
_CHECKPOINT_FOR_DOC = "google/gemma2-7b"
logger = logging.get_logger(__name__)
class Gemma2Config(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`Gemma2Model`]. It is used to instantiate an Gemma2
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 Gemma2-7B.
e.g. [google/gemma2-7b](https://huggingface.co/google/gemma2-7b)
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 256000):
Vocabulary size of the Gemma2 model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`Gemma2Model`]
hidden_size (`int`, *optional*, defaults to 2304):
Dimension of the hidden representations.
intermediate_size (`int`, *optional*, defaults to 9216):
Dimension of the MLP representations.
num_hidden_layers (`int`, *optional*, defaults to 26):
Number of hidden layers in the Transformer decoder.
num_attention_heads (`int`, *optional*, defaults to 8):
Number of attention heads for each attention layer in the Transformer decoder.
num_key_value_heads (`int`, *optional*, defaults to 4):
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 256):
The attention head dimension.
hidden_activation (`str` or `function`, *optional*, defaults to `"gelu_pytorch_tanh"`):
The non-linear activation function (function or string) in the decoder. Will default to `"gelu_pytorch_tanh"`
if not specified. `"gelu_pytorch_tanh"` uses an approximation of the `"gelu"` activation function.
max_position_embeddings (`int`, *optional*, defaults to 8192):
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*, defaults to 0):
Padding token id.
eos_token_id (`int`, *optional*, defaults to 1):
End of stream token id.
bos_token_id (`int`, *optional*, defaults to 2):
Beginning of stream token id.
tie_word_embeddings (`bool`, *optional*, defaults to `True`):
Whether to tie weight embeddings
rope_theta (`float`, *optional*, defaults to 10000.0):
The base period of the RoPE embeddings.
attention_bias (`bool`, defaults to `False`, *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.
query_pre_attn_scalar (`float`, *optional*, defaults to 256): scaling factor used on the attention scores
sliding_window (`int`, *optional*, defaults to 4096): in Gemma2, every other layer uses sliding window attention. This is the
size of the sliding window.
final_logit_softcapping (`float`, *optional*, defaults to 30.0): scaling factor when applying tanh softcapping on the logits.
attn_logit_softcapping (`float`, *optional*, defaults to 50.0): scaling factor when applying tanh softcapping on the attention scores.
cache_implementation (`str`, *optional*, defaults to `"hybrid"`): the cache type to be used with `generate`.
```python
>>> from transformers import Gemma2Model, Gemma2Config
>>> # Initializing a Gemma2 gemma2-7b style configuration
>>> configuration = Gemma2Config()
>>> # Initializing a model from the gemma2-7b style configuration
>>> model = Gemma2Model(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "gemma2"
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",
}
def __init__(
self,
vocab_size=256000,
hidden_size=2304,
intermediate_size=9216,
num_hidden_layers=26,
num_attention_heads=8,
num_key_value_heads=4,
head_dim=256,
hidden_activation="gelu_pytorch_tanh",
max_position_embeddings=8192,
initializer_range=0.02,
rms_norm_eps=1e-6,
use_cache=True,
pad_token_id=0,
eos_token_id=1,
bos_token_id=2,
tie_word_embeddings=True,
rope_theta=10000.0,
attention_bias=False,
attention_dropout=0.0,
query_pre_attn_scalar=256,
sliding_window=4096,
final_logit_softcapping=30.0,
attn_logit_softcapping=50.0,
cache_implementation="hybrid",
**kwargs,
):
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,
)
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.head_dim = head_dim
self.num_key_value_heads = num_key_value_heads
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.hidden_activation = hidden_activation
self.query_pre_attn_scalar = query_pre_attn_scalar
self.sliding_window = sliding_window
self.final_logit_softcapping = final_logit_softcapping
self.attn_logit_softcapping = attn_logit_softcapping
self.cache_implementation = cache_implementation
class Gemma2RMSNorm(GemmaRMSNorm):
pass
class Gemma2MLP(GemmaMLP):
def __init__(self, config):
super().__init__()
self.act_fn = ACT2FN[config.hidden_activation]
def eager_attention_forward(
module: nn.Module,
query: torch.Tensor,
key: torch.Tensor,
value: torch.Tensor,
attention_mask: Optional[torch.Tensor],
dropout: float = 0.0,
scaling: Optional[float] = None,
softcap: Optional[float] = None,
**kwargs,
) -> Tuple[torch.Tensor, torch.Tensor]:
if scaling is None:
scaling = module.head_dim**-0.5
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 softcap is not None:
attn_weights = attn_weights / softcap
attn_weights = torch.tanh(attn_weights)
attn_weights = attn_weights * softcap
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.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 Gemma2Attention(GemmaAttention):
def __init__(self, config: Gemma2Config, layer_idx: int):
super().__init__(config, layer_idx)
self.attn_logit_softcapping = self.config.attn_logit_softcapping
self.attention_dropout = self.config.attention_dropout
self.is_causal = True
self.scaling = config.query_pre_attn_scalar**-0.5
self.sliding_window = config.sliding_window if not bool(layer_idx % 2) else None
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,
"sliding_window": self.sliding_window,
}
key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
# Here we need to slice as we use a static cache by default, but FA2 does not support it
if attention_mask is not None and self.config._attn_implementation == "flash_attention_2":
seq_len = attention_mask.shape[-1]
key_states, value_states = key_states[:, :, :seq_len, :], value_states[:, :, :seq_len, :]
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=self.attention_dropout if self.training else 0.0,
scaling=self.scaling,
sliding_window=self.sliding_window,
softcap=self.attn_logit_softcapping,
**kwargs,
)
attn_output = attn_output.reshape(*input_shape, -1).contiguous()
attn_output = self.o_proj(attn_output)
return attn_output, attn_weights
class Gemma2DecoderLayer(nn.Module):
def __init__(self, config: Gemma2Config, layer_idx: int):
super().__init__()
self.hidden_size = config.hidden_size
self.config = config
self.is_sliding = not bool(layer_idx % 2)
self.self_attn = Gemma2Attention(config=config, layer_idx=layer_idx)
self.mlp = Gemma2MLP(config)
self.input_layernorm = Gemma2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.post_attention_layernorm = Gemma2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.pre_feedforward_layernorm = Gemma2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.post_feedforward_layernorm = Gemma2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.sliding_window = config.sliding_window
def forward(
self,
hidden_states: torch.Tensor,
position_embeddings: Tuple[torch.Tensor, 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,
last_cache_position: int = 0,
**kwargs,
) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
if self.is_sliding and attention_mask is not None: # efficient SDPA and no padding
# In prefill, we may be larger than sliding window
effective_seq_len = max(cache_position.shape[0], self.sliding_window)
# For FA2, the mask is 2D and is of shape [bs, processed_tokens] (not [bs, max_cache_len]),
# thus we must slice from the right (at most `effective_seq_len` elements)
if self.config._attn_implementation == "flash_attention_2":
attention_mask = attention_mask[:, -effective_seq_len:]
# Otherwise, the mask is 4D of shape [bs, 1, query_len, max_cache_len] thus we must slice
# from the left, with an offset if we are beyond the sliding window
else:
min_dtype = torch.finfo(hidden_states.dtype).min
sliding_window_mask = torch.tril(
torch.ones_like(attention_mask, dtype=torch.bool), diagonal=-self.sliding_window
)
attention_mask = torch.where(sliding_window_mask, min_dtype, attention_mask)
# In case we are beyond the sliding window, we need to correctly offset the mask slicing
# `last_cache_position` is equivalent to `cache_position[-1]` but without breaking dynamo
offset = last_cache_position - effective_seq_len
# Should only be used when beyond the sliding window (i.e. offset > 0)
offset = max(0, offset)
attention_mask = attention_mask[:, :, :, offset : offset + effective_seq_len]
residual = hidden_states
hidden_states = self.input_layernorm(hidden_states)
# Self Attention
hidden_states, self_attn_weights = self.self_attn(
hidden_states=hidden_states,
position_embeddings=position_embeddings,
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,
**kwargs,
)
hidden_states = self.post_attention_layernorm(hidden_states)
hidden_states = residual + hidden_states
residual = hidden_states
hidden_states = self.pre_feedforward_layernorm(hidden_states)
hidden_states = self.mlp(hidden_states)
hidden_states = self.post_feedforward_layernorm(hidden_states)
hidden_states = residual + hidden_states
outputs = (hidden_states,)
if output_attentions:
outputs += (self_attn_weights,)
return outputs
class Gemma2Model(GemmaModel):
def __init__(self, config: Gemma2Config):
super().__init__(config)
self.layers = nn.ModuleList(
[Gemma2DecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
)
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[HybridCache] = 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,
last_cache_position: Optional[int] = None,
**flash_attn_kwargs: Unpack[FlashAttentionKwargs],
) -> 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)
if use_cache and past_key_values is None and not self.training:
batch_size, seq_len, _ = inputs_embeds.shape
past_key_values = HybridCache(
self.config,
max_batch_size=batch_size,
max_cache_len=seq_len,
dtype=inputs_embeds.dtype,
)
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)
# This is needed to correctly slice the mask without data-dependent slicing later on if using dynamo tracing
# (retrieving the same value from `cache_position` later on would crash dynamo)
if last_cache_position is None:
last_cache_position = 0
if attention_mask is not None:
# In case a 4d mask is passed directly without using `generate`, we have to rely on cache_position
# It will break dynamo tracing but there are no way around it (and it should never happen in practice)
last_cache_position = (
attention_mask.shape[-1] if attention_mask.dim() == 2 else cache_position[-1].item()
)
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)
# normalized
# Gemma2 downcasts the below to float16, causing sqrt(3072)=55.4256 to become 55.5
# See https://github.com/huggingface/transformers/pull/29402
normalizer = torch.tensor(self.config.hidden_size**0.5, dtype=hidden_states.dtype)
hidden_states = hidden_states * normalizer
# 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(
decoder_layer.__call__,
hidden_states,
position_embeddings,
causal_mask,
position_ids,
past_key_values,
output_attentions,
use_cache,
cache_position,
last_cache_position,
)
else:
layer_outputs = decoder_layer(
hidden_states,
position_embeddings=position_embeddings,
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,
last_cache_position=last_cache_position,
**flash_attn_kwargs,
)
hidden_states = layer_outputs[0]
if output_attentions:
all_self_attns += (layer_outputs[1],)
hidden_states = self.norm(hidden_states)
if output_hidden_states:
all_hidden_states += (hidden_states,)
output = BaseModelOutputWithPast(
last_hidden_state=hidden_states,
past_key_values=past_key_values,
hidden_states=all_hidden_states,
attentions=all_self_attns,
)
return output if return_dict else output.to_tuple()
@torch.no_grad()
def _update_causal_mask(
self,
attention_mask: torch.Tensor,
input_tensor: torch.Tensor,
cache_position: torch.Tensor,
past_key_values: HybridCache,
output_attentions: bool,
):
# Flash Attention currently doesn't support static cache but Gemma2 work only with static cache.
# So we will pass in attention mask as is in any case, not only when ther's padding. Then we'll use its shape
# to cut out keys/values trailing 0 used in static cache. This workaround should be compile compatible
# as it doesn't cause dynamic control issues.
if self.config._attn_implementation == "flash_attention_2":
return attention_mask
dtype, device = input_tensor.dtype, input_tensor.device
sequence_length = input_tensor.shape[1]
if isinstance(past_key_values, HybridCache):
target_length = past_key_values.get_max_cache_shape()
else:
target_length = attention_mask.shape[-1] if attention_mask is not None else input_tensor.shape[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],
)
return causal_mask
class Gemma2ForCausalLM(GemmaForCausalLM):
def __init__(self, config):
super().__init__(config)
self.model = Gemma2Model(config)
self.post_init()
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[HybridCache] = 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,
logits_to_keep: Union[int, torch.Tensor] = 0,
**loss_kwargs,
) -> Union[Tuple, CausalLMOutputWithPast]:
r"""
```python
>>> from transformers import AutoTokenizer, GemmaForCausalLM
>>> model = GemmaForCausalLM.from_pretrained("google/gemma-2-9b")
>>> tokenizer = AutoTokenizer.from_pretrained("google/gemma-2-9b")
>>> 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?"
```"""
if self.training and self.config._attn_implementation != "eager":
logger.warning_once(
"It is strongly recommended to train Gemma2 models with the `eager` attention implementation "
f"instead of `{self.config._attn_implementation}`. Use `eager` with `AutoModelForCausalLM.from_pretrained('<path-to-checkpoint>', attn_implementation='eager')`."
)
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,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
cache_position=cache_position,
**loss_kwargs,
)
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, :])
if self.config.final_logit_softcapping is not None:
logits = logits / self.config.final_logit_softcapping
logits = torch.tanh(logits)
logits = logits * self.config.final_logit_softcapping
loss = None
if labels is not None:
loss = self.loss_function(logits, labels, self.vocab_size, **loss_kwargs)
if not return_dict:
output = (logits,) + outputs[1:]
return (loss,) + output if loss is not None else output
return CausalLMOutputWithPast(
loss=loss,
logits=logits,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
def prepare_inputs_for_generation(
self,
input_ids,
past_key_values=None,
attention_mask=None,
inputs_embeds=None,
cache_position=None,
position_ids=None,
use_cache=True,
logits_to_keep=None,
**kwargs,
):
# Overwritten: has a special cache type, `HybridCache`
# 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 past_key_values is not None:
if (
inputs_embeds is not None # Exception 1
or (is_torchdynamo_compiling() 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]
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 past_key_values:
position_ids = position_ids[:, -input_ids.shape[1] :]
# This `clone` call is needed to avoid recapturing cuda graphs with `torch.compile`'s
# `mode="reduce-overhead`, as otherwise the input `position_ids` would have various stride
# during the decoding. Here, simply using `.contiguous()` is not sufficient as in the
# batch size = 1 case, `position_ids` is already contiguous but with varying stride
# which retriggers a capture.
position_ids = position_ids.clone(memory_format=torch.contiguous_format)
# if `inputs_embeds` are passed, we only want to use them in the 1st generation step
if inputs_embeds is not None and cache_position[0] == 0:
model_inputs = {"inputs_embeds": inputs_embeds, "input_ids": None}
else:
# The clone here is for the same reason as for `position_ids`.
model_inputs = {"input_ids": input_ids.clone(memory_format=torch.contiguous_format), "inputs_embeds": None}
# This is needed to correctly slice the mask without data-dependent slicing later on if using dynamo tracing
# (retrieving the same value from `cache_position` later on would crash dynamo)
model_inputs["last_cache_position"] = attention_mask.shape[-1] if attention_mask is not None else 0
if (
isinstance(past_key_values, HybridCache)
and attention_mask.ndim == 2
and not self.config._attn_implementation == "flash_attention_2"
):
if model_inputs["inputs_embeds"] is not None:
batch_size, sequence_length, _ = model_inputs["inputs_embeds"].shape
device = model_inputs["inputs_embeds"].device
else:
batch_size, sequence_length = model_inputs["input_ids"].shape
device = model_inputs["input_ids"].device
attention_mask = self.model._prepare_4d_causal_attention_mask_with_cache_position(
attention_mask,
sequence_length=sequence_length,
target_length=past_key_values.get_max_cache_shape(),
dtype=self.lm_head.weight.dtype,
device=device,
cache_position=cache_position,
batch_size=batch_size,
)
if logits_to_keep is not None:
model_inputs["logits_to_keep"] = logits_to_keep
model_inputs.update(
{
"position_ids": position_ids,
"cache_position": cache_position,
"past_key_values": past_key_values,
"use_cache": use_cache,
"attention_mask": attention_mask,
}
)
return model_inputs
class Gemma2ForSequenceClassification(GemmaForSequenceClassification):
def __init__(self, config):
super().__init__(config)
self.model = Gemma2Model(config)
self.post_init()
class Gemma2ForTokenClassification(GemmaForTokenClassification):
def __init__(self, config):
super().__init__(config)
self.model = Gemma2Model(config)
self.post_init()
__all__ = [
"Gemma2Config",
"Gemma2ForCausalLM",
"Gemma2Model",
"Gemma2PreTrainedModel", # noqa: F822
"Gemma2ForSequenceClassification",
"Gemma2ForTokenClassification",
]
| transformers/src/transformers/models/gemma2/modular_gemma2.py/0 | {
"file_path": "transformers/src/transformers/models/gemma2/modular_gemma2.py",
"repo_id": "transformers",
"token_count": 14910
} |
# coding=utf-8
# Copyright 2022 KAIST 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 GLPN model."""
import math
from typing import List, Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from ...activations import ACT2FN
from ...modeling_outputs import BaseModelOutput, DepthEstimatorOutput
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,
replace_return_docstrings,
)
from .configuration_glpn import GLPNConfig
logger = logging.get_logger(__name__)
# General docstring
_CONFIG_FOR_DOC = "GLPNConfig"
# Base docstring
_CHECKPOINT_FOR_DOC = "vinvino02/glpn-kitti"
_EXPECTED_OUTPUT_SHAPE = [1, 512, 15, 20]
# 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.segformer.modeling_segformer.SegformerDropPath
class GLPNDropPath(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)
# Copied from transformers.models.segformer.modeling_segformer.SegformerOverlapPatchEmbeddings
class GLPNOverlapPatchEmbeddings(nn.Module):
"""Construct the overlapping patch embeddings."""
def __init__(self, patch_size, stride, num_channels, hidden_size):
super().__init__()
self.proj = nn.Conv2d(
num_channels,
hidden_size,
kernel_size=patch_size,
stride=stride,
padding=patch_size // 2,
)
self.layer_norm = nn.LayerNorm(hidden_size)
def forward(self, pixel_values):
embeddings = self.proj(pixel_values)
_, _, height, width = embeddings.shape
# (batch_size, num_channels, height, width) -> (batch_size, num_channels, height*width) -> (batch_size, height*width, num_channels)
# this can be fed to a Transformer layer
embeddings = embeddings.flatten(2).transpose(1, 2)
embeddings = self.layer_norm(embeddings)
return embeddings, height, width
# Copied from transformers.models.segformer.modeling_segformer.SegformerEfficientSelfAttention
class GLPNEfficientSelfAttention(nn.Module):
"""SegFormer's efficient self-attention mechanism. Employs the sequence reduction process introduced in the [PvT
paper](https://arxiv.org/abs/2102.12122)."""
def __init__(self, config, hidden_size, num_attention_heads, sequence_reduction_ratio):
super().__init__()
self.hidden_size = hidden_size
self.num_attention_heads = num_attention_heads
if self.hidden_size % self.num_attention_heads != 0:
raise ValueError(
f"The hidden size ({self.hidden_size}) is not a multiple of the number of attention "
f"heads ({self.num_attention_heads})"
)
self.attention_head_size = int(self.hidden_size / self.num_attention_heads)
self.all_head_size = self.num_attention_heads * self.attention_head_size
self.query = nn.Linear(self.hidden_size, self.all_head_size)
self.key = nn.Linear(self.hidden_size, self.all_head_size)
self.value = nn.Linear(self.hidden_size, self.all_head_size)
self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
self.sr_ratio = sequence_reduction_ratio
if sequence_reduction_ratio > 1:
self.sr = nn.Conv2d(
hidden_size, hidden_size, kernel_size=sequence_reduction_ratio, stride=sequence_reduction_ratio
)
self.layer_norm = nn.LayerNorm(hidden_size)
def transpose_for_scores(self, hidden_states):
new_shape = hidden_states.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
hidden_states = hidden_states.view(new_shape)
return hidden_states.permute(0, 2, 1, 3)
def forward(
self,
hidden_states,
height,
width,
output_attentions=False,
):
query_layer = self.transpose_for_scores(self.query(hidden_states))
if self.sr_ratio > 1:
batch_size, seq_len, num_channels = hidden_states.shape
# Reshape to (batch_size, num_channels, height, width)
hidden_states = hidden_states.permute(0, 2, 1).reshape(batch_size, num_channels, height, width)
# Apply sequence reduction
hidden_states = self.sr(hidden_states)
# Reshape back to (batch_size, seq_len, num_channels)
hidden_states = hidden_states.reshape(batch_size, num_channels, -1).permute(0, 2, 1)
hidden_states = self.layer_norm(hidden_states)
key_layer = self.transpose_for_scores(self.key(hidden_states))
value_layer = self.transpose_for_scores(self.value(hidden_states))
# 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)
# 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)
outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
return outputs
# Copied from transformers.models.segformer.modeling_segformer.SegformerSelfOutput
class GLPNSelfOutput(nn.Module):
def __init__(self, config, hidden_size):
super().__init__()
self.dense = nn.Linear(hidden_size, hidden_size)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, hidden_states, input_tensor):
hidden_states = self.dense(hidden_states)
hidden_states = self.dropout(hidden_states)
return hidden_states
# Copied from transformers.models.segformer.modeling_segformer.SegformerAttention with Segformer->GLPN
class GLPNAttention(nn.Module):
def __init__(self, config, hidden_size, num_attention_heads, sequence_reduction_ratio):
super().__init__()
self.self = GLPNEfficientSelfAttention(
config=config,
hidden_size=hidden_size,
num_attention_heads=num_attention_heads,
sequence_reduction_ratio=sequence_reduction_ratio,
)
self.output = GLPNSelfOutput(config, hidden_size=hidden_size)
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, height, width, output_attentions=False):
self_outputs = self.self(hidden_states, height, width, 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.segformer.modeling_segformer.SegformerDWConv
class GLPNDWConv(nn.Module):
def __init__(self, dim=768):
super().__init__()
self.dwconv = nn.Conv2d(dim, dim, 3, 1, 1, bias=True, groups=dim)
def forward(self, hidden_states, height, width):
batch_size, seq_len, num_channels = hidden_states.shape
hidden_states = hidden_states.transpose(1, 2).view(batch_size, num_channels, height, width)
hidden_states = self.dwconv(hidden_states)
hidden_states = hidden_states.flatten(2).transpose(1, 2)
return hidden_states
# Copied from transformers.models.segformer.modeling_segformer.SegformerMixFFN with Segformer->GLPN
class GLPNMixFFN(nn.Module):
def __init__(self, config, in_features, hidden_features=None, out_features=None):
super().__init__()
out_features = out_features or in_features
self.dense1 = nn.Linear(in_features, hidden_features)
self.dwconv = GLPNDWConv(hidden_features)
if isinstance(config.hidden_act, str):
self.intermediate_act_fn = ACT2FN[config.hidden_act]
else:
self.intermediate_act_fn = config.hidden_act
self.dense2 = nn.Linear(hidden_features, out_features)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, hidden_states, height, width):
hidden_states = self.dense1(hidden_states)
hidden_states = self.dwconv(hidden_states, height, width)
hidden_states = self.intermediate_act_fn(hidden_states)
hidden_states = self.dropout(hidden_states)
hidden_states = self.dense2(hidden_states)
hidden_states = self.dropout(hidden_states)
return hidden_states
# Copied from transformers.models.segformer.modeling_segformer.SegformerLayer with Segformer->GLPN
class GLPNLayer(nn.Module):
"""This corresponds to the Block class in the original implementation."""
def __init__(self, config, hidden_size, num_attention_heads, drop_path, sequence_reduction_ratio, mlp_ratio):
super().__init__()
self.layer_norm_1 = nn.LayerNorm(hidden_size)
self.attention = GLPNAttention(
config,
hidden_size=hidden_size,
num_attention_heads=num_attention_heads,
sequence_reduction_ratio=sequence_reduction_ratio,
)
self.drop_path = GLPNDropPath(drop_path) if drop_path > 0.0 else nn.Identity()
self.layer_norm_2 = nn.LayerNorm(hidden_size)
mlp_hidden_size = int(hidden_size * mlp_ratio)
self.mlp = GLPNMixFFN(config, in_features=hidden_size, hidden_features=mlp_hidden_size)
def forward(self, hidden_states, height, width, output_attentions=False):
self_attention_outputs = self.attention(
self.layer_norm_1(hidden_states), # in GLPN, layernorm is applied before self-attention
height,
width,
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 (with stochastic depth)
attention_output = self.drop_path(attention_output)
hidden_states = attention_output + hidden_states
mlp_output = self.mlp(self.layer_norm_2(hidden_states), height, width)
# second residual connection (with stochastic depth)
mlp_output = self.drop_path(mlp_output)
layer_output = mlp_output + hidden_states
outputs = (layer_output,) + outputs
return outputs
class GLPNEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
# stochastic depth decay rule
dpr = [x.item() for x in torch.linspace(0, config.drop_path_rate, sum(config.depths))]
# patch embeddings
embeddings = []
for i in range(config.num_encoder_blocks):
embeddings.append(
GLPNOverlapPatchEmbeddings(
patch_size=config.patch_sizes[i],
stride=config.strides[i],
num_channels=config.num_channels if i == 0 else config.hidden_sizes[i - 1],
hidden_size=config.hidden_sizes[i],
)
)
self.patch_embeddings = nn.ModuleList(embeddings)
# Transformer blocks
blocks = []
cur = 0
for i in range(config.num_encoder_blocks):
# each block consists of layers
layers = []
if i != 0:
cur += config.depths[i - 1]
for j in range(config.depths[i]):
layers.append(
GLPNLayer(
config,
hidden_size=config.hidden_sizes[i],
num_attention_heads=config.num_attention_heads[i],
drop_path=dpr[cur + j],
sequence_reduction_ratio=config.sr_ratios[i],
mlp_ratio=config.mlp_ratios[i],
)
)
blocks.append(nn.ModuleList(layers))
self.block = nn.ModuleList(blocks)
# Layer norms
self.layer_norm = nn.ModuleList(
[nn.LayerNorm(config.hidden_sizes[i]) for i in range(config.num_encoder_blocks)]
)
def forward(
self,
pixel_values,
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
batch_size = pixel_values.shape[0]
hidden_states = pixel_values
for idx, x in enumerate(zip(self.patch_embeddings, self.block, self.layer_norm)):
embedding_layer, block_layer, norm_layer = x
# first, obtain patch embeddings
hidden_states, height, width = embedding_layer(hidden_states)
# second, send embeddings through blocks
for i, blk in enumerate(block_layer):
layer_outputs = blk(hidden_states, height, width, output_attentions)
hidden_states = layer_outputs[0]
if output_attentions:
all_self_attentions = all_self_attentions + (layer_outputs[1],)
# third, apply layer norm
hidden_states = norm_layer(hidden_states)
# fourth, optionally reshape back to (batch_size, num_channels, height, width)
hidden_states = hidden_states.reshape(batch_size, height, width, -1).permute(0, 3, 1, 2).contiguous()
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 GLPNPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = GLPNConfig
base_model_prefix = "glpn"
main_input_name = "pixel_values"
_no_split_modules = []
# Copied from transformers.models.segformer.modeling_segformer.SegformerPreTrainedModel._init_weights
def _init_weights(self, module):
"""Initialize the weights"""
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=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)
GLPN_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 ([`GLPNConfig`]): 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.
"""
GLPN_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 [`GLPNImageProcessor.__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.
"""
@add_start_docstrings(
"The bare GLPN encoder (Mix-Transformer) outputting raw hidden-states without any specific head on top.",
GLPN_START_DOCSTRING,
)
class GLPNModel(GLPNPreTrainedModel):
# Copied from transformers.models.segformer.modeling_segformer.SegformerModel.__init__ with Segformer->GLPN
def __init__(self, config):
super().__init__(config)
self.config = config
# hierarchical Transformer encoder
self.encoder = GLPNEncoder(config)
# Initialize weights and apply final processing
self.post_init()
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(GLPN_INPUTS_DOCSTRING.format("(batch_size, sequence_length)"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=BaseModelOutput,
config_class=_CONFIG_FOR_DOC,
modality="vision",
expected_output=_EXPECTED_OUTPUT_SHAPE,
)
# Copied from transformers.models.segformer.modeling_segformer.SegformerModel.forward
def forward(
self,
pixel_values: torch.FloatTensor,
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
encoder_outputs = self.encoder(
pixel_values,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = encoder_outputs[0]
if not return_dict:
return (sequence_output,) + encoder_outputs[1:]
return BaseModelOutput(
last_hidden_state=sequence_output,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
)
class GLPNSelectiveFeatureFusion(nn.Module):
"""
Selective Feature Fusion module, as explained in the [paper](https://arxiv.org/abs/2201.07436) (section 3.4). This
module adaptively selects and integrates local and global features by attaining an attention map for each feature.
"""
def __init__(self, in_channel=64):
super().__init__()
self.convolutional_layer1 = nn.Sequential(
nn.Conv2d(in_channels=int(in_channel * 2), out_channels=in_channel, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(in_channel),
nn.ReLU(),
)
self.convolutional_layer2 = nn.Sequential(
nn.Conv2d(in_channels=in_channel, out_channels=int(in_channel / 2), kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(int(in_channel / 2)),
nn.ReLU(),
)
self.convolutional_layer3 = nn.Conv2d(
in_channels=int(in_channel / 2), out_channels=2, kernel_size=3, stride=1, padding=1
)
self.sigmoid = nn.Sigmoid()
def forward(self, local_features, global_features):
# concatenate features along the channel dimension
features = torch.cat((local_features, global_features), dim=1)
# pass through convolutional layers
features = self.convolutional_layer1(features)
features = self.convolutional_layer2(features)
features = self.convolutional_layer3(features)
# apply sigmoid to get two-channel attention map
attn = self.sigmoid(features)
# construct hybrid features by adding element-wise
hybrid_features = local_features * attn[:, 0, :, :].unsqueeze(1) + global_features * attn[
:, 1, :, :
].unsqueeze(1)
return hybrid_features
class GLPNDecoderStage(nn.Module):
def __init__(self, in_channels, out_channels):
super().__init__()
should_skip = in_channels == out_channels
self.convolution = nn.Conv2d(in_channels, out_channels, kernel_size=1) if not should_skip else nn.Identity()
self.fusion = GLPNSelectiveFeatureFusion(out_channels)
self.upsample = nn.Upsample(scale_factor=2, mode="bilinear", align_corners=False)
def forward(self, hidden_state, residual=None):
hidden_state = self.convolution(hidden_state)
if residual is not None:
hidden_state = self.fusion(hidden_state, residual)
hidden_state = self.upsample(hidden_state)
return hidden_state
hidden_state = self.upsample(hidden_state)
return hidden_state
class GLPNDecoder(nn.Module):
def __init__(self, config):
super().__init__()
# we use features from end -> start
reserved_hidden_sizes = config.hidden_sizes[::-1]
out_channels = config.decoder_hidden_size
self.stages = nn.ModuleList(
[GLPNDecoderStage(hidden_size, out_channels) for hidden_size in reserved_hidden_sizes]
)
# don't fuse in first stage
self.stages[0].fusion = None
self.final_upsample = nn.Upsample(scale_factor=2, mode="bilinear", align_corners=False)
def forward(self, hidden_states: List[torch.Tensor]) -> List[torch.Tensor]:
stage_hidden_states = []
stage_hidden_state = None
for hidden_state, stage in zip(hidden_states[::-1], self.stages):
stage_hidden_state = stage(hidden_state, stage_hidden_state)
stage_hidden_states.append(stage_hidden_state)
stage_hidden_states[-1] = self.final_upsample(stage_hidden_state)
return stage_hidden_states
class SiLogLoss(nn.Module):
r"""
Implements the Scale-invariant log scale loss [Eigen et al., 2014](https://arxiv.org/abs/1406.2283).
$$L=\frac{1}{n} \sum_{i} d_{i}^{2}-\frac{1}{2 n^{2}}\left(\sum_{i} d_{i}^{2}\right)$$ where $d_{i}=\log y_{i}-\log
y_{i}^{*}$.
"""
def __init__(self, lambd=0.5):
super().__init__()
self.lambd = lambd
def forward(self, pred, target):
valid_mask = (target > 0).detach()
diff_log = torch.log(target[valid_mask]) - torch.log(pred[valid_mask])
loss = torch.sqrt(torch.pow(diff_log, 2).mean() - self.lambd * torch.pow(diff_log.mean(), 2))
return loss
class GLPNDepthEstimationHead(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
channels = config.decoder_hidden_size
self.head = nn.Sequential(
nn.Conv2d(channels, channels, kernel_size=3, stride=1, padding=1),
nn.ReLU(inplace=False),
nn.Conv2d(channels, 1, kernel_size=3, stride=1, padding=1),
)
def forward(self, hidden_states: List[torch.Tensor]) -> torch.Tensor:
# use last features of the decoder
hidden_states = hidden_states[self.config.head_in_index]
hidden_states = self.head(hidden_states)
predicted_depth = torch.sigmoid(hidden_states) * self.config.max_depth
predicted_depth = predicted_depth.squeeze(dim=1)
return predicted_depth
@add_start_docstrings(
"""GLPN Model transformer with a lightweight depth estimation head on top e.g. for KITTI, NYUv2.""",
GLPN_START_DOCSTRING,
)
class GLPNForDepthEstimation(GLPNPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.glpn = GLPNModel(config)
self.decoder = GLPNDecoder(config)
self.head = GLPNDepthEstimationHead(config)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(GLPN_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=DepthEstimatorOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
pixel_values: torch.FloatTensor,
labels: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple[torch.Tensor], DepthEstimatorOutput]:
r"""
labels (`torch.FloatTensor` of shape `(batch_size, height, width)`, *optional*):
Ground truth depth estimation maps for computing the loss.
Returns:
Examples:
```python
>>> from transformers import AutoImageProcessor, GLPNForDepthEstimation
>>> import torch
>>> import numpy as np
>>> 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("vinvino02/glpn-kitti")
>>> model = GLPNForDepthEstimation.from_pretrained("vinvino02/glpn-kitti")
>>> # prepare image for the model
>>> inputs = image_processor(images=image, return_tensors="pt")
>>> with torch.no_grad():
... outputs = model(**inputs)
>>> # interpolate to original size
>>> post_processed_output = image_processor.post_process_depth_estimation(
... outputs,
... target_sizes=[(image.height, image.width)],
... )
>>> # visualize the prediction
>>> predicted_depth = post_processed_output[0]["predicted_depth"]
>>> depth = predicted_depth * 255 / predicted_depth.max()
>>> depth = depth.detach().cpu().numpy()
>>> depth = Image.fromarray(depth.astype("uint8"))
```"""
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
)
outputs = self.glpn(
pixel_values,
output_attentions=output_attentions,
output_hidden_states=True, # we need the intermediate hidden states
return_dict=return_dict,
)
hidden_states = outputs.hidden_states if return_dict else outputs[1]
out = self.decoder(hidden_states)
predicted_depth = self.head(out)
loss = None
if labels is not None:
loss_fct = SiLogLoss()
loss = loss_fct(predicted_depth, labels)
if not return_dict:
if output_hidden_states:
output = (predicted_depth,) + outputs[1:]
else:
output = (predicted_depth,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return DepthEstimatorOutput(
loss=loss,
predicted_depth=predicted_depth,
hidden_states=outputs.hidden_states if output_hidden_states else None,
attentions=outputs.attentions,
)
__all__ = ["GLPNForDepthEstimation", "GLPNLayer", "GLPNModel", "GLPNPreTrainedModel"]
| transformers/src/transformers/models/glpn/modeling_glpn.py/0 | {
"file_path": "transformers/src/transformers/models/glpn/modeling_glpn.py",
"repo_id": "transformers",
"token_count": 13171
} |
# coding=utf-8
# Copyright 2018 The Open AI 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.
"""Tokenization classes for OpenAI GPT."""
from typing import Optional, Tuple
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpt2 import GPT2Tokenizer
logger = logging.get_logger(__name__)
VOCAB_FILES_NAMES = {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}
class GPT2TokenizerFast(PreTrainedTokenizerFast):
"""
Construct a "fast" GPT-2 tokenizer (backed by HuggingFace's *tokenizers* library). Based on byte-level
Byte-Pair-Encoding.
This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will
be encoded differently whether it is at the beginning of the sentence (without space) or not:
```python
>>> from transformers import GPT2TokenizerFast
>>> tokenizer = GPT2TokenizerFast.from_pretrained("openai-community/gpt2")
>>> tokenizer("Hello world")["input_ids"]
[15496, 995]
>>> tokenizer(" Hello world")["input_ids"]
[18435, 995]
```
You can get around that behavior by passing `add_prefix_space=True` when instantiating this tokenizer, but since
the model was not pretrained this way, it might yield a decrease in performance.
<Tip>
When used with `is_split_into_words=True`, this tokenizer needs to be instantiated with `add_prefix_space=True`.
</Tip>
This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should
refer to this superclass for more information regarding those methods.
Args:
vocab_file (`str`, *optional*):
Path to the vocabulary file.
merges_file (`str`, *optional*):
Path to the merges file.
tokenizer_file (`str`, *optional*):
Path to [tokenizers](https://github.com/huggingface/tokenizers) file (generally has a .json extension) that
contains everything needed to load the tokenizer.
unk_token (`str`, *optional*, defaults to `"<|endoftext|>"`):
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.
bos_token (`str`, *optional*, defaults to `"<|endoftext|>"`):
The beginning of sequence token.
eos_token (`str`, *optional*, defaults to `"<|endoftext|>"`):
The end of sequence token.
add_prefix_space (`bool`, *optional*, defaults to `False`):
Whether or not to add an initial space to the input. This allows to treat the leading word just as any
other word. (GPT2 tokenizer detect beginning of words by the preceding space).
"""
vocab_files_names = VOCAB_FILES_NAMES
model_input_names = ["input_ids", "attention_mask"]
slow_tokenizer_class = GPT2Tokenizer
def __init__(
self,
vocab_file=None,
merges_file=None,
tokenizer_file=None,
unk_token="<|endoftext|>",
bos_token="<|endoftext|>",
eos_token="<|endoftext|>",
add_prefix_space=False,
**kwargs,
):
super().__init__(
vocab_file=vocab_file,
merges_file=merges_file,
tokenizer_file=tokenizer_file,
unk_token=unk_token,
bos_token=bos_token,
eos_token=eos_token,
add_prefix_space=add_prefix_space,
**kwargs,
)
self.add_bos_token = kwargs.pop("add_bos_token", False)
def _batch_encode_plus(self, *args, **kwargs) -> BatchEncoding:
is_split_into_words = kwargs.get("is_split_into_words", False)
assert self.add_prefix_space or not is_split_into_words, (
f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
"to use it with pretokenized inputs."
)
return super()._batch_encode_plus(*args, **kwargs)
def _encode_plus(self, *args, **kwargs) -> BatchEncoding:
is_split_into_words = kwargs.get("is_split_into_words", False)
assert self.add_prefix_space or not is_split_into_words, (
f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
"to use it with pretokenized inputs."
)
return super()._encode_plus(*args, **kwargs)
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__ = ["GPT2TokenizerFast"]
| transformers/src/transformers/models/gpt2/tokenization_gpt2_fast.py/0 | {
"file_path": "transformers/src/transformers/models/gpt2/tokenization_gpt2_fast.py",
"repo_id": "transformers",
"token_count": 1982
} |
# 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",
]
| transformers/src/transformers/models/helium/modular_helium.py/0 | {
"file_path": "transformers/src/transformers/models/helium/modular_helium.py",
"repo_id": "transformers",
"token_count": 2356
} |
# 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"]
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"]
| transformers/src/transformers/models/idefics2/image_processing_idefics2.py/0 | {
"file_path": "transformers/src/transformers/models/idefics2/image_processing_idefics2.py",
"repo_id": "transformers",
"token_count": 11258
} |
# 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.
"""Convert OpenAI Image GPT checkpoints."""
import argparse
import torch
from transformers import ImageGPTConfig, ImageGPTForCausalLM, load_tf_weights_in_imagegpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def convert_imagegpt_checkpoint_to_pytorch(imagegpt_checkpoint_path, model_size, pytorch_dump_folder_path):
# Construct configuration depending on size
MODELS = {"small": (512, 8, 24), "medium": (1024, 8, 36), "large": (1536, 16, 48)}
n_embd, n_head, n_layer = MODELS[model_size] # set model hyperparameters
config = ImageGPTConfig(n_embd=n_embd, n_layer=n_layer, n_head=n_head)
model = ImageGPTForCausalLM(config)
# Load weights from numpy
load_tf_weights_in_imagegpt(model, config, imagegpt_checkpoint_path)
# Save pytorch-model
pytorch_weights_dump_path = pytorch_dump_folder_path + "/" + WEIGHTS_NAME
pytorch_config_dump_path = pytorch_dump_folder_path + "/" + CONFIG_NAME
print(f"Save PyTorch model to {pytorch_weights_dump_path}")
torch.save(model.state_dict(), pytorch_weights_dump_path)
print(f"Save configuration file to {pytorch_config_dump_path}")
with open(pytorch_config_dump_path, "w", encoding="utf-8") as f:
f.write(config.to_json_string())
if __name__ == "__main__":
parser = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--imagegpt_checkpoint_path",
default=None,
type=str,
required=True,
help="Path to the TensorFlow checkpoint path.",
)
parser.add_argument(
"--model_size",
default=None,
type=str,
required=True,
help="Size of the model (can be either 'small', 'medium' or 'large').",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
args = parser.parse_args()
convert_imagegpt_checkpoint_to_pytorch(
args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
)
| transformers/src/transformers/models/imagegpt/convert_imagegpt_original_tf2_to_pytorch.py/0 | {
"file_path": "transformers/src/transformers/models/imagegpt/convert_imagegpt_original_tf2_to_pytorch.py",
"repo_id": "transformers",
"token_count": 993
} |
# coding=utf-8
# Copyright 2022 Microsoft Research 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 LayoutLMv3 model."""
import collections
import math
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACT2FN
from ...modeling_outputs import (
BaseModelOutput,
QuestionAnsweringModelOutput,
SequenceClassifierOutput,
TokenClassifierOutput,
)
from ...modeling_utils import PreTrainedModel
from ...pytorch_utils import apply_chunking_to_forward
from ...utils import (
add_start_docstrings,
add_start_docstrings_to_model_forward,
logging,
replace_return_docstrings,
torch_int,
)
from .configuration_layoutlmv3 import LayoutLMv3Config
logger = logging.get_logger(__name__)
_CONFIG_FOR_DOC = "LayoutLMv3Config"
LAYOUTLMV3_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 ([`LayoutLMv3Config`]): 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.
"""
LAYOUTLMV3_MODEL_INPUTS_DOCSTRING = r"""
Args:
input_ids (`torch.LongTensor` of shape `({0})`):
Indices of input sequence tokens in the vocabulary.
Note that `sequence_length = token_sequence_length + patch_sequence_length + 1` where `1` is for [CLS]
token. See `pixel_values` for `patch_sequence_length`.
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.
Note that `sequence_length = token_sequence_length + patch_sequence_length + 1` where `1` is for [CLS]
token. See `pixel_values` for `patch_sequence_length`.
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Batch of document images. Each image is divided into patches of shape `(num_channels, config.patch_size,
config.patch_size)` and the total number of patches (=`patch_sequence_length`) equals to `((height /
config.patch_size) * (width / config.patch_size))`.
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**.
Note that `sequence_length = token_sequence_length + patch_sequence_length + 1` where `1` is for [CLS]
token. See `pixel_values` for `patch_sequence_length`.
[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.
Note that `sequence_length = token_sequence_length + patch_sequence_length + 1` where `1` is for [CLS]
token. See `pixel_values` for `patch_sequence_length`.
[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]`.
Note that `sequence_length = token_sequence_length + patch_sequence_length + 1` where `1` is for [CLS]
token. See `pixel_values` for `patch_sequence_length`.
[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.
"""
LAYOUTLMV3_DOWNSTREAM_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.
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Batch of document images. Each image is divided into patches of shape `(num_channels, config.patch_size,
config.patch_size)` and the total number of patches (=`patch_sequence_length`) equals to `((height /
config.patch_size) * (width / config.patch_size))`.
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.
"""
class LayoutLMv3PatchEmbeddings(nn.Module):
"""LayoutLMv3 image (patch) embeddings. This class also automatically interpolates the position embeddings for varying
image sizes."""
def __init__(self, config):
super().__init__()
image_size = (
config.input_size
if isinstance(config.input_size, collections.abc.Iterable)
else (config.input_size, config.input_size)
)
patch_size = (
config.patch_size
if isinstance(config.patch_size, collections.abc.Iterable)
else (config.patch_size, config.patch_size)
)
self.patch_shape = (image_size[0] // patch_size[0], image_size[1] // patch_size[1])
self.proj = nn.Conv2d(config.num_channels, config.hidden_size, kernel_size=patch_size, stride=patch_size)
def forward(self, pixel_values, position_embedding=None):
embeddings = self.proj(pixel_values)
if position_embedding is not None:
# interpolate the position embedding to the corresponding size
position_embedding = position_embedding.view(1, self.patch_shape[0], self.patch_shape[1], -1)
position_embedding = position_embedding.permute(0, 3, 1, 2)
patch_height, patch_width = embeddings.shape[2], embeddings.shape[3]
position_embedding = F.interpolate(position_embedding, size=(patch_height, patch_width), mode="bicubic")
embeddings = embeddings + position_embedding
embeddings = embeddings.flatten(2).transpose(1, 2)
return embeddings
class LayoutLMv3TextEmbeddings(nn.Module):
"""
LayoutLMv3 text embeddings. Same as `RobertaEmbeddings` but with added spatial (layout) 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.token_type_embeddings = nn.Embedding(config.type_vocab_size, 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.padding_idx = config.pad_token_id
self.position_embeddings = nn.Embedding(
config.max_position_embeddings, config.hidden_size, padding_idx=self.padding_idx
)
self.x_position_embeddings = nn.Embedding(config.max_2d_position_embeddings, config.coordinate_size)
self.y_position_embeddings = nn.Embedding(config.max_2d_position_embeddings, config.coordinate_size)
self.h_position_embeddings = nn.Embedding(config.max_2d_position_embeddings, config.shape_size)
self.w_position_embeddings = nn.Embedding(config.max_2d_position_embeddings, config.shape_size)
def calculate_spatial_position_embeddings(self, bbox):
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(torch.clip(bbox[:, :, 3] - bbox[:, :, 1], 0, 1023))
w_position_embeddings = self.w_position_embeddings(torch.clip(bbox[:, :, 2] - bbox[:, :, 0], 0, 1023))
# below is the difference between LayoutLMEmbeddingsV2 (torch.cat) and LayoutLMEmbeddingsV1 (add)
spatial_position_embeddings = torch.cat(
[
left_position_embeddings,
upper_position_embeddings,
right_position_embeddings,
lower_position_embeddings,
h_position_embeddings,
w_position_embeddings,
],
dim=-1,
)
return spatial_position_embeddings
def create_position_ids_from_input_ids(self, input_ids, padding_idx):
"""
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`.
"""
# 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)) * mask
return incremental_indices.long() + padding_idx
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.
"""
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)
def forward(
self,
input_ids=None,
bbox=None,
token_type_ids=None,
position_ids=None,
inputs_embeds=None,
):
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 = self.create_position_ids_from_input_ids(input_ids, self.padding_idx).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 = self.word_embeddings(input_ids)
token_type_embeddings = self.token_type_embeddings(token_type_ids)
embeddings = inputs_embeds + token_type_embeddings
position_embeddings = self.position_embeddings(position_ids)
embeddings += position_embeddings
spatial_position_embeddings = self.calculate_spatial_position_embeddings(bbox)
embeddings = embeddings + spatial_position_embeddings
embeddings = self.LayerNorm(embeddings)
embeddings = self.dropout(embeddings)
return embeddings
class LayoutLMv3PreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = LayoutLMv3Config
base_model_prefix = "layoutlmv3"
def _init_weights(self, module):
"""Initialize the weights"""
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=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)
class LayoutLMv3SelfAttention(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.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.has_relative_attention_bias = config.has_relative_attention_bias
self.has_spatial_attention_bias = config.has_spatial_attention_bias
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 cogview_attention(self, attention_scores, alpha=32):
"""
https://arxiv.org/abs/2105.13290 Section 2.4 Stabilization of training: Precision Bottleneck Relaxation
(PB-Relax). A replacement of the original nn.Softmax(dim=-1)(attention_scores). Seems the new attention_probs
will result in a slower speed and a little bias. Can use torch.allclose(standard_attention_probs,
cogview_attention_probs, atol=1e-08) for comparison. The smaller atol (e.g., 1e-08), the better.
"""
scaled_attention_scores = attention_scores / alpha
max_value = scaled_attention_scores.amax(dim=(-1)).unsqueeze(-1)
new_attention_scores = (scaled_attention_scores - max_value) * alpha
return nn.Softmax(dim=-1)(new_attention_scores)
def forward(
self,
hidden_states,
attention_mask=None,
head_mask=None,
output_attentions=False,
rel_pos=None,
rel_2d_pos=None,
):
mixed_query_layer = self.query(hidden_states)
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)
# Take the dot product between "query" and "key" to get the raw attention scores.
# The attention scores QT K/√d could be significantly larger than input elements, and result in overflow.
# Changing the computational order into QT(K/√d) alleviates the problem. (https://arxiv.org/pdf/2105.13290.pdf)
attention_scores = torch.matmul(query_layer / math.sqrt(self.attention_head_size), key_layer.transpose(-1, -2))
if self.has_relative_attention_bias and self.has_spatial_attention_bias:
attention_scores += (rel_pos + rel_2d_pos) / math.sqrt(self.attention_head_size)
elif self.has_relative_attention_bias:
attention_scores += rel_pos / math.sqrt(self.attention_head_size)
if attention_mask is not None:
# Apply the attention mask is (precomputed for all layers in RobertaModel forward() function)
attention_scores = attention_scores + attention_mask
# Normalize the attention scores to probabilities.
# Use the trick of the CogView paper to stablize training
attention_probs = self.cogview_attention(attention_scores)
# 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,)
return outputs
# Copied from transformers.models.roberta.modeling_roberta.RobertaSelfOutput
class LayoutLMv3SelfOutput(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.layoutlmv2.modeling_layoutlmv2.LayoutLMv2Attention with LayoutLMv2->LayoutLMv3
class LayoutLMv3Attention(nn.Module):
def __init__(self, config):
super().__init__()
self.self = LayoutLMv3SelfAttention(config)
self.output = LayoutLMv3SelfOutput(config)
def forward(
self,
hidden_states,
attention_mask=None,
head_mask=None,
output_attentions=False,
rel_pos=None,
rel_2d_pos=None,
):
self_outputs = self.self(
hidden_states,
attention_mask,
head_mask,
output_attentions,
rel_pos=rel_pos,
rel_2d_pos=rel_2d_pos,
)
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.layoutlmv2.modeling_layoutlmv2.LayoutLMv2Layer with LayoutLMv2->LayoutLMv3
class LayoutLMv3Layer(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 = LayoutLMv3Attention(config)
self.intermediate = LayoutLMv3Intermediate(config)
self.output = LayoutLMv3Output(config)
def forward(
self,
hidden_states,
attention_mask=None,
head_mask=None,
output_attentions=False,
rel_pos=None,
rel_2d_pos=None,
):
self_attention_outputs = self.attention(
hidden_states,
attention_mask,
head_mask,
output_attentions=output_attentions,
rel_pos=rel_pos,
rel_2d_pos=rel_2d_pos,
)
attention_output = self_attention_outputs[0]
outputs = self_attention_outputs[1:] # add self attentions if we output attention weights
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
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
class LayoutLMv3Encoder(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.layer = nn.ModuleList([LayoutLMv3Layer(config) for _ in range(config.num_hidden_layers)])
self.gradient_checkpointing = False
self.has_relative_attention_bias = config.has_relative_attention_bias
self.has_spatial_attention_bias = config.has_spatial_attention_bias
if self.has_relative_attention_bias:
self.rel_pos_bins = config.rel_pos_bins
self.max_rel_pos = config.max_rel_pos
self.rel_pos_bias = nn.Linear(self.rel_pos_bins, config.num_attention_heads, bias=False)
if self.has_spatial_attention_bias:
self.max_rel_2d_pos = config.max_rel_2d_pos
self.rel_2d_pos_bins = config.rel_2d_pos_bins
self.rel_pos_x_bias = nn.Linear(self.rel_2d_pos_bins, config.num_attention_heads, bias=False)
self.rel_pos_y_bias = nn.Linear(self.rel_2d_pos_bins, config.num_attention_heads, bias=False)
def relative_position_bucket(self, relative_position, bidirectional=True, num_buckets=32, max_distance=128):
ret = 0
if bidirectional:
num_buckets //= 2
ret += (relative_position > 0).long() * num_buckets
n = torch.abs(relative_position)
else:
n = torch.max(-relative_position, torch.zeros_like(relative_position))
# now n is in the range [0, inf)
# half of the buckets are for exact increments in positions
max_exact = num_buckets // 2
is_small = n < max_exact
# The other half of the buckets are for logarithmically bigger bins in positions up to max_distance
val_if_large = max_exact + (
torch.log(n.float() / max_exact) / math.log(max_distance / max_exact) * (num_buckets - max_exact)
).to(torch.long)
val_if_large = torch.min(val_if_large, torch.full_like(val_if_large, num_buckets - 1))
ret += torch.where(is_small, n, val_if_large)
return ret
def _cal_1d_pos_emb(self, position_ids):
rel_pos_mat = position_ids.unsqueeze(-2) - position_ids.unsqueeze(-1)
rel_pos = self.relative_position_bucket(
rel_pos_mat,
num_buckets=self.rel_pos_bins,
max_distance=self.max_rel_pos,
)
# Since this is a simple indexing operation that is independent of the input,
# no need to track gradients for this operation
#
# Without this no_grad context, training speed slows down significantly
with torch.no_grad():
rel_pos = self.rel_pos_bias.weight.t()[rel_pos].permute(0, 3, 1, 2)
rel_pos = rel_pos.contiguous()
return rel_pos
def _cal_2d_pos_emb(self, bbox):
position_coord_x = bbox[:, :, 0]
position_coord_y = bbox[:, :, 3]
rel_pos_x_2d_mat = position_coord_x.unsqueeze(-2) - position_coord_x.unsqueeze(-1)
rel_pos_y_2d_mat = position_coord_y.unsqueeze(-2) - position_coord_y.unsqueeze(-1)
rel_pos_x = self.relative_position_bucket(
rel_pos_x_2d_mat,
num_buckets=self.rel_2d_pos_bins,
max_distance=self.max_rel_2d_pos,
)
rel_pos_y = self.relative_position_bucket(
rel_pos_y_2d_mat,
num_buckets=self.rel_2d_pos_bins,
max_distance=self.max_rel_2d_pos,
)
# Since this is a simple indexing operation that is independent of the input,
# no need to track gradients for this operation
#
# Without this no_grad context, training speed slows down significantly
with torch.no_grad():
rel_pos_x = self.rel_pos_x_bias.weight.t()[rel_pos_x].permute(0, 3, 1, 2)
rel_pos_y = self.rel_pos_y_bias.weight.t()[rel_pos_y].permute(0, 3, 1, 2)
rel_pos_x = rel_pos_x.contiguous()
rel_pos_y = rel_pos_y.contiguous()
rel_2d_pos = rel_pos_x + rel_pos_y
return rel_2d_pos
def forward(
self,
hidden_states,
bbox=None,
attention_mask=None,
head_mask=None,
output_attentions=False,
output_hidden_states=False,
return_dict=True,
position_ids=None,
patch_height=None,
patch_width=None,
):
all_hidden_states = () if output_hidden_states else None
all_self_attentions = () if output_attentions else None
rel_pos = self._cal_1d_pos_emb(position_ids) if self.has_relative_attention_bias else None
rel_2d_pos = self._cal_2d_pos_emb(bbox) if self.has_spatial_attention_bias 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,
attention_mask,
layer_head_mask,
output_attentions,
rel_pos,
rel_2d_pos,
)
else:
layer_outputs = layer_module(
hidden_states,
attention_mask,
layer_head_mask,
output_attentions,
rel_pos=rel_pos,
rel_2d_pos=rel_2d_pos,
)
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,
)
# Copied from transformers.models.roberta.modeling_roberta.RobertaIntermediate
class LayoutLMv3Intermediate(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.roberta.modeling_roberta.RobertaOutput
class LayoutLMv3Output(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
@add_start_docstrings(
"The bare LayoutLMv3 Model transformer outputting raw hidden-states without any specific head on top.",
LAYOUTLMV3_START_DOCSTRING,
)
class LayoutLMv3Model(LayoutLMv3PreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.config = config
if config.text_embed:
self.embeddings = LayoutLMv3TextEmbeddings(config)
if config.visual_embed:
# use the default pre-training parameters for fine-tuning (e.g., input_size)
# when the input_size is larger in fine-tuning, we will interpolate the position embeddings in forward
self.patch_embed = LayoutLMv3PatchEmbeddings(config)
size = int(config.input_size / config.patch_size)
self.cls_token = nn.Parameter(torch.zeros(1, 1, config.hidden_size))
self.pos_embed = nn.Parameter(torch.zeros(1, size * size + 1, config.hidden_size))
self.pos_drop = nn.Dropout(p=0.0)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
if self.config.has_relative_attention_bias or self.config.has_spatial_attention_bias:
self.init_visual_bbox(image_size=(size, size))
self.norm = nn.LayerNorm(config.hidden_size, eps=1e-6)
self.encoder = LayoutLMv3Encoder(config)
self.init_weights()
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 init_visual_bbox(self, image_size=(14, 14), max_len=1000):
"""
Create the bounding boxes for the visual (patch) tokens.
"""
visual_bbox_x = torch.div(
torch.arange(0, max_len * (image_size[1] + 1), max_len), image_size[1], rounding_mode="trunc"
)
visual_bbox_y = torch.div(
torch.arange(0, max_len * (image_size[0] + 1), max_len), image_size[0], rounding_mode="trunc"
)
visual_bbox = torch.stack(
[
visual_bbox_x[:-1].repeat(image_size[0], 1),
visual_bbox_y[:-1].repeat(image_size[1], 1).transpose(0, 1),
visual_bbox_x[1:].repeat(image_size[0], 1),
visual_bbox_y[1:].repeat(image_size[1], 1).transpose(0, 1),
],
dim=-1,
).view(-1, 4)
cls_token_box = torch.tensor([[0 + 1, 0 + 1, max_len - 1, max_len - 1]])
self.visual_bbox = torch.cat([cls_token_box, visual_bbox], dim=0)
def calculate_visual_bbox(self, device, dtype, batch_size):
visual_bbox = self.visual_bbox.repeat(batch_size, 1, 1)
visual_bbox = visual_bbox.to(device).type(dtype)
return visual_bbox
def forward_image(self, pixel_values):
embeddings = self.patch_embed(pixel_values)
# add [CLS] token
batch_size, seq_len, _ = embeddings.size()
cls_tokens = self.cls_token.expand(batch_size, -1, -1)
embeddings = torch.cat((cls_tokens, embeddings), dim=1)
# add position embeddings
if self.pos_embed is not None:
embeddings = embeddings + self.pos_embed
embeddings = self.pos_drop(embeddings)
embeddings = self.norm(embeddings)
return embeddings
@add_start_docstrings_to_model_forward(
LAYOUTLMV3_MODEL_INPUTS_DOCSTRING.format("batch_size, token_sequence_length")
)
@replace_return_docstrings(output_type=BaseModelOutput, 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,
pixel_values: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, BaseModelOutput]:
r"""
Returns:
Examples:
```python
>>> from transformers import AutoProcessor, AutoModel
>>> from datasets import load_dataset
>>> processor = AutoProcessor.from_pretrained("microsoft/layoutlmv3-base", apply_ocr=False)
>>> model = AutoModel.from_pretrained("microsoft/layoutlmv3-base")
>>> dataset = load_dataset("nielsr/funsd-layoutlmv3", split="train", trust_remote_code=True)
>>> example = dataset[0]
>>> image = example["image"]
>>> words = example["tokens"]
>>> boxes = example["bboxes"]
>>> encoding = processor(image, words, boxes=boxes, return_tensors="pt")
>>> outputs = model(**encoding)
>>> 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:
input_shape = input_ids.size()
batch_size, seq_length = input_shape
device = input_ids.device
elif inputs_embeds is not None:
input_shape = inputs_embeds.size()[:-1]
batch_size, seq_length = input_shape
device = inputs_embeds.device
elif pixel_values is not None:
batch_size = len(pixel_values)
device = pixel_values.device
else:
raise ValueError("You have to specify either input_ids or inputs_embeds or pixel_values")
if input_ids is not None or inputs_embeds is not None:
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)
if bbox is None:
bbox = torch.zeros(tuple(list(input_shape) + [4]), dtype=torch.long, device=device)
embedding_output = self.embeddings(
input_ids=input_ids,
bbox=bbox,
position_ids=position_ids,
token_type_ids=token_type_ids,
inputs_embeds=inputs_embeds,
)
final_bbox = final_position_ids = None
patch_height = patch_width = None
if pixel_values is not None:
patch_height, patch_width = (
torch_int(pixel_values.shape[2] / self.config.patch_size),
torch_int(pixel_values.shape[3] / self.config.patch_size),
)
visual_embeddings = self.forward_image(pixel_values)
visual_attention_mask = torch.ones(
(batch_size, visual_embeddings.shape[1]), dtype=torch.long, device=device
)
if attention_mask is not None:
attention_mask = torch.cat([attention_mask, visual_attention_mask], dim=1)
else:
attention_mask = visual_attention_mask
if self.config.has_relative_attention_bias or self.config.has_spatial_attention_bias:
if self.config.has_spatial_attention_bias:
visual_bbox = self.calculate_visual_bbox(device, dtype=torch.long, batch_size=batch_size)
if bbox is not None:
final_bbox = torch.cat([bbox, visual_bbox], dim=1)
else:
final_bbox = visual_bbox
visual_position_ids = torch.arange(
0, visual_embeddings.shape[1], dtype=torch.long, device=device
).repeat(batch_size, 1)
if input_ids is not None or inputs_embeds is not None:
position_ids = torch.arange(0, input_shape[1], device=device).unsqueeze(0)
position_ids = position_ids.expand(input_shape)
final_position_ids = torch.cat([position_ids, visual_position_ids], dim=1)
else:
final_position_ids = visual_position_ids
if input_ids is not None or inputs_embeds is not None:
embedding_output = torch.cat([embedding_output, visual_embeddings], dim=1)
else:
embedding_output = visual_embeddings
embedding_output = self.LayerNorm(embedding_output)
embedding_output = self.dropout(embedding_output)
elif self.config.has_relative_attention_bias or self.config.has_spatial_attention_bias:
if self.config.has_spatial_attention_bias:
final_bbox = bbox
if self.config.has_relative_attention_bias:
position_ids = self.embeddings.position_ids[:, : input_shape[1]]
position_ids = position_ids.expand_as(input_ids)
final_position_ids = position_ids
extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(
attention_mask, None, device, dtype=embedding_output.dtype
)
# 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,
bbox=final_bbox,
position_ids=final_position_ids,
attention_mask=extended_attention_mask,
head_mask=head_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
patch_height=patch_height,
patch_width=patch_width,
)
sequence_output = encoder_outputs[0]
if not return_dict:
return (sequence_output,) + encoder_outputs[1:]
return BaseModelOutput(
last_hidden_state=sequence_output,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
)
class LayoutLMv3ClassificationHead(nn.Module):
"""
Head for sentence-level classification tasks. Reference: RobertaClassificationHead
"""
def __init__(self, config, pool_feature=False):
super().__init__()
self.pool_feature = pool_feature
if pool_feature:
self.dense = nn.Linear(config.hidden_size * 3, config.hidden_size)
else:
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
classifier_dropout = (
config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
)
self.dropout = nn.Dropout(classifier_dropout)
self.out_proj = nn.Linear(config.hidden_size, config.num_labels)
def forward(self, x):
x = self.dropout(x)
x = self.dense(x)
x = torch.tanh(x)
x = self.dropout(x)
x = self.out_proj(x)
return x
@add_start_docstrings(
"""
LayoutLMv3 Model with a token classification head on top (a linear layer on top of the final hidden states) e.g.
for sequence labeling (information extraction) tasks such as [FUNSD](https://guillaumejaume.github.io/FUNSD/),
[SROIE](https://rrc.cvc.uab.es/?ch=13), [CORD](https://github.com/clovaai/cord) and
[Kleister-NDA](https://github.com/applicaai/kleister-nda).
""",
LAYOUTLMV3_START_DOCSTRING,
)
class LayoutLMv3ForTokenClassification(LayoutLMv3PreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.layoutlmv3 = LayoutLMv3Model(config)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
if config.num_labels < 10:
self.classifier = nn.Linear(config.hidden_size, config.num_labels)
else:
self.classifier = LayoutLMv3ClassificationHead(config, pool_feature=False)
self.init_weights()
@add_start_docstrings_to_model_forward(
LAYOUTLMV3_DOWNSTREAM_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,
pixel_values: Optional[torch.LongTensor] = 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 AutoProcessor, AutoModelForTokenClassification
>>> from datasets import load_dataset
>>> processor = AutoProcessor.from_pretrained("microsoft/layoutlmv3-base", apply_ocr=False)
>>> model = AutoModelForTokenClassification.from_pretrained("microsoft/layoutlmv3-base", num_labels=7)
>>> dataset = load_dataset("nielsr/funsd-layoutlmv3", split="train", trust_remote_code=True)
>>> example = dataset[0]
>>> image = example["image"]
>>> words = example["tokens"]
>>> boxes = example["bboxes"]
>>> word_labels = example["ner_tags"]
>>> encoding = processor(image, words, boxes=boxes, word_labels=word_labels, return_tensors="pt")
>>> outputs = model(**encoding)
>>> loss = outputs.loss
>>> logits = outputs.logits
```"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.layoutlmv3(
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,
pixel_values=pixel_values,
)
if input_ids is not None:
input_shape = input_ids.size()
else:
input_shape = inputs_embeds.size()[:-1]
seq_length = input_shape[1]
# only take the text part of the output representations
sequence_output = outputs[0][:, :seq_length]
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[1:]
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(
"""
LayoutLMv3 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 text part of the hidden-states output to
compute `span start logits` and `span end logits`).
""",
LAYOUTLMV3_START_DOCSTRING,
)
class LayoutLMv3ForQuestionAnswering(LayoutLMv3PreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.layoutlmv3 = LayoutLMv3Model(config)
self.qa_outputs = LayoutLMv3ClassificationHead(config, pool_feature=False)
self.init_weights()
@add_start_docstrings_to_model_forward(
LAYOUTLMV3_DOWNSTREAM_INPUTS_DOCSTRING.format("batch_size, sequence_length")
)
@replace_return_docstrings(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,
bbox: Optional[torch.LongTensor] = None,
pixel_values: Optional[torch.LongTensor] = 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:
Examples:
```python
>>> from transformers import AutoProcessor, AutoModelForQuestionAnswering
>>> from datasets import load_dataset
>>> import torch
>>> processor = AutoProcessor.from_pretrained("microsoft/layoutlmv3-base", apply_ocr=False)
>>> model = AutoModelForQuestionAnswering.from_pretrained("microsoft/layoutlmv3-base")
>>> dataset = load_dataset("nielsr/funsd-layoutlmv3", split="train", trust_remote_code=True)
>>> example = dataset[0]
>>> image = example["image"]
>>> question = "what's his name?"
>>> words = example["tokens"]
>>> boxes = example["bboxes"]
>>> encoding = processor(image, question, words, boxes=boxes, return_tensors="pt")
>>> start_positions = torch.tensor([1])
>>> end_positions = torch.tensor([3])
>>> outputs = model(**encoding, start_positions=start_positions, end_positions=end_positions)
>>> loss = outputs.loss
>>> start_scores = outputs.start_logits
>>> end_scores = outputs.end_logits
```"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.layoutlmv3(
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,
bbox=bbox,
pixel_values=pixel_values,
)
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[1:]
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,
)
@add_start_docstrings(
"""
LayoutLMv3 Model with a sequence classification head on top (a linear layer on top of the final hidden state of the
[CLS] token) e.g. for document image classification tasks such as the
[RVL-CDIP](https://www.cs.cmu.edu/~aharley/rvl-cdip/) dataset.
""",
LAYOUTLMV3_START_DOCSTRING,
)
class LayoutLMv3ForSequenceClassification(LayoutLMv3PreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.config = config
self.layoutlmv3 = LayoutLMv3Model(config)
self.classifier = LayoutLMv3ClassificationHead(config, pool_feature=False)
self.init_weights()
@add_start_docstrings_to_model_forward(
LAYOUTLMV3_DOWNSTREAM_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,
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,
bbox: Optional[torch.LongTensor] = None,
pixel_values: Optional[torch.LongTensor] = None,
) -> Union[Tuple, SequenceClassifierOutput]:
"""
Returns:
Examples:
```python
>>> from transformers import AutoProcessor, AutoModelForSequenceClassification
>>> from datasets import load_dataset
>>> import torch
>>> processor = AutoProcessor.from_pretrained("microsoft/layoutlmv3-base", apply_ocr=False)
>>> model = AutoModelForSequenceClassification.from_pretrained("microsoft/layoutlmv3-base")
>>> dataset = load_dataset("nielsr/funsd-layoutlmv3", split="train", trust_remote_code=True)
>>> example = dataset[0]
>>> image = example["image"]
>>> words = example["tokens"]
>>> boxes = example["bboxes"]
>>> encoding = processor(image, words, boxes=boxes, return_tensors="pt")
>>> sequence_label = torch.tensor([1])
>>> outputs = model(**encoding, 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.layoutlmv3(
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,
bbox=bbox,
pixel_values=pixel_values,
)
sequence_output = outputs[0][:, 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[1:]
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__ = [
"LayoutLMv3ForQuestionAnswering",
"LayoutLMv3ForSequenceClassification",
"LayoutLMv3ForTokenClassification",
"LayoutLMv3Model",
"LayoutLMv3PreTrainedModel",
]
| transformers/src/transformers/models/layoutlmv3/modeling_layoutlmv3.py/0 | {
"file_path": "transformers/src/transformers/models/layoutlmv3/modeling_layoutlmv3.py",
"repo_id": "transformers",
"token_count": 26417
} |
# coding=utf-8
# Copyright 2022 Meta Platforms, Inc. 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.
"""LeViT model configuration"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
logger = logging.get_logger(__name__)
class LevitConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`LevitModel`]. It is used to instantiate a LeViT
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 LeViT
[facebook/levit-128S](https://huggingface.co/facebook/levit-128S) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
image_size (`int`, *optional*, defaults to 224):
The size of the input image.
num_channels (`int`, *optional*, defaults to 3):
Number of channels in the input image.
kernel_size (`int`, *optional*, defaults to 3):
The kernel size for the initial convolution layers of patch embedding.
stride (`int`, *optional*, defaults to 2):
The stride size for the initial convolution layers of patch embedding.
padding (`int`, *optional*, defaults to 1):
The padding size for the initial convolution layers of patch embedding.
patch_size (`int`, *optional*, defaults to 16):
The patch size for embeddings.
hidden_sizes (`List[int]`, *optional*, defaults to `[128, 256, 384]`):
Dimension of each of the encoder blocks.
num_attention_heads (`List[int]`, *optional*, defaults to `[4, 8, 12]`):
Number of attention heads for each attention layer in each block of the Transformer encoder.
depths (`List[int]`, *optional*, defaults to `[4, 4, 4]`):
The number of layers in each encoder block.
key_dim (`List[int]`, *optional*, defaults to `[16, 16, 16]`):
The size of key in each of the encoder blocks.
drop_path_rate (`int`, *optional*, defaults to 0):
The dropout probability for stochastic depths, used in the blocks of the Transformer encoder.
mlp_ratios (`List[int]`, *optional*, defaults to `[2, 2, 2]`):
Ratio of the size of the hidden layer compared to the size of the input layer of the Mix FFNs in the
encoder blocks.
attention_ratios (`List[int]`, *optional*, defaults to `[2, 2, 2]`):
Ratio of the size of the output dimension compared to input dimension of attention layers.
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 import LevitConfig, LevitModel
>>> # Initializing a LeViT levit-128S style configuration
>>> configuration = LevitConfig()
>>> # Initializing a model (with random weights) from the levit-128S style configuration
>>> model = LevitModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "levit"
def __init__(
self,
image_size=224,
num_channels=3,
kernel_size=3,
stride=2,
padding=1,
patch_size=16,
hidden_sizes=[128, 256, 384],
num_attention_heads=[4, 8, 12],
depths=[4, 4, 4],
key_dim=[16, 16, 16],
drop_path_rate=0,
mlp_ratio=[2, 2, 2],
attention_ratio=[2, 2, 2],
initializer_range=0.02,
**kwargs,
):
super().__init__(**kwargs)
self.image_size = image_size
self.num_channels = num_channels
self.kernel_size = kernel_size
self.stride = stride
self.padding = padding
self.hidden_sizes = hidden_sizes
self.num_attention_heads = num_attention_heads
self.depths = depths
self.key_dim = key_dim
self.drop_path_rate = drop_path_rate
self.patch_size = patch_size
self.attention_ratio = attention_ratio
self.mlp_ratio = mlp_ratio
self.initializer_range = initializer_range
self.down_ops = [
["Subsample", key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
["Subsample", key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
]
# Copied from transformers.models.vit.configuration_vit.ViTOnnxConfig
class LevitOnnxConfig(OnnxConfig):
torch_onnx_minimum_version = version.parse("1.11")
@property
def inputs(self) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
]
)
@property
def atol_for_validation(self) -> float:
return 1e-4
__all__ = ["LevitConfig", "LevitOnnxConfig"]
| transformers/src/transformers/models/levit/configuration_levit.py/0 | {
"file_path": "transformers/src/transformers/models/levit/configuration_levit.py",
"repo_id": "transformers",
"token_count": 2199
} |
# coding=utf-8
# Copyright 2023 Microsoft Research & University of Wisconsin-Madison 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.
"""Llava model configuration"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING, AutoConfig
logger = logging.get_logger(__name__)
class LlavaConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`LlavaForConditionalGeneration`]. It is used to instantiate an
Llava 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 Llava-9B.
e.g. [llava-hf/llava-9b](https://huggingface.co/llava-hf/llava-9b)
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 (`Union[AutoConfig, dict]`, *optional*, defaults to `CLIPVisionConfig`):
The config object or dictionary of the vision backbone.
text_config (`Union[AutoConfig, dict]`, *optional*, defaults to `LlamaConfig`):
The config object or dictionary of the text backbone.
ignore_index (`int`, *optional*, defaults to -100):
The ignore index for the loss function.
image_token_index (`int`, *optional*, defaults to 32000):
The image token index to encode the image prompt.
projector_hidden_act (`str`, *optional*, defaults to `"gelu"`):
The activation function used by the multimodal projector.
vision_feature_select_strategy (`str`, *optional*, defaults to `"default"`):
The feature selection strategy used to select the vision feature from the vision backbone.
Can be one of `"default"` or `"full"`.
vision_feature_layer (`Union[int, List[int]]`, *optional*, defaults to -2):
The index of the layer to select the vision feature. If multiple indices are provided,
the vision feature of the corresponding indices will be concatenated to form the
vision features.
image_seq_length (`int`, *optional*, defaults to 576):
Sequence length of one image embedding.
multimodal_projector_bias (`bool`, *optional*, defaults to `True`):
Whether to use bias in the multimodal projector.
Example:
```python
>>> from transformers import LlavaForConditionalGeneration, LlavaConfig, CLIPVisionConfig, LlamaConfig
>>> # Initializing a CLIP-vision config
>>> vision_config = CLIPVisionConfig()
>>> # Initializing a Llama config
>>> text_config = LlamaConfig()
>>> # Initializing a Llava llava-1.5-7b style configuration
>>> configuration = LlavaConfig(vision_config, text_config)
>>> # Initializing a model from the llava-1.5-7b style configuration
>>> model = LlavaForConditionalGeneration(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "llava"
sub_configs = {"text_config": AutoConfig, "vision_config": AutoConfig}
def __init__(
self,
vision_config=None,
text_config=None,
ignore_index=-100,
image_token_index=32000,
projector_hidden_act="gelu",
vision_feature_select_strategy="default",
vision_feature_layer=-2,
image_seq_length=576,
multimodal_projector_bias=True,
**kwargs,
):
self.ignore_index = ignore_index
self.image_token_index = image_token_index
self.projector_hidden_act = projector_hidden_act
self.image_seq_length = image_seq_length
if vision_feature_select_strategy not in ["default", "full"]:
raise ValueError(
"vision_feature_select_strategy should be one of 'default', 'full'."
f"Got: {vision_feature_select_strategy}"
)
self.vision_feature_select_strategy = vision_feature_select_strategy
self.vision_feature_layer = vision_feature_layer
if isinstance(vision_config, dict):
vision_config["model_type"] = (
vision_config["model_type"] if "model_type" in vision_config else "clip_vision_model"
)
vision_config = CONFIG_MAPPING[vision_config["model_type"]](**vision_config)
elif vision_config is None:
vision_config = CONFIG_MAPPING["clip_vision_model"](
intermediate_size=4096,
hidden_size=1024,
patch_size=14,
image_size=336,
num_hidden_layers=24,
num_attention_heads=16,
vocab_size=32000,
projection_dim=768,
)
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:
text_config = CONFIG_MAPPING["llama"]()
self.text_config = text_config
self.multimodal_projector_bias = multimodal_projector_bias
super().__init__(**kwargs)
__all__ = ["LlavaConfig"]
| transformers/src/transformers/models/llava/configuration_llava.py/0 | {
"file_path": "transformers/src/transformers/models/llava/configuration_llava.py",
"repo_id": "transformers",
"token_count": 2241
} |
# coding=utf-8
# Copyright 2020 The Allen Institute for AI team 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 Longformer model."""
import math
from dataclasses import dataclass
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, gelu
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_code_sample_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
logging,
replace_return_docstrings,
)
from .configuration_longformer import LongformerConfig
logger = logging.get_logger(__name__)
_CHECKPOINT_FOR_DOC = "allenai/longformer-base-4096"
_CONFIG_FOR_DOC = "LongformerConfig"
@dataclass
class LongformerBaseModelOutput(ModelOutput):
"""
Base class for Longformer's outputs, with potential hidden states, local and global 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 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, x +
attention_window + 1)`, where `x` is the number of tokens with global attention mask.
Local attentions weights after the attention softmax, used to compute the weighted average in the
self-attention heads. Those are the attention weights from every token in the sequence to every token with
global attention (first `x` values) and to every token in the attention window (remaining `attention_window
+ 1` values). Note that the first `x` values refer to tokens with fixed positions in the text, but the
remaining `attention_window + 1` values refer to tokens with relative positions: the attention weight of a
token to itself is located at index `x + attention_window / 2` and the `attention_window / 2` preceding
(succeeding) values are the attention weights to the `attention_window / 2` preceding (succeeding) tokens.
If the attention window contains a token with global attention, the attention weight at the corresponding
index is set to 0; the value should be accessed from the first `x` attention weights. If a token has global
attention, the attention weights to all other tokens in `attentions` is set to 0, the values should be
accessed from `global_attentions`.
global_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, x)`,
where `x` is the number of tokens with global attention mask.
Global attentions weights after the attention softmax, used to compute the weighted average in the
self-attention heads. Those are the attention weights from every token with global attention to every token
in the sequence.
"""
last_hidden_state: torch.FloatTensor
hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
global_attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
@dataclass
class LongformerBaseModelOutputWithPooling(ModelOutput):
"""
Base class for Longformer'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)`):
Last layer hidden-state of the first token of the sequence (classification token) further processed by a
Linear layer and a Tanh activation function. The Linear layer weights are trained from the next sentence
prediction (classification) objective during pretraining.
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, x +
attention_window + 1)`, where `x` is the number of tokens with global attention mask.
Local attentions weights after the attention softmax, used to compute the weighted average in the
self-attention heads. Those are the attention weights from every token in the sequence to every token with
global attention (first `x` values) and to every token in the attention window (remaining `attention_window
+ 1` values). Note that the first `x` values refer to tokens with fixed positions in the text, but the
remaining `attention_window + 1` values refer to tokens with relative positions: the attention weight of a
token to itself is located at index `x + attention_window / 2` and the `attention_window / 2` preceding
(succeeding) values are the attention weights to the `attention_window / 2` preceding (succeeding) tokens.
If the attention window contains a token with global attention, the attention weight at the corresponding
index is set to 0; the value should be accessed from the first `x` attention weights. If a token has global
attention, the attention weights to all other tokens in `attentions` is set to 0, the values should be
accessed from `global_attentions`.
global_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, x)`,
where `x` is the number of tokens with global attention mask.
Global attentions weights after the attention softmax, used to compute the weighted average in the
self-attention heads. Those are the attention weights from every token with global attention to every token
in the sequence.
"""
last_hidden_state: torch.FloatTensor
pooler_output: torch.FloatTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
global_attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
@dataclass
class LongformerMaskedLMOutput(ModelOutput):
"""
Base class for masked language models outputs.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Masked language modeling (MLM) loss.
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 + 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, x +
attention_window + 1)`, where `x` is the number of tokens with global attention mask.
Local attentions weights after the attention softmax, used to compute the weighted average in the
self-attention heads. Those are the attention weights from every token in the sequence to every token with
global attention (first `x` values) and to every token in the attention window (remaining `attention_window
+ 1` values). Note that the first `x` values refer to tokens with fixed positions in the text, but the
remaining `attention_window + 1` values refer to tokens with relative positions: the attention weight of a
token to itself is located at index `x + attention_window / 2` and the `attention_window / 2` preceding
(succeeding) values are the attention weights to the `attention_window / 2` preceding (succeeding) tokens.
If the attention window contains a token with global attention, the attention weight at the corresponding
index is set to 0; the value should be accessed from the first `x` attention weights. If a token has global
attention, the attention weights to all other tokens in `attentions` is set to 0, the values should be
accessed from `global_attentions`.
global_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, x)`,
where `x` is the number of tokens with global attention mask.
Global attentions weights after the attention softmax, used to compute the weighted average in the
self-attention heads. Those are the attention weights from every token with global attention to every token
in the sequence.
"""
loss: Optional[torch.FloatTensor] = None
logits: torch.FloatTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
global_attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
@dataclass
class LongformerQuestionAnsweringModelOutput(ModelOutput):
"""
Base class for outputs of question answering Longformer models.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Total span extraction loss is the sum of a Cross-Entropy for the start and end positions.
start_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length)`):
Span-start scores (before SoftMax).
end_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length)`):
Span-end scores (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 + 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, x +
attention_window + 1)`, where `x` is the number of tokens with global attention mask.
Local attentions weights after the attention softmax, used to compute the weighted average in the
self-attention heads. Those are the attention weights from every token in the sequence to every token with
global attention (first `x` values) and to every token in the attention window (remaining `attention_window
+ 1` values). Note that the first `x` values refer to tokens with fixed positions in the text, but the
remaining `attention_window + 1` values refer to tokens with relative positions: the attention weight of a
token to itself is located at index `x + attention_window / 2` and the `attention_window / 2` preceding
(succeeding) values are the attention weights to the `attention_window / 2` preceding (succeeding) tokens.
If the attention window contains a token with global attention, the attention weight at the corresponding
index is set to 0; the value should be accessed from the first `x` attention weights. If a token has global
attention, the attention weights to all other tokens in `attentions` is set to 0, the values should be
accessed from `global_attentions`.
global_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, x)`,
where `x` is the number of tokens with global attention mask.
Global attentions weights after the attention softmax, used to compute the weighted average in the
self-attention heads. Those are the attention weights from every token with global attention to every token
in the sequence.
"""
loss: Optional[torch.FloatTensor] = None
start_logits: torch.FloatTensor = None
end_logits: torch.FloatTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
global_attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
@dataclass
class LongformerSequenceClassifierOutput(ModelOutput):
"""
Base class for outputs of sentence classification models.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Classification (or regression if config.num_labels==1) loss.
logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`):
Classification (or regression if config.num_labels==1) scores (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 + 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, x +
attention_window + 1)`, where `x` is the number of tokens with global attention mask.
Local attentions weights after the attention softmax, used to compute the weighted average in the
self-attention heads. Those are the attention weights from every token in the sequence to every token with
global attention (first `x` values) and to every token in the attention window (remaining `attention_window
+ 1` values). Note that the first `x` values refer to tokens with fixed positions in the text, but the
remaining `attention_window + 1` values refer to tokens with relative positions: the attention weight of a
token to itself is located at index `x + attention_window / 2` and the `attention_window / 2` preceding
(succeeding) values are the attention weights to the `attention_window / 2` preceding (succeeding) tokens.
If the attention window contains a token with global attention, the attention weight at the corresponding
index is set to 0; the value should be accessed from the first `x` attention weights. If a token has global
attention, the attention weights to all other tokens in `attentions` is set to 0, the values should be
accessed from `global_attentions`.
global_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, x)`,
where `x` is the number of tokens with global attention mask.
Global attentions weights after the attention softmax, used to compute the weighted average in the
self-attention heads. Those are the attention weights from every token with global attention to every token
in the sequence.
"""
loss: Optional[torch.FloatTensor] = None
logits: torch.FloatTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
global_attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
@dataclass
class LongformerMultipleChoiceModelOutput(ModelOutput):
"""
Base class for outputs of multiple choice Longformer models.
Args:
loss (`torch.FloatTensor` of shape *(1,)*, *optional*, returned when `labels` is provided):
Classification loss.
logits (`torch.FloatTensor` of shape `(batch_size, num_choices)`):
*num_choices* is the second dimension of the input tensors. (see *input_ids* above).
Classification scores (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 + 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, x +
attention_window + 1)`, where `x` is the number of tokens with global attention mask.
Local attentions weights after the attention softmax, used to compute the weighted average in the
self-attention heads. Those are the attention weights from every token in the sequence to every token with
global attention (first `x` values) and to every token in the attention window (remaining `attention_window
+ 1` values). Note that the first `x` values refer to tokens with fixed positions in the text, but the
remaining `attention_window + 1` values refer to tokens with relative positions: the attention weight of a
token to itself is located at index `x + attention_window / 2` and the `attention_window / 2` preceding
(succeeding) values are the attention weights to the `attention_window / 2` preceding (succeeding) tokens.
If the attention window contains a token with global attention, the attention weight at the corresponding
index is set to 0; the value should be accessed from the first `x` attention weights. If a token has global
attention, the attention weights to all other tokens in `attentions` is set to 0, the values should be
accessed from `global_attentions`.
global_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, x)`,
where `x` is the number of tokens with global attention mask.
Global attentions weights after the attention softmax, used to compute the weighted average in the
self-attention heads. Those are the attention weights from every token with global attention to every token
in the sequence.
"""
loss: Optional[torch.FloatTensor] = None
logits: torch.FloatTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
global_attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
@dataclass
class LongformerTokenClassifierOutput(ModelOutput):
"""
Base class for outputs of token classification models.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided) :
Classification loss.
logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.num_labels)`):
Classification scores (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 + 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, x +
attention_window + 1)`, where `x` is the number of tokens with global attention mask.
Local attentions weights after the attention softmax, used to compute the weighted average in the
self-attention heads. Those are the attention weights from every token in the sequence to every token with
global attention (first `x` values) and to every token in the attention window (remaining `attention_window
+ 1` values). Note that the first `x` values refer to tokens with fixed positions in the text, but the
remaining `attention_window + 1` values refer to tokens with relative positions: the attention weight of a
token to itself is located at index `x + attention_window / 2` and the `attention_window / 2` preceding
(succeeding) values are the attention weights to the `attention_window / 2` preceding (succeeding) tokens.
If the attention window contains a token with global attention, the attention weight at the corresponding
index is set to 0; the value should be accessed from the first `x` attention weights. If a token has global
attention, the attention weights to all other tokens in `attentions` is set to 0, the values should be
accessed from `global_attentions`.
global_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, x)`,
where `x` is the number of tokens with global attention mask.
Global attentions weights after the attention softmax, used to compute the weighted average in the
self-attention heads. Those are the attention weights from every token with global attention to every token
in the sequence.
"""
loss: Optional[torch.FloatTensor] = None
logits: torch.FloatTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
global_attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
def _get_question_end_index(input_ids, sep_token_id):
"""
Computes the index of the first occurrence of `sep_token_id`.
"""
sep_token_indices = (input_ids == sep_token_id).nonzero()
batch_size = input_ids.shape[0]
assert sep_token_indices.shape[1] == 2, "`input_ids` should have two dimensions"
assert sep_token_indices.shape[0] == 3 * batch_size, (
f"There should be exactly three separator tokens: {sep_token_id} in every sample for questions answering. You"
" might also consider to set `global_attention_mask` manually in the forward function to avoid this error."
)
return sep_token_indices.view(batch_size, 3, 2)[:, 0, 1]
def _compute_global_attention_mask(input_ids, sep_token_id, before_sep_token=True):
"""
Computes global attention mask by putting attention on all tokens before `sep_token_id` if `before_sep_token is
True` else after `sep_token_id`.
"""
question_end_index = _get_question_end_index(input_ids, sep_token_id)
question_end_index = question_end_index.unsqueeze(dim=1) # size: batch_size x 1
# bool attention mask with True in locations of global attention
attention_mask = torch.arange(input_ids.shape[1], device=input_ids.device)
if before_sep_token is True:
attention_mask = (attention_mask.expand_as(input_ids) < question_end_index).to(torch.bool)
else:
# last token is separation token and should not be counted and in the middle are two separation tokens
attention_mask = (attention_mask.expand_as(input_ids) > (question_end_index + 1)).to(torch.bool) * (
attention_mask.expand_as(input_ids) < input_ids.shape[-1]
).to(torch.bool)
return attention_mask
def create_position_ids_from_input_ids(input_ids, padding_idx):
"""
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) * mask
return incremental_indices.long() + padding_idx
class LongformerEmbeddings(nn.Module):
"""
Same as BertEmbeddings with a tiny tweak for positional embeddings indexing.
"""
def __init__(self, config):
super().__init__()
self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)
# self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
# any TensorFlow checkpoint file
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.padding_idx = config.pad_token_id
self.position_embeddings = nn.Embedding(
config.max_position_embeddings, config.hidden_size, padding_idx=self.padding_idx
)
def forward(self, input_ids=None, token_type_ids=None, position_ids=None, inputs_embeds=None):
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).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=position_ids.device)
if inputs_embeds is None:
inputs_embeds = self.word_embeddings(input_ids)
position_embeddings = self.position_embeddings(position_ids)
token_type_embeddings = self.token_type_embeddings(token_type_ids)
embeddings = inputs_embeds + position_embeddings + token_type_embeddings
embeddings = self.LayerNorm(embeddings)
embeddings = self.dropout(embeddings)
return embeddings
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 inputs_embeds:
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 LongformerSelfAttention(nn.Module):
def __init__(self, config, layer_id):
super().__init__()
if config.hidden_size % config.num_attention_heads != 0:
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_heads = config.num_attention_heads
self.head_dim = int(config.hidden_size / config.num_attention_heads)
self.embed_dim = config.hidden_size
self.query = nn.Linear(config.hidden_size, self.embed_dim)
self.key = nn.Linear(config.hidden_size, self.embed_dim)
self.value = nn.Linear(config.hidden_size, self.embed_dim)
# separate projection layers for tokens with global attention
self.query_global = nn.Linear(config.hidden_size, self.embed_dim)
self.key_global = nn.Linear(config.hidden_size, self.embed_dim)
self.value_global = nn.Linear(config.hidden_size, self.embed_dim)
self.dropout = config.attention_probs_dropout_prob
self.layer_id = layer_id
attention_window = config.attention_window[self.layer_id]
assert (
attention_window % 2 == 0
), f"`attention_window` for layer {self.layer_id} has to be an even value. Given {attention_window}"
assert (
attention_window > 0
), f"`attention_window` for layer {self.layer_id} has to be positive. Given {attention_window}"
self.one_sided_attn_window_size = attention_window // 2
self.config = config
def forward(
self,
hidden_states,
attention_mask=None,
layer_head_mask=None,
is_index_masked=None,
is_index_global_attn=None,
is_global_attn=None,
output_attentions=False,
):
"""
[`LongformerSelfAttention`] expects *len(hidden_states)* to be multiple of *attention_window*. Padding to
*attention_window* happens in [`LongformerModel.forward`] to avoid redoing the padding on each layer.
The *attention_mask* is changed in [`LongformerModel.forward`] from 0, 1, 2 to:
- -10000: no attention
- 0: local attention
- +10000: global attention
"""
hidden_states = hidden_states.transpose(0, 1)
# project hidden states
query_vectors = self.query(hidden_states)
key_vectors = self.key(hidden_states)
value_vectors = self.value(hidden_states)
seq_len, batch_size, embed_dim = hidden_states.size()
assert (
embed_dim == self.embed_dim
), f"hidden_states should have embed_dim = {self.embed_dim}, but has {embed_dim}"
# normalize query
query_vectors /= math.sqrt(self.head_dim)
query_vectors = query_vectors.view(seq_len, batch_size, self.num_heads, self.head_dim).transpose(0, 1)
key_vectors = key_vectors.view(seq_len, batch_size, self.num_heads, self.head_dim).transpose(0, 1)
attn_scores = self._sliding_chunks_query_key_matmul(
query_vectors, key_vectors, self.one_sided_attn_window_size
)
# values to pad for attention probs
remove_from_windowed_attention_mask = (attention_mask != 0)[:, :, None, None]
# cast to fp32/fp16 then replace 1's with -inf
float_mask = remove_from_windowed_attention_mask.type_as(query_vectors).masked_fill(
remove_from_windowed_attention_mask, torch.finfo(query_vectors.dtype).min
)
# diagonal mask with zeros everywhere and -inf inplace of padding
diagonal_mask = self._sliding_chunks_query_key_matmul(
float_mask.new_ones(size=float_mask.size()), float_mask, self.one_sided_attn_window_size
)
# pad local attention probs
attn_scores += diagonal_mask
assert list(attn_scores.size()) == [
batch_size,
seq_len,
self.num_heads,
self.one_sided_attn_window_size * 2 + 1,
], (
f"local_attn_probs should be of size ({batch_size}, {seq_len}, {self.num_heads},"
f" {self.one_sided_attn_window_size * 2 + 1}), but is of size {attn_scores.size()}"
)
# compute local attention probs from global attention keys and contact over window dim
if is_global_attn:
# compute global attn indices required through out forward fn
(
max_num_global_attn_indices,
is_index_global_attn_nonzero,
is_local_index_global_attn_nonzero,
is_local_index_no_global_attn_nonzero,
) = self._get_global_attn_indices(is_index_global_attn)
# calculate global attn probs from global key
global_key_attn_scores = self._concat_with_global_key_attn_probs(
query_vectors=query_vectors,
key_vectors=key_vectors,
max_num_global_attn_indices=max_num_global_attn_indices,
is_index_global_attn_nonzero=is_index_global_attn_nonzero,
is_local_index_global_attn_nonzero=is_local_index_global_attn_nonzero,
is_local_index_no_global_attn_nonzero=is_local_index_no_global_attn_nonzero,
)
# concat to local_attn_probs
# (batch_size, seq_len, num_heads, extra attention count + 2*window+1)
attn_scores = torch.cat((global_key_attn_scores, attn_scores), dim=-1)
# free memory
del global_key_attn_scores
attn_probs = nn.functional.softmax(
attn_scores, dim=-1, dtype=torch.float32
) # use fp32 for numerical stability
if layer_head_mask is not None:
assert layer_head_mask.size() == (
self.num_heads,
), f"Head mask for a single layer should be of size {(self.num_heads,)}, but is {layer_head_mask.size()}"
attn_probs = layer_head_mask.view(1, 1, -1, 1) * attn_probs
# softmax sometimes inserts NaN if all positions are masked, replace them with 0
attn_probs = torch.masked_fill(attn_probs, is_index_masked[:, :, None, None], 0.0)
attn_probs = attn_probs.type_as(attn_scores)
# free memory
del attn_scores
# apply dropout
attn_probs = nn.functional.dropout(attn_probs, p=self.dropout, training=self.training)
value_vectors = value_vectors.view(seq_len, batch_size, self.num_heads, self.head_dim).transpose(0, 1)
# compute local attention output with global attention value and add
if is_global_attn:
# compute sum of global and local attn
attn_output = self._compute_attn_output_with_global_indices(
value_vectors=value_vectors,
attn_probs=attn_probs,
max_num_global_attn_indices=max_num_global_attn_indices,
is_index_global_attn_nonzero=is_index_global_attn_nonzero,
is_local_index_global_attn_nonzero=is_local_index_global_attn_nonzero,
)
else:
# compute local attn only
attn_output = self._sliding_chunks_matmul_attn_probs_value(
attn_probs, value_vectors, self.one_sided_attn_window_size
)
assert attn_output.size() == (batch_size, seq_len, self.num_heads, self.head_dim), "Unexpected size"
attn_output = attn_output.transpose(0, 1).reshape(seq_len, batch_size, embed_dim).contiguous()
# compute value for global attention and overwrite to attention output
# TODO: remove the redundant computation
if is_global_attn:
global_attn_output, global_attn_probs = self._compute_global_attn_output_from_hidden(
hidden_states=hidden_states,
max_num_global_attn_indices=max_num_global_attn_indices,
layer_head_mask=layer_head_mask,
is_local_index_global_attn_nonzero=is_local_index_global_attn_nonzero,
is_index_global_attn_nonzero=is_index_global_attn_nonzero,
is_local_index_no_global_attn_nonzero=is_local_index_no_global_attn_nonzero,
is_index_masked=is_index_masked,
)
# get only non zero global attn output
nonzero_global_attn_output = global_attn_output[
is_local_index_global_attn_nonzero[0], :, is_local_index_global_attn_nonzero[1]
]
# overwrite values with global attention
attn_output[is_index_global_attn_nonzero[::-1]] = nonzero_global_attn_output.view(
len(is_local_index_global_attn_nonzero[0]), -1
)
# The attention weights for tokens with global attention are
# just filler values, they were never used to compute the output.
# Fill with 0 now, the correct values are in 'global_attn_probs'.
attn_probs[is_index_global_attn_nonzero] = 0
outputs = (attn_output.transpose(0, 1),)
if output_attentions:
outputs += (attn_probs,)
return outputs + (global_attn_probs,) if (is_global_attn and output_attentions) else outputs
@staticmethod
def _pad_and_transpose_last_two_dims(hidden_states_padded, padding):
"""pads rows and then flips rows and columns"""
hidden_states_padded = nn.functional.pad(
hidden_states_padded, padding
) # padding value is not important because it will be overwritten
hidden_states_padded = hidden_states_padded.view(
*hidden_states_padded.size()[:-2], hidden_states_padded.size(-1), hidden_states_padded.size(-2)
)
return hidden_states_padded
@staticmethod
def _pad_and_diagonalize(chunked_hidden_states):
"""
shift every row 1 step right, converting columns into diagonals.
Example:
```python
chunked_hidden_states: [
0.4983,
2.6918,
-0.0071,
1.0492,
-1.8348,
0.7672,
0.2986,
0.0285,
-0.7584,
0.4206,
-0.0405,
0.1599,
2.0514,
-1.1600,
0.5372,
0.2629,
]
window_overlap = num_rows = 4
```
(pad & diagonalize) => [ 0.4983, 2.6918, -0.0071, 1.0492, 0.0000, 0.0000, 0.0000
0.0000, -1.8348, 0.7672, 0.2986, 0.0285, 0.0000, 0.0000 0.0000, 0.0000, -0.7584, 0.4206,
-0.0405, 0.1599, 0.0000 0.0000, 0.0000, 0.0000, 2.0514, -1.1600, 0.5372, 0.2629 ]
"""
total_num_heads, num_chunks, window_overlap, hidden_dim = chunked_hidden_states.size()
chunked_hidden_states = nn.functional.pad(
chunked_hidden_states, (0, window_overlap + 1)
) # total_num_heads x num_chunks x window_overlap x (hidden_dim+window_overlap+1). Padding value is not important because it'll be overwritten
chunked_hidden_states = chunked_hidden_states.view(
total_num_heads, num_chunks, -1
) # total_num_heads x num_chunks x window_overlap*window_overlap+window_overlap
chunked_hidden_states = chunked_hidden_states[
:, :, :-window_overlap
] # total_num_heads x num_chunks x window_overlap*window_overlap
chunked_hidden_states = chunked_hidden_states.view(
total_num_heads, num_chunks, window_overlap, window_overlap + hidden_dim
)
chunked_hidden_states = chunked_hidden_states[:, :, :, :-1]
return chunked_hidden_states
@staticmethod
def _chunk(hidden_states, window_overlap, onnx_export: bool = False):
"""convert into overlapping chunks. Chunk size = 2w, overlap size = w"""
if not onnx_export:
# non-overlapping chunks of size = 2w
hidden_states = hidden_states.view(
hidden_states.size(0),
torch.div(hidden_states.size(1), (window_overlap * 2), rounding_mode="trunc"),
window_overlap * 2,
hidden_states.size(2),
)
# use `as_strided` to make the chunks overlap with an overlap size = window_overlap
chunk_size = list(hidden_states.size())
chunk_size[1] = chunk_size[1] * 2 - 1
chunk_stride = list(hidden_states.stride())
chunk_stride[1] = chunk_stride[1] // 2
return hidden_states.as_strided(size=chunk_size, stride=chunk_stride)
# When exporting to ONNX, use this separate logic
# have to use slow implementation since as_strided, unfold and 2d-tensor indexing aren't supported (yet) in ONNX export
# TODO replace this with
# > return hidden_states.unfold(dimension=1, size=window_overlap * 2, step=window_overlap).transpose(2, 3)
# once `unfold` is supported
# the case hidden_states.size(1) == window_overlap * 2 can also simply return hidden_states.unsqueeze(1), but that's control flow
chunk_size = [
hidden_states.size(0),
torch.div(hidden_states.size(1), window_overlap, rounding_mode="trunc") - 1,
window_overlap * 2,
hidden_states.size(2),
]
overlapping_chunks = torch.empty(chunk_size, device=hidden_states.device)
for chunk in range(chunk_size[1]):
overlapping_chunks[:, chunk, :, :] = hidden_states[
:, chunk * window_overlap : chunk * window_overlap + 2 * window_overlap, :
]
return overlapping_chunks
@staticmethod
def _mask_invalid_locations(input_tensor, affected_seq_len) -> torch.Tensor:
beginning_mask_2d = input_tensor.new_ones(affected_seq_len, affected_seq_len + 1).tril().flip(dims=[0])
beginning_mask = beginning_mask_2d[None, :, None, :]
ending_mask = beginning_mask.flip(dims=(1, 3))
beginning_input = input_tensor[:, :affected_seq_len, :, : affected_seq_len + 1]
beginning_mask = beginning_mask.expand(beginning_input.size())
input_tensor[:, :affected_seq_len, :, : affected_seq_len + 1] = torch.full_like(
beginning_input, -float("inf")
).where(beginning_mask.bool(), beginning_input)
ending_input = input_tensor[:, -affected_seq_len:, :, -(affected_seq_len + 1) :]
ending_mask = ending_mask.expand(ending_input.size())
input_tensor[:, -affected_seq_len:, :, -(affected_seq_len + 1) :] = torch.full_like(
ending_input, -float("inf")
).where(ending_mask.bool(), ending_input)
def _sliding_chunks_query_key_matmul(self, query: torch.Tensor, key: torch.Tensor, window_overlap: int):
"""
Matrix multiplication of query and key tensors using with a sliding window attention pattern. This
implementation splits the input into overlapping chunks of size 2w (e.g. 512 for pretrained Longformer) with an
overlap of size window_overlap
"""
batch_size, seq_len, num_heads, head_dim = query.size()
assert (
seq_len % (window_overlap * 2) == 0
), f"Sequence length should be multiple of {window_overlap * 2}. Given {seq_len}"
assert query.size() == key.size()
chunks_count = torch.div(seq_len, window_overlap, rounding_mode="trunc") - 1
# group batch_size and num_heads dimensions into one, then chunk seq_len into chunks of size window_overlap * 2
query = query.transpose(1, 2).reshape(batch_size * num_heads, seq_len, head_dim)
key = key.transpose(1, 2).reshape(batch_size * num_heads, seq_len, head_dim)
query = self._chunk(query, window_overlap, getattr(self.config, "onnx_export", False))
key = self._chunk(key, window_overlap, getattr(self.config, "onnx_export", False))
# matrix multiplication
# bcxd: batch_size * num_heads x chunks x 2window_overlap x head_dim
# bcyd: batch_size * num_heads x chunks x 2window_overlap x head_dim
# bcxy: batch_size * num_heads x chunks x 2window_overlap x 2window_overlap
diagonal_chunked_attention_scores = torch.einsum("bcxd,bcyd->bcxy", (query, key)) # multiply
# convert diagonals into columns
diagonal_chunked_attention_scores = self._pad_and_transpose_last_two_dims(
diagonal_chunked_attention_scores, padding=(0, 0, 0, 1)
)
# allocate space for the overall attention matrix where the chunks are combined. The last dimension
# has (window_overlap * 2 + 1) columns. The first (window_overlap) columns are the window_overlap lower triangles (attention from a word to
# window_overlap previous words). The following column is attention score from each word to itself, then
# followed by window_overlap columns for the upper triangle.
diagonal_attention_scores = diagonal_chunked_attention_scores.new_zeros(
(batch_size * num_heads, chunks_count + 1, window_overlap, window_overlap * 2 + 1)
)
# copy parts from diagonal_chunked_attention_scores into the combined matrix of attentions
# - copying the main diagonal and the upper triangle
diagonal_attention_scores[:, :-1, :, window_overlap:] = diagonal_chunked_attention_scores[
:, :, :window_overlap, : window_overlap + 1
]
diagonal_attention_scores[:, -1, :, window_overlap:] = diagonal_chunked_attention_scores[
:, -1, window_overlap:, : window_overlap + 1
]
# - copying the lower triangle
diagonal_attention_scores[:, 1:, :, :window_overlap] = diagonal_chunked_attention_scores[
:, :, -(window_overlap + 1) : -1, window_overlap + 1 :
]
diagonal_attention_scores[:, 0, 1:window_overlap, 1:window_overlap] = diagonal_chunked_attention_scores[
:, 0, : window_overlap - 1, 1 - window_overlap :
]
# separate batch_size and num_heads dimensions again
diagonal_attention_scores = diagonal_attention_scores.view(
batch_size, num_heads, seq_len, 2 * window_overlap + 1
).transpose(2, 1)
self._mask_invalid_locations(diagonal_attention_scores, window_overlap)
return diagonal_attention_scores
def _sliding_chunks_matmul_attn_probs_value(
self, attn_probs: torch.Tensor, value: torch.Tensor, window_overlap: int
):
"""
Same as _sliding_chunks_query_key_matmul but for attn_probs and value tensors. Returned tensor will be of the
same shape as `attn_probs`
"""
batch_size, seq_len, num_heads, head_dim = value.size()
assert seq_len % (window_overlap * 2) == 0
assert attn_probs.size()[:3] == value.size()[:3]
assert attn_probs.size(3) == 2 * window_overlap + 1
chunks_count = torch.div(seq_len, window_overlap, rounding_mode="trunc") - 1
# group batch_size and num_heads dimensions into one, then chunk seq_len into chunks of size 2 window overlap
chunked_attn_probs = attn_probs.transpose(1, 2).reshape(
batch_size * num_heads,
torch.div(seq_len, window_overlap, rounding_mode="trunc"),
window_overlap,
2 * window_overlap + 1,
)
# group batch_size and num_heads dimensions into one
value = value.transpose(1, 2).reshape(batch_size * num_heads, seq_len, head_dim)
# pad seq_len with w at the beginning of the sequence and another window overlap at the end
padded_value = nn.functional.pad(value, (0, 0, window_overlap, window_overlap), value=-1)
# chunk padded_value into chunks of size 3 window overlap and an overlap of size window overlap
chunked_value_size = (batch_size * num_heads, chunks_count + 1, 3 * window_overlap, head_dim)
chunked_value_stride = padded_value.stride()
chunked_value_stride = (
chunked_value_stride[0],
window_overlap * chunked_value_stride[1],
chunked_value_stride[1],
chunked_value_stride[2],
)
chunked_value = padded_value.as_strided(size=chunked_value_size, stride=chunked_value_stride)
chunked_attn_probs = self._pad_and_diagonalize(chunked_attn_probs)
context = torch.einsum("bcwd,bcdh->bcwh", (chunked_attn_probs, chunked_value))
return context.view(batch_size, num_heads, seq_len, head_dim).transpose(1, 2)
@staticmethod
def _get_global_attn_indices(is_index_global_attn):
"""compute global attn indices required throughout forward pass"""
# helper variable
num_global_attn_indices = is_index_global_attn.long().sum(dim=1)
# max number of global attn indices in batch
max_num_global_attn_indices = num_global_attn_indices.max()
# indices of global attn
is_index_global_attn_nonzero = is_index_global_attn.nonzero(as_tuple=True)
# helper variable
is_local_index_global_attn = torch.arange(
max_num_global_attn_indices, device=is_index_global_attn.device
) < num_global_attn_indices.unsqueeze(dim=-1)
# location of the non-padding values within global attention indices
is_local_index_global_attn_nonzero = is_local_index_global_attn.nonzero(as_tuple=True)
# location of the padding values within global attention indices
is_local_index_no_global_attn_nonzero = (is_local_index_global_attn == 0).nonzero(as_tuple=True)
return (
max_num_global_attn_indices,
is_index_global_attn_nonzero,
is_local_index_global_attn_nonzero,
is_local_index_no_global_attn_nonzero,
)
def _concat_with_global_key_attn_probs(
self,
key_vectors,
query_vectors,
max_num_global_attn_indices,
is_index_global_attn_nonzero,
is_local_index_global_attn_nonzero,
is_local_index_no_global_attn_nonzero,
):
batch_size = key_vectors.shape[0]
# create only global key vectors
key_vectors_only_global = key_vectors.new_zeros(
batch_size, max_num_global_attn_indices, self.num_heads, self.head_dim
)
key_vectors_only_global[is_local_index_global_attn_nonzero] = key_vectors[is_index_global_attn_nonzero]
# (batch_size, seq_len, num_heads, max_num_global_attn_indices)
attn_probs_from_global_key = torch.einsum("blhd,bshd->blhs", (query_vectors, key_vectors_only_global))
# need to transpose since ONNX export only supports consecutive indexing: https://pytorch.org/docs/stable/onnx.html#writes-sets
attn_probs_from_global_key = attn_probs_from_global_key.transpose(1, 3)
attn_probs_from_global_key[
is_local_index_no_global_attn_nonzero[0], is_local_index_no_global_attn_nonzero[1], :, :
] = torch.finfo(attn_probs_from_global_key.dtype).min
attn_probs_from_global_key = attn_probs_from_global_key.transpose(1, 3)
return attn_probs_from_global_key
def _compute_attn_output_with_global_indices(
self,
value_vectors,
attn_probs,
max_num_global_attn_indices,
is_index_global_attn_nonzero,
is_local_index_global_attn_nonzero,
):
batch_size = attn_probs.shape[0]
# cut local attn probs to global only
attn_probs_only_global = attn_probs.narrow(-1, 0, max_num_global_attn_indices)
# get value vectors for global only
value_vectors_only_global = value_vectors.new_zeros(
batch_size, max_num_global_attn_indices, self.num_heads, self.head_dim
)
value_vectors_only_global[is_local_index_global_attn_nonzero] = value_vectors[is_index_global_attn_nonzero]
# use `matmul` because `einsum` crashes sometimes with fp16
# attn = torch.einsum('blhs,bshd->blhd', (selected_attn_probs, selected_v))
# compute attn output only global
attn_output_only_global = torch.matmul(
attn_probs_only_global.transpose(1, 2).clone(), value_vectors_only_global.transpose(1, 2).clone()
).transpose(1, 2)
# reshape attn probs
attn_probs_without_global = attn_probs.narrow(
-1, max_num_global_attn_indices, attn_probs.size(-1) - max_num_global_attn_indices
).contiguous()
# compute attn output with global
attn_output_without_global = self._sliding_chunks_matmul_attn_probs_value(
attn_probs_without_global, value_vectors, self.one_sided_attn_window_size
)
return attn_output_only_global + attn_output_without_global
def _compute_global_attn_output_from_hidden(
self,
hidden_states,
max_num_global_attn_indices,
layer_head_mask,
is_local_index_global_attn_nonzero,
is_index_global_attn_nonzero,
is_local_index_no_global_attn_nonzero,
is_index_masked,
):
seq_len, batch_size = hidden_states.shape[:2]
# prepare global hidden states
global_attn_hidden_states = hidden_states.new_zeros(max_num_global_attn_indices, batch_size, self.embed_dim)
global_attn_hidden_states[is_local_index_global_attn_nonzero[::-1]] = hidden_states[
is_index_global_attn_nonzero[::-1]
]
# global key, query, value
global_query_vectors_only_global = self.query_global(global_attn_hidden_states)
global_key_vectors = self.key_global(hidden_states)
global_value_vectors = self.value_global(hidden_states)
# normalize
global_query_vectors_only_global /= math.sqrt(self.head_dim)
# reshape
global_query_vectors_only_global = (
global_query_vectors_only_global.contiguous()
.view(max_num_global_attn_indices, batch_size * self.num_heads, self.head_dim)
.transpose(0, 1)
) # (batch_size * self.num_heads, max_num_global_attn_indices, head_dim)
global_key_vectors = (
global_key_vectors.contiguous().view(-1, batch_size * self.num_heads, self.head_dim).transpose(0, 1)
) # batch_size * self.num_heads, seq_len, head_dim)
global_value_vectors = (
global_value_vectors.contiguous().view(-1, batch_size * self.num_heads, self.head_dim).transpose(0, 1)
) # batch_size * self.num_heads, seq_len, head_dim)
# compute attn scores
global_attn_scores = torch.bmm(global_query_vectors_only_global, global_key_vectors.transpose(1, 2))
assert list(global_attn_scores.size()) == [
batch_size * self.num_heads,
max_num_global_attn_indices,
seq_len,
], (
"global_attn_scores have the wrong size. Size should be"
f" {(batch_size * self.num_heads, max_num_global_attn_indices, seq_len)}, but is"
f" {global_attn_scores.size()}."
)
global_attn_scores = global_attn_scores.view(batch_size, self.num_heads, max_num_global_attn_indices, seq_len)
# need to transpose since ONNX export only supports consecutive indexing: https://pytorch.org/docs/stable/onnx.html#writes-sets
global_attn_scores = global_attn_scores.transpose(1, 2)
global_attn_scores[
is_local_index_no_global_attn_nonzero[0], is_local_index_no_global_attn_nonzero[1], :, :
] = torch.finfo(global_attn_scores.dtype).min
global_attn_scores = global_attn_scores.transpose(1, 2)
global_attn_scores = global_attn_scores.masked_fill(
is_index_masked[:, None, None, :],
torch.finfo(global_attn_scores.dtype).min,
)
global_attn_scores = global_attn_scores.view(batch_size * self.num_heads, max_num_global_attn_indices, seq_len)
# compute global attn probs
global_attn_probs_float = nn.functional.softmax(
global_attn_scores, dim=-1, dtype=torch.float32
) # use fp32 for numerical stability
# apply layer head masking
if layer_head_mask is not None:
assert layer_head_mask.size() == (
self.num_heads,
), f"Head mask for a single layer should be of size {(self.num_heads,)}, but is {layer_head_mask.size()}"
global_attn_probs_float = layer_head_mask.view(1, -1, 1, 1) * global_attn_probs_float.view(
batch_size, self.num_heads, max_num_global_attn_indices, seq_len
)
global_attn_probs_float = global_attn_probs_float.view(
batch_size * self.num_heads, max_num_global_attn_indices, seq_len
)
global_attn_probs = nn.functional.dropout(
global_attn_probs_float.type_as(global_attn_scores), p=self.dropout, training=self.training
)
# global attn output
global_attn_output = torch.bmm(global_attn_probs, global_value_vectors)
assert list(global_attn_output.size()) == [
batch_size * self.num_heads,
max_num_global_attn_indices,
self.head_dim,
], (
"global_attn_output tensor has the wrong size. Size should be"
f" {(batch_size * self.num_heads, max_num_global_attn_indices, self.head_dim)}, but is"
f" {global_attn_output.size()}."
)
global_attn_probs = global_attn_probs.view(batch_size, self.num_heads, max_num_global_attn_indices, seq_len)
global_attn_output = global_attn_output.view(
batch_size, self.num_heads, max_num_global_attn_indices, self.head_dim
)
return global_attn_output, global_attn_probs
# Copied from transformers.models.bert.modeling_bert.BertSelfOutput
class LongformerSelfOutput(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 LongformerAttention(nn.Module):
def __init__(self, config, layer_id=0):
super().__init__()
self.self = LongformerSelfAttention(config, layer_id)
self.output = LongformerSelfOutput(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,
attention_mask=None,
layer_head_mask=None,
is_index_masked=None,
is_index_global_attn=None,
is_global_attn=None,
output_attentions=False,
):
self_outputs = self.self(
hidden_states,
attention_mask=attention_mask,
layer_head_mask=layer_head_mask,
is_index_masked=is_index_masked,
is_index_global_attn=is_index_global_attn,
is_global_attn=is_global_attn,
output_attentions=output_attentions,
)
attn_output = self.output(self_outputs[0], hidden_states)
outputs = (attn_output,) + self_outputs[1:]
return outputs
# Copied from transformers.models.bert.modeling_bert.BertIntermediate
class LongformerIntermediate(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
class LongformerOutput(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 LongformerLayer(nn.Module):
def __init__(self, config, layer_id=0):
super().__init__()
self.attention = LongformerAttention(config, layer_id)
self.intermediate = LongformerIntermediate(config)
self.output = LongformerOutput(config)
self.chunk_size_feed_forward = config.chunk_size_feed_forward
self.seq_len_dim = 1
def forward(
self,
hidden_states,
attention_mask=None,
layer_head_mask=None,
is_index_masked=None,
is_index_global_attn=None,
is_global_attn=None,
output_attentions=False,
):
self_attn_outputs = self.attention(
hidden_states,
attention_mask=attention_mask,
layer_head_mask=layer_head_mask,
is_index_masked=is_index_masked,
is_index_global_attn=is_index_global_attn,
is_global_attn=is_global_attn,
output_attentions=output_attentions,
)
attn_output = self_attn_outputs[0]
outputs = self_attn_outputs[1:]
layer_output = apply_chunking_to_forward(
self.ff_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attn_output
)
outputs = (layer_output,) + outputs
return outputs
def ff_chunk(self, attn_output):
intermediate_output = self.intermediate(attn_output)
layer_output = self.output(intermediate_output, attn_output)
return layer_output
class LongformerEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.layer = nn.ModuleList([LongformerLayer(config, layer_id=i) for i in range(config.num_hidden_layers)])
self.gradient_checkpointing = False
def forward(
self,
hidden_states,
attention_mask=None,
head_mask=None,
padding_len=0,
output_attentions=False,
output_hidden_states=False,
return_dict=True,
):
is_index_masked = attention_mask < 0
is_index_global_attn = attention_mask > 0
# Record `is_global_attn == True` to enable ONNX export
is_global_attn = is_index_global_attn.flatten().any().item()
all_hidden_states = () if output_hidden_states else None
all_attentions = () if output_attentions else None # All local attentions.
all_global_attentions = () if (output_attentions and is_global_attn) else None
# check if head_mask has a correct number of layers specified if desired
if head_mask is not None:
assert head_mask.size()[0] == (
len(self.layer)
), f"The head_mask should be specified for {len(self.layer)} layers, but it is for {head_mask.size()[0]}."
for idx, layer_module in enumerate(self.layer):
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
layer_module.__call__,
hidden_states,
attention_mask,
head_mask[idx] if head_mask is not None else None,
is_index_masked,
is_index_global_attn,
is_global_attn,
output_attentions,
)
else:
layer_outputs = layer_module(
hidden_states,
attention_mask=attention_mask,
layer_head_mask=head_mask[idx] if head_mask is not None else None,
is_index_masked=is_index_masked,
is_index_global_attn=is_index_global_attn,
is_global_attn=is_global_attn,
output_attentions=output_attentions,
)
hidden_states = layer_outputs[0]
if output_attentions:
# bzs x seq_len x num_attn_heads x (num_global_attn + attention_window_len + 1) => bzs x num_attn_heads x seq_len x (num_global_attn + attention_window_len + 1)
all_attentions = all_attentions + (layer_outputs[1].transpose(1, 2),)
if is_global_attn:
# bzs x num_attn_heads x num_global_attn x seq_len => bzs x num_attn_heads x seq_len x num_global_attn
all_global_attentions = all_global_attentions + (layer_outputs[2].transpose(2, 3),)
# Add last layer
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
# undo padding if necessary
# unpad `hidden_states` because the calling function is expecting a length == input_ids.size(1)
hidden_states = hidden_states[:, : hidden_states.shape[1] - padding_len]
if output_hidden_states:
all_hidden_states = tuple([state[:, : state.shape[1] - padding_len] for state in all_hidden_states])
if output_attentions:
all_attentions = tuple([state[:, :, : state.shape[2] - padding_len, :] for state in all_attentions])
if not return_dict:
return tuple(
v for v in [hidden_states, all_hidden_states, all_attentions, all_global_attentions] if v is not None
)
return LongformerBaseModelOutput(
last_hidden_state=hidden_states,
hidden_states=all_hidden_states,
attentions=all_attentions,
global_attentions=all_global_attentions,
)
# Copied from transformers.models.bert.modeling_bert.BertPooler
class LongformerPooler(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.roberta.modeling_roberta.RobertaLMHead with Roberta->Longformer
class LongformerLMHead(nn.Module):
"""Longformer 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):
# To tie those two weights if they get disconnected (on TPU or when the bias is resized)
# For accelerate compatibility and to not break backward compatibility
if self.decoder.bias.device.type == "meta":
self.decoder.bias = self.bias
else:
self.bias = self.decoder.bias
class LongformerPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = LongformerConfig
base_model_prefix = "longformer"
supports_gradient_checkpointing = True
_no_split_modules = ["LongformerSelfAttention"]
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, nn.LayerNorm):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
LONGFORMER_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 ([`LongformerConfig`]): 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.
"""
LONGFORMER_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)
global_attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*):
Mask to decide the attention given on each token, local attention or global attention. Tokens with global
attention attends to all other tokens, and all other tokens attend to them. This is important for
task-specific finetuning because it makes the model more flexible at representing the task. For example,
for classification, the <s> token should be given global attention. For QA, all question tokens should also
have global attention. Please refer to the [Longformer paper](https://arxiv.org/abs/2004.05150) for more
details. Mask values selected in `[0, 1]`:
- 0 for local attention (a sliding window attention),
- 1 for global attention (tokens that attend to all other tokens, and all other tokens attend to them).
head_mask (`torch.Tensor` of shape `(num_layers, num_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 `(num_layers, num_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**.
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)
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 Longformer Model outputting raw hidden-states without any specific head on top.",
LONGFORMER_START_DOCSTRING,
)
class LongformerModel(LongformerPreTrainedModel):
"""
This class copied code from [`RobertaModel`] and overwrote standard self-attention with longformer self-attention
to provide the ability to process long sequences following the self-attention approach described in [Longformer:
the Long-Document Transformer](https://arxiv.org/abs/2004.05150) by Iz Beltagy, Matthew E. Peters, and Arman Cohan.
Longformer self-attention combines a local (sliding window) and global attention to extend to long documents
without the O(n^2) increase in memory and compute.
The self-attention module `LongformerSelfAttention` implemented here supports the combination of local and global
attention but it lacks support for autoregressive attention and dilated attention. Autoregressive and dilated
attention are more relevant for autoregressive language modeling than finetuning on downstream tasks. Future
release will add support for autoregressive attention, but the support for dilated attention requires a custom CUDA
kernel to be memory and compute efficient.
"""
def __init__(self, config, add_pooling_layer=True):
super().__init__(config)
self.config = config
if isinstance(config.attention_window, int):
assert config.attention_window % 2 == 0, "`config.attention_window` has to be an even value"
assert config.attention_window > 0, "`config.attention_window` has to be positive"
config.attention_window = [config.attention_window] * config.num_hidden_layers # one value per layer
else:
assert len(config.attention_window) == config.num_hidden_layers, (
"`len(config.attention_window)` should equal `config.num_hidden_layers`. "
f"Expected {config.num_hidden_layers}, given {len(config.attention_window)}"
)
self.embeddings = LongformerEmbeddings(config)
self.encoder = LongformerEncoder(config)
self.pooler = LongformerPooler(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)
def _pad_to_window_size(
self,
input_ids: torch.Tensor,
attention_mask: torch.Tensor,
token_type_ids: torch.Tensor,
position_ids: torch.Tensor,
inputs_embeds: torch.Tensor,
pad_token_id: int,
):
"""A helper function to pad tokens and mask to work with implementation of Longformer self-attention."""
# padding
attention_window = (
self.config.attention_window
if isinstance(self.config.attention_window, int)
else max(self.config.attention_window)
)
assert attention_window % 2 == 0, f"`attention_window` should be an even value. Given {attention_window}"
input_shape = input_ids.shape if input_ids is not None else inputs_embeds.shape
batch_size, seq_len = input_shape[:2]
padding_len = (attention_window - seq_len % attention_window) % attention_window
# this path should be recorded in the ONNX export, it is fine with padding_len == 0 as well
if padding_len > 0:
logger.warning_once(
f"Input ids are automatically padded to be a multiple of "
f"`config.attention_window`: {attention_window}"
)
if input_ids is not None:
input_ids = nn.functional.pad(input_ids, (0, padding_len), value=pad_token_id)
if position_ids is not None:
# pad with position_id = pad_token_id as in modeling_roberta.RobertaEmbeddings
position_ids = nn.functional.pad(position_ids, (0, padding_len), value=pad_token_id)
if inputs_embeds is not None:
input_ids_padding = inputs_embeds.new_full(
(batch_size, padding_len),
self.config.pad_token_id,
dtype=torch.long,
)
inputs_embeds_padding = self.embeddings(input_ids_padding)
inputs_embeds = torch.cat([inputs_embeds, inputs_embeds_padding], dim=-2)
attention_mask = nn.functional.pad(
attention_mask, (0, padding_len), value=0
) # no attention on the padding tokens
token_type_ids = nn.functional.pad(token_type_ids, (0, padding_len), value=0) # pad with token_type_id = 0
return padding_len, input_ids, attention_mask, token_type_ids, position_ids, inputs_embeds
def _merge_to_attention_mask(self, attention_mask: torch.Tensor, global_attention_mask: torch.Tensor):
# longformer self attention expects attention mask to have 0 (no attn), 1 (local attn), 2 (global attn)
# (global_attention_mask + 1) => 1 for local attention, 2 for global attention
# => final attention_mask => 0 for no attention, 1 for local attention 2 for global attention
if attention_mask is not None:
attention_mask = attention_mask * (global_attention_mask + 1)
else:
# simply use `global_attention_mask` as `attention_mask`
# if no `attention_mask` is given
attention_mask = global_attention_mask + 1
return attention_mask
@add_start_docstrings_to_model_forward(LONGFORMER_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=LongformerBaseModelOutputWithPooling, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
global_attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, LongformerBaseModelOutputWithPooling]:
r"""
Returns:
Examples:
```python
>>> import torch
>>> from transformers import LongformerModel, AutoTokenizer
>>> model = LongformerModel.from_pretrained("allenai/longformer-base-4096")
>>> tokenizer = AutoTokenizer.from_pretrained("allenai/longformer-base-4096")
>>> SAMPLE_TEXT = " ".join(["Hello world! "] * 1000) # long input document
>>> input_ids = torch.tensor(tokenizer.encode(SAMPLE_TEXT)).unsqueeze(0) # batch of size 1
>>> attention_mask = torch.ones(
... input_ids.shape, dtype=torch.long, device=input_ids.device
... ) # initialize to local attention
>>> global_attention_mask = torch.zeros(
... input_ids.shape, dtype=torch.long, device=input_ids.device
... ) # initialize to global attention to be deactivated for all tokens
>>> global_attention_mask[
... :,
... [
... 1,
... 4,
... 21,
... ],
... ] = 1 # Set global attention to random tokens for the sake of this example
>>> # Usually, set global attention based on the task. For example,
>>> # classification: the <s> token
>>> # QA: question tokens
>>> # LM: potentially on the beginning of sentences and paragraphs
>>> outputs = model(input_ids, attention_mask=attention_mask, global_attention_mask=global_attention_mask)
>>> sequence_output = outputs.last_hidden_state
>>> pooled_output = outputs.pooler_output
```"""
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)
# merge `global_attention_mask` and `attention_mask`
if global_attention_mask is not None:
attention_mask = self._merge_to_attention_mask(attention_mask, global_attention_mask)
padding_len, input_ids, attention_mask, token_type_ids, position_ids, inputs_embeds = self._pad_to_window_size(
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
inputs_embeds=inputs_embeds,
pad_token_id=self.config.pad_token_id,
)
# 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)[
:, 0, 0, :
]
embedding_output = 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,
attention_mask=extended_attention_mask,
head_mask=head_mask,
padding_len=padding_len,
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 LongformerBaseModelOutputWithPooling(
last_hidden_state=sequence_output,
pooler_output=pooled_output,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
global_attentions=encoder_outputs.global_attentions,
)
@add_start_docstrings("""Longformer Model with a `language modeling` head on top.""", LONGFORMER_START_DOCSTRING)
class LongformerForMaskedLM(LongformerPreTrainedModel):
_tied_weights_keys = ["lm_head.decoder"]
def __init__(self, config):
super().__init__(config)
self.longformer = LongformerModel(config, add_pooling_layer=False)
self.lm_head = LongformerLMHead(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
@add_start_docstrings_to_model_forward(LONGFORMER_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=LongformerMaskedLMOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
global_attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
labels: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, LongformerMaskedLMOutput]:
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.
Returns:
Mask filling example:
```python
>>> from transformers import AutoTokenizer, LongformerForMaskedLM
>>> tokenizer = AutoTokenizer.from_pretrained("allenai/longformer-base-4096")
>>> model = LongformerForMaskedLM.from_pretrained("allenai/longformer-base-4096")
```
Let's try a very long input.
```python
>>> TXT = (
... "My friends are <mask> but they eat too many carbs."
... + " That's why I decide not to eat with them." * 300
... )
>>> input_ids = tokenizer([TXT], return_tensors="pt")["input_ids"]
>>> logits = model(input_ids).logits
>>> masked_index = (input_ids[0] == tokenizer.mask_token_id).nonzero().item()
>>> probs = logits[0, masked_index].softmax(dim=0)
>>> values, predictions = probs.topk(5)
>>> tokenizer.decode(predictions).split()
['healthy', 'skinny', 'thin', 'good', 'vegetarian']
```"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.longformer(
input_ids,
attention_mask=attention_mask,
global_attention_mask=global_attention_mask,
head_mask=head_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
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()
labels = labels.to(prediction_scores.device)
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 LongformerMaskedLMOutput(
loss=masked_lm_loss,
logits=prediction_scores,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
global_attentions=outputs.global_attentions,
)
@add_start_docstrings(
"""
Longformer Model transformer with a sequence classification/regression head on top (a linear layer on top of the
pooled output) e.g. for GLUE tasks.
""",
LONGFORMER_START_DOCSTRING,
)
class LongformerForSequenceClassification(LongformerPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.config = config
self.longformer = LongformerModel(config, add_pooling_layer=False)
self.classifier = LongformerClassificationHead(config)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(LONGFORMER_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint="jpwahle/longformer-base-plagiarism-detection",
output_type=LongformerSequenceClassifierOutput,
config_class=_CONFIG_FOR_DOC,
expected_output="'ORIGINAL'",
expected_loss=5.44,
)
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
global_attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
labels: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, LongformerSequenceClassifierOutput]:
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
if global_attention_mask is None:
logger.warning_once("Initializing global attention on CLS token...")
global_attention_mask = torch.zeros_like(input_ids)
# global attention on cls token
global_attention_mask[:, 0] = 1
outputs = self.longformer(
input_ids,
attention_mask=attention_mask,
global_attention_mask=global_attention_mask,
head_mask=head_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
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:
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 LongformerSequenceClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
global_attentions=outputs.global_attentions,
)
class LongformerClassificationHead(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, hidden_states, **kwargs):
hidden_states = hidden_states[:, 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)
output = self.out_proj(hidden_states)
return output
@add_start_docstrings(
"""
Longformer Model with a span classification head on top for extractive question-answering tasks like SQuAD /
TriviaQA (a linear layers on top of the hidden-states output to compute `span start logits` and `span end logits`).
""",
LONGFORMER_START_DOCSTRING,
)
class LongformerForQuestionAnswering(LongformerPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.longformer = LongformerModel(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(LONGFORMER_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=LongformerQuestionAnsweringModelOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
global_attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
start_positions: Optional[torch.Tensor] = None,
end_positions: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, LongformerQuestionAnsweringModelOutput]:
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:
Examples:
```python
>>> from transformers import AutoTokenizer, LongformerForQuestionAnswering
>>> import torch
>>> tokenizer = AutoTokenizer.from_pretrained("allenai/longformer-large-4096-finetuned-triviaqa")
>>> model = LongformerForQuestionAnswering.from_pretrained("allenai/longformer-large-4096-finetuned-triviaqa")
>>> question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"
>>> encoding = tokenizer(question, text, return_tensors="pt")
>>> input_ids = encoding["input_ids"]
>>> # default is local attention everywhere
>>> # the forward method will automatically set global attention on question tokens
>>> attention_mask = encoding["attention_mask"]
>>> outputs = model(input_ids, attention_mask=attention_mask)
>>> start_logits = outputs.start_logits
>>> end_logits = outputs.end_logits
>>> all_tokens = tokenizer.convert_ids_to_tokens(input_ids[0].tolist())
>>> answer_tokens = all_tokens[torch.argmax(start_logits) : torch.argmax(end_logits) + 1]
>>> answer = tokenizer.decode(
... tokenizer.convert_tokens_to_ids(answer_tokens)
... ) # remove space prepending space token
```"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if global_attention_mask is None:
if input_ids is None:
logger.warning(
"It is not possible to automatically generate the `global_attention_mask` because input_ids is"
" None. Please make sure that it is correctly set."
)
else:
# set global attention on question tokens automatically
global_attention_mask = _compute_global_attention_mask(input_ids, self.config.sep_token_id)
outputs = self.longformer(
input_ids,
attention_mask=attention_mask,
global_attention_mask=global_attention_mask,
head_mask=head_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
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 LongformerQuestionAnsweringModelOutput(
loss=total_loss,
start_logits=start_logits,
end_logits=end_logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
global_attentions=outputs.global_attentions,
)
@add_start_docstrings(
"""
Longformer 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.
""",
LONGFORMER_START_DOCSTRING,
)
class LongformerForTokenClassification(LongformerPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.longformer = LongformerModel(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(LONGFORMER_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint="brad1141/Longformer-finetuned-norm",
output_type=LongformerTokenClassifierOutput,
config_class=_CONFIG_FOR_DOC,
expected_output=(
"['Evidence', 'Evidence', 'Evidence', 'Evidence', 'Evidence', 'Evidence', 'Evidence', 'Evidence',"
" 'Evidence', 'Evidence', 'Evidence', 'Evidence']"
),
expected_loss=0.63,
)
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
global_attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
labels: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, LongformerTokenClassifierOutput]:
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.longformer(
input_ids,
attention_mask=attention_mask,
global_attention_mask=global_attention_mask,
head_mask=head_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
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()
labels = labels.to(logits.device)
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 LongformerTokenClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
global_attentions=outputs.global_attentions,
)
@add_start_docstrings(
"""
Longformer 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.
""",
LONGFORMER_START_DOCSTRING,
)
class LongformerForMultipleChoice(LongformerPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.longformer = LongformerModel(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(
LONGFORMER_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length")
)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=LongformerMultipleChoiceModelOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
global_attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
labels: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, LongformerMultipleChoiceModelOutput]:
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)
"""
num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1]
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
# set global attention on question tokens
if global_attention_mask is None and input_ids is not None:
logger.warning_once("Initializing global attention on multiple choice...")
# put global attention on all tokens after `config.sep_token_id`
global_attention_mask = torch.stack(
[
_compute_global_attention_mask(input_ids[:, i], self.config.sep_token_id, before_sep_token=False)
for i in range(num_choices)
],
dim=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_global_attention_mask = (
global_attention_mask.view(-1, global_attention_mask.size(-1))
if global_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.longformer(
flat_input_ids,
position_ids=flat_position_ids,
token_type_ids=flat_token_type_ids,
attention_mask=flat_attention_mask,
global_attention_mask=flat_global_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()
labels = labels.to(reshaped_logits.device)
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 LongformerMultipleChoiceModelOutput(
loss=loss,
logits=reshaped_logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
global_attentions=outputs.global_attentions,
)
__all__ = [
"LongformerForMaskedLM",
"LongformerForMultipleChoice",
"LongformerForQuestionAnswering",
"LongformerForSequenceClassification",
"LongformerForTokenClassification",
"LongformerModel",
"LongformerPreTrainedModel",
"LongformerSelfAttention",
]
| transformers/src/transformers/models/longformer/modeling_longformer.py/0 | {
"file_path": "transformers/src/transformers/models/longformer/modeling_longformer.py",
"repo_id": "transformers",
"token_count": 46959
} |
# coding=utf-8
# Copyright 2018 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.
"""Convert LXMERT checkpoint."""
import argparse
import torch
from transformers import LxmertConfig, LxmertForPreTraining, load_tf_weights_in_lxmert
from transformers.utils import logging
logging.set_verbosity_info()
def convert_tf_checkpoint_to_pytorch(tf_checkpoint_path, config_file, pytorch_dump_path):
# Initialise PyTorch model
config = LxmertConfig.from_json_file(config_file)
print(f"Building PyTorch model from configuration: {config}")
model = LxmertForPreTraining(config)
# Load weights from tf checkpoint
load_tf_weights_in_lxmert(model, config, tf_checkpoint_path)
# Save pytorch-model
print(f"Save PyTorch model to {pytorch_dump_path}")
torch.save(model.state_dict(), pytorch_dump_path)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
)
parser.add_argument(
"--config_file",
default=None,
type=str,
required=True,
help="The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.",
)
parser.add_argument(
"--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
args = parser.parse_args()
convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)
| transformers/src/transformers/models/lxmert/convert_lxmert_original_tf_checkpoint_to_pytorch.py/0 | {
"file_path": "transformers/src/transformers/models/lxmert/convert_lxmert_original_tf_checkpoint_to_pytorch.py",
"repo_id": "transformers",
"token_count": 723
} |
# coding=utf-8
# Copyright 2024 state-spaces/mamba2 org 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.
"""This script can be used to convert checkpoints provided in the `mamba2_ssm` library into the format provided in HuggingFace `transformers`. It depends on the `mamba2_ssm` package to be installed."""
import argparse
import json
from functools import partial
from os import path
from typing import Dict, Optional
import torch
from safetensors import safe_open
from safetensors.torch import save_model
from transformers import GPTNeoXTokenizerFast, LlamaTokenizerFast, Mamba2Config, Mamba2ForCausalLM
def load_state_dict_from_safetensors(mamba2_checkpoint_path: str, ckpt_name: str) -> Dict[str, torch.Tensor]:
# Load weights and config from paths
original_state_dict = {}
with safe_open(path.join(mamba2_checkpoint_path, ckpt_name), framework="pt") as f:
for k in f.keys():
newk = k.removeprefix("model.")
original_state_dict[newk] = f.get_tensor(k).clone()
return original_state_dict
def load_state_dict_from_torch(mamba2_checkpoint_path: str, ckpt_name: str) -> Dict[str, torch.Tensor]:
return torch.load(path.join(mamba2_checkpoint_path, ckpt_name), map_location="cpu")
def convert_ssm_config_to_hf_config(config_ssm: Dict, mamba2_model_dict: Dict) -> Mamba2Config:
"""Convert a Mamba2Config from mamba_ssm to a Mamba2Config from here."""
hf_config = Mamba2Config()
# Switch to a different dict depending on model type
config_dict = mamba2_model_dict
# Set important values from config and recalculate other resulting entries
hf_config.hidden_size = config_ssm[config_dict["hidden_size"]]
hf_config.num_heads = (hf_config.hidden_size * hf_config.expand) // hf_config.head_dim
hf_config.num_hidden_layers = config_ssm[config_dict["num_hidden_layers"]]
hf_config.n_groups = config_ssm.get(config_dict["n_groups"], 1)
hf_config.tie_word_embeddings = config_ssm["tie_embeddings"]
hf_config.bos_token_id = config_dict["bos_token_id"]
hf_config.pad_token_id = config_dict["pad_token_id"]
hf_config.eos_token_id = config_dict["eos_token_id"]
# Padded vocab size, mostly of 16 but 32 is also very common in different models
vocab_size = config_ssm["vocab_size"]
pad_vocab_size_multiple = config_ssm["pad_vocab_size_multiple"]
if (vocab_size % pad_vocab_size_multiple) != 0:
vocab_size += pad_vocab_size_multiple - (vocab_size % pad_vocab_size_multiple)
hf_config.vocab_size = vocab_size
return hf_config
def load_and_save_tokenizer(
mamba2_model_type: str,
output_dir: str,
tokenizer_model_path: Optional[str] = None,
) -> None:
tokenizer = None
# Load tokenizer
if tokenizer_model_path is not None and mamba2_model_type == "codestral":
tokenizer_class = LlamaTokenizerFast
tokenizer = tokenizer_class(tokenizer_model_path, legacy=False, from_slow=True)
elif mamba2_model_type == "mamba_ssm":
tokenizer = GPTNeoXTokenizerFast.from_pretrained("state-spaces/mamba-130m-hf", padding_side="left")
# Save tokenizer
if tokenizer is not None:
tokenizer.save_pretrained(output_dir)
_MAMBA2_MODELS_DICT = {
"codestral": {
"hidden_size": "dim",
"num_hidden_layers": "n_layers",
"n_groups": "n_groups",
"bos_token_id": 0,
"pad_token_id": 1,
"eos_token_id": 2,
"config_name": "params.json",
"load_state_dict": partial(load_state_dict_from_safetensors, ckpt_name="consolidated.safetensors"),
"load_and_save_tokenizer": partial(load_and_save_tokenizer, "codestral"),
},
"mamba_ssm": {
"hidden_size": "d_model",
"num_hidden_layers": "n_layer",
"n_groups": "ngroups",
"bos_token_id": 0,
"pad_token_id": 0,
"eos_token_id": 0,
"config_name": "config.json",
"load_state_dict": partial(load_state_dict_from_torch, ckpt_name="pytorch_model.bin"),
"load_and_save_tokenizer": partial(load_and_save_tokenizer, "mamba_ssm"),
},
}
def convert_mamba2_checkpoint_file_to_huggingface_model_file(
mamba2_checkpoint_path: str,
mamba2_model_type: str,
precision: str,
output_dir: str,
tokenizer_model_path: Optional[str] = None,
) -> None:
mamba2_model_dict = _MAMBA2_MODELS_DICT[mamba2_model_type]
# Load and save config based on name
config_path = path.join(mamba2_checkpoint_path, mamba2_model_dict["config_name"])
with open(config_path, "r", encoding="utf-8") as json_file:
config = json.load(json_file)
hf_config = convert_ssm_config_to_hf_config(config_ssm=config, mamba2_model_dict=mamba2_model_dict)
hf_config.save_pretrained(output_dir)
# Load state dict of the original model and transfer to hf model
original_state_dict = mamba2_model_dict["load_state_dict"](mamba2_checkpoint_path=mamba2_checkpoint_path)
hf_model = Mamba2ForCausalLM(hf_config)
hf_model.load_state_dict(original_state_dict)
# Save new model to pytorch_dump_path
dtype = torch.float32 if precision == "fp32" else (torch.bfloat16 if precision == "bf16" else torch.float16)
save_model(hf_model.to(dtype), path.join(output_dir, "model.safetensors"), metadata={"format": "pt"})
# Load and save tokenizer
mamba2_model_dict["load_and_save_tokenizer"](output_dir=output_dir, tokenizer_model_path=tokenizer_model_path)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"-i",
"--mamba2_checkpoint_directory",
type=str,
required=True,
help="Path to a directory containing the `pytorch_model.bin` or `.safetensors` mamba2_ssm checkpoint file to be converted.",
)
parser.add_argument(
"-m",
"--mamba2_model_type",
type=str,
default="mamba_ssm",
const="mamba_ssm",
required=True,
choices=("codestral", "mamba_ssm"),
help="The model type the conversion will be performed on. Can choose from either `codestral` or `mamba_ssm`.",
)
parser.add_argument(
"-p",
"--precision",
type=str,
default="fp16",
const="fp16",
required=True,
choices=("fp32", "fp16", "bf16"),
help="The precision the model will be saved in. Select from fp32, fp16 or bf16.",
)
parser.add_argument(
"-o", "--output_dir", type=str, required=True, help="Path to directory to save the converted output model to."
)
parser.add_argument(
"-t",
"--tokenizer_model_path",
type=str,
default=None,
required=False,
help="Path to a `codestral` tokenizer file.",
)
args = parser.parse_args()
convert_mamba2_checkpoint_file_to_huggingface_model_file(
args.mamba2_checkpoint_directory,
args.mamba2_model_type,
args.precision,
args.output_dir,
args.tokenizer_model_path,
)
| transformers/src/transformers/models/mamba2/convert_mamba2_ssm_checkpoint_to_pytorch.py/0 | {
"file_path": "transformers/src/transformers/models/mamba2/convert_mamba2_ssm_checkpoint_to_pytorch.py",
"repo_id": "transformers",
"token_count": 3066
} |
# 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.
"""
Fast tokenization class for MarkupLM. It overwrites 2 methods of the slow tokenizer class, namely _batch_encode_plus
and _encode_plus, in which the Rust tokenizer is used.
"""
import json
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union
from tokenizers import processors
from ...file_utils import PaddingStrategy, TensorType, add_end_docstrings
from ...tokenization_utils_base import (
ENCODE_KWARGS_DOCSTRING,
AddedToken,
BatchEncoding,
EncodedInput,
PreTokenizedInput,
TextInput,
TextInputPair,
TruncationStrategy,
)
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_markuplm import MARKUPLM_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING, MarkupLMTokenizer
logger = logging.get_logger(__name__)
VOCAB_FILES_NAMES = {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}
@lru_cache()
def bytes_to_unicode():
"""
Returns list of utf-8 byte and a mapping to unicode strings. We specifically avoids mapping to whitespace/control
characters the bpe code barfs on. The reversible bpe codes work on unicode strings. This means you need a large #
of unicode characters in your vocab if you want to avoid UNKs. When you're at something like a 10B token dataset
you end up needing around 5K for decent coverage. This is a significant percentage of your normal, say, 32K bpe
vocab. To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
"""
bs = (
list(range(ord("!"), ord("~") + 1)) + list(range(ord("¡"), ord("¬") + 1)) + list(range(ord("®"), ord("ÿ") + 1))
)
cs = bs[:]
n = 0
for b in range(2**8):
if b not in bs:
bs.append(b)
cs.append(2**8 + n)
n += 1
cs = [chr(n) for n in cs]
return dict(zip(bs, cs))
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
class MarkupLMTokenizerFast(PreTrainedTokenizerFast):
r"""
Construct a MarkupLM tokenizer. Based on byte-level Byte-Pair-Encoding (BPE).
[`MarkupLMTokenizerFast`] can be used to turn HTML strings into to token-level `input_ids`, `attention_mask`,
`token_type_ids`, `xpath_tags_seq` and `xpath_tags_seq`. This tokenizer inherits from [`PreTrainedTokenizer`] which
contains most of the main methods.
Users should refer to this superclass for more information regarding those methods.
Args:
vocab_file (`str`):
Path to the vocabulary file.
merges_file (`str`):
Path to the merges file.
errors (`str`, *optional*, defaults to `"replace"`):
Paradigm to follow when decoding bytes to UTF-8. See
[bytes.decode](https://docs.python.org/3/library/stdtypes.html#bytes.decode) for more information.
bos_token (`str`, *optional*, defaults to `"<s>"`):
The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
<Tip>
When building a sequence using special tokens, this is not the token that is used for the beginning of
sequence. The token used is the `cls_token`.
</Tip>
eos_token (`str`, *optional*, defaults to `"</s>"`):
The end of sequence token.
<Tip>
When building a sequence using special tokens, this is not the token that is used for the end of sequence.
The token used is the `sep_token`.
</Tip>
sep_token (`str`, *optional*, defaults to `"</s>"`):
The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
sequence classification or for a text and a question for question answering. It is also used as the last
token of a sequence built with special tokens.
cls_token (`str`, *optional*, defaults to `"<s>"`):
The classifier token which is used when doing sequence classification (classification of the whole sequence
instead of per-token classification). It is the first token of the sequence when built with special tokens.
unk_token (`str`, *optional*, defaults to `"<unk>"`):
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.
pad_token (`str`, *optional*, defaults to `"<pad>"`):
The token used for padding, for example when batching sequences of different lengths.
mask_token (`str`, *optional*, defaults to `"<mask>"`):
The token used for masking values. This is the token used when training this model with masked language
modeling. This is the token which the model will try to predict.
add_prefix_space (`bool`, *optional*, defaults to `False`):
Whether or not to add an initial space to the input. This allows to treat the leading word just as any
other word. (RoBERTa tokenizer detect beginning of words by the preceding space).
"""
vocab_files_names = VOCAB_FILES_NAMES
slow_tokenizer_class = MarkupLMTokenizer
def __init__(
self,
vocab_file,
merges_file,
tags_dict,
tokenizer_file=None,
errors="replace",
bos_token="<s>",
eos_token="</s>",
sep_token="</s>",
cls_token="<s>",
unk_token="<unk>",
pad_token="<pad>",
mask_token="<mask>",
add_prefix_space=False,
max_depth=50,
max_width=1000,
pad_width=1001,
pad_token_label=-100,
only_label_first_subword=True,
trim_offsets=False,
**kwargs,
):
bos_token = AddedToken(bos_token, lstrip=False, rstrip=False) if isinstance(bos_token, str) else bos_token
eos_token = AddedToken(eos_token, lstrip=False, rstrip=False) if isinstance(eos_token, str) else eos_token
sep_token = AddedToken(sep_token, lstrip=False, rstrip=False) if isinstance(sep_token, str) else sep_token
cls_token = AddedToken(cls_token, lstrip=False, rstrip=False) if isinstance(cls_token, str) else cls_token
unk_token = AddedToken(unk_token, lstrip=False, rstrip=False) if isinstance(unk_token, str) else unk_token
pad_token = AddedToken(pad_token, lstrip=False, rstrip=False) if isinstance(pad_token, str) else pad_token
# Mask token behave like a normal word, i.e. include the space before it
mask_token = AddedToken(mask_token, lstrip=True, rstrip=False) if isinstance(mask_token, str) else mask_token
super().__init__(
vocab_file=vocab_file,
merges_file=merges_file,
tags_dict=tags_dict,
tokenizer_file=tokenizer_file,
errors=errors,
bos_token=bos_token,
eos_token=eos_token,
unk_token=unk_token,
sep_token=sep_token,
cls_token=cls_token,
pad_token=pad_token,
mask_token=mask_token,
add_prefix_space=add_prefix_space,
trim_offsets=trim_offsets,
max_depth=max_depth,
max_width=max_width,
pad_width=pad_width,
pad_token_label=pad_token_label,
only_label_first_subword=only_label_first_subword,
**kwargs,
)
if trim_offsets:
# Not implemented yet, because we need to chain two post processors which is not possible yet
# We need to wait for https://github.com/huggingface/tokenizers/pull/1005
# With `trim_offsets=False` we don't need to do add `processors.ByteLevel(trim_offsets=False)`
# because it's not doing anything
raise NotImplementedError(
"`trim_offsets=True` is not implemented for MarkupLMTokenizerFast. Please set it to False."
)
self.tags_dict = tags_dict
tokenizer_component = "post_processor"
tokenizer_component_instance = getattr(self.backend_tokenizer, tokenizer_component, None)
if tokenizer_component_instance:
state = json.loads(tokenizer_component_instance.__getstate__())
# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
if "sep" in state:
state["sep"] = tuple(state["sep"])
if "cls" in state:
state["cls"] = tuple(state["cls"])
changes_to_apply = False
if state.get("add_prefix_space", add_prefix_space) != add_prefix_space:
state["add_prefix_space"] = add_prefix_space
changes_to_apply = True
if changes_to_apply:
component_class = getattr(processors, state.pop("type"))
new_value = component_class(**state)
setattr(self.backend_tokenizer, tokenizer_component, new_value)
# additional properties
self.max_depth = max_depth
self.max_width = max_width
self.pad_width = pad_width
self.unk_tag_id = len(self.tags_dict)
self.pad_tag_id = self.unk_tag_id + 1
self.pad_xpath_tags_seq = [self.pad_tag_id] * self.max_depth
self.pad_xpath_subs_seq = [self.pad_width] * self.max_depth
self.pad_token_label = pad_token_label
self.only_label_first_subword = only_label_first_subword
def get_xpath_seq(self, xpath):
"""
Given the xpath expression of one particular node (like "/html/body/div/li[1]/div/span[2]"), return a list of
tag IDs and corresponding subscripts, taking into account max depth.
"""
xpath_tags_list = []
xpath_subs_list = []
xpath_units = xpath.split("/")
for unit in xpath_units:
if not unit.strip():
continue
name_subs = unit.strip().split("[")
tag_name = name_subs[0]
sub = 0 if len(name_subs) == 1 else int(name_subs[1][:-1])
xpath_tags_list.append(self.tags_dict.get(tag_name, self.unk_tag_id))
xpath_subs_list.append(min(self.max_width, sub))
xpath_tags_list = xpath_tags_list[: self.max_depth]
xpath_subs_list = xpath_subs_list[: self.max_depth]
xpath_tags_list += [self.pad_tag_id] * (self.max_depth - len(xpath_tags_list))
xpath_subs_list += [self.pad_width] * (self.max_depth - len(xpath_subs_list))
return xpath_tags_list, xpath_subs_list
@add_end_docstrings(ENCODE_KWARGS_DOCSTRING, MARKUPLM_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
def __call__(
self,
text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]],
text_pair: Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None,
xpaths: Union[List[List[int]], List[List[List[int]]]] = None,
node_labels: Optional[Union[List[int], List[List[int]]]] = 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,
padding_side: Optional[bool] = None,
return_tensors: Optional[Union[str, TensorType]] = None,
return_token_type_ids: Optional[bool] = None,
return_attention_mask: Optional[bool] = None,
return_overflowing_tokens: bool = False,
return_special_tokens_mask: bool = False,
return_offsets_mapping: bool = False,
return_length: bool = False,
verbose: bool = True,
**kwargs,
) -> BatchEncoding:
"""
Main method to tokenize and prepare for the model one or several sequence(s) or one or several pair(s) of
sequences with nodes, xpaths and optional labels.
Args:
text (`str`, `List[str]`, `List[List[str]]`):
The sequence or batch of sequences to be encoded. Each sequence can be a string, a list of strings
(words of a single example or questions of a batch of examples) or a list of list of strings (batch of
words).
text_pair (`List[str]`, `List[List[str]]`):
The sequence or batch of sequences to be encoded. Each sequence should be a list of strings
(pretokenized string).
xpaths (`List[List[int]]`, `List[List[List[int]]]`):
Node-level xpaths. Each bounding box should be normalized to be on a 0-1000 scale.
node_labels (`List[int]`, `List[List[int]]`, *optional*):
Node-level integer labels (for token classification tasks).
"""
# Input type checking for clearer error
def _is_valid_text_input(t):
if isinstance(t, str):
# Strings are fine
return True
elif isinstance(t, (list, tuple)):
# List are fine as long as they are...
if len(t) == 0:
# ... empty
return True
elif isinstance(t[0], str):
# ... list of strings
return True
elif isinstance(t[0], (list, tuple)):
# ... list with an empty list or with a list of strings
return len(t[0]) == 0 or isinstance(t[0][0], str)
else:
return False
else:
return False
if text_pair is not None:
# in case text + text_pair are provided, text = questions, text_pair = nodes
if not _is_valid_text_input(text):
raise ValueError("text input must of type `str` (single example) or `List[str]` (batch of examples). ")
if not isinstance(text_pair, (list, tuple)):
raise ValueError(
"Nodes must be of type `List[str]` (single pretokenized example), "
"or `List[List[str]]` (batch of pretokenized examples)."
)
else:
# in case only text is provided => must be nodes
if not isinstance(text, (list, tuple)):
raise ValueError(
"Nodes must be of type `List[str]` (single pretokenized example), "
"or `List[List[str]]` (batch of pretokenized examples)."
)
if text_pair is not None:
is_batched = isinstance(text, (list, tuple))
else:
is_batched = isinstance(text, (list, tuple)) and text and isinstance(text[0], (list, tuple))
nodes = text if text_pair is None else text_pair
assert xpaths is not None, "You must provide corresponding xpaths"
if is_batched:
assert len(nodes) == len(xpaths), "You must provide nodes and xpaths for an equal amount of examples"
for nodes_example, xpaths_example in zip(nodes, xpaths):
assert len(nodes_example) == len(xpaths_example), "You must provide as many nodes as there are xpaths"
else:
assert len(nodes) == len(xpaths), "You must provide as many nodes as there are xpaths"
if is_batched:
if text_pair is not None and len(text) != len(text_pair):
raise ValueError(
f"batch length of `text`: {len(text)} does not match batch length of `text_pair`:"
f" {len(text_pair)}."
)
batch_text_or_text_pairs = list(zip(text, text_pair)) if text_pair is not None else text
is_pair = bool(text_pair is not None)
return self.batch_encode_plus(
batch_text_or_text_pairs=batch_text_or_text_pairs,
is_pair=is_pair,
xpaths=xpaths,
node_labels=node_labels,
add_special_tokens=add_special_tokens,
padding=padding,
truncation=truncation,
max_length=max_length,
stride=stride,
pad_to_multiple_of=pad_to_multiple_of,
padding_side=padding_side,
return_tensors=return_tensors,
return_token_type_ids=return_token_type_ids,
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_length=return_length,
verbose=verbose,
**kwargs,
)
else:
return self.encode_plus(
text=text,
text_pair=text_pair,
xpaths=xpaths,
node_labels=node_labels,
add_special_tokens=add_special_tokens,
padding=padding,
truncation=truncation,
max_length=max_length,
stride=stride,
pad_to_multiple_of=pad_to_multiple_of,
padding_side=padding_side,
return_tensors=return_tensors,
return_token_type_ids=return_token_type_ids,
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_length=return_length,
verbose=verbose,
**kwargs,
)
@add_end_docstrings(ENCODE_KWARGS_DOCSTRING, MARKUPLM_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
def batch_encode_plus(
self,
batch_text_or_text_pairs: Union[
List[TextInput],
List[TextInputPair],
List[PreTokenizedInput],
],
is_pair: bool = None,
xpaths: Optional[List[List[List[int]]]] = None,
node_labels: Optional[Union[List[int], List[List[int]]]] = 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,
padding_side: Optional[bool] = None,
return_tensors: Optional[Union[str, TensorType]] = None,
return_token_type_ids: Optional[bool] = None,
return_attention_mask: Optional[bool] = None,
return_overflowing_tokens: bool = False,
return_special_tokens_mask: bool = False,
return_offsets_mapping: bool = False,
return_length: bool = False,
verbose: bool = True,
**kwargs,
) -> BatchEncoding:
# Backward compatibility for 'truncation_strategy', 'pad_to_max_length'
padding_strategy, truncation_strategy, max_length, kwargs = self._get_padding_truncation_strategies(
padding=padding,
truncation=truncation,
max_length=max_length,
pad_to_multiple_of=pad_to_multiple_of,
verbose=verbose,
**kwargs,
)
return self._batch_encode_plus(
batch_text_or_text_pairs=batch_text_or_text_pairs,
is_pair=is_pair,
xpaths=xpaths,
node_labels=node_labels,
add_special_tokens=add_special_tokens,
padding_strategy=padding_strategy,
truncation_strategy=truncation_strategy,
max_length=max_length,
stride=stride,
pad_to_multiple_of=pad_to_multiple_of,
padding_side=padding_side,
return_tensors=return_tensors,
return_token_type_ids=return_token_type_ids,
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_length=return_length,
verbose=verbose,
**kwargs,
)
def tokenize(self, text: str, pair: Optional[str] = None, add_special_tokens: bool = False, **kwargs) -> List[str]:
batched_input = [(text, pair)] if pair else [text]
encodings = self._tokenizer.encode_batch(
batched_input, add_special_tokens=add_special_tokens, is_pretokenized=False, **kwargs
)
return encodings[0].tokens
@add_end_docstrings(ENCODE_KWARGS_DOCSTRING, MARKUPLM_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
def encode_plus(
self,
text: Union[TextInput, PreTokenizedInput],
text_pair: Optional[PreTokenizedInput] = None,
xpaths: Optional[List[List[int]]] = None,
node_labels: Optional[List[int]] = 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,
padding_side: Optional[bool] = None,
return_tensors: Optional[Union[str, TensorType]] = None,
return_token_type_ids: Optional[bool] = None,
return_attention_mask: Optional[bool] = None,
return_overflowing_tokens: bool = False,
return_special_tokens_mask: bool = False,
return_offsets_mapping: bool = False,
return_length: bool = False,
verbose: bool = True,
**kwargs,
) -> BatchEncoding:
"""
Tokenize and prepare for the model a sequence or a pair of sequences. .. warning:: This method is deprecated,
`__call__` should be used instead.
Args:
text (`str`, `List[str]`, `List[List[str]]`):
The first sequence to be encoded. This can be a string, a list of strings or a list of list of strings.
text_pair (`List[str]` or `List[int]`, *optional*):
Optional second sequence to be encoded. This can be a list of strings (words of a single example) or a
list of list of strings (words of a batch of examples).
"""
# Backward compatibility for 'truncation_strategy', 'pad_to_max_length'
padding_strategy, truncation_strategy, max_length, kwargs = self._get_padding_truncation_strategies(
padding=padding,
truncation=truncation,
max_length=max_length,
pad_to_multiple_of=pad_to_multiple_of,
verbose=verbose,
**kwargs,
)
return self._encode_plus(
text=text,
xpaths=xpaths,
text_pair=text_pair,
node_labels=node_labels,
add_special_tokens=add_special_tokens,
padding_strategy=padding_strategy,
truncation_strategy=truncation_strategy,
max_length=max_length,
stride=stride,
pad_to_multiple_of=pad_to_multiple_of,
padding_side=padding_side,
return_tensors=return_tensors,
return_token_type_ids=return_token_type_ids,
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_length=return_length,
verbose=verbose,
**kwargs,
)
def _batch_encode_plus(
self,
batch_text_or_text_pairs: Union[
List[TextInput],
List[TextInputPair],
List[PreTokenizedInput],
],
is_pair: bool = None,
xpaths: Optional[List[List[List[int]]]] = None,
node_labels: Optional[List[List[int]]] = None,
add_special_tokens: bool = True,
padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE,
max_length: Optional[int] = None,
stride: int = 0,
pad_to_multiple_of: Optional[int] = None,
padding_side: Optional[bool] = None,
return_tensors: Optional[str] = None,
return_token_type_ids: Optional[bool] = None,
return_attention_mask: Optional[bool] = None,
return_overflowing_tokens: bool = False,
return_special_tokens_mask: bool = False,
return_offsets_mapping: bool = False,
return_length: bool = False,
verbose: bool = True,
) -> BatchEncoding:
if not isinstance(batch_text_or_text_pairs, list):
raise TypeError(f"batch_text_or_text_pairs has to be a list (got {type(batch_text_or_text_pairs)})")
# Set the truncation and padding strategy and restore the initial configuration
self.set_truncation_and_padding(
padding_strategy=padding_strategy,
truncation_strategy=truncation_strategy,
max_length=max_length,
stride=stride,
pad_to_multiple_of=pad_to_multiple_of,
padding_side=padding_side,
)
if is_pair:
batch_text_or_text_pairs = [([text], text_pair) for text, text_pair in batch_text_or_text_pairs]
encodings = self._tokenizer.encode_batch(
batch_text_or_text_pairs,
add_special_tokens=add_special_tokens,
is_pretokenized=True, # we set this to True as MarkupLM always expects pretokenized inputs
)
# Convert encoding to dict
# `Tokens` is a tuple of (List[Dict[str, List[List[int]]]] or List[Dict[str, 2D-Tensor]],
# List[EncodingFast]) with nested dimensions corresponding to batch, overflows, sequence length
tokens_and_encodings = [
self._convert_encoding(
encoding=encoding,
return_token_type_ids=return_token_type_ids,
return_attention_mask=return_attention_mask,
return_overflowing_tokens=return_overflowing_tokens,
return_special_tokens_mask=return_special_tokens_mask,
return_offsets_mapping=True
if node_labels is not None
else return_offsets_mapping, # we use offsets to create the labels
return_length=return_length,
verbose=verbose,
)
for encoding in encodings
]
# Convert the output to have dict[list] from list[dict] and remove the additional overflows dimension
# From (variable) shape (batch, overflows, sequence length) to ~ (batch * overflows, sequence length)
# (we say ~ because the number of overflow varies with the example in the batch)
#
# To match each overflowing sample with the original sample in the batch
# we add an overflow_to_sample_mapping array (see below)
sanitized_tokens = {}
for key in tokens_and_encodings[0][0].keys():
stack = [e for item, _ in tokens_and_encodings for e in item[key]]
sanitized_tokens[key] = stack
sanitized_encodings = [e for _, item in tokens_and_encodings for e in item]
# If returning overflowing tokens, we need to return a mapping
# from the batch idx to the original sample
if return_overflowing_tokens:
overflow_to_sample_mapping = []
for i, (toks, _) in enumerate(tokens_and_encodings):
overflow_to_sample_mapping += [i] * len(toks["input_ids"])
sanitized_tokens["overflow_to_sample_mapping"] = overflow_to_sample_mapping
for input_ids in sanitized_tokens["input_ids"]:
self._eventual_warn_about_too_long_sequence(input_ids, max_length, verbose)
# create the token-level xpaths tags and subscripts
xpath_tags_seq = []
xpath_subs_seq = []
for batch_index in range(len(sanitized_tokens["input_ids"])):
if return_overflowing_tokens:
original_index = sanitized_tokens["overflow_to_sample_mapping"][batch_index]
else:
original_index = batch_index
xpath_tags_seq_example = []
xpath_subs_seq_example = []
for id, sequence_id, word_id in zip(
sanitized_tokens["input_ids"][batch_index],
sanitized_encodings[batch_index].sequence_ids,
sanitized_encodings[batch_index].word_ids,
):
if word_id is not None:
if is_pair and sequence_id == 0:
xpath_tags_seq_example.append(self.pad_xpath_tags_seq)
xpath_subs_seq_example.append(self.pad_xpath_subs_seq)
else:
xpath_tags_list, xpath_subs_list = self.get_xpath_seq(xpaths[original_index][word_id])
xpath_tags_seq_example.extend([xpath_tags_list])
xpath_subs_seq_example.extend([xpath_subs_list])
else:
if id in [self.cls_token_id, self.sep_token_id, self.pad_token_id]:
xpath_tags_seq_example.append(self.pad_xpath_tags_seq)
xpath_subs_seq_example.append(self.pad_xpath_subs_seq)
else:
raise ValueError("Id not recognized")
xpath_tags_seq.append(xpath_tags_seq_example)
xpath_subs_seq.append(xpath_subs_seq_example)
sanitized_tokens["xpath_tags_seq"] = xpath_tags_seq
sanitized_tokens["xpath_subs_seq"] = xpath_subs_seq
# optionally, create the labels
if node_labels is not None:
labels = []
for batch_index in range(len(sanitized_tokens["input_ids"])):
if return_overflowing_tokens:
original_index = sanitized_tokens["overflow_to_sample_mapping"][batch_index]
else:
original_index = batch_index
labels_example = []
for id, offset, word_id in zip(
sanitized_tokens["input_ids"][batch_index],
sanitized_tokens["offset_mapping"][batch_index],
sanitized_encodings[batch_index].word_ids,
):
if word_id is not None:
if self.only_label_first_subword:
if offset[0] == 0:
# Use the real label id for the first token of the word, and padding ids for the remaining tokens
labels_example.append(node_labels[original_index][word_id])
else:
labels_example.append(self.pad_token_label)
else:
labels_example.append(node_labels[original_index][word_id])
else:
labels_example.append(self.pad_token_label)
labels.append(labels_example)
sanitized_tokens["labels"] = labels
# finally, remove offsets if the user didn't want them
if not return_offsets_mapping:
del sanitized_tokens["offset_mapping"]
return BatchEncoding(sanitized_tokens, sanitized_encodings, tensor_type=return_tensors)
def _encode_plus(
self,
text: Union[TextInput, PreTokenizedInput],
text_pair: Optional[PreTokenizedInput] = None,
xpaths: Optional[List[List[int]]] = None,
node_labels: Optional[List[int]] = None,
add_special_tokens: bool = True,
padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE,
max_length: Optional[int] = None,
stride: int = 0,
pad_to_multiple_of: Optional[int] = None,
padding_side: Optional[bool] = None,
return_tensors: Optional[bool] = None,
return_token_type_ids: Optional[bool] = None,
return_attention_mask: Optional[bool] = None,
return_overflowing_tokens: bool = False,
return_special_tokens_mask: bool = False,
return_offsets_mapping: bool = False,
return_length: bool = False,
verbose: bool = True,
**kwargs,
) -> BatchEncoding:
# make it a batched input
# 2 options:
# 1) only text, in case text must be a list of str
# 2) text + text_pair, in which case text = str and text_pair a list of str
batched_input = [(text, text_pair)] if text_pair else [text]
batched_xpaths = [xpaths]
batched_node_labels = [node_labels] if node_labels is not None else None
batched_output = self._batch_encode_plus(
batched_input,
is_pair=bool(text_pair is not None),
xpaths=batched_xpaths,
node_labels=batched_node_labels,
add_special_tokens=add_special_tokens,
padding_strategy=padding_strategy,
truncation_strategy=truncation_strategy,
max_length=max_length,
stride=stride,
pad_to_multiple_of=pad_to_multiple_of,
padding_side=padding_side,
return_tensors=return_tensors,
return_token_type_ids=return_token_type_ids,
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_length=return_length,
verbose=verbose,
**kwargs,
)
# Return tensor is None, then we can remove the leading batch axis
# Overflowing tokens are returned as a batch of output so we keep them in this case
if return_tensors is None and not return_overflowing_tokens:
batched_output = BatchEncoding(
{
key: value[0] if len(value) > 0 and isinstance(value[0], list) else value
for key, value in batched_output.items()
},
batched_output.encodings,
)
self._eventual_warn_about_too_long_sequence(batched_output["input_ids"], max_length, verbose)
return batched_output
def _pad(
self,
encoded_inputs: Union[Dict[str, EncodedInput], BatchEncoding],
max_length: Optional[int] = None,
padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
pad_to_multiple_of: Optional[int] = None,
padding_side: Optional[bool] = None,
return_attention_mask: Optional[bool] = None,
) -> dict:
"""
Args:
Pad encoded inputs (on left/right and up to predefined length or max length in the batch)
encoded_inputs:
Dictionary of tokenized inputs (`List[int]`) or batch of tokenized inputs (`List[List[int]]`).
max_length: maximum length of the returned list and optionally padding length (see below).
Will truncate by taking into account the special tokens.
padding_strategy: PaddingStrategy to use for padding.
- PaddingStrategy.LONGEST Pad to the longest sequence in the batch
- PaddingStrategy.MAX_LENGTH: Pad to the max length (default)
- PaddingStrategy.DO_NOT_PAD: Do not pad
The tokenizer padding sides are defined in self.padding_side:
- 'left': pads on the left of the sequences
- 'right': pads on the right of the sequences
pad_to_multiple_of: (optional) Integer if set will pad the sequence to a multiple of the provided value.
This is especially useful to enable the use of Tensor Core on NVIDIA hardware with compute capability
`>= 7.5` (Volta).
padding_side:
The side on which the model should have padding applied. Should be selected between ['right', 'left'].
Default value is picked from the class attribute of the same name.
return_attention_mask:
(optional) Set to False to avoid returning attention mask (default: set to model specifics)
"""
# Load from model defaults
if return_attention_mask is None:
return_attention_mask = "attention_mask" in self.model_input_names
required_input = encoded_inputs[self.model_input_names[0]]
if padding_strategy == PaddingStrategy.LONGEST:
max_length = len(required_input)
if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
max_length = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
needs_to_be_padded = padding_strategy != PaddingStrategy.DO_NOT_PAD and len(required_input) != max_length
# Initialize attention mask if not present.
if return_attention_mask and "attention_mask" not in encoded_inputs:
encoded_inputs["attention_mask"] = [1] * len(required_input)
if needs_to_be_padded:
difference = max_length - len(required_input)
padding_side = padding_side if padding_side is not None else self.padding_side
if padding_side == "right":
if return_attention_mask:
encoded_inputs["attention_mask"] = encoded_inputs["attention_mask"] + [0] * difference
if "token_type_ids" in encoded_inputs:
encoded_inputs["token_type_ids"] = (
encoded_inputs["token_type_ids"] + [self.pad_token_type_id] * difference
)
if "xpath_tags_seq" in encoded_inputs:
encoded_inputs["xpath_tags_seq"] = (
encoded_inputs["xpath_tags_seq"] + [self.pad_xpath_tags_seq] * difference
)
if "xpath_subs_seq" in encoded_inputs:
encoded_inputs["xpath_subs_seq"] = (
encoded_inputs["xpath_subs_seq"] + [self.pad_xpath_subs_seq] * difference
)
if "labels" in encoded_inputs:
encoded_inputs["labels"] = encoded_inputs["labels"] + [self.pad_token_label] * difference
if "special_tokens_mask" in encoded_inputs:
encoded_inputs["special_tokens_mask"] = encoded_inputs["special_tokens_mask"] + [1] * difference
encoded_inputs[self.model_input_names[0]] = required_input + [self.pad_token_id] * difference
elif padding_side == "left":
if return_attention_mask:
encoded_inputs["attention_mask"] = [0] * difference + encoded_inputs["attention_mask"]
if "token_type_ids" in encoded_inputs:
encoded_inputs["token_type_ids"] = [self.pad_token_type_id] * difference + encoded_inputs[
"token_type_ids"
]
if "xpath_tags_seq" in encoded_inputs:
encoded_inputs["xpath_tags_seq"] = [self.pad_xpath_tags_seq] * difference + encoded_inputs[
"xpath_tags_seq"
]
if "xpath_subs_seq" in encoded_inputs:
encoded_inputs["xpath_subs_seq"] = [self.pad_xpath_subs_seq] * difference + encoded_inputs[
"xpath_subs_seq"
]
if "labels" in encoded_inputs:
encoded_inputs["labels"] = [self.pad_token_label] * difference + encoded_inputs["labels"]
if "special_tokens_mask" in encoded_inputs:
encoded_inputs["special_tokens_mask"] = [1] * difference + encoded_inputs["special_tokens_mask"]
encoded_inputs[self.model_input_names[0]] = [self.pad_token_id] * difference + required_input
else:
raise ValueError("Invalid padding strategy:" + str(padding_side))
return encoded_inputs
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. A RoBERTa sequence has the following format:
- single sequence: `<s> X </s>`
- pair of sequences: `<s> A </s></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.
"""
if token_ids_1 is None:
return [self.cls_token_id] + token_ids_0 + [self.sep_token_id]
cls = [self.cls_token_id]
sep = [self.sep_token_id]
return cls + token_ids_0 + sep + token_ids_1 + sep
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. RoBERTa does not
make use of token type ids, therefore a list of zeros is returned.
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 zeros.
"""
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 + token_ids_1 + sep) * [0]
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__ = ["MarkupLMTokenizerFast"]
| transformers/src/transformers/models/markuplm/tokenization_markuplm_fast.py/0 | {
"file_path": "transformers/src/transformers/models/markuplm/tokenization_markuplm_fast.py",
"repo_id": "transformers",
"token_count": 20109
} |
# Copyright 2022 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.
import argparse
import importlib.util
import json
import os
import re
import sys
import types
import torch
from huggingface_hub import split_torch_state_dict_into_shards
from packaging import version
from transformers import AutoTokenizer, GPT2Config
from transformers.modeling_utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
def add_checkpointing_args(parser):
parser.add_argument("--megatron-path", type=str, default=None, help="Base directory of Megatron repository")
parser.add_argument(
"--convert_checkpoint_from_megatron_to_transformers",
action="store_true",
help=(
"If True, convert a Megatron checkpoint to a Transformers checkpoint. "
"If False, convert a Transformers checkpoint to a Megatron checkpoint."
),
)
parser.add_argument(
"--load_path",
type=str,
required=True,
help="Path to the checkpoint to convert.",
)
parser.add_argument(
"--save_path",
type=str,
required=True,
help="Path to the converted checkpoint.",
)
parser.add_argument("--print-checkpoint-structure", action="store_true")
return parser
def add_megatron_checkpoint_args(parser):
parser.add_argument(
"--target_tensor_model_parallel_size",
type=int,
default=1,
help=(
"The tensor model parallel size of the converted checkpoint. "
"Only used when converting a Transformers checkpoint to a Megatron checkpoint."
),
)
parser.add_argument(
"--target_pipeline_model_parallel_size",
type=int,
default=1,
help=(
"The pipeline model parallel size of the converted checkpoint. "
"Only used when converting a Transformers checkpoint to a Megatron checkpoint."
),
)
parser.add_argument(
"--target_data_parallel_size",
type=int,
default=1,
help=(
"The data parallel size of the converted checkpoint. "
"Only used when converting a Transformers checkpoint to a Megatron checkpoint."
),
)
parser.add_argument(
"--target_params_dtype",
type=str,
default="fp32",
help=(
"The dtype of the converted checkpoint. "
"Only used when converting a Transformers checkpoint to a Megatron checkpoint."
),
)
parser.add_argument(
"--make_vocab_size_divisible_by",
type=int,
default=128,
help=(
"Pad the vocab size to be divisible by this value. "
"This is added for computational efficieny reasons. "
"Only used when converting a Transformers checkpoint to a Megatron checkpoint."
),
)
parser.add_argument(
"--use_distributed_optimizer",
action="store_true",
help=(
"If True, use the distributed optimizer. "
"Only used when converting a Transformers checkpoint to a Megatron checkpoint."
),
)
return parser
def add_transformers_checkpoint_args(parser):
parser.add_argument(
"--tokenizer_name",
type=str,
default=None,
help=(
"The name of the pre-trained tokenizer to save. "
"If not None, the tokenizer will be saved. "
"Only used when converting a Megatron checkpoint to a Transformers checkpoint."
),
)
parser.add_argument(
"--max_shard_size",
type=str,
default="10GB",
help=(
"The maximum size for a checkpoint before being sharded. Checkpoints shard will then be each of size "
"lower than this size. If expressed as a string, needs to be digits followed by a unit (like `5MB`). "
"Only used when converting a Megatron checkpoint to a Transformers checkpoint."
),
)
return parser
# The simple map of names for "automated" rules.
megatron_to_transformers = {
"attention.dense": ".attn.c_proj.",
"self_attention.dense": ".attn.c_proj.",
"mlp.dense_h_to_4h": ".mlp.c_fc.",
"mlp.dense_4h_to_h": ".mlp.c_proj.",
}
transformers_to_megatron = {v[1:-1]: k for k, v in megatron_to_transformers.items()}
tensor_parallel_params = [
# megatron-lm layers to merge across tp ranks
"self_attention.query_key_value.weight",
"self_attention.query_key_value.bias",
"self_attention.dense.weight",
"mlp.dense_h_to_4h.weight",
"mlp.dense_h_to_4h.bias",
"mlp.dense_4h_to_h.weight",
# deprecated
"attention.query_key_value.weight",
"attention.query_key_value.bias",
"attention.dense.weight",
# transformers layers to split across tp ranks
"attn.c_attn.weight",
"attn.c_attn.bias",
"attn.c_proj.weight",
"mlp.c_fc.weight",
"mlp.c_fc.bias",
"mlp.c_proj.weight",
]
def recursive_print(name, val, spaces=0):
"""
Recursively print the structure of a checkpoint. This function is taken from `convert_megatron_gpt2_checkpoint.py`
Args:
name (str): the name of the current tensor parameter
val (Tuple(int)): the shape of the current tensor parameter
spaces (int): the number of spaces to print before the output for a nested structure
"""
# Format the message.
if name is None:
msg = None
else:
fmt = "." * max(0, spaces - 2) + "# {:" + str(50 - spaces) + "s}"
msg = fmt.format(name)
# Print and recurse (if needed).
if isinstance(val, dict):
if msg is not None:
print(msg)
for k in val.keys():
recursive_print(k, val[k], spaces + 2)
elif isinstance(val, torch.Tensor):
print(msg, ":", val.size())
else:
print(msg, ":", val)
def megatron_to_transformers_fix_query_key_value_ordering(
param, checkpoint_version, num_splits, num_heads, hidden_size
):
"""
Permutes layout of param tensor to [num_splits * num_heads * hidden_size, :] for compatibility with later versions
of NVIDIA Megatron-LM. The inverse operation is performed inside Megatron-LM to read checkpoints:
https://github.com/NVIDIA/Megatron-LM/blob/v2.4/megatron/checkpointing.py#L209 If param is the weight tensor of the
self-attention block, the returned tensor will have to be transposed one more time to be read by HuggingFace GPT2.
This function is taken from `convert_megatron_gpt2_checkpoint.py`
Args:
param (torch.Tensor): the tensor to permute
checkpoint_version (int): the version of the checkpoint.
num_splits (int): the number of projections, usually 3 for (Query, Key, Value)
num_heads (int): the number of attention heads
hidden_size (int): the hidden size per head
"""
input_shape = param.size()
if checkpoint_version == 1.0:
# version 1.0 stores [num_heads * hidden_size * num_splits, :]
saved_shape = (num_heads, hidden_size, num_splits) + input_shape[1:]
param = param.view(*saved_shape)
param = param.transpose(0, 2)
param = param.transpose(1, 2).contiguous()
elif checkpoint_version >= 2.0:
# other versions store [num_heads * num_splits * hidden_size, :]
saved_shape = (num_heads, num_splits, hidden_size) + input_shape[1:]
param = param.view(*saved_shape)
param = param.transpose(0, 1).contiguous()
param = param.view(*input_shape)
return param
def transformers_to_megatron_fix_query_key_value_ordering(
param, checkpoint_version, num_splits, num_heads, hidden_size
):
"""
Permutes layout of param tensor to the one compatible with respective NVIDIA Megatron-LM chekpoint versions. Input
is [num_splits * num_heads * hidden_size, :] and output is [num_heads * hidden_size * num_splits, :] for version
1.0 and [num_heads * num_splits * hidden_size, :] for version 2.0 and later. If param is the weight tensor of the
self-attention block, the param needs to be already transposed before calling this function.
Args:
param (torch.Tensor): the tensor to permute
checkpoint_version (int): the version of the checkpoint.
num_splits (int): the number of projections, usually 3 for (Query, Key, Value)
num_heads (int): the number of attention heads
hidden_size (int): the hidden size per head
"""
# Input is [num_splits * num_heads * hidden_size, :]
input_shape = param.size()
if checkpoint_version == 1.0:
# version 1.0 stores [num_heads * hidden_size * num_splits, :]
current_shape = (num_splits, num_heads, hidden_size) + input_shape[1:]
param = param.view(*current_shape)
param = param.transpose(0, 2)
param = param.transpose(1, 2).contiguous()
elif checkpoint_version >= 2.0:
# other versions store [num_heads * num_splits * hidden_size, :]
current_shape = (num_splits, num_heads, hidden_size) + input_shape[1:]
param = param.view(*current_shape)
param = param.transpose(0, 1).contiguous()
param = param.view(*input_shape)
return param
def merge_transformers_sharded_states(path, num_checkpoints):
"""
Merge sharded checkpoints from transformers into a single checkpoint.
Args:
path (str): the path to the sharded checkpoints
num_checkpoints (int): the number of checkpoints to merge
"""
state_dict = {}
for i in range(1, num_checkpoints + 1):
checkpoint_path = os.path.join(path, f"pytorch_model-{i:05d}-of-{num_checkpoints:05d}.bin")
current_chunk = torch.load(checkpoint_path, map_location="cpu")
state_dict.update(current_chunk)
return state_dict
def get_megatron_sharded_states(args, tp_size, pp_size, pp_rank):
"""
Get sharded checkpoints from NVIDIA Megatron-LM checkpoint based on the provided tensor parallel size, pipeline
parallel size and pipeline parallel rank.
Args:
args (argparse.Namespace): the arguments to the script
tp_size (int): the tensor parallel size
pp_size (int): the pipeline parallel size
pp_rank (int): the pipeline parallel rank
"""
tp_state_dicts = []
for i in range(tp_size):
sub_dir_name = f"mp_rank_{i:02d}" if pp_size == 1 else f"mp_rank_{i:02d}_{pp_rank:03d}"
for checkpoint_name in ["model_optim_rng.pt", "model_rng.pt"]:
checkpoint_path = os.path.join(args.load_path, sub_dir_name, checkpoint_name)
if os.path.isfile(checkpoint_path):
break
state_dict = torch.load(checkpoint_path, map_location="cpu")
tp_state_dicts.append(state_dict)
return tp_state_dicts
def get_element_from_dict_by_path(d, path):
"""
Get element from dictionary by path. If element is not present, recursively add empty dictionaries.
Args:
d (dict): the dictionary to get the element from
path (list): the path to the element which is delimited by "."
"""
path = path.split(".")
for k in path:
if k not in d:
d[k] = {}
d = d[k]
return d
def convert_checkpoint_from_megatron_to_transformers(args):
"""
Convert NVIDIA Megatron-LM checkpoint to HuggingFace Transformers checkpoint. This handles Megatron checkpoints
with different tensor parallelism and pipeline parallelism sizes. It saves the converted checkpoint into shards
using HuggingFace Transformers checkpoint sharding functionality. This greatly extends the functionality of
`convert_megatron_gpt2_checkpoint.py`
Args:
args (argparse.Namespace): the arguments to the script
"""
# Load Megatron-LM checkpoint arguments from the state dict
sub_dirs = os.listdir(args.load_path)
possible_sub_dirs = ["mp_rank_00", "mp_rank_00_000"]
for sub_dir in possible_sub_dirs:
if sub_dir in sub_dirs:
rank0_checkpoint_name = os.listdir(os.path.join(args.load_path, sub_dir))[0]
rank0_checkpoint_path = os.path.join(args.load_path, sub_dir, rank0_checkpoint_name)
break
print(f"Loading Megatron-LM checkpoint arguments from: {rank0_checkpoint_path}")
state_dict = torch.load(rank0_checkpoint_path, map_location="cpu")
megatron_args = state_dict.get("args", None)
if megatron_args is None:
raise ValueError(
"Megatron-LM checkpoint does not contain arguments. This utility only supports Megatron-LM checkpoints"
" containing all the megatron arguments. This is because it loads all config related to model"
" architecture, the tensor and pipeline model parallel size from the checkpoint insead of user having to"
" manually specify all the details. Please save Megatron-LM checkpoint along with all the megatron"
" arguments to use this utility."
)
# Create Transformers GPT2 config from Megatron-LM arguments
if megatron_args is not None:
if megatron_args.bias_gelu_fusion:
activation_function = "gelu_fast"
elif megatron_args.openai_gelu:
activation_function = "gelu_new"
else:
activation_function = "gelu"
else:
# in the very early days this used to be "gelu_new"
activation_function = "gelu_new"
vocab_size = (
megatron_args.padded_vocab_size
if getattr(megatron_args, "orig_vocab_size", None) is None
else megatron_args.orig_vocab_size
)
print(vocab_size)
config = GPT2Config(
vocab_size=vocab_size,
n_positions=megatron_args.max_position_embeddings,
n_embd=megatron_args.hidden_size,
n_layer=megatron_args.num_layers,
n_head=megatron_args.num_attention_heads,
n_inner=megatron_args.ffn_hidden_size,
activation_function=activation_function,
resid_pdrop=0.1,
embd_pdrop=0.1,
attn_pdrop=0.1,
layer_norm_epsilon=1e-5,
initializer_range=0.02,
summary_type="cls_index",
summary_use_proj=True,
summary_activation=None,
summary_proj_to_labels=True,
summary_first_dropout=0.1,
scale_attn_weights=True,
use_cache=True,
bos_token_id=vocab_size - 1,
eos_token_id=vocab_size - 1,
architectures=["GPT2LMHeadModel"],
)
output_state_dict = {}
checkpoint_version = state_dict.get("checkpoint_version", 0.0)
tp_size = megatron_args.tensor_model_parallel_size
pp_size = megatron_args.pipeline_model_parallel_size
dtype = torch.float32
# The regex to extract layer names.
layer_re = re.compile(r"layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)")
# Convert.
print("Converting")
# Embeddings
print("Converting embeddings")
tp_state_dicts = get_megatron_sharded_states(args, tp_size, pp_size, 0)
# Convert and store the position embeddings.
position_embeddings = get_element_from_dict_by_path(
tp_state_dicts[0], "model.language_model.embedding.position_embeddings.weight"
)
output_state_dict["transformer.wpe.weight"] = position_embeddings.to(dtype)
# Convert and store the word embeddings.
word_embeddings = torch.cat(
[
get_element_from_dict_by_path(
tp_state_dicts[tp_rank], "model.language_model.embedding.word_embeddings.weight"
)
for tp_rank in range(tp_size)
],
dim=0,
)
word_embeddings = word_embeddings[:vocab_size].to(dtype)
output_state_dict["transformer.wte.weight"] = word_embeddings
# Transformer Layers
print("Converting transformer layers")
# The number of heads.
heads = config.n_head
# The hidden_size per head.
hidden_size_per_head = config.n_embd // config.n_head
n_positions = config.n_positions
num_layers = config.num_hidden_layers // pp_size
for pp_rank in range(pp_size):
if pp_size > 0:
print(f"Converting pipeline parallel rank {pp_rank}")
tp_state_dicts = get_megatron_sharded_states(args, tp_size, pp_size, pp_rank)
# The transformer.
path = (
"model.language_model.transformer"
if "transformer" in get_element_from_dict_by_path(tp_state_dicts[0], "model.language_model").keys()
else "model.language_model.encoder"
)
# Extract the layers.
for key, val in get_element_from_dict_by_path(tp_state_dicts[0], path).items():
# Match the name.
m = layer_re.match(key)
# Stop if that's not a layer
if m is None:
break
# The index of the layer.
layer_idx = int(m.group(1)) + pp_rank * num_layers
# The name of the operation.
op_name = m.group(2)
# Is it a weight or a bias?
weight_or_bias = m.group(3)
# The name of the layer.
layer_name = f"transformer.h.{layer_idx}"
if op_name + "." + weight_or_bias not in tensor_parallel_params:
params = val.to(dtype)
else:
dim = 1 if op_name in ["self_attention.dense", "mlp.dense_4h_to_h", "attention.dense"] else 0
params = torch.cat(
[val]
+ [
get_element_from_dict_by_path(tp_state_dicts[tp_rank], f"{path}")[key]
for tp_rank in range(1, tp_size)
],
dim=dim,
).to(dtype)
# For layernorm(s), simply store the layer norm.
if op_name.endswith("layernorm"):
ln_name = "ln_1" if op_name.startswith("input") else "ln_2"
output_state_dict[layer_name + "." + ln_name + "." + weight_or_bias] = params
# Transpose the QKV matrix.
elif (
op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
) and weight_or_bias == "weight":
# Insert a tensor of 1x1xDxD bias.
causal_mask = torch.tril(torch.ones((n_positions, n_positions), dtype=dtype)).view(
1, 1, n_positions, n_positions
)
output_state_dict[layer_name + ".attn.bias"] = causal_mask
# Insert a "dummy" tensor for masked_bias.
masked_bias = torch.tensor(-1e4, dtype=dtype)
output_state_dict[layer_name + ".attn.masked_bias"] = masked_bias
out_val = megatron_to_transformers_fix_query_key_value_ordering(
params,
checkpoint_version,
3,
heads,
hidden_size_per_head,
)
# Megatron stores (3*D) x D but transformers-GPT2 expects D x 3*D.
out_val = out_val.transpose(0, 1).contiguous()
# Store.
output_state_dict[layer_name + ".attn.c_attn.weight"] = out_val
# Transpose the bias.
elif (
op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
) and weight_or_bias == "bias":
out_val = megatron_to_transformers_fix_query_key_value_ordering(
params, checkpoint_version, 3, heads, hidden_size_per_head
)
# Store. No change of shape.
output_state_dict[layer_name + ".attn.c_attn.bias"] = out_val
# Transpose the weights.
elif weight_or_bias == "weight":
out_name = megatron_to_transformers[op_name]
output_state_dict[layer_name + out_name + "weight"] = params.transpose(0, 1)
# Copy the bias.
elif weight_or_bias == "bias":
out_name = megatron_to_transformers[op_name]
output_state_dict[layer_name + out_name + "bias"] = params
if config.n_layer != (layer_idx + 1):
raise ValueError(f"Expected {config.n_layer} layers but found {layer_idx + 1}")
# The final layernorm.
print("Converting final layernorm")
params = get_element_from_dict_by_path(tp_state_dicts[0], str(path))
output_state_dict["transformer.ln_f.weight"] = params["final_layernorm.weight"].to(dtype)
output_state_dict["transformer.ln_f.bias"] = params["final_layernorm.bias"].to(dtype)
# For LM head, transformers' wants the matrix to weight embeddings.
print("Converting LM head")
output_state_dict["lm_head.weight"] = word_embeddings.to(dtype)
# It should be done!
print("Conversion from Megatron-LM to Transformers is done!")
# Print the structure of converted state dict.
if args.print_checkpoint_structure:
recursive_print(None, output_state_dict)
# Add tokenizer class info to config
# see https://github.com/huggingface/transformers/issues/13906)
if args.tokenizer_name is None:
tokenizer_name = "openai-community/gpt2"
else:
tokenizer_name = args.tokenizer_name
tokenizer = AutoTokenizer.from_pretrained(tokenizer_name)
tokenizer_class = type(tokenizer).__name__
config.tokenizer_class = tokenizer_class
# Store the config to file.
print("Saving config")
config.save_pretrained(args.save_path)
# Save tokenizer based on args
if args.tokenizer_name is not None:
print(f"Adding {tokenizer_class} tokenizer files")
tokenizer.save_pretrained(args.save_path)
# Store the state_dict to file.
max_shard_size = int(args.max_shard_size) if args.max_shard_size.isdigit() else args.max_shard_size
state_dict_split = split_torch_state_dict_into_shards(output_state_dict, max_shard_size=max_shard_size)
shards = index = None
for tensors in state_dict_split.filename_to_tensors.values():
shards = {tensor: state_dict[tensor] for tensor in tensors}
if state_dict_split.is_sharded:
index = {
"metadata": state_dict_split.metadata,
"weight_map": state_dict_split.tensor_to_filename,
}
# Save the model
for shard_file, shard in shards.items():
torch.save(shard, os.path.join(args.save_path, shard_file))
if index is None:
print(f"Model weights saved in {os.path.join(args.save_path, WEIGHTS_NAME)}")
else:
save_index_file = os.path.join(args.save_path, WEIGHTS_INDEX_NAME)
# Save the index as well
with open(save_index_file, "w", encoding="utf-8") as f:
content = json.dumps(index, indent=2, sort_keys=True) + "\n"
f.write(content)
print(
f"The model is bigger than the maximum size per checkpoint ({args.max_shard_size}) and is going to be "
f"split in {len(shards)} checkpoint shards. You can find where each parameters has been saved in the "
f"index located at {save_index_file}."
)
def convert_checkpoint_from_transformers_to_megatron(args):
"""
Convert a checkpoint from HuggingFace Transformers to Megatron-LM. This allows converted checkpoints with variable
tensor parallelism and pipeline parallelism sizes. It takes as input a checkpoint from HuggingFace Transformers
which can have multiple shards.
Args:
args (argparse.Namespace): the arguments to the script
"""
os.makedirs(args.save_path, exist_ok=True)
# Search in directory above this
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), os.path.pardir)))
if args.megatron_path is not None:
sys.path.insert(0, args.megatron_path)
megatron_exists = importlib.util.find_spec("megatron") is not None
if megatron_exists:
from megatron.core import package_info
if version.parse(package_info.__version__) >= version.parse("0.6.0"):
from megatron.training.tokenizer.tokenizer import _vocab_size_with_padding
else:
from megatron.tokenizer.tokenizer import _vocab_size_with_padding
else:
print("Unable to import Megatron, please specify the path to Megatron using --megatron-path. Exiting.")
exit(1)
# load the transformers model state dict and config
sub_dirs = [x for x in os.listdir(args.load_path) if x.startswith("pytorch_model")]
if len(sub_dirs) == 1:
checkpoint_name = "pytorch_model.bin"
state_dict = torch.load(os.path.join(args.load_path, checkpoint_name), map_location="cpu")
else:
num_checkpoints = len(sub_dirs) - 1
state_dict = merge_transformers_sharded_states(args.load_path, num_checkpoints)
config = GPT2Config.from_pretrained(args.load_path)
# Saving the tracker file
tracker_filepath = os.path.join(args.save_path, "latest_checkpointed_iteration.txt")
with open(tracker_filepath, "w") as f:
f.write("release")
# create `release` dir in args.load_path
release_dir = os.path.join(args.save_path, "release")
os.makedirs(release_dir, exist_ok=True)
# megatron args
megatron_args = {
"orig_vocab_size": config.vocab_size,
"max_position_embeddings": config.n_positions,
"hidden_size": config.n_embd,
"num_layers": config.n_layer,
"num_attention_heads": config.n_head,
"ffn_hidden_size": config.n_inner,
"tensor_model_parallel_size": args.target_tensor_model_parallel_size,
"pipeline_model_parallel_size": args.target_pipeline_model_parallel_size,
"data_parallel_size": args.target_data_parallel_size,
"make_vocab_size_divisible_by": args.make_vocab_size_divisible_by,
"rank": 0,
"tokenizer_type": "GPT2BPETokenizer",
}
if config.activation_function == "gelu":
megatron_args["bias_gelu_fusion"] = False
megatron_args["openai_gelu"] = False
elif config.activation_function == "gelu_fast":
megatron_args["bias_gelu_fusion"] = True
megatron_args["openai_gelu"] = False
elif config.activation_function == "gelu_new":
megatron_args["bias_gelu_fusion"] = False
megatron_args["openai_gelu"] = True
margs = types.SimpleNamespace()
for k, v in megatron_args.items():
setattr(margs, k, v)
# params dtype
if args.target_params_dtype == "fp16":
dtype = torch.float16
elif args.target_params_dtype == "bf16":
dtype = torch.bfloat16
else:
dtype = torch.float32
setattr(margs, "params_dtype", dtype)
# save dummy optim state dict
dummy_optim_state_dict = {}
dummy_optim_state_dict["optimizer"] = {
"step": 0,
"param_groups": [
{
"lr": 0.0,
"beta1": 0.0,
"beta2": 0.0,
"eps": 0.0,
"weight_decay": 0.0,
"correct_bias": False,
"params": [],
}
],
}
if args.use_distributed_optimizer:
for i in range(args.target_pipeline_model_parallel_size):
for j in range(args.target_tensor_model_parallel_size):
for k in range(args.target_data_parallel_size):
if args.target_pipeline_model_parallel_size == 1:
checkpoint_dir = f"mp_rank_{j:02d}_{k:03d}"
else:
checkpoint_dir = f"mp_rank_{j:02d}_{i:03d}_{k:03d}"
checkpoint_dir = os.path.join(release_dir, checkpoint_dir)
os.makedirs(checkpoint_dir, exist_ok=True)
torch.save(
dummy_optim_state_dict,
os.path.join(checkpoint_dir, "optim.pt"),
)
# Convert.
print("Converting")
output_state_dict = []
for i in range(args.target_tensor_model_parallel_size):
output_state_dict.append({})
# Embedding layer
print("converting embedding layer")
pos_embedding = state_dict["transformer.wpe.weight"].to(dtype)
word_embedding = state_dict["transformer.wte.weight"].to(dtype)
orig_vocab_size = config.vocab_size
padded_vocab_size = _vocab_size_with_padding(orig_vocab_size, margs)
setattr(margs, "padded_vocab_size", padded_vocab_size)
# Cut out extra padding we don't need
if orig_vocab_size > padded_vocab_size:
full_word_embed = word_embedding[0:padded_vocab_size, :]
# Expanding embedding to larger size by replicating final entry
elif orig_vocab_size < padded_vocab_size:
padding_size = padded_vocab_size - orig_vocab_size
full_word_embed = torch.cat((word_embedding, word_embedding[-1].unsqueeze(0).expand(padding_size, -1)))
# Same size!
else:
full_word_embed = word_embedding
# Split into new tensor model parallel sizes
out_word_embed = torch.chunk(full_word_embed, args.target_tensor_model_parallel_size, dim=0)
for i in range(args.target_tensor_model_parallel_size):
pos_emb_dict = get_element_from_dict_by_path(
output_state_dict[i], "model.language_model.embedding.position_embeddings"
)
pos_emb_dict["weight"] = pos_embedding
word_emb_dict = get_element_from_dict_by_path(
output_state_dict[i], "model.language_model.embedding.word_embeddings"
)
word_emb_dict["weight"] = out_word_embed[i].clone()
# Transformer layers
print("converting transformer layers")
if config.num_attention_heads % args.target_tensor_model_parallel_size != 0:
raise ValueError(
f"Number of attention heads ({config.num_attention_heads}) must be divisible by number of tensor parallelism"
f" ({args.target_tensor_model_parallel_size})"
)
if config.num_hidden_layers % args.target_pipeline_model_parallel_size != 0:
raise ValueError(
f"Number of layers ({config.num_hidden_layers}) must be divisible by number of pipeline parallelism"
f" ({args.target_pipeline_model_parallel_size})"
)
num_layers = config.num_hidden_layers // args.target_pipeline_model_parallel_size
layer_re = re.compile(r"transformer.h\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)")
# The number of heads.
heads = config.n_head
# The hidden_size per head.
hidden_size_per_head = config.n_embd // config.n_head
for pp_rank in range(args.target_pipeline_model_parallel_size):
layer_offset = pp_rank * num_layers
if pp_rank > 0:
output_state_dict = []
for i in range(args.target_tensor_model_parallel_size):
output_state_dict.append({})
for layer in range(num_layers):
pp_layer_id = layer + layer_offset
layers_to_copy = [
layer_name
for layer_name in state_dict.keys()
if layer_name.startswith(f"transformer.h.{pp_layer_id}.")
]
for layer_name in layers_to_copy:
m = layer_re.match(layer_name)
# Stop if that's not a layer
if m is None:
break
# The index of the layer.
_ = int(m.group(1))
# The name of the operation.
op_name = m.group(2)
# Is it a weight or a bias?
weight_or_bias = m.group(3)
params = state_dict[layer_name].to(dtype)
# handle layernorm
if op_name.startswith("ln"):
out_name = "input_layernorm" if op_name.endswith("1") else "post_attention_layernorm"
layer_name = f"layers.{layer}.{out_name}.{weight_or_bias}"
# handle attention K, V, Q weights
elif op_name.startswith("attn.c_attn") and weight_or_bias == "weight":
# transformers stores D X (3*D) but Megatron-LM expects (3*D) X D.
params = params.transpose(0, 1).contiguous()
params = transformers_to_megatron_fix_query_key_value_ordering(
params,
3.0,
3,
heads,
hidden_size_per_head,
)
layer_name = f"layers.{layer}.self_attention.query_key_value.{weight_or_bias}"
# handle attention K, V, Q bias
elif op_name.startswith("attn.c_attn") and weight_or_bias == "bias":
params = transformers_to_megatron_fix_query_key_value_ordering(
params,
3.0,
3,
heads,
hidden_size_per_head,
)
layer_name = f"layers.{layer}.self_attention.query_key_value.{weight_or_bias}"
# handle attention and mlp weights
elif weight_or_bias == "weight":
out_name = transformers_to_megatron.get(op_name, None)
if out_name is None:
continue
params = params.transpose(0, 1)
layer_name = f"layers.{layer}.{out_name}.{weight_or_bias}"
# handle attention and mlp bias
elif weight_or_bias == "bias":
out_name = transformers_to_megatron.get(op_name, None)
if out_name is None:
continue
layer_name = f"layers.{layer}.{out_name}.{weight_or_bias}"
# skip
else:
continue
if op_name + "." + weight_or_bias in tensor_parallel_params:
dim = 1 if op_name in ["attn.c_proj", "mlp.c_proj"] else 0
params = torch.chunk(params, args.target_tensor_model_parallel_size, dim=dim)
for i in range(args.target_tensor_model_parallel_size):
params_dict = get_element_from_dict_by_path(output_state_dict[i], "model.language_model.encoder")
params_dict[layer_name] = (
params[i].clone() if (op_name + "." + weight_or_bias in tensor_parallel_params) else params
)
if pp_rank == args.target_pipeline_model_parallel_size - 1:
# handle final layernorm
for weight_or_bias in ["weight", "bias"]:
params = state_dict[f"transformer.ln_f.{weight_or_bias}"].to(dtype)
layer_name = f"final_layernorm.{weight_or_bias}"
for i in range(args.target_tensor_model_parallel_size):
params_dict = get_element_from_dict_by_path(output_state_dict[i], "model.language_model.encoder")
params_dict[layer_name] = params
# add the LM head
for i in range(args.target_tensor_model_parallel_size):
params_dict = get_element_from_dict_by_path(output_state_dict[i], "model.word_embeddings_for_head")
params_dict["weight"] = out_word_embed[i].clone()
# saving the state dict as per the tp_rank and pp_rank
for tp_rank in range(args.target_tensor_model_parallel_size):
output_state_dict[tp_rank]["checkpoint_version"] = 3.0
output_state_dict[tp_rank]["args"] = margs
checkpoint_dir = (
f"mp_rank_{tp_rank:02d}"
if args.target_pipeline_model_parallel_size == 1
else f"mp_rank_{tp_rank:02d}_{pp_rank:03d}"
)
if args.use_distributed_optimizer:
checkpoint_name = "model_rng.pt"
else:
checkpoint_name = "model_optim_rng.pt"
output_state_dict[tp_rank]["optimizer"] = dummy_optim_state_dict["optimizer"]
checkpoint_dir = os.path.join(release_dir, checkpoint_dir)
os.makedirs(checkpoint_dir, exist_ok=True)
checkpoint_path = os.path.join(checkpoint_dir, checkpoint_name)
if args.print_checkpoint_structure:
print(
f"Checkpoint structure of model state dict shard belonging to TP rank {tp_rank} and PP rank"
f" {pp_rank}:"
)
recursive_print(None, output_state_dict[tp_rank])
torch.save(output_state_dict[tp_rank], checkpoint_path)
def main():
parser = argparse.ArgumentParser()
parser = add_checkpointing_args(parser)
parser = add_megatron_checkpoint_args(parser)
parser = add_transformers_checkpoint_args(parser)
args = parser.parse_args()
if args.convert_checkpoint_from_megatron_to_transformers:
convert_checkpoint_from_megatron_to_transformers(args)
else:
convert_checkpoint_from_transformers_to_megatron(args)
if __name__ == "__main__":
main()
| transformers/src/transformers/models/megatron_gpt2/checkpoint_reshaping_and_interoperability.py/0 | {
"file_path": "transformers/src/transformers/models/megatron_gpt2/checkpoint_reshaping_and_interoperability.py",
"repo_id": "transformers",
"token_count": 16798
} |
# coding=utf-8
# Copyright 2024 Mistral AI 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 Mistral model."""
import math
import warnings
from typing import List, Optional, Tuple, Union
import tensorflow as tf
from ...modeling_tf_outputs import (
TFBaseModelOutputWithPast,
TFCausalLMOutputWithPast,
TFSequenceClassifierOutputWithPast,
)
from ...modeling_tf_utils import (
TFCausalLanguageModelingLoss,
TFPreTrainedModel,
TFSequenceClassificationLoss,
get_initializer,
get_tf_activation,
keras,
keras_serializable,
unpack_inputs,
)
from ...tf_utils import check_embeddings_within_bounds, shape_list, stable_softmax
from ...utils import (
add_start_docstrings,
add_start_docstrings_to_model_forward,
logging,
)
from .configuration_mistral import MistralConfig
logger = logging.get_logger(__name__)
_CONFIG_FOR_DOC = "MistralConfig"
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 TFMistralRMSNorm(keras.layers.Layer):
def __init__(self, hidden_size, eps=1e-6, **kwargs):
"""
TFMistralRMSNorm is equivalent to T5LayerNorm
"""
super().__init__(**kwargs)
self.hidden_size = hidden_size
self.variance_epsilon = eps
def build(self, input_shape=None):
self.weight = self.add_weight(
name="weight",
shape=self.hidden_size,
initializer="ones",
)
if self.built:
return
self.built = True
def call(self, hidden_states):
input_dtype = hidden_states.dtype
hidden_states = tf.cast(hidden_states, tf.float32)
variance = tf.reduce_mean(tf.square(hidden_states), axis=-1, keepdims=True)
hidden_states = tf.divide(hidden_states, tf.sqrt(variance + self.variance_epsilon))
return self.weight * tf.cast(hidden_states, input_dtype)
# Verification: https://colab.research.google.com/gist/ariG23498/f8d8131b795a131b93d99e70ee93c192/scratchpad.ipynb
class TFMistralRotaryEmbedding(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 = 1.0 / (self.base ** (tf.range(start=0, limit=self.dim, delta=2, dtype=tf.float32) / self.dim))
def call(self, x, seq_len=None):
# x: [bs, num_attention_heads, seq_len, head_size]
t = tf.cast(tf.range(seq_len, dtype=tf.int64), self.inv_freq.dtype)
freqs = tf.einsum("i,j->ij", t, self.inv_freq)
emb = tf.concat([freqs, freqs], axis=-1)
cos_values = tf.cast(tf.cos(emb), x.dtype)
sin_values = tf.cast(tf.sin(emb), x.dtype)
cos_values = cos_values[:seq_len]
cos_values = tf.cast(cos_values, dtype=x.dtype)
sin_values = sin_values[:seq_len]
sin_values = tf.cast(sin_values, dtype=x.dtype)
return (cos_values, sin_values)
def rotate_half(x):
"""Rotates half the hidden dims of the input."""
mid_length = shape_list(x)[-1] // 2
x1 = x[..., :mid_length]
x2 = x[..., mid_length:]
return tf.concat([-x2, x1], axis=-1)
# Verification: https://colab.research.google.com/gist/ariG23498/bb8474baeb33f4ae6ed7d77da5f7e7a4/scratchpad.ipynb
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 (`tf.Tensor`): The query tensor.
k (`tf.Tensor`): The key tensor.
cos (`tf.Tensor`): The cosine part of the rotary embedding.
sin (`tf.Tensor`): The sine part of the rotary embedding.
position_ids (`tf.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(tf.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
"""
cos = tf.expand_dims(tf.gather(cos, position_ids), unsqueeze_dim)
sin = tf.expand_dims(tf.gather(sin, position_ids), unsqueeze_dim)
q_embed = (q * cos) + (rotate_half(q) * sin)
k_embed = (k * cos) + (rotate_half(k) * sin)
return q_embed, k_embed
class TFMistralMLP(keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.config = config
self.hidden_size = config.hidden_size
self.intermediate_size = config.intermediate_size
self.gate_proj = keras.layers.Dense(self.intermediate_size, use_bias=False, name="gate_proj")
self.up_proj = keras.layers.Dense(self.intermediate_size, use_bias=False, name="up_proj")
self.down_proj = keras.layers.Dense(self.hidden_size, use_bias=False, name="down_proj")
self.act_fn = get_tf_activation(config.hidden_act)
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, "up_proj", None) is not None:
with tf.name_scope(self.up_proj.name):
self.up_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,))
# Verification: https://colab.research.google.com/gist/ariG23498/556d443d491966763ce2e7eee336efed/scratchpad.ipynb
def repeat_kv(hidden_states: tf.Tensor, n_rep: int) -> tf.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 = shape_list(hidden_states)
if n_rep == 1:
return hidden_states
hidden_states = tf.expand_dims(hidden_states, 2)
hidden_states = tf.repeat(hidden_states, repeats=n_rep, axis=2)
return tf.reshape(hidden_states, (batch, num_key_value_heads * n_rep, slen, head_dim))
class TFMistralAttention(keras.layers.Layer):
"""
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: MistralConfig, layer_idx: Optional[int] = None, **kwargs):
super().__init__(**kwargs)
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.max_position_embeddings = config.max_position_embeddings
self.rope_theta = config.rope_theta
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 = keras.layers.Dense(self.num_heads * self.head_dim, use_bias=False, name="q_proj")
self.k_proj = keras.layers.Dense(self.num_key_value_heads * self.head_dim, use_bias=False, name="k_proj")
self.v_proj = keras.layers.Dense(self.num_key_value_heads * self.head_dim, use_bias=False, name="v_proj")
self.o_proj = keras.layers.Dense(self.hidden_size, use_bias=False, name="o_proj")
self.rotary_emb = TFMistralRotaryEmbedding(
self.head_dim,
max_position_embeddings=self.max_position_embeddings,
base=self.rope_theta,
name="rotary_emb",
)
self.dropout = keras.layers.Dropout(rate=self.attention_dropout)
def _shape(self, tensor: tf.Tensor, seq_len: int, bsz: int):
tensor = tf.reshape(tensor, (bsz, seq_len, self.num_heads, self.head_dim))
tensor = tf.transpose(tensor, perm=(0, 2, 1, 3))
return tensor
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=None,
**kwargs,
) -> Tuple[tf.Tensor, Optional[tf.Tensor], Optional[Tuple[tf.Tensor]]]:
if "padding_mask" in kwargs:
warnings.warn(
"Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
)
bsz, q_len, _ = shape_list(hidden_states)
query_states = self.q_proj(hidden_states)
key_states = self.k_proj(hidden_states)
value_states = self.v_proj(hidden_states)
query_states = tf.transpose(
tf.reshape(query_states, (bsz, q_len, self.num_heads, self.head_dim)), perm=(0, 2, 1, 3)
)
key_states = tf.transpose(
tf.reshape(key_states, (bsz, q_len, self.num_key_value_heads, self.head_dim)), perm=(0, 2, 1, 3)
)
value_states = tf.transpose(
tf.reshape(value_states, (bsz, q_len, self.num_key_value_heads, self.head_dim)), perm=(0, 2, 1, 3)
)
kv_seq_len = shape_list(key_states)[-2]
if past_key_value is not None:
kv_seq_len += past_key_value[0].shape[-2]
cos, sin = self.rotary_emb(
x=value_states,
seq_len=kv_seq_len,
)
query_states, key_states = apply_rotary_pos_emb(
q=query_states,
k=key_states,
cos=cos,
sin=sin,
position_ids=position_ids,
)
if past_key_value is not None:
# resue 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
# 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 = tf.matmul(query_states, key_states, transpose_b=True) / math.sqrt(self.head_dim)
if attention_mask is not None:
attn_weights = attn_weights + attention_mask
# upcast attention to fp32
attn_weights = stable_softmax(attn_weights, axis=-1)
attn_weights = tf.cast(attn_weights, query_states.dtype)
attn_weights = self.dropout(
attn_weights,
training=training,
)
attn_output = tf.matmul(attn_weights, value_states)
attn_output = tf.transpose(attn_output, perm=(0, 2, 1, 3))
attn_output = tf.reshape(attn_output, (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
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((self.hidden_size,))
if getattr(self, "k_proj", None) is not None:
with tf.name_scope(self.k_proj.name):
self.k_proj.build((self.hidden_size,))
if getattr(self, "v_proj", None) is not None:
with tf.name_scope(self.v_proj.name):
self.v_proj.build((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,))
class TFMistralDecoderLayer(keras.layers.Layer):
def __init__(self, config: MistralConfig, layer_idx: int, **kwargs):
super().__init__(**kwargs)
self.hidden_size = config.hidden_size
self.self_attn = TFMistralAttention(config, layer_idx, name="self_attn")
self.mlp = TFMistralMLP(config, name="mlp")
self.input_layernorm = TFMistralRMSNorm(config.hidden_size, eps=config.rms_norm_eps, name="input_layernorm")
self.post_attention_layernorm = TFMistralRMSNorm(
config.hidden_size, eps=config.rms_norm_eps, name="post_attention_layernorm"
)
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,
**kwargs,
) -> 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, 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(tf.Tensor)`, *optional*): cached past key and value projection states
"""
if "padding_mask" in kwargs:
warnings.warn(
"Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
)
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 = 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,)
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)
@keras_serializable
class TFMistralMainLayer(keras.layers.Layer):
"""
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`MistralDecoderLayer`]
Args:
config: MistralConfig
"""
config_class = MistralConfig
def __init__(self, config: MistralConfig, **kwargs):
super().__init__(**kwargs)
self.padding_idx = config.pad_token_id
self.vocab_size = config.vocab_size
self.hidden_size = config.hidden_size
# TF and PT Embedding check: https://colab.research.google.com/gist/ariG23498/2b9826818875c9c4968c79cb19f55f2c/scratchpad.ipynb
self.embed_tokens = keras.layers.Embedding(
input_dim=config.vocab_size,
output_dim=config.hidden_size,
name="embed_tokens",
)
self.layers = [
TFMistralDecoderLayer(config, layer_idx, name=f"layers.{layer_idx}")
for layer_idx in range(config.num_hidden_layers)
]
self._attn_implementation = config._attn_implementation
self.norm = TFMistralRMSNorm(config.hidden_size, eps=config.rms_norm_eps, name="norm")
self.config = config
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
def call(
self,
input_ids: tf.Tensor = 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,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, TFBaseModelOutputWithPast]:
# 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 position_ids is None:
position_ids = tf.range(
start=past_key_values_length, limit=seq_length + past_key_values_length, dtype=tf.int64
)
position_ids = tf.reshape(tf.expand_dims(position_ids, 0), (-1, seq_length))
else:
position_ids = tf.cast(tf.reshape(position_ids, (-1, seq_length)), tf.int64)
if inputs_embeds is None:
check_embeddings_within_bounds(input_ids, self.config.vocab_size)
inputs_embeds = self.embed_tokens(input_ids)
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
# 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.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
layer_outputs = decoder_layer(
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 = 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 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 TFBaseModelOutputWithPast(
last_hidden_state=hidden_states,
past_key_values=next_cache,
hidden_states=all_hidden_states,
attentions=all_self_attns,
)
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, "norm", None) is not None:
with tf.name_scope(self.norm.name):
self.norm.build(None)
if getattr(self, "layers", None) is not None:
for layer in self.layers:
with tf.name_scope(layer.name):
layer.build(None)
MISTRAL_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 `model` 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 ([`MistralConfig`]): 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 Mistral Model outputting raw hidden-states without any specific head on top.",
MISTRAL_START_DOCSTRING,
)
class TFMistralPreTrainedModel(TFPreTrainedModel):
config_class = MistralConfig
base_model_prefix = "model"
MISTRAL_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 (`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 (`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(tf.Tensor))`, *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`.
One formats is allowed:
- 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)`). 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 (`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 Mistral Model outputting raw hidden-states without any specific head on top.",
MISTRAL_START_DOCSTRING,
)
class TFMistralModel(TFMistralPreTrainedModel):
def __init__(self, config: MistralConfig, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.model = TFMistralMainLayer(config, name="model")
@unpack_inputs
@add_start_docstrings_to_model_forward(MISTRAL_INPUTS_DOCSTRING)
def call(
self,
input_ids: tf.Tensor = 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,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, TFBaseModelOutputWithPast]:
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,
return_dict=return_dict,
)
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 TFMistralForCausalLM(TFMistralPreTrainedModel, TFCausalLanguageModelingLoss):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.model = TFMistralMainLayer(config, name="model")
self.vocab_size = config.vocab_size
self.lm_head = keras.layers.Dense(
config.vocab_size,
use_bias=False,
kernel_initializer=get_initializer(config.initializer_range),
name="lm_head",
)
self.config = config
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
@unpack_inputs
@add_start_docstrings_to_model_forward(MISTRAL_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
def call(
self,
input_ids: tf.Tensor = 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,
labels: Optional[tf.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, TFCausalLMOutputWithPast]:
r"""
Args:
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]`.
"""
# 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,
return_dict=return_dict,
)
hidden_states = outputs[0]
logits = self.lm_head(hidden_states)
logits = tf.cast(logits, tf.float32)
loss = None
if labels is not None:
# shift labels to the left and cut last logit token
shifted_logits = logits[:, :-1]
labels = labels[:, 1:]
loss = self.hf_compute_loss(labels, shifted_logits)
if not return_dict:
output = (logits,) + outputs[1:]
return ((loss,) + output) if loss is not None else output
return TFCausalLMOutputWithPast(
loss=loss,
logits=logits,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
def prepare_inputs_for_generation(
self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
):
# Omit tokens covered by past_key_values
if past_key_values:
input_ids = tf.expand_dims(input_ids[:, -1], -1)
position_ids = kwargs.get("position_ids", 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": input_ids,
"attention_mask": attention_mask,
"position_ids": position_ids,
"past_key_values": past_key_values,
"use_cache": kwargs.get("use_cache"),
}
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((self.config.hidden_size,))
@add_start_docstrings(
"""
The Mistral Model transformer with a sequence classification head on top (linear layer).
[`MistralForSequenceClassification`] 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).
""",
MISTRAL_START_DOCSTRING,
)
class TFMistralForSequenceClassification(TFMistralPreTrainedModel, TFSequenceClassificationLoss):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.num_labels = config.num_labels
self.model = TFMistralMainLayer(config, name="model")
self.score = keras.layers.Dense(
self.num_labels,
use_bias=False,
kernel_initializer=get_initializer(config.initializer_range),
name="score",
)
self.config = config
def get_input_embeddings(self):
return self.model.embed_tokens
def set_input_embeddings(self, value):
self.model.embed_tokens = value
@unpack_inputs
@add_start_docstrings_to_model_forward(MISTRAL_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
def call(
self,
input_ids: tf.Tensor = 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,
labels: Optional[tf.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, TFSequenceClassifierOutputWithPast]:
r"""
Args:
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]`.
"""
transformer_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,
return_dict=return_dict,
)
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, "model", None) is not None:
with tf.name_scope(self.model.name):
self.model.build(None)
if getattr(self, "score", None) is not None:
with tf.name_scope(self.score.name):
self.score.build((self.config.hidden_size,))
| transformers/src/transformers/models/mistral/modeling_tf_mistral.py/0 | {
"file_path": "transformers/src/transformers/models/mistral/modeling_tf_mistral.py",
"repo_id": "transformers",
"token_count": 19628
} |
# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
# This file was automatically generated from src/transformers/models/modernbert/modular_modernbert.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_modernbert.py file directly. One of our CI enforces this.
# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
# Copyright 2024 Answer.AI, LightOn, and contributors, 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.
import math
from contextlib import nullcontext
from typing import Dict, Optional, Tuple, Union
import torch
import torch.nn.functional as F
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACT2FN
from ...modeling_attn_mask_utils import _prepare_4d_attention_mask
from ...modeling_outputs import BaseModelOutput, MaskedLMOutput, SequenceClassifierOutput, TokenClassifierOutput
from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS
from ...modeling_utils import PreTrainedModel
from ...utils import (
add_code_sample_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
is_flash_attn_2_available,
logging,
)
from ...utils.import_utils import is_triton_available
from .configuration_modernbert import ModernBertConfig
if is_flash_attn_2_available():
from flash_attn.flash_attn_interface import flash_attn_varlen_qkvpacked_func
from flash_attn.layers.rotary import RotaryEmbedding
from flash_attn.ops.triton.rotary import apply_rotary
else:
RotaryEmbedding = object
logger = logging.get_logger(__name__)
_CHECKPOINT_FOR_DOC = "answerdotai/ModernBERT-base"
_CONFIG_FOR_DOC = "ModernBertConfig"
class ApplyRotaryEmbUnpad(torch.autograd.Function):
@staticmethod
def forward(
ctx,
qkv,
cos,
sin,
cu_seqlens: Optional[torch.Tensor] = None,
max_seqlen: Optional[int] = None,
):
# (total_nnz, 3, nheads, headdim)
qkv = qkv.contiguous()
total_nnz, _three, _nheads, headdim = qkv.shape
# We need qkv to be contiguous so that when we reshape to combine (3, nheads) dimensions,
# we get the same tensor
# qk = rearrange(qkv[:, :2], "b_s t h d -> b_s (t h) d")
qk = qkv[:, :2].view(total_nnz, -1, headdim)
apply_rotary(
qk,
cos,
sin,
seqlen_offsets=0,
cu_seqlens=cu_seqlens,
max_seqlen=max_seqlen,
interleaved=False,
inplace=True,
)
ctx.save_for_backward(cos, sin, cu_seqlens)
ctx.max_seqlen = max_seqlen
return qkv
@staticmethod
def backward(ctx, do):
cos, sin, cu_seqlens = ctx.saved_tensors
do = do.contiguous()
total_nnz, _three, _nheads, headdim = do.shape
# We need dqkv to be contiguous so that when we reshape to combine (3, nheads) dimensions,
# we get the same tensor
dqk = do[:, :2].view(total_nnz, -1, headdim)
apply_rotary(
dqk,
cos,
sin,
seqlen_offsets=0,
cu_seqlens=cu_seqlens,
max_seqlen=ctx.max_seqlen,
interleaved=False,
inplace=True,
conjugate=True,
)
return do, None, None, None, None, None, None
def apply_rotary_unpadded(
qkv,
cos,
sin,
cu_seqlens: Optional[torch.Tensor] = None,
max_seqlen: Optional[int] = None,
):
"""
Arguments:
qkv: (total_nnz, 3, nheads, headdim) - input tensor for packed QKV.
cos, sin: (seqlen_rotary, rotary_dim / 2)
interleaved: if True, rotate pairs of even and odd dimensions (GPT-J style) instead
of 1st half and 2nd half (GPT-NeoX style).
inplace: if True, apply rotary embedding in-place.
seqlen_offsets: (batch_size,) or int. Each sequence in x is shifted by this amount.
Most commonly used in inference when we have KV cache.
cu_seqlens: (batch + 1,) or None
max_seqlen: int
Return:
out: (total_nnz, dim)
rotary_dim must be <= headdim
Apply rotary embedding to the first rotary_dim of x.
"""
return ApplyRotaryEmbUnpad.apply(qkv, cos, sin, cu_seqlens, max_seqlen)
class ModernBertUnpaddedRotaryEmbedding(RotaryEmbedding):
"""
The rotary position embeddings applied directly to unpadded sequences.
"""
def __init__(
self,
dim: int,
base: float = 10000.0,
max_seqlen: Optional[int] = None,
device: Optional[torch.device] = None,
dtype: Optional[torch.dtype] = None,
):
"""
max_seqlen: if max_seqlen, device, and dtype are provided, we precompute the cos_sin_cache
up to max_seqlen. If the max_seqlen, device, or dtype during training/inference differ,
the cos_sin_cache wll be recomputed during the forward pass.
"""
super().__init__(dim=dim, base=base, pos_idx_in_fp32=True, device=device, interleaved=False)
self.max_seqlen = max_seqlen
if max_seqlen is not None and device is not None and dtype is not None:
self._update_cos_sin_cache(max_seqlen, device=device, dtype=dtype)
def forward(
self,
qkv: torch.Tensor,
cu_seqlens: torch.Tensor,
max_seqlen: Optional[int] = None,
) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
"""
Apply rotary embedding *inplace* to qkv.
qkv: (total_nnz, 3, nheads, headdim)
cu_seqlens: (batch + 1,) cumulative sequence lengths
max_seqlen: int max seq length in the batch
"""
if max_seqlen is not None:
self._update_cos_sin_cache(max_seqlen, device=qkv.device, dtype=qkv.dtype)
qkv = apply_rotary_unpadded(
qkv,
self._cos_cached,
self._sin_cached,
cu_seqlens=cu_seqlens,
max_seqlen=max_seqlen,
)
return qkv
def extra_repr(self) -> str:
return f"dim={self.dim}, base={self.base}, scale_base={self.scale_base}"
class ModernBertEmbeddings(nn.Module):
"""
Same as BertEmbeddings with a tiny tweak for positional embeddings indexing.
"""
def __init__(self, config: ModernBertConfig):
super().__init__()
self.config = config
self.tok_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
self.norm = nn.LayerNorm(config.hidden_size, eps=config.norm_eps, bias=config.norm_bias)
self.drop = nn.Dropout(config.embedding_dropout)
@torch.compile(dynamic=True)
def compiled_embeddings(self, input_ids: torch.LongTensor) -> torch.Tensor:
return self.drop(self.norm(self.tok_embeddings(input_ids)))
def forward(
self, input_ids: torch.LongTensor = None, inputs_embeds: Optional[torch.Tensor] = None
) -> torch.Tensor:
if inputs_embeds is not None:
hidden_states = self.drop(self.norm(inputs_embeds))
else:
hidden_states = (
self.compiled_embeddings(input_ids)
if self.config.reference_compile
else self.drop(self.norm(self.tok_embeddings(input_ids)))
)
return hidden_states
class ModernBertMLP(nn.Module):
"""Applies the GLU at the end of each ModernBERT layer.
Compared to the default BERT architecture, this block replaces :class:`~transformers.model.bert.modeling_bert.BertIntermediate`
and :class:`~transformers.model.bert.modeling_bert.SelfOutput` with a single module that has similar functionality.
"""
def __init__(self, config: ModernBertConfig):
super().__init__()
self.config = config
self.Wi = nn.Linear(config.hidden_size, int(config.intermediate_size) * 2, bias=config.mlp_bias)
self.act = ACT2FN[config.hidden_activation]
self.drop = nn.Dropout(config.mlp_dropout)
self.Wo = nn.Linear(config.intermediate_size, config.hidden_size, bias=config.mlp_bias)
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
input, gate = self.Wi(hidden_states).chunk(2, dim=-1)
return self.Wo(self.drop(self.act(input) * gate))
class ModernBertRotaryEmbedding(nn.Module):
def __init__(self, config: ModernBertConfig, dim: int, base: float, device: Optional[torch.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(None, device, dim=dim, base=base)
self.register_buffer("inv_freq", inv_freq, persistent=False)
self.original_inv_freq = self.inv_freq
def _dynamic_frequency_update(self, position_ids, device):
"""
dynamic RoPE layers should recompute `inv_freq` in the following situations:
1 - growing beyond the cached sequence length (allow scaling)
2 - the current sequence length is in the original scale (avoid losing precision with small sequences)
"""
seq_len = torch.max(position_ids) + 1
if seq_len > self.max_seq_len_cached: # growth
inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device, seq_len=seq_len)
self.register_buffer("inv_freq", inv_freq, persistent=False) # TODO joao: may break with compilation
self.max_seq_len_cached = seq_len
if seq_len < self.original_max_seq_len and self.max_seq_len_cached > self.original_max_seq_len: # reset
# This .to() is needed if the model has been moved to a device after being initialized (because
# the buffer is automatically moved, but not the original copy)
self.original_inv_freq = self.original_inv_freq.to(device)
self.register_buffer("inv_freq", self.original_inv_freq, persistent=False)
self.max_seq_len_cached = self.original_max_seq_len
@torch.no_grad()
def forward(self, x, position_ids):
if "dynamic" in self.rope_type:
self._dynamic_frequency_update(position_ids, device=x.device)
# Core RoPE block
inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1)
position_ids_expanded = position_ids[:, None, :].float()
# Force float32 (see https://github.com/huggingface/transformers/pull/29285)
device_type = x.device.type
device_type = device_type if isinstance(device_type, str) and device_type != "mps" else "cpu"
with torch.autocast(device_type=device_type, enabled=False):
freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
emb = torch.cat((freqs, freqs), dim=-1)
cos = emb.cos()
sin = emb.sin()
# Advanced RoPE types (e.g. yarn) apply a post-processing scaling factor, equivalent to scaling attention
cos = cos * self.attention_scaling
sin = sin * self.attention_scaling
return cos.to(dtype=x.dtype), sin.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=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 eager_attention_forward(
module: "ModernBertAttention",
qkv: torch.Tensor,
attention_mask: torch.Tensor,
sliding_window_mask: torch.Tensor,
position_ids: Optional[torch.LongTensor],
local_attention: Tuple[int, int],
bs: int,
dim: int,
output_attentions: Optional[bool] = False,
**_kwargs,
) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]:
# qkv: [batch_size, seqlen, 3, nheads, headdim]
cos, sin = module.rotary_emb(qkv, position_ids=position_ids)
query, key, value = qkv.transpose(3, 1).unbind(dim=2)
# query, key, value: [batch_size, heads, seq_len, head_dim]
query, key = apply_rotary_pos_emb(query, key, cos, sin)
scale = module.head_dim**-0.5
attn_weights = torch.matmul(query, key.transpose(2, 3)) * scale
if local_attention != (-1, -1):
attention_mask = sliding_window_mask
attn_weights = attn_weights + attention_mask
# upcast attention to fp32
attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
attn_weights = nn.functional.dropout(attn_weights, p=module.attention_dropout, training=module.training)
attn_output = torch.matmul(attn_weights, value)
attn_output = attn_output.transpose(1, 2).contiguous()
attn_output = attn_output.view(bs, -1, dim)
if output_attentions:
return (attn_output, attn_weights)
return (attn_output,)
def flash_attention_forward(
module: "ModernBertAttention",
qkv: torch.Tensor,
rotary_emb: ModernBertUnpaddedRotaryEmbedding,
cu_seqlens: torch.Tensor,
max_seqlen: int,
local_attention: Tuple[int, int],
bs: int,
dim: int,
target_dtype: torch.dtype = torch.bfloat16,
**_kwargs,
) -> Tuple[torch.Tensor]:
# (total_seqlen, 3, nheads, headdim)
qkv = rotary_emb(qkv, cu_seqlens=cu_seqlens, max_seqlen=max_seqlen)
convert_dtype = qkv.dtype not in (torch.float16, torch.bfloat16)
if convert_dtype:
# FA2 implementation only supports fp16 and bf16. If FA2 is supported,
# bfloat16 must be supported as of FA2 2.5.7. (Turing GPUs not supported)
orig_dtype = qkv.dtype
qkv = qkv.to(target_dtype)
attn = flash_attn_varlen_qkvpacked_func(
qkv,
cu_seqlens=cu_seqlens,
max_seqlen=max_seqlen,
dropout_p=module.attention_dropout if module.training else 0.0,
deterministic=module.deterministic_flash_attn,
window_size=local_attention,
)
attn = attn.to(orig_dtype) # type: ignore
else:
attn = flash_attn_varlen_qkvpacked_func(
qkv,
cu_seqlens=cu_seqlens,
max_seqlen=max_seqlen,
dropout_p=module.attention_dropout if module.training else 0.0,
deterministic=module.deterministic_flash_attn,
window_size=local_attention,
)
return (attn.view(bs, dim),)
def sdpa_attention_forward(
module: "ModernBertAttention",
qkv: torch.Tensor,
attention_mask: torch.Tensor,
sliding_window_mask: torch.Tensor,
position_ids: Optional[torch.LongTensor],
local_attention: Tuple[int, int],
bs: int,
dim: int,
**_kwargs,
) -> Tuple[torch.Tensor]:
# qkv: [batch_size, seqlen, 3, nheads, headdim]
cos, sin = module.rotary_emb(qkv, position_ids=position_ids)
query, key, value = qkv.transpose(3, 1).unbind(dim=2)
# query, key, value: [batch_size, heads, seq_len, head_dim]
query, key = apply_rotary_pos_emb(query, key, cos, sin)
if local_attention != (-1, -1):
attention_mask = sliding_window_mask
attn_output = (
F.scaled_dot_product_attention(
query,
key,
value,
dropout_p=module.attention_dropout if module.training else 0.0,
attn_mask=attention_mask,
)
.transpose(1, 2)
.contiguous()
)
attn_output = attn_output.view(bs, -1, dim)
return (attn_output,)
MODERNBERT_ATTENTION_FUNCTION = {
"flash_attention_2": flash_attention_forward,
"eager": eager_attention_forward,
"sdpa": sdpa_attention_forward,
}
class ModernBertAttention(nn.Module):
"""Performs multi-headed self attention on a batch of unpadded sequences.
If Flash Attention 2 is installed, this module uses Flash Attention to improve throughput.
If Flash Attention 2 is not installed, the implementation will use PyTorch's SDPA kernel,
which requires padding and unpadding inputs, adding some overhead.
See `forward` method for additional details.
"""
def __init__(self, config: ModernBertConfig, layer_id: Optional[int] = None):
super().__init__()
self.config = config
self.layer_id = layer_id
if config.hidden_size % config.num_attention_heads != 0:
raise ValueError(
f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention heads ({config.num_attention_heads})"
)
self.attention_dropout = config.attention_dropout
self.deterministic_flash_attn = config.deterministic_flash_attn
self.num_heads = config.num_attention_heads
self.head_dim = config.hidden_size // config.num_attention_heads
self.all_head_size = self.head_dim * self.num_heads
self.Wqkv = nn.Linear(config.hidden_size, 3 * self.all_head_size, bias=config.attention_bias)
if layer_id % config.global_attn_every_n_layers != 0:
self.local_attention = (config.local_attention // 2, config.local_attention // 2)
else:
self.local_attention = (-1, -1)
rope_theta = config.global_rope_theta
max_position_embeddings = config.max_position_embeddings
if self.local_attention != (-1, -1):
if config.local_rope_theta is not None:
rope_theta = config.local_rope_theta
max_position_embeddings = config.local_attention
if config._attn_implementation == "flash_attention_2":
self.rotary_emb = ModernBertUnpaddedRotaryEmbedding(
dim=self.head_dim, max_seqlen=max_position_embeddings, base=rope_theta
)
else:
self.rotary_emb = ModernBertRotaryEmbedding(config=config, dim=self.head_dim, base=rope_theta)
self.Wo = nn.Linear(config.hidden_size, config.hidden_size, bias=config.attention_bias)
self.out_drop = nn.Dropout(config.attention_dropout) if config.attention_dropout > 0.0 else nn.Identity()
self.pruned_heads = set()
def forward(
self,
hidden_states: torch.Tensor,
output_attentions: Optional[bool] = False,
**kwargs,
) -> torch.Tensor:
qkv = self.Wqkv(hidden_states)
bs = hidden_states.shape[0]
if self.config._attn_implementation == "flash_attention_2":
qkv = qkv.view(-1, 3, self.num_heads, self.head_dim)
else:
qkv = qkv.view(bs, -1, 3, self.num_heads, self.head_dim)
attn_outputs = MODERNBERT_ATTENTION_FUNCTION[self.config._attn_implementation](
self,
qkv=qkv,
rotary_emb=self.rotary_emb,
local_attention=self.local_attention,
bs=bs,
dim=self.all_head_size,
output_attentions=output_attentions,
**kwargs,
)
hidden_states = attn_outputs[0]
hidden_states = self.out_drop(self.Wo(hidden_states))
return (hidden_states,) + attn_outputs[1:] # add attentions if outputted
class ModernBertEncoderLayer(nn.Module):
def __init__(self, config: ModernBertConfig, layer_id: Optional[int] = None):
super().__init__()
self.config = config
if layer_id == 0:
self.attn_norm = nn.Identity()
else:
self.attn_norm = nn.LayerNorm(config.hidden_size, eps=config.norm_eps, bias=config.norm_bias)
self.attn = ModernBertAttention(config=config, layer_id=layer_id)
self.mlp_norm = nn.LayerNorm(config.hidden_size, eps=config.norm_eps, bias=config.norm_bias)
self.mlp = ModernBertMLP(config)
@torch.compile(dynamic=True)
def compiled_mlp(self, hidden_states: torch.Tensor) -> torch.Tensor:
return self.mlp(self.mlp_norm(hidden_states))
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
sliding_window_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
cu_seqlens: Optional[torch.Tensor] = None,
max_seqlen: Optional[int] = None,
output_attentions: Optional[bool] = False,
) -> torch.Tensor:
attn_outputs = self.attn(
self.attn_norm(hidden_states),
attention_mask=attention_mask,
sliding_window_mask=sliding_window_mask,
position_ids=position_ids,
cu_seqlens=cu_seqlens,
max_seqlen=max_seqlen,
output_attentions=output_attentions,
)
hidden_states = hidden_states + attn_outputs[0]
mlp_output = (
self.compiled_mlp(hidden_states)
if self.config.reference_compile
else self.mlp(self.mlp_norm(hidden_states))
)
hidden_states = hidden_states + mlp_output
return (hidden_states,) + attn_outputs[1:] # add attentions if outputted
MODERNBERT_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 ([`ModernBertConfig`]):
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 ModernBert Model outputting raw hidden-states without any specific head on top.",
MODERNBERT_START_DOCSTRING,
)
class ModernBertPreTrainedModel(PreTrainedModel):
config_class = ModernBertConfig
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = ["ModernBertEmbeddings", "ModernBertEncoderLayer"]
_supports_flash_attn_2 = True
_supports_sdpa = True
_supports_flex_attn = False
def _init_weights(self, module: nn.Module):
cutoff_factor = self.config.initializer_cutoff_factor
if cutoff_factor is None:
cutoff_factor = 3
def init_weight(module: nn.Module, std: float):
nn.init.trunc_normal_(
module.weight,
mean=0.0,
std=std,
a=-cutoff_factor * std,
b=cutoff_factor * std,
)
if isinstance(module, nn.Linear):
if module.bias is not None:
nn.init.zeros_(module.bias)
stds = {
"in": self.config.initializer_range,
"out": self.config.initializer_range / math.sqrt(2.0 * self.config.num_hidden_layers),
"embedding": self.config.initializer_range,
"final_out": self.config.hidden_size**-0.5,
}
if isinstance(module, ModernBertEmbeddings):
init_weight(module.tok_embeddings, stds["embedding"])
elif isinstance(module, ModernBertMLP):
init_weight(module.Wi, stds["in"])
init_weight(module.Wo, stds["out"])
elif isinstance(module, ModernBertAttention):
init_weight(module.Wqkv, stds["in"])
init_weight(module.Wo, stds["out"])
elif isinstance(module, ModernBertPredictionHead):
init_weight(module.dense, stds["out"])
elif isinstance(module, ModernBertForMaskedLM):
init_weight(module.decoder, stds["out"])
elif isinstance(module, (ModernBertForSequenceClassification, ModernBertForTokenClassification)):
init_weight(module.classifier, stds["final_out"])
@classmethod
def _autoset_attn_implementation(
cls,
config,
use_flash_attention_2: bool = False,
torch_dtype: Optional[torch.dtype] = None,
device_map: Optional[Union[str, Dict[str, int]]] = None,
check_device_map: bool = True,
):
# If the user didn't specify anything, try to use flash_attention_2 if available.
# Otherwise we fall back to the default SDPA -> Eager from the super() method.
# ModernBert's FA2 implementation correctly handles non-fp16/bf16 dtypes, we don't
# need the FA2 warning for non-fp16/bf16 dtypes so we set fp16 for the FA2 check.
if config._attn_implementation_internal is None:
config._attn_implementation_internal = "flash_attention_2"
try:
return cls._check_and_enable_flash_attn_2(
config,
torch_dtype=torch.float16,
device_map=device_map,
hard_check_only=False,
check_device_map=check_device_map,
)
except (ValueError, ImportError):
config._attn_implementation_internal = None
return super()._autoset_attn_implementation(
config,
use_flash_attention_2=use_flash_attention_2,
torch_dtype=torch.float16,
device_map=device_map,
check_device_map=check_device_map,
)
def _maybe_set_compile(self):
if self.config.reference_compile is False:
return
if hasattr(self, "hf_device_map") and len(self.hf_device_map) > 1:
if self.config.reference_compile:
logger.warning_once(
"If `accelerate` split the model across devices, `torch.compile` will not work. "
"Falling back to non-compiled mode."
)
self.config.reference_compile = False
if self.device.type == "mps":
if self.config.reference_compile:
logger.warning_once(
"Compiling the model with `torch.compile` and using a `torch.mps` device is not supported. "
"Falling back to non-compiled mode."
)
self.config.reference_compile = False
if self.device.type == "cpu":
if self.config.reference_compile:
logger.warning_once(
"Compiling the model with `torch.compile` and using a `torch.cpu` device is not supported. "
"Falling back to non-compiled mode."
)
self.config.reference_compile = False
if self.config.reference_compile is None:
self.config.reference_compile = is_triton_available()
def resize_token_embeddings(self, *args, **kwargs):
model_embeds = super().resize_token_embeddings(*args, **kwargs)
if self.config.reference_compile in {True, None}:
if self.config.reference_compile:
logger.warning_once(
"Resizing token embeddings with `torch.compile` is not supported. Falling back to non-compiled mode."
)
self.config.reference_compile = False
return model_embeds
def _unpad_modernbert_input(
inputs: torch.Tensor,
attention_mask: torch.Tensor,
position_ids: Optional[torch.Tensor] = None,
labels: Optional[torch.Tensor] = None,
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, int, Optional[torch.Tensor], Optional[torch.Tensor]]:
"""
Remove padding from input sequences.
Args:
inputs: (batch, seqlen, ...) or (batch, seqlen)
attention_mask: (batch, seqlen), bool / int, 1 means valid and 0 means not valid.
position_ids: (batch, seqlen), int, position ids
labels: (batch, seqlen), int, labels
Returns:
unpadded_inputs: (total_nnz, ...), where total_nnz = number of tokens selected in attention_mask.
indices: (total_nnz)
cu_seqlens: (batch + 1), the cumulative sequence lengths
max_seqlen_in_batch: int
unpadded_position_ids: (total_nnz) or None
unpadded_labels: (total_nnz) or None
"""
seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
max_seqlen_in_batch = int(seqlens_in_batch.max().item())
cu_seqlens = torch.nn.functional.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.int32), (1, 0))
if inputs.dim() == 2:
unpadded_inputs = inputs.flatten()[indices]
else:
batch, seqlen, *rest = inputs.shape
shape = batch * seqlen
unpadded_inputs = inputs.view(shape, *rest)[indices]
unpadded_position_ids = position_ids.flatten()[indices] if position_ids is not None else None
unpadded_labels = labels.flatten()[indices] if labels is not None else None
return unpadded_inputs, indices, cu_seqlens, max_seqlen_in_batch, unpadded_position_ids, unpadded_labels
def _pad_modernbert_output(
inputs: torch.Tensor,
indices: torch.Tensor,
batch: int,
seqlen: int,
) -> torch.Tensor:
"""
Add padding to sequences.
Args:
inputs: (total_nnz, ...) or (total_nnz,), where total_nnz = number of tokens selected in attention_mask.
indices: (total_nnz)
batch: int, batch size
seqlen: int, max sequence length
Returns:
padded_inputs: (batch, seqlen, ...) or (batch, seqlen)
"""
if inputs.dim() == 1:
output = torch.zeros(batch * seqlen, dtype=inputs.dtype, device=inputs.device)
output[indices] = inputs
padded_inputs = output.view(batch, seqlen)
else:
_, *rest = inputs.shape
output = torch.zeros(batch * seqlen, *rest, dtype=inputs.dtype, device=inputs.device)
output[indices] = inputs
padded_inputs = output.view(batch, seqlen, *rest)
return padded_inputs
MODERNBERT_INPUTS_DOCSTRING = r"""
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. With Flash Attention 2.0, 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 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**.
sliding_window_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding or far-away tokens. In ModernBert, only every few layers
perform global attention, while the rest perform local attention. This mask is used to avoid attending to
far-away tokens in the local attention layers when not using Flash Attention.
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)
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.
indices (`torch.Tensor` of shape `(total_unpadded_tokens,)`, *optional*):
Indices of the non-padding tokens in the input sequence. Used for unpadding the output.
cu_seqlens (`torch.Tensor` of shape `(batch + 1,)`, *optional*):
Cumulative sequence lengths of the input sequences. Used to index the unpadded tensors.
max_seqlen (`int`, *optional*):
Maximum sequence length in the batch excluding padding tokens. Used to unpad input_ids and pad output tensors.
batch_size (`int`, *optional*):
Batch size of the input sequences. Used to pad the output tensors.
seq_len (`int`, *optional*):
Sequence length of the input sequences including padding tokens. Used to pad the output tensors.
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 ModernBert Model outputting raw hidden-states without any specific head on top.",
MODERNBERT_START_DOCSTRING,
)
class ModernBertModel(ModernBertPreTrainedModel):
def __init__(self, config: ModernBertConfig):
super().__init__(config)
self.config = config
self.embeddings = ModernBertEmbeddings(config)
self.layers = nn.ModuleList(
[ModernBertEncoderLayer(config, layer_id) for layer_id in range(config.num_hidden_layers)]
)
self.final_norm = nn.LayerNorm(config.hidden_size, eps=config.norm_eps, bias=config.norm_bias)
self.gradient_checkpointing = False
self.post_init()
def get_input_embeddings(self):
return self.embeddings.tok_embeddings
def set_input_embeddings(self, value):
self.embeddings.tok_embeddings = value
@add_start_docstrings_to_model_forward(MODERNBERT_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=BaseModelOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
sliding_window_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
indices: Optional[torch.Tensor] = None,
cu_seqlens: Optional[torch.Tensor] = None,
max_seqlen: Optional[int] = None,
batch_size: Optional[int] = None,
seq_len: Optional[int] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple[torch.Tensor, ...], 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
if (input_ids is None) ^ (inputs_embeds is not None):
raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
all_hidden_states = () if output_hidden_states else None
all_self_attentions = () if output_attentions else None
self._maybe_set_compile()
if input_ids is not None:
self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
if batch_size is None and seq_len is None:
if inputs_embeds is not None:
batch_size, seq_len = inputs_embeds.shape[:2]
else:
batch_size, seq_len = input_ids.shape[:2]
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_len), device=device, dtype=torch.bool)
repad = False
if self.config._attn_implementation == "flash_attention_2":
if indices is None and cu_seqlens is None and max_seqlen is None:
repad = True
if inputs_embeds is None:
with torch.no_grad():
input_ids, indices, cu_seqlens, max_seqlen, *_ = _unpad_modernbert_input(
inputs=input_ids, attention_mask=attention_mask
)
else:
inputs_embeds, indices, cu_seqlens, max_seqlen, *_ = _unpad_modernbert_input(
inputs=inputs_embeds, attention_mask=attention_mask
)
else:
if position_ids is None:
position_ids = torch.arange(seq_len, device=device).unsqueeze(0)
attention_mask, sliding_window_mask = self._update_attention_mask(
attention_mask, output_attentions=output_attentions
)
hidden_states = self.embeddings(input_ids=input_ids, inputs_embeds=inputs_embeds)
for encoder_layer in self.layers:
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
encoder_layer.__call__,
hidden_states,
attention_mask,
sliding_window_mask,
position_ids,
cu_seqlens,
max_seqlen,
output_attentions,
)
else:
layer_outputs = encoder_layer(
hidden_states,
attention_mask=attention_mask,
sliding_window_mask=sliding_window_mask,
position_ids=position_ids,
cu_seqlens=cu_seqlens,
max_seqlen=max_seqlen,
output_attentions=output_attentions,
)
hidden_states = layer_outputs[0]
if output_attentions and len(layer_outputs) > 1:
all_self_attentions = all_self_attentions + (layer_outputs[1],)
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
hidden_states = self.final_norm(hidden_states)
if repad:
hidden_states = _pad_modernbert_output(
inputs=hidden_states, indices=indices, batch=batch_size, seqlen=seq_len
)
if all_hidden_states is not None:
all_hidden_states = tuple(
_pad_modernbert_output(inputs=hs, indices=indices, batch=batch_size, seqlen=seq_len)
for hs in all_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,
)
def _update_attention_mask(self, attention_mask: torch.Tensor, output_attentions: bool) -> torch.Tensor:
if output_attentions:
if self.config._attn_implementation == "sdpa":
logger.warning_once(
"Outputting attentions is only supported with the 'eager' attention implementation, "
'not with "sdpa". Falling back to `attn_implementation="eager"`.'
)
self.config._attn_implementation = "eager"
elif self.config._attn_implementation != "eager":
logger.warning_once(
"Outputting attentions is only supported with the eager attention implementation, "
f'not with {self.config._attn_implementation}. Consider setting `attn_implementation="eager"`.'
" Setting `output_attentions=False`."
)
global_attention_mask = _prepare_4d_attention_mask(attention_mask, self.dtype)
# Create position indices
rows = torch.arange(global_attention_mask.shape[2]).unsqueeze(0)
# Calculate distance between positions
distance = torch.abs(rows - rows.T)
# Create sliding window mask (1 for positions within window, 0 outside)
window_mask = (
(distance <= self.config.local_attention // 2).unsqueeze(0).unsqueeze(0).to(attention_mask.device)
)
# Combine with existing mask
sliding_window_mask = global_attention_mask.masked_fill(window_mask.logical_not(), torch.finfo(self.dtype).min)
return global_attention_mask, sliding_window_mask
class ModernBertPredictionHead(nn.Module):
def __init__(self, config: ModernBertConfig):
super().__init__()
self.config = config
self.dense = nn.Linear(config.hidden_size, config.hidden_size, config.classifier_bias)
self.act = ACT2FN[config.classifier_activation]
self.norm = nn.LayerNorm(config.hidden_size, eps=config.norm_eps, bias=config.norm_bias)
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
return self.norm(self.act(self.dense(hidden_states)))
@add_start_docstrings(
"The ModernBert Model with a decoder head on top that is used for masked language modeling.",
MODERNBERT_START_DOCSTRING,
)
class ModernBertForMaskedLM(ModernBertPreTrainedModel):
_tied_weights_keys = ["decoder.weight"]
def __init__(self, config: ModernBertConfig):
super().__init__(config)
self.config = config
self.model = ModernBertModel(config)
self.head = ModernBertPredictionHead(config)
self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=config.decoder_bias)
self.sparse_prediction = self.config.sparse_prediction
self.sparse_pred_ignore_index = self.config.sparse_pred_ignore_index
# Initialize weights and apply final processing
self.post_init()
def get_output_embeddings(self):
return self.decoder
def set_output_embeddings(self, new_embeddings: nn.Linear):
self.decoder = new_embeddings
@torch.compile(dynamic=True)
def compiled_head(self, output: torch.Tensor) -> torch.Tensor:
return self.decoder(self.head(output))
@add_start_docstrings_to_model_forward(MODERNBERT_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=MaskedLMOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
sliding_window_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
labels: Optional[torch.Tensor] = None,
indices: Optional[torch.Tensor] = None,
cu_seqlens: Optional[torch.Tensor] = None,
max_seqlen: Optional[int] = None,
batch_size: Optional[int] = None,
seq_len: Optional[int] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
**kwargs,
) -> Union[Tuple[torch.Tensor], MaskedLMOutput]:
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
self._maybe_set_compile()
if self.config._attn_implementation == "flash_attention_2":
if indices is None and cu_seqlens is None and max_seqlen is None:
if batch_size is None and seq_len is None:
if inputs_embeds is not None:
batch_size, seq_len = inputs_embeds.shape[:2]
else:
batch_size, seq_len = input_ids.shape[:2]
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_len), device=device, dtype=torch.bool)
if inputs_embeds is None:
with torch.no_grad():
input_ids, indices, cu_seqlens, max_seqlen, position_ids, labels = _unpad_modernbert_input(
inputs=input_ids, attention_mask=attention_mask, position_ids=position_ids, labels=labels
)
else:
inputs_embeds, indices, cu_seqlens, max_seqlen, position_ids, labels = _unpad_modernbert_input(
inputs=inputs_embeds, attention_mask=attention_mask, position_ids=position_ids, labels=labels
)
outputs = self.model(
input_ids=input_ids,
attention_mask=attention_mask,
sliding_window_mask=sliding_window_mask,
position_ids=position_ids,
inputs_embeds=inputs_embeds,
indices=indices,
cu_seqlens=cu_seqlens,
max_seqlen=max_seqlen,
batch_size=batch_size,
seq_len=seq_len,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
last_hidden_state = outputs[0]
if self.sparse_prediction and labels is not None:
# flatten labels and output first
labels = labels.view(-1)
last_hidden_state = last_hidden_state.view(labels.shape[0], -1)
# then filter out the non-masked tokens
mask_tokens = labels != self.sparse_pred_ignore_index
last_hidden_state = last_hidden_state[mask_tokens]
labels = labels[mask_tokens]
logits = (
self.compiled_head(last_hidden_state)
if self.config.reference_compile
else self.decoder(self.head(last_hidden_state))
)
loss = None
if labels is not None:
loss = self.loss_function(logits, labels, vocab_size=self.config.vocab_size)
if self.config._attn_implementation == "flash_attention_2":
with nullcontext() if self.config.repad_logits_with_grad or labels is None else torch.no_grad():
logits = _pad_modernbert_output(inputs=logits, indices=indices, batch=batch_size, seqlen=seq_len)
if not return_dict:
output = (logits,)
return ((loss,) + output) if loss is not None else output
return MaskedLMOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
@add_start_docstrings(
"The ModernBert Model with a sequence classification head on top that performs pooling.",
MODERNBERT_START_DOCSTRING,
)
class ModernBertForSequenceClassification(ModernBertPreTrainedModel):
def __init__(self, config: ModernBertConfig):
super().__init__(config)
self.num_labels = config.num_labels
self.config = config
self.model = ModernBertModel(config)
self.head = ModernBertPredictionHead(config)
self.drop = torch.nn.Dropout(config.classifier_dropout)
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(MODERNBERT_INPUTS_DOCSTRING)
@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.Tensor] = None,
sliding_window_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
labels: Optional[torch.Tensor] = None,
indices: Optional[torch.Tensor] = None,
cu_seqlens: Optional[torch.Tensor] = None,
max_seqlen: Optional[int] = None,
batch_size: Optional[int] = None,
seq_len: Optional[int] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
**kwargs,
) -> Union[Tuple[torch.Tensor], 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
self._maybe_set_compile()
outputs = self.model(
input_ids=input_ids,
attention_mask=attention_mask,
sliding_window_mask=sliding_window_mask,
position_ids=position_ids,
inputs_embeds=inputs_embeds,
indices=indices,
cu_seqlens=cu_seqlens,
max_seqlen=max_seqlen,
batch_size=batch_size,
seq_len=seq_len,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
last_hidden_state = outputs[0]
if self.config.classifier_pooling == "cls":
last_hidden_state = last_hidden_state[:, 0]
elif self.config.classifier_pooling == "mean":
last_hidden_state = (last_hidden_state * attention_mask.unsqueeze(-1)).sum(dim=1) / attention_mask.sum(
dim=1, keepdim=True
)
pooled_output = self.head(last_hidden_state)
pooled_output = self.drop(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,)
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(
"The ModernBert Model with a token classification head on top, e.g. for Named Entity Recognition (NER) tasks.",
MODERNBERT_START_DOCSTRING,
)
class ModernBertForTokenClassification(ModernBertPreTrainedModel):
def __init__(self, config: ModernBertConfig):
super().__init__(config)
self.num_labels = config.num_labels
self.model = ModernBertModel(config)
self.head = ModernBertPredictionHead(config)
self.drop = torch.nn.Dropout(config.classifier_dropout)
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(MODERNBERT_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,
sliding_window_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
labels: Optional[torch.Tensor] = None,
indices: Optional[torch.Tensor] = None,
cu_seqlens: Optional[torch.Tensor] = None,
max_seqlen: Optional[int] = None,
batch_size: Optional[int] = None,
seq_len: Optional[int] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple[torch.Tensor], 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]`.
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
self._maybe_set_compile()
outputs = self.model(
input_ids=input_ids,
attention_mask=attention_mask,
sliding_window_mask=sliding_window_mask,
position_ids=position_ids,
inputs_embeds=inputs_embeds,
indices=indices,
cu_seqlens=cu_seqlens,
max_seqlen=max_seqlen,
batch_size=batch_size,
seq_len=seq_len,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
last_hidden_state = outputs[0]
last_hidden_state = self.head(last_hidden_state)
last_hidden_state = self.drop(last_hidden_state)
logits = self.classifier(last_hidden_state)
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[1:]
return ((loss,) + output) if loss is not None else output
return TokenClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
__all__ = [
"ModernBertModel",
"ModernBertPreTrainedModel",
"ModernBertForMaskedLM",
"ModernBertForSequenceClassification",
"ModernBertForTokenClassification",
]
| transformers/src/transformers/models/modernbert/modeling_modernbert.py/0 | {
"file_path": "transformers/src/transformers/models/modernbert/modeling_modernbert.py",
"repo_id": "transformers",
"token_count": 26299
} |
# coding=utf-8
# Copyright 2023 Meta 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.
"""MusicGen model configuration"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig
logger = logging.get_logger(__name__)
class MusicgenDecoderConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of an [`MusicgenDecoder`]. It is used to instantiate a
MusicGen 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 MusicGen
[facebook/musicgen-small](https://huggingface.co/facebook/musicgen-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 2048):
Vocabulary size of the MusicgenDecoder model. Defines the number of different tokens that can be
represented by the `inputs_ids` passed when calling [`MusicgenDecoder`].
hidden_size (`int`, *optional*, defaults to 1024):
Dimensionality of the layers and the pooler layer.
num_hidden_layers (`int`, *optional*, defaults to 24):
Number of decoder layers.
num_attention_heads (`int`, *optional*, defaults to 16):
Number of attention heads for each attention layer in the Transformer block.
ffn_dim (`int`, *optional*, defaults to 4096):
Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer block.
activation_function (`str` or `function`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the decoder 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, text_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.
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).
initializer_factor (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
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.
scale_embedding (`bool`, *optional*, defaults to `False`):
Scale embeddings by diving by sqrt(hidden_size).
use_cache (`bool`, *optional*, defaults to `True`):
Whether the model should return the last key/values attentions (not used by all models)
num_codebooks (`int`, *optional*, defaults to 4):
The number of parallel codebooks forwarded to the model.
tie_word_embeddings(`bool`, *optional*, defaults to `False`):
Whether input and output word embeddings should be tied.
audio_channels (`int`, *optional*, defaults to 1
Number of channels in the audio data. Either 1 for mono or 2 for stereo. Stereo models generate a separate
audio stream for the left/right output channels. Mono models generate a single audio stream output.
"""
model_type = "musicgen_decoder"
base_config_key = "decoder_config"
keys_to_ignore_at_inference = ["past_key_values"]
def __init__(
self,
vocab_size=2048,
max_position_embeddings=2048,
num_hidden_layers=24,
ffn_dim=4096,
num_attention_heads=16,
layerdrop=0.0,
use_cache=True,
activation_function="gelu",
hidden_size=1024,
dropout=0.1,
attention_dropout=0.0,
activation_dropout=0.0,
initializer_factor=0.02,
scale_embedding=False,
num_codebooks=4,
audio_channels=1,
pad_token_id=2048,
bos_token_id=2048,
eos_token_id=None,
tie_word_embeddings=False,
**kwargs,
):
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
self.hidden_size = hidden_size
self.ffn_dim = ffn_dim
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.dropout = dropout
self.attention_dropout = attention_dropout
self.activation_dropout = activation_dropout
self.activation_function = activation_function
self.initializer_factor = initializer_factor
self.layerdrop = layerdrop
self.use_cache = use_cache
self.scale_embedding = scale_embedding # scale factor will be sqrt(d_model) if True
self.num_codebooks = num_codebooks
if audio_channels not in [1, 2]:
raise ValueError(f"Expected 1 (mono) or 2 (stereo) audio channels, got {audio_channels} channels.")
self.audio_channels = audio_channels
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,
)
class MusicgenConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`MusicgenModel`]. It is used to instantiate a
MusicGen model according to the specified arguments, defining the text encoder, audio encoder and MusicGen decoder
configs.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
kwargs (*optional*):
Dictionary of keyword arguments. Notably:
- **text_encoder** ([`PretrainedConfig`], *optional*) -- An instance of a configuration object that
defines the text encoder config.
- **audio_encoder** ([`PretrainedConfig`], *optional*) -- An instance of a configuration object that
defines the audio encoder config.
- **decoder** ([`PretrainedConfig`], *optional*) -- An instance of a configuration object that defines
the decoder config.
Example:
```python
>>> from transformers import (
... MusicgenConfig,
... MusicgenDecoderConfig,
... T5Config,
... EncodecConfig,
... MusicgenForConditionalGeneration,
... )
>>> # Initializing text encoder, audio encoder, and decoder model configurations
>>> text_encoder_config = T5Config()
>>> audio_encoder_config = EncodecConfig()
>>> decoder_config = MusicgenDecoderConfig()
>>> configuration = MusicgenConfig.from_sub_models_config(
... text_encoder_config, audio_encoder_config, decoder_config
... )
>>> # Initializing a MusicgenForConditionalGeneration (with random weights) from the facebook/musicgen-small style configuration
>>> model = MusicgenForConditionalGeneration(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
>>> config_text_encoder = model.config.text_encoder
>>> config_audio_encoder = model.config.audio_encoder
>>> config_decoder = model.config.decoder
>>> # Saving the model, including its configuration
>>> model.save_pretrained("musicgen-model")
>>> # loading model and config from pretrained folder
>>> musicgen_config = MusicgenConfig.from_pretrained("musicgen-model")
>>> model = MusicgenForConditionalGeneration.from_pretrained("musicgen-model", config=musicgen_config)
```"""
model_type = "musicgen"
sub_configs = {
"text_encoder": AutoConfig,
"audio_encoder": AutoConfig,
"decoder": MusicgenDecoderConfig,
}
is_composition = True
def __init__(self, **kwargs):
super().__init__(**kwargs)
if "text_encoder" not in kwargs or "audio_encoder" not in kwargs or "decoder" not in kwargs:
raise ValueError("Config has to be initialized with text_encoder, audio_encoder and decoder config")
text_encoder_config = kwargs.pop("text_encoder")
text_encoder_model_type = text_encoder_config.pop("model_type")
audio_encoder_config = kwargs.pop("audio_encoder")
audio_encoder_model_type = audio_encoder_config.pop("model_type")
decoder_config = kwargs.pop("decoder")
self.text_encoder = AutoConfig.for_model(text_encoder_model_type, **text_encoder_config)
self.audio_encoder = AutoConfig.for_model(audio_encoder_model_type, **audio_encoder_config)
self.decoder = MusicgenDecoderConfig(**decoder_config)
self.is_encoder_decoder = True
@classmethod
def from_sub_models_config(
cls,
text_encoder_config: PretrainedConfig,
audio_encoder_config: PretrainedConfig,
decoder_config: MusicgenDecoderConfig,
**kwargs,
):
r"""
Instantiate a [`MusicgenConfig`] (or a derived class) from text encoder, audio encoder and decoder
configurations.
Returns:
[`MusicgenConfig`]: An instance of a configuration object
"""
return cls(
text_encoder=text_encoder_config.to_dict(),
audio_encoder=audio_encoder_config.to_dict(),
decoder=decoder_config.to_dict(),
**kwargs,
)
@property
# This is a property because you might want to change the codec model on the fly
def sampling_rate(self):
return self.audio_encoder.sampling_rate
__all__ = ["MusicgenConfig", "MusicgenDecoderConfig"]
| transformers/src/transformers/models/musicgen/configuration_musicgen.py/0 | {
"file_path": "transformers/src/transformers/models/musicgen/configuration_musicgen.py",
"repo_id": "transformers",
"token_count": 4078
} |
# 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 Nougat.
"""
from typing import Dict, List, Optional, Union
from transformers.tokenization_utils_base import PreTokenizedInput, TextInput, TruncationStrategy
from ...processing_utils import ProcessorMixin
from ...utils import PaddingStrategy, TensorType
class NougatProcessor(ProcessorMixin):
r"""
Constructs a Nougat processor which wraps a Nougat image processor and a Nougat tokenizer into a single processor.
[`NougatProcessor`] offers all the functionalities of [`NougatImageProcessor`] and [`NougatTokenizerFast`]. See the
[`~NougatProcessor.__call__`] and [`~NougatProcessor.decode`] for more information.
Args:
image_processor ([`NougatImageProcessor`]):
An instance of [`NougatImageProcessor`]. The image processor is a required input.
tokenizer ([`NougatTokenizerFast`]):
An instance of [`NougatTokenizerFast`]. The tokenizer is a required input.
"""
attributes = ["image_processor", "tokenizer"]
image_processor_class = "AutoImageProcessor"
tokenizer_class = "AutoTokenizer"
def __init__(self, image_processor, tokenizer):
super().__init__(image_processor, tokenizer)
self.current_processor = self.image_processor
def __call__(
self,
images=None,
text=None,
do_crop_margin: bool = None,
do_resize: bool = None,
size: Dict[str, int] = None,
resample: "PILImageResampling" = None, # noqa: F821
do_thumbnail: bool = None,
do_align_long_axis: bool = None,
do_pad: bool = None,
do_rescale: bool = None,
rescale_factor: Union[int, float] = None,
do_normalize: bool = None,
image_mean: Optional[Union[float, List[float]]] = None,
image_std: Optional[Union[float, List[float]]] = None,
data_format: Optional["ChannelDimension"] = "channels_first", # noqa: F821
input_data_format: Optional[Union[str, "ChannelDimension"]] = None, # noqa: F821
text_pair: Optional[Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]]] = None,
text_target: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,
text_pair_target: Optional[
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,
is_split_into_words: bool = False,
pad_to_multiple_of: Optional[int] = None,
return_tensors: Optional[Union[str, TensorType]] = None,
return_token_type_ids: Optional[bool] = None,
return_attention_mask: Optional[bool] = None,
return_overflowing_tokens: bool = False,
return_special_tokens_mask: bool = False,
return_offsets_mapping: bool = False,
return_length: bool = False,
verbose: bool = True,
):
if images is None and text is None:
raise ValueError("You need to specify either an `images` or `text` input to process.")
if images is not None:
inputs = self.image_processor(
images,
do_crop_margin=do_crop_margin,
do_resize=do_resize,
size=size,
resample=resample,
do_thumbnail=do_thumbnail,
do_align_long_axis=do_align_long_axis,
do_pad=do_pad,
do_rescale=do_rescale,
rescale_factor=rescale_factor,
do_normalize=do_normalize,
image_mean=image_mean,
image_std=image_std,
return_tensors=return_tensors,
data_format=data_format,
input_data_format=input_data_format,
)
if text is not None:
encodings = self.tokenizer(
text,
text_pair=text_pair,
text_target=text_target,
text_pair_target=text_pair_target,
add_special_tokens=add_special_tokens,
padding=padding,
truncation=truncation,
max_length=max_length,
stride=stride,
is_split_into_words=is_split_into_words,
pad_to_multiple_of=pad_to_multiple_of,
return_tensors=return_tensors,
return_token_type_ids=return_token_type_ids,
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_length=return_length,
verbose=verbose,
)
if text is None:
return inputs
elif images is None:
return encodings
else:
inputs["labels"] = encodings["input_ids"]
return inputs
def batch_decode(self, *args, **kwargs):
"""
This method forwards all its arguments to NougatTokenizer'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 NougatTokenizer'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, *args, **kwargs):
"""
This method forwards all its arguments to NougatTokenizer's [`~PreTrainedTokenizer.post_process_generation`].
Please refer to the docstring of this method for more information.
"""
return self.tokenizer.post_process_generation(*args, **kwargs)
__all__ = ["NougatProcessor"]
| transformers/src/transformers/models/nougat/processing_nougat.py/0 | {
"file_path": "transformers/src/transformers/models/nougat/processing_nougat.py",
"repo_id": "transformers",
"token_count": 2945
} |
# coding=utf-8
# Copyright 2018 The OpenAI Team Authors and HuggingFace Inc. team.
# Copyright (c) 2018, 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.
"""OpenAI GPT configuration"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
logger = logging.get_logger(__name__)
class OpenAIGPTConfig(PretrainedConfig):
"""
This is the configuration class to store the configuration of a [`OpenAIGPTModel`] or a [`TFOpenAIGPTModel`]. It is
used to instantiate a GPT 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
[openai-community/openai-gpt](https://huggingface.co/openai-community/openai-gpt) architecture from OpenAI.
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 40478):
Vocabulary size of the GPT-2 model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`OpenAIGPTModel`] or [`TFOpenAIGPTModel`].
n_positions (`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).
n_embd (`int`, *optional*, defaults to 768):
Dimensionality of the embeddings and hidden states.
n_layer (`int`, *optional*, defaults to 12):
Number of hidden layers in the Transformer encoder.
n_head (`int`, *optional*, defaults to 12):
Number of attention heads for each attention layer in the Transformer encoder.
afn (`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.
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-05):
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.
summary_type (`str`, *optional*, defaults to `"cls_index"`):
Argument used when doing sequence summary, used in the models [`OpenAIGPTDoubleHeadsModel`] and
[`OpenAIGPTDoubleHeadsModel`].
Has to be one of the following options:
- `"last"`: Take the last token hidden state (like XLNet).
- `"first"`: Take the first token hidden state (like BERT).
- `"mean"`: Take the mean of all tokens hidden states.
- `"cls_index"`: Supply a Tensor of classification token position (like GPT/GPT-2).
- `"attn"`: Not implemented now, use multi-head attention.
summary_use_proj (`bool`, *optional*, defaults to `True`):
Argument used when doing sequence summary, used in the models [`OpenAIGPTDoubleHeadsModel`] and
[`OpenAIGPTDoubleHeadsModel`].
Whether or not to add a projection after the vector extraction.
summary_activation (`str`, *optional*):
Argument used when doing sequence summary, used in the models [`OpenAIGPTDoubleHeadsModel`] and
[`OpenAIGPTDoubleHeadsModel`].
Pass `"tanh"` for a tanh activation to the output, any other value will result in no activation.
summary_proj_to_labels (`bool`, *optional*, defaults to `True`):
Argument used when doing sequence summary, used in the models [`OpenAIGPTDoubleHeadsModel`] and
[`OpenAIGPTDoubleHeadsModel`].
Whether the projection outputs should have `config.num_labels` or `config.hidden_size` classes.
summary_first_dropout (`float`, *optional*, defaults to 0.1):
Argument used when doing sequence summary, used in the models [`OpenAIGPTDoubleHeadsModel`] and
[`OpenAIGPTDoubleHeadsModel`].
The dropout ratio to be used after the projection and activation.
Examples:
```python
>>> from transformers import OpenAIGPTConfig, OpenAIGPTModel
>>> # Initializing a GPT configuration
>>> configuration = OpenAIGPTConfig()
>>> # Initializing a model (with random weights) from the configuration
>>> model = OpenAIGPTModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "openai-gpt"
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=40478,
n_positions=512,
n_embd=768,
n_layer=12,
n_head=12,
afn="gelu",
resid_pdrop=0.1,
embd_pdrop=0.1,
attn_pdrop=0.1,
layer_norm_epsilon=1e-5,
initializer_range=0.02,
summary_type="cls_index",
summary_use_proj=True,
summary_activation=None,
summary_proj_to_labels=True,
summary_first_dropout=0.1,
**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.afn = afn
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.summary_type = summary_type
self.summary_use_proj = summary_use_proj
self.summary_activation = summary_activation
self.summary_first_dropout = summary_first_dropout
self.summary_proj_to_labels = summary_proj_to_labels
super().__init__(**kwargs)
__all__ = ["OpenAIGPTConfig"]
| transformers/src/transformers/models/openai/configuration_openai.py/0 | {
"file_path": "transformers/src/transformers/models/openai/configuration_openai.py",
"repo_id": "transformers",
"token_count": 2757
} |
# coding=utf-8
# Copyright 2023 Google AI 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 OWLv2 model."""
from dataclasses import dataclass
from functools import lru_cache
from typing import Any, Dict, Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import Tensor, 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,
is_vision_available,
logging,
replace_return_docstrings,
torch_int,
)
from .configuration_owlv2 import Owlv2Config, Owlv2TextConfig, Owlv2VisionConfig
if is_vision_available():
from transformers.image_transforms import center_to_corners_format
logger = logging.get_logger(__name__)
_CHECKPOINT_FOR_DOC = "google/owlv2-base-patch16-ensemble"
# See all Owlv2 models at https://huggingface.co/models?filter=owlv2
# Copied from transformers.models.clip.modeling_clip.contrastive_loss with clip->owlv2
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->owlv2
def owlv2_loss(similarity: torch.Tensor) -> torch.Tensor:
caption_loss = contrastive_loss(similarity)
image_loss = contrastive_loss(similarity.t())
return (caption_loss + image_loss) / 2.0
@dataclass
class Owlv2Output(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.
text_embeds (`torch.FloatTensor` of shape `(batch_size * num_max_text_queries, output_dim`):
The text embeddings obtained by applying the projection layer to the pooled output of [`Owlv2TextModel`].
image_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim`):
The image embeddings obtained by applying the projection layer to the pooled output of
[`Owlv2VisionModel`].
text_model_output (Tuple[`BaseModelOutputWithPooling`]):
The output of the [`Owlv2TextModel`].
vision_model_output (`BaseModelOutputWithPooling`):
The output of the [`Owlv2VisionModel`].
"""
loss: Optional[torch.FloatTensor] = None
logits_per_image: torch.FloatTensor = None
logits_per_text: torch.FloatTensor = None
text_embeds: torch.FloatTensor = None
image_embeds: 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()
)
# Copied from transformers.loss.loss_for_object_detection._upcast
def _upcast(t: Tensor) -> Tensor:
# Protects from numerical overflows in multiplications by upcasting to the equivalent higher type
if t.is_floating_point():
return t if t.dtype in (torch.float32, torch.float64) else t.float()
else:
return t if t.dtype in (torch.int32, torch.int64) else t.int()
# Copied from transformers.loss.loss_for_object_detection.box_area
def box_area(boxes: Tensor) -> Tensor:
"""
Computes the area of a set of bounding boxes, which are specified by its (x1, y1, x2, y2) coordinates.
Args:
boxes (`torch.FloatTensor` of shape `(number_of_boxes, 4)`):
Boxes for which the area will be computed. They are expected to be in (x1, y1, x2, y2) format with `0 <= x1
< x2` and `0 <= y1 < y2`.
Returns:
`torch.FloatTensor`: a tensor containing the area for each box.
"""
boxes = _upcast(boxes)
return (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1])
# Copied from transformers.loss.loss_for_object_detection.box_iou
def box_iou(boxes1, boxes2):
area1 = box_area(boxes1)
area2 = box_area(boxes2)
left_top = torch.max(boxes1[:, None, :2], boxes2[:, :2]) # [N,M,2]
right_bottom = torch.min(boxes1[:, None, 2:], boxes2[:, 2:]) # [N,M,2]
width_height = (right_bottom - left_top).clamp(min=0) # [N,M,2]
inter = width_height[:, :, 0] * width_height[:, :, 1] # [N,M]
union = area1[:, None] + area2 - inter
iou = inter / union
return iou, union
# Copied from transformers.loss.loss_for_object_detection.generalized_box_iou
def generalized_box_iou(boxes1, boxes2):
"""
Generalized IoU from https://giou.stanford.edu/. The boxes should be in [x0, y0, x1, y1] (corner) format.
Returns:
`torch.FloatTensor`: a [N, M] pairwise matrix, where N = len(boxes1) and M = len(boxes2)
"""
# degenerate boxes gives inf / nan results
# so do an early check
if not (boxes1[:, 2:] >= boxes1[:, :2]).all():
raise ValueError(f"boxes1 must be in [x0, y0, x1, y1] (corner) format, but got {boxes1}")
if not (boxes2[:, 2:] >= boxes2[:, :2]).all():
raise ValueError(f"boxes2 must be in [x0, y0, x1, y1] (corner) format, but got {boxes2}")
iou, union = box_iou(boxes1, boxes2)
top_left = torch.min(boxes1[:, None, :2], boxes2[:, :2])
bottom_right = torch.max(boxes1[:, None, 2:], boxes2[:, 2:])
width_height = (bottom_right - top_left).clamp(min=0) # [N,M,2]
area = width_height[:, :, 0] * width_height[:, :, 1]
return iou - (area - union) / area
@dataclass
class Owlv2ObjectDetectionOutput(ModelOutput):
"""
Output type of [`Owlv2ForObjectDetection`].
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_patches, num_queries)`):
Classification logits (including no-object) for all queries.
objectness_logits (`torch.FloatTensor` of shape `(batch_size, num_patches, 1)`):
The objectness logits of all image patches. OWL-ViT represents images as a set of image patches where the
total number of patches is (image_size / patch_size)**2.
pred_boxes (`torch.FloatTensor` of shape `(batch_size, num_patches, 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 [`~Owlv2ImageProcessor.post_process_object_detection`] to retrieve the
unnormalized bounding boxes.
text_embeds (`torch.FloatTensor` of shape `(batch_size, num_max_text_queries, output_dim`):
The text embeddings obtained by applying the projection layer to the pooled output of [`Owlv2TextModel`].
image_embeds (`torch.FloatTensor` of shape `(batch_size, patch_size, patch_size, output_dim`):
Pooled output of [`Owlv2VisionModel`]. OWLv2 represents images as a set of image patches and computes image
embeddings for each patch.
class_embeds (`torch.FloatTensor` of shape `(batch_size, num_patches, hidden_size)`):
Class embeddings of all image patches. OWLv2 represents images as a set of image patches where the total
number of patches is (image_size / patch_size)**2.
text_model_output (Tuple[`BaseModelOutputWithPooling`]):
The output of the [`Owlv2TextModel`].
vision_model_output (`BaseModelOutputWithPooling`):
The output of the [`Owlv2VisionModel`].
"""
loss: Optional[torch.FloatTensor] = None
loss_dict: Optional[Dict] = None
logits: torch.FloatTensor = None
objectness_logits: torch.FloatTensor = None
pred_boxes: torch.FloatTensor = None
text_embeds: torch.FloatTensor = None
image_embeds: torch.FloatTensor = None
class_embeds: 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()
)
@dataclass
# Copied from transformers.models.owlvit.modeling_owlvit.OwlViTImageGuidedObjectDetectionOutput with OwlViT->Owlv2,OWL-ViT->OWLv2
class Owlv2ImageGuidedObjectDetectionOutput(ModelOutput):
"""
Output type of [`Owlv2ForObjectDetection.image_guided_detection`].
Args:
logits (`torch.FloatTensor` of shape `(batch_size, num_patches, num_queries)`):
Classification logits (including no-object) for all queries.
target_pred_boxes (`torch.FloatTensor` of shape `(batch_size, num_patches, 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 target image in the batch
(disregarding possible padding). You can use [`~Owlv2ImageProcessor.post_process_object_detection`] to
retrieve the unnormalized bounding boxes.
query_pred_boxes (`torch.FloatTensor` of shape `(batch_size, num_patches, 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 query image in the batch
(disregarding possible padding). You can use [`~Owlv2ImageProcessor.post_process_object_detection`] to
retrieve the unnormalized bounding boxes.
image_embeds (`torch.FloatTensor` of shape `(batch_size, patch_size, patch_size, output_dim`):
Pooled output of [`Owlv2VisionModel`]. OWLv2 represents images as a set of image patches and computes
image embeddings for each patch.
query_image_embeds (`torch.FloatTensor` of shape `(batch_size, patch_size, patch_size, output_dim`):
Pooled output of [`Owlv2VisionModel`]. OWLv2 represents images as a set of image patches and computes
image embeddings for each patch.
class_embeds (`torch.FloatTensor` of shape `(batch_size, num_patches, hidden_size)`):
Class embeddings of all image patches. OWLv2 represents images as a set of image patches where the total
number of patches is (image_size / patch_size)**2.
text_model_output (Tuple[`BaseModelOutputWithPooling`]):
The output of the [`Owlv2TextModel`].
vision_model_output (`BaseModelOutputWithPooling`):
The output of the [`Owlv2VisionModel`].
"""
logits: torch.FloatTensor = None
image_embeds: torch.FloatTensor = None
query_image_embeds: torch.FloatTensor = None
target_pred_boxes: torch.FloatTensor = None
query_pred_boxes: torch.FloatTensor = None
class_embeds: 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()
)
# Copied from transformers.models.owlvit.modeling_owlvit.OwlViTVisionEmbeddings with OwlViT->Owlv2
class Owlv2VisionEmbeddings(nn.Module):
def __init__(self, config: Owlv2VisionConfig):
super().__init__()
self.patch_size = config.patch_size
self.config = config
self.embed_dim = config.hidden_size
self.class_embedding = nn.Parameter(torch.randn(config.hidden_size))
self.patch_embedding = nn.Conv2d(
in_channels=config.num_channels,
out_channels=self.embed_dim,
kernel_size=config.patch_size,
stride=config.patch_size,
bias=False,
)
self.num_patches = (config.image_size // config.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)
# Copied from transformers.models.clip.modeling_clip.CLIPVisionEmbeddings.interpolate_pos_encoding
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: bool = False) -> torch.Tensor:
batch_size, _, height, width = pixel_values.shape
patch_embeds = self.patch_embedding(pixel_values) # shape = [batch_size, num_channels, height, width]
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.owlvit.modeling_owlvit.OwlViTTextEmbeddings with OwlViT->Owlv2
class Owlv2TextEmbeddings(nn.Module):
def __init__(self, config: Owlv2TextConfig):
super().__init__()
self.token_embedding = nn.Embedding(config.vocab_size, config.hidden_size)
self.position_embedding = nn.Embedding(config.max_position_embeddings, config.hidden_size)
# 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]
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
# Copied from transformers.models.owlvit.modeling_owlvit.OwlViTAttention with OwlViT->Owlv2
class Owlv2Attention(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], Optional[Tuple[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 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)
# For int8 compatibility, sometimes the `attn_probs` are in `fp32`
attn_probs = attn_probs.to(value_states.dtype)
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->Owlv2
class Owlv2MLP(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->Owlv2
class Owlv2EncoderLayer(nn.Module):
def __init__(self, config: Owlv2Config):
super().__init__()
self.embed_dim = config.hidden_size
self.self_attn = Owlv2Attention(config)
self.layer_norm1 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
self.mlp = Owlv2MLP(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.owlvit.modeling_owlvit.OwlViTPreTrainedModel with OwlViT->Owlv2,owlvit->owlv2
class Owlv2PreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = Owlv2Config
base_model_prefix = "owlv2"
supports_gradient_checkpointing = True
_no_split_modules = ["Owlv2EncoderLayer"]
def _init_weights(self, module):
"""Initialize the weights"""
factor = self.config.initializer_factor
if isinstance(module, Owlv2TextEmbeddings):
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, Owlv2VisionEmbeddings):
factor = self.config.initializer_factor
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, Owlv2Attention):
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, Owlv2MLP):
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)
elif isinstance(module, Owlv2Model):
nn.init.normal_(
module.text_projection.weight,
std=module.text_embed_dim**-0.5 * self.config.initializer_factor,
)
nn.init.normal_(
module.visual_projection.weight,
std=module.vision_embed_dim**-0.5 * self.config.initializer_factor,
)
if isinstance(module, nn.LayerNorm):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
if isinstance(module, nn.Linear) and module.bias is not None:
module.bias.data.zero_()
OWLV2_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 ([`Owvl2Config`]): 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.
"""
OWLV2_TEXT_INPUTS_DOCSTRING = r"""
Args:
input_ids (`torch.LongTensor` of shape `(batch_size * num_max_text_queries, 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 (`torch.Tensor` of shape `(batch_size, num_max_text_queries, 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.
"""
OWLV2_VISION_INPUTS_DOCSTRING = r"""
Args:
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Pixel 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.
"""
OWLV2_INPUTS_DOCSTRING = r"""
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, 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)
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Pixel values.
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)
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.
interpolate_pos_encoding (`bool`, *optional*, defaults `False`):
Whether to interpolate the pre-trained position encodings.
return_base_image_embeds (`bool`, *optional*):
Whether or not to return the base image embeddings.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
OWLV2_OBJECT_DETECTION_INPUTS_DOCSTRING = r"""
Args:
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Pixel values.
input_ids (`torch.LongTensor` of shape `(batch_size * num_max_text_queries, sequence_length)`, *optional*):
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.Tensor` of shape `(batch_size, num_max_text_queries, 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_hidden_states (`bool`, *optional*):
Whether or not to return the last hidden state. See `text_model_last_hidden_state` and
`vision_model_last_hidden_state` 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.
"""
OWLV2_IMAGE_GUIDED_OBJECT_DETECTION_INPUTS_DOCSTRING = r"""
Args:
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Pixel values.
query_pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Pixel values of query image(s) to be detected. Pass in one query image per target image.
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.
"""
# Copied from transformers.models.owlvit.modeling_owlvit.OwlViTEncoder with OwlViT->Owlv2
class Owlv2Encoder(nn.Module):
"""
Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a
[`Owlv2EncoderLayer`].
Args:
config: Owlv2Config
"""
def __init__(self, config: Owlv2Config):
super().__init__()
self.layers = nn.ModuleList([Owlv2EncoderLayer(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)`).
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 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,
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
)
# Copied from transformers.models.owlvit.modeling_owlvit.OwlViTTextTransformer with OWLVIT->OWLV2,OwlViT->Owlv2
class Owlv2TextTransformer(nn.Module):
def __init__(self, config: Owlv2TextConfig):
super().__init__()
self.config = config
embed_dim = config.hidden_size
self.embeddings = Owlv2TextEmbeddings(config)
self.encoder = Owlv2Encoder(config)
self.final_layer_norm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
@add_start_docstrings_to_model_forward(OWLV2_TEXT_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=Owlv2TextConfig)
def forward(
self,
input_ids: torch.Tensor,
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
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)
# num_samples, seq_len = input_shape where num_samples = batch_size * num_max_text_queries
# OWLV2'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:
# [num_samples, seq_len] -> [num_samples, 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)
# take features from the end of tokens embedding (end of 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(torch.int).argmax(dim=-1).to(last_hidden_state.device),
]
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,
)
# Copied from transformers.models.owlvit.modeling_owlvit.OwlViTTextModel with google/owlvit-base-patch32->google/owlv2-base-patch16, OWLVIT->OWLV2,OwlViT->Owlv2
class Owlv2TextModel(Owlv2PreTrainedModel):
config_class = Owlv2TextConfig
def __init__(self, config: Owlv2TextConfig):
super().__init__(config)
self.text_model = Owlv2TextTransformer(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(OWLV2_TEXT_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=Owlv2TextConfig)
def forward(
self,
input_ids: torch.Tensor,
attention_mask: 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 AutoProcessor, Owlv2TextModel
>>> model = Owlv2TextModel.from_pretrained("google/owlv2-base-patch16")
>>> processor = AutoProcessor.from_pretrained("google/owlv2-base-patch16")
>>> inputs = processor(
... text=[["a photo of a cat", "a photo of a dog"], ["photo of a astranaut"]], return_tensors="pt"
... )
>>> outputs = model(**inputs)
>>> last_hidden_state = outputs.last_hidden_state
>>> pooled_output = outputs.pooler_output # pooled (EOS token) states
```"""
# Get embeddings for all text queries in all batch samples
return self.text_model(
input_ids=input_ids,
attention_mask=attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
# Copied from transformers.models.owlvit.modeling_owlvit.OwlViTVisionTransformer with OWLVIT->OWLV2,OwlViT->Owlv2
class Owlv2VisionTransformer(nn.Module):
def __init__(self, config: Owlv2VisionConfig):
super().__init__()
self.config = config
self.embeddings = Owlv2VisionEmbeddings(config)
self.pre_layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.encoder = Owlv2Encoder(config)
self.post_layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
@add_start_docstrings_to_model_forward(OWLV2_VISION_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=Owlv2VisionConfig)
def forward(
self,
pixel_values: torch.FloatTensor,
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
# Cast the input to the expected `dtype`
expected_input_dtype = self.embeddings.patch_embedding.weight.dtype
pixel_values = pixel_values.to(expected_input_dtype)
hidden_states = self.embeddings(pixel_values, interpolate_pos_encoding=interpolate_pos_encoding)
hidden_states = self.pre_layernorm(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,
)
# Copied from transformers.models.owlvit.modeling_owlvit.OwlViTVisionModel with OWLVIT->OWLV2,OwlViT->Owlv2,google/owlvit-base-patch32->google/owlv2-base-patch16
class Owlv2VisionModel(Owlv2PreTrainedModel):
config_class = Owlv2VisionConfig
main_input_name = "pixel_values"
def __init__(self, config: Owlv2VisionConfig):
super().__init__(config)
self.vision_model = Owlv2VisionTransformer(config)
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self) -> nn.Module:
return self.vision_model.embeddings.patch_embedding
@add_start_docstrings_to_model_forward(OWLV2_VISION_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=Owlv2VisionConfig)
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:
Examples:
```python
>>> from PIL import Image
>>> import requests
>>> from transformers import AutoProcessor, Owlv2VisionModel
>>> model = Owlv2VisionModel.from_pretrained("google/owlv2-base-patch16")
>>> processor = AutoProcessor.from_pretrained("google/owlv2-base-patch16")
>>> 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,
interpolate_pos_encoding=interpolate_pos_encoding,
return_dict=return_dict,
)
@add_start_docstrings(OWLV2_START_DOCSTRING)
# Copied from transformers.models.owlvit.modeling_owlvit.OwlViTModel with google/owlvit-base-patch32->google/owlv2-base-patch16-ensemble, OWLVIT->OWLV2,OwlViT->Owlv2,owlvit->owlv2,OWL-ViT->OWLv2
class Owlv2Model(Owlv2PreTrainedModel):
config_class = Owlv2Config
def __init__(self, config: Owlv2Config):
super().__init__(config)
if not isinstance(config.text_config, Owlv2TextConfig):
raise TypeError(
"config.text_config is expected to be of type Owlv2TextConfig but is of type"
f" {type(config.text_config)}."
)
if not isinstance(config.vision_config, Owlv2VisionConfig):
raise TypeError(
"config.vision_config is expected to be of type Owlv2VisionConfig 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.text_embed_dim = text_config.hidden_size
self.vision_embed_dim = vision_config.hidden_size
self.text_model = Owlv2TextTransformer(text_config)
self.vision_model = Owlv2VisionTransformer(vision_config)
self.visual_projection = nn.Linear(self.vision_embed_dim, self.projection_dim, bias=False)
self.text_projection = nn.Linear(self.text_embed_dim, self.projection_dim, bias=False)
self.logit_scale = nn.Parameter(torch.tensor(config.logit_scale_init_value))
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(OWLV2_TEXT_INPUTS_DOCSTRING)
def get_text_features(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: 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 [`Owlv2TextModel`].
Examples:
```python
>>> from transformers import AutoProcessor, Owlv2Model
>>> model = Owlv2Model.from_pretrained("google/owlv2-base-patch16-ensemble")
>>> processor = AutoProcessor.from_pretrained("google/owlv2-base-patch16-ensemble")
>>> inputs = processor(
... text=[["a photo of a cat", "a photo of a dog"], ["photo of a astranaut"]], return_tensors="pt"
... )
>>> text_features = model.get_text_features(**inputs)
```"""
# Use OWLv2 model's config for some fields (if specified) instead of those of vision & text components.
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
# Get embeddings for all text queries in all batch samples
text_output = self.text_model(input_ids=input_ids, attention_mask=attention_mask, return_dict=return_dict)
pooled_output = text_output[1]
text_features = self.text_projection(pooled_output)
return text_features
@add_start_docstrings_to_model_forward(OWLV2_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,
interpolate_pos_encoding: bool = False,
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 [`Owlv2VisionModel`].
Examples:
```python
>>> from PIL import Image
>>> import requests
>>> from transformers import AutoProcessor, Owlv2Model
>>> model = Owlv2Model.from_pretrained("google/owlv2-base-patch16-ensemble")
>>> processor = AutoProcessor.from_pretrained("google/owlv2-base-patch16-ensemble")
>>> 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 OWLv2 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,
interpolate_pos_encoding=interpolate_pos_encoding,
return_dict=return_dict,
)
pooled_output = vision_outputs[1]
image_features = self.visual_projection(pooled_output)
return image_features
@add_start_docstrings_to_model_forward(OWLV2_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=Owlv2Output, config_class=Owlv2Config)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
pixel_values: Optional[torch.FloatTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
return_loss: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
interpolate_pos_encoding: bool = False,
return_base_image_embeds: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, Owlv2Output]:
r"""
Returns:
Examples:
```python
>>> from PIL import Image
>>> import requests
>>> from transformers import AutoProcessor, Owlv2Model
>>> model = Owlv2Model.from_pretrained("google/owlv2-base-patch16-ensemble")
>>> processor = AutoProcessor.from_pretrained("google/owlv2-base-patch16-ensemble")
>>> 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")
>>> 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 OWLv2 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,
interpolate_pos_encoding=interpolate_pos_encoding,
return_dict=return_dict,
)
# Get embeddings for all text queries in all batch samples
text_outputs = self.text_model(
input_ids=input_ids,
attention_mask=attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
text_embeds = text_outputs[1]
text_embeds = self.text_projection(text_embeds)
image_embeds = vision_outputs[1]
image_embeds = self.visual_projection(image_embeds)
# normalized features
image_embeds = image_embeds / torch.linalg.norm(image_embeds, ord=2, dim=-1, keepdim=True)
text_embeds_norm = text_embeds / torch.linalg.norm(text_embeds, ord=2, dim=-1, keepdim=True)
# cosine similarity as logits and set it on the correct device
logit_scale = self.logit_scale.exp().to(image_embeds.device)
logits_per_text = torch.matmul(text_embeds_norm, image_embeds.t()) * logit_scale
logits_per_image = logits_per_text.t()
loss = None
if return_loss:
loss = owlv2_loss(logits_per_text)
text_embeds = text_embeds_norm
if not return_dict:
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 Owlv2Output(
loss=loss,
logits_per_image=logits_per_image,
logits_per_text=logits_per_text,
text_embeds=text_embeds,
image_embeds=image_embeds,
text_model_output=text_outputs,
vision_model_output=vision_outputs,
)
# Copied from transformers.models.owlvit.modeling_owlvit.OwlViTBoxPredictionHead with OwlViT->Owlv2
class Owlv2BoxPredictionHead(nn.Module):
def __init__(self, config: Owlv2Config, out_dim: int = 4):
super().__init__()
width = config.vision_config.hidden_size
self.dense0 = nn.Linear(width, width)
self.dense1 = nn.Linear(width, width)
self.gelu = nn.GELU()
self.dense2 = nn.Linear(width, out_dim)
def forward(self, image_features: torch.Tensor) -> torch.FloatTensor:
output = self.dense0(image_features)
output = self.gelu(output)
output = self.dense1(output)
output = self.gelu(output)
output = self.dense2(output)
return output
# Copied from transformers.models.owlvit.modeling_owlvit.OwlViTClassPredictionHead with OwlViT->Owlv2
class Owlv2ClassPredictionHead(nn.Module):
def __init__(self, config: Owlv2Config):
super().__init__()
out_dim = config.text_config.hidden_size
self.query_dim = config.vision_config.hidden_size
self.dense0 = nn.Linear(self.query_dim, out_dim)
self.logit_shift = nn.Linear(self.query_dim, 1)
self.logit_scale = nn.Linear(self.query_dim, 1)
self.elu = nn.ELU()
def forward(
self,
image_embeds: torch.FloatTensor,
query_embeds: Optional[torch.FloatTensor],
query_mask: Optional[torch.Tensor],
) -> Tuple[torch.FloatTensor]:
image_class_embeds = self.dense0(image_embeds)
if query_embeds is None:
device = image_class_embeds.device
batch_size, num_patches = image_class_embeds.shape[:2]
pred_logits = torch.zeros((batch_size, num_patches, self.query_dim)).to(device)
return (pred_logits, image_class_embeds)
# Normalize image and text features
image_class_embeds = image_class_embeds / (torch.linalg.norm(image_class_embeds, dim=-1, keepdim=True) + 1e-6)
query_embeds = query_embeds / (torch.linalg.norm(query_embeds, dim=-1, keepdim=True) + 1e-6)
# Get class predictions
pred_logits = torch.einsum("...pd,...qd->...pq", image_class_embeds, query_embeds)
# Apply a learnable shift and scale to logits
logit_shift = self.logit_shift(image_embeds)
logit_scale = self.logit_scale(image_embeds)
logit_scale = self.elu(logit_scale) + 1
pred_logits = (pred_logits + logit_shift) * logit_scale
if query_mask is not None:
if query_mask.ndim > 1:
query_mask = torch.unsqueeze(query_mask, dim=-2)
pred_logits = torch.where(query_mask == 0, torch.finfo(pred_logits.dtype).min, pred_logits)
pred_logits = pred_logits.to(torch.float32)
return (pred_logits, image_class_embeds)
class Owlv2ForObjectDetection(Owlv2PreTrainedModel):
config_class = Owlv2Config
def __init__(self, config: Owlv2Config):
super().__init__(config)
self.owlv2 = Owlv2Model(config)
self.class_head = Owlv2ClassPredictionHead(config)
self.box_head = Owlv2BoxPredictionHead(config)
self.objectness_head = Owlv2BoxPredictionHead(config, out_dim=1)
self.layer_norm = nn.LayerNorm(config.vision_config.hidden_size, eps=config.vision_config.layer_norm_eps)
self.sigmoid = nn.Sigmoid()
self.config = config
self.num_patches_height = self.config.vision_config.image_size // self.config.vision_config.patch_size
self.num_patches_width = self.config.vision_config.image_size // self.config.vision_config.patch_size
self.box_bias = self.compute_box_bias(self.num_patches_height, self.num_patches_width)
@staticmethod
# Copied from transformers.models.owlvit.modeling_owlvit.OwlViTForObjectDetection.normalize_grid_corner_coordinates
def normalize_grid_corner_coordinates(num_patches_height: int, num_patches_width: int) -> torch.Tensor:
# Create grid coordinates using torch
x_coordinates = torch.arange(1, num_patches_width + 1, dtype=torch.float32)
y_coordinates = torch.arange(1, num_patches_height + 1, dtype=torch.float32)
xx, yy = torch.meshgrid(x_coordinates, y_coordinates, indexing="xy")
# Stack the coordinates and divide by their respective patch counts
box_coordinates = torch.stack((xx, yy), dim=-1)
box_coordinates[..., 0] /= num_patches_width
box_coordinates[..., 1] /= num_patches_height
# Flatten (h, w, 2) -> (h*w, 2)
box_coordinates = box_coordinates.view(-1, 2)
return box_coordinates
def objectness_predictor(self, image_features: torch.FloatTensor) -> torch.FloatTensor:
"""Predicts the probability that each image feature token is an object.
Args:
image_features (`torch.FloatTensor` of shape `(batch_size, num_patches, hidden_dim)`)):
Features extracted from the image.
Returns:
Objectness scores.
"""
image_features = image_features.detach()
objectness_logits = self.objectness_head(image_features)
objectness_logits = objectness_logits[..., 0]
return objectness_logits
@lru_cache(maxsize=2)
# Copied from transformers.models.owlvit.modeling_owlvit.OwlViTForObjectDetection.compute_box_bias
def compute_box_bias(
self, num_patches_height: int, num_patches_width: int, feature_map: Optional[torch.FloatTensor] = None
) -> torch.Tensor:
if feature_map is not None:
raise ValueError("feature_map has been deprecated as an input. Please pass in num_patches instead")
# The box center is biased to its position on the feature grid
box_coordinates = self.normalize_grid_corner_coordinates(num_patches_height, num_patches_width)
box_coordinates = torch.clip(box_coordinates, 0.0, 1.0)
# Unnormalize xy
box_coord_bias = torch.log(box_coordinates + 1e-4) - torch.log1p(-box_coordinates + 1e-4)
# The box size is biased to the patch size
box_size = torch.full_like(box_coord_bias, 1.0)
box_size[..., 0] /= num_patches_width
box_size[..., 1] /= num_patches_height
box_size_bias = torch.log(box_size + 1e-4) - torch.log1p(-box_size + 1e-4)
# Compute box bias
box_bias = torch.cat([box_coord_bias, box_size_bias], dim=-1)
return box_bias
# Copied from transformers.models.owlvit.modeling_owlvit.OwlViTForObjectDetection.box_predictor
def box_predictor(
self,
image_feats: torch.FloatTensor,
feature_map: torch.FloatTensor,
interpolate_pos_encoding: bool = False,
) -> torch.FloatTensor:
"""
Args:
image_feats:
Features extracted from the image, returned by the `image_text_embedder` method.
feature_map:
A spatial re-arrangement of image_features, also returned by the `image_text_embedder` method.
interpolate_pos_encoding:
Whether to interpolate the pre-trained position encodings.
Returns:
pred_boxes:
List of predicted boxes (cxcywh normalized to 0, 1) nested within a dictionary.
"""
# Bounding box detection head [batch_size, num_boxes, 4].
pred_boxes = self.box_head(image_feats)
# Compute the location of each token on the grid and use it to compute a bias for the bbox prediction
if interpolate_pos_encoding:
_, num_patches_height, num_patches_width, _ = feature_map.shape
box_bias = self.compute_box_bias(num_patches_height, num_patches_width)
else:
box_bias = self.box_bias
box_bias = box_bias.to(feature_map.device)
pred_boxes += box_bias
pred_boxes = self.sigmoid(pred_boxes)
return pred_boxes
# Copied from transformers.models.owlvit.modeling_owlvit.OwlViTForObjectDetection.class_predictor
def class_predictor(
self,
image_feats: torch.FloatTensor,
query_embeds: Optional[torch.FloatTensor] = None,
query_mask: Optional[torch.Tensor] = None,
) -> Tuple[torch.FloatTensor]:
"""
Args:
image_feats:
Features extracted from the `image_text_embedder`.
query_embeds:
Text query embeddings.
query_mask:
Must be provided with query_embeddings. A mask indicating which query embeddings are valid.
"""
(pred_logits, image_class_embeds) = self.class_head(image_feats, query_embeds, query_mask)
return (pred_logits, image_class_embeds)
# Copied from transformers.models.owlvit.modeling_owlvit.OwlViTForObjectDetection.image_text_embedder with owlvit->owlv2
def image_text_embedder(
self,
input_ids: torch.Tensor,
pixel_values: torch.FloatTensor,
attention_mask: torch.Tensor,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
interpolate_pos_encoding: bool = False,
) -> Tuple[torch.FloatTensor]:
# Encode text and image
outputs = self.owlv2(
pixel_values=pixel_values,
input_ids=input_ids,
attention_mask=attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
interpolate_pos_encoding=interpolate_pos_encoding,
return_dict=True,
)
if interpolate_pos_encoding:
_, _, height, width = pixel_values.shape
num_patches_height = height // self.config.vision_config.patch_size
num_patches_width = width // self.config.vision_config.patch_size
else:
num_patches_height = self.num_patches_height
num_patches_width = self.num_patches_width
# Get image embeddings
last_hidden_state = outputs.vision_model_output[0]
image_embeds = self.owlv2.vision_model.post_layernorm(last_hidden_state)
# Resize class token
class_token_out = torch.broadcast_to(image_embeds[:, :1, :], image_embeds[:, :-1].shape)
# Merge image embedding with class tokens
image_embeds = image_embeds[:, 1:, :] * class_token_out
image_embeds = self.layer_norm(image_embeds)
# Resize to [batch_size, num_patches_height, num_patches_width, hidden_size]
new_size = (
image_embeds.shape[0],
num_patches_height,
num_patches_width,
image_embeds.shape[-1],
)
image_embeds = image_embeds.reshape(new_size)
text_embeds = outputs[-4]
return (text_embeds, image_embeds, outputs)
# Copied from transformers.models.owlvit.modeling_owlvit.OwlViTForObjectDetection.image_embedder with owlvit->owlv2, OwlViTModel->Owlv2Model
def image_embedder(
self,
pixel_values: torch.FloatTensor,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
interpolate_pos_encoding: bool = False,
) -> Tuple[torch.FloatTensor]:
# Get Owlv2Model vision embeddings (same as CLIP)
vision_outputs = self.owlv2.vision_model(
pixel_values=pixel_values, interpolate_pos_encoding=interpolate_pos_encoding, return_dict=True
)
if interpolate_pos_encoding:
_, _, height, width = pixel_values.shape
num_patches_height = height // self.config.vision_config.patch_size
num_patches_width = width // self.config.vision_config.patch_size
else:
num_patches_height = self.num_patches_height
num_patches_width = self.num_patches_width
# Apply post_layernorm to last_hidden_state, return non-projected output
last_hidden_state = vision_outputs[0]
image_embeds = self.owlv2.vision_model.post_layernorm(last_hidden_state)
# Resize class token
class_token_out = torch.broadcast_to(image_embeds[:, :1, :], image_embeds[:, :-1].shape)
# Merge image embedding with class tokens
image_embeds = image_embeds[:, 1:, :] * class_token_out
image_embeds = self.layer_norm(image_embeds)
# Resize to [batch_size, num_patches_height, num_patches_width, hidden_size]
new_size = (
image_embeds.shape[0],
num_patches_height,
num_patches_width,
image_embeds.shape[-1],
)
image_embeds = image_embeds.reshape(new_size)
return (image_embeds, vision_outputs)
# Copied from transformers.models.owlvit.modeling_owlvit.OwlViTForObjectDetection.embed_image_query
def embed_image_query(
self,
query_image_features: torch.FloatTensor,
query_feature_map: torch.FloatTensor,
interpolate_pos_encoding: bool = False,
) -> torch.FloatTensor:
_, class_embeds = self.class_predictor(query_image_features)
pred_boxes = self.box_predictor(query_image_features, query_feature_map, interpolate_pos_encoding)
pred_boxes_as_corners = center_to_corners_format(pred_boxes)
# Loop over query images
best_class_embeds = []
best_box_indices = []
pred_boxes_device = pred_boxes_as_corners.device
for i in range(query_image_features.shape[0]):
each_query_box = torch.tensor([[0, 0, 1, 1]], device=pred_boxes_device)
each_query_pred_boxes = pred_boxes_as_corners[i]
ious, _ = box_iou(each_query_box, each_query_pred_boxes)
# If there are no overlapping boxes, fall back to generalized IoU
if torch.all(ious[0] == 0.0):
ious = generalized_box_iou(each_query_box, each_query_pred_boxes)
# Use an adaptive threshold to include all boxes within 80% of the best IoU
iou_threshold = torch.max(ious) * 0.8
selected_inds = (ious[0] >= iou_threshold).nonzero()
if selected_inds.numel():
selected_embeddings = class_embeds[i][selected_inds.squeeze(1)]
mean_embeds = torch.mean(class_embeds[i], axis=0)
mean_sim = torch.einsum("d,id->i", mean_embeds, selected_embeddings)
best_box_ind = selected_inds[torch.argmin(mean_sim)]
best_class_embeds.append(class_embeds[i][best_box_ind])
best_box_indices.append(best_box_ind)
if best_class_embeds:
query_embeds = torch.stack(best_class_embeds)
box_indices = torch.stack(best_box_indices)
else:
query_embeds, box_indices = None, None
return query_embeds, box_indices, pred_boxes
@add_start_docstrings_to_model_forward(OWLV2_IMAGE_GUIDED_OBJECT_DETECTION_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=Owlv2ImageGuidedObjectDetectionOutput, config_class=Owlv2Config)
def image_guided_detection(
self,
pixel_values: torch.FloatTensor,
query_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,
) -> Owlv2ImageGuidedObjectDetectionOutput:
r"""
Returns:
Examples:
```python
>>> import requests
>>> from PIL import Image
>>> import torch
>>> from transformers import AutoProcessor, Owlv2ForObjectDetection
>>> processor = AutoProcessor.from_pretrained("google/owlv2-base-patch16-ensemble")
>>> model = Owlv2ForObjectDetection.from_pretrained("google/owlv2-base-patch16-ensemble")
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> query_url = "http://images.cocodataset.org/val2017/000000001675.jpg"
>>> query_image = Image.open(requests.get(query_url, stream=True).raw)
>>> inputs = processor(images=image, query_images=query_image, return_tensors="pt")
>>> # forward pass
>>> with torch.no_grad():
... outputs = model.image_guided_detection(**inputs)
>>> target_sizes = torch.Tensor([image.size[::-1]])
>>> # Convert outputs (bounding boxes and class logits) to Pascal VOC format (xmin, ymin, xmax, ymax)
>>> results = processor.post_process_image_guided_detection(
... outputs=outputs, threshold=0.9, nms_threshold=0.3, target_sizes=target_sizes
... )
>>> i = 0 # Retrieve predictions for the first image
>>> boxes, scores = results[i]["boxes"], results[i]["scores"]
>>> for box, score in zip(boxes, scores):
... box = [round(i, 2) for i in box.tolist()]
... print(f"Detected similar object with confidence {round(score.item(), 3)} at location {box}")
Detected similar object with confidence 0.938 at location [327.31, 54.94, 547.39, 268.06]
Detected similar object with confidence 0.959 at location [5.78, 360.65, 619.12, 366.39]
Detected similar object with confidence 0.902 at location [2.85, 360.01, 627.63, 380.8]
Detected similar object with confidence 0.985 at location [176.98, -29.45, 672.69, 182.83]
Detected similar object with confidence 1.0 at location [6.53, 14.35, 624.87, 470.82]
Detected similar object with confidence 0.998 at location [579.98, 29.14, 615.49, 489.05]
Detected similar object with confidence 0.985 at location [206.15, 10.53, 247.74, 466.01]
Detected similar object with confidence 0.947 at location [18.62, 429.72, 646.5, 457.72]
Detected similar object with confidence 0.996 at location [523.88, 20.69, 586.84, 483.18]
Detected similar object with confidence 0.998 at location [3.39, 360.59, 617.29, 499.21]
Detected similar object with confidence 0.969 at location [4.47, 449.05, 614.5, 474.76]
Detected similar object with confidence 0.966 at location [31.44, 463.65, 654.66, 471.07]
Detected similar object with confidence 0.924 at location [30.93, 468.07, 635.35, 475.39]
```"""
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
# Compute feature maps for the input and query images
query_feature_map = self.image_embedder(
pixel_values=query_pixel_values, interpolate_pos_encoding=interpolate_pos_encoding
)[0]
feature_map, vision_outputs = self.image_embedder(
pixel_values=pixel_values,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
interpolate_pos_encoding=interpolate_pos_encoding,
)
batch_size, num_patches_height, num_patches_width, hidden_dim = feature_map.shape
image_feats = torch.reshape(feature_map, (batch_size, num_patches_height * num_patches_width, hidden_dim))
batch_size, num_patches_height, num_patches_width, hidden_dim = query_feature_map.shape
query_image_feats = torch.reshape(
query_feature_map, (batch_size, num_patches_height * num_patches_width, hidden_dim)
)
# Get top class embedding and best box index for each query image in batch
query_embeds, best_box_indices, query_pred_boxes = self.embed_image_query(
query_image_feats, query_feature_map, interpolate_pos_encoding
)
# Predict object classes [batch_size, num_patches, num_queries+1]
(pred_logits, class_embeds) = self.class_predictor(image_feats=image_feats, query_embeds=query_embeds)
# Predict object boxes
target_pred_boxes = self.box_predictor(image_feats, feature_map, interpolate_pos_encoding)
if not return_dict:
output = (
feature_map,
query_feature_map,
target_pred_boxes,
query_pred_boxes,
pred_logits,
class_embeds,
vision_outputs.to_tuple(),
)
output = tuple(x for x in output if x is not None)
return output
return Owlv2ImageGuidedObjectDetectionOutput(
image_embeds=feature_map,
query_image_embeds=query_feature_map,
target_pred_boxes=target_pred_boxes,
query_pred_boxes=query_pred_boxes,
logits=pred_logits,
class_embeds=class_embeds,
text_model_output=None,
vision_model_output=vision_outputs,
)
@add_start_docstrings_to_model_forward(OWLV2_OBJECT_DETECTION_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=Owlv2ObjectDetectionOutput, config_class=Owlv2Config)
def forward(
self,
input_ids: torch.Tensor,
pixel_values: torch.FloatTensor,
attention_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
interpolate_pos_encoding: bool = False,
return_dict: Optional[bool] = None,
) -> Owlv2ObjectDetectionOutput:
r"""
Returns:
Examples:
```python
>>> import requests
>>> from PIL import Image
>>> import torch
>>> from transformers import Owlv2Processor, Owlv2ForObjectDetection
>>> processor = Owlv2Processor.from_pretrained("google/owlv2-base-patch16-ensemble")
>>> model = Owlv2ForObjectDetection.from_pretrained("google/owlv2-base-patch16-ensemble")
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> text_labels = [["a photo of a cat", "a photo of a dog"]]
>>> inputs = processor(text=text_labels, images=image, return_tensors="pt")
>>> outputs = model(**inputs)
>>> # Target image sizes (height, width) to rescale box predictions [batch_size, 2]
>>> target_sizes = torch.tensor([(image.height, image.width)])
>>> # Convert outputs (bounding boxes and class logits) to Pascal VOC format (xmin, ymin, xmax, ymax)
>>> results = processor.post_process_grounded_object_detection(
... outputs=outputs, target_sizes=target_sizes, threshold=0.1, text_labels=text_labels
... )
>>> # Retrieve predictions for the first image for the corresponding text queries
>>> result = results[0]
>>> boxes, scores, text_labels = result["boxes"], result["scores"], result["text_labels"]
>>> for box, score, text_label in zip(boxes, scores, text_labels):
... box = [round(i, 2) for i in box.tolist()]
... print(f"Detected {text_label} with confidence {round(score.item(), 3)} at location {box}")
Detected a photo of a cat with confidence 0.614 at location [341.67, 23.39, 642.32, 371.35]
Detected a photo of a cat with confidence 0.665 at location [6.75, 51.96, 326.62, 473.13]
```"""
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
# Embed images and text queries
query_embeds, feature_map, outputs = self.image_text_embedder(
input_ids=input_ids,
pixel_values=pixel_values,
attention_mask=attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
interpolate_pos_encoding=interpolate_pos_encoding,
)
# Text and vision model outputs
text_outputs = outputs.text_model_output
vision_outputs = outputs.vision_model_output
batch_size, num_patches_height, num_patches_width, hidden_dim = feature_map.shape
image_feats = torch.reshape(feature_map, (batch_size, num_patches_height * num_patches_width, hidden_dim))
# Reshape from [batch_size * max_text_queries, hidden_dim] -> [batch_size, max_text_queries, hidden_dim]
max_text_queries = input_ids.shape[0] // batch_size
query_embeds = query_embeds.reshape(batch_size, max_text_queries, query_embeds.shape[-1])
# If first token is 0, then this is a padded query [batch_size, num_queries].
input_ids = input_ids.reshape(batch_size, max_text_queries, input_ids.shape[-1])
query_mask = input_ids[..., 0] > 0
# Predict object classes [batch_size, num_patches, num_queries+1]
(pred_logits, class_embeds) = self.class_predictor(image_feats, query_embeds, query_mask)
# Predict objectness
objectness_logits = self.objectness_predictor(image_feats)
# Predict object boxes
pred_boxes = self.box_predictor(image_feats, feature_map, interpolate_pos_encoding)
if not return_dict:
output = (
pred_logits,
objectness_logits,
pred_boxes,
query_embeds,
feature_map,
class_embeds,
text_outputs.to_tuple(),
vision_outputs.to_tuple(),
)
output = tuple(x for x in output if x is not None)
return output
return Owlv2ObjectDetectionOutput(
image_embeds=feature_map,
text_embeds=query_embeds,
pred_boxes=pred_boxes,
logits=pred_logits,
objectness_logits=objectness_logits,
class_embeds=class_embeds,
text_model_output=text_outputs,
vision_model_output=vision_outputs,
)
__all__ = ["Owlv2Model", "Owlv2PreTrainedModel", "Owlv2TextModel", "Owlv2VisionModel", "Owlv2ForObjectDetection"]
| transformers/src/transformers/models/owlv2/modeling_owlv2.py/0 | {
"file_path": "transformers/src/transformers/models/owlv2/modeling_owlv2.py",
"repo_id": "transformers",
"token_count": 36595
} |
# coding=utf-8
# Copyright 2023 IBM and 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.
"""PatchTSMixer model configuration"""
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
logger = logging.get_logger(__name__)
class PatchTSMixerConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`PatchTSMixerModel`]. It is used to instantiate a
PatchTSMixer 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 PatchTSMixer
[ibm/patchtsmixer-etth1-pretrain](https://huggingface.co/ibm/patchtsmixer-etth1-pretrain) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
context_length (`int`, *optional*, defaults to 32):
The context/history length for the input sequence.
patch_length (`int`, *optional*, defaults to 8):
The patch length for the input sequence.
num_input_channels (`int`, *optional*, defaults to 1):
Number of input variates. For Univariate, set it to 1.
patch_stride (`int`, *optional*, defaults to 8):
Determines the overlap between two consecutive patches. Set it to patch_length (or greater), if we want
non-overlapping patches.
num_parallel_samples (`int`, *optional*, defaults to 100):
The number of samples to generate in parallel for probabilistic forecast.
d_model (`int`, *optional*, defaults to 8):
Hidden dimension of the model. Recommended to set it as a multiple of patch_length (i.e. 2-5X of
patch_length). Larger value indicates more complex model.
expansion_factor (`int`, *optional*, defaults to 2):
Expansion factor to use inside MLP. Recommended range is 2-5. Larger value indicates more complex model.
num_layers (`int`, *optional*, defaults to 3):
Number of layers to use. Recommended range is 3-15. Larger value indicates more complex model.
dropout (`float`, *optional*, defaults to 0.2):
The dropout probability the `PatchTSMixer` backbone. Recommended range is 0.2-0.7
mode (`str`, *optional*, defaults to `"common_channel"`):
Mixer Mode. Determines how to process the channels. Allowed values: "common_channel", "mix_channel". In
"common_channel" mode, we follow Channel-independent modelling with no explicit channel-mixing. Channel
mixing happens in an implicit manner via shared weights across channels. (preferred first approach) In
"mix_channel" mode, we follow explicit channel-mixing in addition to patch and feature mixer. (preferred
approach when channel correlations are very important to model)
gated_attn (`bool`, *optional*, defaults to `True`):
Enable Gated Attention.
norm_mlp (`str`, *optional*, defaults to `"LayerNorm"`):
Normalization layer (BatchNorm or LayerNorm).
self_attn (`bool`, *optional*, defaults to `False`):
Enable Tiny self attention across patches. This can be enabled when the output of Vanilla PatchTSMixer with
gated attention is not satisfactory. Enabling this leads to explicit pair-wise attention and modelling
across patches.
self_attn_heads (`int`, *optional*, defaults to 1):
Number of self-attention heads. Works only when `self_attn` is set to `True`.
use_positional_encoding (`bool`, *optional*, defaults to `False`):
Enable the use of positional embedding for the tiny self-attention layers. Works only when `self_attn` is
set to `True`.
positional_encoding_type (`str`, *optional*, defaults to `"sincos"`):
Positional encodings. Options `"random"` and `"sincos"` are supported. Works only when
`use_positional_encoding` is set to `True`
scaling (`string` or `bool`, *optional*, defaults to `"std"`):
Whether to scale the input targets via "mean" scaler, "std" scaler or no scaler if `None`. If `True`, the
scaler is set to "mean".
loss (`string`, *optional*, defaults to `"mse"`):
The loss function for the model corresponding to the `distribution_output` head. For parametric
distributions it is the negative log likelihood ("nll") and for point estimates it is the mean squared
error "mse".
init_std (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated normal weight initialization distribution.
post_init (`bool`, *optional*, defaults to `False`):
Whether to use custom weight initialization from `transformers` library, or the default initialization in
`PyTorch`. Setting it to `False` performs `PyTorch` weight initialization.
norm_eps (`float`, *optional*, defaults to 1e-05):
A value added to the denominator for numerical stability of normalization.
mask_type (`str`, *optional*, defaults to `"random"`):
Type of masking to use for Masked Pretraining mode. Allowed values are "random", "forecast". In Random
masking, points are masked randomly. In Forecast masking, points are masked towards the end.
random_mask_ratio (`float`, *optional*, defaults to 0.5):
Masking ratio to use when `mask_type` is `random`. Higher value indicates more masking.
num_forecast_mask_patches (`int` or `list`, *optional*, defaults to `[2]`):
Number of patches to be masked at the end of each batch sample. If it is an integer, all the samples in the
batch will have the same number of masked patches. If it is a list, samples in the batch will be randomly
masked by numbers defined in the list. This argument is only used for forecast pretraining.
mask_value (`float`, *optional*, defaults to `0.0`):
Mask value to use.
masked_loss (`bool`, *optional*, defaults to `True`):
Whether to compute pretraining loss only at the masked portions, or on the entire output.
channel_consistent_masking (`bool`, *optional*, defaults to `True`):
When true, masking will be same across all channels of a timeseries. Otherwise, masking positions will vary
across channels.
unmasked_channel_indices (`list`, *optional*):
Channels that are not masked during pretraining.
head_dropout (`float`, *optional*, defaults to 0.2):
The dropout probability the `PatchTSMixer` head.
distribution_output (`string`, *optional*, defaults to `"student_t"`):
The distribution emission head for the model when loss is "nll". Could be either "student_t", "normal" or
"negative_binomial".
prediction_length (`int`, *optional*, defaults to 16):
Number of time steps to forecast for a forecasting task. Also known as the Forecast Horizon.
prediction_channel_indices (`list`, *optional*):
List of channel indices to forecast. If None, forecast all channels. Target data is expected to have all
channels and we explicitly filter the channels in prediction and target before loss computation.
num_targets (`int`, *optional*, defaults to 3):
Number of targets (dimensionality of the regressed variable) for a regression task.
output_range (`list`, *optional*):
Output range to restrict for the regression task. Defaults to None.
head_aggregation (`str`, *optional*, defaults to `"max_pool"`):
Aggregation mode to enable for classification or regression task. Allowed values are `None`, "use_last",
"max_pool", "avg_pool".
Example:
```python
>>> from transformers import PatchTSMixerConfig, PatchTSMixerModel
>>> # Initializing a default PatchTSMixer configuration
>>> configuration = PatchTSMixerConfig()
>>> # Randomly initializing a model (with random weights) from the configuration
>>> model = PatchTSMixerModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "patchtsmixer"
attribute_map = {
"hidden_size": "d_model",
"num_hidden_layers": "num_layers",
}
def __init__(
self,
# Time series specific configuration
context_length: int = 32,
patch_length: int = 8,
num_input_channels: int = 1,
patch_stride: int = 8,
num_parallel_samples: int = 100,
# General model configuration
d_model: int = 8,
expansion_factor: int = 2,
num_layers: int = 3,
dropout: float = 0.2,
mode: str = "common_channel",
gated_attn: bool = True,
norm_mlp: str = "LayerNorm",
self_attn: bool = False,
self_attn_heads: int = 1,
use_positional_encoding: bool = False,
positional_encoding_type: str = "sincos",
scaling: Optional[Union[str, bool]] = "std",
loss: str = "mse",
init_std: float = 0.02,
post_init: bool = False,
norm_eps: float = 1e-5,
# Pretrain model configuration
mask_type: str = "random",
random_mask_ratio: float = 0.5,
num_forecast_mask_patches: Optional[Union[List[int], int]] = [2],
mask_value: int = 0,
masked_loss: bool = True,
channel_consistent_masking: bool = True,
unmasked_channel_indices: Optional[List[int]] = None,
# General head configuration
head_dropout: float = 0.2,
distribution_output: str = "student_t",
# Prediction head configuration
prediction_length: int = 16,
prediction_channel_indices: list = None,
# Classification/Regression configuration
num_targets: int = 3,
output_range: list = None,
head_aggregation: str = "max_pool",
**kwargs,
):
self.num_input_channels = num_input_channels
self.context_length = context_length
self.patch_length = patch_length
self.patch_stride = patch_stride
self.d_model = d_model
self.expansion_factor = expansion_factor
self.num_layers = num_layers
self.dropout = dropout
self.mode = mode
self.gated_attn = gated_attn
self.norm_mlp = norm_mlp
self.scaling = scaling
self.head_dropout = head_dropout
self.num_patches = (max(context_length, patch_length) - patch_length) // patch_stride + 1
self.mask_type = mask_type
self.random_mask_ratio = random_mask_ratio
self.num_forecast_mask_patches = num_forecast_mask_patches
self.mask_value = mask_value
self.channel_consistent_masking = channel_consistent_masking
self.masked_loss = masked_loss
self.patch_last = True
self.use_positional_encoding = use_positional_encoding
self.positional_encoding_type = positional_encoding_type
self.prediction_length = prediction_length
self.prediction_channel_indices = prediction_channel_indices
self.num_targets = num_targets
self.output_range = output_range
self.head_aggregation = head_aggregation
self.self_attn = self_attn
self.self_attn_heads = self_attn_heads
self.init_std = init_std
self.post_init = post_init
self.distribution_output = distribution_output
self.loss = loss
self.num_parallel_samples = num_parallel_samples
self.unmasked_channel_indices = unmasked_channel_indices
self.norm_eps = norm_eps
super().__init__(**kwargs)
__all__ = ["PatchTSMixerConfig"]
| transformers/src/transformers/models/patchtsmixer/configuration_patchtsmixer.py/0 | {
"file_path": "transformers/src/transformers/models/patchtsmixer/configuration_patchtsmixer.py",
"repo_id": "transformers",
"token_count": 4643
} |
# 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.
"""Pixtral model configuration"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
logger = logging.get_logger(__name__)
class PixtralVisionConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`PixtralVisionModel`]. It is used to instantiate an
Pixtral vision encoder according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a similar configuration to the vision encoder used by Pixtral-12B.
e.g. [pixtral-hf/pixtral-9b](https://huggingface.co/pixtral-hf/pixtral-9b)
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):
Dimension of the hidden representations.
intermediate_size (`int`, *optional*, defaults to 4096):
Dimension of the MLP representations.
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 in the Transformer encoder.
num_channels (`int`, *optional*, defaults to 3):
Number of input channels in the input images.
image_size (`int`, *optional*, defaults to 1024):
Max dimension of the input images.
patch_size (`int`, *optional*, defaults to 16):
Size of the image patches.
hidden_act (`str`, *optional*, defaults to `"gelu"`):
Activation function used in the hidden layers.
attention_dropout (`float`, *optional*, defaults to 0.0):
Dropout probability for the attention layers.
rope_theta (`float`, *optional*, defaults to 10000.0):
The base period of the RoPE embeddings.
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 import PixtralVisionModel, PixtralVisionConfig
>>> # Initializing a Pixtral-12B style configuration
>>> config = PixtralVisionConfig()
>>> # Initializing a model (with randomly initialized weights) from the configuration
>>> model = PixtralVisionModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "pixtral"
def __init__(
self,
hidden_size=1024,
intermediate_size=4096,
num_hidden_layers=24,
num_attention_heads=16,
num_channels=3,
image_size=1024,
patch_size=16,
hidden_act="gelu",
attention_dropout=0.0,
rope_theta=10000.0,
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.hidden_act = hidden_act
self.rope_theta = rope_theta
self.head_dim = hidden_size // num_attention_heads
self.initializer_range = initializer_range
__all__ = ["PixtralVisionConfig"]
| transformers/src/transformers/models/pixtral/configuration_pixtral.py/0 | {
"file_path": "transformers/src/transformers/models/pixtral/configuration_pixtral.py",
"repo_id": "transformers",
"token_count": 1518
} |
# coding=utf-8
# Copyright 2022 Sea AI Lab 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 PoolFormer model."""
import collections.abc
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 BaseModelOutputWithNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_utils import PreTrainedModel
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_poolformer import PoolFormerConfig
logger = logging.get_logger(__name__)
# General docstring
_CONFIG_FOR_DOC = "PoolFormerConfig"
# Base docstring
_CHECKPOINT_FOR_DOC = "sail/poolformer_s12"
_EXPECTED_OUTPUT_SHAPE = [1, 512, 7, 7]
# Image classification docstring
_IMAGE_CLASS_CHECKPOINT = "sail/poolformer_s12"
_IMAGE_CLASS_EXPECTED_OUTPUT = "tabby, tabby cat"
# 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->PoolFormer
class PoolFormerDropPath(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 PoolFormerEmbeddings(nn.Module):
"""
Construct Patch Embeddings.
"""
def __init__(self, hidden_size, num_channels, patch_size, stride, padding, norm_layer=None):
super().__init__()
patch_size = patch_size if isinstance(patch_size, collections.abc.Iterable) else (patch_size, patch_size)
stride = stride if isinstance(stride, collections.abc.Iterable) else (stride, stride)
padding = padding if isinstance(padding, collections.abc.Iterable) else (padding, padding)
self.projection = nn.Conv2d(num_channels, hidden_size, kernel_size=patch_size, stride=stride, padding=padding)
self.norm = norm_layer(hidden_size) if norm_layer else nn.Identity()
def forward(self, pixel_values):
embeddings = self.projection(pixel_values)
embeddings = self.norm(embeddings)
return embeddings
class PoolFormerGroupNorm(nn.GroupNorm):
"""
Group Normalization with 1 group. Input: tensor in shape [B, C, H, W]
"""
def __init__(self, num_channels, **kwargs):
super().__init__(1, num_channels, **kwargs)
class PoolFormerPooling(nn.Module):
def __init__(self, pool_size):
super().__init__()
self.pool = nn.AvgPool2d(pool_size, stride=1, padding=pool_size // 2, count_include_pad=False)
def forward(self, hidden_states):
return self.pool(hidden_states) - hidden_states
class PoolFormerOutput(nn.Module):
def __init__(self, config, dropout_prob, hidden_size, intermediate_size):
super().__init__()
self.conv1 = nn.Conv2d(hidden_size, intermediate_size, 1)
self.conv2 = nn.Conv2d(intermediate_size, hidden_size, 1)
self.drop = PoolFormerDropPath(dropout_prob)
if isinstance(config.hidden_act, str):
self.act_fn = ACT2FN[config.hidden_act]
else:
self.act_fn = config.hidden_act
def forward(self, hidden_states):
hidden_states = self.conv1(hidden_states)
hidden_states = self.act_fn(hidden_states)
hidden_states = self.drop(hidden_states)
hidden_states = self.conv2(hidden_states)
hidden_states = self.drop(hidden_states)
return hidden_states
class PoolFormerLayer(nn.Module):
"""This corresponds to the 'PoolFormerBlock' class in the original implementation."""
def __init__(self, config, num_channels, pool_size, hidden_size, intermediate_size, drop_path):
super().__init__()
self.pooling = PoolFormerPooling(pool_size)
self.output = PoolFormerOutput(config, drop_path, hidden_size, intermediate_size)
self.before_norm = PoolFormerGroupNorm(num_channels)
self.after_norm = PoolFormerGroupNorm(num_channels)
# Useful for training neural nets
self.drop_path = PoolFormerDropPath(drop_path) if drop_path > 0.0 else nn.Identity()
self.use_layer_scale = config.use_layer_scale
if config.use_layer_scale:
self.layer_scale_1 = nn.Parameter(
config.layer_scale_init_value * torch.ones((num_channels)), requires_grad=True
)
self.layer_scale_2 = nn.Parameter(
config.layer_scale_init_value * torch.ones((num_channels)), requires_grad=True
)
def forward(self, hidden_states):
if self.use_layer_scale:
pooling_output = self.pooling(self.before_norm(hidden_states))
scaled_op = self.layer_scale_1.unsqueeze(-1).unsqueeze(-1) * pooling_output
# First residual connection
hidden_states = hidden_states + self.drop_path(scaled_op)
outputs = ()
layer_output = self.output(self.after_norm(hidden_states))
scaled_op = self.layer_scale_2.unsqueeze(-1).unsqueeze(-1) * layer_output
# Second residual connection
output = hidden_states + self.drop_path(scaled_op)
outputs = (output,) + outputs
return outputs
else:
pooling_output = self.drop_path(self.pooling(self.before_norm(hidden_states)))
# First residual connection
hidden_states = pooling_output + hidden_states
outputs = ()
# Second residual connection inside the PoolFormerOutput block
layer_output = self.drop_path(self.output(self.after_norm(hidden_states)))
output = hidden_states + layer_output
outputs = (output,) + outputs
return outputs
class PoolFormerEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
# stochastic depth decay rule
dpr = [x.item() for x in torch.linspace(0, config.drop_path_rate, sum(config.depths))]
# patch embeddings
embeddings = []
for i in range(config.num_encoder_blocks):
embeddings.append(
PoolFormerEmbeddings(
patch_size=config.patch_sizes[i],
stride=config.strides[i],
padding=config.padding[i],
num_channels=config.num_channels if i == 0 else config.hidden_sizes[i - 1],
hidden_size=config.hidden_sizes[i],
)
)
self.patch_embeddings = nn.ModuleList(embeddings)
# Transformer blocks
blocks = []
cur = 0
for i in range(config.num_encoder_blocks):
# each block consists of layers
layers = []
if i != 0:
cur += config.depths[i - 1]
for j in range(config.depths[i]):
layers.append(
PoolFormerLayer(
config,
num_channels=config.hidden_sizes[i],
pool_size=config.pool_size,
hidden_size=config.hidden_sizes[i],
intermediate_size=int(config.hidden_sizes[i] * config.mlp_ratio),
drop_path=dpr[cur + j],
)
)
blocks.append(nn.ModuleList(layers))
self.block = nn.ModuleList(blocks)
def forward(self, pixel_values, output_hidden_states=False, return_dict=True):
all_hidden_states = () if output_hidden_states else None
hidden_states = pixel_values
for idx, layers in enumerate(zip(self.patch_embeddings, self.block)):
embedding_layer, block_layer = layers
# Get patch embeddings from hidden_states
hidden_states = embedding_layer(hidden_states)
# Send the embeddings through the blocks
for _, blk in enumerate(block_layer):
layer_outputs = blk(hidden_states)
hidden_states = layer_outputs[0]
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] if v is not None)
return BaseModelOutputWithNoAttention(last_hidden_state=hidden_states, hidden_states=all_hidden_states)
class PoolFormerPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = PoolFormerConfig
base_model_prefix = "poolformer"
main_input_name = "pixel_values"
_no_split_modules = ["PoolFormerLayer"]
def _init_weights(self, module):
"""Initialize the weights"""
if isinstance(module, (nn.Linear, nn.Conv2d)):
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):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
POOLFORMER_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 ([`PoolFormerConfig`]): 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.
"""
POOLFORMER_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
[`PoolFormerImageProcessor.__call__`] for details.
"""
@add_start_docstrings(
"The bare PoolFormer Model transformer outputting raw hidden-states without any specific head on top.",
POOLFORMER_START_DOCSTRING,
)
class PoolFormerModel(PoolFormerPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.config = config
self.encoder = PoolFormerEncoder(config)
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.embeddings.patch_embeddings
@add_start_docstrings_to_model_forward(POOLFORMER_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=BaseModelOutputWithNoAttention,
config_class=_CONFIG_FOR_DOC,
modality="vision",
expected_output=_EXPECTED_OUTPUT_SHAPE,
)
def forward(
self,
pixel_values: Optional[torch.FloatTensor] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, BaseModelOutputWithNoAttention]:
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")
encoder_outputs = self.encoder(
pixel_values,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = encoder_outputs[0]
if not return_dict:
return (sequence_output, None) + encoder_outputs[1:]
return BaseModelOutputWithNoAttention(
last_hidden_state=sequence_output,
hidden_states=encoder_outputs.hidden_states,
)
class PoolFormerFinalPooler(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
def forward(self, hidden_states):
output = self.dense(hidden_states)
return output
@add_start_docstrings(
"""
PoolFormer Model transformer with an image classification head on top
""",
POOLFORMER_START_DOCSTRING,
)
class PoolFormerForImageClassification(PoolFormerPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.poolformer = PoolFormerModel(config)
# Final norm
self.norm = PoolFormerGroupNorm(config.hidden_sizes[-1])
# Classifier head
self.classifier = (
nn.Linear(config.hidden_sizes[-1], 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(POOLFORMER_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT,
output_type=ImageClassifierOutputWithNoAttention,
config_class=_CONFIG_FOR_DOC,
expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT,
)
def forward(
self,
pixel_values: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, ImageClassifierOutputWithNoAttention]:
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.poolformer(
pixel_values,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = outputs[0]
logits = self.classifier(self.norm(sequence_output).mean([-2, -1]))
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 ImageClassifierOutputWithNoAttention(loss=loss, logits=logits, hidden_states=outputs.hidden_states)
__all__ = ["PoolFormerForImageClassification", "PoolFormerModel", "PoolFormerPreTrainedModel"]
| transformers/src/transformers/models/poolformer/modeling_poolformer.py/0 | {
"file_path": "transformers/src/transformers/models/poolformer/modeling_poolformer.py",
"repo_id": "transformers",
"token_count": 7387
} |
# coding=utf-8
# Copyright 2025 The Qwen Team 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 Qwen2.5-VL model."""
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint
from torch.nn import CrossEntropyLoss
from transformers.models.qwen2_vl.configuration_qwen2_vl import Qwen2VLConfig
from transformers.models.qwen2_vl.image_processing_qwen2_vl import Qwen2VLImageProcessor
from transformers.models.qwen2_vl.modeling_qwen2_vl import (
PatchEmbed,
PatchMerger,
Qwen2RMSNorm,
Qwen2VLCausalLMOutputWithPast,
Qwen2VLForConditionalGeneration,
Qwen2VLModel,
Qwen2VLPreTrainedModel,
VisionAttention,
VisionRotaryEmbedding,
VisionSdpaAttention,
)
from transformers.models.qwen2_vl.processing_qwen2_vl import Qwen2VLProcessor
from ...activations import ACT2FN
from ...cache_utils import StaticCache
from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import BatchFeature
from ...image_utils import ImageInput, VideoInput
from ...processing_utils import ProcessingKwargs, Unpack, VideosKwargs
from ...tokenization_utils_base import PreTokenizedInput, TextInput
from ...utils import is_flash_attn_2_available, is_torchdynamo_compiling
if is_flash_attn_2_available():
from flash_attn import flash_attn_varlen_func
from flash_attn.layers.rotary import apply_rotary_emb
else:
flash_attn_varlen_func = None
apply_rotary_emb = None
def apply_rotary_pos_emb_flashatt(tensor: torch.Tensor, freqs: torch.Tensor) -> torch.Tensor:
tensor_ = tensor.float()
cos = freqs.cos().float()
sin = freqs.sin().float()
output = apply_rotary_emb(tensor_, cos, sin).type_as(tensor)
return output
class Qwen2_5_VLVisionConfig(PretrainedConfig):
model_type = "qwen2_5_vl"
base_config_key = "vision_config"
def __init__(
self,
depth=32,
hidden_size=3584,
hidden_act="silu",
intermediate_size=3420,
num_heads=16,
in_channels=3,
patch_size=14,
spatial_merge_size=2,
temporal_patch_size=2,
tokens_per_second=4,
window_size=112,
out_hidden_size=3584,
fullatt_block_indexes=[7, 15, 23, 31],
**kwargs,
):
super().__init__(**kwargs)
self.depth = depth
self.hidden_size = hidden_size
self.hidden_act = hidden_act
self.intermediate_size = intermediate_size
self.num_heads = num_heads
self.in_channels = in_channels
self.patch_size = patch_size
self.spatial_merge_size = spatial_merge_size
self.temporal_patch_size = temporal_patch_size
self.tokens_per_second = tokens_per_second
self.window_size = window_size
self.fullatt_block_indexes = fullatt_block_indexes
self.out_hidden_size = out_hidden_size
class Qwen2_5_VLConfig(Qwen2VLConfig):
model_type = "qwen2_5_vl"
sub_configs = {"vision_config": Qwen2_5_VLVisionConfig}
class Qwen2_5_VLMLP(nn.Module):
def __init__(self, config, bias: bool = False):
super().__init__()
self.hidden_size = config.hidden_size
self.intermediate_size = config.intermediate_size
self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=bias)
self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=bias)
self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=bias)
self.act_fn = ACT2FN[config.hidden_act]
def forward(self, hidden_state):
return self.down_proj(self.act_fn(self.gate_proj(hidden_state)) * self.up_proj(hidden_state))
class Qwen2_5_VisionPatchEmbed(PatchEmbed):
pass
class Qwen2_5_VisionRotaryEmbedding(VisionRotaryEmbedding):
pass
class Qwen2_5_VLPatchMerger(PatchMerger):
def __init__(self, dim: int, context_dim: int, spatial_merge_size: int = 2) -> None:
super().__init__(dim, context_dim, spatial_merge_size)
self.ln_q = Qwen2RMSNorm(context_dim, eps=1e-6)
class Qwen2_5_VLVisionFlashAttention2(nn.Module):
def __init__(self, dim: int, num_heads: int = 16) -> None:
super().__init__()
self.num_heads = num_heads
self.qkv = nn.Linear(dim, dim * 3, bias=True)
self.proj = nn.Linear(dim, dim)
def forward(
self,
hidden_states: torch.Tensor,
cu_seqlens: torch.Tensor,
rotary_pos_emb: torch.Tensor = None,
) -> torch.Tensor:
seq_length = hidden_states.shape[0]
q, k, v = self.qkv(hidden_states).reshape(seq_length, 3, self.num_heads, -1).permute(1, 0, 2, 3).unbind(0)
q = apply_rotary_pos_emb_flashatt(q.unsqueeze(0), rotary_pos_emb).squeeze(0)
k = apply_rotary_pos_emb_flashatt(k.unsqueeze(0), rotary_pos_emb).squeeze(0)
max_seqlen = (cu_seqlens[1:] - cu_seqlens[:-1]).max().item()
attn_output = flash_attn_varlen_func(q, k, v, cu_seqlens, cu_seqlens, max_seqlen, max_seqlen).reshape(
seq_length, -1
)
attn_output = self.proj(attn_output)
return attn_output
class Qwen2_5_VLVisionAttention(VisionAttention):
pass
class Qwen2_5_VLVisionSdpaAttention(VisionSdpaAttention):
pass
QWEN2_5_VL_VISION_ATTENTION_CLASSES = {
"eager": Qwen2_5_VLVisionAttention,
"flash_attention_2": Qwen2_5_VLVisionFlashAttention2,
"sdpa": Qwen2_5_VLVisionSdpaAttention,
}
class Qwen2_5_VLVisionBlock(nn.Module):
def __init__(self, config, attn_implementation: str = "sdpa") -> None:
super().__init__()
self.norm1 = Qwen2RMSNorm(config.hidden_size, eps=1e-6)
self.norm2 = Qwen2RMSNorm(config.hidden_size, eps=1e-6)
self.attn = QWEN2_5_VL_VISION_ATTENTION_CLASSES[attn_implementation](
config.hidden_size, num_heads=config.num_heads
)
self.mlp = Qwen2_5_VLMLP(config, bias=True)
def forward(self, hidden_states, cu_seqlens, rotary_pos_emb) -> torch.Tensor:
hidden_states = hidden_states + self.attn(
self.norm1(hidden_states),
cu_seqlens=cu_seqlens,
rotary_pos_emb=rotary_pos_emb,
)
hidden_states = hidden_states + self.mlp(self.norm2(hidden_states))
return hidden_states
class Qwen2_5_VLPreTrainedModel(Qwen2VLPreTrainedModel):
pass
class Qwen2_5_VisionTransformerPretrainedModel(Qwen2_5_VLPreTrainedModel):
config_class = Qwen2_5_VLVisionConfig
_no_split_modules = ["Qwen2_5_VLVisionBlock"]
def __init__(self, config, *inputs, **kwargs) -> None:
super().__init__(config, *inputs, **kwargs)
self.spatial_merge_size = config.spatial_merge_size
self.patch_size = config.patch_size
self.fullatt_block_indexes = config.fullatt_block_indexes
self.window_size = config.window_size
self.spatial_merge_unit = self.spatial_merge_size * self.spatial_merge_size
self.patch_embed = Qwen2_5_VisionPatchEmbed(
patch_size=config.patch_size,
temporal_patch_size=config.temporal_patch_size,
in_channels=config.in_channels,
embed_dim=config.hidden_size,
)
head_dim = config.hidden_size // config.num_heads
self.rotary_pos_emb = Qwen2_5_VisionRotaryEmbedding(head_dim // 2)
self.blocks = nn.ModuleList(
[Qwen2_5_VLVisionBlock(config, config._attn_implementation) for _ in range(config.depth)]
)
self.merger = Qwen2_5_VLPatchMerger(
dim=config.out_hidden_size,
context_dim=config.hidden_size,
spatial_merge_size=config.spatial_merge_size,
)
self.gradient_checkpointing = False
def rot_pos_emb(self, grid_thw):
pos_ids = []
for t, h, w in grid_thw:
hpos_ids = torch.arange(h).unsqueeze(1).expand(-1, w)
hpos_ids = hpos_ids.reshape(
h // self.spatial_merge_size,
self.spatial_merge_size,
w // self.spatial_merge_size,
self.spatial_merge_size,
)
hpos_ids = hpos_ids.permute(0, 2, 1, 3)
hpos_ids = hpos_ids.flatten()
wpos_ids = torch.arange(w).unsqueeze(0).expand(h, -1)
wpos_ids = wpos_ids.reshape(
h // self.spatial_merge_size,
self.spatial_merge_size,
w // self.spatial_merge_size,
self.spatial_merge_size,
)
wpos_ids = wpos_ids.permute(0, 2, 1, 3)
wpos_ids = wpos_ids.flatten()
pos_ids.append(torch.stack([hpos_ids, wpos_ids], dim=-1).repeat(t, 1))
pos_ids = torch.cat(pos_ids, dim=0)
max_grid_size = grid_thw[:, 1:].max()
rotary_pos_emb_full = self.rotary_pos_emb(max_grid_size)
rotary_pos_emb = rotary_pos_emb_full[pos_ids].flatten(1)
return rotary_pos_emb
def get_window_index(self, grid_thw):
window_index: list = []
cu_window_seqlens: list = [0]
window_index_id = 0
vit_merger_window_size = self.window_size // self.spatial_merge_size // self.patch_size
for grid_t, grid_h, grid_w in grid_thw:
llm_grid_h, llm_grid_w = (
grid_h // self.spatial_merge_size,
grid_w // self.spatial_merge_size,
)
index = torch.arange(grid_t * llm_grid_h * llm_grid_w).reshape(grid_t, llm_grid_h, llm_grid_w)
pad_h = vit_merger_window_size - llm_grid_h % vit_merger_window_size
pad_w = vit_merger_window_size - llm_grid_w % vit_merger_window_size
num_windows_h = (llm_grid_h + pad_h) // vit_merger_window_size
num_windows_w = (llm_grid_w + pad_w) // vit_merger_window_size
index_padded = F.pad(index, (0, pad_w, 0, pad_h), "constant", -100)
index_padded = index_padded.reshape(
grid_t,
num_windows_h,
vit_merger_window_size,
num_windows_w,
vit_merger_window_size,
)
index_padded = index_padded.permute(0, 1, 3, 2, 4).reshape(
grid_t,
num_windows_h * num_windows_w,
vit_merger_window_size,
vit_merger_window_size,
)
seqlens = (index_padded != -100).sum([2, 3]).reshape(-1)
index_padded = index_padded.reshape(-1)
index_new = index_padded[index_padded != -100]
window_index.append(index_new + window_index_id)
cu_seqlens_tmp = seqlens.cumsum(0) * self.spatial_merge_unit + cu_window_seqlens[-1]
cu_window_seqlens.extend(cu_seqlens_tmp.tolist())
window_index_id += (grid_t * llm_grid_h * llm_grid_w).item()
window_index = torch.cat(window_index, dim=0)
return window_index, cu_window_seqlens
def forward(self, hidden_states: torch.Tensor, grid_thw: torch.Tensor) -> torch.Tensor:
"""
Args:
hidden_states (`torch.Tensor` of shape `(batch_size, seq_len, hidden_size)`):
The final hidden states of the model.
grid_thw (`torch.Tensor` of shape `(num_images_or_videos, 3)`):
The temporal, height and width of feature shape of each image in LLM.
Returns:
`torch.Tensor`: hidden_states.
"""
hidden_states = self.patch_embed(hidden_states)
rotary_pos_emb = self.rot_pos_emb(grid_thw)
window_index, cu_window_seqlens = self.get_window_index(grid_thw)
cu_window_seqlens = torch.tensor(
cu_window_seqlens,
device=hidden_states.device,
dtype=grid_thw.dtype if torch.jit.is_tracing() else torch.int32,
)
cu_window_seqlens = torch.unique_consecutive(cu_window_seqlens)
seq_len, _ = hidden_states.size()
hidden_states = hidden_states.reshape(seq_len // self.spatial_merge_unit, self.spatial_merge_unit, -1)
hidden_states = hidden_states[window_index, :, :]
hidden_states = hidden_states.reshape(seq_len, -1)
rotary_pos_emb = rotary_pos_emb.reshape(seq_len // self.spatial_merge_unit, self.spatial_merge_unit, -1)
rotary_pos_emb = rotary_pos_emb[window_index, :, :]
rotary_pos_emb = rotary_pos_emb.reshape(seq_len, -1)
cu_seqlens = torch.repeat_interleave(grid_thw[:, 1] * grid_thw[:, 2], grid_thw[:, 0]).cumsum(
dim=0,
# Select dtype based on the following factors:
# - FA2 requires that cu_seqlens_q must have dtype int32
# - torch.onnx.export requires that cu_seqlens_q must have same dtype as grid_thw
# See https://github.com/huggingface/transformers/pull/34852 for more information
dtype=grid_thw.dtype if torch.jit.is_tracing() else torch.int32,
)
cu_seqlens = F.pad(cu_seqlens, (1, 0), value=0)
for layer_num, blk in enumerate(self.blocks):
if layer_num in self.fullatt_block_indexes:
cu_seqlens_now = cu_seqlens
else:
cu_seqlens_now = cu_window_seqlens
if self.gradient_checkpointing and self.training:
hidden_states = self._gradient_checkpointing_func(
blk.__call__, hidden_states, cu_seqlens_now, rotary_pos_emb
)
else:
hidden_states = blk(
hidden_states,
cu_seqlens=cu_seqlens_now,
rotary_pos_emb=rotary_pos_emb,
)
hidden_states = self.merger(hidden_states)
reverse_indices = torch.argsort(window_index)
hidden_states = hidden_states[reverse_indices, :]
return hidden_states
class Qwen2_5_VLModel(Qwen2VLModel):
pass
@dataclass
class Qwen2_5_VLCausalLMOutputWithPast(Qwen2VLCausalLMOutputWithPast):
pass
class Qwen2_5_VLForConditionalGeneration(Qwen2VLForConditionalGeneration):
config_class = Qwen2_5_VLConfig
_no_split_modules = ["Qwen2VLDecoderLayer", "Qwen2_5_VLVisionBlock"]
def __init__(self, config):
super().__init__(config)
self.visual = Qwen2_5_VisionTransformerPretrainedModel._from_config(config.vision_config)
def get_rope_index(
self,
input_ids: Optional[torch.LongTensor] = None,
image_grid_thw: Optional[torch.LongTensor] = None,
video_grid_thw: Optional[torch.LongTensor] = None,
second_per_grid_ts: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
) -> Tuple[torch.Tensor, torch.Tensor]:
"""
Calculate the 3D rope index based on image and video's temporal, height and width in LLM.
Explanation:
Each embedding sequence contains vision embedding and text embedding or just contains text embedding.
For pure text embedding sequence, the rotary position embedding has no difference with modern LLMs.
Examples:
input_ids: [T T T T T], here T is for text.
temporal position_ids: [0, 1, 2, 3, 4]
height position_ids: [0, 1, 2, 3, 4]
width position_ids: [0, 1, 2, 3, 4]
For vision and text embedding sequence, we calculate 3D rotary position embedding for vision part
and 1D rotary position embeddin for text part.
Examples:
Temporal (Time): 3 patches, representing different segments of the video in time.
Height: 2 patches, dividing each frame vertically.
Width: 2 patches, dividing each frame horizontally.
We also have some important parameters:
fps (Frames Per Second): The video's frame rate, set to 1. This means one frame is processed each second.
tokens_per_second: This is a crucial parameter. It dictates how many "time-steps" or "temporal tokens" are conceptually packed into a one-second interval of the video. In this case, we have 25 tokens per second. So each second of the video will be represented with 25 separate time points. It essentially defines the temporal granularity.
temporal_patch_size: The number of frames that compose one temporal patch. Here, it's 2 frames.
interval: The step size for the temporal position IDs, calculated as tokens_per_second * temporal_patch_size / fps. In this case, 25 * 2 / 1 = 50. This means that each temporal patch will be have a difference of 50 in the temporal position IDs.
input_ids: [V V V V V V V V V V V V T T T T T], here V is for vision.
vision temporal position_ids: [0, 0, 0, 0, 50, 50, 50, 50, 100, 100, 100, 100]
vision height position_ids: [0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1]
vision width position_ids: [0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1]
text temporal position_ids: [101, 102, 103, 104, 105]
text height position_ids: [101, 102, 103, 104, 105]
text width position_ids: [101, 102, 103, 104, 105]
Here we calculate the text start position_ids as the max vision position_ids plus 1.
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.
image_grid_thw (`torch.LongTensor` of shape `(num_images, 3)`, *optional*):
The temporal, height and width of feature shape of each image in LLM.
video_grid_thw (`torch.LongTensor` of shape `(num_videos, 3)`, *optional*):
The temporal, height and width of feature shape of each video in LLM.
second_per_grid_ts (`torch.Tensor` of shape `(num_videos)`, *optional*):
The time interval (in seconds) for each grid along the temporal dimension in the 3D position 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**.
Returns:
position_ids (`torch.LongTensor` of shape `(3, batch_size, sequence_length)`)
mrope_position_deltas (`torch.Tensor` of shape `(batch_size)`)
"""
spatial_merge_size = self.config.vision_config.spatial_merge_size
image_token_id = self.config.image_token_id
video_token_id = self.config.video_token_id
vision_start_token_id = self.config.vision_start_token_id
mrope_position_deltas = []
if input_ids is not None and (image_grid_thw is not None or video_grid_thw is not None):
total_input_ids = input_ids
if attention_mask is None:
attention_mask = torch.ones_like(total_input_ids)
position_ids = torch.ones(
3,
input_ids.shape[0],
input_ids.shape[1],
dtype=input_ids.dtype,
device=input_ids.device,
)
image_index, video_index = 0, 0
attention_mask = attention_mask.to(total_input_ids.device)
for i, input_ids in enumerate(total_input_ids):
input_ids = input_ids[attention_mask[i] == 1]
image_nums, video_nums = 0, 0
vision_start_indices = torch.argwhere(input_ids == vision_start_token_id).squeeze(1)
vision_tokens = input_ids[vision_start_indices + 1]
image_nums = (vision_tokens == image_token_id).sum()
video_nums = (vision_tokens == video_token_id).sum()
input_tokens = input_ids.tolist()
llm_pos_ids_list: list = []
st = 0
remain_images, remain_videos = image_nums, video_nums
for _ in range(image_nums + video_nums):
if image_token_id in input_tokens and remain_images > 0:
ed_image = input_tokens.index(image_token_id, st)
else:
ed_image = len(input_tokens) + 1
if video_token_id in input_tokens and remain_videos > 0:
ed_video = input_tokens.index(video_token_id, st)
else:
ed_video = len(input_tokens) + 1
if ed_image < ed_video:
t, h, w = (
image_grid_thw[image_index][0],
image_grid_thw[image_index][1],
image_grid_thw[image_index][2],
)
second_per_grid_t = 0
image_index += 1
remain_images -= 1
ed = ed_image
else:
t, h, w = (
video_grid_thw[video_index][0],
video_grid_thw[video_index][1],
video_grid_thw[video_index][2],
)
if second_per_grid_ts is not None:
second_per_grid_t = second_per_grid_ts[video_index]
else:
second_per_grid_t = 1.0
video_index += 1
remain_videos -= 1
ed = ed_video
llm_grid_t, llm_grid_h, llm_grid_w = (
t.item(),
h.item() // spatial_merge_size,
w.item() // spatial_merge_size,
)
text_len = ed - st
st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0
llm_pos_ids_list.append(torch.arange(text_len).view(1, -1).expand(3, -1) + st_idx)
range_tensor = torch.arange(llm_grid_t).view(-1, 1)
expanded_range = range_tensor.expand(-1, llm_grid_h * llm_grid_w)
time_tensor = expanded_range * second_per_grid_t * self.config.vision_config.tokens_per_second
time_tensor_long = time_tensor.long()
t_index = time_tensor_long.flatten()
h_index = torch.arange(llm_grid_h).view(1, -1, 1).expand(llm_grid_t, -1, llm_grid_w).flatten()
w_index = torch.arange(llm_grid_w).view(1, 1, -1).expand(llm_grid_t, llm_grid_h, -1).flatten()
llm_pos_ids_list.append(torch.stack([t_index, h_index, w_index]) + text_len + st_idx)
st = ed + llm_grid_t * llm_grid_h * llm_grid_w
if st < len(input_tokens):
st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0
text_len = len(input_tokens) - st
llm_pos_ids_list.append(torch.arange(text_len).view(1, -1).expand(3, -1) + st_idx)
llm_positions = torch.cat(llm_pos_ids_list, dim=1).reshape(3, -1)
position_ids[..., i, attention_mask[i] == 1] = llm_positions.to(position_ids.device)
mrope_position_deltas.append(llm_positions.max() + 1 - len(total_input_ids[i]))
mrope_position_deltas = torch.tensor(mrope_position_deltas, device=input_ids.device).unsqueeze(1)
return position_ids, mrope_position_deltas
else:
if attention_mask is not None:
position_ids = attention_mask.long().cumsum(-1) - 1
position_ids.masked_fill_(attention_mask == 0, 1)
position_ids = position_ids.unsqueeze(0).expand(3, -1, -1).to(attention_mask.device)
max_position_ids = position_ids.max(0, keepdim=False)[0].max(-1, keepdim=True)[0]
mrope_position_deltas = max_position_ids + 1 - attention_mask.shape[-1]
else:
position_ids = (
torch.arange(input_ids.shape[1], device=input_ids.device)
.view(1, 1, -1)
.expand(3, input_ids.shape[0], -1)
)
mrope_position_deltas = torch.zeros(
[input_ids.shape[0], 1],
device=input_ids.device,
dtype=input_ids.dtype,
)
return position_ids, mrope_position_deltas
def forward(
self,
input_ids: 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,
return_dict: Optional[bool] = None,
pixel_values: Optional[torch.Tensor] = None,
pixel_values_videos: Optional[torch.FloatTensor] = None,
image_grid_thw: Optional[torch.LongTensor] = None,
video_grid_thw: Optional[torch.LongTensor] = None,
rope_deltas: Optional[torch.LongTensor] = None,
cache_position: Optional[torch.LongTensor] = None,
second_per_grid_ts: Optional[torch.Tensor] = None,
) -> Union[Tuple, Qwen2_5_VLCausalLMOutputWithPast]:
r"""
Args:
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 PIL import Image
>>> import requests
>>> from transformers import AutoProcessor, Qwen2_5_VLForConditionalGeneration
>>> model = Qwen2_5_VLForConditionalGeneration.from_pretrained("Qwen/Qwen2.5-VL-7B-Instruct")
>>> processor = AutoProcessor.from_pretrained("Qwen/Qwen2.5-VL-7B-Instruct")
>>> messages = [
{
"role": "user",
"content": [
{"type": "image"},
{"type": "text", "text": "What is shown in this image?"},
],
},
]
>>> url = "https://www.ilankelman.org/stopsigns/australia.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
>>> inputs = processor(text=[text], images=[image], vision_infos=[vision_infos])
>>> # 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]
"The image shows a street scene with a red stop sign in the foreground. In the background, there is a large red gate with Chinese characters ..."
```"""
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 None:
inputs_embeds = self.model.embed_tokens(input_ids)
if pixel_values is not None:
pixel_values = pixel_values.type(self.visual.dtype)
image_embeds = self.visual(pixel_values, grid_thw=image_grid_thw)
n_image_tokens = (input_ids == self.config.image_token_id).sum().item()
n_image_features = image_embeds.shape[0]
if n_image_tokens != n_image_features:
raise ValueError(
f"Image features and image tokens do not match: tokens: {n_image_tokens}, features {n_image_features}"
)
mask = input_ids == self.config.image_token_id
mask_unsqueezed = mask.unsqueeze(-1)
mask_expanded = mask_unsqueezed.expand_as(inputs_embeds)
image_mask = mask_expanded.to(inputs_embeds.device)
image_embeds = image_embeds.to(inputs_embeds.device, inputs_embeds.dtype)
inputs_embeds = inputs_embeds.masked_scatter(image_mask, image_embeds)
if pixel_values_videos is not None:
pixel_values_videos = pixel_values_videos.type(self.visual.dtype)
video_embeds = self.visual(pixel_values_videos, grid_thw=video_grid_thw)
n_video_tokens = (input_ids == self.config.video_token_id).sum().item()
n_video_features = video_embeds.shape[0]
if n_video_tokens != n_video_features:
raise ValueError(
f"Video features and video tokens do not match: tokens: {n_video_tokens}, features {n_video_features}"
)
mask = input_ids == self.config.video_token_id
mask_unsqueezed = mask.unsqueeze(-1)
mask_expanded = mask_unsqueezed.expand_as(inputs_embeds)
video_mask = mask_expanded.to(inputs_embeds.device)
video_embeds = video_embeds.to(inputs_embeds.device, inputs_embeds.dtype)
inputs_embeds = inputs_embeds.masked_scatter(video_mask, video_embeds)
if attention_mask is not None:
attention_mask = attention_mask.to(inputs_embeds.device)
# if we get 4D attention mask we cannot calculate rope deltas anymore. TODO @raushan fixme
if position_ids is None and (attention_mask is None or attention_mask.ndim == 2):
# calculate RoPE index once per generation in the pre-fill stage only
if (
(cache_position is not None and cache_position[0] == 0)
or self.rope_deltas is None
or (past_key_values is None or past_key_values.get_seq_length() == 0)
):
position_ids, rope_deltas = self.get_rope_index(
input_ids,
image_grid_thw,
video_grid_thw,
second_per_grid_ts,
attention_mask,
)
self.rope_deltas = rope_deltas
# then use the prev pre-calculated rope-deltas to get the correct position ids
else:
batch_size, seq_length, _ = inputs_embeds.shape
delta = (
(cache_position[0] + self.rope_deltas).to(inputs_embeds.device)
if cache_position is not None
else 0
)
position_ids = torch.arange(seq_length, device=inputs_embeds.device)
position_ids = position_ids.view(1, -1).expand(batch_size, -1)
if cache_position is not None: # otherwise `deltas` is an int `0`
delta = delta.repeat_interleave(batch_size // delta.shape[0], dim=0)
position_ids = position_ids.add(delta)
position_ids = position_ids.unsqueeze(0).expand(3, -1, -1)
outputs = self.model(
input_ids=None,
position_ids=position_ids,
attention_mask=attention_mask,
past_key_values=past_key_values,
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 = outputs[0]
logits = self.lm_head(hidden_states)
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
loss_fct = CrossEntropyLoss()
shift_logits = shift_logits.view(-1, self.config.vocab_size)
shift_labels = shift_labels.view(-1)
# Enable model parallelism
shift_labels = shift_labels.to(shift_logits.device)
loss = loss_fct(shift_logits, shift_labels)
if not return_dict:
output = (logits,) + outputs[1:]
return (loss,) + output if loss is not None else output
return Qwen2_5_VLCausalLMOutputWithPast(
loss=loss,
logits=logits,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
rope_deltas=self.rope_deltas,
)
def prepare_inputs_for_generation(
self,
input_ids,
past_key_values=None,
attention_mask=None,
inputs_embeds=None,
cache_position=None,
position_ids=None,
use_cache=True,
pixel_values=None,
pixel_values_videos=None,
image_grid_thw=None,
video_grid_thw=None,
second_per_grid_ts=None,
**kwargs,
):
# Overwritten -- in specific circumstances we don't want to forward image inputs to the model
# 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)
# Exception 4: If input_embeds are passed then slice it through `cache_position`, to keep only the unprocessed tokens and
# generate the first token for each sequence. Later use the generated Input ids for continuation.
if past_key_values is not None:
if inputs_embeds is not None and input_ids.shape[1] == 0: # Exception 4
inputs_embeds = inputs_embeds[:, -cache_position.shape[0] :]
elif (
inputs_embeds is not None # Exception 1
or (is_torchdynamo_compiling() 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]
if cache_position[0] != 0:
pixel_values = None
pixel_values_videos = None
# if `inputs_embeds` are passed, we only want to use them in the 1st generation step
if inputs_embeds is not None and len(cache_position) == inputs_embeds.shape[1]:
model_inputs = {"inputs_embeds": inputs_embeds, "input_ids": None}
else:
model_inputs = {"input_ids": input_ids, "inputs_embeds": None}
if isinstance(past_key_values, StaticCache) and attention_mask.ndim == 2:
if model_inputs["inputs_embeds"] is not None:
batch_size, sequence_length, _ = inputs_embeds.shape
device = inputs_embeds.device
else:
batch_size, sequence_length = input_ids.shape
device = input_ids.device
attention_mask = self.model._prepare_4d_causal_attention_mask_with_cache_position(
attention_mask,
sequence_length=sequence_length,
target_length=past_key_values.get_max_cache_shape(),
dtype=self.lm_head.weight.dtype,
device=device,
cache_position=cache_position,
batch_size=batch_size,
config=self.config,
past_key_values=past_key_values,
)
model_inputs.update(
{
"position_ids": position_ids,
"past_key_values": past_key_values,
"use_cache": use_cache,
"attention_mask": attention_mask,
"pixel_values": pixel_values,
"pixel_values_videos": pixel_values_videos,
"image_grid_thw": image_grid_thw,
"video_grid_thw": video_grid_thw,
"cache_position": cache_position,
"second_per_grid_ts": second_per_grid_ts,
}
)
return model_inputs
class Qwen2_5_VLImageProcessor(Qwen2VLImageProcessor):
r"""
Constructs a Qwen2.5-VL image processor that dynamically resizes images based on the original images.
Args:
do_resize (`bool`, *optional*, defaults to `True`):
Whether to resize the image's (height, width) dimensions.
resample (`PILImageResampling`, *optional*, defaults to `Resampling.BICUBIC`):
Resampling filter to use when resizing the image.
do_rescale (`bool`, *optional*, defaults to `True`):
Whether to rescale the image by the specified scale `rescale_factor`.
rescale_factor (`int` or `float`, *optional*, defaults to `1/255`):
Scale factor to use if rescaling the image.
do_normalize (`bool`, *optional*, defaults to `True`):
Whether to normalize the image.
image_mean (`float` or `List[float]`, *optional*, defaults to `[0.48145466, 0.4578275, 0.40821073]`):
Mean to use if normalizing the image. This is a float or list of floats for each channel in the image.
image_std (`float` or `List[float]`, *optional*, defaults to `[0.26862954, 0.26130258, 0.27577711]`):
Standard deviation to use if normalizing the image. This is a float or list of floats for each channel in the image.
do_convert_rgb (`bool`, *optional*, defaults to `True`):
Whether to convert the image to RGB.
min_pixels (`int`, *optional*, defaults to `56 * 56`):
The min pixels of the image to resize the image.
max_pixels (`int`, *optional*, defaults to `28 * 28 * 1280`):
The max pixels of the image to resize the image.
patch_size (`int`, *optional*, defaults to 14):
The spacial patch size of the vision encoder.
temporal_patch_size (`int`, *optional*, defaults to 2):
The temporal patch size of the vision encoder.
merge_size (`int`, *optional*, defaults to 2):
The merge size of the vision encoder to llm encoder.
"""
model_input_names = [
"pixel_values",
"image_grid_thw",
"pixel_values_videos",
"video_grid_thw",
"second_per_grid_ts",
]
class Qwen2_5_VLVideosProcessorKwargs(VideosKwargs, total=False):
fps: Union[List[float], float]
class Qwen2_5_VLProcessorKwargs(ProcessingKwargs, total=False):
videos_kwargs: Qwen2_5_VLVideosProcessorKwargs
_defaults = {
"text_kwargs": {
"padding": False,
},
"videos_kwargs": {"fps": 2.0},
}
class Qwen2_5_VLProcessor(Qwen2VLProcessor):
r"""
Constructs a Qwen2.5-VL processor which wraps a Qwen2.5-VL image processor and a Qwen2 tokenizer into a single processor.
[`Qwen2_5_VLProcessor`] offers all the functionalities of [`Qwen2_5_VLImageProcessor`] and [`Qwen2TokenizerFast`]. See the
[`~Qwen2_5_VLProcessor.__call__`] and [`~Qwen2_5_VLProcessor.decode`] for more information.
Args:
image_processor ([`Qwen2_5_VLImageProcessor`], *optional*):
The image processor is a required input.
tokenizer ([`Qwen2TokenizerFast`], *optional*):
The tokenizer is a required input.
chat_template (`str`, *optional*): A Jinja template which will be used to convert lists of messages
in a chat into a tokenizable string.
"""
image_processor_class = "Qwen2_5_VLImageProcessor"
def __call__(
self,
images: ImageInput = None,
text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,
videos: VideoInput = None,
**kwargs: Unpack[Qwen2_5_VLProcessorKwargs],
) -> BatchFeature:
"""
Main method to prepare for the model one or several sequences(s) and image(s). This method forwards the `text`
and `kwargs` arguments to Qwen2TokenizerFast's [`~Qwen2TokenizerFast.__call__`] if `text` is not `None` to encode
the text. To prepare the vision inputs, this method forwards the `vision_infos` and `kwrags` arguments to
Qwen2_5_VLImageProcessor's [`~Qwen2_5_VLImageProcessor.__call__`] if `vision_infos` is not `None`.
Args:
images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `List[PIL.Image.Image]`, `List[np.ndarray]`, `List[torch.Tensor]`):
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 (`str`, `List[str]`, `List[List[str]]`):
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).
videos (`np.ndarray`, `torch.Tensor`, `List[np.ndarray]`, `List[torch.Tensor]`):
The image or batch of videos to be prepared. Each video can be a 4D NumPy array or PyTorch
tensor, or a nested list of 3D frames. Both channels-first and channels-last formats are supported.
return_tensors (`str` or [`~utils.TensorType`], *optional*):
If set, will return tensors of a particular framework. Acceptable values are:
- `'tf'`: Return TensorFlow `tf.constant` objects.
- `'pt'`: Return PyTorch `torch.Tensor` objects.
- `'np'`: Return NumPy `np.ndarray` objects.
- `'jax'`: Return JAX `jnp.ndarray` objects.
Returns:
[`BatchFeature`]: A [`BatchFeature`] with the following fields:
- **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`.
- **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
`return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not
`None`).
- **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`.
- **pixel_values_videos** -- Pixel values of videos to be fed to a model. Returned when `videos` is not `None`.
- **image_grid_thw** -- List of image 3D grid in LLM. Returned when `images` is not `None`.
- **video_grid_thw** -- List of video 3D grid in LLM. Returned when `videos` is not `None`.
- **second_per_grid_ts** -- List of video seconds per time grid. Returned when `videos` is not `None`.
"""
output_kwargs = self._merge_kwargs(
Qwen2_5_VLProcessorKwargs,
tokenizer_init_kwargs=self.tokenizer.init_kwargs,
**kwargs,
)
if images is not None:
image_inputs = self.image_processor(images=images, videos=None, **output_kwargs["images_kwargs"])
image_grid_thw = image_inputs["image_grid_thw"]
else:
image_inputs = {}
image_grid_thw = None
if videos is not None:
videos_inputs = self.image_processor(images=None, videos=videos, **output_kwargs["images_kwargs"])
video_grid_thw = videos_inputs["video_grid_thw"]
fps = output_kwargs["videos_kwargs"].pop("fps", 2.0)
if isinstance(fps, (int, float)):
second_per_grid_ts = [self.image_processor.temporal_patch_size / fps] * len(video_grid_thw)
elif hasattr(fps, "__len__") and len(fps) == len(video_grid_thw):
second_per_grid_ts = [self.image_processor.temporal_patch_size / tmp for tmp in fps]
else:
raise ValueError(
f"The length of fps ({len(fps) if hasattr(fps, '__len__') else fps}) must be equal to the length of video_grid_thw ({len(video_grid_thw)}) or fps should be a single number."
)
videos_inputs.update({"second_per_grid_ts": second_per_grid_ts})
else:
videos_inputs = {}
video_grid_thw = None
if not isinstance(text, list):
text = [text]
if image_grid_thw is not None:
merge_length = self.image_processor.merge_size**2
index = 0
for i in range(len(text)):
while self.image_token in text[i]:
text[i] = text[i].replace(
self.image_token,
"<|placeholder|>" * (image_grid_thw[index].prod() // merge_length),
1,
)
index += 1
text[i] = text[i].replace("<|placeholder|>", self.image_token)
if video_grid_thw is not None:
merge_length = self.image_processor.merge_size**2
index = 0
for i in range(len(text)):
while self.video_token in text[i]:
text[i] = text[i].replace(
self.video_token,
"<|placeholder|>" * (video_grid_thw[index].prod() // merge_length),
1,
)
index += 1
text[i] = text[i].replace("<|placeholder|>", self.video_token)
text_inputs = self.tokenizer(text, **output_kwargs["text_kwargs"])
return BatchFeature(data={**text_inputs, **image_inputs, **videos_inputs})
__all__ = [
"Qwen2_5_VLConfig",
"Qwen2_5_VLForConditionalGeneration",
"Qwen2_5_VLModel",
"Qwen2_5_VLPreTrainedModel",
"Qwen2_5_VLImageProcessor",
"Qwen2_5_VLProcessor",
]
| transformers/src/transformers/models/qwen2_5_vl/modular_qwen2_5_vl.py/0 | {
"file_path": "transformers/src/transformers/models/qwen2_5_vl/modular_qwen2_5_vl.py",
"repo_id": "transformers",
"token_count": 22748
} |
# coding=utf-8
# Copyright 2020, The RAG 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.
"""RAG model configuration"""
from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings
RAG_CONFIG_DOC = r"""
[`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and
can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information.
Args:
title_sep (`str`, *optional*, defaults to `" / "`):
Separator inserted between the title and the text of the retrieved document when calling [`RagRetriever`].
doc_sep (`str`, *optional*, defaults to `" // "`):
Separator inserted between the text of the retrieved document and the original input when calling
[`RagRetriever`].
n_docs (`int`, *optional*, defaults to 5):
Number of documents to retrieve.
max_combined_length (`int`, *optional*, defaults to 300):
Max length of contextualized input returned by [`~RagRetriever.__call__`].
retrieval_vector_size (`int`, *optional*, defaults to 768):
Dimensionality of the document embeddings indexed by [`RagRetriever`].
retrieval_batch_size (`int`, *optional*, defaults to 8):
Retrieval batch size, defined as the number of queries issues concurrently to the faiss index encapsulated
[`RagRetriever`].
dataset (`str`, *optional*, defaults to `"wiki_dpr"`):
A dataset identifier of the indexed dataset in HuggingFace Datasets (list all available datasets and ids
using `datasets.list_datasets()`).
dataset_split (`str`, *optional*, defaults to `"train"`)
Which split of the `dataset` to load.
index_name (`str`, *optional*, defaults to `"compressed"`)
The index name of the index associated with the `dataset`. One can choose between `"legacy"`, `"exact"` and
`"compressed"`.
index_path (`str`, *optional*)
The path to the serialized faiss index on disk.
passages_path (`str`, *optional*):
A path to text passages compatible with the faiss index. Required if using
[`~models.rag.retrieval_rag.LegacyIndex`]
use_dummy_dataset (`bool`, *optional*, defaults to `False`)
Whether to load a "dummy" variant of the dataset specified by `dataset`.
label_smoothing (`float`, *optional*, defaults to 0.0):
Only relevant if `return_loss` is set to `True`. Controls the `epsilon` parameter value for label smoothing
in the loss calculation. If set to 0, no label smoothing is performed.
do_marginalize (`bool`, *optional*, defaults to `False`):
If `True`, the logits are marginalized over all documents by making use of
`torch.nn.functional.log_softmax`.
reduce_loss (`bool`, *optional*, defaults to `False`):
Whether or not to reduce the NLL loss using the `torch.Tensor.sum` operation.
do_deduplication (`bool`, *optional*, defaults to `True`):
Whether or not to deduplicate the generations from different context documents for a given input. Has to be
set to `False` if used while training with distributed backend.
exclude_bos_score (`bool`, *optional*, defaults to `False`):
Whether or not to disregard the BOS token when computing the loss.
output_retrieved(`bool`, *optional*, defaults to `False`):
If set to `True`, `retrieved_doc_embeds`, `retrieved_doc_ids`, `context_input_ids` and
`context_attention_mask` are returned. See returned tensors for more detail.
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models).
forced_eos_token_id (`int`, *optional*):
The id of the token to force as the last generated token when `max_length` is reached. Usually set to
`eos_token_id`.
"""
@add_start_docstrings(RAG_CONFIG_DOC)
class RagConfig(PretrainedConfig):
model_type = "rag"
is_composition = True
def __init__(
self,
vocab_size=None,
is_encoder_decoder=True,
prefix=None,
bos_token_id=None,
pad_token_id=None,
eos_token_id=None,
decoder_start_token_id=None,
title_sep=" / ",
doc_sep=" // ",
n_docs=5,
max_combined_length=300,
retrieval_vector_size=768,
retrieval_batch_size=8,
dataset="wiki_dpr",
dataset_split="train",
index_name="compressed",
index_path=None,
passages_path=None,
use_dummy_dataset=False,
reduce_loss=False,
label_smoothing=0.0,
do_deduplication=True,
exclude_bos_score=False,
do_marginalize=False,
output_retrieved=False,
use_cache=True,
forced_eos_token_id=None,
dataset_revision=None,
**kwargs,
):
super().__init__(
bos_token_id=bos_token_id,
pad_token_id=pad_token_id,
eos_token_id=eos_token_id,
decoder_start_token_id=decoder_start_token_id,
forced_eos_token_id=forced_eos_token_id,
is_encoder_decoder=is_encoder_decoder,
prefix=prefix,
vocab_size=vocab_size,
**kwargs,
)
if "question_encoder" not in kwargs or "generator" not in kwargs:
raise ValueError(
f"A configuraton of type {self.model_type} cannot be instantiated because "
f"both `question_encoder` and `generator` sub-configurations were not passed, only {kwargs}"
)
question_encoder_config = kwargs.pop("question_encoder")
question_encoder_model_type = question_encoder_config.pop("model_type")
decoder_config = kwargs.pop("generator")
decoder_model_type = decoder_config.pop("model_type")
from ..auto.configuration_auto import AutoConfig
self.question_encoder = AutoConfig.for_model(question_encoder_model_type, **question_encoder_config)
self.generator = AutoConfig.for_model(decoder_model_type, **decoder_config)
self.reduce_loss = reduce_loss
self.label_smoothing = label_smoothing
self.exclude_bos_score = exclude_bos_score
self.do_marginalize = do_marginalize
self.title_sep = title_sep
self.doc_sep = doc_sep
self.n_docs = n_docs
self.max_combined_length = max_combined_length
self.dataset = dataset
self.dataset_split = dataset_split
self.index_name = index_name
self.retrieval_vector_size = retrieval_vector_size
self.retrieval_batch_size = retrieval_batch_size
self.passages_path = passages_path
self.index_path = index_path
self.use_dummy_dataset = use_dummy_dataset
self.dataset_revision = dataset_revision
self.output_retrieved = output_retrieved
self.do_deduplication = do_deduplication
self.use_cache = use_cache
if self.forced_eos_token_id is None:
self.forced_eos_token_id = getattr(self.generator, "forced_eos_token_id", None)
@classmethod
def from_question_encoder_generator_configs(
cls, question_encoder_config: PretrainedConfig, generator_config: PretrainedConfig, **kwargs
) -> PretrainedConfig:
r"""
Instantiate a [`EncoderDecoderConfig`] (or a derived class) from a pre-trained encoder model configuration and
decoder model configuration.
Returns:
[`EncoderDecoderConfig`]: An instance of a configuration object
"""
return cls(question_encoder=question_encoder_config.to_dict(), generator=generator_config.to_dict(), **kwargs)
__all__ = ["RagConfig"]
| transformers/src/transformers/models/rag/configuration_rag.py/0 | {
"file_path": "transformers/src/transformers/models/rag/configuration_rag.py",
"repo_id": "transformers",
"token_count": 3452
} |
# coding=utf-8
# Copyright 2022 Meta Platforms, Inc. 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.
"""RegNet model configuration"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
logger = logging.get_logger(__name__)
class RegNetConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`RegNetModel`]. It is used to instantiate a RegNet
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 RegNet
[facebook/regnet-y-040](https://huggingface.co/facebook/regnet-y-040) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
num_channels (`int`, *optional*, defaults to 3):
The number of input channels.
embedding_size (`int`, *optional*, defaults to 64):
Dimensionality (hidden size) for the embedding layer.
hidden_sizes (`List[int]`, *optional*, defaults to `[256, 512, 1024, 2048]`):
Dimensionality (hidden size) at each stage.
depths (`List[int]`, *optional*, defaults to `[3, 4, 6, 3]`):
Depth (number of layers) for each stage.
layer_type (`str`, *optional*, defaults to `"y"`):
The layer to use, it can be either `"x" or `"y"`. An `x` layer is a ResNet's BottleNeck layer with
`reduction` fixed to `1`. While a `y` layer is a `x` but with squeeze and excitation. Please refer to the
paper for a detailed explanation of how these layers were constructed.
hidden_act (`str`, *optional*, defaults to `"relu"`):
The non-linear activation function in each block. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"`
are supported.
downsample_in_first_stage (`bool`, *optional*, defaults to `False`):
If `True`, the first stage will downsample the inputs using a `stride` of 2.
Example:
```python
>>> from transformers import RegNetConfig, RegNetModel
>>> # Initializing a RegNet regnet-y-40 style configuration
>>> configuration = RegNetConfig()
>>> # Initializing a model from the regnet-y-40 style configuration
>>> model = RegNetModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```
"""
model_type = "regnet"
layer_types = ["x", "y"]
def __init__(
self,
num_channels=3,
embedding_size=32,
hidden_sizes=[128, 192, 512, 1088],
depths=[2, 6, 12, 2],
groups_width=64,
layer_type="y",
hidden_act="relu",
**kwargs,
):
super().__init__(**kwargs)
if layer_type not in self.layer_types:
raise ValueError(f"layer_type={layer_type} is not one of {','.join(self.layer_types)}")
self.num_channels = num_channels
self.embedding_size = embedding_size
self.hidden_sizes = hidden_sizes
self.depths = depths
self.groups_width = groups_width
self.layer_type = layer_type
self.hidden_act = hidden_act
# always downsample in the first stage
self.downsample_in_first_stage = True
__all__ = ["RegNetConfig"]
| transformers/src/transformers/models/regnet/configuration_regnet.py/0 | {
"file_path": "transformers/src/transformers/models/regnet/configuration_regnet.py",
"repo_id": "transformers",
"token_count": 1433
} |
# coding=utf-8
# Copyright 2023 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
from flax.core.frozen_dict import FrozenDict, freeze, unfreeze
from flax.traverse_util import flatten_dict, unflatten_dict
from ...modeling_flax_outputs import (
FlaxBaseModelOutputWithNoAttention,
FlaxBaseModelOutputWithPoolingAndNoAttention,
FlaxImageClassifierOutputWithNoAttention,
)
from ...modeling_flax_utils import (
ACT2FN,
FlaxPreTrainedModel,
append_replace_return_docstrings,
overwrite_call_docstring,
)
from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward
from .configuration_resnet import ResNetConfig
RESNET_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, saving and converting weights from PyTorch models)
This model is also a
[flax.linen.Module](https://flax.readthedocs.io/en/latest/api_reference/flax.linen/module.html) subclass. Use it as
a regular Flax linen 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 ([`ResNetConfig`]): 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`].
"""
RESNET_INPUTS_DOCSTRING = r"""
Args:
pixel_values (`jax.numpy.float32` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
[`AutoImageProcessor.__call__`] for details.
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 Identity(nn.Module):
"""Identity function."""
@nn.compact
def __call__(self, x, **kwargs):
return x
class FlaxResNetConvLayer(nn.Module):
out_channels: int
kernel_size: int = 3
stride: int = 1
activation: Optional[str] = "relu"
dtype: jnp.dtype = jnp.float32
def setup(self):
self.convolution = nn.Conv(
self.out_channels,
kernel_size=(self.kernel_size, self.kernel_size),
strides=self.stride,
padding=self.kernel_size // 2,
dtype=self.dtype,
use_bias=False,
kernel_init=nn.initializers.variance_scaling(2.0, mode="fan_out", distribution="normal", dtype=self.dtype),
)
self.normalization = nn.BatchNorm(momentum=0.9, epsilon=1e-05, dtype=self.dtype)
self.activation_func = ACT2FN[self.activation] if self.activation is not None else Identity()
def __call__(self, x: jnp.ndarray, deterministic: bool = True) -> jnp.ndarray:
hidden_state = self.convolution(x)
hidden_state = self.normalization(hidden_state, use_running_average=deterministic)
hidden_state = self.activation_func(hidden_state)
return hidden_state
class FlaxResNetEmbeddings(nn.Module):
"""
ResNet Embeddings (stem) composed of a single aggressive convolution.
"""
config: ResNetConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
self.embedder = FlaxResNetConvLayer(
self.config.embedding_size,
kernel_size=7,
stride=2,
activation=self.config.hidden_act,
dtype=self.dtype,
)
self.max_pool = partial(nn.max_pool, window_shape=(3, 3), strides=(2, 2), padding=((1, 1), (1, 1)))
def __call__(self, pixel_values: jnp.ndarray, deterministic: bool = True) -> jnp.ndarray:
num_channels = pixel_values.shape[-1]
if num_channels != self.config.num_channels:
raise ValueError(
"Make sure that the channel dimension of the pixel values match with the one set in the configuration."
)
embedding = self.embedder(pixel_values, deterministic=deterministic)
embedding = self.max_pool(embedding)
return embedding
class FlaxResNetShortCut(nn.Module):
"""
ResNet shortcut, used to project the residual features to the correct size. If needed, it is also used to
downsample the input using `stride=2`.
"""
out_channels: int
stride: int = 2
dtype: jnp.dtype = jnp.float32
def setup(self):
self.convolution = nn.Conv(
self.out_channels,
kernel_size=(1, 1),
strides=self.stride,
use_bias=False,
kernel_init=nn.initializers.variance_scaling(2.0, mode="fan_out", distribution="truncated_normal"),
dtype=self.dtype,
)
self.normalization = nn.BatchNorm(momentum=0.9, epsilon=1e-05, dtype=self.dtype)
def __call__(self, x: jnp.ndarray, deterministic: bool = True) -> jnp.ndarray:
hidden_state = self.convolution(x)
hidden_state = self.normalization(hidden_state, use_running_average=deterministic)
return hidden_state
class FlaxResNetBasicLayerCollection(nn.Module):
out_channels: int
stride: int = 1
dtype: jnp.dtype = jnp.float32
def setup(self):
self.layer = [
FlaxResNetConvLayer(self.out_channels, stride=self.stride, dtype=self.dtype),
FlaxResNetConvLayer(self.out_channels, activation=None, dtype=self.dtype),
]
def __call__(self, hidden_state: jnp.ndarray, deterministic: bool = True) -> jnp.ndarray:
for layer in self.layer:
hidden_state = layer(hidden_state, deterministic=deterministic)
return hidden_state
class FlaxResNetBasicLayer(nn.Module):
"""
A classic ResNet's residual layer composed by two `3x3` convolutions.
"""
in_channels: int
out_channels: int
stride: int = 1
activation: Optional[str] = "relu"
dtype: jnp.dtype = jnp.float32
def setup(self):
should_apply_shortcut = self.in_channels != self.out_channels or self.stride != 1
self.shortcut = (
FlaxResNetShortCut(self.out_channels, stride=self.stride, dtype=self.dtype)
if should_apply_shortcut
else None
)
self.layer = FlaxResNetBasicLayerCollection(
out_channels=self.out_channels,
stride=self.stride,
dtype=self.dtype,
)
self.activation_func = ACT2FN[self.activation]
def __call__(self, hidden_state, deterministic: bool = True):
residual = hidden_state
hidden_state = self.layer(hidden_state, deterministic=deterministic)
if self.shortcut is not None:
residual = self.shortcut(residual, deterministic=deterministic)
hidden_state += residual
hidden_state = self.activation_func(hidden_state)
return hidden_state
class FlaxResNetBottleNeckLayerCollection(nn.Module):
out_channels: int
stride: int = 1
activation: Optional[str] = "relu"
reduction: int = 4
dtype: jnp.dtype = jnp.float32
def setup(self):
reduces_channels = self.out_channels // self.reduction
self.layer = [
FlaxResNetConvLayer(reduces_channels, kernel_size=1, dtype=self.dtype, name="0"),
FlaxResNetConvLayer(reduces_channels, stride=self.stride, dtype=self.dtype, name="1"),
FlaxResNetConvLayer(self.out_channels, kernel_size=1, activation=None, dtype=self.dtype, name="2"),
]
def __call__(self, hidden_state: jnp.ndarray, deterministic: bool = True) -> jnp.ndarray:
for layer in self.layer:
hidden_state = layer(hidden_state, deterministic=deterministic)
return hidden_state
class FlaxResNetBottleNeckLayer(nn.Module):
"""
A classic ResNet's bottleneck layer composed by three `3x3` convolutions. The first `1x1` convolution reduces the
input by a factor of `reduction` in order to make the second `3x3` convolution faster. The last `1x1` convolution
remaps the reduced features to `out_channels`.
"""
in_channels: int
out_channels: int
stride: int = 1
activation: Optional[str] = "relu"
reduction: int = 4
dtype: jnp.dtype = jnp.float32
def setup(self):
should_apply_shortcut = self.in_channels != self.out_channels or self.stride != 1
self.shortcut = (
FlaxResNetShortCut(self.out_channels, stride=self.stride, dtype=self.dtype)
if should_apply_shortcut
else None
)
self.layer = FlaxResNetBottleNeckLayerCollection(
self.out_channels,
stride=self.stride,
activation=self.activation,
reduction=self.reduction,
dtype=self.dtype,
)
self.activation_func = ACT2FN[self.activation]
def __call__(self, hidden_state: jnp.ndarray, deterministic: bool = True) -> jnp.ndarray:
residual = hidden_state
if self.shortcut is not None:
residual = self.shortcut(residual, deterministic=deterministic)
hidden_state = self.layer(hidden_state, deterministic)
hidden_state += residual
hidden_state = self.activation_func(hidden_state)
return hidden_state
class FlaxResNetStageLayersCollection(nn.Module):
"""
A ResNet stage composed by stacked layers.
"""
config: ResNetConfig
in_channels: int
out_channels: int
stride: int = 2
depth: int = 2
dtype: jnp.dtype = jnp.float32
def setup(self):
layer = FlaxResNetBottleNeckLayer if self.config.layer_type == "bottleneck" else FlaxResNetBasicLayer
layers = [
# downsampling is done in the first layer with stride of 2
layer(
self.in_channels,
self.out_channels,
stride=self.stride,
activation=self.config.hidden_act,
dtype=self.dtype,
name="0",
),
]
for i in range(self.depth - 1):
layers.append(
layer(
self.out_channels,
self.out_channels,
activation=self.config.hidden_act,
dtype=self.dtype,
name=str(i + 1),
)
)
self.layers = layers
def __call__(self, x: jnp.ndarray, deterministic: bool = True) -> jnp.ndarray:
hidden_state = x
for layer in self.layers:
hidden_state = layer(hidden_state, deterministic=deterministic)
return hidden_state
class FlaxResNetStage(nn.Module):
"""
A ResNet stage composed by stacked layers.
"""
config: ResNetConfig
in_channels: int
out_channels: int
stride: int = 2
depth: int = 2
dtype: jnp.dtype = jnp.float32
def setup(self):
self.layers = FlaxResNetStageLayersCollection(
self.config,
in_channels=self.in_channels,
out_channels=self.out_channels,
stride=self.stride,
depth=self.depth,
dtype=self.dtype,
)
def __call__(self, x: jnp.ndarray, deterministic: bool = True) -> jnp.ndarray:
return self.layers(x, deterministic=deterministic)
class FlaxResNetStageCollection(nn.Module):
config: ResNetConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
in_out_channels = zip(self.config.hidden_sizes, self.config.hidden_sizes[1:])
stages = [
FlaxResNetStage(
self.config,
self.config.embedding_size,
self.config.hidden_sizes[0],
stride=2 if self.config.downsample_in_first_stage else 1,
depth=self.config.depths[0],
dtype=self.dtype,
name="0",
)
]
for i, ((in_channels, out_channels), depth) in enumerate(zip(in_out_channels, self.config.depths[1:])):
stages.append(
FlaxResNetStage(self.config, in_channels, out_channels, depth=depth, dtype=self.dtype, name=str(i + 1))
)
self.stages = stages
def __call__(
self,
hidden_state: jnp.ndarray,
output_hidden_states: bool = False,
deterministic: bool = True,
) -> FlaxBaseModelOutputWithNoAttention:
hidden_states = () if output_hidden_states else None
for stage_module in self.stages:
if output_hidden_states:
hidden_states = hidden_states + (hidden_state.transpose(0, 3, 1, 2),)
hidden_state = stage_module(hidden_state, deterministic=deterministic)
return hidden_state, hidden_states
class FlaxResNetEncoder(nn.Module):
config: ResNetConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
self.stages = FlaxResNetStageCollection(self.config, dtype=self.dtype)
def __call__(
self,
hidden_state: jnp.ndarray,
output_hidden_states: bool = False,
return_dict: bool = True,
deterministic: bool = True,
) -> FlaxBaseModelOutputWithNoAttention:
hidden_state, hidden_states = self.stages(
hidden_state, output_hidden_states=output_hidden_states, deterministic=deterministic
)
if output_hidden_states:
hidden_states = hidden_states + (hidden_state.transpose(0, 3, 1, 2),)
if not return_dict:
return tuple(v for v in [hidden_state, hidden_states] if v is not None)
return FlaxBaseModelOutputWithNoAttention(
last_hidden_state=hidden_state,
hidden_states=hidden_states,
)
class FlaxResNetPreTrainedModel(FlaxPreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = ResNetConfig
base_model_prefix = "resnet"
main_input_name = "pixel_values"
module_class: nn.Module = None
def __init__(
self,
config: ResNetConfig,
input_shape=(1, 224, 224, 3),
seed: int = 0,
dtype: jnp.dtype = jnp.float32,
_do_init: bool = True,
**kwargs,
):
module = self.module_class(config=config, dtype=dtype, **kwargs)
if input_shape is None:
input_shape = (1, config.image_size, config.image_size, config.num_channels)
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
pixel_values = jnp.zeros(input_shape, dtype=self.dtype)
rngs = {"params": rng}
random_params = self.module.init(rngs, pixel_values, return_dict=False)
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
@add_start_docstrings_to_model_forward(RESNET_INPUTS_DOCSTRING)
def __call__(
self,
pixel_values,
params: dict = None,
train: bool = False,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
):
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
pixel_values = jnp.transpose(pixel_values, (0, 2, 3, 1))
# Handle any PRNG if needed
rngs = {}
return self.module.apply(
{
"params": params["params"] if params is not None else self.params["params"],
"batch_stats": params["batch_stats"] if params is not None else self.params["batch_stats"],
},
jnp.array(pixel_values, dtype=jnp.float32),
not train,
output_hidden_states,
return_dict,
rngs=rngs,
mutable=["batch_stats"] if train else False, # Returing tuple with batch_stats only when train is True
)
class FlaxResNetModule(nn.Module):
config: ResNetConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.embedder = FlaxResNetEmbeddings(self.config, dtype=self.dtype)
self.encoder = FlaxResNetEncoder(self.config, dtype=self.dtype)
# Adaptive average pooling used in resnet
self.pooler = partial(
nn.avg_pool,
padding=((0, 0), (0, 0)),
)
def __call__(
self,
pixel_values,
deterministic: bool = True,
output_hidden_states: bool = False,
return_dict: bool = True,
) -> FlaxBaseModelOutputWithPoolingAndNoAttention:
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
embedding_output = self.embedder(pixel_values, deterministic=deterministic)
encoder_outputs = self.encoder(
embedding_output,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
deterministic=deterministic,
)
last_hidden_state = encoder_outputs[0]
pooled_output = self.pooler(
last_hidden_state,
window_shape=(last_hidden_state.shape[1], last_hidden_state.shape[2]),
strides=(last_hidden_state.shape[1], last_hidden_state.shape[2]),
).transpose(0, 3, 1, 2)
last_hidden_state = last_hidden_state.transpose(0, 3, 1, 2)
if not return_dict:
return (last_hidden_state, pooled_output) + encoder_outputs[1:]
return FlaxBaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=last_hidden_state,
pooler_output=pooled_output,
hidden_states=encoder_outputs.hidden_states,
)
@add_start_docstrings(
"The bare ResNet model outputting raw features without any specific head on top.",
RESNET_START_DOCSTRING,
)
class FlaxResNetModel(FlaxResNetPreTrainedModel):
module_class = FlaxResNetModule
FLAX_VISION_MODEL_DOCSTRING = """
Returns:
Examples:
```python
>>> from transformers import AutoImageProcessor, FlaxResNetModel
>>> 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("microsoft/resnet-50")
>>> model = FlaxResNetModel.from_pretrained("microsoft/resnet-50")
>>> inputs = image_processor(images=image, return_tensors="np")
>>> outputs = model(**inputs)
>>> last_hidden_states = outputs.last_hidden_state
```
"""
overwrite_call_docstring(FlaxResNetModel, FLAX_VISION_MODEL_DOCSTRING)
append_replace_return_docstrings(
FlaxResNetModel, output_type=FlaxBaseModelOutputWithPoolingAndNoAttention, config_class=ResNetConfig
)
class FlaxResNetClassifierCollection(nn.Module):
config: ResNetConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
self.classifier = nn.Dense(self.config.num_labels, dtype=self.dtype, name="1")
def __call__(self, x: jnp.ndarray) -> jnp.ndarray:
return self.classifier(x)
class FlaxResNetForImageClassificationModule(nn.Module):
config: ResNetConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
self.resnet = FlaxResNetModule(config=self.config, dtype=self.dtype)
if self.config.num_labels > 0:
self.classifier = FlaxResNetClassifierCollection(self.config, dtype=self.dtype)
else:
self.classifier = Identity()
def __call__(
self,
pixel_values=None,
deterministic: bool = True,
output_hidden_states=None,
return_dict=None,
):
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.resnet(
pixel_values,
deterministic=deterministic,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
pooled_output = outputs.pooler_output if return_dict else outputs[1]
logits = self.classifier(pooled_output[:, :, 0, 0])
if not return_dict:
output = (logits,) + outputs[2:]
return output
return FlaxImageClassifierOutputWithNoAttention(logits=logits, hidden_states=outputs.hidden_states)
@add_start_docstrings(
"""
ResNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
ImageNet.
""",
RESNET_START_DOCSTRING,
)
class FlaxResNetForImageClassification(FlaxResNetPreTrainedModel):
module_class = FlaxResNetForImageClassificationModule
FLAX_VISION_CLASSIF_DOCSTRING = """
Returns:
Example:
```python
>>> from transformers import AutoImageProcessor, FlaxResNetForImageClassification
>>> from PIL import Image
>>> import jax
>>> import requests
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> image_processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50")
>>> model = FlaxResNetForImageClassification.from_pretrained("microsoft/resnet-50")
>>> inputs = image_processor(images=image, return_tensors="np")
>>> outputs = model(**inputs)
>>> logits = outputs.logits
>>> # model predicts one of the 1000 ImageNet classes
>>> predicted_class_idx = jax.numpy.argmax(logits, axis=-1)
>>> print("Predicted class:", model.config.id2label[predicted_class_idx.item()])
```
"""
overwrite_call_docstring(FlaxResNetForImageClassification, FLAX_VISION_CLASSIF_DOCSTRING)
append_replace_return_docstrings(
FlaxResNetForImageClassification, output_type=FlaxImageClassifierOutputWithNoAttention, config_class=ResNetConfig
)
__all__ = ["FlaxResNetForImageClassification", "FlaxResNetModel", "FlaxResNetPreTrainedModel"]
| transformers/src/transformers/models/resnet/modeling_flax_resnet.py/0 | {
"file_path": "transformers/src/transformers/models/resnet/modeling_flax_resnet.py",
"repo_id": "transformers",
"token_count": 10406
} |
# 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.
"""SeamlessM4Tv2 model configuration"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
logger = logging.get_logger(__name__)
class SeamlessM4Tv2Config(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`~SeamlessM4Tv2Model`]. It is used to instantiate
an SeamlessM4Tv2 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 SeamlessM4Tv2
[""](https://huggingface.co/"") 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 256102):
Vocabulary size of the text modality of the SeamlessM4Tv2 model. Defines the number of different tokens
that can be represented by the `inputs_ids` passed when calling [`~SeamlessM4Tv2Model`],
[`~SeamlessM4Tv2ForTextToSpeech`] or [`~SeamlessM4Tv2ForTextToText`].
t2u_vocab_size (`int`, *optional*, defaults to 10082):
Unit vocabulary size of the SeamlessM4Tv2 model. Defines the number of different "unit tokens" that can be
represented by the `inputs_ids` passed when calling the Text-To-Units sub-model of [`~SeamlessM4Tv2Model`],
[`~SeamlessM4Tv2ForSpeechToSpeech`] or [`~SeamlessM4Tv2ForTextToSpeech`].
char_vocab_size (`int`, *optional*, defaults to 10943):
Character vocabulary size of the SeamlessM4Tv2 model. Defines the number of different character tokens that
can be represented by the `char_inputs_ids` passed when calling the Text-To-Units sub-model of
[`~SeamlessM4Tv2Model`], [`~SeamlessM4Tv2ForSpeechToSpeech`] or [`~SeamlessM4Tv2ForTextToSpeech`].
> Parameters shared across sub-models
hidden_size (`int`, *optional*, defaults to 1024):
Dimensionality of the "intermediate" layers in the architecture.
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-05):
The epsilon used by the layer 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).
max_position_embeddings (`int`, *optional*, defaults to 4096):
The maximum sequence length that this model text encoder and decoder might ever be used with. Typically set
this to something large just in case (e.g., 512 or 1024 or 2048).
is_encoder_decoder (`bool`, *optional*, defaults to `True`):
Whether the model is used as an encoder/decoder or not.
encoder_layerdrop (`float`, *optional*, defaults to 0.05):
The LayerDrop probability for the encoders. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556)
for more details.
decoder_layerdrop (`float`, *optional*, defaults to 0.05):
The LayerDrop probability for the decoders. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556)
for more details.
activation_function (`str` or `function`, *optional*, defaults to `"relu"`):
The non-linear activation function (function or string) in the decoder and feed-forward layers. If string,
`"gelu"`, `"relu"`, `"selu"`, `"swish"` and `"gelu_new"` are supported.
dropout (`float`, *optional*, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings, encoder, decoder, and pooler.
attention_dropout (`float`, *optional*, defaults to 0.1):
The dropout probability for all attention layers.
activation_dropout (`float`, *optional*, defaults to 0.0):
The dropout probability for all activation layers in the model.
scale_embedding (`bool`, *optional*, defaults to `True`):
Scale embeddings by diving by sqrt(d_model).
> Text encoder and text decoder specific parameters
encoder_layers (`int`, *optional*, defaults to 24):
Number of hidden layers in the Transformer text encoder.
encoder_ffn_dim (`int`, *optional*, defaults to 8192):
Dimension of the "intermediate" (i.e., feed-forward) layer in the Transformer text encoder.
encoder_attention_heads (`int`, *optional*, defaults to 16):
Number of attention heads for each attention layer in the Transformer text encoder.
decoder_layers (`int`, *optional*, defaults to 24):
Number of hidden layers in the Transformer text decoder.
decoder_ffn_dim (`int`, *optional*, defaults to 8192):
Dimension of the "intermediate" (i.e., feed-forward) layer in the Transformer text decoder.
decoder_attention_heads (`int`, *optional*, defaults to 16):
Number of attention heads for each attention layer in the Transformer text decoder.
decoder_start_token_id (`int`, *optional*, defaults to 3):
If an encoder-decoder model starts decoding with a different token than _bos_, the id of that token. Only
applied in the text decoder.
max_new_tokens (`int`, *optional*, defaults to 256):
The maximum numbers of text tokens to generate, ignoring the number of tokens in the prompt.
pad_token_id (`int`, *optional*, defaults to 0):
The id of the _padding_ text token. Only applied to the text-decoder model.
bos_token_id (`int`, *optional*, defaults to 2):
The id of the _beginning-of-stream_ text token. Only applied to the text-decoder model.
eos_token_id (`int`, *optional*, defaults to 3):
The id of the _end-of-stream_ text token. Only applied to the text-decoder model.
> Speech encoder specific parameters
speech_encoder_layers (`int`, *optional*, defaults to 24):
Number of hidden layers in the Transformer speech encoder.
speech_encoder_attention_heads (`int`, *optional*, defaults to 16):
Number of attention heads for each attention layer in the Transformer speech encoder.
speech_encoder_intermediate_size (`int`, *optional*, defaults to 4096):
Dimension of the "intermediate" (i.e., feed-forward) layer in the Transformer speech encoder.
speech_encoder_hidden_act (`str` or `function`, *optional*, defaults to `"swish"`):
The non-linear activation function (function or string) in the speech encoder. If string, `"gelu"`,
`"relu"`, `"selu"`, `"swish"` and `"gelu_new"` are supported.
speech_encoder_dropout (`float`, *optional*, defaults to 0.0):
The dropout probability for all layers in the speech encoder.
add_adapter (`bool`, *optional*, defaults to `True`):
Add an adapter layer on top of the speech encoder.
speech_encoder_layerdrop (`float`, *optional*, defaults to 0.1):
The LayerDrop probability for the speech encoder. See the [LayerDrop paper](see
https://arxiv.org/abs/1909.11556) for more details.
feature_projection_input_dim (`int`, *optional*, defaults to 160):
Input dimension of the input feature projection of the speech encoder, i.e the dimension after processing
input audios with [`SeamlessM4TFeatureExtractor`].
adaptor_kernel_size (`int`, *optional*, defaults to 8):
Kernel size of the convolutional layers in the adapter network. Only relevant if `add_adapter is True`.
adaptor_stride (`int`, *optional*, defaults to 8):
Stride of the convolutional layers in the adapter network. Only relevant if `add_adapter is True`.
adaptor_dropout (`float`, *optional*, defaults to 0.1):
The dropout probability for all layers in the speech adapter.
num_adapter_layers (`int`, *optional*, defaults to 1):
Number of convolutional layers that should be used in the adapter network. Only relevant if `add_adapter is
True`.
position_embeddings_type (`str`, *optional*, defaults to `"relative_key"`):
Can be specified to `relative_key`. If left to `None`, no relative position embedding is applied. Only
applied to the speech encoder. For more information on `"relative_key"`, please refer to [Self-Attention
with Relative Position Representations (Shaw et al.)](https://arxiv.org/abs/1803.02155).
conv_depthwise_kernel_size (`int`, *optional*, defaults to 31):
Kernel size of convolutional depthwise 1D layer in Conformer blocks. Only applied to the speech encoder.
left_max_position_embeddings (`int`, *optional*, defaults to 64):
The left clipping value for relative positions.
right_max_position_embeddings (`int`, *optional*, defaults to 8):
The right clipping value for relative positions.
speech_encoder_chunk_size (`int`, *optional*, defaults to 20000): The size of each attention chunk.
speech_encoder_left_chunk_num (`int`, *optional*, defaults to 128):
Number of chunks on the left up to which lookahead is allowed.
> Text-To-Unit (t2u) model specific parameters
t2u_bos_token_id (`int`, *optional*, defaults to 0):
The id of the _beginning-of-stream_ unit token. Only applied to the text-to-unit seq2seq model.
t2u_pad_token_id (`int`, *optional*, defaults to 1):
The id of the _padding_ unit token. Only applied to the text-to-unit seq2seq model.
t2u_eos_token_id (`int`, *optional*, defaults to 2):
The id of the _end-of-stream_ unit token. Only applied to the text-to-unit seq2seq model.
t2u_encoder_layers (`int`, *optional*, defaults to 6):
Number of hidden layers in the Transformer text-to-unit encoder.
t2u_encoder_ffn_dim (`int`, *optional*, defaults to 8192):
Dimension of the "intermediate" (i.e., feed-forward) layer in the Transformer text-to-unit encoder.
t2u_encoder_attention_heads (`int`, *optional*, defaults to 16):
Number of attention heads for each attention layer in the Transformer text-to-unit encoder.
t2u_decoder_layers (`int`, *optional*, defaults to 6):
Number of hidden layers in the Transformer text-to-unit decoder.
t2u_decoder_ffn_dim (`int`, *optional*, defaults to 8192):
Dimension of the "intermediate" (i.e., feed-forward) layer in the Transformer text-to-unit decoder.
t2u_decoder_attention_heads (`int`, *optional*, defaults to 16):
Number of attention heads for each attention layer in the Transformer text-to-unit decoder.
t2u_max_position_embeddings (`int`, *optional*, defaults to 4096):
The maximum sequence length that this model text-to-unit component might ever be used with. Typically set
this to something large just in case (e.g., 512 or 1024 or 2048).
t2u_variance_predictor_embed_dim (`int`, *optional*, defaults to 1024):
The projection dimension of the text-to-unit's duration predictor.
t2u_variance_predictor_hidden_dim (`int`, *optional*, defaults to 256):
Internal dimension of the text-to-unit's duration predictor.
t2u_variance_predictor_kernel_size (`int`, *optional*, defaults to 3):
Kernel size of the convolutional layers of the text-to-unit's duration predictor.
t2u_variance_pred_dropout (`float`, *optional*, defaults to 0.5):
The dropout probability of the text-to-unit's duration predictor.
> Hifi-Gan Vocoder specific parameters
sampling_rate (`int`, *optional*, defaults to 16000):
The sampling rate at which the output audio will be generated, expressed in hertz (Hz).
upsample_initial_channel (`int`, *optional*, defaults to 512):
The number of input channels into the hifi-gan upsampling network. Applies to the vocoder only.
upsample_rates (`Tuple[int]` or `List[int]`, *optional*, defaults to `[5, 4, 4, 2, 2]`):
A tuple of integers defining the stride of each 1D convolutional layer in the vocoder upsampling network.
The length of *upsample_rates* defines the number of convolutional layers and has to match the length of
*upsample_kernel_sizes*. Applies to the vocoder only.
upsample_kernel_sizes (`Tuple[int]` or `List[int]`, *optional*, defaults to `[11, 8, 8, 4, 4]`):
A tuple of integers defining the kernel size of each 1D convolutional layer in the vocoder upsampling
network. The length of *upsample_kernel_sizes* defines the number of convolutional layers and has to match
the length of *upsample_rates*. Applies to the vocoder only.
resblock_kernel_sizes (`Tuple[int]` or `List[int]`, *optional*, defaults to `[3, 7, 11]`):
A tuple of integers defining the kernel sizes of the vocoder 1D convolutional layers in the multi-receptive
field fusion (MRF) module. Applies to the vocoder only.
resblock_dilation_sizes (`Tuple[Tuple[int]]` or `List[List[int]]`, *optional*, defaults to `[[1, 3, 5], [1, 3, 5], [1, 3, 5]]`):
A nested tuple of integers defining the dilation rates of the vocoder dilated 1D convolutional layers in
the multi-receptive field fusion (MRF) module. Applies to the vocoder only.
leaky_relu_slope (`float`, *optional*, defaults to 0.1):
The angle of the negative slope used by the leaky ReLU activation in the vocoder. Applies to the vocoder
only.
unit_hifi_gan_vocab_size (`int`, *optional*, defaults to 10000):
Vocabulary size of the SeamlessM4Tv2 vocoder. Defines the number of different unit tokens that can be
represented by the `inputs_ids` passed when calling the vocoder of [`~SeamlessM4Tv2Model`],
[`~SeamlessM4Tv2ForSpeechToSpeech`] or [`~SeamlessM4Tv2ForTextToSpeech`].
unit_embed_dim (`int`, *optional*, defaults to 1280):
The projection dimension of the input ids given to the hifi-gan vocoder. Applies to the vocoder only.
lang_embed_dim (`int`, *optional*, defaults to 256):
The projection dimension of the target language given to the hifi-gan vocoder. Applies to the vocoder only.
spkr_embed_dim (`int`, *optional*, defaults to 256):
The projection dimension of the speaker id given to the hifi-gan vocoder. Applies to the vocoder only.
vocoder_num_langs (`int`, *optional*, defaults to 36):
Number of langs supported by the vocoder. Might be different from `t2u_num_langs`.
vocoder_num_spkrs (`int`, *optional*, defaults to 200):
Number of speakers supported by the vocoder.
variance_predictor_kernel_size (`int`, *optional*, defaults to 3):
Kernel size of the duration predictor. Applies to the vocoder only.
var_pred_dropout (`float`, *optional*, defaults to 0.5):
The dropout probability of the duration predictor. Applies to the vocoder only.
vocoder_offset (`int`, *optional*, defaults to 4):
Offset the unit token ids by this number to account for symbol tokens. Applies to the vocoder only.
```python
>>> from transformers import SeamlessM4Tv2Model, SeamlessM4Tv2Config
>>> # Initializing a SeamlessM4Tv2 "" style configuration
>>> configuration = SeamlessM4Tv2Config()
>>> # Initializing a model from the "" style configuration
>>> model = SeamlessM4Tv2Model(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "seamless_m4t_v2"
def __init__(
self,
vocab_size=256102,
t2u_vocab_size=10082,
char_vocab_size=10943,
# shared config
hidden_size=1024,
initializer_range=0.02,
layer_norm_eps=1e-5,
use_cache=True,
max_position_embeddings=4096,
is_encoder_decoder=True,
encoder_layerdrop=0.05,
decoder_layerdrop=0.05,
activation_function="relu",
dropout=0.1,
attention_dropout=0.1,
activation_dropout=0.0,
scale_embedding=True,
# text encoder|decoder
encoder_layers=24,
encoder_ffn_dim=8192,
encoder_attention_heads=16,
decoder_layers=24,
decoder_ffn_dim=8192,
decoder_attention_heads=16,
decoder_start_token_id=3,
max_new_tokens=256,
pad_token_id=0,
bos_token_id=2,
eos_token_id=3,
# speech_encoder
speech_encoder_layers=24,
speech_encoder_attention_heads=16,
speech_encoder_intermediate_size=4096,
speech_encoder_hidden_act="swish",
speech_encoder_dropout=0.0,
add_adapter=True,
speech_encoder_layerdrop=0.1,
feature_projection_input_dim=160,
adaptor_kernel_size=8,
adaptor_stride=8,
adaptor_dropout=0.1,
num_adapter_layers=1,
position_embeddings_type="relative_key",
conv_depthwise_kernel_size=31,
left_max_position_embeddings=64,
right_max_position_embeddings=8,
speech_encoder_chunk_size=20000,
speech_encoder_left_chunk_num=128,
# t2u config
t2u_bos_token_id=0,
t2u_pad_token_id=1,
t2u_eos_token_id=2,
t2u_encoder_layers=6,
t2u_encoder_ffn_dim=8192,
t2u_encoder_attention_heads=16,
t2u_decoder_layers=6,
t2u_decoder_ffn_dim=8192,
t2u_decoder_attention_heads=16,
t2u_max_position_embeddings=4096,
t2u_variance_predictor_embed_dim=1024,
t2u_variance_predictor_hidden_dim=256,
t2u_variance_predictor_kernel_size=3,
t2u_variance_pred_dropout=0.5,
# hifi-gan vocoder config
sampling_rate=16000,
upsample_initial_channel=512,
upsample_rates=[5, 4, 4, 2, 2],
upsample_kernel_sizes=[11, 8, 8, 4, 4],
resblock_kernel_sizes=[3, 7, 11],
resblock_dilation_sizes=[[1, 3, 5], [1, 3, 5], [1, 3, 5]],
leaky_relu_slope=0.1,
# specific to Code Hifi-Gan
unit_hifi_gan_vocab_size=10000,
unit_embed_dim=1280,
lang_embed_dim=256,
spkr_embed_dim=256,
vocoder_num_langs=36,
vocoder_num_spkrs=200,
variance_predictor_kernel_size=3,
var_pred_dropout=0.5,
vocoder_offset=4,
**kwargs,
):
# overall_config
self.vocab_size = vocab_size
self.t2u_vocab_size = t2u_vocab_size
self.char_vocab_size = char_vocab_size
self.hidden_size = hidden_size
self.initializer_range = initializer_range
self.layer_norm_eps = layer_norm_eps
self.max_position_embeddings = max_position_embeddings
self.use_cache = use_cache
self.max_new_tokens = max_new_tokens
self.encoder_layerdrop = encoder_layerdrop
self.decoder_layerdrop = decoder_layerdrop
self.activation_function = activation_function
self.dropout = dropout
self.attention_dropout = attention_dropout
self.activation_dropout = activation_dropout
self.scale_embedding = scale_embedding
# for proper config init
self.num_attention_heads = decoder_attention_heads
self.num_hidden_layers = decoder_layers
# text|unit encoder|decoder
self.encoder_layers = encoder_layers
self.encoder_ffn_dim = encoder_ffn_dim
self.encoder_attention_heads = encoder_attention_heads
self.decoder_layers = decoder_layers
self.decoder_ffn_dim = decoder_ffn_dim
self.decoder_attention_heads = decoder_attention_heads
# speech_encoder
self.speech_encoder_layers = speech_encoder_layers
self.speech_encoder_hidden_act = speech_encoder_hidden_act
self.speech_encoder_dropout = speech_encoder_dropout
self.speech_encoder_attention_heads = speech_encoder_attention_heads
self.speech_encoder_layerdrop = speech_encoder_layerdrop
self.speech_encoder_intermediate_size = speech_encoder_intermediate_size
self.feature_projection_input_dim = feature_projection_input_dim
self.adaptor_kernel_size = adaptor_kernel_size
self.adaptor_stride = adaptor_stride
self.adaptor_dropout = adaptor_dropout
self.num_adapter_layers = num_adapter_layers
self.position_embeddings_type = position_embeddings_type
self.conv_depthwise_kernel_size = conv_depthwise_kernel_size
self.add_adapter = add_adapter
self.left_max_position_embeddings = left_max_position_embeddings
self.right_max_position_embeddings = right_max_position_embeddings
self.speech_encoder_chunk_size = speech_encoder_chunk_size
self.speech_encoder_left_chunk_num = speech_encoder_left_chunk_num
# t2u config
self.t2u_bos_token_id = t2u_bos_token_id
self.t2u_pad_token_id = t2u_pad_token_id
self.t2u_eos_token_id = t2u_eos_token_id
self.t2u_encoder_layers = t2u_encoder_layers
self.t2u_encoder_ffn_dim = t2u_encoder_ffn_dim
self.t2u_encoder_attention_heads = t2u_encoder_attention_heads
self.t2u_decoder_layers = t2u_decoder_layers
self.t2u_decoder_ffn_dim = t2u_decoder_ffn_dim
self.t2u_decoder_attention_heads = t2u_decoder_attention_heads
self.t2u_max_position_embeddings = t2u_max_position_embeddings
self.t2u_variance_predictor_embed_dim = t2u_variance_predictor_embed_dim # TODO: add to docstrings
self.t2u_variance_predictor_hidden_dim = t2u_variance_predictor_hidden_dim # TODO: add to docstrings
self.t2u_variance_predictor_kernel_size = t2u_variance_predictor_kernel_size # TODO: add to docstrings
self.t2u_variance_pred_dropout = t2u_variance_pred_dropout # TODO: add to docstrings
# hifi-gan vocoder config
# original parameters specific to Hifi-Gan
self.sampling_rate = sampling_rate
self.upsample_initial_channel = upsample_initial_channel
self.upsample_rates = upsample_rates
self.upsample_kernel_sizes = upsample_kernel_sizes
self.resblock_kernel_sizes = resblock_kernel_sizes
self.resblock_dilation_sizes = resblock_dilation_sizes
self.leaky_relu_slope = leaky_relu_slope
# specific to Code Hifi-Gan
self.unit_hifi_gan_vocab_size = unit_hifi_gan_vocab_size
self.unit_embed_dim = unit_embed_dim
self.lang_embed_dim = lang_embed_dim
self.spkr_embed_dim = spkr_embed_dim
self.vocoder_num_langs = vocoder_num_langs
self.vocoder_num_spkrs = vocoder_num_spkrs
self.variance_predictor_kernel_size = variance_predictor_kernel_size
self.var_pred_dropout = var_pred_dropout
self.vocoder_offset = vocoder_offset
super().__init__(
pad_token_id=pad_token_id,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
decoder_start_token_id=decoder_start_token_id,
is_encoder_decoder=is_encoder_decoder,
max_position_embeddings=max_position_embeddings,
**kwargs,
)
__all__ = ["SeamlessM4Tv2Config"]
| transformers/src/transformers/models/seamless_m4t_v2/configuration_seamless_m4t_v2.py/0 | {
"file_path": "transformers/src/transformers/models/seamless_m4t_v2/configuration_seamless_m4t_v2.py",
"repo_id": "transformers",
"token_count": 9883
} |
# coding=utf-8
# Copyright 2021 The HuggingFace Inc. team.
# Copyright (c) 2018, 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.
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig
logger = logging.get_logger(__name__)
class SpeechEncoderDecoderConfig(PretrainedConfig):
r"""
[`SpeechEncoderDecoderConfig`] is the configuration class to store the configuration of a
[`SpeechEncoderDecoderModel`]. It is used to instantiate an Encoder Decoder model according to the specified
arguments, defining the encoder and decoder configs.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
kwargs (*optional*):
Dictionary of keyword arguments. Notably:
- **encoder** ([`PretrainedConfig`], *optional*) -- An instance of a configuration object that defines
the encoder config.
- **decoder** ([`PretrainedConfig`], *optional*) -- An instance of a configuration object that defines
the decoder config.
Examples:
```python
>>> from transformers import BertConfig, Wav2Vec2Config, SpeechEncoderDecoderConfig, SpeechEncoderDecoderModel
>>> # Initializing a Wav2Vec2 & BERT style configuration
>>> config_encoder = Wav2Vec2Config()
>>> config_decoder = BertConfig()
>>> config = SpeechEncoderDecoderConfig.from_encoder_decoder_configs(config_encoder, config_decoder)
>>> # Initializing a Wav2Vec2Bert model from a Wav2Vec2 & google-bert/bert-base-uncased style configurations
>>> model = SpeechEncoderDecoderModel(config=config)
>>> # Accessing the model configuration
>>> config_encoder = model.config.encoder
>>> config_decoder = model.config.decoder
>>> # set decoder config to causal lm
>>> config_decoder.is_decoder = True
>>> config_decoder.add_cross_attention = True
>>> # Saving the model, including its configuration
>>> model.save_pretrained("my-model")
>>> # loading model and config from pretrained folder
>>> encoder_decoder_config = SpeechEncoderDecoderConfig.from_pretrained("my-model")
>>> model = SpeechEncoderDecoderModel.from_pretrained("my-model", config=encoder_decoder_config)
```"""
model_type = "speech-encoder-decoder"
sub_configs = {"encoder": AutoConfig, "decoder": AutoConfig}
is_composition = True
def __init__(self, **kwargs):
super().__init__(**kwargs)
if "encoder" not in kwargs or "decoder" not in kwargs:
raise ValueError(
f"A configuraton of type {self.model_type} cannot be instantiated because not both `encoder` and"
f" `decoder` sub-configurations are passed, but only {kwargs}"
)
encoder_config = kwargs.pop("encoder")
encoder_model_type = encoder_config.pop("model_type")
decoder_config = kwargs.pop("decoder")
decoder_model_type = decoder_config.pop("model_type")
self.encoder = AutoConfig.for_model(encoder_model_type, **encoder_config)
self.decoder = AutoConfig.for_model(decoder_model_type, **decoder_config)
self.is_encoder_decoder = True
@classmethod
def from_encoder_decoder_configs(
cls, encoder_config: PretrainedConfig, decoder_config: PretrainedConfig, **kwargs
) -> PretrainedConfig:
r"""
Instantiate a [`SpeechEncoderDecoderConfig`] (or a derived class) from a pre-trained encoder model
configuration and decoder model configuration.
Returns:
[`SpeechEncoderDecoderConfig`]: An instance of a configuration object
"""
logger.info("Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config")
decoder_config.is_decoder = True
decoder_config.add_cross_attention = True
return cls(encoder=encoder_config.to_dict(), decoder=decoder_config.to_dict(), **kwargs)
__all__ = ["SpeechEncoderDecoderConfig"]
| transformers/src/transformers/models/speech_encoder_decoder/configuration_speech_encoder_decoder.py/0 | {
"file_path": "transformers/src/transformers/models/speech_encoder_decoder/configuration_speech_encoder_decoder.py",
"repo_id": "transformers",
"token_count": 1636
} |
# 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.
"""Convert SpeechT5 checkpoint."""
import argparse
import torch
from transformers import (
SpeechT5Config,
SpeechT5FeatureExtractor,
SpeechT5ForSpeechToSpeech,
SpeechT5ForSpeechToText,
SpeechT5ForTextToSpeech,
SpeechT5Processor,
SpeechT5Tokenizer,
logging,
)
from transformers.tokenization_utils import AddedToken
logging.set_verbosity_info()
logger = logging.get_logger("transformers.models.speecht5")
MAPPING_SPEECH_ENCODER_PRENET = {
"speech_encoder_prenet.layer_norm": "speecht5.encoder.prenet.feature_projection.layer_norm",
"speech_encoder_prenet.post_extract_proj": "speecht5.encoder.prenet.feature_projection.projection",
"speech_encoder_prenet.pos_conv.0": "speecht5.encoder.prenet.pos_conv_embed.conv",
"speech_encoder_prenet.mask_emb": "speecht5.encoder.prenet.masked_spec_embed",
}
MAPPING_TEXT_ENCODER_PRENET = {
"text_encoder_prenet.encoder_prenet.0": "speecht5.encoder.prenet.embed_tokens",
"text_encoder_prenet.encoder_prenet.1.alpha": "speecht5.encoder.prenet.encode_positions.alpha",
}
MAPPING_SPEECH_DECODER_PRENET = {
"speech_decoder_prenet.decoder_prenet.0.0.prenet.0.0": "speecht5.decoder.prenet.layers.0",
"speech_decoder_prenet.decoder_prenet.0.0.prenet.1.0": "speecht5.decoder.prenet.layers.1",
"speech_decoder_prenet.decoder_prenet.0.1": "speecht5.decoder.prenet.final_layer",
"speech_decoder_prenet.decoder_prenet.1.alpha": "speecht5.decoder.prenet.encode_positions.alpha",
"speech_decoder_prenet.spkembs_layer.0": "speecht5.decoder.prenet.speaker_embeds_layer",
}
MAPPING_SPEECH_DECODER_POSTNET = {
"speech_decoder_postnet.feat_out": "speech_decoder_postnet.feat_out",
"speech_decoder_postnet.prob_out": "speech_decoder_postnet.prob_out",
"speech_decoder_postnet.postnet.postnet.0.0": "speech_decoder_postnet.layers.0.conv",
"speech_decoder_postnet.postnet.postnet.0.1": "speech_decoder_postnet.layers.0.batch_norm",
"speech_decoder_postnet.postnet.postnet.1.0": "speech_decoder_postnet.layers.1.conv",
"speech_decoder_postnet.postnet.postnet.1.1": "speech_decoder_postnet.layers.1.batch_norm",
"speech_decoder_postnet.postnet.postnet.2.0": "speech_decoder_postnet.layers.2.conv",
"speech_decoder_postnet.postnet.postnet.2.1": "speech_decoder_postnet.layers.2.batch_norm",
"speech_decoder_postnet.postnet.postnet.3.0": "speech_decoder_postnet.layers.3.conv",
"speech_decoder_postnet.postnet.postnet.3.1": "speech_decoder_postnet.layers.3.batch_norm",
"speech_decoder_postnet.postnet.postnet.4.0": "speech_decoder_postnet.layers.4.conv",
"speech_decoder_postnet.postnet.postnet.4.1": "speech_decoder_postnet.layers.4.batch_norm",
}
MAPPING_TEXT_DECODER_PRENET = {
"text_decoder_prenet.embed_tokens": "speecht5.decoder.prenet.embed_tokens",
}
MAPPING_TEXT_DECODER_POSTNET = {
"text_decoder_postnet.output_projection": "text_decoder_postnet.lm_head",
}
MAPPING_ENCODER = {
"encoder.layers.*.self_attn.k_proj": "speecht5.encoder.wrapped_encoder.layers.*.attention.k_proj",
"encoder.layers.*.self_attn.v_proj": "speecht5.encoder.wrapped_encoder.layers.*.attention.v_proj",
"encoder.layers.*.self_attn.q_proj": "speecht5.encoder.wrapped_encoder.layers.*.attention.q_proj",
"encoder.layers.*.self_attn.out_proj": "speecht5.encoder.wrapped_encoder.layers.*.attention.out_proj",
"encoder.layers.*.self_attn_layer_norm": "speecht5.encoder.wrapped_encoder.layers.*.layer_norm",
"encoder.layers.*.fc1": "speecht5.encoder.wrapped_encoder.layers.*.feed_forward.intermediate_dense",
"encoder.layers.*.fc2": "speecht5.encoder.wrapped_encoder.layers.*.feed_forward.output_dense",
"encoder.layers.*.final_layer_norm": "speecht5.encoder.wrapped_encoder.layers.*.final_layer_norm",
"encoder.layer_norm": "speecht5.encoder.wrapped_encoder.layer_norm",
"encoder.pos_emb.pe_k": "speecht5.encoder.wrapped_encoder.embed_positions.pe_k",
}
MAPPING_DECODER = {
"decoder.layers.*.self_attn.k_proj": "speecht5.decoder.wrapped_decoder.layers.*.self_attn.k_proj",
"decoder.layers.*.self_attn.v_proj": "speecht5.decoder.wrapped_decoder.layers.*.self_attn.v_proj",
"decoder.layers.*.self_attn.q_proj": "speecht5.decoder.wrapped_decoder.layers.*.self_attn.q_proj",
"decoder.layers.*.self_attn.out_proj": "speecht5.decoder.wrapped_decoder.layers.*.self_attn.out_proj",
"decoder.layers.*.self_attn_layer_norm": "speecht5.decoder.wrapped_decoder.layers.*.self_attn_layer_norm",
"decoder.layers.*.encoder_attn.k_proj": "speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.k_proj",
"decoder.layers.*.encoder_attn.v_proj": "speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.v_proj",
"decoder.layers.*.encoder_attn.q_proj": "speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.q_proj",
"decoder.layers.*.encoder_attn.out_proj": "speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.out_proj",
"decoder.layers.*.encoder_attn_layer_norm": "speecht5.decoder.wrapped_decoder.layers.*.encoder_attn_layer_norm",
"decoder.layers.*.fc1": "speecht5.decoder.wrapped_decoder.layers.*.feed_forward.intermediate_dense",
"decoder.layers.*.fc2": "speecht5.decoder.wrapped_decoder.layers.*.feed_forward.output_dense",
"decoder.layers.*.final_layer_norm": "speecht5.decoder.wrapped_decoder.layers.*.final_layer_norm",
}
MAPPING_S2T = {
**MAPPING_SPEECH_ENCODER_PRENET,
**MAPPING_ENCODER,
**MAPPING_DECODER,
**MAPPING_TEXT_DECODER_PRENET,
**MAPPING_TEXT_DECODER_POSTNET,
}
MAPPING_T2S = {
**MAPPING_TEXT_ENCODER_PRENET,
**MAPPING_ENCODER,
**MAPPING_DECODER,
**MAPPING_SPEECH_DECODER_PRENET,
**MAPPING_SPEECH_DECODER_POSTNET,
}
MAPPING_S2S = {
**MAPPING_SPEECH_ENCODER_PRENET,
**MAPPING_ENCODER,
**MAPPING_DECODER,
**MAPPING_SPEECH_DECODER_PRENET,
**MAPPING_SPEECH_DECODER_POSTNET,
}
TOP_LEVEL_KEYS = []
IGNORE_KEYS = [
"encoder.version",
"encoder.layers.*.norm_k.weight",
"encoder.layers.*.norm_k.bias",
"decoder.version",
"decoder.layers.*.norm_k.weight",
"decoder.layers.*.norm_k.bias",
"decoder.pos_emb.pe_k",
"speech_encoder_prenet.embed_positions._float_tensor",
"text_decoder_prenet.embed_positions._float_tensor",
]
IGNORE_KEYS_S2T = IGNORE_KEYS + [
"encoder.proj",
"text_encoder_prenet.*",
"speech_decoder_prenet.*",
"speech_decoder_postnet.*",
]
IGNORE_KEYS_T2S = IGNORE_KEYS + [
"encoder.proj",
"speech_encoder_prenet.*",
"text_decoder_prenet.*",
"text_decoder_postnet.*",
]
IGNORE_KEYS_S2S = IGNORE_KEYS + [
"encoder.proj",
"text_encoder_prenet.*",
"text_decoder_prenet.*",
"text_decoder_postnet.*",
]
def set_recursively(hf_pointer, key, value, full_name, weight_type):
for attribute in key.split("."):
hf_pointer = getattr(hf_pointer, attribute)
if weight_type is not None:
hf_shape = getattr(hf_pointer, weight_type).shape
else:
hf_shape = hf_pointer.shape
if hf_shape != value.shape:
raise ValueError(
f"Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"
f" {value.shape} for {full_name}"
)
if weight_type == "weight":
hf_pointer.weight.data = value
elif weight_type == "weight_g":
hf_pointer.weight_g.data = value
elif weight_type == "weight_v":
hf_pointer.weight_v.data = value
elif weight_type == "bias":
hf_pointer.bias.data = value
elif weight_type == "running_mean":
hf_pointer.running_mean.data = value
elif weight_type == "running_var":
hf_pointer.running_var.data = value
elif weight_type == "num_batches_tracked":
hf_pointer.num_batches_tracked.data = value
else:
hf_pointer.data = value
logger.info(f"{key + ('.' + weight_type if weight_type is not None else '')} was initialized from {full_name}.")
def should_ignore(name, ignore_keys):
for key in ignore_keys:
if key.endswith(".*"):
if name.startswith(key[:-1]):
return True
elif ".*." in key:
prefix, suffix = key.split(".*.")
if prefix in name and suffix in name:
return True
elif key in name:
return True
return False
def recursively_load_weights(fairseq_dict, hf_model, task):
unused_weights = []
if task == "s2t":
feature_encoder = hf_model.speecht5.encoder.prenet.feature_encoder
MAPPING = MAPPING_S2T
IGNORE_KEYS = IGNORE_KEYS_S2T
elif task == "t2s":
feature_encoder = None
MAPPING = MAPPING_T2S
IGNORE_KEYS = IGNORE_KEYS_T2S
elif task == "s2s":
feature_encoder = hf_model.speecht5.encoder.prenet.feature_encoder
MAPPING = MAPPING_S2S
IGNORE_KEYS = IGNORE_KEYS_S2S
else:
raise ValueError(f"Unsupported task: {task}")
for name, value in fairseq_dict.items():
if should_ignore(name, IGNORE_KEYS):
logger.info(f"{name} was ignored")
continue
is_used = False
if "conv_layers" in name:
load_conv_layer(
name,
value,
feature_encoder,
unused_weights,
hf_model.config.feat_extract_norm == "group",
)
is_used = True
else:
for key, mapped_key in MAPPING.items():
# mapped_key = "speecht5." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
if "*" in key:
prefix, suffix = key.split(".*.")
if prefix in name and suffix in name:
key = suffix
# if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
if key in name:
is_used = True
if "*" in mapped_key:
layer_index = name.split(key)[0].split(".")[-2]
mapped_key = mapped_key.replace("*", layer_index)
if "weight_g" in name:
weight_type = "weight_g"
elif "weight_v" in name:
weight_type = "weight_v"
elif "bias" in name:
weight_type = "bias"
elif "weight" in name:
weight_type = "weight"
elif "running_mean" in name:
weight_type = "running_mean"
elif "running_var" in name:
weight_type = "running_var"
elif "num_batches_tracked" in name:
weight_type = "num_batches_tracked"
else:
weight_type = None
set_recursively(hf_model, mapped_key, value, name, weight_type)
continue
if not is_used:
unused_weights.append(name)
logger.warning(f"Unused weights: {unused_weights}")
def load_conv_layer(full_name, value, feature_extractor, unused_weights, use_group_norm):
name = full_name.split("conv_layers.")[-1]
items = name.split(".")
layer_id = int(items[0])
type_id = int(items[1])
if type_id == 0:
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
raise ValueError(
f"{full_name} has size {value.shape}, but"
f" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."
)
feature_extractor.conv_layers[layer_id].conv.bias.data = value
logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}.")
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
raise ValueError(
f"{full_name} has size {value.shape}, but"
f" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."
)
feature_extractor.conv_layers[layer_id].conv.weight.data = value
logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}.")
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
raise ValueError(
f"{full_name} has size {value.shape}, but"
f" {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found."
)
feature_extractor.conv_layers[layer_id].layer_norm.bias.data = value
logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.")
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
raise ValueError(
f"{full_name} has size {value.shape}, but"
f" {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found."
)
feature_extractor.conv_layers[layer_id].layer_norm.weight.data = value
logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.")
else:
unused_weights.append(full_name)
@torch.no_grad()
def convert_speecht5_checkpoint(
task,
checkpoint_path,
pytorch_dump_folder_path,
config_path=None,
vocab_path=None,
repo_id=None,
):
"""
Copy/paste/tweak model's weights to transformers design.
"""
if config_path is not None:
config = SpeechT5Config.from_pretrained(config_path)
else:
config = SpeechT5Config()
if task == "s2t":
config.max_length = config.max_text_positions
model = SpeechT5ForSpeechToText(config)
elif task == "t2s":
config.max_speech_positions = 1876
config.max_text_positions = 600
config.max_length = config.max_speech_positions
model = SpeechT5ForTextToSpeech(config)
elif task == "s2s":
config.max_speech_positions = 1876
config.max_length = config.max_speech_positions
model = SpeechT5ForSpeechToSpeech(config)
else:
raise ValueError(f"Unknown task name: {task}")
if vocab_path:
tokenizer = SpeechT5Tokenizer(vocab_path, model_max_length=config.max_text_positions)
# Mask token behaves like a normal word, i.e. include the space before it
mask_token = AddedToken("<mask>", lstrip=True, rstrip=False)
tokenizer.mask_token = mask_token
tokenizer.add_special_tokens({"mask_token": mask_token})
tokenizer.add_tokens(["<ctc_blank>"])
feature_extractor = SpeechT5FeatureExtractor()
processor = SpeechT5Processor(tokenizer=tokenizer, feature_extractor=feature_extractor)
processor.save_pretrained(pytorch_dump_folder_path)
fairseq_checkpoint = torch.load(checkpoint_path)
recursively_load_weights(fairseq_checkpoint["model"], model, task)
model.save_pretrained(pytorch_dump_folder_path)
if repo_id:
print("Pushing to the hub...")
processor.push_to_hub(repo_id)
model.push_to_hub(repo_id)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--task",
default="s2t",
type=str,
help="Type of the SpeechT5 model you'd like to convert. Should be one of 's2t', 't2s', 's2s'.",
)
parser.add_argument("--checkpoint_path", required=True, default=None, type=str, help="Path to fairseq checkpoint")
parser.add_argument("--vocab_path", default=None, type=str, help="Path to SentencePiece model")
parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
parser.add_argument(
"--pytorch_dump_folder_path", required=True, default=None, type=str, help="Path to the output PyTorch model."
)
parser.add_argument(
"--push_to_hub", default=None, type=str, help="Where to upload the converted model on the 🤗 hub."
)
args = parser.parse_args()
convert_speecht5_checkpoint(
args.task,
args.checkpoint_path,
args.pytorch_dump_folder_path,
args.config_path,
args.vocab_path,
args.push_to_hub,
)
| transformers/src/transformers/models/speecht5/convert_speecht5_original_pytorch_checkpoint_to_pytorch.py/0 | {
"file_path": "transformers/src/transformers/models/speecht5/convert_speecht5_original_pytorch_checkpoint_to_pytorch.py",
"repo_id": "transformers",
"token_count": 7959
} |
# 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.
"""PyTorch SuperPoint model."""
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
from torch import nn
from transformers import PreTrainedModel
from transformers.modeling_outputs import (
BaseModelOutputWithNoAttention,
)
from transformers.models.superpoint.configuration_superpoint import SuperPointConfig
from ...utils import (
ModelOutput,
add_start_docstrings,
add_start_docstrings_to_model_forward,
logging,
)
logger = logging.get_logger(__name__)
_CONFIG_FOR_DOC = "SuperPointConfig"
_CHECKPOINT_FOR_DOC = "magic-leap-community/superpoint"
def remove_keypoints_from_borders(
keypoints: torch.Tensor, scores: torch.Tensor, border: int, height: int, width: int
) -> Tuple[torch.Tensor, torch.Tensor]:
"""Removes keypoints (and their associated scores) that are too close to the border"""
mask_h = (keypoints[:, 0] >= border) & (keypoints[:, 0] < (height - border))
mask_w = (keypoints[:, 1] >= border) & (keypoints[:, 1] < (width - border))
mask = mask_h & mask_w
return keypoints[mask], scores[mask]
def top_k_keypoints(keypoints: torch.Tensor, scores: torch.Tensor, k: int) -> Tuple[torch.Tensor, torch.Tensor]:
"""Keeps the k keypoints with highest score"""
if k >= len(keypoints):
return keypoints, scores
scores, indices = torch.topk(scores, k, dim=0)
return keypoints[indices], scores
def simple_nms(scores: torch.Tensor, nms_radius: int) -> torch.Tensor:
"""Applies non-maximum suppression on scores"""
if nms_radius < 0:
raise ValueError("Expected positive values for nms_radius")
def max_pool(x):
return nn.functional.max_pool2d(x, kernel_size=nms_radius * 2 + 1, stride=1, padding=nms_radius)
zeros = torch.zeros_like(scores)
max_mask = scores == max_pool(scores)
for _ in range(2):
supp_mask = max_pool(max_mask.float()) > 0
supp_scores = torch.where(supp_mask, zeros, scores)
new_max_mask = supp_scores == max_pool(supp_scores)
max_mask = max_mask | (new_max_mask & (~supp_mask))
return torch.where(max_mask, scores, zeros)
@dataclass
class SuperPointKeypointDescriptionOutput(ModelOutput):
"""
Base class for outputs of image point description models. Due to the nature of keypoint detection, the number of
keypoints is not fixed and can vary from image to image, which makes batching non-trivial. In the batch of images,
the maximum number of keypoints is set as the dimension of the keypoints, scores and descriptors tensors. The mask
tensor is used to indicate which values in the keypoints, scores and descriptors tensors are keypoint information
and which are padding.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*):
Loss computed during training.
keypoints (`torch.FloatTensor` of shape `(batch_size, num_keypoints, 2)`):
Relative (x, y) coordinates of predicted keypoints in a given image.
scores (`torch.FloatTensor` of shape `(batch_size, num_keypoints)`):
Scores of predicted keypoints.
descriptors (`torch.FloatTensor` of shape `(batch_size, num_keypoints, descriptor_size)`):
Descriptors of predicted keypoints.
mask (`torch.BoolTensor` of shape `(batch_size, num_keypoints)`):
Mask indicating which values in keypoints, scores and descriptors are keypoint information.
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 stage) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states
(also called feature maps) of the model at the output of each stage.
"""
loss: Optional[torch.FloatTensor] = None
keypoints: Optional[torch.IntTensor] = None
scores: Optional[torch.FloatTensor] = None
descriptors: Optional[torch.FloatTensor] = None
mask: Optional[torch.BoolTensor] = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
class SuperPointConvBlock(nn.Module):
def __init__(
self, config: SuperPointConfig, in_channels: int, out_channels: int, add_pooling: bool = False
) -> None:
super().__init__()
self.conv_a = nn.Conv2d(
in_channels,
out_channels,
kernel_size=3,
stride=1,
padding=1,
)
self.conv_b = nn.Conv2d(
out_channels,
out_channels,
kernel_size=3,
stride=1,
padding=1,
)
self.relu = nn.ReLU(inplace=True)
self.pool = nn.MaxPool2d(kernel_size=2, stride=2) if add_pooling else None
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidden_states = self.relu(self.conv_a(hidden_states))
hidden_states = self.relu(self.conv_b(hidden_states))
if self.pool is not None:
hidden_states = self.pool(hidden_states)
return hidden_states
class SuperPointEncoder(nn.Module):
"""
SuperPoint encoder module. It is made of 4 convolutional layers with ReLU activation and max pooling, reducing the
dimensionality of the image.
"""
def __init__(self, config: SuperPointConfig) -> None:
super().__init__()
# SuperPoint uses 1 channel images
self.input_dim = 1
conv_blocks = []
conv_blocks.append(
SuperPointConvBlock(config, self.input_dim, config.encoder_hidden_sizes[0], add_pooling=True)
)
for i in range(1, len(config.encoder_hidden_sizes) - 1):
conv_blocks.append(
SuperPointConvBlock(
config, config.encoder_hidden_sizes[i - 1], config.encoder_hidden_sizes[i], add_pooling=True
)
)
conv_blocks.append(
SuperPointConvBlock(
config, config.encoder_hidden_sizes[-2], config.encoder_hidden_sizes[-1], add_pooling=False
)
)
self.conv_blocks = nn.ModuleList(conv_blocks)
def forward(
self,
input,
output_hidden_states: Optional[bool] = False,
return_dict: Optional[bool] = True,
) -> Union[Tuple, BaseModelOutputWithNoAttention]:
all_hidden_states = () if output_hidden_states else None
for conv_block in self.conv_blocks:
input = conv_block(input)
if output_hidden_states:
all_hidden_states = all_hidden_states + (input,)
output = input
if not return_dict:
return tuple(v for v in [output, all_hidden_states] if v is not None)
return BaseModelOutputWithNoAttention(
last_hidden_state=output,
hidden_states=all_hidden_states,
)
class SuperPointInterestPointDecoder(nn.Module):
"""
The SuperPointInterestPointDecoder uses the output of the SuperPointEncoder to compute the keypoint with scores.
The scores are first computed by a convolutional layer, then a softmax is applied to get a probability distribution
over the 65 possible keypoint classes. The keypoints are then extracted from the scores by thresholding and
non-maximum suppression. Post-processing is then applied to remove keypoints too close to the image borders as well
as to keep only the k keypoints with highest score.
"""
def __init__(self, config: SuperPointConfig) -> None:
super().__init__()
self.keypoint_threshold = config.keypoint_threshold
self.max_keypoints = config.max_keypoints
self.nms_radius = config.nms_radius
self.border_removal_distance = config.border_removal_distance
self.relu = nn.ReLU(inplace=True)
self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
self.conv_score_a = nn.Conv2d(
config.encoder_hidden_sizes[-1],
config.decoder_hidden_size,
kernel_size=3,
stride=1,
padding=1,
)
self.conv_score_b = nn.Conv2d(
config.decoder_hidden_size, config.keypoint_decoder_dim, kernel_size=1, stride=1, padding=0
)
def forward(self, encoded: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
scores = self._get_pixel_scores(encoded)
keypoints, scores = self._extract_keypoints(scores)
return keypoints, scores
def _get_pixel_scores(self, encoded: torch.Tensor) -> torch.Tensor:
"""Based on the encoder output, compute the scores for each pixel of the image"""
scores = self.relu(self.conv_score_a(encoded))
scores = self.conv_score_b(scores)
scores = nn.functional.softmax(scores, 1)[:, :-1]
batch_size, _, height, width = scores.shape
scores = scores.permute(0, 2, 3, 1).reshape(batch_size, height, width, 8, 8)
scores = scores.permute(0, 1, 3, 2, 4).reshape(batch_size, height * 8, width * 8)
scores = simple_nms(scores, self.nms_radius)
return scores
def _extract_keypoints(self, scores: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
"""
Based on their scores, extract the pixels that represent the keypoints that will be used for descriptors computation.
The keypoints are in the form of relative (x, y) coordinates.
"""
_, height, width = scores.shape
# Threshold keypoints by score value
keypoints = torch.nonzero(scores[0] > self.keypoint_threshold)
scores = scores[0][tuple(keypoints.t())]
# Discard keypoints near the image borders
keypoints, scores = remove_keypoints_from_borders(
keypoints, scores, self.border_removal_distance, height * 8, width * 8
)
# Keep the k keypoints with highest score
if self.max_keypoints >= 0:
keypoints, scores = top_k_keypoints(keypoints, scores, self.max_keypoints)
# Convert (y, x) to (x, y)
keypoints = torch.flip(keypoints, [1]).float()
return keypoints, scores
class SuperPointDescriptorDecoder(nn.Module):
"""
The SuperPointDescriptorDecoder uses the outputs of both the SuperPointEncoder and the
SuperPointInterestPointDecoder to compute the descriptors at the keypoints locations.
The descriptors are first computed by a convolutional layer, then normalized to have a norm of 1. The descriptors
are then interpolated at the keypoints locations.
"""
def __init__(self, config: SuperPointConfig) -> None:
super().__init__()
self.relu = nn.ReLU(inplace=True)
self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
self.conv_descriptor_a = nn.Conv2d(
config.encoder_hidden_sizes[-1],
config.decoder_hidden_size,
kernel_size=3,
stride=1,
padding=1,
)
self.conv_descriptor_b = nn.Conv2d(
config.decoder_hidden_size,
config.descriptor_decoder_dim,
kernel_size=1,
stride=1,
padding=0,
)
def forward(self, encoded: torch.Tensor, keypoints: torch.Tensor) -> torch.Tensor:
"""Based on the encoder output and the keypoints, compute the descriptors for each keypoint"""
descriptors = self.conv_descriptor_b(self.relu(self.conv_descriptor_a(encoded)))
descriptors = nn.functional.normalize(descriptors, p=2, dim=1)
descriptors = self._sample_descriptors(keypoints[None], descriptors[0][None], 8)[0]
# [descriptor_dim, num_keypoints] -> [num_keypoints, descriptor_dim]
descriptors = torch.transpose(descriptors, 0, 1)
return descriptors
@staticmethod
def _sample_descriptors(keypoints, descriptors, scale: int = 8) -> torch.Tensor:
"""Interpolate descriptors at keypoint locations"""
batch_size, num_channels, height, width = descriptors.shape
keypoints = keypoints - scale / 2 + 0.5
divisor = torch.tensor([[(width * scale - scale / 2 - 0.5), (height * scale - scale / 2 - 0.5)]])
divisor = divisor.to(keypoints)
keypoints /= divisor
keypoints = keypoints * 2 - 1 # normalize to (-1, 1)
kwargs = {"align_corners": True}
# [batch_size, num_channels, num_keypoints, 2] -> [batch_size, num_channels, num_keypoints, 2]
keypoints = keypoints.view(batch_size, 1, -1, 2)
descriptors = nn.functional.grid_sample(descriptors, keypoints, mode="bilinear", **kwargs)
# [batch_size, descriptor_decoder_dim, num_channels, num_keypoints] -> [batch_size, descriptor_decoder_dim, num_keypoints]
descriptors = descriptors.reshape(batch_size, num_channels, -1)
descriptors = nn.functional.normalize(descriptors, p=2, dim=1)
return descriptors
class SuperPointPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = SuperPointConfig
base_model_prefix = "superpoint"
main_input_name = "pixel_values"
supports_gradient_checkpointing = False
def _init_weights(self, module: Union[nn.Linear, nn.Conv2d, nn.LayerNorm]) -> None:
"""Initialize the weights"""
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=self.config.initializer_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)
def extract_one_channel_pixel_values(self, pixel_values: torch.FloatTensor) -> torch.FloatTensor:
"""
Assuming pixel_values has shape (batch_size, 3, height, width), and that all channels values are the same,
extract the first channel value to get a tensor of shape (batch_size, 1, height, width) for SuperPoint. This is
a workaround for the issue discussed in :
https://github.com/huggingface/transformers/pull/25786#issuecomment-1730176446
Args:
pixel_values: torch.FloatTensor of shape (batch_size, 3, height, width)
Returns:
pixel_values: torch.FloatTensor of shape (batch_size, 1, height, width)
"""
return pixel_values[:, 0, :, :][:, None, :, :]
SUPERPOINT_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 ([`SuperPointConfig`]): 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.
"""
SUPERPOINT_INPUTS_DOCSTRING = r"""
Args:
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Pixel values can be obtained using [`SuperPointImageProcessor`]. See
[`SuperPointImageProcessor.__call__`] for details.
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(
"SuperPoint model outputting keypoints and descriptors.",
SUPERPOINT_START_DOCSTRING,
)
class SuperPointForKeypointDetection(SuperPointPreTrainedModel):
"""
SuperPoint model. It consists of a SuperPointEncoder, a SuperPointInterestPointDecoder and a
SuperPointDescriptorDecoder. SuperPoint was proposed in `SuperPoint: Self-Supervised Interest Point Detection and
Description <https://arxiv.org/abs/1712.07629>`__ by Daniel DeTone, Tomasz Malisiewicz, and Andrew Rabinovich. It
is a fully convolutional neural network that extracts keypoints and descriptors from an image. It is trained in a
self-supervised manner, using a combination of a photometric loss and a loss based on the homographic adaptation of
keypoints. It is made of a convolutional encoder and two decoders: one for keypoints and one for descriptors.
"""
def __init__(self, config: SuperPointConfig) -> None:
super().__init__(config)
self.config = config
self.encoder = SuperPointEncoder(config)
self.keypoint_decoder = SuperPointInterestPointDecoder(config)
self.descriptor_decoder = SuperPointDescriptorDecoder(config)
self.post_init()
@add_start_docstrings_to_model_forward(SUPERPOINT_INPUTS_DOCSTRING)
def forward(
self,
pixel_values: torch.FloatTensor,
labels: Optional[torch.LongTensor] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, SuperPointKeypointDescriptionOutput]:
"""
Examples:
```python
>>> from transformers import AutoImageProcessor, SuperPointForKeypointDetection
>>> 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("magic-leap-community/superpoint")
>>> model = SuperPointForKeypointDetection.from_pretrained("magic-leap-community/superpoint")
>>> inputs = processor(image, return_tensors="pt")
>>> outputs = model(**inputs)
```"""
loss = None
if labels is not None:
raise ValueError("SuperPoint does not support training for now.")
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
pixel_values = self.extract_one_channel_pixel_values(pixel_values)
batch_size, _, height, width = pixel_values.shape
encoder_outputs = self.encoder(
pixel_values,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
last_hidden_state = encoder_outputs[0]
list_keypoints_scores = [
self.keypoint_decoder(last_hidden_state[None, ...]) for last_hidden_state in last_hidden_state
]
list_keypoints = [keypoints_scores[0] for keypoints_scores in list_keypoints_scores]
list_scores = [keypoints_scores[1] for keypoints_scores in list_keypoints_scores]
list_descriptors = [
self.descriptor_decoder(last_hidden_state[None, ...], keypoints[None, ...])
for last_hidden_state, keypoints in zip(last_hidden_state, list_keypoints)
]
maximum_num_keypoints = max(keypoints.shape[0] for keypoints in list_keypoints)
keypoints = torch.zeros((batch_size, maximum_num_keypoints, 2), device=pixel_values.device)
scores = torch.zeros((batch_size, maximum_num_keypoints), device=pixel_values.device)
descriptors = torch.zeros(
(batch_size, maximum_num_keypoints, self.config.descriptor_decoder_dim),
device=pixel_values.device,
)
mask = torch.zeros((batch_size, maximum_num_keypoints), device=pixel_values.device, dtype=torch.int)
for i, (_keypoints, _scores, _descriptors) in enumerate(zip(list_keypoints, list_scores, list_descriptors)):
keypoints[i, : _keypoints.shape[0]] = _keypoints
scores[i, : _scores.shape[0]] = _scores
descriptors[i, : _descriptors.shape[0]] = _descriptors
mask[i, : _scores.shape[0]] = 1
# Convert to relative coordinates
keypoints = keypoints / torch.tensor([width, height], device=keypoints.device)
hidden_states = encoder_outputs[1] if output_hidden_states else None
if not return_dict:
return tuple(v for v in [loss, keypoints, scores, descriptors, mask, hidden_states] if v is not None)
return SuperPointKeypointDescriptionOutput(
loss=loss,
keypoints=keypoints,
scores=scores,
descriptors=descriptors,
mask=mask,
hidden_states=hidden_states,
)
__all__ = ["SuperPointForKeypointDetection", "SuperPointPreTrainedModel"]
| transformers/src/transformers/models/superpoint/modeling_superpoint.py/0 | {
"file_path": "transformers/src/transformers/models/superpoint/modeling_superpoint.py",
"repo_id": "transformers",
"token_count": 8555
} |
# coding=utf-8
# Copyright 2022 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 Swin2SR Transformer model."""
import collections.abc
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, ImageSuperResolutionOutput
from ...modeling_utils import PreTrainedModel
from ...pytorch_utils import find_pruneable_heads_and_indices, meshgrid, prune_linear_layer
from ...utils import (
ModelOutput,
add_code_sample_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
logging,
replace_return_docstrings,
)
from .configuration_swin2sr import Swin2SRConfig
logger = logging.get_logger(__name__)
# General docstring
_CONFIG_FOR_DOC = "Swin2SRConfig"
# Base docstring
_CHECKPOINT_FOR_DOC = "caidas/swin2SR-classical-sr-x2-64"
_EXPECTED_OUTPUT_SHAPE = [1, 180, 488, 648]
@dataclass
class Swin2SREncoderOutput(ModelOutput):
"""
Swin2SR 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)`.
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.
"""
last_hidden_state: torch.FloatTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None
# Copied from transformers.models.swin.modeling_swin.window_partition
def window_partition(input_feature, window_size):
"""
Partitions the given input into windows.
"""
batch_size, height, width, num_channels = input_feature.shape
input_feature = input_feature.view(
batch_size, height // window_size, window_size, width // window_size, window_size, num_channels
)
windows = input_feature.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, num_channels)
return windows
# Copied from transformers.models.swin.modeling_swin.window_reverse
def window_reverse(windows, window_size, height, width):
"""
Merges windows to produce higher resolution features.
"""
num_channels = windows.shape[-1]
windows = windows.view(-1, height // window_size, width // window_size, window_size, window_size, num_channels)
windows = windows.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, height, width, num_channels)
return windows
# 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.swin.modeling_swin.SwinDropPath with Swin->Swin2SR
class Swin2SRDropPath(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 Swin2SREmbeddings(nn.Module):
"""
Construct the patch and optional position embeddings.
"""
def __init__(self, config):
super().__init__()
self.patch_embeddings = Swin2SRPatchEmbeddings(config)
num_patches = self.patch_embeddings.num_patches
if config.use_absolute_embeddings:
self.position_embeddings = nn.Parameter(torch.zeros(1, num_patches + 1, config.embed_dim))
else:
self.position_embeddings = None
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.window_size = config.window_size
def forward(self, pixel_values: Optional[torch.FloatTensor]) -> Tuple[torch.Tensor]:
embeddings, output_dimensions = self.patch_embeddings(pixel_values)
if self.position_embeddings is not None:
embeddings = embeddings + self.position_embeddings
embeddings = self.dropout(embeddings)
return embeddings, output_dimensions
class Swin2SRPatchEmbeddings(nn.Module):
def __init__(self, config, normalize_patches=True):
super().__init__()
num_channels = config.embed_dim
image_size, patch_size = config.image_size, config.patch_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)
patches_resolution = [image_size[0] // patch_size[0], image_size[1] // patch_size[1]]
self.patches_resolution = patches_resolution
self.num_patches = patches_resolution[0] * patches_resolution[1]
self.projection = nn.Conv2d(num_channels, config.embed_dim, kernel_size=patch_size, stride=patch_size)
self.layernorm = nn.LayerNorm(config.embed_dim) if normalize_patches else None
def forward(self, embeddings: Optional[torch.FloatTensor]) -> Tuple[torch.Tensor, Tuple[int]]:
embeddings = self.projection(embeddings)
_, _, height, width = embeddings.shape
output_dimensions = (height, width)
embeddings = embeddings.flatten(2).transpose(1, 2)
if self.layernorm is not None:
embeddings = self.layernorm(embeddings)
return embeddings, output_dimensions
class Swin2SRPatchUnEmbeddings(nn.Module):
r"""Image to Patch Unembedding"""
def __init__(self, config):
super().__init__()
self.embed_dim = config.embed_dim
def forward(self, embeddings, x_size):
batch_size, height_width, num_channels = embeddings.shape
embeddings = embeddings.transpose(1, 2).view(batch_size, self.embed_dim, x_size[0], x_size[1]) # B Ph*Pw C
return embeddings
# Copied from transformers.models.swinv2.modeling_swinv2.Swinv2PatchMerging with Swinv2->Swin2SR
class Swin2SRPatchMerging(nn.Module):
"""
Patch Merging Layer.
Args:
input_resolution (`Tuple[int]`):
Resolution of input feature.
dim (`int`):
Number of input channels.
norm_layer (`nn.Module`, *optional*, defaults to `nn.LayerNorm`):
Normalization layer class.
"""
def __init__(self, input_resolution: Tuple[int], dim: int, norm_layer: nn.Module = nn.LayerNorm) -> None:
super().__init__()
self.input_resolution = input_resolution
self.dim = dim
self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
self.norm = norm_layer(2 * dim)
def maybe_pad(self, input_feature, height, width):
should_pad = (height % 2 == 1) or (width % 2 == 1)
if should_pad:
pad_values = (0, 0, 0, width % 2, 0, height % 2)
input_feature = nn.functional.pad(input_feature, pad_values)
return input_feature
def forward(self, input_feature: torch.Tensor, input_dimensions: Tuple[int, int]) -> torch.Tensor:
height, width = input_dimensions
# `dim` is height * width
batch_size, dim, num_channels = input_feature.shape
input_feature = input_feature.view(batch_size, height, width, num_channels)
# pad input to be disible by width and height, if needed
input_feature = self.maybe_pad(input_feature, height, width)
# [batch_size, height/2, width/2, num_channels]
input_feature_0 = input_feature[:, 0::2, 0::2, :]
# [batch_size, height/2, width/2, num_channels]
input_feature_1 = input_feature[:, 1::2, 0::2, :]
# [batch_size, height/2, width/2, num_channels]
input_feature_2 = input_feature[:, 0::2, 1::2, :]
# [batch_size, height/2, width/2, num_channels]
input_feature_3 = input_feature[:, 1::2, 1::2, :]
# [batch_size, height/2 * width/2, 4*num_channels]
input_feature = torch.cat([input_feature_0, input_feature_1, input_feature_2, input_feature_3], -1)
input_feature = input_feature.view(batch_size, -1, 4 * num_channels) # [batch_size, height/2 * width/2, 4*C]
input_feature = self.reduction(input_feature)
input_feature = self.norm(input_feature)
return input_feature
# Copied from transformers.models.swinv2.modeling_swinv2.Swinv2SelfAttention with Swinv2->Swin2SR
class Swin2SRSelfAttention(nn.Module):
def __init__(self, config, dim, num_heads, window_size, pretrained_window_size=[0, 0]):
super().__init__()
if dim % num_heads != 0:
raise ValueError(
f"The hidden size ({dim}) is not a multiple of the number of attention heads ({num_heads})"
)
self.num_attention_heads = num_heads
self.attention_head_size = int(dim / num_heads)
self.all_head_size = self.num_attention_heads * self.attention_head_size
self.window_size = (
window_size if isinstance(window_size, collections.abc.Iterable) else (window_size, window_size)
)
self.pretrained_window_size = pretrained_window_size
self.logit_scale = nn.Parameter(torch.log(10 * torch.ones((num_heads, 1, 1))))
# mlp to generate continuous relative position bias
self.continuous_position_bias_mlp = nn.Sequential(
nn.Linear(2, 512, bias=True), nn.ReLU(inplace=True), nn.Linear(512, num_heads, bias=False)
)
# get relative_coords_table
relative_coords_h = torch.arange(-(self.window_size[0] - 1), self.window_size[0], dtype=torch.int64).float()
relative_coords_w = torch.arange(-(self.window_size[1] - 1), self.window_size[1], dtype=torch.int64).float()
relative_coords_table = (
torch.stack(meshgrid([relative_coords_h, relative_coords_w], indexing="ij"))
.permute(1, 2, 0)
.contiguous()
.unsqueeze(0)
) # [1, 2*window_height - 1, 2*window_width - 1, 2]
if pretrained_window_size[0] > 0:
relative_coords_table[:, :, :, 0] /= pretrained_window_size[0] - 1
relative_coords_table[:, :, :, 1] /= pretrained_window_size[1] - 1
elif window_size > 1:
relative_coords_table[:, :, :, 0] /= self.window_size[0] - 1
relative_coords_table[:, :, :, 1] /= self.window_size[1] - 1
relative_coords_table *= 8 # normalize to -8, 8
relative_coords_table = (
torch.sign(relative_coords_table) * torch.log2(torch.abs(relative_coords_table) + 1.0) / math.log2(8)
)
# set to same dtype as mlp weight
relative_coords_table = relative_coords_table.to(next(self.continuous_position_bias_mlp.parameters()).dtype)
self.register_buffer("relative_coords_table", relative_coords_table, persistent=False)
# get pair-wise relative position index for each token inside the window
coords_h = torch.arange(self.window_size[0])
coords_w = torch.arange(self.window_size[1])
coords = torch.stack(meshgrid([coords_h, coords_w], indexing="ij"))
coords_flatten = torch.flatten(coords, 1)
relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]
relative_coords = relative_coords.permute(1, 2, 0).contiguous()
relative_coords[:, :, 0] += self.window_size[0] - 1
relative_coords[:, :, 1] += self.window_size[1] - 1
relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
relative_position_index = relative_coords.sum(-1)
self.register_buffer("relative_position_index", relative_position_index, persistent=False)
self.query = nn.Linear(self.all_head_size, self.all_head_size, bias=config.qkv_bias)
self.key = nn.Linear(self.all_head_size, self.all_head_size, bias=False)
self.value = nn.Linear(self.all_head_size, self.all_head_size, bias=config.qkv_bias)
self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
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: torch.Tensor,
attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = False,
) -> Tuple[torch.Tensor]:
batch_size, dim, num_channels = hidden_states.shape
mixed_query_layer = self.query(hidden_states)
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)
# cosine attention
attention_scores = nn.functional.normalize(query_layer, dim=-1) @ nn.functional.normalize(
key_layer, dim=-1
).transpose(-2, -1)
logit_scale = torch.clamp(self.logit_scale, max=math.log(1.0 / 0.01)).exp()
attention_scores = attention_scores * logit_scale
relative_position_bias_table = self.continuous_position_bias_mlp(self.relative_coords_table).view(
-1, self.num_attention_heads
)
# [window_height*window_width,window_height*window_width,num_attention_heads]
relative_position_bias = relative_position_bias_table[self.relative_position_index.view(-1)].view(
self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1
)
# [num_attention_heads,window_height*window_width,window_height*window_width]
relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww
relative_position_bias = 16 * torch.sigmoid(relative_position_bias)
attention_scores = attention_scores + relative_position_bias.unsqueeze(0)
if attention_mask is not None:
# Apply the attention mask is (precomputed for all layers in Swin2SRModel forward() function)
mask_shape = attention_mask.shape[0]
attention_scores = attention_scores.view(
batch_size // mask_shape, mask_shape, self.num_attention_heads, dim, dim
) + attention_mask.unsqueeze(1).unsqueeze(0)
attention_scores = attention_scores + attention_mask.unsqueeze(1).unsqueeze(0)
attention_scores = attention_scores.view(-1, self.num_attention_heads, dim, dim)
# 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,)
return outputs
# Copied from transformers.models.swin.modeling_swin.SwinSelfOutput with Swin->Swin2SR
class Swin2SRSelfOutput(nn.Module):
def __init__(self, config, dim):
super().__init__()
self.dense = nn.Linear(dim, dim)
self.dropout = nn.Dropout(config.attention_probs_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
# Copied from transformers.models.swinv2.modeling_swinv2.Swinv2Attention with Swinv2->Swin2SR
class Swin2SRAttention(nn.Module):
def __init__(self, config, dim, num_heads, window_size, pretrained_window_size=0):
super().__init__()
self.self = Swin2SRSelfAttention(
config=config,
dim=dim,
num_heads=num_heads,
window_size=window_size,
pretrained_window_size=pretrained_window_size
if isinstance(pretrained_window_size, collections.abc.Iterable)
else (pretrained_window_size, pretrained_window_size),
)
self.output = Swin2SRSelfOutput(config, dim)
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,
output_attentions: Optional[bool] = False,
) -> Tuple[torch.Tensor]:
self_outputs = self.self(hidden_states, attention_mask, 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
# Copied from transformers.models.swin.modeling_swin.SwinIntermediate with Swin->Swin2SR
class Swin2SRIntermediate(nn.Module):
def __init__(self, config, dim):
super().__init__()
self.dense = nn.Linear(dim, int(config.mlp_ratio * dim))
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.swin.modeling_swin.SwinOutput with Swin->Swin2SR
class Swin2SROutput(nn.Module):
def __init__(self, config, dim):
super().__init__()
self.dense = nn.Linear(int(config.mlp_ratio * dim), dim)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidden_states = self.dense(hidden_states)
hidden_states = self.dropout(hidden_states)
return hidden_states
# Copied from transformers.models.swinv2.modeling_swinv2.Swinv2Layer with Swinv2->Swin2SR
class Swin2SRLayer(nn.Module):
def __init__(
self, config, dim, input_resolution, num_heads, drop_path_rate=0.0, shift_size=0, pretrained_window_size=0
):
super().__init__()
self.input_resolution = input_resolution
window_size, shift_size = self._compute_window_shift(
(config.window_size, config.window_size), (shift_size, shift_size)
)
self.window_size = window_size[0]
self.shift_size = shift_size[0]
self.attention = Swin2SRAttention(
config=config,
dim=dim,
num_heads=num_heads,
window_size=self.window_size,
pretrained_window_size=pretrained_window_size
if isinstance(pretrained_window_size, collections.abc.Iterable)
else (pretrained_window_size, pretrained_window_size),
)
self.layernorm_before = nn.LayerNorm(dim, eps=config.layer_norm_eps)
self.drop_path = Swin2SRDropPath(drop_path_rate) if drop_path_rate > 0.0 else nn.Identity()
self.intermediate = Swin2SRIntermediate(config, dim)
self.output = Swin2SROutput(config, dim)
self.layernorm_after = nn.LayerNorm(dim, eps=config.layer_norm_eps)
def _compute_window_shift(self, target_window_size, target_shift_size) -> Tuple[Tuple[int, int], Tuple[int, int]]:
window_size = [r if r <= w else w for r, w in zip(self.input_resolution, target_window_size)]
shift_size = [0 if r <= w else s for r, w, s in zip(self.input_resolution, window_size, target_shift_size)]
return window_size, shift_size
def get_attn_mask(self, height, width, dtype):
if self.shift_size > 0:
# calculate attention mask for shifted window multihead self attention
img_mask = torch.zeros((1, height, width, 1), dtype=dtype)
height_slices = (
slice(0, -self.window_size),
slice(-self.window_size, -self.shift_size),
slice(-self.shift_size, None),
)
width_slices = (
slice(0, -self.window_size),
slice(-self.window_size, -self.shift_size),
slice(-self.shift_size, None),
)
count = 0
for height_slice in height_slices:
for width_slice in width_slices:
img_mask[:, height_slice, width_slice, :] = count
count += 1
mask_windows = window_partition(img_mask, self.window_size)
mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))
else:
attn_mask = None
return attn_mask
def maybe_pad(self, hidden_states, height, width):
pad_right = (self.window_size - width % self.window_size) % self.window_size
pad_bottom = (self.window_size - height % self.window_size) % self.window_size
pad_values = (0, 0, 0, pad_right, 0, pad_bottom)
hidden_states = nn.functional.pad(hidden_states, pad_values)
return hidden_states, pad_values
def forward(
self,
hidden_states: torch.Tensor,
input_dimensions: Tuple[int, int],
head_mask: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = False,
) -> Tuple[torch.Tensor, torch.Tensor]:
height, width = input_dimensions
batch_size, _, channels = hidden_states.size()
shortcut = hidden_states
# pad hidden_states to multiples of window size
hidden_states = hidden_states.view(batch_size, height, width, channels)
hidden_states, pad_values = self.maybe_pad(hidden_states, height, width)
_, height_pad, width_pad, _ = hidden_states.shape
# cyclic shift
if self.shift_size > 0:
shifted_hidden_states = torch.roll(hidden_states, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
else:
shifted_hidden_states = hidden_states
# partition windows
hidden_states_windows = window_partition(shifted_hidden_states, self.window_size)
hidden_states_windows = hidden_states_windows.view(-1, self.window_size * self.window_size, channels)
attn_mask = self.get_attn_mask(height_pad, width_pad, dtype=hidden_states.dtype)
if attn_mask is not None:
attn_mask = attn_mask.to(hidden_states_windows.device)
attention_outputs = self.attention(
hidden_states_windows, attn_mask, head_mask, output_attentions=output_attentions
)
attention_output = attention_outputs[0]
attention_windows = attention_output.view(-1, self.window_size, self.window_size, channels)
shifted_windows = window_reverse(attention_windows, self.window_size, height_pad, width_pad)
# reverse cyclic shift
if self.shift_size > 0:
attention_windows = torch.roll(shifted_windows, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
else:
attention_windows = shifted_windows
was_padded = pad_values[3] > 0 or pad_values[5] > 0
if was_padded:
attention_windows = attention_windows[:, :height, :width, :].contiguous()
attention_windows = attention_windows.view(batch_size, height * width, channels)
hidden_states = self.layernorm_before(attention_windows)
hidden_states = shortcut + self.drop_path(hidden_states)
layer_output = self.intermediate(hidden_states)
layer_output = self.output(layer_output)
layer_output = hidden_states + self.drop_path(self.layernorm_after(layer_output))
layer_outputs = (layer_output, attention_outputs[1]) if output_attentions else (layer_output,)
return layer_outputs
class Swin2SRStage(nn.Module):
"""
This corresponds to the Residual Swin Transformer Block (RSTB) in the original implementation.
"""
def __init__(self, config, dim, input_resolution, depth, num_heads, drop_path, pretrained_window_size=0):
super().__init__()
self.config = config
self.dim = dim
self.layers = nn.ModuleList(
[
Swin2SRLayer(
config=config,
dim=dim,
input_resolution=input_resolution,
num_heads=num_heads,
shift_size=0 if (i % 2 == 0) else config.window_size // 2,
pretrained_window_size=pretrained_window_size,
)
for i in range(depth)
]
)
if config.resi_connection == "1conv":
self.conv = nn.Conv2d(dim, dim, 3, 1, 1)
elif config.resi_connection == "3conv":
# to save parameters and memory
self.conv = nn.Sequential(
nn.Conv2d(dim, dim // 4, 3, 1, 1),
nn.LeakyReLU(negative_slope=0.2, inplace=True),
nn.Conv2d(dim // 4, dim // 4, 1, 1, 0),
nn.LeakyReLU(negative_slope=0.2, inplace=True),
nn.Conv2d(dim // 4, dim, 3, 1, 1),
)
self.patch_embed = Swin2SRPatchEmbeddings(config, normalize_patches=False)
self.patch_unembed = Swin2SRPatchUnEmbeddings(config)
def forward(
self,
hidden_states: torch.Tensor,
input_dimensions: Tuple[int, int],
head_mask: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = False,
) -> Tuple[torch.Tensor]:
residual = hidden_states
height, width = input_dimensions
for i, layer_module in enumerate(self.layers):
layer_head_mask = head_mask[i] if head_mask is not None else None
layer_outputs = layer_module(hidden_states, input_dimensions, layer_head_mask, output_attentions)
hidden_states = layer_outputs[0]
output_dimensions = (height, width, height, width)
hidden_states = self.patch_unembed(hidden_states, input_dimensions)
hidden_states = self.conv(hidden_states)
hidden_states, _ = self.patch_embed(hidden_states)
hidden_states = hidden_states + residual
stage_outputs = (hidden_states, output_dimensions)
if output_attentions:
stage_outputs += layer_outputs[1:]
return stage_outputs
class Swin2SREncoder(nn.Module):
def __init__(self, config, grid_size):
super().__init__()
self.num_stages = len(config.depths)
self.config = config
dpr = [x.item() for x in torch.linspace(0, config.drop_path_rate, sum(config.depths))]
self.stages = nn.ModuleList(
[
Swin2SRStage(
config=config,
dim=config.embed_dim,
input_resolution=(grid_size[0], grid_size[1]),
depth=config.depths[stage_idx],
num_heads=config.num_heads[stage_idx],
drop_path=dpr[sum(config.depths[:stage_idx]) : sum(config.depths[: stage_idx + 1])],
pretrained_window_size=0,
)
for stage_idx in range(self.num_stages)
]
)
self.gradient_checkpointing = False
def forward(
self,
hidden_states: torch.Tensor,
input_dimensions: Tuple[int, int],
head_mask: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = False,
output_hidden_states: Optional[bool] = False,
return_dict: Optional[bool] = True,
) -> Union[Tuple, Swin2SREncoderOutput]:
all_input_dimensions = ()
all_hidden_states = () if output_hidden_states else None
all_self_attentions = () if output_attentions else None
if output_hidden_states:
all_hidden_states += (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, input_dimensions, layer_head_mask, output_attentions
)
else:
layer_outputs = stage_module(hidden_states, input_dimensions, layer_head_mask, output_attentions)
hidden_states = layer_outputs[0]
output_dimensions = layer_outputs[1]
input_dimensions = (output_dimensions[-2], output_dimensions[-1])
all_input_dimensions += (input_dimensions,)
if output_hidden_states:
all_hidden_states += (hidden_states,)
if output_attentions:
all_self_attentions += layer_outputs[2:]
if not return_dict:
return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None)
return Swin2SREncoderOutput(
last_hidden_state=hidden_states,
hidden_states=all_hidden_states,
attentions=all_self_attentions,
)
class Swin2SRPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = Swin2SRConfig
base_model_prefix = "swin2sr"
main_input_name = "pixel_values"
supports_gradient_checkpointing = True
def _init_weights(self, module):
"""Initialize the weights"""
if isinstance(module, (nn.Linear, nn.Conv2d)):
torch.nn.init.trunc_normal_(module.weight.data, std=self.config.initializer_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)
SWIN2SR_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 ([`Swin2SRConfig`]): 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.
"""
SWIN2SR_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
[`Swin2SRImageProcessor.__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.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
@add_start_docstrings(
"The bare Swin2SR Model transformer outputting raw hidden-states without any specific head on top.",
SWIN2SR_START_DOCSTRING,
)
class Swin2SRModel(Swin2SRPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.config = config
if config.num_channels == 3 and config.num_channels_out == 3:
rgb_mean = (0.4488, 0.4371, 0.4040)
self.mean = torch.Tensor(rgb_mean).view(1, 3, 1, 1)
else:
self.mean = torch.zeros(1, 1, 1, 1)
self.img_range = config.img_range
self.first_convolution = nn.Conv2d(config.num_channels, config.embed_dim, 3, 1, 1)
self.embeddings = Swin2SREmbeddings(config)
self.encoder = Swin2SREncoder(config, grid_size=self.embeddings.patch_embeddings.patches_resolution)
self.layernorm = nn.LayerNorm(config.embed_dim, eps=config.layer_norm_eps)
self.patch_unembed = Swin2SRPatchUnEmbeddings(config)
self.conv_after_body = nn.Conv2d(config.embed_dim, config.embed_dim, 3, 1, 1)
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.embeddings.patch_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} See base
class PreTrainedModel
"""
for layer, heads in heads_to_prune.items():
self.encoder.layer[layer].attention.prune_heads(heads)
def pad_and_normalize(self, pixel_values):
_, _, height, width = pixel_values.size()
# 1. pad
window_size = self.config.window_size
modulo_pad_height = (window_size - height % window_size) % window_size
modulo_pad_width = (window_size - width % window_size) % window_size
pixel_values = nn.functional.pad(pixel_values, (0, modulo_pad_width, 0, modulo_pad_height), "reflect")
# 2. normalize
self.mean = self.mean.type_as(pixel_values)
pixel_values = (pixel_values - self.mean) * self.img_range
return pixel_values
@add_start_docstrings_to_model_forward(SWIN2SR_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=BaseModelOutput,
config_class=_CONFIG_FOR_DOC,
modality="vision",
expected_output=_EXPECTED_OUTPUT_SHAPE,
)
def forward(
self,
pixel_values: torch.FloatTensor,
head_mask: Optional[torch.FloatTensor] = 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
# 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))
_, _, height, width = pixel_values.shape
# some preprocessing: padding + normalization
pixel_values = self.pad_and_normalize(pixel_values)
embeddings = self.first_convolution(pixel_values)
embedding_output, input_dimensions = self.embeddings(embeddings)
encoder_outputs = self.encoder(
embedding_output,
input_dimensions,
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)
sequence_output = self.patch_unembed(sequence_output, (height, width))
sequence_output = self.conv_after_body(sequence_output) + embeddings
if not return_dict:
output = (sequence_output,) + encoder_outputs[1:]
return output
return BaseModelOutput(
last_hidden_state=sequence_output,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
)
class Upsample(nn.Module):
"""Upsample module.
Args:
scale (`int`):
Scale factor. Supported scales: 2^n and 3.
num_features (`int`):
Channel number of intermediate features.
"""
def __init__(self, scale, num_features):
super().__init__()
self.scale = scale
if (scale & (scale - 1)) == 0:
# scale = 2^n
for i in range(int(math.log(scale, 2))):
self.add_module(f"convolution_{i}", nn.Conv2d(num_features, 4 * num_features, 3, 1, 1))
self.add_module(f"pixelshuffle_{i}", nn.PixelShuffle(2))
elif scale == 3:
self.convolution = nn.Conv2d(num_features, 9 * num_features, 3, 1, 1)
self.pixelshuffle = nn.PixelShuffle(3)
else:
raise ValueError(f"Scale {scale} is not supported. Supported scales: 2^n and 3.")
def forward(self, hidden_state):
if (self.scale & (self.scale - 1)) == 0:
for i in range(int(math.log(self.scale, 2))):
hidden_state = self.__getattr__(f"convolution_{i}")(hidden_state)
hidden_state = self.__getattr__(f"pixelshuffle_{i}")(hidden_state)
elif self.scale == 3:
hidden_state = self.convolution(hidden_state)
hidden_state = self.pixelshuffle(hidden_state)
return hidden_state
class UpsampleOneStep(nn.Module):
"""UpsampleOneStep module (the difference with Upsample is that it always only has 1conv + 1pixelshuffle)
Used in lightweight SR to save parameters.
Args:
scale (int):
Scale factor. Supported scales: 2^n and 3.
in_channels (int):
Channel number of intermediate features.
out_channels (int):
Channel number of output features.
"""
def __init__(self, scale, in_channels, out_channels):
super().__init__()
self.conv = nn.Conv2d(in_channels, (scale**2) * out_channels, 3, 1, 1)
self.pixel_shuffle = nn.PixelShuffle(scale)
def forward(self, x):
x = self.conv(x)
x = self.pixel_shuffle(x)
return x
class PixelShuffleUpsampler(nn.Module):
def __init__(self, config, num_features):
super().__init__()
self.conv_before_upsample = nn.Conv2d(config.embed_dim, num_features, 3, 1, 1)
self.activation = nn.LeakyReLU(inplace=True)
self.upsample = Upsample(config.upscale, num_features)
self.final_convolution = nn.Conv2d(num_features, config.num_channels_out, 3, 1, 1)
def forward(self, sequence_output):
x = self.conv_before_upsample(sequence_output)
x = self.activation(x)
x = self.upsample(x)
x = self.final_convolution(x)
return x
class NearestConvUpsampler(nn.Module):
def __init__(self, config, num_features):
super().__init__()
if config.upscale != 4:
raise ValueError("The nearest+conv upsampler only supports an upscale factor of 4 at the moment.")
self.conv_before_upsample = nn.Conv2d(config.embed_dim, num_features, 3, 1, 1)
self.activation = nn.LeakyReLU(inplace=True)
self.conv_up1 = nn.Conv2d(num_features, num_features, 3, 1, 1)
self.conv_up2 = nn.Conv2d(num_features, num_features, 3, 1, 1)
self.conv_hr = nn.Conv2d(num_features, num_features, 3, 1, 1)
self.final_convolution = nn.Conv2d(num_features, config.num_channels_out, 3, 1, 1)
self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
def forward(self, sequence_output):
sequence_output = self.conv_before_upsample(sequence_output)
sequence_output = self.activation(sequence_output)
sequence_output = self.lrelu(
self.conv_up1(torch.nn.functional.interpolate(sequence_output, scale_factor=2, mode="nearest"))
)
sequence_output = self.lrelu(
self.conv_up2(torch.nn.functional.interpolate(sequence_output, scale_factor=2, mode="nearest"))
)
reconstruction = self.final_convolution(self.lrelu(self.conv_hr(sequence_output)))
return reconstruction
class PixelShuffleAuxUpsampler(nn.Module):
def __init__(self, config, num_features):
super().__init__()
self.upscale = config.upscale
self.conv_bicubic = nn.Conv2d(config.num_channels, num_features, 3, 1, 1)
self.conv_before_upsample = nn.Conv2d(config.embed_dim, num_features, 3, 1, 1)
self.activation = nn.LeakyReLU(inplace=True)
self.conv_aux = nn.Conv2d(num_features, config.num_channels, 3, 1, 1)
self.conv_after_aux = nn.Sequential(nn.Conv2d(3, num_features, 3, 1, 1), nn.LeakyReLU(inplace=True))
self.upsample = Upsample(config.upscale, num_features)
self.final_convolution = nn.Conv2d(num_features, config.num_channels_out, 3, 1, 1)
def forward(self, sequence_output, bicubic, height, width):
bicubic = self.conv_bicubic(bicubic)
sequence_output = self.conv_before_upsample(sequence_output)
sequence_output = self.activation(sequence_output)
aux = self.conv_aux(sequence_output)
sequence_output = self.conv_after_aux(aux)
sequence_output = (
self.upsample(sequence_output)[:, :, : height * self.upscale, : width * self.upscale]
+ bicubic[:, :, : height * self.upscale, : width * self.upscale]
)
reconstruction = self.final_convolution(sequence_output)
return reconstruction, aux
@add_start_docstrings(
"""
Swin2SR Model transformer with an upsampler head on top for image super resolution and restoration.
""",
SWIN2SR_START_DOCSTRING,
)
class Swin2SRForImageSuperResolution(Swin2SRPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.swin2sr = Swin2SRModel(config)
self.upsampler = config.upsampler
self.upscale = config.upscale
# Upsampler
num_features = 64
if self.upsampler == "pixelshuffle":
self.upsample = PixelShuffleUpsampler(config, num_features)
elif self.upsampler == "pixelshuffle_aux":
self.upsample = PixelShuffleAuxUpsampler(config, num_features)
elif self.upsampler == "pixelshuffledirect":
# for lightweight SR (to save parameters)
self.upsample = UpsampleOneStep(config.upscale, config.embed_dim, config.num_channels_out)
elif self.upsampler == "nearest+conv":
# for real-world SR (less artifacts)
self.upsample = NearestConvUpsampler(config, num_features)
else:
# for image denoising and JPEG compression artifact reduction
self.final_convolution = nn.Conv2d(config.embed_dim, config.num_channels_out, 3, 1, 1)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(SWIN2SR_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=ImageSuperResolutionOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
pixel_values: Optional[torch.FloatTensor] = None,
head_mask: 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, ImageSuperResolutionOutput]:
r"""
Returns:
Example:
```python
>>> import torch
>>> import numpy as np
>>> from PIL import Image
>>> import requests
>>> from transformers import AutoImageProcessor, Swin2SRForImageSuperResolution
>>> processor = AutoImageProcessor.from_pretrained("caidas/swin2SR-classical-sr-x2-64")
>>> model = Swin2SRForImageSuperResolution.from_pretrained("caidas/swin2SR-classical-sr-x2-64")
>>> url = "https://huggingface.co/spaces/jjourney1125/swin2sr/resolve/main/samples/butterfly.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> # prepare image for the model
>>> inputs = processor(image, return_tensors="pt")
>>> # forward pass
>>> with torch.no_grad():
... outputs = model(**inputs)
>>> output = outputs.reconstruction.data.squeeze().float().cpu().clamp_(0, 1).numpy()
>>> output = np.moveaxis(output, source=0, destination=-1)
>>> output = (output * 255.0).round().astype(np.uint8) # float32 to uint8
>>> # you can visualize `output` with `Image.fromarray`
```"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
loss = None
if labels is not None:
raise NotImplementedError("Training is not supported at the moment")
height, width = pixel_values.shape[2:]
if self.config.upsampler == "pixelshuffle_aux":
bicubic = nn.functional.interpolate(
pixel_values,
size=(height * self.upscale, width * self.upscale),
mode="bicubic",
align_corners=False,
)
outputs = self.swin2sr(
pixel_values,
head_mask=head_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = outputs[0]
if self.upsampler in ["pixelshuffle", "pixelshuffledirect", "nearest+conv"]:
reconstruction = self.upsample(sequence_output)
elif self.upsampler == "pixelshuffle_aux":
reconstruction, aux = self.upsample(sequence_output, bicubic, height, width)
aux = aux / self.swin2sr.img_range + self.swin2sr.mean
else:
reconstruction = pixel_values + self.final_convolution(sequence_output)
reconstruction = reconstruction / self.swin2sr.img_range + self.swin2sr.mean
reconstruction = reconstruction[:, :, : height * self.upscale, : width * self.upscale]
if not return_dict:
output = (reconstruction,) + outputs[1:]
return ((loss,) + output) if loss is not None else output
return ImageSuperResolutionOutput(
loss=loss,
reconstruction=reconstruction,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
__all__ = ["Swin2SRForImageSuperResolution", "Swin2SRModel", "Swin2SRPreTrainedModel"]
| transformers/src/transformers/models/swin2sr/modeling_swin2sr.py/0 | {
"file_path": "transformers/src/transformers/models/swin2sr/modeling_swin2sr.py",
"repo_id": "transformers",
"token_count": 21738
} |
# coding=utf-8
# Copyright 2021 T5 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.
"""Flax T5 model."""
import copy
from typing import Callable, 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 import partitioning as nn_partitioning
from flax.linen.attention import dot_product_attention_weights
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey
from ...modeling_flax_outputs import (
FlaxBaseModelOutput,
FlaxBaseModelOutputWithPastAndCrossAttentions,
FlaxCausalLMOutputWithCrossAttentions,
FlaxSeq2SeqLMOutput,
FlaxSeq2SeqModelOutput,
)
from ...modeling_flax_utils import (
ACT2FN,
FlaxPreTrainedModel,
append_call_sample_docstring,
append_replace_return_docstrings,
overwrite_call_docstring,
)
from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
from .configuration_t5 import T5Config
logger = logging.get_logger(__name__)
_CHECKPOINT_FOR_DOC = "google-t5/t5-small"
_CONFIG_FOR_DOC = "T5Config"
remat = nn_partitioning.remat
# Copied from transformers.models.bart.modeling_flax_bart.shift_tokens_right
def shift_tokens_right(input_ids: jnp.ndarray, pad_token_id: int, decoder_start_token_id: int) -> jnp.ndarray:
"""
Shift input ids one token to the right.
"""
shifted_input_ids = jnp.zeros_like(input_ids)
shifted_input_ids = shifted_input_ids.at[:, 1:].set(input_ids[:, :-1])
shifted_input_ids = shifted_input_ids.at[:, 0].set(decoder_start_token_id)
shifted_input_ids = jnp.where(shifted_input_ids == -100, pad_token_id, shifted_input_ids)
return shifted_input_ids
class FlaxT5LayerNorm(nn.Module):
hidden_size: int
dtype: jnp.dtype = jnp.float32
eps: float = 1e-6
weight_init: Callable[..., np.ndarray] = jax.nn.initializers.ones
def setup(self):
self.weight = self.param("weight", self.weight_init, (self.hidden_size,))
def __call__(self, hidden_states):
"""
Construct a layernorm module in the T5 style; No bias and no subtraction of mean.
"""
# layer norm should always be calculated in float32
variance = jnp.power(hidden_states.astype("f4"), 2).mean(axis=-1, keepdims=True)
hidden_states = hidden_states / jnp.sqrt(variance + self.eps)
return self.weight * hidden_states
class FlaxT5DenseActDense(nn.Module):
config: T5Config
dtype: jnp.dtype = jnp.float32
def setup(self):
wi_init_std = self.config.initializer_factor * (self.config.d_model**-0.5)
wo_init_std = self.config.initializer_factor * (self.config.d_ff**-0.5)
self.wi = nn.Dense(
self.config.d_ff,
use_bias=False,
kernel_init=jax.nn.initializers.normal(wi_init_std),
dtype=self.dtype,
)
self.wo = nn.Dense(
self.config.d_model,
use_bias=False,
kernel_init=jax.nn.initializers.normal(wo_init_std),
dtype=self.dtype,
)
self.dropout = nn.Dropout(self.config.dropout_rate)
self.act = ACT2FN[self.config.dense_act_fn]
def __call__(self, hidden_states, deterministic=True):
hidden_states = self.wi(hidden_states)
hidden_states = self.act(hidden_states)
hidden_states = self.dropout(hidden_states, deterministic=deterministic)
hidden_states = self.wo(hidden_states)
return hidden_states
class FlaxT5DenseGatedActDense(nn.Module):
config: T5Config
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
wi_init_std = self.config.initializer_factor * (self.config.d_model**-0.5)
wo_init_std = self.config.initializer_factor * (self.config.d_ff**-0.5)
self.wi_0 = nn.Dense(
self.config.d_ff,
use_bias=False,
kernel_init=jax.nn.initializers.normal(wi_init_std),
dtype=self.dtype,
)
self.wi_1 = nn.Dense(
self.config.d_ff,
use_bias=False,
kernel_init=jax.nn.initializers.normal(wi_init_std),
dtype=self.dtype,
)
self.wo = nn.Dense(
self.config.d_model,
use_bias=False,
kernel_init=jax.nn.initializers.normal(wo_init_std),
dtype=self.dtype,
)
self.dropout = nn.Dropout(self.config.dropout_rate)
self.act = ACT2FN[self.config.dense_act_fn]
def __call__(self, hidden_states, deterministic):
hidden_gelu = self.act(self.wi_0(hidden_states))
hidden_linear = self.wi_1(hidden_states)
hidden_states = hidden_gelu * hidden_linear
hidden_states = self.dropout(hidden_states, deterministic=deterministic)
hidden_states = self.wo(hidden_states)
return hidden_states
class FlaxT5LayerFF(nn.Module):
config: T5Config
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
if self.config.is_gated_act:
self.DenseReluDense = FlaxT5DenseGatedActDense(self.config, dtype=self.dtype)
else:
self.DenseReluDense = FlaxT5DenseActDense(self.config, dtype=self.dtype)
self.layer_norm = FlaxT5LayerNorm(self.config.d_model, eps=self.config.layer_norm_epsilon, dtype=self.dtype)
self.dropout = nn.Dropout(self.config.dropout_rate)
def __call__(self, hidden_states, deterministic=True):
forwarded_states = self.layer_norm(hidden_states)
forwarded_states = self.DenseReluDense(forwarded_states, deterministic=deterministic)
hidden_states = hidden_states + self.dropout(forwarded_states, deterministic=deterministic)
return hidden_states
class FlaxT5Attention(nn.Module):
config: T5Config
has_relative_attention_bias: bool = False
causal: bool = False
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.relative_attention_num_buckets = self.config.relative_attention_num_buckets
self.relative_attention_max_distance = self.config.relative_attention_max_distance
self.d_model = self.config.d_model
self.key_value_proj_dim = self.config.d_kv
self.n_heads = self.config.num_heads
self.dropout = self.config.dropout_rate
self.inner_dim = self.n_heads * self.key_value_proj_dim
q_init_std = self.config.initializer_factor * ((self.inner_dim * self.key_value_proj_dim) ** -0.5)
kv_init_std = self.config.initializer_factor * (self.inner_dim**-0.5)
o_init_std = self.config.initializer_factor * (self.inner_dim**-0.5)
self.q = nn.Dense(
self.inner_dim,
use_bias=False,
kernel_init=jax.nn.initializers.normal(q_init_std),
dtype=self.dtype,
)
self.k = nn.Dense(
self.inner_dim,
use_bias=False,
kernel_init=jax.nn.initializers.normal(kv_init_std),
dtype=self.dtype,
)
self.v = nn.Dense(
self.inner_dim,
use_bias=False,
kernel_init=jax.nn.initializers.normal(kv_init_std),
dtype=self.dtype,
)
self.o = nn.Dense(
self.d_model,
use_bias=False,
kernel_init=jax.nn.initializers.normal(o_init_std),
dtype=self.dtype,
)
if self.has_relative_attention_bias:
self.relative_attention_bias = nn.Embed(
self.relative_attention_num_buckets,
self.n_heads,
embedding_init=jax.nn.initializers.normal(kv_init_std),
dtype=self.dtype,
)
@staticmethod
def _relative_position_bucket(relative_position, bidirectional=True, num_buckets=32, max_distance=128):
"""
Adapted from Mesh Tensorflow:
https://github.com/tensorflow/mesh/blob/0cb87fe07da627bf0b7e60475d59f95ed6b5be3d/mesh_tensorflow/transformer/transformer_layers.py#L593
Translate relative position to a bucket number for relative attention. The relative position is defined as
memory_position - query_position, i.e. the distance in tokens from the attending position to the attended-to
position. If bidirectional=False, then positive relative positions are invalid. We use smaller buckets for
small absolute relative_position and larger buckets for larger absolute relative_positions. All relative
positions >=max_distance map to the same bucket. All relative positions <=-max_distance map to the same bucket.
This should allow for more graceful generalization to longer sequences than the model has been trained on
"""
relative_buckets = 0
if bidirectional:
num_buckets //= 2
relative_buckets += (relative_position > 0) * num_buckets
relative_position = jnp.abs(relative_position)
else:
relative_position = -jnp.clip(relative_position, a_max=0)
# now relative_position is in the range [0, inf)
# half of the buckets are for exact increments in positions
max_exact = num_buckets // 2
is_small = relative_position < max_exact
# The other half of the buckets are for logarithmically bigger bins in positions up to max_distance
relative_position_if_large = max_exact + (
jnp.log(relative_position / max_exact) / jnp.log(max_distance / max_exact) * (num_buckets - max_exact)
)
relative_position_if_large = jnp.clip(relative_position_if_large, a_max=num_buckets - 1)
relative_buckets += jnp.where(is_small, relative_position, relative_position_if_large)
return relative_buckets.astype("i4")
def compute_bias(self, query_length, key_length):
"""Compute binned relative position bias"""
context_position = jnp.arange(query_length, dtype="i4")[:, None]
memory_position = jnp.arange(key_length, dtype="i4")[None, :]
relative_position = memory_position - context_position
relative_position_bucket = self._relative_position_bucket(
relative_position,
bidirectional=(not self.causal),
num_buckets=self.relative_attention_num_buckets,
max_distance=self.relative_attention_max_distance,
)
values = self.relative_attention_bias(relative_position_bucket)
values = values.transpose((2, 0, 1))[None, :, :, :]
return values
def _split_heads(self, hidden_states):
return hidden_states.reshape(hidden_states.shape[:2] + (self.n_heads, self.key_value_proj_dim))
def _merge_heads(self, hidden_states):
return hidden_states.reshape(hidden_states.shape[:2] + (self.inner_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 slighly 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 = jax.lax.dynamic_update_slice(cached_key.value, key, indices)
value = jax.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 _create_position_bias(
self, key_states, query_states, attention_mask, init_cache, seq_length, causal_attention_mask_shift
):
cache_is_filled = self.causal and self.has_variable("cache", "cached_key") and (not init_cache)
key_length = key_states.shape[1]
query_length = key_length if cache_is_filled else query_states.shape[1]
if self.has_relative_attention_bias:
position_bias = self.compute_bias(query_length, key_length)
elif attention_mask is not None:
position_bias = jnp.zeros_like(attention_mask)
else:
position_bias = jnp.zeros((1, self.n_heads, query_length, key_length), dtype=self.dtype)
# if key and values are already calculated, only the last query position bias should be taken
if cache_is_filled:
max_decoder_length = self.variables["cache"]["cached_key"].shape[1]
position_bias = jax.lax.dynamic_slice(
position_bias,
(0, 0, causal_attention_mask_shift, 0),
(1, self.n_heads, seq_length, max_decoder_length),
)
return position_bias
def __call__(
self,
hidden_states,
attention_mask=None,
key_value_states=None,
position_bias=None,
use_cache=False,
output_attentions=False,
deterministic=True,
init_cache=False,
):
"""
Self-attention (if key_value_states is None) or attention over source sentence (provided by key_value_states).
"""
batch_size, seq_length = hidden_states.shape[:2]
# q, k, v projections
query_states = self.q(hidden_states) # (batch_size, n_heads, seq_length, dim_per_head)
key_states = self.k(hidden_states) if key_value_states is None else self.k(key_value_states)
value_states = self.v(hidden_states) if key_value_states is None else self.v(key_value_states)
# reshape to (batch_size, seq_length, n_heads, head_dim)
query_states = self._split_heads(query_states)
key_states = self._split_heads(key_states)
value_states = self._split_heads(value_states)
# counter-act scaling in dot_product_attention_weights function
query_states *= jnp.sqrt(query_states.shape[-1])
# for fast decoding causal attention mask should be shifted
causal_attention_mask_shift = (
self.variables["cache"]["cache_index"] if (self.has_variable("cache", "cached_key") and self.causal) else 0
)
# create causal attention_mask; attention_mask has to be defined when model is causal
if self.causal:
causal_attention_mask = make_causal_mask(attention_mask, dtype="bool")
# fast decoding for generate requires special attention_mask
if self.has_variable("cache", "cached_key"):
max_decoder_length = self.variables["cache"]["cached_key"].shape[1]
causal_attention_mask = jax.lax.dynamic_slice(
causal_attention_mask,
(0, 0, causal_attention_mask_shift, 0),
(1, 1, seq_length, max_decoder_length),
)
# broadcast causal attention mask & attention mask to fit for merge
causal_attention_mask = jnp.broadcast_to(
causal_attention_mask, (batch_size,) + causal_attention_mask.shape[1:]
)
attention_mask = jnp.broadcast_to(
jnp.expand_dims(attention_mask, axis=(-3, -2)), causal_attention_mask.shape
)
attention_mask = combine_masks(attention_mask, causal_attention_mask)
elif attention_mask is not None:
attention_mask = jnp.expand_dims(attention_mask, axis=(-3, -2))
# During fast autoregressive decoding, we feed one position at a time,
# and cache the keys and values step by step.
if self.causal and (self.has_variable("cache", "cached_key") or init_cache):
key_states, value_states, attention_mask = self._concatenate_to_cache(
key_states, value_states, query_states, attention_mask
)
# replace masked positions with -10_000
if attention_mask is not None:
mask_value = jnp.finfo(self.dtype).min
attention_mask = jax.lax.select(
attention_mask > 0,
jnp.full(attention_mask.shape, 0.0).astype(self.dtype),
jnp.full(attention_mask.shape, mask_value).astype(self.dtype),
)
if position_bias is None:
# compute position bias (only for first layer)
position_bias = self._create_position_bias(
key_states, query_states, attention_mask, init_cache, seq_length, causal_attention_mask_shift
)
if attention_mask is not None:
position_bias = position_bias + attention_mask
# create dropout rng
dropout_rng = None
if not deterministic and self.dropout > 0.0:
dropout_rng = self.make_rng("dropout")
# Softmax(QK^T)
attn_weights = dot_product_attention_weights(
query_states,
key_states,
bias=position_bias,
dropout_rng=dropout_rng,
dropout_rate=self.dropout,
broadcast_dropout=True,
deterministic=deterministic,
dtype=self.dtype,
)
# multiply with value states
attn_output = jnp.einsum("...hqk,...khd->...qhd", attn_weights, value_states)
# bring back to (batch_size, seq_length, d_model)
attn_output = self._merge_heads(attn_output)
# apply output matrix
attn_output = self.o(attn_output)
outputs = (attn_output, position_bias)
if output_attentions:
outputs = outputs + (attn_weights,)
return outputs
class FlaxT5LayerSelfAttention(nn.Module):
config: T5Config
has_relative_attention_bias: bool = False
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.SelfAttention = FlaxT5Attention(
self.config,
has_relative_attention_bias=self.has_relative_attention_bias,
causal=self.config.causal,
dtype=self.dtype,
)
self.layer_norm = FlaxT5LayerNorm(self.config.d_model, eps=self.config.layer_norm_epsilon, dtype=self.dtype)
self.dropout = nn.Dropout(self.config.dropout_rate)
def __call__(
self,
hidden_states,
attention_mask=None,
position_bias=None,
output_attentions=False,
deterministic=True,
init_cache=False,
):
normed_hidden_states = self.layer_norm(hidden_states)
attention_output = self.SelfAttention(
normed_hidden_states,
attention_mask=attention_mask,
position_bias=position_bias,
output_attentions=output_attentions,
deterministic=deterministic,
init_cache=init_cache,
)
hidden_states = hidden_states + self.dropout(attention_output[0], deterministic=deterministic)
outputs = (hidden_states,) + attention_output[1:] # add attentions if we output them
return outputs
class FlaxT5LayerCrossAttention(nn.Module):
config: T5Config
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.EncDecAttention = FlaxT5Attention(
self.config, has_relative_attention_bias=False, causal=False, dtype=self.dtype
)
self.layer_norm = FlaxT5LayerNorm(self.config.d_model, eps=self.config.layer_norm_epsilon, dtype=self.dtype)
self.dropout = nn.Dropout(self.config.dropout_rate)
def __call__(
self,
hidden_states,
key_value_states,
attention_mask=None,
position_bias=None,
output_attentions=False,
deterministic=True,
):
normed_hidden_states = self.layer_norm(hidden_states)
attention_output = self.EncDecAttention(
normed_hidden_states,
attention_mask=attention_mask,
key_value_states=key_value_states,
position_bias=position_bias,
output_attentions=output_attentions,
)
hidden_states = hidden_states + self.dropout(attention_output[0], deterministic=deterministic)
outputs = (hidden_states,) + attention_output[1:] # add attentions if we output them
return outputs
class FlaxT5Block(nn.Module):
config: T5Config
has_relative_attention_bias: bool = False
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.causal = self.config.causal
self.layer = (
FlaxT5LayerSelfAttention(
self.config,
has_relative_attention_bias=self.has_relative_attention_bias,
name=str(0),
dtype=self.dtype,
),
)
feed_forward_index = 1
if self.causal:
self.layer += (FlaxT5LayerCrossAttention(self.config, name=str(1), dtype=self.dtype),)
feed_forward_index += 1
self.layer += (FlaxT5LayerFF(self.config, name=str(feed_forward_index), dtype=self.dtype),)
def __call__(
self,
hidden_states,
attention_mask=None,
position_bias=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
encoder_decoder_position_bias=None,
output_attentions=False,
return_dict=True,
deterministic=True,
init_cache=False,
):
self_attention_outputs = self.layer[0](
hidden_states,
attention_mask=attention_mask,
position_bias=position_bias,
output_attentions=output_attentions,
deterministic=deterministic,
init_cache=init_cache,
)
hidden_states = self_attention_outputs[0]
attention_outputs = self_attention_outputs[1:] # Keep self-attention outputs and relative position weights
do_cross_attention = self.causal and encoder_hidden_states is not None
if do_cross_attention:
cross_attention_outputs = self.layer[1](
hidden_states,
key_value_states=encoder_hidden_states,
attention_mask=encoder_attention_mask,
position_bias=encoder_decoder_position_bias,
output_attentions=output_attentions,
deterministic=deterministic,
)
hidden_states = cross_attention_outputs[0]
# Keep cross-attention outputs and relative position weights
attention_outputs = attention_outputs + cross_attention_outputs[1:]
# Apply Feed Forward layer
hidden_states = self.layer[-1](hidden_states, deterministic=deterministic)
outputs = (hidden_states,)
outputs = outputs + attention_outputs
# returns hidden-states, present_key_value_states, (self-attention position bias), (self-attention weights),
# (cross-attention position bias), (cross-attention weights)
return outputs
class FlaxT5LayerCollection(nn.Module):
config: T5Config
has_relative_attention_bias: bool
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.layer = FlaxT5Block(
self.config, has_relative_attention_bias=self.has_relative_attention_bias, dtype=self.dtype
)
def __call__(
self,
hidden_states,
attention_mask=None,
position_bias=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
encoder_decoder_position_bias=None,
output_attentions=False,
deterministic=True,
init_cache=False,
):
return self.layer(
hidden_states,
attention_mask=attention_mask,
position_bias=position_bias,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
encoder_decoder_position_bias=encoder_decoder_position_bias,
output_attentions=output_attentions,
deterministic=deterministic,
init_cache=init_cache,
)
class FlaxT5BlockCollection(nn.Module):
config: T5Config
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
gradient_checkpointing: bool = False
def setup(self):
self.causal = self.config.causal
if self.gradient_checkpointing:
FlaxT5CheckpointLayer = remat(FlaxT5LayerCollection, static_argnums=(6, 7, 8))
self.blocks = [
FlaxT5CheckpointLayer(
self.config,
has_relative_attention_bias=(i == 0),
dtype=self.dtype,
name=str(i),
)
for i in range(self.config.num_layers)
]
else:
self.blocks = [
FlaxT5LayerCollection(
self.config,
has_relative_attention_bias=(i == 0),
dtype=self.dtype,
name=str(i),
)
for i in range(self.config.num_layers)
]
def __call__(
self,
hidden_states=None,
attention_mask=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
output_attentions: bool = False,
output_hidden_states: bool = False,
deterministic: bool = True,
init_cache: bool = False,
):
# Prepare head mask if needed
all_hidden_states = () if output_hidden_states else None
all_attentions = () if output_attentions else None
all_cross_attentions = () if (output_attentions and self.causal) else None
position_bias = None
encoder_decoder_position_bias = None
for i, layer_module in enumerate(self.blocks):
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
layer_outputs = layer_module(
hidden_states,
attention_mask,
position_bias,
encoder_hidden_states,
encoder_attention_mask,
encoder_decoder_position_bias,
output_attentions,
deterministic,
init_cache,
)
hidden_states = layer_outputs[0]
# We share the position biases between the layers - the first layer store them
# layer_outputs = hidden-states, key-value-states (self-attention position bias), (self-attention weights),
# (cross-attention position bias), (cross-attention weights)
position_bias = layer_outputs[1]
if self.causal and encoder_hidden_states is not None:
encoder_decoder_position_bias = layer_outputs[3 if output_attentions else 2]
if output_attentions:
all_attentions = all_attentions + (layer_outputs[2],)
if self.causal:
all_cross_attentions = all_cross_attentions + (layer_outputs[4],)
return FlaxBaseModelOutputWithPastAndCrossAttentions(
last_hidden_state=hidden_states,
hidden_states=all_hidden_states,
attentions=all_attentions,
cross_attentions=all_cross_attentions,
)
class FlaxT5Stack(nn.Module):
config: T5Config
embed_tokens: nn.Embed
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
gradient_checkpointing: bool = False
def setup(self):
self.causal = self.config.causal
self.block = FlaxT5BlockCollection(
self.config, dtype=self.dtype, gradient_checkpointing=self.gradient_checkpointing
)
self.final_layer_norm = FlaxT5LayerNorm(
self.config.d_model, eps=self.config.layer_norm_epsilon, dtype=self.dtype
)
self.dropout = nn.Dropout(self.config.dropout_rate)
def __call__(
self,
input_ids=None,
attention_mask=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
output_attentions: bool = False,
output_hidden_states: bool = False,
return_dict: bool = True,
deterministic: bool = True,
init_cache: bool = False,
):
hidden_states = self.embed_tokens(input_ids)
hidden_states = self.dropout(hidden_states, deterministic=deterministic)
outputs = self.block(
hidden_states,
attention_mask=attention_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
deterministic=deterministic,
init_cache=init_cache,
)
hidden_states = outputs[0]
hidden_states = self.final_layer_norm(hidden_states)
hidden_states = self.dropout(hidden_states, deterministic=deterministic)
# Add last layer
all_hidden_states = None
if output_hidden_states:
all_hidden_states = outputs.hidden_states
all_hidden_states = all_hidden_states + (hidden_states,)
if not return_dict:
if output_hidden_states:
return (
hidden_states,
all_hidden_states,
) + outputs[2:]
return (hidden_states,) + outputs[1:]
return FlaxBaseModelOutputWithPastAndCrossAttentions(
last_hidden_state=hidden_states,
hidden_states=all_hidden_states,
attentions=outputs.attentions,
cross_attentions=outputs.cross_attentions,
)
T5_ENCODE_INPUTS_DOCSTRING = r"""
Args:
input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. T5 is a model with relative position embeddings so you
should be able to pad the inputs on both the right and the left.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for detail.
To know more on how to prepare `input_ids` for pretraining take a look a [T5 Training](./t5#training).
attention_mask (`jnp.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)
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.
"""
T5_DECODE_INPUTS_DOCSTRING = r"""
Args:
decoder_input_ids (`jnp.ndarray` of shape `(batch_size, target_sequence_length)`):
Indices of decoder input sequence tokens in the vocabulary.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are decoder input IDs?](../glossary#decoder-input-ids)
For training, `decoder_input_ids` should be provided.
encoder_outputs (`tuple(tuple(jnp.ndarray)`):
Tuple consists of (`last_hidden_state`, *optional*: `hidden_states`, *optional*: `attentions`)
`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.
encoder_attention_mask (`jnp.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)
decoder_attention_mask (`jnp.ndarray` 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.
If you want to change padding behavior, you should modify to your needs. See diagram 1 in [the
paper](https://arxiv.org/abs/1910.13461) for more information on the default strategy.
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.
"""
T5_INPUTS_DOCSTRING = r"""
Args:
input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. T5 is a model with relative position embeddings so you
should be able to pad the inputs on both the right and the left.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for detail.
[What are input IDs?](../glossary#input-ids)
To know more on how to prepare `input_ids` for pretraining take a look a [T5 Training](./t5#training).
attention_mask (`jnp.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)
decoder_input_ids (`jnp.ndarray` of shape `(batch_size, target_sequence_length)`, *optional*):
Indices of decoder input sequence tokens in the vocabulary.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are decoder input IDs?](../glossary#decoder-input-ids)
T5 uses the `pad_token_id` as the starting token for `decoder_input_ids` generation. If `past_key_values`
is used, optionally only the last `decoder_input_ids` have to be input (see `past_key_values`).
To know more on how to prepare `decoder_input_ids` for pretraining take a look at [T5
Training](./t5#training).
decoder_attention_mask (`jnp.ndarray` 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.
encoder_outputs (`tuple(tuple(jnp.ndarray)`, *optional*):
Tuple consists of (`last_hidden_state`, `optional`: *hidden_states*, `optional`: *attentions*)
`last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)` 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(jnp.ndarray))` 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)`.
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 FlaxT5PreTrainedModel(FlaxPreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = T5Config
base_model_prefix = "transformer"
module_class: nn.Module = None
def __init__(
self,
config: T5Config,
input_shape: Tuple[int] = (1, 1),
seed: int = 0,
dtype: jnp.dtype = jnp.float32,
_do_init: bool = True,
gradient_checkpointing: bool = False,
**kwargs,
):
module = self.module_class(config=config, dtype=dtype, gradient_checkpointing=gradient_checkpointing, **kwargs)
super().__init__(config, module, input_shape=input_shape, seed=seed, dtype=dtype, _do_init=_do_init)
def enable_gradient_checkpointing(self):
self._module = self.module_class(
config=self.config,
dtype=self.dtype,
gradient_checkpointing=True,
)
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)
args = [input_ids, attention_mask]
if self.module_class not in [FlaxT5EncoderModule]:
decoder_input_ids = jnp.ones_like(input_ids)
decoder_attention_mask = jnp.ones_like(input_ids)
args.extend([decoder_input_ids, decoder_attention_mask])
params_rng, dropout_rng = jax.random.split(rng)
rngs = {"params": params_rng, "dropout": dropout_rng}
random_params = self.module.init(
rngs,
*args,
)["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
@add_start_docstrings_to_model_forward(T5_INPUTS_DOCSTRING)
def __call__(
self,
input_ids: jnp.ndarray,
attention_mask: Optional[jnp.ndarray] = None,
decoder_input_ids: jnp.ndarray = None,
decoder_attention_mask: Optional[jnp.ndarray] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
train: bool = False,
params: dict = None,
dropout_rng: PRNGKey = 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
if decoder_input_ids is None:
raise ValueError(
"Make sure to provide both `input_ids` and `decoder_input_ids`. `decoder_input_ids` is not passed"
" here."
)
# prepare encoder inputs
if attention_mask is None:
attention_mask = jnp.ones_like(input_ids)
# prepare decoder inputs
if decoder_attention_mask is None:
decoder_attention_mask = jnp.ones_like(decoder_input_ids)
# Handle any PRNG if needed
rngs = {"dropout": dropout_rng} if dropout_rng is not None else {}
return self.module.apply(
{"params": params or self.params},
input_ids=jnp.array(input_ids, dtype="i4"),
attention_mask=jnp.array(attention_mask, dtype="i4"),
decoder_input_ids=jnp.array(decoder_input_ids, dtype="i4"),
decoder_attention_mask=jnp.array(decoder_attention_mask, dtype="i4"),
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
deterministic=not train,
rngs=rngs,
)
def init_cache(self, batch_size, max_length, encoder_outputs):
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.
encoder_outputs (`Union[FlaxBaseModelOutput, tuple(tuple(jnp.ndarray)]`):
`encoder_outputs` consists of (`last_hidden_state`, *optional*: `hidden_states`, *optional*:
`attentions`). `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.
"""
# init input variables to retrieve cache
decoder_input_ids = jnp.ones((batch_size, max_length), dtype="i4")
decoder_attention_mask = jnp.ones_like(decoder_input_ids)
def _decoder_forward(module, decoder_input_ids, decoder_attention_mask, **kwargs):
decoder_module = module._get_decoder_module()
return decoder_module(
decoder_input_ids,
decoder_attention_mask,
**kwargs,
)
init_variables = self.module.init(
jax.random.PRNGKey(0),
decoder_input_ids=decoder_input_ids,
decoder_attention_mask=decoder_attention_mask,
encoder_hidden_states=encoder_outputs[0],
init_cache=True,
method=_decoder_forward, # we only need to call the decoder to init the cache
)
return unfreeze(init_variables["cache"])
@add_start_docstrings(T5_ENCODE_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=FlaxBaseModelOutput, config_class=T5Config)
def encode(
self,
input_ids: jnp.ndarray,
attention_mask: Optional[jnp.ndarray] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
train: bool = False,
params: dict = None,
dropout_rng: PRNGKey = None,
):
r"""
Returns:
Example:
```python
>>> from transformers import AutoTokenizer, FlaxT5ForConditionalGeneration
>>> tokenizer = AutoTokenizer.from_pretrained("google-t5/t5-small")
>>> model = FlaxT5ForConditionalGeneration.from_pretrained("google-t5/t5-small")
>>> text = "My friends are cool but they eat too many carbs."
>>> inputs = tokenizer(text, return_tensors="np")
>>> encoder_outputs = model.encode(**inputs)
```"""
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
if attention_mask is None:
attention_mask = jnp.ones_like(input_ids)
# Handle any PRNG if needed
rngs = {}
if dropout_rng is not None:
rngs["dropout"] = dropout_rng
def _encoder_forward(module, input_ids, attention_mask, **kwargs):
encode_module = module._get_encoder_module()
return encode_module(input_ids, attention_mask, **kwargs)
return self.module.apply(
{"params": params or self.params},
input_ids=jnp.array(input_ids, dtype="i4"),
attention_mask=jnp.array(attention_mask, dtype="i4"),
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
deterministic=not train,
rngs=rngs,
method=_encoder_forward,
)
@add_start_docstrings(T5_DECODE_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=FlaxBaseModelOutputWithPastAndCrossAttentions, config_class=T5Config)
def decode(
self,
decoder_input_ids,
encoder_outputs,
encoder_attention_mask: Optional[jnp.ndarray] = None,
decoder_attention_mask: Optional[jnp.ndarray] = None,
past_key_values: dict = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
train: bool = False,
params: dict = None,
dropout_rng: PRNGKey = None,
):
r"""
Returns:
Example:
```python
>>> from transformers import AutoTokenizer, FlaxT5ForConditionalGeneration
>>> import jax.numpy as jnp
>>> tokenizer = AutoTokenizer.from_pretrained("google-t5/t5-small")
>>> model = FlaxT5ForConditionalGeneration.from_pretrained("google-t5/t5-small")
>>> text = "My friends are cool but they eat too many carbs."
>>> inputs = tokenizer(text, return_tensors="np")
>>> encoder_outputs = model.encode(**inputs)
>>> decoder_start_token_id = model.config.decoder_start_token_id
>>> decoder_input_ids = jnp.ones((inputs.input_ids.shape[0], 1), dtype="i4") * decoder_start_token_id
>>> outputs = model.decode(decoder_input_ids, encoder_outputs)
>>> logits = outputs.logits
```"""
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
encoder_hidden_states = encoder_outputs[0]
if encoder_attention_mask is None:
batch_size, sequence_length = encoder_hidden_states.shape[:2]
encoder_attention_mask = jnp.ones((batch_size, sequence_length))
batch_size, sequence_length = decoder_input_ids.shape
if decoder_attention_mask is None:
decoder_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 FlaxT5Attention module
if past_key_values:
inputs["cache"] = past_key_values
mutable = ["cache"]
else:
mutable = False
def _decoder_forward(module, decoder_input_ids, decoder_attention_mask, **kwargs):
decoder_module = module._get_decoder_module()
return decoder_module(
decoder_input_ids,
decoder_attention_mask,
**kwargs,
)
outputs = self.module.apply(
inputs,
decoder_input_ids=jnp.array(decoder_input_ids, dtype="i4"),
decoder_attention_mask=jnp.array(decoder_attention_mask, dtype="i4"),
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=jnp.array(encoder_attention_mask, dtype="i4"),
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
deterministic=not train,
rngs=rngs,
mutable=mutable,
method=_decoder_forward,
)
# add updated cache to model output
if past_key_values is not None and return_dict:
outputs, past = outputs
outputs["past_key_values"] = unfreeze(past["cache"])
return outputs
elif past_key_values is not None and not return_dict:
outputs, past = outputs
outputs = outputs[:1] + (unfreeze(past["cache"]),) + outputs[1:]
return outputs
T5_START_DOCSTRING = r"""
The T5 model was proposed in [Exploring the Limits of Transfer Learning with a Unified Text-to-Text
Transformer](https://arxiv.org/abs/1910.10683) by Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan
Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu. It's an encoder decoder transformer pre-trained in a
text-to-text denoising generative setting.
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 ([`T5Config`]): 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`].
"""
@add_start_docstrings(
"The bare T5 Model transformer outputting raw hidden-stateswithout any specific head on top.",
T5_START_DOCSTRING,
)
class FlaxT5Module(nn.Module):
config: T5Config
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
gradient_checkpointing: bool = False
def _get_encoder_module(self):
return self.encoder
def _get_decoder_module(self):
return self.decoder
def setup(self):
self.shared = nn.Embed(
self.config.vocab_size,
self.config.d_model,
embedding_init=jax.nn.initializers.normal(self.config.initializer_factor * 1.0),
dtype=self.dtype,
)
encoder_config = copy.deepcopy(self.config)
encoder_config.causal = False
self.encoder = FlaxT5Stack(
encoder_config,
embed_tokens=self.shared,
dtype=self.dtype,
gradient_checkpointing=self.gradient_checkpointing,
)
decoder_config = copy.deepcopy(self.config)
decoder_config.causal = True
decoder_config.num_layers = self.config.num_decoder_layers
self.decoder = FlaxT5Stack(
decoder_config,
embed_tokens=self.shared,
dtype=self.dtype,
gradient_checkpointing=self.gradient_checkpointing,
)
def __call__(
self,
input_ids=None,
attention_mask=None,
decoder_input_ids=None,
decoder_attention_mask=None,
encoder_outputs=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
deterministic: bool = True,
):
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
# Encode if needed (training, first prediction pass)
encoder_outputs = self.encoder(
input_ids=input_ids,
attention_mask=attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
deterministic=deterministic,
)
# Decode
decoder_outputs = self.decoder(
input_ids=decoder_input_ids,
attention_mask=decoder_attention_mask,
encoder_hidden_states=encoder_outputs[0],
encoder_attention_mask=attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
deterministic=deterministic,
)
if not return_dict:
return decoder_outputs + encoder_outputs
return FlaxSeq2SeqModelOutput(
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,
)
class FlaxT5Model(FlaxT5PreTrainedModel):
module_class = FlaxT5Module
append_call_sample_docstring(FlaxT5Model, _CHECKPOINT_FOR_DOC, FlaxSeq2SeqModelOutput, _CONFIG_FOR_DOC)
FLAX_T5_MODEL_DOCSTRING = """
Returns:
Example:
```python
>>> from transformers import AutoTokenizer, FlaxT5Model
>>> tokenizer = AutoTokenizer.from_pretrained("google-t5/t5-small")
>>> model = FlaxT5Model.from_pretrained("google-t5/t5-small")
>>> input_ids = tokenizer(
... "Studies have been shown that owning a dog is good for you", return_tensors="np"
... ).input_ids
>>> decoder_input_ids = tokenizer("Studies show that", return_tensors="np").input_ids
>>> # preprocess: Prepend decoder_input_ids with start token which is pad token for T5Model.
>>> # This is not needed for torch's T5ForConditionalGeneration as it does this internally using labels arg.
>>> decoder_input_ids = model._shift_right(decoder_input_ids)
>>> # forward pass
>>> outputs = model(input_ids=input_ids, decoder_input_ids=decoder_input_ids)
>>> last_hidden_states = outputs.last_hidden_state
```
"""
overwrite_call_docstring(FlaxT5Model, T5_INPUTS_DOCSTRING + FLAX_T5_MODEL_DOCSTRING)
append_replace_return_docstrings(FlaxT5Model, output_type=FlaxSeq2SeqLMOutput, config_class=_CONFIG_FOR_DOC)
@add_start_docstrings(
"The bare T5 Model transformer outputting encoder's raw hidden-states without any specific head on top.",
T5_START_DOCSTRING,
)
class FlaxT5EncoderModule(nn.Module):
config: T5Config
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
gradient_checkpointing: bool = False
def setup(self):
self.shared = nn.Embed(
self.config.vocab_size,
self.config.d_model,
embedding_init=jax.nn.initializers.normal(self.config.initializer_factor * 1.0),
dtype=self.dtype,
)
encoder_config = copy.deepcopy(self.config)
encoder_config.is_decoder = False
encoder_config.is_encoder_decoder = False
encoder_config.causal = False
self.encoder = FlaxT5Stack(
encoder_config,
embed_tokens=self.shared,
dtype=self.dtype,
gradient_checkpointing=self.gradient_checkpointing,
)
def __call__(
self,
input_ids=None,
attention_mask=None,
output_attentions=False,
output_hidden_states=False,
return_dict: bool = True,
deterministic: bool = True,
):
# Encode if needed (training, first prediction pass)
encoder_outputs = self.encoder(
input_ids=input_ids,
attention_mask=attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
deterministic=deterministic,
)
return encoder_outputs
class FlaxT5EncoderModel(FlaxT5PreTrainedModel):
module_class = FlaxT5EncoderModule
@add_start_docstrings_to_model_forward(T5_ENCODE_INPUTS_DOCSTRING)
def __call__(
self,
input_ids: jnp.ndarray,
attention_mask: Optional[jnp.ndarray] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
train: bool = False,
params: dict = None,
dropout_rng: PRNGKey = 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
# prepare encoder inputs
if attention_mask is None:
attention_mask = jnp.ones_like(input_ids)
# Handle any PRNG if needed
rngs = {"dropout": dropout_rng} if dropout_rng is not None else {}
return self.module.apply(
{"params": params or self.params},
input_ids=jnp.array(input_ids, dtype="i4"),
attention_mask=jnp.array(attention_mask, dtype="i4"),
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
deterministic=not train,
rngs=rngs,
)
@add_start_docstrings("""T5 Model with a `language modeling` head on top.""", T5_START_DOCSTRING)
class FlaxT5ForConditionalGenerationModule(nn.Module):
config: T5Config
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
gradient_checkpointing: bool = False
def _get_encoder_module(self):
return self.encoder
def _get_decoder_module(self):
return self.decoder
def setup(self):
self.model_dim = self.config.d_model
self.shared = nn.Embed(
self.config.vocab_size,
self.config.d_model,
embedding_init=jax.nn.initializers.normal(self.config.initializer_factor),
dtype=self.dtype,
)
encoder_config = copy.deepcopy(self.config)
encoder_config.causal = False
encoder_config.use_cache = False
encoder_config.is_encoder_decoder = False
self.encoder = FlaxT5Stack(
encoder_config, self.shared, dtype=self.dtype, gradient_checkpointing=self.gradient_checkpointing
)
decoder_config = copy.deepcopy(self.config)
decoder_config.causal = True
decoder_config.is_encoder_decoder = False
decoder_config.num_layers = self.config.num_decoder_layers
self.decoder = FlaxT5Stack(
decoder_config, self.shared, dtype=self.dtype, gradient_checkpointing=self.gradient_checkpointing
)
self.lm_head = nn.Dense(
self.config.vocab_size,
use_bias=False,
kernel_init=jax.nn.initializers.normal(self.config.initializer_factor),
dtype=self.dtype,
)
def __call__(
self,
input_ids=None,
attention_mask=None,
decoder_input_ids=None,
decoder_attention_mask=None,
encoder_outputs=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
deterministic: bool = True,
):
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
# Encode
encoder_outputs = self.encoder(
input_ids=input_ids,
attention_mask=attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
deterministic=deterministic,
)
hidden_states = encoder_outputs[0]
# Decode
decoder_outputs = self.decoder(
input_ids=decoder_input_ids,
attention_mask=decoder_attention_mask,
encoder_hidden_states=hidden_states,
encoder_attention_mask=attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
deterministic=deterministic,
)
sequence_output = decoder_outputs[0]
if self.config.tie_word_embeddings:
# Rescale output before projecting on vocab
# See https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/transformer/transformer.py#L586
sequence_output = sequence_output * (self.model_dim**-0.5)
if self.config.tie_word_embeddings:
shared_embedding = self.shared.variables["params"]["embedding"]
lm_logits = self.lm_head.apply({"params": {"kernel": shared_embedding.T}}, sequence_output)
else:
lm_logits = self.lm_head(sequence_output)
if not return_dict:
return (lm_logits,) + decoder_outputs[1:] + encoder_outputs
return FlaxSeq2SeqLMOutput(
logits=lm_logits,
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,
)
class FlaxT5ForConditionalGeneration(FlaxT5PreTrainedModel):
module_class = FlaxT5ForConditionalGenerationModule
@add_start_docstrings(T5_DECODE_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=FlaxCausalLMOutputWithCrossAttentions, config_class=T5Config)
def decode(
self,
decoder_input_ids,
encoder_outputs,
encoder_attention_mask: Optional[jnp.ndarray] = None,
decoder_attention_mask: Optional[jnp.ndarray] = None,
past_key_values: dict = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
train: bool = False,
params: dict = None,
dropout_rng: PRNGKey = None,
):
r"""
Returns:
Example:
```python
>>> from transformers import AutoTokenizer, FlaxT5ForConditionalGeneration
>>> import jax.numpy as jnp
>>> tokenizer = AutoTokenizer.from_pretrained("google-t5/t5-small")
>>> model = FlaxT5ForConditionalGeneration.from_pretrained("google-t5/t5-small")
>>> text = "summarize: My friends are cool but they eat too many carbs."
>>> inputs = tokenizer(text, return_tensors="np")
>>> encoder_outputs = model.encode(**inputs)
>>> decoder_start_token_id = model.config.decoder_start_token_id
>>> decoder_input_ids = jnp.ones((inputs.input_ids.shape[0], 1), dtype="i4") * decoder_start_token_id
>>> outputs = model.decode(decoder_input_ids, encoder_outputs)
>>> logits = outputs.logits
```"""
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
encoder_hidden_states = encoder_outputs[0]
if encoder_attention_mask is None:
batch_size, sequence_length = encoder_hidden_states.shape[:2]
encoder_attention_mask = jnp.ones((batch_size, sequence_length))
batch_size, sequence_length = decoder_input_ids.shape
if decoder_attention_mask is None:
decoder_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 FlaxT5Attention module
if past_key_values:
inputs["cache"] = past_key_values
mutable = ["cache"]
else:
mutable = False
def _decoder_forward(module, decoder_input_ids, decoder_attention_mask, **kwargs):
decoder_module = module._get_decoder_module()
decoder_outputs = decoder_module(
decoder_input_ids,
decoder_attention_mask,
**kwargs,
)
sequence_output = decoder_outputs[0]
if self.config.tie_word_embeddings:
# Rescale output before projecting on vocab
# See https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/transformer/transformer.py#L586
sequence_output = sequence_output * (self.config.d_model**-0.5)
if self.config.tie_word_embeddings:
shared_embedding = module.shared.variables["params"]["embedding"]
lm_logits = module.lm_head.apply({"params": {"kernel": shared_embedding.T}}, sequence_output)
else:
lm_logits = module.lm_head(sequence_output)
return lm_logits, decoder_outputs
outputs = self.module.apply(
inputs,
decoder_input_ids=jnp.array(decoder_input_ids, dtype="i4"),
decoder_attention_mask=jnp.array(decoder_attention_mask, dtype="i4"),
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=jnp.array(encoder_attention_mask, dtype="i4"),
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
deterministic=not train,
rngs=rngs,
mutable=mutable,
method=_decoder_forward,
)
if past_key_values is None:
lm_logits, decoder_outputs = outputs
else:
(lm_logits, decoder_outputs), past = outputs
if return_dict:
outputs = FlaxCausalLMOutputWithCrossAttentions(
logits=lm_logits,
hidden_states=decoder_outputs.hidden_states,
attentions=decoder_outputs.attentions,
cross_attentions=decoder_outputs.cross_attentions,
)
else:
outputs = (lm_logits,) + decoder_outputs[1:]
# add updated cache to model output
if past_key_values is not None and return_dict:
outputs["past_key_values"] = unfreeze(past["cache"])
return outputs
elif past_key_values is not None and not return_dict:
outputs = outputs[:1] + (unfreeze(past["cache"]),) + outputs[1:]
return outputs
def prepare_inputs_for_generation(
self,
decoder_input_ids,
max_length,
attention_mask: Optional[jax.Array] = None,
decoder_attention_mask: Optional[jax.Array] = None,
encoder_outputs=None,
**kwargs,
):
# initializing the cache
batch_size, seq_length = decoder_input_ids.shape
past_key_values = self.init_cache(batch_size, max_length, encoder_outputs)
# 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 the decoder 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 decoder_attention_mask is not None:
extended_attention_mask = jax.lax.dynamic_update_slice(
extended_attention_mask, decoder_attention_mask, (0, 0)
)
return {
"past_key_values": past_key_values,
"encoder_outputs": encoder_outputs,
"encoder_attention_mask": attention_mask,
"decoder_attention_mask": extended_attention_mask,
}
def update_inputs_for_generation(self, model_outputs, model_kwargs):
model_kwargs["past_key_values"] = model_outputs.past_key_values
return model_kwargs
FLAX_T5_CONDITIONAL_GENERATION_DOCSTRING = """
Returns:
Example:
```python
>>> from transformers import AutoTokenizer, FlaxT5ForConditionalGeneration
>>> tokenizer = AutoTokenizer.from_pretrained("google-t5/t5-small")
>>> model = FlaxT5ForConditionalGeneration.from_pretrained("google-t5/t5-small")
>>> ARTICLE_TO_SUMMARIZE = "summarize: My friends are cool but they eat too many carbs."
>>> inputs = tokenizer([ARTICLE_TO_SUMMARIZE], return_tensors="np")
>>> # Generate Summary
>>> summary_ids = model.generate(inputs["input_ids"]).sequences
>>> print(tokenizer.decode(summary_ids[0], skip_special_tokens=True, clean_up_tokenization_spaces=False))
```
"""
overwrite_call_docstring(
FlaxT5ForConditionalGeneration, T5_INPUTS_DOCSTRING + FLAX_T5_CONDITIONAL_GENERATION_DOCSTRING
)
append_replace_return_docstrings(
FlaxT5ForConditionalGeneration, output_type=FlaxSeq2SeqLMOutput, config_class=_CONFIG_FOR_DOC
)
__all__ = ["FlaxT5EncoderModel", "FlaxT5ForConditionalGeneration", "FlaxT5Model", "FlaxT5PreTrainedModel"]
| transformers/src/transformers/models/t5/modeling_flax_t5.py/0 | {
"file_path": "transformers/src/transformers/models/t5/modeling_flax_t5.py",
"repo_id": "transformers",
"token_count": 32751
} |
# 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.
"""Configuration for TimmWrapper models"""
from typing import Any, Dict
from ...configuration_utils import PretrainedConfig
from ...utils import is_timm_available, logging, requires_backends
if is_timm_available():
from timm.data import ImageNetInfo, infer_imagenet_subset
logger = logging.get_logger(__name__)
class TimmWrapperConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration for a timm backbone [`TimmWrapper`].
It is used to instantiate a timm model according to the specified arguments, defining the model.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Config loads imagenet label descriptions and stores them in `id2label` attribute, `label2id` attribute for default
imagenet models is set to `None` due to occlusions in the label descriptions.
Args:
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
do_pooling (`bool`, *optional*, defaults to `True`):
Whether to do pooling for the last_hidden_state in `TimmWrapperModel` or not.
Example:
```python
>>> from transformers import TimmWrapperModel
>>> # Initializing a timm model
>>> model = TimmWrapperModel.from_pretrained("timm/resnet18.a1_in1k")
>>> # Accessing the model configuration
>>> configuration = model.config
```
"""
model_type = "timm_wrapper"
def __init__(self, initializer_range: float = 0.02, do_pooling: bool = True, **kwargs):
self.initializer_range = initializer_range
self.do_pooling = do_pooling
super().__init__(**kwargs)
@classmethod
def from_dict(cls, config_dict: Dict[str, Any], **kwargs):
label_names = config_dict.get("label_names", None)
is_custom_model = "num_labels" in kwargs or "id2label" in kwargs
# if no labels added to config, use imagenet labeller in timm
if label_names is None and not is_custom_model:
requires_backends(cls, ["timm"])
imagenet_subset = infer_imagenet_subset(config_dict)
if imagenet_subset:
dataset_info = ImageNetInfo(imagenet_subset)
synsets = dataset_info.label_names()
label_descriptions = dataset_info.label_descriptions(as_dict=True)
label_names = [label_descriptions[synset] for synset in synsets]
if label_names is not None and not is_custom_model:
kwargs["id2label"] = dict(enumerate(label_names))
# if all label names are unique, create label2id mapping as well
if len(set(label_names)) == len(label_names):
kwargs["label2id"] = {name: i for i, name in enumerate(label_names)}
else:
kwargs["label2id"] = None
# timm config stores the `num_classes` attribute in both the root of config and in the "pretrained_cfg" dict.
# We are removing these attributes in order to have the native `transformers` num_labels attribute in config
# and to avoid duplicate attributes
num_labels_in_kwargs = kwargs.pop("num_labels", None)
num_labels_in_dict = config_dict.pop("num_classes", None)
# passed num_labels has priority over num_classes in config_dict
kwargs["num_labels"] = num_labels_in_kwargs or num_labels_in_dict
# pop num_classes from "pretrained_cfg",
# it is not necessary to have it, only root one is used in timm
if "pretrained_cfg" in config_dict and "num_classes" in config_dict["pretrained_cfg"]:
config_dict["pretrained_cfg"].pop("num_classes", None)
return super().from_dict(config_dict, **kwargs)
def to_dict(self) -> Dict[str, Any]:
output = super().to_dict()
output["num_classes"] = self.num_labels
output["label_names"] = list(self.id2label.values())
output.pop("id2label", None)
output.pop("label2id", None)
return output
__all__ = ["TimmWrapperConfig"]
| transformers/src/transformers/models/timm_wrapper/configuration_timm_wrapper.py/0 | {
"file_path": "transformers/src/transformers/models/timm_wrapper/configuration_timm_wrapper.py",
"repo_id": "transformers",
"token_count": 1782
} |
# coding=utf-8
# Copyright 2024 Microsoft Research 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 UDOP model."""
import collections
import logging
import math
import random
from abc import ABC, abstractmethod
from copy import deepcopy
from dataclasses import dataclass
from typing import Any, Dict, Optional, Sequence, Tuple, Union
import torch
from torch import Tensor, nn
from torch.nn import CrossEntropyLoss
from transformers import UdopConfig
from transformers.modeling_outputs import (
Seq2SeqLMOutput,
Seq2SeqModelOutput,
)
from ...activations import ACT2FN
from ...cache_utils import Cache, DynamicCache, EncoderDecoderCache, StaticCache
from ...generation import GenerationMixin
from ...modeling_attn_mask_utils import AttentionMaskConverter
from ...modeling_utils import PreTrainedModel
from ...pytorch_utils import find_pruneable_heads_and_indices, prune_linear_layer
from ...utils import (
ModelOutput,
add_start_docstrings,
add_start_docstrings_to_model_forward,
is_torchdynamo_compiling,
replace_return_docstrings,
)
logger = logging.getLogger(__name__)
_CONFIG_FOR_DOC = "UdopConfig"
UDOP_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.
Args:
config ([`UdopConfig`]): 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.
"""
UDOP_INPUTS_DOCSTRING = r"""
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. UDOP is a model with relative position embeddings so
you should be able to pad the inputs on both the right and the left. Indices can be obtained using
[`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for detail.
[What are input IDs?](../glossary#input-ids)
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)
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.
Note that `sequence_length = token_sequence_length + patch_sequence_length + 1` where `1` is for [CLS]
token. See `pixel_values` for `patch_sequence_length`.
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Batch of document images. Each image is divided into patches of shape `(num_channels, config.patch_size,
config.patch_size)` and the total number of patches (=`patch_sequence_length`) equals to `((height /
config.patch_size) * (width / config.patch_size))`.
visual_bbox (`torch.LongTensor` of shape `(batch_size, patch_sequence_length, 4)`, *optional*):
Bounding boxes of each patch in the image. If not provided, bounding boxes are created in the model.
decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
Indices of decoder input sequence tokens in the vocabulary. Indices can be obtained using
[`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details.
[What are decoder input IDs?](../glossary#decoder-input-ids) T5 uses the `pad_token_id` as the starting
token for `decoder_input_ids` generation. If `past_key_values` is used, optionally only the last
`decoder_input_ids` have to be input (see `past_key_values`). To know more on how to prepare
`decoder_input_ids` for pretraining take a look at [T5 Training](./t5#training).
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.
head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
Mask to nullify selected heads of the self-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.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
Mask to nullify selected heads of the self-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 `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
Mask to nullify selected heads of the cross-attention modules in the decoder. 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`, `optional`: *hidden_states*, `optional`: *attentions*)
`last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)` 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))` 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.
decoder_inputs_embeds (`torch.FloatTensor` of shape `(batch_size, target_sequence_length, hidden_size)`, *optional*):
Optionally, instead of passing `decoder_input_ids` you can choose to directly pass an embedded
representation. If `past_key_values` is used, optionally only the last `decoder_inputs_embeds` have to be
input (see `past_key_values`). This is useful if you want more control over how to convert
`decoder_input_ids` indices into associated vectors than the model's internal embedding lookup matrix. If
`decoder_input_ids` and `decoder_inputs_embeds` are both unset, `decoder_inputs_embeds` takes the value of
`inputs_embeds`.
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. It is used to update the
cache in the correct position and to infer the complete sequence length.
"""
UDOP_ENCODER_INPUTS_DOCSTRING = r"""
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. T5 is a model with relative position embeddings so you
should be able to pad the inputs on both the right and the left.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for detail.
To know more on how to prepare `input_ids` for pretraining take a look a [T5 Training](./t5#training).
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)
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.
Note that `sequence_length = token_sequence_length + patch_sequence_length + 1` where `1` is for [CLS]
token. See `pixel_values` for `patch_sequence_length`.
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Batch of document images. Each image is divided into patches of shape `(num_channels, config.patch_size,
config.patch_size)` and the total number of patches (=`patch_sequence_length`) equals to `((height /
config.patch_size) * (width / config.patch_size))`.
visual_bbox (`torch.LongTensor` of shape `(batch_size, patch_sequence_length, 4)`, *optional*):
Bounding boxes of each patch in the image. If not provided, bounding boxes are created in the model.
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*):
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.
"""
@dataclass
class BaseModelOutputWithAttentionMask(ModelOutput):
"""
Class for the model's outputs that may also contain a past key/values (to speed up sequential decoding). Includes
an additional attention mask.
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.
cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` and
`config.add_cross_attention=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 of the decoder's cross-attention layer, after the attention softmax,
used to compute the weighted average in the cross-attention heads.
"""
last_hidden_state: torch.FloatTensor = None
attention_mask: 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
cross_attentions: Optional[Tuple[torch.FloatTensor]] = None
def get_visual_bbox(image_size=224, patch_size=16):
image_feature_pool_shape = [image_size // patch_size, image_size // patch_size]
visual_bbox_x = torch.arange(0, 1.0 * (image_feature_pool_shape[1] + 1), 1.0)
visual_bbox_x /= image_feature_pool_shape[1]
visual_bbox_y = torch.arange(0, 1.0 * (image_feature_pool_shape[0] + 1), 1.0)
visual_bbox_y /= image_feature_pool_shape[0]
visual_bbox_input = torch.stack(
[
visual_bbox_x[:-1].repeat(image_feature_pool_shape[0], 1),
visual_bbox_y[:-1].repeat(image_feature_pool_shape[1], 1).transpose(0, 1),
visual_bbox_x[1:].repeat(image_feature_pool_shape[0], 1),
visual_bbox_y[1:].repeat(image_feature_pool_shape[1], 1).transpose(0, 1),
],
dim=-1,
)
visual_bbox_input = visual_bbox_input.view(-1, 4)
return visual_bbox_input
def pad_sequence(seq, target_len, pad_value=0):
if isinstance(seq, torch.Tensor):
n = seq.shape[0]
else:
n = len(seq)
seq = torch.tensor(seq)
m = target_len - n
if m > 0:
ret = torch.stack([pad_value] * m).to(seq)
seq = torch.cat([seq, ret], dim=0)
return seq[:target_len]
def combine_image_text_embeddings(
image_embeddings,
inputs_embeds,
bbox,
visual_bbox,
attention_mask=None,
num_patches=14,
max_len=0,
image_size=224,
patch_size=16,
):
"""
Combine the image and text embeddings for the input to the encoder/decoder of UDOP.
First, the image embeddings are created by checking for each visual patch if it is inside the bounding box of a
token. If it is, the visual patch is combined with the token embedding. Then, the visual bounding boxes are combined
with the text bounding boxes. Finally, the visual bounding boxes are combined with the text attention mask.
"""
sequence_length = num_patches
ocr_points_x = torch.clip(
torch.floor((bbox[:, :, 0] + bbox[:, :, 2]) / 2.0 * sequence_length).long(), 0, sequence_length - 1
)
ocr_points_y = (
torch.clip(torch.floor((bbox[:, :, 1] + bbox[:, :, 3]) / 2.0 * sequence_length).long(), 0, sequence_length - 1)
* sequence_length
)
ocr_points = ocr_points_x + ocr_points_y
# make sure bounding boxes are of type float to calculate means
bbox = bbox.to(torch.float64)
target_seg = (bbox.mean(-1) == 0.0) | (bbox.mean(-1) == 1.0)
repeated_vision_embeds = torch.gather(
image_embeddings, 1, ocr_points.unsqueeze(-1).repeat(1, 1, image_embeddings.size(-1))
)
repeated_vision_embeds[target_seg] = 0.0
inputs_embeds += repeated_vision_embeds
patch_inds = torch.full_like(image_embeddings[:, :, 0], True).bool()
ind = torch.cat(
[
torch.arange(len(ocr_points))[:, None].repeat(1, ocr_points.size(-1))[:, :, None].to(ocr_points),
ocr_points[:, :, None],
],
dim=-1,
)
ind = ind.flatten(0, 1)
rows, cols = zip(*ind)
patch_inds[rows, cols] = False
input_vision_patches = [image_embeddings[i][patch_inds[i]] for i in range(len(patch_inds))]
if visual_bbox is None:
visual_bbox = get_visual_bbox(image_size=image_size, patch_size=patch_size)
visual_bbox = visual_bbox.unsqueeze(0).repeat(image_embeddings.size(0), 1, 1)
visual_bbox = visual_bbox.to(image_embeddings.device)
visual_bbox = [visual_bbox[i][patch_inds[i]] for i in range(len(patch_inds))]
if attention_mask is not None:
visual_attention_mask = [torch.tensor([1] * len(item)).to(attention_mask) for item in visual_bbox]
if max_len == 0:
max_len = image_embeddings.size(1)
else:
max_len = max_len - inputs_embeds.size(1)
inputs_vision_patches = torch.stack(
[pad_sequence(item, max_len, torch.zeros_like(image_embeddings[0, 0])) for item in input_vision_patches]
)
visual_bbox = torch.stack([pad_sequence(item, max_len, torch.zeros_like(bbox[0, 0])) for item in visual_bbox])
if attention_mask is not None:
visual_attention_mask = torch.stack(
[pad_sequence(item, max_len, torch.zeros_like(attention_mask[0, 0])) for item in visual_attention_mask]
)
inputs_embeds = torch.cat([inputs_embeds, inputs_vision_patches], 1)
bbox = torch.cat([bbox, visual_bbox], 1)
if attention_mask is not None:
attention_mask = torch.cat([attention_mask, visual_attention_mask], 1)
return inputs_embeds, bbox, attention_mask
class UdopPatchEmbeddings(nn.Module):
"""2D Image to Patch Embeddings"""
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.proj = nn.Conv2d(num_channels, hidden_size, kernel_size=patch_size, stride=patch_size)
def forward(self, pixel_values):
batch_size, num_channels, height, width = pixel_values.shape
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.proj(pixel_values)
embeddings = embeddings.flatten(2).transpose(1, 2)
return embeddings
class UdopPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models. Based on `T5PreTrainedModel`.
"""
config_class = UdopConfig
base_model_prefix = "transformer"
supports_gradient_checkpointing = True
_supports_cache_class = True
_supports_static_cache = False
_keep_in_fp32_modules = ["wo"]
def _init_weights(self, module):
"""Initialize the weights"""
factor = self.config.initializer_factor # Used for testing weights initialization
if isinstance(module, UdopLayerNorm):
module.weight.data.fill_(factor * 1.0)
elif isinstance(module, nn.Embedding):
module.weight.data.normal_(mean=0.0, std=factor)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
elif isinstance(module, 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=factor).to(
module.weight.dtype
)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, RelativePositionBiasBase):
factor = self.config.initializer_factor
d_model = self.config.d_model
module.relative_attention_bias.weight.data.normal_(mean=0.0, std=factor * ((d_model) ** -0.5))
elif isinstance(module, UdopModel):
# Mesh TensorFlow embeddings initialization
# See https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/layers.py#L1624
module.shared.weight.data.normal_(mean=0.0, std=factor * 1.0)
elif isinstance(module, UdopForConditionalGeneration):
if hasattr(module, "lm_head") and not self.config.tie_word_embeddings:
module.lm_head.weight.data.normal_(mean=0.0, std=factor * 1.0)
elif isinstance(module, UdopDenseActDense):
# Mesh TensorFlow FF initialization
# See https://github.com/tensorflow/mesh/blob/master/mesh_tensorflow/transformer/transformer_layers.py#L56
# and https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/layers.py#L89
module.wi.weight.data.normal_(mean=0.0, std=factor * ((self.config.d_model) ** -0.5))
if hasattr(module.wi, "bias") and module.wi.bias is not None:
module.wi.bias.data.zero_()
module.wo.weight.data.normal_(mean=0.0, std=factor * ((self.config.d_ff) ** -0.5))
if hasattr(module.wo, "bias") and module.wo.bias is not None:
module.wo.bias.data.zero_()
elif isinstance(module, UdopDenseGatedActDense):
module.wi_0.weight.data.normal_(mean=0.0, std=factor * ((self.config.d_model) ** -0.5))
if hasattr(module.wi_0, "bias") and module.wi_0.bias is not None:
module.wi_0.bias.data.zero_()
module.wi_1.weight.data.normal_(mean=0.0, std=factor * ((self.config.d_model) ** -0.5))
if hasattr(module.wi_1, "bias") and module.wi_1.bias is not None:
module.wi_1.bias.data.zero_()
module.wo.weight.data.normal_(mean=0.0, std=factor * ((self.config.d_ff) ** -0.5))
if hasattr(module.wo, "bias") and module.wo.bias is not None:
module.wo.bias.data.zero_()
elif isinstance(module, UdopAttention):
# Mesh TensorFlow attention initialization to avoid scaling before softmax
# See https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/transformer/attention.py#L136
d_model = self.config.d_model
key_value_proj_dim = self.config.d_kv
n_heads = self.config.num_heads
module.q.weight.data.normal_(mean=0.0, std=factor * ((d_model * key_value_proj_dim) ** -0.5))
module.k.weight.data.normal_(mean=0.0, std=factor * (d_model**-0.5))
module.v.weight.data.normal_(mean=0.0, std=factor * (d_model**-0.5))
module.o.weight.data.normal_(mean=0.0, std=factor * ((n_heads * key_value_proj_dim) ** -0.5))
if module.has_relative_attention_bias:
module.relative_attention_bias.weight.data.normal_(mean=0.0, std=factor * ((d_model) ** -0.5))
# Copied from transformers.models.prophetnet.modeling_prophetnet.ProphetNetPreTrainedModel._shift_right with ProphetNet->Udop
def _shift_right(self, input_ids):
decoder_start_token_id = self.config.decoder_start_token_id
pad_token_id = self.config.pad_token_id
assert decoder_start_token_id is not None, (
"self.model.config.decoder_start_token_id has to be defined. In Udop it is usually set to the"
" pad_token_id. See Udop docs for more information"
)
# shift inputs to the right
shifted_input_ids = input_ids.new_zeros(input_ids.shape)
shifted_input_ids[..., 1:] = input_ids[..., :-1].clone()
shifted_input_ids[..., 0] = decoder_start_token_id
assert pad_token_id is not None, "self.model.config.pad_token_id has to be defined."
# replace possible -100 values in labels by `pad_token_id`
shifted_input_ids.masked_fill_(shifted_input_ids == -100, pad_token_id)
assert torch.all(shifted_input_ids >= 0).item(), "Verify that `shifted_input_ids` has only positive values"
return shifted_input_ids
# Copied from transformers.models.t5.modeling_t5.T5LayerNorm with T5->Udop
class UdopLayerNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-6):
"""
Construct a layernorm module in the Udop style. No bias and no subtraction of mean.
"""
super().__init__()
self.weight = nn.Parameter(torch.ones(hidden_size))
self.variance_epsilon = eps
def forward(self, hidden_states):
# Udop uses a layer_norm which only scales and doesn't shift, which is also known as Root Mean
# Square Layer Normalization https://arxiv.org/abs/1910.07467 thus varience is calculated
# w/o mean and there is no bias. Additionally we want to make sure that the accumulation for
# half-precision inputs is done in fp32
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
# Copied from transformers.models.t5.modeling_t5.T5DenseActDense with T5->Udop
class UdopDenseActDense(nn.Module):
def __init__(self, config: UdopConfig):
super().__init__()
self.wi = nn.Linear(config.d_model, config.d_ff, bias=False)
self.wo = nn.Linear(config.d_ff, config.d_model, bias=False)
self.dropout = nn.Dropout(config.dropout_rate)
self.act = ACT2FN[config.dense_act_fn]
def forward(self, hidden_states):
hidden_states = self.wi(hidden_states)
hidden_states = self.act(hidden_states)
hidden_states = self.dropout(hidden_states)
if (
isinstance(self.wo.weight, torch.Tensor)
and hidden_states.dtype != self.wo.weight.dtype
and self.wo.weight.dtype != torch.int8
):
hidden_states = hidden_states.to(self.wo.weight.dtype)
hidden_states = self.wo(hidden_states)
return hidden_states
# Copied from transformers.models.t5.modeling_t5.T5DenseGatedActDense with T5->Udop
class UdopDenseGatedActDense(nn.Module):
def __init__(self, config: UdopConfig):
super().__init__()
self.wi_0 = nn.Linear(config.d_model, config.d_ff, bias=False)
self.wi_1 = nn.Linear(config.d_model, config.d_ff, bias=False)
self.wo = nn.Linear(config.d_ff, config.d_model, bias=False)
self.dropout = nn.Dropout(config.dropout_rate)
self.act = ACT2FN[config.dense_act_fn]
def forward(self, hidden_states):
hidden_gelu = self.act(self.wi_0(hidden_states))
hidden_linear = self.wi_1(hidden_states)
hidden_states = hidden_gelu * hidden_linear
hidden_states = self.dropout(hidden_states)
# To make 8bit quantization work for google/flan-t5-xxl, self.wo is kept in float32.
# See https://github.com/huggingface/transformers/issues/20287
# we also make sure the weights are not in `int8` in case users will force `_keep_in_fp32_modules` to be `None``
if (
isinstance(self.wo.weight, torch.Tensor)
and hidden_states.dtype != self.wo.weight.dtype
and self.wo.weight.dtype != torch.int8
):
hidden_states = hidden_states.to(self.wo.weight.dtype)
hidden_states = self.wo(hidden_states)
return hidden_states
# Copied from transformers.models.t5.modeling_t5.T5LayerFF with T5->Udop
class UdopLayerFF(nn.Module):
def __init__(self, config: UdopConfig):
super().__init__()
if config.is_gated_act:
self.DenseReluDense = UdopDenseGatedActDense(config)
else:
self.DenseReluDense = UdopDenseActDense(config)
self.layer_norm = UdopLayerNorm(config.d_model, eps=config.layer_norm_epsilon)
self.dropout = nn.Dropout(config.dropout_rate)
def forward(self, hidden_states):
forwarded_states = self.layer_norm(hidden_states)
forwarded_states = self.DenseReluDense(forwarded_states)
hidden_states = hidden_states + self.dropout(forwarded_states)
return hidden_states
# Copied from transformers.models.t5.modeling_t5.T5Attention with T5->Udop
class UdopAttention(nn.Module):
def __init__(
self,
config: UdopConfig,
has_relative_attention_bias=False,
layer_idx: Optional[int] = None,
):
super().__init__()
self.is_decoder = config.is_decoder
self.has_relative_attention_bias = has_relative_attention_bias
self.relative_attention_num_buckets = config.relative_attention_num_buckets
self.relative_attention_max_distance = config.relative_attention_max_distance
self.d_model = config.d_model
self.key_value_proj_dim = config.d_kv
self.n_heads = config.num_heads
self.dropout = config.dropout_rate
self.inner_dim = self.n_heads * self.key_value_proj_dim
self.layer_idx = layer_idx
if layer_idx is None and self.is_decoder:
logger.warning_once(
f"Instantiating a decoder {self.__class__.__name__} without passing `layer_idx` is not recommended and "
"will to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` "
"when creating this class."
)
# Mesh TensorFlow initialization to avoid scaling before softmax
self.q = nn.Linear(self.d_model, self.inner_dim, bias=False)
self.k = nn.Linear(self.d_model, self.inner_dim, bias=False)
self.v = nn.Linear(self.d_model, self.inner_dim, bias=False)
self.o = nn.Linear(self.inner_dim, self.d_model, bias=False)
if self.has_relative_attention_bias:
self.relative_attention_bias = nn.Embedding(self.relative_attention_num_buckets, self.n_heads)
self.pruned_heads = set()
self.gradient_checkpointing = False
def prune_heads(self, heads):
if len(heads) == 0:
return
heads, index = find_pruneable_heads_and_indices(
heads, self.n_heads, self.key_value_proj_dim, self.pruned_heads
)
# Prune linear layers
self.q = prune_linear_layer(self.q, index)
self.k = prune_linear_layer(self.k, index)
self.v = prune_linear_layer(self.v, index)
self.o = prune_linear_layer(self.o, index, dim=1)
# Update hyper params
self.n_heads = self.n_heads - len(heads)
self.inner_dim = self.key_value_proj_dim * self.n_heads
self.pruned_heads = self.pruned_heads.union(heads)
@staticmethod
def _relative_position_bucket(relative_position, bidirectional=True, num_buckets=32, max_distance=128):
"""
Adapted from Mesh Tensorflow:
https://github.com/tensorflow/mesh/blob/0cb87fe07da627bf0b7e60475d59f95ed6b5be3d/mesh_tensorflow/transformer/transformer_layers.py#L593
Translate relative position to a bucket number for relative attention. The relative position is defined as
memory_position - query_position, i.e. the distance in tokens from the attending position to the attended-to
position. If bidirectional=False, then positive relative positions are invalid. We use smaller buckets for
small absolute relative_position and larger buckets for larger absolute relative_positions. All relative
positions >=max_distance map to the same bucket. All relative positions <=-max_distance map to the same bucket.
This should allow for more graceful generalization to longer sequences than the model has been trained on
Args:
relative_position: an int32 Tensor
bidirectional: a boolean - whether the attention is bidirectional
num_buckets: an integer
max_distance: an integer
Returns:
a Tensor with the same shape as relative_position, containing int32 values in the range [0, num_buckets)
"""
relative_buckets = 0
if bidirectional:
num_buckets //= 2
relative_buckets += (relative_position > 0).to(torch.long) * num_buckets
relative_position = torch.abs(relative_position)
else:
relative_position = -torch.min(relative_position, torch.zeros_like(relative_position))
# now relative_position is in the range [0, inf)
# half of the buckets are for exact increments in positions
max_exact = num_buckets // 2
is_small = relative_position < max_exact
# The other half of the buckets are for logarithmically bigger bins in positions up to max_distance
relative_position_if_large = max_exact + (
torch.log(relative_position.float() / max_exact)
/ math.log(max_distance / max_exact)
* (num_buckets - max_exact)
).to(torch.long)
relative_position_if_large = torch.min(
relative_position_if_large, torch.full_like(relative_position_if_large, num_buckets - 1)
)
relative_buckets += torch.where(is_small, relative_position, relative_position_if_large)
return relative_buckets
def compute_bias(self, query_length, key_length, device=None, cache_position=None):
"""Compute binned relative position bias"""
if device is None:
device = self.relative_attention_bias.weight.device
if cache_position is None:
context_position = torch.arange(query_length, dtype=torch.long, device=device)[:, None]
else:
context_position = cache_position[:, None].to(device)
memory_position = torch.arange(key_length, dtype=torch.long, device=device)[None, :]
relative_position = memory_position - context_position # shape (query_length, key_length)
relative_position_bucket = self._relative_position_bucket(
relative_position, # shape (query_length, key_length)
bidirectional=(not self.is_decoder),
num_buckets=self.relative_attention_num_buckets,
max_distance=self.relative_attention_max_distance,
)
values = self.relative_attention_bias(relative_position_bucket) # shape (query_length, key_length, num_heads)
values = values.permute([2, 0, 1]).unsqueeze(0) # shape (1, num_heads, query_length, key_length)
return values
def forward(
self,
hidden_states,
mask=None,
key_value_states=None,
position_bias=None,
past_key_value=None,
layer_head_mask=None,
query_length=None,
use_cache=False,
output_attentions=False,
cache_position=None,
):
"""
Self-attention (if key_value_states is None) or attention over source sentence (provided by key_value_states).
"""
# Input is (batch_size, seq_length, dim)
# Mask is (batch_size, 1, 1, key_length) (non-causal encoder) or (batch_size, 1, seq_length, key_length) (causal decoder)
batch_size, seq_length = hidden_states.shape[:2]
# 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
query_states = self.q(hidden_states)
query_states = query_states.view(batch_size, -1, self.n_heads, self.key_value_proj_dim).transpose(1, 2)
if past_key_value is not None:
is_updated = past_key_value.is_updated.get(self.layer_idx)
if is_cross_attention:
# after the first generated id, we can subsequently re-use all key/value_states from cache
curr_past_key_value = past_key_value.cross_attention_cache
else:
curr_past_key_value = past_key_value.self_attention_cache
current_states = key_value_states if is_cross_attention else hidden_states
if is_cross_attention and past_key_value is not None and is_updated:
# reuse k,v, cross_attentions
key_states = curr_past_key_value.key_cache[self.layer_idx]
value_states = curr_past_key_value.value_cache[self.layer_idx]
else:
key_states = self.k(current_states)
value_states = self.v(current_states)
key_states = key_states.view(batch_size, -1, self.n_heads, self.key_value_proj_dim).transpose(1, 2)
value_states = value_states.view(batch_size, -1, self.n_heads, self.key_value_proj_dim).transpose(1, 2)
if past_key_value is not None:
# save all key/value_states to cache to be re-used for fast auto-regressive generation
cache_position = cache_position if not is_cross_attention else None
key_states, value_states = curr_past_key_value.update(
key_states, value_states, self.layer_idx, {"cache_position": cache_position}
)
# set flag that curr layer for cross-attn is already updated so we can re-use in subsequent calls
if is_cross_attention:
past_key_value.is_updated[self.layer_idx] = True
# compute scores, equivalent of torch.einsum("bnqd,bnkd->bnqk", query_states, key_states), compatible with onnx op>9
scores = torch.matmul(query_states, key_states.transpose(3, 2))
if position_bias is None:
key_length = key_states.shape[-2]
# cache position is 0-indexed so we add 1 to get the real length of queries (aka with past)
real_seq_length = query_length if query_length is not None else cache_position[-1] + 1
if not self.has_relative_attention_bias:
position_bias = torch.zeros(
(1, self.n_heads, seq_length, key_length), device=scores.device, dtype=scores.dtype
)
if self.gradient_checkpointing and self.training:
position_bias.requires_grad = True
else:
position_bias = self.compute_bias(
real_seq_length, key_length, device=scores.device, cache_position=cache_position
)
position_bias = position_bias[:, :, -seq_length:, :]
if mask is not None:
causal_mask = mask[:, :, :, : key_states.shape[-2]]
position_bias = position_bias + causal_mask
if self.pruned_heads:
mask = torch.ones(position_bias.shape[1])
mask[list(self.pruned_heads)] = 0
position_bias_masked = position_bias[:, mask.bool()]
else:
position_bias_masked = position_bias
scores += position_bias_masked
# (batch_size, n_heads, seq_length, key_length)
attn_weights = nn.functional.softmax(scores.float(), dim=-1).type_as(scores)
attn_weights = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)
# Mask heads if we want to
if layer_head_mask is not None:
attn_weights = attn_weights * layer_head_mask
attn_output = torch.matmul(attn_weights, value_states)
attn_output = attn_output.transpose(1, 2).contiguous()
attn_output = attn_output.view(batch_size, -1, self.inner_dim)
attn_output = self.o(attn_output)
outputs = (attn_output, past_key_value, position_bias)
if output_attentions:
outputs = outputs + (attn_weights,)
return outputs
# Copied from transformers.models.t5.modeling_t5.T5LayerSelfAttention with T5->Udop
class UdopLayerSelfAttention(nn.Module):
def __init__(self, config, has_relative_attention_bias=False, layer_idx: Optional[int] = None):
super().__init__()
self.SelfAttention = UdopAttention(
config, has_relative_attention_bias=has_relative_attention_bias, layer_idx=layer_idx
)
self.layer_norm = UdopLayerNorm(config.d_model, eps=config.layer_norm_epsilon)
self.dropout = nn.Dropout(config.dropout_rate)
def forward(
self,
hidden_states,
attention_mask=None,
position_bias=None,
layer_head_mask=None,
past_key_value=None,
use_cache=False,
output_attentions=False,
cache_position=None,
):
normed_hidden_states = self.layer_norm(hidden_states)
attention_output = self.SelfAttention(
normed_hidden_states,
mask=attention_mask,
position_bias=position_bias,
layer_head_mask=layer_head_mask,
past_key_value=past_key_value,
use_cache=use_cache,
output_attentions=output_attentions,
cache_position=cache_position,
)
hidden_states = hidden_states + self.dropout(attention_output[0])
outputs = (hidden_states,) + attention_output[1:] # add attentions if we output them
return outputs
# Copied from transformers.models.t5.modeling_t5.T5LayerCrossAttention with T5->Udop
class UdopLayerCrossAttention(nn.Module):
def __init__(self, config, layer_idx: Optional[int] = None):
super().__init__()
self.EncDecAttention = UdopAttention(config, has_relative_attention_bias=False, layer_idx=layer_idx)
self.layer_norm = UdopLayerNorm(config.d_model, eps=config.layer_norm_epsilon)
self.dropout = nn.Dropout(config.dropout_rate)
def forward(
self,
hidden_states,
key_value_states,
attention_mask=None,
position_bias=None,
layer_head_mask=None,
past_key_value=None,
use_cache=False,
query_length=None,
output_attentions=False,
cache_position=None,
):
normed_hidden_states = self.layer_norm(hidden_states)
attention_output = self.EncDecAttention(
normed_hidden_states,
mask=attention_mask,
key_value_states=key_value_states,
position_bias=position_bias,
layer_head_mask=layer_head_mask,
past_key_value=past_key_value,
use_cache=use_cache,
query_length=query_length,
output_attentions=output_attentions,
cache_position=cache_position,
)
layer_output = hidden_states + self.dropout(attention_output[0])
outputs = (layer_output,) + attention_output[1:] # add attentions if we output them
return outputs
# Copied from transformers.models.t5.modeling_t5.T5Block with T5->Udop
class UdopBlock(nn.Module):
def __init__(self, config, has_relative_attention_bias=False, layer_idx: Optional[int] = None):
super().__init__()
self.is_decoder = config.is_decoder
self.layer = nn.ModuleList()
self.layer.append(
UdopLayerSelfAttention(
config, has_relative_attention_bias=has_relative_attention_bias, layer_idx=layer_idx
)
)
if self.is_decoder:
self.layer.append(UdopLayerCrossAttention(config, layer_idx=layer_idx))
self.layer.append(UdopLayerFF(config))
def forward(
self,
hidden_states,
attention_mask=None,
position_bias=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
encoder_decoder_position_bias=None,
layer_head_mask=None,
cross_attn_layer_head_mask=None,
past_key_value=None,
use_cache=False,
output_attentions=False,
return_dict=True,
cache_position=None,
):
self_attention_outputs = self.layer[0](
hidden_states,
attention_mask=attention_mask,
position_bias=position_bias,
layer_head_mask=layer_head_mask,
past_key_value=past_key_value,
use_cache=use_cache,
output_attentions=output_attentions,
cache_position=cache_position,
)
hidden_states, past_key_value = self_attention_outputs[:2]
attention_outputs = self_attention_outputs[2:] # Keep self-attention outputs and relative position weights
# clamp inf values to enable fp16 training
if hidden_states.dtype == torch.float16:
clamp_value = torch.where(
torch.isinf(hidden_states).any(),
torch.finfo(hidden_states.dtype).max - 1000,
torch.finfo(hidden_states.dtype).max,
)
hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)
do_cross_attention = self.is_decoder and encoder_hidden_states is not None
if do_cross_attention:
cross_attention_outputs = self.layer[1](
hidden_states,
key_value_states=encoder_hidden_states,
attention_mask=encoder_attention_mask,
position_bias=encoder_decoder_position_bias,
layer_head_mask=cross_attn_layer_head_mask,
past_key_value=past_key_value,
query_length=cache_position[-1] + 1,
use_cache=use_cache,
output_attentions=output_attentions,
)
hidden_states, past_key_value = cross_attention_outputs[:2]
# clamp inf values to enable fp16 training
if hidden_states.dtype == torch.float16:
clamp_value = torch.where(
torch.isinf(hidden_states).any(),
torch.finfo(hidden_states.dtype).max - 1000,
torch.finfo(hidden_states.dtype).max,
)
hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)
# Keep cross-attention outputs and relative position weights
attention_outputs = attention_outputs + cross_attention_outputs[2:]
# Apply Feed Forward layer
hidden_states = self.layer[-1](hidden_states)
# clamp inf values to enable fp16 training
if hidden_states.dtype == torch.float16:
clamp_value = torch.where(
torch.isinf(hidden_states).any(),
torch.finfo(hidden_states.dtype).max - 1000,
torch.finfo(hidden_states.dtype).max,
)
hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)
outputs = (hidden_states,)
if use_cache:
outputs = outputs + (past_key_value,) + attention_outputs
else:
outputs = outputs + attention_outputs
return outputs # hidden-states, past_key_value, (self-attention position bias), (self-attention weights), (cross-attention position bias), (cross-attention weights)
class UdopCellEmbeddings(nn.Module):
def __init__(self, max_2d_position_embeddings=501, hidden_size=1024):
super(UdopCellEmbeddings, self).__init__()
self.max_2d_position_embeddings = max_2d_position_embeddings
self.x_position_embeddings = nn.Embedding(max_2d_position_embeddings, hidden_size)
self.y_position_embeddings = nn.Embedding(max_2d_position_embeddings, hidden_size)
def forward(self, bbox):
bbox = torch.clip(bbox, 0.0, 1.0)
bbox = (bbox * (self.max_2d_position_embeddings - 1)).long()
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])
embeddings = (
left_position_embeddings
+ upper_position_embeddings
+ right_position_embeddings
+ lower_position_embeddings
)
return embeddings
# get function for bucket computation
# protected member access seems to be lesser evil than copy paste whole function
get_relative_position_bucket = UdopAttention._relative_position_bucket
AUGMENTATION_RANGE = (0.80, 1.25)
class RelativePositionBiasBase(nn.Module, ABC):
"""
Base class of relative biases.
Args:
num_heads (`int`):
Number of attention heads in the model, it will create embeddings of size `num_heads`, which will be added to the scores of each token pair.
relative_attention_num_buckets (`int`, *optional*, defaults to 32):
Pair token metric (distance in the sequence, distance in pixels etc.) will be bucketed, parameter is defining number of such
buckets.
bidirectional (`bool`, *optional*, defaults to `True`):
Whether the distance should be bidirectional for a pair of tokens. If `False`, then distance(tok1, tok2) == distance(tok2, tok1).
scaling_factor (`int`, *optional*, defaults to 1):
Defining factor which will be used to scale relative distance.
max_distance (`int`, *optional*, defaults to 128):
All distances above this value will end up in the one/same bucket.
augmentation (`bool`, *optional*, defaults to `False`):
Whether to multiply relative distances by a random scalar.
expand (`bool`, *optional*, defaults to `False`):
Whether to expand an existing pretrained model with subsequent additions of prefix_bucket.
"""
def __init__(
self,
num_heads=None,
relative_attention_num_buckets=32,
bidirectional=True,
scaling_factor=1,
max_distance=128,
level="tokens",
augmentation=False,
prefix_bucket=False,
expand=False,
):
super(RelativePositionBiasBase, self).__init__()
self.prefix_bucket = prefix_bucket
self.augmentation = augmentation
self.level = level
self.max_distance = max_distance
self.scaling_factor = scaling_factor
self.bidirectional = bidirectional
self.num_heads = num_heads
self.expand = expand
self.relative_attention_num_buckets = relative_attention_num_buckets
extra_head = 2 if prefix_bucket and not self.expand else 0
self.relative_attention_bias = nn.Embedding(self.relative_attention_num_buckets + extra_head, self.num_heads)
@abstractmethod
def prepare_input(
self,
attention_mask: Optional[Tensor] = None,
bbox: Optional[Dict[str, Any]] = None,
) -> Tensor:
pass
def get_bucket(self, attention_mask: Optional[Tensor] = None, bbox: Optional[Dict[str, Any]] = None) -> Tensor:
relative_position = self.prepare_input(attention_mask, bbox)
rp_bucket: Tensor = get_relative_position_bucket(
relative_position,
bidirectional=self.bidirectional,
num_buckets=self.relative_attention_num_buckets,
max_distance=self.max_distance,
)
return rp_bucket
def get_relative_position(self, positions):
context_position = positions[:, :, None]
memory_position = positions[:, None, :]
relative_position = memory_position - context_position
if self.augmentation and self.training:
relative_position *= random.uniform(*AUGMENTATION_RANGE)
relative_position *= self.scaling_factor
return relative_position.to(torch.long)
def forward(self, attention_mask: Optional[Tensor] = None, bbox: Optional[Dict[str, Any]] = None) -> Tensor:
# re-using pretrained model with subsequent addition of prefix_bucket
if self.expand and self.prefix_bucket:
new_bias = nn.Embedding(self.relative_attention_num_buckets + 2, self.num_heads)
new_bias.weight.data[: self.relative_attention_num_buckets] = self.relative_attention_bias.weight.data
new_bias.weight.data[self.relative_attention_num_buckets :] = 0.1
self.relative_attention_bias = new_bias
self.expand = False
rp_bucket = self.get_bucket(attention_mask, bbox)
if self.prefix_bucket:
if rp_bucket.size(0) == 1 and attention_mask.size(0) > 1:
rp_bucket = rp_bucket.repeat(attention_mask.size(0), 1, 1)
# based on assumption that prefix bboxes are negative
is_prefix = bbox[:, :, 1] < 0
num_prefix = is_prefix.sum(-1)
for idx, num_prefix_row in enumerate(num_prefix.cpu().numpy()):
rp_bucket[idx, :num_prefix_row, num_prefix_row:] = self.relative_attention_num_buckets
rp_bucket[idx, num_prefix_row:, :num_prefix_row] = self.relative_attention_num_buckets + 1
values: Tensor = self.relative_attention_bias(rp_bucket)
if values.dim() != 4:
raise ValueError("Wrong dimension of values tensor")
values = values.permute([0, 3, 1, 2])
return values
class RelativePositionBias1D(RelativePositionBiasBase):
def __init__(self, scaling_factor=1, max_distance=128, **kwargs):
"""
Reimplementation of T5 relative position bias. Distance between given tokens is their distance in the sequence.
Parameters are the same as in base class
"""
super().__init__(scaling_factor=scaling_factor, max_distance=max_distance, **kwargs)
def prepare_input(self, attention_mask: Optional[Tensor] = None, bbox: Optional[Dict[str, Any]] = None) -> Tensor:
if self.scaling_factor != 1:
raise ValueError("No need to scale 1d features")
relative_position = self.get_relative_position(
torch.arange(attention_mask.size(1), dtype=torch.long, device=attention_mask.device)[None, :]
)
return relative_position
class RelativePositionBiasHorizontal(RelativePositionBiasBase):
def __init__(self, scaling_factor=100, max_distance=100, **kwargs):
"""
Represents in the bucket embeddings horizontal distance between two tokens. Parameters are the same as in base
class
"""
super().__init__(scaling_factor=scaling_factor, max_distance=max_distance, **kwargs)
def prepare_input(self, attention_mask: Optional[Tensor] = None, bbox: Optional[Dict[str, Any]] = None) -> Tensor:
if not self.scaling_factor > 1.0:
raise ValueError("Need to scale the values of bboxes, as there are in small (0,1) range")
if bbox is None:
raise ValueError("Bbox is required for horizontal relative position bias")
# get x positions of left point of bbox
horizontal_position: Tensor = bbox[:, :, [0, 2]].mean(dim=-1)
return self.get_relative_position(horizontal_position)
class RelativePositionBiasVertical(RelativePositionBiasBase):
def __init__(self, scaling_factor=100, max_distance=100, **kwargs):
"""
Represents in the bucket embeddings vertical distance between two tokens. Parameters are the same as in base
class
"""
super().__init__(scaling_factor=scaling_factor, max_distance=max_distance, **kwargs)
def prepare_input(self, attention_mask: Optional[Tensor] = None, bbox: Optional[Dict[str, Any]] = None) -> Tensor:
if not self.scaling_factor > 1.0:
raise ValueError("Need to scale the values of bboxes, as there are in small (0,1) range")
if bbox is None:
raise ValueError("Bbox is required for vertical relative position bias")
# get y positions of middle of bbox
vertical_position: Tensor = bbox[:, :, [1, 3]].mean(dim=-1)
return self.get_relative_position(vertical_position)
class RelativePositionBiasAggregated(nn.Module):
def __init__(self, modules: Sequence[RelativePositionBiasBase]):
"""
Class which sums up various computed biases.
Args:
modules (Sequence[RelativePositionBiasBase]):
List of relative bias modules.
"""
super().__init__()
self.biases = nn.ModuleList(modules)
def forward(
self, attention_mask: Optional[Tensor] = None, bbox: Optional[Dict[str, Any]] = None
) -> Union[float, Tensor]:
output = 0.0
for bias in self.biases: # type: ignore
output = bias(attention_mask, bbox) + output
return output
BIAS_CLASSES = {
"1d": RelativePositionBias1D,
"horizontal": RelativePositionBiasHorizontal,
"vertical": RelativePositionBiasVertical,
}
def create_relative_bias(config: UdopConfig) -> Sequence[RelativePositionBiasBase]:
"""
Creates empty list or one/multiple relative biases.
:param config: Model's configuration :return: Sequence with created bias modules.
"""
bias_list = []
if hasattr(config, "relative_bias_args"):
for bias_kwargs_org in config.relative_bias_args:
bias_kwargs = deepcopy(bias_kwargs_org)
bias_type = bias_kwargs.pop("type")
model_num_heads = config.num_heads if hasattr(config, "num_heads") else config.num_attention_heads
if "num_heads" in bias_kwargs:
if bias_kwargs["num_heads"] != model_num_heads:
raise ValueError("Number of heads must match num of heads in the model")
else:
bias_kwargs["num_heads"] = model_num_heads
bias_list.append(BIAS_CLASSES[bias_type](**bias_kwargs)) # type: ignore
return bias_list
class UdopStack(UdopPreTrainedModel):
"""
This class is based on `T5Stack`, but modified to take into account the image modality as well as 2D position
embeddings.
"""
def __init__(self, config, embed_tokens=None, embed_patches=None):
super().__init__(config)
self.embed_tokens = embed_tokens
self.embed_patches = embed_patches
self.is_decoder = config.is_decoder
self._max_length = config.max_length
self.num_layers = config.num_layers
self.block = nn.ModuleList(
[UdopBlock(config, has_relative_attention_bias=bool(i == 0), layer_idx=i) for i in range(self.num_layers)]
)
self.final_layer_norm = UdopLayerNorm(config.d_model, eps=config.layer_norm_epsilon)
self.dropout = nn.Dropout(config.dropout_rate)
if not self.is_decoder:
self.cell_2d_embedding = UdopCellEmbeddings(config.max_2d_position_embeddings, config.hidden_size)
# get weights from encoder position bias
self.relative_bias = self._get_relative_bias(config)
def _tie_weights(self):
for bias in self.relative_bias.biases:
if isinstance(bias, RelativePositionBias1D):
self._tie_or_clone_weights(
bias.relative_attention_bias, self.block[0].layer[0].SelfAttention.relative_attention_bias
)
@staticmethod
def _get_relative_bias(config: UdopConfig) -> RelativePositionBiasAggregated:
relative_bias_list = create_relative_bias(config)
return RelativePositionBiasAggregated(relative_bias_list)
def get_input_embeddings(self):
return self.embed_tokens
def get_output_embeddings(self):
return self.embed_tokens
def set_input_embeddings(self, new_embeddings):
self.embed_tokens = new_embeddings
def forward(
self,
input_ids=None,
attention_mask=None,
bbox=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
inputs_embeds=None,
pixel_values=None,
visual_bbox=None,
image_embeddings=None,
position_bias=None,
head_mask=None,
cross_attn_head_mask=None,
past_key_values=None,
use_cache=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
cache_position=None,
):
use_cache = use_cache if use_cache is not None else self.config.use_cache
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
# input embeddings processing
if input_ids is not None and inputs_embeds is not None:
err_msg_prefix = "decoder_" if self.is_decoder else ""
raise ValueError(
f"You cannot specify both {err_msg_prefix}inputs and {err_msg_prefix}inputs_embeds at the same time"
)
elif input_ids is not None and torch.numel(input_ids) > 0:
input_shape = input_ids.size()
input_ids = input_ids.view(-1, input_shape[-1])
elif inputs_embeds is None and input_ids is not None and torch.numel(input_ids) == 0:
input_ids = torch.full((4, 1024), self.config.pad_token_id, device=input_ids.device, dtype=input_ids.dtype)
attention_mask = torch.zeros((4, 1024), device=input_ids.device, dtype=input_ids.dtype)
bbox = torch.zeros((4, 1024, 4), device=input_ids.device, dtype=input_ids.dtype)
input_shape = input_ids.size()
position_bias = torch.zeros_like(self.get_extended_attention_mask(attention_mask, input_shape))
# encoder_attention_mask = attention_mask
logger.warning("Empty batch")
elif inputs_embeds is not None:
input_shape = inputs_embeds.size()[:-1]
else:
err_msg_prefix = "decoder_" if self.is_decoder else ""
raise ValueError(f"You have to specify either {err_msg_prefix}inputs or {err_msg_prefix}inputs_embeds")
if inputs_embeds is None:
if self.embed_tokens is None:
raise ValueError("You have to intialize the model with valid token embeddings")
inputs_embeds = self.embed_tokens(input_ids)
if pixel_values is not None:
image_embeddings = self.embed_patches(pixel_values)
if image_embeddings is not None:
# combine visual and OCR text embeddings
num_patches = self.config.image_size // self.config.patch_size
inputs_embeds, bbox, attention_mask = combine_image_text_embeddings(
image_embeddings,
inputs_embeds,
bbox,
visual_bbox,
attention_mask,
num_patches,
0,
self.config.image_size,
self.config.patch_size,
)
input_shape = inputs_embeds.size()[:-1]
if not self.is_decoder and bbox is not None:
inputs_embeds += self.cell_2d_embedding(bbox)
batch_size, seq_length = input_shape
if use_cache is True:
assert self.is_decoder, "`use_cache` can only be set to `True` if {} is used as a decoder".format(self)
# initialize past_key_values
return_legacy_cache = False
return_self_attention_cache = False
if self.is_decoder and (use_cache or past_key_values is not None):
if isinstance(past_key_values, Cache) and not isinstance(past_key_values, EncoderDecoderCache):
return_self_attention_cache = True
past_key_values = EncoderDecoderCache(past_key_values, DynamicCache())
elif not isinstance(past_key_values, EncoderDecoderCache):
return_legacy_cache = True
logger.warning_once(
"Passing a tuple of `past_key_values` is deprecated and will be removed in Transformers v4.48.0. "
"You should pass an instance of `EncoderDecoderCache` instead, e.g. "
"`past_key_values=EncoderDecoderCache.from_legacy_cache(past_key_values)`."
)
past_key_values = EncoderDecoderCache.from_legacy_cache(past_key_values)
elif past_key_values is None:
past_key_values = EncoderDecoderCache(DynamicCache(), DynamicCache())
elif not self.is_decoder:
# do not pass cache object down the line for encoder stack
# it messes indexing later in decoder-stack because cache object is modified in-place
past_key_values = None
past_key_values_length = past_key_values.get_seq_length() if past_key_values is not None else 0
if cache_position is None:
cache_position = torch.arange(
past_key_values_length, past_key_values_length + seq_length, device=inputs_embeds.device
)
if attention_mask is None and not is_torchdynamo_compiling():
# required mask seq length can be calculated via length of past cache
mask_seq_length = past_key_values_length + seq_length
attention_mask = torch.ones(batch_size, mask_seq_length, device=inputs_embeds.device)
if self.config.is_decoder:
causal_mask = self._update_causal_mask(
attention_mask,
inputs_embeds,
cache_position,
past_key_values.self_attention_cache if past_key_values is not None else None,
output_attentions,
)
else:
causal_mask = attention_mask[:, None, None, :]
causal_mask = causal_mask.to(dtype=inputs_embeds.dtype)
causal_mask = (1.0 - causal_mask) * torch.finfo(inputs_embeds.dtype).min
if self.is_decoder and encoder_attention_mask is not None:
encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
else:
encoder_extended_attention_mask = None
# Prepare head mask if needed
head_mask = self.get_head_mask(head_mask, self.num_layers)
all_hidden_states = () if output_hidden_states else None
all_attentions = () if output_attentions else None
all_cross_attentions = () if (output_attentions and self.is_decoder) else None
if self.is_decoder: # modified lines
position_bias = None
else:
position_bias = self.relative_bias(attention_mask=attention_mask, bbox=bbox)
position_bias = position_bias + causal_mask
encoder_decoder_position_bias = None
hidden_states = inputs_embeds
hidden_states = self.dropout(hidden_states)
for i, layer_module in enumerate(self.block):
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
layer_outputs = layer_module(
hidden_states,
attention_mask=causal_mask,
position_bias=position_bias,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_extended_attention_mask,
encoder_decoder_position_bias=encoder_decoder_position_bias,
layer_head_mask=head_mask[i],
past_key_value=past_key_values,
use_cache=use_cache,
output_attentions=output_attentions,
cache_position=cache_position,
)
# layer_outputs is a tuple with:
# hidden-states, key-value-states, (self-attention weights), (self-attention position bias), (cross-attention weights), (cross-attention position bias)
if use_cache is False: # MP fixes
layer_outputs = layer_outputs[:1] + (None,) + layer_outputs[1:]
hidden_states, next_decoder_cache = layer_outputs[:2]
# We share the position biases between the layers - the first layer store them
# layer_outputs = hidden-states, key-value-states (self-attention weights),
# (self-attention position bias), (cross-attention weights), (cross-attention position bias)
position_bias = layer_outputs[2]
if self.is_decoder and encoder_hidden_states is not None:
encoder_decoder_position_bias = layer_outputs[4 if output_attentions else 3]
if output_attentions:
all_attentions = all_attentions + (layer_outputs[2],) # We keep only self-attention weights for now
if self.is_decoder:
all_cross_attentions = all_cross_attentions + (layer_outputs[5],)
hidden_states = self.final_layer_norm(hidden_states)
hidden_states = self.dropout(hidden_states)
# Add last layer
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
next_cache = next_decoder_cache if use_cache else None
if return_self_attention_cache:
next_cache = past_key_values.self_attention_cache
if return_legacy_cache:
next_cache = past_key_values.to_legacy_cache()
if not return_dict:
return tuple(
v
for v in [
hidden_states,
attention_mask,
next_cache,
all_hidden_states,
all_attentions,
all_cross_attentions,
]
if v is not None
)
return BaseModelOutputWithAttentionMask(
last_hidden_state=hidden_states,
attention_mask=attention_mask,
past_key_values=next_cache,
hidden_states=all_hidden_states,
attentions=all_attentions,
cross_attentions=all_cross_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,
):
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
# 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.LlamaPreTrainedModel._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 plcae 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, :]
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 bare UDOP encoder-decoder Transformer outputting raw hidden-states without any specific head on top.",
UDOP_START_DOCSTRING,
)
class UdopModel(UdopPreTrainedModel):
_tied_weights_keys = [
"encoder.embed_tokens.weight",
"decoder.embed_tokens.weight",
"encoder.embed_patches.proj.weight",
"encoder.embed_patches.proj.bias",
"encoder.relative_bias.biases.0.relative_attention_bias.weight",
"decoder.relative_bias.biases.0.relative_attention_bias.weight",
]
def __init__(self, config):
super(UdopModel, self).__init__(config)
# text and image embeddings
self.shared = nn.Embedding(config.vocab_size, config.d_model)
self.patch_embed = UdopPatchEmbeddings(config)
encoder_config = deepcopy(config)
encoder_config.is_decoder = False
encoder_config.use_cache = False
encoder_config.is_encoder_decoder = False
self.encoder = UdopStack(encoder_config, self.shared, self.patch_embed)
decoder_config = deepcopy(config)
decoder_config.is_decoder = True
decoder_config.is_encoder_decoder = False
decoder_config.num_layers = config.num_decoder_layers
self.decoder = UdopStack(decoder_config, self.shared)
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.shared
def set_input_embeddings(self, new_embeddings):
self.shared = new_embeddings
self.encoder.set_input_embeddings(new_embeddings)
self.decoder.set_input_embeddings(new_embeddings)
def get_encoder(self):
return self.encoder
def get_decoder(self):
return self.decoder
@add_start_docstrings_to_model_forward(UDOP_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=Seq2SeqModelOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Tensor = None,
attention_mask: Tensor = None,
bbox: Dict[str, Any] = None,
pixel_values: Optional[Tensor] = None,
visual_bbox: Dict[str, Any] = None,
decoder_input_ids: Optional[Tensor] = None,
decoder_attention_mask: Optional[Tensor] = None,
inputs_embeds: Optional[Tensor] = None,
encoder_outputs: Optional[Tensor] = None,
past_key_values: Optional[Tensor] = None,
head_mask: Optional[Tensor] = None,
decoder_inputs_embeds: Optional[Tensor] = None,
decoder_head_mask: Optional[Tensor] = None,
cross_attn_head_mask: Optional[Tensor] = None,
use_cache=True,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
cache_position: Optional[torch.LongTensor] = None,
) -> Tuple[Tensor, ...]:
r"""
Returns:
Example:
```python
>>> from transformers import AutoProcessor, AutoModel
>>> from datasets import load_dataset
>>> import torch
>>> # load model and processor
>>> # in this case, we already have performed OCR ourselves
>>> # so we initialize the processor with `apply_ocr=False`
>>> processor = AutoProcessor.from_pretrained("microsoft/udop-large", apply_ocr=False)
>>> model = AutoModel.from_pretrained("microsoft/udop-large")
>>> # load an example image, along with the words and coordinates
>>> # which were extracted using an OCR engine
>>> dataset = load_dataset("nielsr/funsd-layoutlmv3", split="train", trust_remote_code=True)
>>> example = dataset[0]
>>> image = example["image"]
>>> words = example["tokens"]
>>> boxes = example["bboxes"]
>>> inputs = processor(image, words, boxes=boxes, return_tensors="pt")
>>> decoder_input_ids = torch.tensor([[model.config.decoder_start_token_id]])
>>> # forward pass
>>> outputs = model(**inputs, decoder_input_ids=decoder_input_ids)
>>> last_hidden_states = outputs.last_hidden_state
>>> list(last_hidden_states.shape)
[1, 1, 1024]
```"""
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
# Encode if needed (training, first prediction pass)
if encoder_outputs is None:
encoder_outputs = self.encoder(
input_ids=input_ids,
attention_mask=attention_mask,
bbox=bbox,
pixel_values=pixel_values,
visual_bbox=visual_bbox,
inputs_embeds=inputs_embeds,
head_mask=head_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
hidden_states = encoder_outputs[0]
encoder_attention_mask = encoder_outputs.attention_mask if return_dict else encoder_outputs[1]
# Decode
decoder_outputs = self.decoder(
input_ids=decoder_input_ids,
attention_mask=decoder_attention_mask,
inputs_embeds=decoder_inputs_embeds,
past_key_values=past_key_values,
encoder_hidden_states=hidden_states,
encoder_attention_mask=encoder_attention_mask,
head_mask=decoder_head_mask,
cross_attn_head_mask=cross_attn_head_mask,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
cache_position=cache_position,
)
if not return_dict:
# we filter out the attention mask
decoder_outputs = tuple(value for idx, value in enumerate(decoder_outputs) if idx != 1)
encoder_outputs = tuple(value for idx, value in enumerate(encoder_outputs) if idx != 1)
return decoder_outputs + encoder_outputs
return Seq2SeqModelOutput(
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,
)
@add_start_docstrings(
"""The UDOP encoder-decoder Transformer with a language modeling head on top, enabling to generate text given document
images and an optional prompt.
This class is based on [`T5ForConditionalGeneration`], extended to deal with images and layout (2D) data.""",
UDOP_START_DOCSTRING,
)
class UdopForConditionalGeneration(UdopPreTrainedModel, GenerationMixin):
_tied_weights_keys = [
"encoder.embed_tokens.weight",
"decoder.embed_tokens.weight",
"encoder.embed_patches.proj.weight",
"encoder.embed_patches.proj.bias",
"encoder.relative_bias.biases.0.relative_attention_bias.weight",
"decoder.relative_bias.biases.0.relative_attention_bias.weight",
"lm_head.weight",
]
def __init__(self, config):
super(UdopForConditionalGeneration, self).__init__(config)
# text and image embeddings
self.shared = nn.Embedding(config.vocab_size, config.d_model)
self.patch_embed = UdopPatchEmbeddings(config)
encoder_config = deepcopy(config)
encoder_config.is_decoder = False
encoder_config.use_cache = False
encoder_config.is_encoder_decoder = False
self.encoder = UdopStack(encoder_config, self.shared, self.patch_embed)
decoder_config = deepcopy(config)
decoder_config.is_decoder = True
decoder_config.is_encoder_decoder = False
decoder_config.num_layers = config.num_decoder_layers
self.decoder = UdopStack(decoder_config, self.shared)
# The weights of the language modeling head are shared with those of the encoder and decoder
self.lm_head = nn.Linear(config.d_model, config.vocab_size, bias=False)
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.shared
def set_input_embeddings(self, new_embeddings):
self.shared = new_embeddings
self.encoder.set_input_embeddings(new_embeddings)
self.decoder.set_input_embeddings(new_embeddings)
def set_output_embeddings(self, new_embeddings):
self.lm_head = new_embeddings
def get_output_embeddings(self):
return self.lm_head
def get_encoder(self):
return self.encoder
def get_decoder(self):
return self.decoder
@add_start_docstrings_to_model_forward(UDOP_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=Seq2SeqLMOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Tensor = None,
attention_mask: Tensor = None,
bbox: Dict[str, Any] = None,
pixel_values: Optional[Tensor] = None,
visual_bbox: Dict[str, Any] = None,
decoder_input_ids: Optional[Tensor] = None,
decoder_attention_mask: Optional[Tensor] = None,
inputs_embeds: Optional[Tensor] = None,
encoder_outputs: Optional[Tensor] = None,
past_key_values: Optional[Tensor] = None,
head_mask: Optional[Tensor] = None,
decoder_inputs_embeds: Optional[Tensor] = None,
decoder_head_mask: Optional[Tensor] = None,
cross_attn_head_mask: Optional[Tensor] = None,
use_cache=True,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
labels: Optional[Tensor] = None,
cache_position: Optional[torch.LongTensor] = None,
) -> Tuple[Tensor, ...]:
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 AutoProcessor, UdopForConditionalGeneration
>>> from datasets import load_dataset
>>> # load model and processor
>>> # in this case, we already have performed OCR ourselves
>>> # so we initialize the processor with `apply_ocr=False`
>>> processor = AutoProcessor.from_pretrained("microsoft/udop-large", apply_ocr=False)
>>> model = UdopForConditionalGeneration.from_pretrained("microsoft/udop-large")
>>> # load an example image, along with the words and coordinates
>>> # which were extracted using an OCR engine
>>> dataset = load_dataset("nielsr/funsd-layoutlmv3", split="train", trust_remote_code=True)
>>> example = dataset[0]
>>> image = example["image"]
>>> words = example["tokens"]
>>> boxes = example["bboxes"]
>>> # one can use the various task prefixes (prompts) used during pre-training
>>> # e.g. the task prefix for DocVQA is "Question answering. "
>>> question = "Question answering. What is the date on the form?"
>>> encoding = processor(image, question, text_pair=words, boxes=boxes, return_tensors="pt")
>>> # autoregressive generation
>>> predicted_ids = model.generate(**encoding)
>>> print(processor.batch_decode(predicted_ids, skip_special_tokens=True)[0])
9/30/92
```"""
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 decoder_input_ids is None and labels is not None:
decoder_input_ids = self._shift_right(labels)
# Encode if needed (training, first prediction pass)
if encoder_outputs is None:
encoder_outputs = self.encoder(
input_ids=input_ids,
bbox=bbox,
visual_bbox=visual_bbox,
pixel_values=pixel_values,
attention_mask=attention_mask,
inputs_embeds=inputs_embeds,
head_mask=head_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
hidden_states = encoder_outputs[0]
encoder_attention_mask = encoder_outputs.attention_mask if return_dict else encoder_outputs[1]
# Decode
decoder_outputs = self.decoder(
input_ids=decoder_input_ids,
attention_mask=decoder_attention_mask,
inputs_embeds=decoder_inputs_embeds,
past_key_values=past_key_values,
encoder_hidden_states=hidden_states,
encoder_attention_mask=encoder_attention_mask,
head_mask=decoder_head_mask,
cross_attn_head_mask=cross_attn_head_mask,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
cache_position=cache_position,
)
sequence_output = decoder_outputs[0]
if self.config.tie_word_embeddings:
# Rescale output before projecting on vocab
# See https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/transformer/transformer.py#L586
sequence_output = sequence_output * (self.config.d_model**-0.5)
lm_logits = self.lm_head(sequence_output)
loss = None
if labels is not None:
loss_fct = CrossEntropyLoss(ignore_index=-100)
loss = loss_fct(lm_logits.view(-1, lm_logits.size(-1)), labels.view(-1))
if not return_dict:
output = (lm_logits,) + decoder_outputs[2:] + (encoder_outputs[0],) + encoder_outputs[2:]
return ((loss,) + output) if loss is not None else output
return Seq2SeqLMOutput(
loss=loss,
logits=lm_logits,
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,
)
# Copied from transformers.models.t5.modeling_t5.T5ForConditionalGeneration._reorder_cache
def _reorder_cache(self, past_key_values, beam_idx):
# if decoder past is not included in output
# speedy decoding is disabled and no need to reorder
if past_key_values is None:
logger.warning("You might want to consider setting `use_cache=True` to speed up decoding")
return past_key_values
reordered_decoder_past = ()
for layer_past_states in past_key_values:
# get the correct batch idx from layer past batch dim
# batch dim of `past` is at 2nd position
reordered_layer_past_states = ()
for layer_past_state in layer_past_states:
# need to set correct `past` for each of the four key / value states
reordered_layer_past_states = reordered_layer_past_states + (
layer_past_state.index_select(0, beam_idx.to(layer_past_state.device)),
)
if reordered_layer_past_states[0].shape != layer_past_states[0].shape:
raise ValueError(
f"reordered_layer_past_states[0] shape {reordered_layer_past_states[0].shape} and layer_past_states[0] shape {layer_past_states[0].shape} mismatched"
)
if len(reordered_layer_past_states) != len(layer_past_states):
raise ValueError(
f"length of reordered_layer_past_states {len(reordered_layer_past_states)} and length of layer_past_states {len(layer_past_states)} mismatched"
)
reordered_decoder_past = reordered_decoder_past + (reordered_layer_past_states,)
return reordered_decoder_past
@add_start_docstrings(
"The bare UDOP Model transformer outputting encoder's raw hidden-states without any specific head on top.",
UDOP_START_DOCSTRING,
)
class UdopEncoderModel(UdopPreTrainedModel):
_tied_weights_keys = [
"encoder.embed_tokens.weight",
"encoder.embed_patches.proj.weight",
"encoder.embed_patches.proj.bias",
"encoder.relative_bias.biases.0.relative_attention_bias.weight",
]
def __init__(self, config: UdopConfig):
super().__init__(config)
# text and image embeddings
self.shared = nn.Embedding(config.vocab_size, config.d_model)
self.patch_embed = UdopPatchEmbeddings(config)
encoder_config = deepcopy(config)
encoder_config.is_decoder = False
encoder_config.use_cache = False
encoder_config.is_encoder_decoder = False
self.encoder = UdopStack(encoder_config, self.shared, self.patch_embed)
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.shared
def set_input_embeddings(self, new_embeddings):
self.shared = new_embeddings
self.encoder.set_input_embeddings(new_embeddings)
def get_encoder(self):
return self.encoder
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.block[layer].layer[0].SelfAttention.prune_heads(heads)
@add_start_docstrings_to_model_forward(UDOP_ENCODER_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=BaseModelOutputWithAttentionMask, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Tensor = None,
bbox: Dict[str, Any] = None,
attention_mask: Tensor = None,
pixel_values: Optional[Tensor] = None,
visual_bbox: Dict[str, Any] = None,
head_mask: Optional[Tensor] = None,
inputs_embeds: Optional[Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple[torch.FloatTensor], BaseModelOutputWithAttentionMask]:
r"""
Returns:
Example:
```python
>>> from transformers import AutoProcessor, UdopEncoderModel
>>> from huggingface_hub import hf_hub_download
>>> from datasets import load_dataset
>>> # load model and processor
>>> # in this case, we already have performed OCR ourselves
>>> # so we initialize the processor with `apply_ocr=False`
>>> processor = AutoProcessor.from_pretrained("microsoft/udop-large", apply_ocr=False)
>>> model = UdopEncoderModel.from_pretrained("microsoft/udop-large")
>>> # load an example image, along with the words and coordinates
>>> # which were extracted using an OCR engine
>>> dataset = load_dataset("nielsr/funsd-layoutlmv3", split="train", trust_remote_code=True)
>>> example = dataset[0]
>>> image = example["image"]
>>> words = example["tokens"]
>>> boxes = example["bboxes"]
>>> encoding = processor(image, words, boxes=boxes, return_tensors="pt")
>>> outputs = model(**encoding)
>>> 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
encoder_outputs = self.encoder(
input_ids=input_ids,
bbox=bbox,
visual_bbox=visual_bbox,
pixel_values=pixel_values,
attention_mask=attention_mask,
inputs_embeds=inputs_embeds,
head_mask=head_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
return encoder_outputs
__all__ = ["UdopForConditionalGeneration", "UdopPreTrainedModel", "UdopModel", "UdopEncoderModel"]
| transformers/src/transformers/models/udop/modeling_udop.py/0 | {
"file_path": "transformers/src/transformers/models/udop/modeling_udop.py",
"repo_id": "transformers",
"token_count": 43263
} |
# 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 VideoLlava.
"""
from typing import List, Optional, Union
import numpy as np
from ...feature_extraction_utils import BatchFeature
from ...image_utils import ImageInput, get_image_size, to_numpy_array
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType, logging
logger = logging.get_logger(__name__)
class VideoLlavaProcessor(ProcessorMixin):
r"""
Constructs a VideoLlava processor which wraps a VideoLlava image processor and a Llava tokenizer into a single processor.
[`VideoLlavaProcessor`] offers all the functionalities of [`VideoLlavaImageProcessor`] and [`LlamaTokenizerFast`]. See the
[`~VideoLlavaProcessor.__call__`] and [`~VideoLlavaProcessor.decode`] for more information.
Args:
image_processor ([`VideoLlavaImageProcessor`], *optional*):
The image processor is a required input.
tokenizer ([`LlamaTokenizerFast`], *optional*):
The tokenizer is a required input.
patch_size (`int`, *optional*, defaults to 14):
Patch size from the vision tower.
vision_feature_select_strategy (`str`, *optional*, defaults to `"default"`):
The feature selection strategy used to select the vision feature from the vision backbone.
Shoudl be same as in model's config
image_token (`str`, *optional*, defaults to `"<image>"`):
Special token used to denote image location.
video_token (`str`, *optional*, defaults to `"<video>"`):
Special token used to denote video location.
chat_template (`str`, *optional*): A Jinja template which will be used to convert lists of messages
in a chat into a tokenizable string.
num_additional_image_tokens (`int`, *optional*, defaults to 1):
Number of additional tokens added to the image embeddings, such as CLS (+1). If the backbone has no CLS or other
extra tokens appended, no need to set this arg.
"""
attributes = ["image_processor", "tokenizer"]
valid_kwargs = [
"chat_template",
"patch_size",
"vision_feature_select_strategy",
"image_token",
"video_token",
"num_additional_image_tokens",
]
image_processor_class = "VideoLlavaImageProcessor"
tokenizer_class = "AutoTokenizer"
def __init__(
self,
image_processor=None,
tokenizer=None,
patch_size=14,
vision_feature_select_strategy="default",
image_token="<image>", # set the default and let users change if they have peculiar special tokens in rare cases
video_token="<video>",
chat_template=None,
num_additional_image_tokens=1,
**kwargs,
):
self.patch_size = patch_size
self.num_additional_image_tokens = num_additional_image_tokens
self.vision_feature_select_strategy = vision_feature_select_strategy
self.image_token = tokenizer.image_token if hasattr(tokenizer, "image_token") else image_token
self.video_token = tokenizer.video_token if hasattr(tokenizer, "video_token") else video_token
super().__init__(image_processor, tokenizer, chat_template=chat_template)
def __call__(
self,
text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,
images: ImageInput = None,
videos: ImageInput = None,
padding: Union[bool, str, PaddingStrategy] = False,
truncation: Union[bool, str, TruncationStrategy] = None,
max_length=None,
return_tensors: Optional[Union[str, TensorType]] = TensorType.PYTORCH,
) -> BatchFeature:
"""
Main method to prepare for the model one or several sequences(s) and image(s). This method forwards the `text`
and `kwargs` arguments to LlamaTokenizerFast's [`~LlamaTokenizerFast.__call__`] if `text` is not `None` to encode
the text. To prepare the image(s), this method forwards the `images` and `kwrags` arguments to
VideoLlavaImageProcessor's [`~VideoLlavaImageProcessor.__call__`] if `images` is not `None`. Please refer to the doctsring
of the above two methods for more information.
Args:
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).
images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `List[PIL.Image.Image]`, `List[np.ndarray]`, `List[torch.Tensor]`):
The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch
tensor. In case of a NumPy array/PyTorch tensor, each image should be of shape (C, H, W), where C is a
number of channels, H and W are image height and width.
videos (`np.ndarray`, `torch.Tensor`, `List[np.ndarray]`, `List[torch.Tensor]`):
Video frames to preprocess. Expects a single or batch of video frames in NumPy array or PyTorch
tensor. Each video should be of shape (T, C, H, W), where T is number of frames, C is
number of channels, H and W are image height and width.
padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):
Select a strategy to pad the returned sequences (according to the model's padding side and padding
index) among:
- `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single
sequence if provided).
- `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
acceptable input length for the model if that argument is not provided.
- `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different
lengths).
max_length (`int`, *optional*):
Maximum length of the returned list and optionally padding length (see above).
truncation (`bool`, *optional*):
Activates truncation to cut input sequences longer than `max_length` to `max_length`.
return_tensors (`str` or [`~utils.TensorType`], *optional*):
If set, will return tensors of a particular framework. Acceptable values are:
- `'tf'`: Return TensorFlow `tf.constant` objects.
- `'pt'`: Return PyTorch `torch.Tensor` objects.
- `'np'`: Return NumPy `np.ndarray` objects.
- `'jax'`: Return JAX `jnp.ndarray` objects.
Returns:
[`BatchFeature`]: A [`BatchFeature`] with the following fields:
- **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`.
- **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
`return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not
`None`).
- **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`.
"""
data = {}
if images is not None or videos is not None:
encoded_images = self.image_processor(images=images, videos=videos, return_tensors=return_tensors)
data.update(encoded_images)
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")
prompt_strings = text
if encoded_images is not None:
if "pixel_values_images" in encoded_images.keys():
height, width = get_image_size(to_numpy_array(encoded_images.get("pixel_values_images")[0]))
num_frames = 1
if "pixel_values_videos" in encoded_images.keys():
one_video = encoded_images.get("pixel_values_videos")[0]
if isinstance(encoded_images.get("pixel_values_videos")[0], (list, tuple)):
one_video = np.array(one_video)
else:
one_video = to_numpy_array(one_video)
height, width = get_image_size(one_video[0])
num_frames = one_video.shape[0] # frame dim is always after batch dim
num_image_tokens = (height // self.patch_size) * (
width // self.patch_size
) + self.num_additional_image_tokens
num_video_tokens = num_image_tokens * num_frames
num_image_tokens = (height // self.patch_size) * (
width // self.patch_size
) + self.num_additional_image_tokens
num_video_tokens = num_image_tokens * num_frames
if self.vision_feature_select_strategy == "default":
num_image_tokens -= 1
prompt_strings = []
for sample in text:
sample = sample.replace(self.image_token, self.image_token * num_image_tokens)
sample = sample.replace(self.video_token, self.video_token * num_video_tokens)
prompt_strings.append(sample)
text_inputs = self.tokenizer(
prompt_strings,
return_tensors=return_tensors,
padding=padding,
truncation=truncation,
max_length=max_length,
)
data.update(text_inputs)
return BatchFeature(data=data)
# Copied from transformers.models.clip.processing_clip.CLIPProcessor.batch_decode with CLIP->Llama
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)
# Copied from transformers.models.clip.processing_clip.CLIPProcessor.decode with CLIP->Llama
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
# Copied from transformers.models.clip.processing_clip.CLIPProcessor.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))
__all__ = ["VideoLlavaProcessor"]
| transformers/src/transformers/models/video_llava/processing_video_llava.py/0 | {
"file_path": "transformers/src/transformers/models/video_llava/processing_video_llava.py",
"repo_id": "transformers",
"token_count": 4739
} |
# 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.
import argparse
import torch
from huggingface_hub import hf_hub_download
from transformers import (
AddedToken,
AutoConfig,
AutoTokenizer,
CLIPImageProcessor,
LlavaProcessor,
VipLlavaConfig,
VipLlavaForConditionalGeneration,
)
KEYS_TO_MODIFY_MAPPING = {
"model.vision_tower.": "",
"model.mm_projector": "multi_modal_projector",
"model": "model.model",
"vision_model.model": "vision_model",
"lm_head": "language_model.lm_head",
"model.model": "language_model.model",
"multi_modal_projector.0": "multi_modal_projector.linear_1",
"multi_modal_projector.2": "multi_modal_projector.linear_2",
"final_linear.0": "linear_1",
"final_linear.2": "linear_2",
"multi_modal_projector.clip_layernorm": "multi_modal_projector.projector_layernorm",
}
# Copied from transformers.models.llava.convert_llava_weights_to_hf.convert_state_dict_to_hf
def convert_state_dict_to_hf(state_dict):
new_state_dict = {}
for key, value in state_dict.items():
if key.endswith(".inv_freq"):
continue
for key_to_modify, new_key in KEYS_TO_MODIFY_MAPPING.items():
if key_to_modify in key:
key = key.replace(key_to_modify, new_key)
new_state_dict[key] = value
return new_state_dict
def convert_vipllava_llama_to_hf(text_model_id, vision_model_id, output_hub_path, old_state_dict_id):
torch.set_default_dtype(torch.float16)
text_config = AutoConfig.from_pretrained(text_model_id)
tokenizer = AutoTokenizer.from_pretrained(text_model_id)
tokenizer.add_tokens(AddedToken("<image>", special=True, normalized=False), special_tokens=True)
tokenizer.add_special_tokens({"pad_token": "<pad>"})
image_processor = CLIPImageProcessor.from_pretrained(vision_model_id)
processor = LlavaProcessor(tokenizer=tokenizer, image_processor=image_processor)
config = VipLlavaConfig(text_config=text_config)
config.pad_token_id = 32001
with torch.device("meta"):
model = VipLlavaForConditionalGeneration(config)
# Pad to 64 for performance reasons
pad_shape = 64
state_dict_path = hf_hub_download(old_state_dict_id, "model_state_dict_7b.bin")
state_dict = torch.load(state_dict_path, map_location="cpu")
state_dict = convert_state_dict_to_hf(state_dict)
model.load_state_dict(state_dict, strict=True, assign=True)
pre_expansion_embeddings = model.language_model.model.embed_tokens.weight.data
mu = torch.mean(pre_expansion_embeddings, dim=0).float()
n = pre_expansion_embeddings.size()[0]
sigma = ((pre_expansion_embeddings - mu).T @ (pre_expansion_embeddings - mu)) / n
dist = torch.distributions.multivariate_normal.MultivariateNormal(mu, covariance_matrix=1e-5 * sigma)
# We add an image token so we resize the model
model.resize_token_embeddings(config.text_config.vocab_size + 2, pad_shape)
model.language_model.model.embed_tokens.weight.data[32000:] = torch.stack(
tuple((dist.sample() for _ in range(model.language_model.model.embed_tokens.weight.data[32000:].shape[0]))),
dim=0,
)
model.language_model.lm_head.weight.data[32000:] = torch.stack(
tuple((dist.sample() for _ in range(model.language_model.lm_head.weight.data[32000:].shape[0]))),
dim=0,
)
model.push_to_hub(output_hub_path)
processor.push_to_hub(output_hub_path)
def main():
parser = argparse.ArgumentParser()
parser.add_argument(
"--text_model_id",
help="Hub location of the text model",
)
parser.add_argument(
"--vision_model_id",
help="Hub location of the vision model",
)
parser.add_argument(
"--output_hub_path",
help="Location on the hub of the converted model",
)
parser.add_argument(
"--old_state_dict_id",
help="Location on the hub of the raw state dict of the original model. The filename needs to be `model_state_dict.bin`",
)
args = parser.parse_args()
convert_vipllava_llama_to_hf(
args.text_model_id, args.vision_model_id, args.output_hub_path, args.old_state_dict_id
)
if __name__ == "__main__":
main()
| transformers/src/transformers/models/vipllava/convert_vipllava_weights_to_hf.py/0 | {
"file_path": "transformers/src/transformers/models/vipllava/convert_vipllava_weights_to_hf.py",
"repo_id": "transformers",
"token_count": 1889
} |
# coding=utf-8
# Copyright 2021 The UCLA NLP 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.
"""PyTorch VisualBERT model."""
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import CrossEntropyLoss, KLDivLoss, LogSoftmax
from ...activations import ACT2FN
from ...modeling_outputs import (
BaseModelOutput,
BaseModelOutputWithPooling,
MultipleChoiceModelOutput,
SequenceClassifierOutput,
)
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,
)
from .configuration_visual_bert import VisualBertConfig
logger = logging.get_logger(__name__)
_CONFIG_FOR_DOC = "VisualBertConfig"
_CHECKPOINT_FOR_DOC = "uclanlp/visualbert-vqa-coco-pre"
class VisualBertEmbeddings(nn.Module):
"""Construct the embeddings from word, position and token_type embeddings and visual 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.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)
# self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
# any TensorFlow checkpoint file
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
)
# For Visual Features
# Token type and position embedding for image features
self.visual_token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)
self.visual_position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
if config.special_visual_initialize:
self.visual_token_type_embeddings.weight.data = nn.Parameter(
self.token_type_embeddings.weight.data.clone(), requires_grad=True
)
self.visual_position_embeddings.weight.data = nn.Parameter(
self.position_embeddings.weight.data.clone(), requires_grad=True
)
self.visual_projection = nn.Linear(config.visual_embedding_dim, config.hidden_size)
def forward(
self,
input_ids=None,
token_type_ids=None,
position_ids=None,
inputs_embeds=None,
visual_embeds=None,
visual_token_type_ids=None,
image_text_alignment=None,
):
if input_ids is not None:
input_shape = input_ids.size()
else:
input_shape = inputs_embeds.size()[:-1]
seq_length = input_shape[1]
if position_ids is None:
position_ids = self.position_ids[:, :seq_length]
if inputs_embeds is None:
inputs_embeds = self.word_embeddings(input_ids)
if token_type_ids is None:
token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device)
token_type_embeddings = self.token_type_embeddings(token_type_ids)
embeddings = inputs_embeds + token_type_embeddings
# Absolute Position Embeddings
position_embeddings = self.position_embeddings(position_ids)
embeddings += position_embeddings
if visual_embeds is not None:
if visual_token_type_ids is None:
visual_token_type_ids = torch.ones(
visual_embeds.size()[:-1], dtype=torch.long, device=self.position_ids.device
)
visual_embeds = self.visual_projection(visual_embeds)
visual_token_type_embeddings = self.visual_token_type_embeddings(visual_token_type_ids)
if image_text_alignment is not None:
# image_text_alignment = Batch x image_length x alignment_number.
# Each element denotes the position of the word corresponding to the image feature. -1 is the padding value.
dtype = token_type_embeddings.dtype
image_text_alignment_mask = (image_text_alignment != -1).long()
# Get rid of the -1.
image_text_alignment = image_text_alignment_mask * image_text_alignment
# Batch x image_length x alignment length x dim
visual_position_embeddings = self.position_embeddings(image_text_alignment)
visual_position_embeddings *= image_text_alignment_mask.to(dtype=dtype).unsqueeze(-1)
visual_position_embeddings = visual_position_embeddings.sum(2)
# We want to averge along the alignment_number dimension.
image_text_alignment_mask = image_text_alignment_mask.to(dtype=dtype).sum(2)
if (image_text_alignment_mask == 0).sum() != 0:
image_text_alignment_mask[image_text_alignment_mask == 0] = 1 # Avoid divide by zero error
logger.warning(
"Found 0 values in `image_text_alignment_mask`. Setting them to 1 to avoid divide-by-zero"
" error."
)
visual_position_embeddings = visual_position_embeddings / image_text_alignment_mask.unsqueeze(-1)
visual_position_ids = torch.zeros(
*visual_embeds.size()[:-1], dtype=torch.long, device=visual_embeds.device
)
# When fine-tuning the detector , the image_text_alignment is sometimes padded too long.
if visual_position_embeddings.size(1) != visual_embeds.size(1):
if visual_position_embeddings.size(1) < visual_embeds.size(1):
raise ValueError(
f"Visual position embeddings length: {visual_position_embeddings.size(1)} "
f"should be the same as `visual_embeds` length: {visual_embeds.size(1)}"
)
visual_position_embeddings = visual_position_embeddings[:, : visual_embeds.size(1), :]
visual_position_embeddings = visual_position_embeddings + self.visual_position_embeddings(
visual_position_ids
)
else:
visual_position_ids = torch.zeros(
*visual_embeds.size()[:-1], dtype=torch.long, device=visual_embeds.device
)
visual_position_embeddings = self.visual_position_embeddings(visual_position_ids)
visual_embeddings = visual_embeds + visual_position_embeddings + visual_token_type_embeddings
embeddings = torch.cat((embeddings, visual_embeddings), dim=1)
embeddings = self.LayerNorm(embeddings)
embeddings = self.dropout(embeddings)
return embeddings
class VisualBertSelfAttention(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.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)
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,
output_attentions=False,
):
mixed_query_layer = self.query(hidden_states)
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)
# 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 VisualBertSelfAttentionModel 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,)
return outputs
# Copied from transformers.models.bert.modeling_bert.BertSelfOutput with Bert->VisualBert
class VisualBertSelfOutput(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 VisualBertAttention(nn.Module):
def __init__(self, config):
super().__init__()
self.self = VisualBertSelfAttention(config)
self.output = VisualBertSelfOutput(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,
attention_mask=None,
head_mask=None,
output_attentions=False,
):
self_outputs = self.self(
hidden_states,
attention_mask,
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
# Copied from transformers.models.bert.modeling_bert.BertIntermediate with Bert->VisualBert
class VisualBertIntermediate(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->VisualBert
class VisualBertOutput(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 VisualBertLayer(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 = VisualBertAttention(config)
self.intermediate = VisualBertIntermediate(config)
self.output = VisualBertOutput(config)
def forward(
self,
hidden_states,
attention_mask=None,
head_mask=None,
output_attentions=False,
):
self_attention_outputs = self.attention(
hidden_states,
attention_mask,
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
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
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
class VisualBertEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.layer = nn.ModuleList([VisualBertLayer(config) for _ in range(config.num_hidden_layers)])
self.gradient_checkpointing = False
def forward(
self,
hidden_states,
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
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,
attention_mask,
layer_head_mask,
output_attentions,
)
else:
layer_outputs = layer_module(hidden_states, 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,
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
)
# Copied from transformers.models.bert.modeling_bert.BertPooler with Bert->VisualBert
class VisualBertPooler(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->VisualBert
class VisualBertPredictionHeadTransform(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->VisualBert
class VisualBertLMPredictionHead(nn.Module):
def __init__(self, config):
super().__init__()
self.transform = VisualBertPredictionHeadTransform(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.BertPreTrainingHeads with Bert->VisualBert
class VisualBertPreTrainingHeads(nn.Module):
def __init__(self, config):
super().__init__()
self.predictions = VisualBertLMPredictionHead(config)
self.seq_relationship = nn.Linear(config.hidden_size, 2)
def forward(self, sequence_output, pooled_output):
prediction_scores = self.predictions(sequence_output)
seq_relationship_score = self.seq_relationship(pooled_output)
return prediction_scores, seq_relationship_score
class VisualBertPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = VisualBertConfig
base_model_prefix = "visual_bert"
supports_gradient_checkpointing = True
def _init_weights(self, module):
"""Initialize the weights"""
if isinstance(module, (nn.Linear, nn.Embedding)):
# 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)
elif isinstance(module, nn.LayerNorm):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
if isinstance(module, nn.Linear) and module.bias is not None:
module.bias.data.zero_()
@dataclass
class VisualBertForPreTrainingOutput(ModelOutput):
"""
Output type of [`VisualBertForPreTraining`].
Args:
loss (*optional*, returned when `labels` is provided, `torch.FloatTensor` of shape `(1,)`):
Total loss as the sum of the masked language modeling loss and the sentence-image prediction
(classification) loss.
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).
seq_relationship_logits (`torch.FloatTensor` of shape `(batch_size, 2)`):
Prediction scores of the sentence-image prediction (classification) head (scores of True/False continuation
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 + 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.
"""
loss: Optional[torch.FloatTensor] = None
prediction_logits: torch.FloatTensor = None
seq_relationship_logits: torch.FloatTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None
VISUAL_BERT_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 ([`VisualBertConfig`]): 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.
"""
VISUAL_BERT_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.
visual_embeds (`torch.FloatTensor` of shape `(batch_size, visual_seq_length, visual_embedding_dim)`, *optional*):
The embedded representation of the visual inputs, generally derived using using an object detector.
visual_attention_mask (`torch.FloatTensor` of shape `(batch_size, visual_seq_length)`, *optional*):
Mask to avoid performing attention on visual embeddings. 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)
visual_token_type_ids (`torch.LongTensor` of shape `(batch_size, visual_seq_length)`, *optional*):
Segment token indices to indicate different portions of the visual embeds.
[What are token type IDs?](../glossary#token-type-ids) The authors of VisualBERT set the
*visual_token_type_ids* to *1* for all tokens.
image_text_alignment (`torch.LongTensor` of shape `(batch_size, visual_seq_length, alignment_number)`, *optional*):
Image-Text alignment uses to decide the position IDs of the visual embeddings.
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 VisualBert Model transformer outputting raw hidden-states without any specific head on top.",
VISUAL_BERT_START_DOCSTRING,
)
class VisualBertModel(VisualBertPreTrainedModel):
"""
The model can behave as an encoder (with only self-attention) 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.embeddings = VisualBertEmbeddings(config)
self.encoder = VisualBertEncoder(config)
self.pooler = VisualBertPooler(config) if add_pooling_layer else None
self.bypass_transformer = config.bypass_transformer
if self.bypass_transformer:
self.additional_layer = VisualBertLayer(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(VISUAL_BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.LongTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.LongTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
visual_embeds: Optional[torch.FloatTensor] = None,
visual_attention_mask: Optional[torch.LongTensor] = None,
visual_token_type_ids: Optional[torch.LongTensor] = None,
image_text_alignment: Optional[torch.LongTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPooling]:
r"""
Returns:
Example:
```python
# Assumption: *get_visual_embeddings(image)* gets the visual embeddings of the image.
from transformers import AutoTokenizer, VisualBertModel
import torch
tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased")
model = VisualBertModel.from_pretrained("uclanlp/visualbert-vqa-coco-pre")
inputs = tokenizer("The capital of France is Paris.", return_tensors="pt")
visual_embeds = get_visual_embeddings(image).unsqueeze(0)
visual_token_type_ids = torch.ones(visual_embeds.shape[:-1], dtype=torch.long)
visual_attention_mask = torch.ones(visual_embeds.shape[:-1], dtype=torch.float)
inputs.update(
{
"visual_embeds": visual_embeds,
"visual_token_type_ids": visual_token_type_ids,
"visual_attention_mask": visual_attention_mask,
}
)
outputs = model(**inputs)
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")
batch_size, seq_length = input_shape
device = input_ids.device if input_ids is not None else inputs_embeds.device
if visual_embeds is not None:
visual_input_shape = visual_embeds.size()[:-1]
if attention_mask is None:
attention_mask = torch.ones(input_shape, device=device)
if visual_embeds is not None and visual_attention_mask is None:
visual_attention_mask = torch.ones(visual_input_shape, 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.
if visual_embeds is not None:
combined_attention_mask = torch.cat((attention_mask, visual_attention_mask), dim=-1)
extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(
combined_attention_mask, (batch_size, input_shape + visual_input_shape)
)
else:
extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(
attention_mask, (batch_size, 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 = self.embeddings(
input_ids=input_ids,
position_ids=position_ids,
token_type_ids=token_type_ids,
inputs_embeds=inputs_embeds,
visual_embeds=visual_embeds,
visual_token_type_ids=visual_token_type_ids,
image_text_alignment=image_text_alignment,
)
if self.bypass_transformer and visual_embeds is not None:
text_length = input_ids.size(1)
text_embedding_output = embedding_output[:, :text_length, :]
visual_embedding_output = embedding_output[:, text_length:, :]
text_extended_attention_mask = extended_attention_mask[:, :, text_length, :text_length]
encoded_outputs = self.encoder(
text_embedding_output,
attention_mask=text_extended_attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = encoded_outputs[0]
concatenated_input = torch.cat((sequence_output, visual_embedding_output), dim=1)
sequence_output = self.additional_layer(concatenated_input, extended_attention_mask)
pooled_output = self.pooler(sequence_output) if self.pooler is not None else None
else:
encoder_outputs = self.encoder(
embedding_output,
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 BaseModelOutputWithPooling(
last_hidden_state=sequence_output,
pooler_output=pooled_output,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
)
@add_start_docstrings(
"""
VisualBert Model with two heads on top as done during the pretraining: a `masked language modeling` head and a
`sentence-image prediction (classification)` head.
""",
VISUAL_BERT_START_DOCSTRING,
)
class VisualBertForPreTraining(VisualBertPreTrainedModel):
_tied_weights_keys = ["cls.predictions.decoder.weight", "cls.predictions.decoder.bias"]
def __init__(self, config):
super().__init__(config)
self.visual_bert = VisualBertModel(config)
self.cls = VisualBertPreTrainingHeads(config)
# Initialize weights and apply final processing
self.post_init()
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(VISUAL_BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=VisualBertForPreTrainingOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.LongTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.LongTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
visual_embeds: Optional[torch.FloatTensor] = None,
visual_attention_mask: Optional[torch.LongTensor] = None,
visual_token_type_ids: Optional[torch.LongTensor] = None,
image_text_alignment: Optional[torch.LongTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
labels: Optional[torch.LongTensor] = None,
sentence_image_labels: Optional[torch.LongTensor] = None,
) -> Union[Tuple[torch.Tensor], VisualBertForPreTrainingOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size, total_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]`
sentence_image_labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the sentence-image prediction (classification) loss. Input should be a sequence pair
(see `input_ids` docstring) Indices should be in `[0, 1]`:
- 0 indicates sequence B is a matching pair of sequence A for the given image,
- 1 indicates sequence B is a random sequence w.r.t A for the given image.
Returns:
Example:
```python
# Assumption: *get_visual_embeddings(image)* gets the visual embeddings of the image in the batch.
from transformers import AutoTokenizer, VisualBertForPreTraining
tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased")
model = VisualBertForPreTraining.from_pretrained("uclanlp/visualbert-vqa-coco-pre")
inputs = tokenizer("The capital of France is [MASK].", return_tensors="pt")
visual_embeds = get_visual_embeddings(image).unsqueeze(0)
visual_token_type_ids = torch.ones(visual_embeds.shape[:-1], dtype=torch.long)
visual_attention_mask = torch.ones(visual_embeds.shape[:-1], dtype=torch.float)
inputs.update(
{
"visual_embeds": visual_embeds,
"visual_token_type_ids": visual_token_type_ids,
"visual_attention_mask": visual_attention_mask,
}
)
max_length = inputs["input_ids"].shape[-1] + visual_embeds.shape[-2]
labels = tokenizer(
"The capital of France is Paris.", return_tensors="pt", padding="max_length", max_length=max_length
)["input_ids"]
sentence_image_labels = torch.tensor(1).unsqueeze(0) # Batch_size
outputs = model(**inputs, labels=labels, sentence_image_labels=sentence_image_labels)
loss = outputs.loss
prediction_logits = outputs.prediction_logits
seq_relationship_logits = outputs.seq_relationship_logits
```"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if labels is not None:
total_size = attention_mask.size(-1) + visual_attention_mask.size(-1)
if labels.size(-1) != total_size:
raise ValueError(
"The labels provided should have same sequence length as total attention mask. "
f"Found labels with sequence length {labels.size(-1)}, expected {total_size}."
)
outputs = self.visual_bert(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
visual_embeds=visual_embeds,
visual_attention_mask=visual_attention_mask,
visual_token_type_ids=visual_token_type_ids,
image_text_alignment=image_text_alignment,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output, pooled_output = outputs[:2]
prediction_scores, seq_relationship_score = self.cls(sequence_output, pooled_output)
total_loss = None
if labels is not None and sentence_image_labels is not None:
loss_fct = CrossEntropyLoss()
masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
sentence_image_loss = loss_fct(seq_relationship_score.view(-1, 2), sentence_image_labels.view(-1))
total_loss = masked_lm_loss + sentence_image_loss
elif labels is not None:
loss_fct = CrossEntropyLoss()
total_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
if not return_dict:
output = (prediction_scores, seq_relationship_score) + outputs[2:]
return ((total_loss,) + output) if total_loss is not None else output
return VisualBertForPreTrainingOutput(
loss=total_loss,
prediction_logits=prediction_scores,
seq_relationship_logits=seq_relationship_score,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
@add_start_docstrings(
"""
VisualBert Model with a multiple choice classification head on top (a linear layer on top of the pooled output and
a softmax) e.g. for VCR tasks.
""",
VISUAL_BERT_START_DOCSTRING,
)
class VisualBertForMultipleChoice(VisualBertPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.visual_bert = VisualBertModel(config)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.cls = nn.Linear(config.hidden_size, 1)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(
VISUAL_BERT_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length")
)
@replace_return_docstrings(output_type=MultipleChoiceModelOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.LongTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.LongTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
visual_embeds: Optional[torch.FloatTensor] = None,
visual_attention_mask: Optional[torch.LongTensor] = None,
visual_token_type_ids: Optional[torch.LongTensor] = None,
image_text_alignment: Optional[torch.LongTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
labels: Optional[torch.LongTensor] = None,
) -> Union[Tuple[torch.Tensor], MultipleChoiceModelOutput]:
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)
Returns:
Example:
```python
# Assumption: *get_visual_embeddings(image)* gets the visual embeddings of the image in the batch.
from transformers import AutoTokenizer, VisualBertForMultipleChoice
import torch
tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased")
model = VisualBertForMultipleChoice.from_pretrained("uclanlp/visualbert-vcr")
prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced."
choice0 = "It is eaten with a fork and a knife."
choice1 = "It is eaten while held in the hand."
visual_embeds = get_visual_embeddings(image)
# (batch_size, num_choices, visual_seq_length, visual_embedding_dim)
visual_embeds = visual_embeds.expand(1, 2, *visual_embeds.shape)
visual_token_type_ids = torch.ones(visual_embeds.shape[:-1], dtype=torch.long)
visual_attention_mask = torch.ones(visual_embeds.shape[:-1], dtype=torch.float)
labels = torch.tensor(0).unsqueeze(0) # choice0 is correct (according to Wikipedia ;)), batch size 1
encoding = tokenizer([[prompt, prompt], [choice0, choice1]], return_tensors="pt", padding=True)
# batch size is 1
inputs_dict = {k: v.unsqueeze(0) for k, v in encoding.items()}
inputs_dict.update(
{
"visual_embeds": visual_embeds,
"visual_attention_mask": visual_attention_mask,
"visual_token_type_ids": visual_token_type_ids,
"labels": labels,
}
)
outputs = model(**inputs_dict)
loss = outputs.loss
logits = outputs.logits
```"""
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]
input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None
attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None
token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None
position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None
inputs_embeds = (
inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1))
if inputs_embeds is not None
else None
)
visual_embeds = (
visual_embeds.view(-1, visual_embeds.size(-2), visual_embeds.size(-1))
if visual_embeds is not None
else None
)
visual_attention_mask = (
visual_attention_mask.view(-1, visual_attention_mask.size(-1))
if visual_attention_mask is not None
else None
)
visual_token_type_ids = (
visual_token_type_ids.view(-1, visual_token_type_ids.size(-1))
if visual_token_type_ids is not None
else None
)
outputs = self.visual_bert(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
visual_embeds=visual_embeds,
visual_attention_mask=visual_attention_mask,
visual_token_type_ids=visual_token_type_ids,
image_text_alignment=image_text_alignment,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
_, pooled_output = outputs[0], outputs[1]
pooled_output = self.dropout(pooled_output)
logits = self.cls(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(
"""
VisualBert Model with a classification/regression head on top (a dropout and a linear layer on top of the pooled
output) for VQA.
""",
VISUAL_BERT_START_DOCSTRING,
)
class VisualBertForQuestionAnswering(VisualBertPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.visual_bert = VisualBertModel(config)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.cls = nn.Linear(config.hidden_size, config.num_labels)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(VISUAL_BERT_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,
attention_mask: Optional[torch.LongTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.LongTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
visual_embeds: Optional[torch.FloatTensor] = None,
visual_attention_mask: Optional[torch.LongTensor] = None,
visual_token_type_ids: Optional[torch.LongTensor] = None,
image_text_alignment: Optional[torch.LongTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
labels: Optional[torch.LongTensor] = None,
) -> Union[Tuple[torch.Tensor], SequenceClassifierOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size, total_sequence_length)`, *optional*):
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
config.num_labels - 1]`. A KLDivLoss is computed between the labels and the returned logits.
Returns:
Example:
```python
# Assumption: *get_visual_embeddings(image)* gets the visual embeddings of the image in the batch.
from transformers import AutoTokenizer, VisualBertForQuestionAnswering
import torch
tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased")
model = VisualBertForQuestionAnswering.from_pretrained("uclanlp/visualbert-vqa")
text = "Who is eating the apple?"
inputs = tokenizer(text, return_tensors="pt")
visual_embeds = get_visual_embeddings(image).unsqueeze(0)
visual_token_type_ids = torch.ones(visual_embeds.shape[:-1], dtype=torch.long)
visual_attention_mask = torch.ones(visual_embeds.shape[:-1], dtype=torch.float)
inputs.update(
{
"visual_embeds": visual_embeds,
"visual_token_type_ids": visual_token_type_ids,
"visual_attention_mask": visual_attention_mask,
}
)
labels = torch.tensor([[0.0, 1.0]]).unsqueeze(0) # Batch size 1, Num labels 2
outputs = model(**inputs, labels=labels)
loss = outputs.loss
scores = outputs.logits
```"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
# Get the index of the last text token
index_to_gather = attention_mask.sum(1) - 2 # as in original code
outputs = self.visual_bert(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
visual_embeds=visual_embeds,
visual_attention_mask=visual_attention_mask,
visual_token_type_ids=visual_token_type_ids,
image_text_alignment=image_text_alignment,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = outputs[0]
# TO-CHECK: From the original code
index_to_gather = (
index_to_gather.unsqueeze(-1).unsqueeze(-1).expand(index_to_gather.size(0), 1, sequence_output.size(-1))
)
pooled_output = torch.gather(sequence_output, 1, index_to_gather)
pooled_output = self.dropout(pooled_output)
logits = self.cls(pooled_output)
reshaped_logits = logits.view(-1, self.num_labels)
loss = None
if labels is not None:
loss_fct = nn.KLDivLoss(reduction="batchmean")
log_softmax = nn.LogSoftmax(dim=-1)
reshaped_logits = log_softmax(reshaped_logits)
loss = loss_fct(reshaped_logits, labels.contiguous())
if not return_dict:
output = (reshaped_logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return SequenceClassifierOutput(
loss=loss,
logits=reshaped_logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
@add_start_docstrings(
"""
VisualBert Model with a sequence classification head on top (a dropout and a linear layer on top of the pooled
output) for Visual Reasoning e.g. for NLVR task.
""",
VISUAL_BERT_START_DOCSTRING,
)
class VisualBertForVisualReasoning(VisualBertPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.visual_bert = VisualBertModel(config)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.cls = nn.Linear(config.hidden_size, config.num_labels) # 2
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(VISUAL_BERT_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,
attention_mask: Optional[torch.LongTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.LongTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
visual_embeds: Optional[torch.FloatTensor] = None,
visual_attention_mask: Optional[torch.LongTensor] = None,
visual_token_type_ids: Optional[torch.LongTensor] = None,
image_text_alignment: Optional[torch.LongTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
labels: Optional[torch.LongTensor] = None,
) -> Union[Tuple[torch.Tensor], 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]`. A classification loss is computed (Cross-Entropy) against these labels.
Returns:
Example:
```python
# Assumption: *get_visual_embeddings(image)* gets the visual embeddings of the image in the batch.
from transformers import AutoTokenizer, VisualBertForVisualReasoning
import torch
tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased")
model = VisualBertForVisualReasoning.from_pretrained("uclanlp/visualbert-nlvr2")
text = "Who is eating the apple?"
inputs = tokenizer(text, return_tensors="pt")
visual_embeds = get_visual_embeddings(image).unsqueeze(0)
visual_token_type_ids = torch.ones(visual_embeds.shape[:-1], dtype=torch.long)
visual_attention_mask = torch.ones(visual_embeds.shape[:-1], dtype=torch.float)
inputs.update(
{
"visual_embeds": visual_embeds,
"visual_token_type_ids": visual_token_type_ids,
"visual_attention_mask": visual_attention_mask,
}
)
labels = torch.tensor(1).unsqueeze(0) # Batch size 1, Num choices 2
outputs = model(**inputs, labels=labels)
loss = outputs.loss
scores = outputs.logits
```"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.visual_bert(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
visual_embeds=visual_embeds,
visual_attention_mask=visual_attention_mask,
visual_token_type_ids=visual_token_type_ids,
image_text_alignment=image_text_alignment,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
# sequence_output = outputs[0]
pooled_output = outputs[1]
pooled_output = self.dropout(pooled_output)
logits = self.cls(pooled_output)
reshaped_logits = logits.contiguous()
loss = None
if labels is not None:
loss_fct = CrossEntropyLoss()
loss = loss_fct(reshaped_logits, labels.view(-1))
if not return_dict:
output = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return SequenceClassifierOutput(
loss=loss,
logits=reshaped_logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
class VisualBertRegionToPhraseAttention(nn.Module):
def __init__(self, config):
super().__init__()
if config.hidden_size % config.num_attention_heads != 0:
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 = 1 # 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)
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, query, key, attention_mask):
attention_mask = attention_mask.to(query.dtype)
attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)
attention_mask = (1.0 - attention_mask) * torch.finfo(query.dtype).min
mixed_query_layer = self.query(query)
mixed_key_layer = self.key(key)
query_layer = self.transpose_for_scores(mixed_query_layer)
key_layer = self.transpose_for_scores(mixed_key_layer)
attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
attention_scores = attention_scores / math.sqrt(self.attention_head_size)
attention_scores = attention_scores + attention_mask
attention_scores = attention_scores.squeeze(1)
return attention_scores
@add_start_docstrings(
"""
VisualBert Model with a Masked Language Modeling head and an attention layer on top for Region-to-Phrase Alignment
e.g. for Flickr30 Entities task.
""",
VISUAL_BERT_START_DOCSTRING,
)
class VisualBertForRegionToPhraseAlignment(VisualBertPreTrainedModel):
_tied_weights_keys = ["cls.predictions.decoder.bias"]
def __init__(self, config):
super().__init__(config)
self.visual_bert = VisualBertModel(config)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.cls = VisualBertPreTrainingHeads(config)
self.attention = VisualBertRegionToPhraseAttention(config)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(VISUAL_BERT_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,
attention_mask: Optional[torch.LongTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.LongTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
visual_embeds: Optional[torch.FloatTensor] = None,
visual_attention_mask: Optional[torch.LongTensor] = None,
visual_token_type_ids: Optional[torch.LongTensor] = None,
image_text_alignment: Optional[torch.LongTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
region_to_phrase_position: Optional[torch.LongTensor] = None,
labels: Optional[torch.LongTensor] = None,
) -> Union[Tuple[torch.Tensor], SequenceClassifierOutput]:
r"""
region_to_phrase_position (`torch.LongTensor` of shape `(batch_size, total_sequence_length)`, *optional*):
The positions depicting the position of the image embedding corresponding to the textual tokens.
labels (`torch.LongTensor` of shape `(batch_size, total_sequence_length, visual_sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. KLDivLoss is computed against these labels and the
outputs from the attention layer.
Returns:
Example:
```python
# Assumption: *get_visual_embeddings(image)* gets the visual embeddings of the image in the batch.
from transformers import AutoTokenizer, VisualBertForRegionToPhraseAlignment
import torch
tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased")
model = VisualBertForRegionToPhraseAlignment.from_pretrained("uclanlp/visualbert-vqa-coco-pre")
text = "Who is eating the apple?"
inputs = tokenizer(text, return_tensors="pt")
visual_embeds = get_visual_embeddings(image).unsqueeze(0)
visual_token_type_ids = torch.ones(visual_embeds.shape[:-1], dtype=torch.long)
visual_attention_mask = torch.ones(visual_embeds.shape[:-1], dtype=torch.float)
region_to_phrase_position = torch.ones((1, inputs["input_ids"].shape[-1] + visual_embeds.shape[-2]))
inputs.update(
{
"region_to_phrase_position": region_to_phrase_position,
"visual_embeds": visual_embeds,
"visual_token_type_ids": visual_token_type_ids,
"visual_attention_mask": visual_attention_mask,
}
)
labels = torch.ones(
(1, inputs["input_ids"].shape[-1] + visual_embeds.shape[-2], visual_embeds.shape[-2])
) # Batch size 1
outputs = model(**inputs, labels=labels)
loss = outputs.loss
scores = outputs.logits
```"""
if region_to_phrase_position is None:
raise ValueError("`region_to_phrase_position` should not be None when using Flickr Model.")
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.visual_bert(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
visual_embeds=visual_embeds,
visual_attention_mask=visual_attention_mask,
visual_token_type_ids=visual_token_type_ids,
image_text_alignment=image_text_alignment,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = outputs[0]
region_to_phrase_position_mask = (region_to_phrase_position != -1).long()
# Make the -1 become 0
region_to_phrase_position = region_to_phrase_position * region_to_phrase_position_mask
# Selected_positions = batch x selected position x dim
expanded_region_to_phrase_positions = region_to_phrase_position.unsqueeze(2).expand(
region_to_phrase_position.size(0), region_to_phrase_position.size(1), sequence_output.size(2)
)
selected_positions = sequence_output.gather(1, expanded_region_to_phrase_positions)
# Visual Features = batch x visual_feature_length x dim
# This will need separate image and visual masks.
visual_features = sequence_output[:, attention_mask.size(1) :]
if visual_features.size(1) != visual_attention_mask.size(1):
raise ValueError(
f"Visual features length :{visual_features.size(1)} should be the same"
f" as visual attention mask length: {visual_attention_mask.size(1)}."
)
logits = self.attention(selected_positions, visual_features, visual_attention_mask)
loss = None
if labels is not None:
# scores = batch x selected position x visual_feature
# scores = selected_positions.bmm(visual_features.transpose(1,2))
# label = batch x selected_postion x needed position
loss_fct = KLDivLoss(reduction="batchmean")
log_softmax = LogSoftmax(dim=-1)
scores = log_softmax(logits)
labels = labels.contiguous()
loss = loss_fct(scores, 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,
)
__all__ = [
"VisualBertForMultipleChoice",
"VisualBertForPreTraining",
"VisualBertForQuestionAnswering",
"VisualBertForRegionToPhraseAlignment",
"VisualBertForVisualReasoning",
"VisualBertLayer",
"VisualBertModel",
"VisualBertPreTrainedModel",
]
| transformers/src/transformers/models/visual_bert/modeling_visual_bert.py/0 | {
"file_path": "transformers/src/transformers/models/visual_bert/modeling_visual_bert.py",
"repo_id": "transformers",
"token_count": 29242
} |
# coding=utf-8
# Copyright 2024 University of Sydney 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 VitPose model."""
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from ...modeling_utils import PreTrainedModel
from ...utils import (
ModelOutput,
add_start_docstrings,
add_start_docstrings_to_model_forward,
logging,
replace_return_docstrings,
)
from ...utils.backbone_utils import load_backbone
from .configuration_vitpose import VitPoseConfig
logger = logging.get_logger(__name__)
# General docstring
_CONFIG_FOR_DOC = "VitPoseConfig"
@dataclass
class VitPoseEstimatorOutput(ModelOutput):
"""
Class for outputs of pose estimation models.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Loss is not supported at this moment. See https://github.com/ViTAE-Transformer/ViTPose/tree/main/mmpose/models/losses for further detail.
heatmaps (`torch.FloatTensor` of shape `(batch_size, num_keypoints, height, width)`):
Heatmaps as predicted by 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 stage) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states
(also called feature maps) of the model at the output of each stage.
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, patch_size,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
"""
loss: Optional[torch.FloatTensor] = None
heatmaps: torch.FloatTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
class VitPosePreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = VitPoseConfig
base_model_prefix = "vit"
main_input_name = "pixel_values"
supports_gradient_checkpointing = 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)
VITPOSE_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 ([`VitPoseConfig`]): 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.
"""
VITPOSE_INPUTS_DOCSTRING = r"""
Args:
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Pixel values can be obtained using [`VitPoseImageProcessor`]. See
[`VitPoseImageProcessor.__call__`] for details.
dataset_index (`torch.Tensor` of shape `(batch_size,)`):
Index to use in the Mixture-of-Experts (MoE) blocks of the backbone.
This corresponds to the dataset index used during training, e.g. For the single dataset index 0 refers to the corresponding dataset. For the multiple datasets index 0 refers to dataset A (e.g. MPII) and index 1 refers to dataset B (e.g. CrowdPose).
flip_pairs (`torch.tensor`, *optional*):
Whether to mirror pairs of keypoints (for example, left ear -- right ear).
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 flip_back(output_flipped, flip_pairs, target_type="gaussian-heatmap"):
"""Flip the flipped heatmaps back to the original form.
Args:
output_flipped (`torch.tensor` of shape `(batch_size, num_keypoints, height, width)`):
The output heatmaps obtained from the flipped images.
flip_pairs (`torch.Tensor` of shape `(num_keypoints, 2)`):
Pairs of keypoints which are mirrored (for example, left ear -- right ear).
target_type (`str`, *optional*, defaults to `"gaussian-heatmap"`):
Target type to use. Can be gaussian-heatmap or combined-target.
gaussian-heatmap: Classification target with gaussian distribution.
combined-target: The combination of classification target (response map) and regression target (offset map).
Paper ref: Huang et al. The Devil is in the Details: Delving into Unbiased Data Processing for Human Pose Estimation (CVPR 2020).
Returns:
torch.Tensor: heatmaps that flipped back to the original image
"""
if target_type not in ["gaussian-heatmap", "combined-target"]:
raise ValueError("target_type should be gaussian-heatmap or combined-target")
if output_flipped.ndim != 4:
raise ValueError("output_flipped should be [batch_size, num_keypoints, height, width]")
batch_size, num_keypoints, height, width = output_flipped.shape
channels = 1
if target_type == "combined-target":
channels = 3
output_flipped[:, 1::3, ...] = -output_flipped[:, 1::3, ...]
output_flipped = output_flipped.reshape(batch_size, -1, channels, height, width)
output_flipped_back = output_flipped.clone()
# Swap left-right parts
for left, right in flip_pairs.tolist():
output_flipped_back[:, left, ...] = output_flipped[:, right, ...]
output_flipped_back[:, right, ...] = output_flipped[:, left, ...]
output_flipped_back = output_flipped_back.reshape((batch_size, num_keypoints, height, width))
# Flip horizontally
output_flipped_back = output_flipped_back.flip(-1)
return output_flipped_back
class VitPoseSimpleDecoder(nn.Module):
"""
Simple decoding head consisting of a ReLU activation, 4x upsampling and a 3x3 convolution, turning the
feature maps into heatmaps.
"""
def __init__(self, config) -> None:
super().__init__()
self.activation = nn.ReLU()
self.upsampling = nn.Upsample(scale_factor=config.scale_factor, mode="bilinear", align_corners=False)
self.conv = nn.Conv2d(
config.backbone_config.hidden_size, config.num_labels, kernel_size=3, stride=1, padding=1
)
def forward(self, hidden_state: torch.Tensor, flip_pairs: Optional[torch.Tensor] = None) -> torch.Tensor:
# Transform input: ReLU + upsample
hidden_state = self.activation(hidden_state)
hidden_state = self.upsampling(hidden_state)
heatmaps = self.conv(hidden_state)
if flip_pairs is not None:
heatmaps = flip_back(heatmaps, flip_pairs)
return heatmaps
class VitPoseClassicDecoder(nn.Module):
"""
Classic decoding head consisting of a 2 deconvolutional blocks, followed by a 1x1 convolution layer,
turning the feature maps into heatmaps.
"""
def __init__(self, config: VitPoseConfig):
super().__init__()
self.deconv1 = nn.ConvTranspose2d(
config.backbone_config.hidden_size, 256, kernel_size=4, stride=2, padding=1, bias=False
)
self.batchnorm1 = nn.BatchNorm2d(256)
self.relu1 = nn.ReLU()
self.deconv2 = nn.ConvTranspose2d(256, 256, kernel_size=4, stride=2, padding=1, bias=False)
self.batchnorm2 = nn.BatchNorm2d(256)
self.relu2 = nn.ReLU()
self.conv = nn.Conv2d(256, config.num_labels, kernel_size=1, stride=1, padding=0)
def forward(self, hidden_state: torch.Tensor, flip_pairs: Optional[torch.Tensor] = None):
hidden_state = self.deconv1(hidden_state)
hidden_state = self.batchnorm1(hidden_state)
hidden_state = self.relu1(hidden_state)
hidden_state = self.deconv2(hidden_state)
hidden_state = self.batchnorm2(hidden_state)
hidden_state = self.relu2(hidden_state)
heatmaps = self.conv(hidden_state)
if flip_pairs is not None:
heatmaps = flip_back(heatmaps, flip_pairs)
return heatmaps
@add_start_docstrings(
"The VitPose model with a pose estimation head on top.",
VITPOSE_START_DOCSTRING,
)
class VitPoseForPoseEstimation(VitPosePreTrainedModel):
def __init__(self, config: VitPoseConfig) -> None:
super().__init__(config)
self.backbone = load_backbone(config)
# add backbone attributes
if not hasattr(self.backbone.config, "hidden_size"):
raise ValueError("The backbone should have a hidden_size attribute")
if not hasattr(self.backbone.config, "image_size"):
raise ValueError("The backbone should have an image_size attribute")
if not hasattr(self.backbone.config, "patch_size"):
raise ValueError("The backbone should have a patch_size attribute")
self.head = VitPoseSimpleDecoder(config) if config.use_simple_decoder else VitPoseClassicDecoder(config)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(VITPOSE_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=VitPoseEstimatorOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
pixel_values: torch.Tensor,
dataset_index: Optional[torch.Tensor] = None,
flip_pairs: Optional[torch.Tensor] = None,
labels: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[tuple, VitPoseEstimatorOutput]:
"""
Returns:
Examples:
```python
>>> from transformers import AutoImageProcessor, VitPoseForPoseEstimation
>>> import torch
>>> from PIL import Image
>>> import requests
>>> processor = AutoImageProcessor.from_pretrained("usyd-community/vitpose-base-simple")
>>> model = VitPoseForPoseEstimation.from_pretrained("usyd-community/vitpose-base-simple")
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> boxes = [[[412.8, 157.61, 53.05, 138.01], [384.43, 172.21, 15.12, 35.74]]]
>>> inputs = processor(image, boxes=boxes, return_tensors="pt")
>>> with torch.no_grad():
... outputs = model(**inputs)
>>> heatmaps = outputs.heatmaps
```"""
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
loss = None
if labels is not None:
raise NotImplementedError("Training is not yet supported")
outputs = self.backbone.forward_with_filtered_kwargs(
pixel_values,
dataset_index=dataset_index,
output_hidden_states=output_hidden_states,
output_attentions=output_attentions,
return_dict=return_dict,
)
# Turn output hidden states in tensor of shape (batch_size, num_channels, height, width)
sequence_output = outputs.feature_maps[-1] if return_dict else outputs[0][-1]
batch_size = sequence_output.shape[0]
patch_height = self.config.backbone_config.image_size[0] // self.config.backbone_config.patch_size[0]
patch_width = self.config.backbone_config.image_size[1] // self.config.backbone_config.patch_size[1]
sequence_output = (
sequence_output.permute(0, 2, 1).reshape(batch_size, -1, patch_height, patch_width).contiguous()
)
heatmaps = self.head(sequence_output, flip_pairs=flip_pairs)
if not return_dict:
if output_hidden_states:
output = (heatmaps,) + outputs[1:]
else:
output = (heatmaps,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return VitPoseEstimatorOutput(
loss=loss,
heatmaps=heatmaps,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
__all__ = ["VitPosePreTrainedModel", "VitPoseForPoseEstimation"]
| transformers/src/transformers/models/vitpose/modeling_vitpose.py/0 | {
"file_path": "transformers/src/transformers/models/vitpose/modeling_vitpose.py",
"repo_id": "transformers",
"token_count": 5724
} |
# 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.
"""Convert Wav2Vec2 checkpoint."""
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
Wav2Vec2Config,
Wav2Vec2CTCTokenizer,
Wav2Vec2FeatureExtractor,
Wav2Vec2ForCTC,
Wav2Vec2ForPreTraining,
Wav2Vec2Processor,
logging,
)
from transformers.models.wav2vec2.modeling_wav2vec2 import Wav2Vec2ForSequenceClassification
logging.set_verbosity_info()
logger = logging.get_logger(__name__)
MAPPING = {
"post_extract_proj": "feature_projection.projection",
"encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
"self_attn.k_proj": "encoder.layers.*.attention.k_proj",
"self_attn.v_proj": "encoder.layers.*.attention.v_proj",
"self_attn.q_proj": "encoder.layers.*.attention.q_proj",
"self_attn.out_proj": "encoder.layers.*.attention.out_proj",
"self_attn_layer_norm": "encoder.layers.*.layer_norm",
"fc1": "encoder.layers.*.feed_forward.intermediate_dense",
"fc2": "encoder.layers.*.feed_forward.output_dense",
"final_layer_norm": "encoder.layers.*.final_layer_norm",
"encoder.layer_norm": "encoder.layer_norm",
"adapter_layer": "encoder.layers.*.adapter_layer",
"w2v_model.layer_norm": "feature_projection.layer_norm",
"quantizer.weight_proj": "quantizer.weight_proj",
"quantizer.vars": "quantizer.codevectors",
"project_q": "project_q",
"final_proj": "project_hid",
"w2v_encoder.proj": "lm_head",
"mask_emb": "masked_spec_embed",
"pooling_layer.linear": "projector",
"pooling_layer.projection": "classifier",
}
TOP_LEVEL_KEYS = [
"lm_head",
"quantizer.weight_proj",
"quantizer.codevectors",
"project_q",
"project_hid",
"projector",
"classifier",
]
def read_txt_into_dict(filename):
result = {}
with open(filename, "r") as file:
for line_number, line in enumerate(file):
line = line.strip()
if line:
words = line.split()
key = line_number
value = words[0]
result[key] = value
return result
def set_recursively(key, value, full_name, weight_type, hf_pointer):
for attribute in key.split("."):
hf_pointer = getattr(hf_pointer, attribute)
hf_param_name = None
for param_key in PARAM_MAPPING.keys():
if full_name.endswith(param_key):
hf_param_name = PARAM_MAPPING[full_name.split(".")[-1]]
weight_type = "param"
# fairseq uses nn.utils.weight_norm() while transformers switches to nn.utils.parametrizations.weight_norm()
# the mapping between two versions:
# https://github.com/pytorch/pytorch/blob/56935684c3dfad7841c83c719eeebecb560fe466/torch/nn/utils/parametrizations.py#L389-L395
if weight_type is not None and weight_type != "param":
if weight_type == "weight_g" and not hasattr(hf_pointer, "weight_g"):
hf_shape = hf_pointer.parametrizations.weight.original0.shape
elif weight_type == "weight_v" and not hasattr(hf_pointer, "weight_v"):
hf_shape = hf_pointer.parametrizations.weight.original1.shape
else:
hf_shape = getattr(hf_pointer, weight_type).shape
elif weight_type is not None and weight_type == "param":
shape_pointer = hf_pointer
for attribute in hf_param_name.split("."):
shape_pointer = getattr(shape_pointer, attribute)
hf_shape = shape_pointer.shape
# let's reduce dimension
value = value[0]
else:
hf_shape = hf_pointer.shape
if hf_shape != value.shape:
raise ValueError(
f"Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"
f" {value.shape} for {full_name}"
)
if weight_type == "weight":
hf_pointer.weight.data = value
elif weight_type == "weight_g":
if hasattr(hf_pointer, "weight_g"):
hf_pointer.weight_g.data = value
else:
hf_pointer.parametrizations.weight.original0.data = value
elif weight_type == "weight_v":
if hasattr(hf_pointer, "weight_v"):
hf_pointer.weight_v.data = value
else:
hf_pointer.parametrizations.weight.original1.data = value
elif weight_type == "bias":
hf_pointer.bias.data = value
elif weight_type == "param":
for attribute in hf_param_name.split("."):
hf_pointer = getattr(hf_pointer, attribute)
hf_pointer.data = value
else:
hf_pointer.data = value
logger.info(f"{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.")
def rename_dict(key, value, full_name, weight_type, hf_dict):
hf_param_name = None
for param_key in PARAM_MAPPING.keys():
if full_name.endswith(param_key):
hf_param_name = PARAM_MAPPING[full_name.split(".")[-1]]
weight_type = "param"
if weight_type is not None and weight_type != "param":
full_key = ".".join([key, weight_type])
elif weight_type is not None and weight_type == "param":
full_key = ".".join([key, hf_param_name])
else:
full_key = key
hf_dict[full_key] = value if "lm_head" in full_key else value[0]
PARAM_MAPPING = {
"W_a": "linear_1.weight",
"W_b": "linear_2.weight",
"b_a": "linear_1.bias",
"b_b": "linear_2.bias",
"ln_W": "norm.weight",
"ln_b": "norm.bias",
}
def load_wav2vec2_layer(name, value, hf_model=None, hf_dict=None):
is_used = False
for key, mapped_key in MAPPING.items():
mapped_key = "wav2vec2." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
is_used = True
if "*" in mapped_key:
layer_index = name.split(key)[0].split(".")[-2]
mapped_key = mapped_key.replace("*", layer_index)
if "weight_g" in name:
weight_type = "weight_g"
elif "weight_v" in name:
weight_type = "weight_v"
elif "bias" in name:
weight_type = "bias"
elif "weight" in name:
# TODO: don't match quantizer.weight_proj
weight_type = "weight"
else:
weight_type = None
if hf_dict is not None:
rename_dict(mapped_key, value, name, weight_type, hf_dict)
else:
set_recursively(mapped_key, value, name, weight_type, hf_model)
return is_used
return is_used
def recursively_load_weights(fairseq_model, hf_model, is_headless):
unused_weights = []
fairseq_dict = fairseq_model.state_dict()
feature_extractor = hf_model.wav2vec2.feature_extractor
for name, value in fairseq_dict.items():
is_used = False
if "conv_layers" in name:
load_conv_layer(
name,
value,
feature_extractor,
unused_weights,
hf_model.config.feat_extract_norm == "group",
)
is_used = True
else:
is_used = load_wav2vec2_layer(name, value, hf_model)
if not is_used:
unused_weights.append(name)
logger.warning(f"Unused weights: {unused_weights}")
def load_conv_layer(full_name, value, feature_extractor, unused_weights, use_group_norm):
name = full_name.split("conv_layers.")[-1]
items = name.split(".")
layer_id = int(items[0])
type_id = int(items[1])
if type_id == 0:
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
raise ValueError(
f"{full_name} has size {value.shape}, but"
f" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."
)
feature_extractor.conv_layers[layer_id].conv.bias.data = value
logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}.")
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
raise ValueError(
f"{full_name} has size {value.shape}, but"
f" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."
)
feature_extractor.conv_layers[layer_id].conv.weight.data = value
logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}.")
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
raise ValueError(
f"{full_name} has size {value.shape}, but"
f" {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found."
)
feature_extractor.conv_layers[layer_id].layer_norm.bias.data = value
logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.")
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
raise ValueError(
f"{full_name} has size {value.shape}, but"
f" {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found."
)
feature_extractor.conv_layers[layer_id].layer_norm.weight.data = value
logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.")
else:
unused_weights.append(full_name)
@torch.no_grad()
def convert_wav2vec2_checkpoint(
checkpoint_path, pytorch_dump_folder_path, config_path=None, dict_path=None, is_finetuned=True, is_seq_class=False
):
"""
Copy/paste/tweak model's weights to transformers design.
"""
if config_path is not None:
config = Wav2Vec2Config.from_pretrained(config_path)
else:
config = Wav2Vec2Config()
if is_seq_class:
id2label = read_txt_into_dict(dict_path)
config.id2label = id2label
hf_wav2vec = Wav2Vec2ForSequenceClassification(config)
feature_extractor = Wav2Vec2FeatureExtractor(
feature_size=1,
sampling_rate=16000,
padding_value=0,
do_normalize=True,
return_attention_mask=True,
)
feature_extractor.save_pretrained(pytorch_dump_folder_path)
elif is_finetuned:
if dict_path:
target_dict = Dictionary.load(dict_path)
# important change bos & pad token id since CTC symbol is <pad> and
# not <s> as in fairseq
config.bos_token_id = target_dict.pad_index
config.pad_token_id = target_dict.bos_index
config.eos_token_id = target_dict.eos_index
config.vocab_size = len(target_dict.symbols)
vocab_path = os.path.join(pytorch_dump_folder_path, "vocab.json")
if not os.path.isdir(pytorch_dump_folder_path):
logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(pytorch_dump_folder_path))
return
os.makedirs(pytorch_dump_folder_path, exist_ok=True)
vocab_dict = target_dict.indices
# fairseq has the <pad> and <s> switched
vocab_dict["<pad>"] = 0
vocab_dict["<s>"] = 1
with open(vocab_path, "w", encoding="utf-8") as vocab_handle:
json.dump(vocab_dict, vocab_handle)
tokenizer = Wav2Vec2CTCTokenizer(
vocab_path,
unk_token=target_dict.unk_word,
pad_token=target_dict.pad_word,
bos_token=target_dict.bos_word,
eos_token=target_dict.eos_word,
word_delimiter_token="|",
do_lower_case=False,
)
return_attention_mask = True if config.feat_extract_norm == "layer" else False
feature_extractor = Wav2Vec2FeatureExtractor(
feature_size=1,
sampling_rate=16000,
padding_value=0,
do_normalize=True,
return_attention_mask=return_attention_mask,
)
processor = Wav2Vec2Processor(feature_extractor=feature_extractor, tokenizer=tokenizer)
processor.save_pretrained(pytorch_dump_folder_path)
hf_wav2vec = Wav2Vec2ForCTC(config)
else:
hf_wav2vec = Wav2Vec2ForPreTraining(config)
if is_finetuned or is_seq_class:
model, _, _ = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path], arg_overrides={"data": "/".join(dict_path.split("/")[:-1])}
)
else:
task_arg = argparse.Namespace(task="audio_pretraining")
task = fairseq.tasks.setup_task(task_arg)
model, _, _ = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path], task=task)
model = model[0].eval()
recursively_load_weights(model, hf_wav2vec, not is_finetuned)
hf_wav2vec.save_pretrained(pytorch_dump_folder_path)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
parser.add_argument("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model")
parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
parser.add_argument(
"--not_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
)
parser.add_argument(
"--is_seq_class",
action="store_true",
help="Whether the model to convert is a fine-tuned sequence classification model or not",
)
args = parser.parse_args()
is_finetuned = not args.not_finetuned and not args.is_seq_class
convert_wav2vec2_checkpoint(
args.checkpoint_path,
args.pytorch_dump_folder_path,
args.config_path,
args.dict_path,
is_finetuned,
args.is_seq_class,
)
| transformers/src/transformers/models/wav2vec2/convert_wav2vec2_original_pytorch_checkpoint_to_pytorch.py/0 | {
"file_path": "transformers/src/transformers/models/wav2vec2/convert_wav2vec2_original_pytorch_checkpoint_to_pytorch.py",
"repo_id": "transformers",
"token_count": 6994
} |
# coding=utf-8
# Copyright 2022 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.
"""PyTorch Wav2Vec2-Conformer model."""
import math
import warnings
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
import torch.utils.checkpoint
from torch import 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_outputs import (
BaseModelOutput,
CausalLMOutput,
SequenceClassifierOutput,
TokenClassifierOutput,
Wav2Vec2BaseModelOutput,
XVectorOutput,
)
from ...modeling_utils import PreTrainedModel
from ...utils import (
ModelOutput,
add_code_sample_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
is_peft_available,
logging,
replace_return_docstrings,
)
from .configuration_wav2vec2_conformer import Wav2Vec2ConformerConfig
logger = logging.get_logger(__name__)
_HIDDEN_STATES_START_POSITION = 2
# General docstring
_CONFIG_FOR_DOC = "Wav2Vec2ConformerConfig"
# Base docstring
_CHECKPOINT_FOR_DOC = "facebook/wav2vec2-conformer-rope-large-960h-ft"
_EXPECTED_OUTPUT_SHAPE = [1, 292, 1024]
# CTC docstring
_CTC_EXPECTED_OUTPUT = "'MISTER QUILTER IS THE APOSTLE OF THE MIDDLE CLASSES AND WE ARE GLAD TO WELCOME HIS GOSPEL'"
_CTC_EXPECTED_LOSS = 64.21
@dataclass
# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForPreTrainingOutput with Wav2Vec2->Wav2Vec2Conformer
class Wav2Vec2ConformerForPreTrainingOutput(ModelOutput):
"""
Output type of [`Wav2Vec2ConformerForPreTraining`], with potential hidden states and attentions.
Args:
loss (*optional*, returned when `sample_negative_indices` are passed, `torch.FloatTensor` of shape `(1,)`):
Total loss as the sum of the contrastive loss (L_m) and the diversity loss (L_d) as stated in the [official
paper](https://arxiv.org/pdf/2006.11477.pdf) . (classification) loss.
projected_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.proj_codevector_dim)`):
Hidden-states of the model projected to *config.proj_codevector_dim* that can be used to predict the masked
projected quantized states.
projected_quantized_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.proj_codevector_dim)`):
Quantized extracted feature vectors projected to *config.proj_codevector_dim* representing the positive
target vectors for contrastive loss.
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.
contrastive_loss (*optional*, returned when `sample_negative_indices` are passed, `torch.FloatTensor` of shape `(1,)`):
The contrastive loss (L_m) as stated in the [official paper](https://arxiv.org/pdf/2006.11477.pdf) .
diversity_loss (*optional*, returned when `sample_negative_indices` are passed, `torch.FloatTensor` of shape `(1,)`):
The diversity loss (L_d) as stated in the [official paper](https://arxiv.org/pdf/2006.11477.pdf) .
"""
loss: Optional[torch.FloatTensor] = None
projected_states: torch.FloatTensor = None
projected_quantized_states: torch.FloatTensor = None
codevector_perplexity: torch.FloatTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None
contrastive_loss: Optional[torch.FloatTensor] = None
diversity_loss: Optional[torch.FloatTensor] = None
# Copied from transformers.models.wav2vec2.modeling_wav2vec2._compute_mask_indices
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.sum(-1).detach().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
# Copied from transformers.models.wav2vec2.modeling_wav2vec2._sample_negative_indices
def _sample_negative_indices(
features_shape: Tuple, num_negatives: int, mask_time_indices: Optional[np.ndarray] = None
):
"""
Sample `num_negatives` vectors from feature vectors.
"""
batch_size, sequence_length = features_shape
# generate indices of the positive vectors themselves, repeat them `num_negatives` times
sequence_length_range = np.arange(sequence_length)
# get `num_negatives` random vector indices from the same utterance
sampled_negative_indices = np.zeros(shape=(batch_size, sequence_length, num_negatives), dtype=np.int32)
mask_time_indices = (
mask_time_indices.astype(bool) if mask_time_indices is not None else np.ones(features_shape, dtype=bool)
)
for batch_idx in range(batch_size):
high = mask_time_indices[batch_idx].sum() - 1
mapped_masked_indices = sequence_length_range[mask_time_indices[batch_idx]]
feature_indices = np.broadcast_to(np.arange(high + 1)[:, None], (high + 1, num_negatives))
sampled_indices = np.random.randint(0, high, size=(high + 1, num_negatives))
# avoid sampling the same positive vector, but keep the distribution uniform
sampled_indices[sampled_indices >= feature_indices] += 1
# remap to actual indices
sampled_negative_indices[batch_idx][mask_time_indices[batch_idx]] = mapped_masked_indices[sampled_indices]
# correct for batch size
sampled_negative_indices[batch_idx] += batch_idx * sequence_length
return sampled_negative_indices
# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2NoLayerNormConvLayer with Wav2Vec2->Wav2Vec2Conformer
class Wav2Vec2ConformerNoLayerNormConvLayer(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
# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2LayerNormConvLayer with Wav2Vec2->Wav2Vec2Conformer
class Wav2Vec2ConformerLayerNormConvLayer(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
# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2GroupNormConvLayer with Wav2Vec2->Wav2Vec2Conformer
class Wav2Vec2ConformerGroupNormConvLayer(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
# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2PositionalConvEmbedding with Wav2Vec2->Wav2Vec2Conformer
class Wav2Vec2ConformerPositionalConvEmbedding(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,
)
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 = Wav2Vec2ConformerSamePadLayer(config.num_conv_pos_embeddings)
self.activation = ACT2FN[config.feat_extract_activation]
def forward(self, hidden_states):
hidden_states = hidden_states.transpose(1, 2)
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 Wav2Vec2ConformerRotaryPositionalEmbedding(nn.Module):
"""Rotary positional embedding
Reference : https://blog.eleuther.ai/rotary-embeddings/ Paper: https://arxiv.org/pdf/2104.09864.pdf
"""
def __init__(self, config):
super().__init__()
dim = config.hidden_size // config.num_attention_heads
base = config.rotary_embedding_base
inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.int64).float() / dim))
self.register_buffer("inv_freq", inv_freq)
self.cached_sequence_length = None
self.cached_rotary_positional_embedding = None
def forward(self, hidden_states):
sequence_length = hidden_states.shape[1]
if sequence_length == self.cached_sequence_length and self.cached_rotary_positional_embedding is not None:
return self.cached_rotary_positional_embedding
self.cached_sequence_length = sequence_length
# Embeddings are computed in the dtype of the inv_freq constant
time_stamps = torch.arange(sequence_length).type_as(self.inv_freq)
freqs = torch.einsum("i,j->ij", time_stamps, self.inv_freq)
embeddings = torch.cat((freqs, freqs), dim=-1)
cos_embeddings = embeddings.cos()[:, None, None, :]
sin_embeddings = embeddings.sin()[:, None, None, :]
# Computed embeddings are cast to the dtype of the hidden state inputs
self.cached_rotary_positional_embedding = torch.stack([cos_embeddings, sin_embeddings]).type_as(hidden_states)
return self.cached_rotary_positional_embedding
class Wav2Vec2ConformerRelPositionalEmbedding(nn.Module):
"""Relative positional encoding module."""
def __init__(self, config):
super().__init__()
self.max_len = config.max_source_positions
self.d_model = config.hidden_size
self.pe = None
self.extend_pe(torch.tensor(0.0).expand(1, self.max_len))
def extend_pe(self, x):
# Reset the positional encodings
if self.pe is not None:
# self.pe contains both positive and negative parts
# the length of self.pe is 2 * input_len - 1
if self.pe.size(1) >= x.size(1) * 2 - 1:
if self.pe.dtype != x.dtype or self.pe.device != x.device:
self.pe = self.pe.to(dtype=x.dtype, device=x.device)
return
# Suppose `i` is the position of query vector and `j` is the
# position of key vector. We use positive relative positions when keys
# are to the left (i>j) and negative relative positions otherwise (i<j).
pe_positive = torch.zeros(x.size(1), self.d_model)
pe_negative = torch.zeros(x.size(1), self.d_model)
position = torch.arange(0, x.size(1), dtype=torch.int64).float().unsqueeze(1)
div_term = torch.exp(
torch.arange(0, self.d_model, 2, dtype=torch.int64).float() * -(math.log(10000.0) / self.d_model)
)
pe_positive[:, 0::2] = torch.sin(position * div_term)
pe_positive[:, 1::2] = torch.cos(position * div_term)
pe_negative[:, 0::2] = torch.sin(-1 * position * div_term)
pe_negative[:, 1::2] = torch.cos(-1 * position * div_term)
# Reverse the order of positive indices and concat both positive and
# negative indices. This is used to support the shifting trick
# as in https://arxiv.org/abs/1901.02860
pe_positive = torch.flip(pe_positive, [0]).unsqueeze(0)
pe_negative = pe_negative[1:].unsqueeze(0)
pe = torch.cat([pe_positive, pe_negative], dim=1)
self.pe = pe.to(device=x.device, dtype=x.dtype)
def forward(self, hidden_states: torch.Tensor):
self.extend_pe(hidden_states)
start_idx = self.pe.size(1) // 2 - hidden_states.size(1) + 1
end_idx = self.pe.size(1) // 2 + hidden_states.size(1)
relative_position_embeddings = self.pe[:, start_idx:end_idx]
return relative_position_embeddings
# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2SamePadLayer with Wav2Vec2->Wav2Vec2Conformer
class Wav2Vec2ConformerSamePadLayer(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
# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2FeatureEncoder with Wav2Vec2->Wav2Vec2Conformer
class Wav2Vec2ConformerFeatureEncoder(nn.Module):
"""Construct the features from raw audio waveform"""
def __init__(self, config):
super().__init__()
if config.feat_extract_norm == "group":
conv_layers = [Wav2Vec2ConformerGroupNormConvLayer(config, layer_id=0)] + [
Wav2Vec2ConformerNoLayerNormConvLayer(config, layer_id=i + 1)
for i in range(config.num_feat_extract_layers - 1)
]
elif config.feat_extract_norm == "layer":
conv_layers = [
Wav2Vec2ConformerLayerNormConvLayer(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
# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2FeatureProjection with Wav2Vec2->Wav2Vec2Conformer
class Wav2Vec2ConformerFeatureProjection(nn.Module):
def __init__(self, config):
super().__init__()
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
norm_hidden_states = self.layer_norm(hidden_states)
hidden_states = self.projection(norm_hidden_states)
hidden_states = self.dropout(hidden_states)
return hidden_states, norm_hidden_states
# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2FeedForward with Wav2Vec2->Wav2Vec2Conformer
class Wav2Vec2ConformerFeedForward(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
class Wav2Vec2ConformerConvolutionModule(nn.Module):
"""Convolution block used in the conformer block"""
def __init__(self, config):
super().__init__()
if (config.conv_depthwise_kernel_size - 1) % 2 == 1:
raise ValueError("`config.conv_depthwise_kernel_size` should be a odd number for 'SAME' padding")
self.layer_norm = nn.LayerNorm(config.hidden_size)
self.pointwise_conv1 = nn.Conv1d(
config.hidden_size,
2 * config.hidden_size,
kernel_size=1,
stride=1,
padding=0,
bias=False,
)
self.glu = nn.GLU(dim=1)
self.depthwise_conv = nn.Conv1d(
config.hidden_size,
config.hidden_size,
config.conv_depthwise_kernel_size,
stride=1,
padding=(config.conv_depthwise_kernel_size - 1) // 2,
groups=config.hidden_size,
bias=False,
)
self.batch_norm = nn.BatchNorm1d(config.hidden_size)
self.activation = ACT2FN[config.hidden_act]
self.pointwise_conv2 = nn.Conv1d(
config.hidden_size,
config.hidden_size,
kernel_size=1,
stride=1,
padding=0,
bias=False,
)
self.dropout = nn.Dropout(config.conformer_conv_dropout)
def forward(self, hidden_states):
hidden_states = self.layer_norm(hidden_states)
# exchange the temporal dimension and the feature dimension
hidden_states = hidden_states.transpose(1, 2)
# GLU mechanism
# => (batch, 2*channel, dim)
hidden_states = self.pointwise_conv1(hidden_states)
# => (batch, channel, dim)
hidden_states = self.glu(hidden_states)
# 1D Depthwise Conv
hidden_states = self.depthwise_conv(hidden_states)
hidden_states = self.batch_norm(hidden_states)
hidden_states = self.activation(hidden_states)
hidden_states = self.pointwise_conv2(hidden_states)
hidden_states = self.dropout(hidden_states)
hidden_states = hidden_states.transpose(1, 2)
return hidden_states
class Wav2Vec2ConformerSelfAttention(nn.Module):
"""Construct an Wav2Vec2ConformerSelfAttention object.
Can be enhanced with rotary or relative position embeddings.
"""
def __init__(self, config):
super().__init__()
self.head_size = config.hidden_size // config.num_attention_heads
self.num_heads = config.num_attention_heads
self.position_embeddings_type = config.position_embeddings_type
self.linear_q = nn.Linear(config.hidden_size, config.hidden_size)
self.linear_k = nn.Linear(config.hidden_size, config.hidden_size)
self.linear_v = nn.Linear(config.hidden_size, config.hidden_size)
self.linear_out = nn.Linear(config.hidden_size, config.hidden_size)
self.dropout = nn.Dropout(p=config.attention_dropout)
if self.position_embeddings_type == "relative":
# linear transformation for positional encoding
self.linear_pos = nn.Linear(config.hidden_size, config.hidden_size, bias=False)
# these two learnable bias are used in matrix c and matrix d
# as described in https://arxiv.org/abs/1901.02860 Section 3.3
self.pos_bias_u = nn.Parameter(torch.zeros(self.num_heads, self.head_size))
self.pos_bias_v = nn.Parameter(torch.zeros(self.num_heads, self.head_size))
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
relative_position_embeddings: Optional[torch.Tensor] = None,
output_attentions: bool = False,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
# self-attention mechanism
batch_size, sequence_length, hidden_size = hidden_states.size()
# make sure query/key states can be != value states
query_key_states = hidden_states
value_states = hidden_states
if self.position_embeddings_type == "rotary":
if relative_position_embeddings is None:
raise ValueError(
"`relative_position_embeddings` has to be defined when `self.position_embeddings_type == 'rotary'"
)
query_key_states = self._apply_rotary_embedding(query_key_states, relative_position_embeddings)
# project query_key_states and value_states
query = self.linear_q(query_key_states).view(batch_size, -1, self.num_heads, self.head_size)
key = self.linear_k(query_key_states).view(batch_size, -1, self.num_heads, self.head_size)
value = self.linear_v(value_states).view(batch_size, -1, self.num_heads, self.head_size)
# => (batch, head, time1, d_k)
query = query.transpose(1, 2)
key = key.transpose(1, 2)
value = value.transpose(1, 2)
if self.position_embeddings_type == "relative":
if relative_position_embeddings is None:
raise ValueError(
"`relative_position_embeddings` has to be defined when `self.position_embeddings_type =="
" 'relative'"
)
# apply relative_position_embeddings to qk scores
# as proposed in Transformer_XL: https://arxiv.org/abs/1901.02860
scores = self._apply_relative_embeddings(
query=query, key=key, relative_position_embeddings=relative_position_embeddings
)
else:
scores = torch.matmul(query, key.transpose(-2, -1)) / math.sqrt(self.head_size)
# apply attention_mask if necessary
if attention_mask is not None:
scores = scores + attention_mask
# => (batch, head, time1, time2)
probs = torch.softmax(scores, dim=-1)
probs = self.dropout(probs)
# => (batch, head, time1, d_k)
hidden_states = torch.matmul(probs, value)
# => (batch, time1, hidden_size)
hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, self.num_heads * self.head_size)
hidden_states = self.linear_out(hidden_states)
return hidden_states, probs
def _apply_rotary_embedding(self, hidden_states, relative_position_embeddings):
batch_size, sequence_length, hidden_size = hidden_states.size()
hidden_states = hidden_states.view(batch_size, sequence_length, self.num_heads, self.head_size)
cos = relative_position_embeddings[0, :sequence_length, ...]
sin = relative_position_embeddings[1, :sequence_length, ...]
# rotate hidden_states with rotary embeddings
hidden_states = hidden_states.transpose(0, 1)
rotated_states_begin = hidden_states[..., : self.head_size // 2]
rotated_states_end = hidden_states[..., self.head_size // 2 :]
rotated_states = torch.cat((-rotated_states_end, rotated_states_begin), dim=rotated_states_begin.ndim - 1)
hidden_states = (hidden_states * cos) + (rotated_states * sin)
hidden_states = hidden_states.transpose(0, 1)
hidden_states = hidden_states.view(batch_size, sequence_length, self.num_heads * self.head_size)
return hidden_states
def _apply_relative_embeddings(self, query, key, relative_position_embeddings):
# 1. project positional embeddings
# => (batch, head, 2*time1-1, d_k)
proj_relative_position_embeddings = self.linear_pos(relative_position_embeddings)
proj_relative_position_embeddings = proj_relative_position_embeddings.view(
relative_position_embeddings.size(0), -1, self.num_heads, self.head_size
)
proj_relative_position_embeddings = proj_relative_position_embeddings.transpose(1, 2)
proj_relative_position_embeddings = proj_relative_position_embeddings.transpose(2, 3)
# 2. Add bias to query
# => (batch, head, time1, d_k)
query = query.transpose(1, 2)
q_with_bias_u = (query + self.pos_bias_u).transpose(1, 2)
q_with_bias_v = (query + self.pos_bias_v).transpose(1, 2)
# 3. attention score: first compute matrix a and matrix c
# as described in https://arxiv.org/abs/1901.02860 Section 3.3
# => (batch, head, time1, time2)
scores_ac = torch.matmul(q_with_bias_u, key.transpose(-2, -1))
# 4. then compute matrix b and matrix d
# => (batch, head, time1, 2*time1-1)
scores_bd = torch.matmul(q_with_bias_v, proj_relative_position_embeddings)
# 5. shift matrix b and matrix d
zero_pad = torch.zeros((*scores_bd.size()[:3], 1), device=scores_bd.device, dtype=scores_bd.dtype)
scores_bd_padded = torch.cat([zero_pad, scores_bd], dim=-1)
scores_bd_padded_shape = scores_bd.size()[:2] + (scores_bd.shape[3] + 1, scores_bd.shape[2])
scores_bd_padded = scores_bd_padded.view(*scores_bd_padded_shape)
scores_bd = scores_bd_padded[:, :, 1:].view_as(scores_bd)
scores_bd = scores_bd[:, :, :, : scores_bd.size(-1) // 2 + 1]
# 6. sum matrices
# => (batch, head, time1, time2)
scores = (scores_ac + scores_bd) / math.sqrt(self.head_size)
return scores
class Wav2Vec2ConformerEncoderLayer(nn.Module):
"""Conformer block based on https://arxiv.org/abs/2005.08100."""
def __init__(self, config):
super().__init__()
embed_dim = config.hidden_size
dropout = config.attention_dropout
# Feed-forward 1
self.ffn1_layer_norm = nn.LayerNorm(embed_dim)
self.ffn1 = Wav2Vec2ConformerFeedForward(config)
# Self-Attention
self.self_attn_layer_norm = nn.LayerNorm(embed_dim)
self.self_attn_dropout = nn.Dropout(dropout)
self.self_attn = Wav2Vec2ConformerSelfAttention(config)
# Conformer Convolution
self.conv_module = Wav2Vec2ConformerConvolutionModule(config)
# Feed-forward 2
self.ffn2_layer_norm = nn.LayerNorm(embed_dim)
self.ffn2 = Wav2Vec2ConformerFeedForward(config)
self.final_layer_norm = nn.LayerNorm(embed_dim)
def forward(
self,
hidden_states,
attention_mask: Optional[torch.Tensor] = None,
relative_position_embeddings: Optional[torch.Tensor] = None,
output_attentions: bool = False,
):
hidden_states = hidden_states
# 1. Feed-Forward 1 layer
residual = hidden_states
hidden_states = self.ffn1_layer_norm(hidden_states)
hidden_states = self.ffn1(hidden_states)
hidden_states = hidden_states * 0.5 + residual
residual = hidden_states
# 2. Self-Attention layer
hidden_states = self.self_attn_layer_norm(hidden_states)
hidden_states, attn_weigts = self.self_attn(
hidden_states=hidden_states,
attention_mask=attention_mask,
relative_position_embeddings=relative_position_embeddings,
output_attentions=output_attentions,
)
hidden_states = self.self_attn_dropout(hidden_states)
hidden_states = hidden_states + residual
# 3. Convolutional Layer
residual = hidden_states
hidden_states = self.conv_module(hidden_states)
hidden_states = residual + hidden_states
# 4. Feed-Forward 2 Layer
residual = hidden_states
hidden_states = self.ffn2_layer_norm(hidden_states)
hidden_states = self.ffn2(hidden_states)
hidden_states = hidden_states * 0.5 + residual
hidden_states = self.final_layer_norm(hidden_states)
return hidden_states, attn_weigts
class Wav2Vec2ConformerEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
if config.position_embeddings_type == "relative":
self.embed_positions = Wav2Vec2ConformerRelPositionalEmbedding(config)
elif config.position_embeddings_type == "rotary":
self.embed_positions = Wav2Vec2ConformerRotaryPositionalEmbedding(config)
else:
self.embed_positions = None
self.pos_conv_embed = Wav2Vec2ConformerPositionalConvEmbedding(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([Wav2Vec2ConformerEncoderLayer(config) for _ in range(config.num_hidden_layers)])
self.gradient_checkpointing = False
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 output 0
expand_attention_mask = attention_mask.unsqueeze(-1).repeat(1, 1, hidden_states.shape[2])
hidden_states[~expand_attention_mask] = 0.0
# 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]
)
hidden_states = self.dropout(hidden_states)
if self.embed_positions is not None:
relative_position_embeddings = self.embed_positions(hidden_states)
else:
relative_position_embeddings = None
synced_gpus = is_deepspeed_zero3_enabled() or is_fsdp_managed_module(self)
for i, layer in enumerate(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,
relative_position_embeddings,
output_attentions,
)
else:
layer_outputs = layer(
hidden_states,
attention_mask=attention_mask,
relative_position_embeddings=relative_position_embeddings,
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,
)
# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2GumbelVectorQuantizer with Wav2Vec2->Wav2Vec2Conformer
class Wav2Vec2ConformerGumbelVectorQuantizer(nn.Module):
"""
Vector quantization using gumbel softmax. See `[CATEGORICAL REPARAMETERIZATION WITH
GUMBEL-SOFTMAX](https://arxiv.org/pdf/1611.01144.pdf) for more information.
"""
def __init__(self, config):
super().__init__()
self.num_groups = config.num_codevector_groups
self.num_vars = config.num_codevectors_per_group
if config.codevector_dim % self.num_groups != 0:
raise ValueError(
f"`config.codevector_dim {config.codevector_dim} must be divisible "
f"by `config.num_codevector_groups` {self.num_groups} for concatenation"
)
# storage for codebook variables (codewords)
self.codevectors = nn.Parameter(
torch.FloatTensor(1, self.num_groups * self.num_vars, config.codevector_dim // self.num_groups)
)
self.weight_proj = nn.Linear(config.conv_dim[-1], self.num_groups * self.num_vars)
# can be decayed for training
self.temperature = 2
@staticmethod
def _compute_perplexity(probs, mask=None):
if mask is not None:
mask_extended = mask.flatten()[:, None, None].expand(probs.shape)
probs = torch.where(mask_extended, probs, torch.zeros_like(probs))
marginal_probs = probs.sum(dim=0) / mask.sum()
else:
marginal_probs = probs.mean(dim=0)
perplexity = torch.exp(-torch.sum(marginal_probs * torch.log(marginal_probs + 1e-7), dim=-1)).sum()
return perplexity
def forward(self, hidden_states, mask_time_indices=None):
batch_size, sequence_length, hidden_size = hidden_states.shape
# project to codevector dim
hidden_states = self.weight_proj(hidden_states)
hidden_states = hidden_states.view(batch_size * sequence_length * self.num_groups, -1)
if self.training:
# sample code vector probs via gumbel in differentiateable way
codevector_probs = nn.functional.gumbel_softmax(
hidden_states.float(), tau=self.temperature, hard=True
).type_as(hidden_states)
# compute perplexity
codevector_soft_dist = torch.softmax(
hidden_states.view(batch_size * sequence_length, self.num_groups, -1).float(), dim=-1
)
perplexity = self._compute_perplexity(codevector_soft_dist, mask_time_indices)
else:
# take argmax in non-differentiable way
# comptute hard codevector distribution (one hot)
codevector_idx = hidden_states.argmax(dim=-1)
codevector_probs = hidden_states.new_zeros(hidden_states.shape).scatter_(
-1, codevector_idx.view(-1, 1), 1.0
)
codevector_probs = codevector_probs.view(batch_size * sequence_length, self.num_groups, -1)
perplexity = self._compute_perplexity(codevector_probs, mask_time_indices)
codevector_probs = codevector_probs.view(batch_size * sequence_length, -1)
# use probs to retrieve codevectors
codevectors_per_group = codevector_probs.unsqueeze(-1) * self.codevectors
codevectors = codevectors_per_group.view(batch_size * sequence_length, self.num_groups, self.num_vars, -1)
codevectors = codevectors.sum(-2).view(batch_size, sequence_length, -1)
return codevectors, perplexity
# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2Adapter with Wav2Vec2->Wav2Vec2Conformer
class Wav2Vec2ConformerAdapter(nn.Module):
def __init__(self, config):
super().__init__()
# feature dim might need to be down-projected
if config.output_hidden_size != config.hidden_size:
self.proj = nn.Linear(config.hidden_size, config.output_hidden_size)
self.proj_layer_norm = nn.LayerNorm(config.output_hidden_size)
else:
self.proj = self.proj_layer_norm = None
self.layers = nn.ModuleList(Wav2Vec2ConformerAdapterLayer(config) for _ in range(config.num_adapter_layers))
self.layerdrop = config.layerdrop
def forward(self, hidden_states):
# down project hidden_states if necessary
if self.proj is not None and self.proj_layer_norm is not None:
hidden_states = self.proj(hidden_states)
hidden_states = self.proj_layer_norm(hidden_states)
hidden_states = hidden_states.transpose(1, 2)
for layer in self.layers:
layerdrop_prob = np.random.random()
if not self.training or (layerdrop_prob > self.layerdrop):
hidden_states = layer(hidden_states)
hidden_states = hidden_states.transpose(1, 2)
return hidden_states
# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2AdapterLayer with Wav2Vec2->Wav2Vec2Conformer
class Wav2Vec2ConformerAdapterLayer(nn.Module):
def __init__(self, config):
super().__init__()
self.conv = nn.Conv1d(
config.output_hidden_size,
2 * config.output_hidden_size,
config.adapter_kernel_size,
stride=config.adapter_stride,
padding=1,
)
def forward(self, hidden_states):
hidden_states = self.conv(hidden_states)
hidden_states = nn.functional.glu(hidden_states, dim=1)
return hidden_states
class Wav2Vec2ConformerPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = Wav2Vec2ConformerConfig
base_model_prefix = "wav2vec2_conformer"
main_input_name = "input_values"
supports_gradient_checkpointing = True
def _init_weights(self, module):
"""Initialize the weights"""
# Wav2Vec2ForPreTraining last 2 linear layers need standard Linear init.
if isinstance(module, Wav2Vec2ConformerForPreTraining):
module.project_hid.reset_parameters()
module.project_q.reset_parameters()
module.project_hid._is_hf_initialized = True
module.project_q._is_hf_initialized = True
# gumbel softmax requires special init
elif isinstance(module, Wav2Vec2ConformerGumbelVectorQuantizer):
module.weight_proj.weight.data.normal_(mean=0.0, std=1)
module.weight_proj.bias.data.zero_()
nn.init.uniform_(module.codevectors)
elif isinstance(module, Wav2Vec2ConformerSelfAttention):
if hasattr(module, "pos_bias_u"):
nn.init.xavier_uniform_(module.pos_bias_u)
if hasattr(module, "pos_bias_v"):
nn.init.xavier_uniform_(module.pos_bias_v)
elif isinstance(module, Wav2Vec2ConformerPositionalConvEmbedding):
nn.init.normal_(
module.conv.weight,
mean=0,
std=2 * math.sqrt(1 / (module.conv.kernel_size[0] * module.conv.in_channels)),
)
nn.init.constant_(module.conv.bias, 0)
elif isinstance(module, Wav2Vec2ConformerFeatureProjection):
k = math.sqrt(1 / module.projection.in_features)
nn.init.uniform_(module.projection.weight, a=-k, b=k)
nn.init.uniform_(module.projection.bias, a=-k, b=k)
elif isinstance(module, nn.Linear):
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)):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
elif isinstance(module, nn.Conv1d):
nn.init.kaiming_normal_(module.weight)
if module.bias is not None:
k = math.sqrt(module.groups / (module.in_channels * module.kernel_size[0]))
nn.init.uniform_(module.bias, a=-k, b=k)
def _get_feat_extract_output_lengths(
self, input_lengths: Union[torch.LongTensor, int], add_adapter: Optional[bool] = None
):
"""
Computes the output length of the convolutional layers
"""
add_adapter = self.config.add_adapter if add_adapter is None else add_adapter
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)
if add_adapter:
for _ in range(self.config.num_adapter_layers):
input_lengths = _conv_out_length(input_lengths, 1, self.config.adapter_stride)
return input_lengths
def _get_feature_vector_attention_mask(
self, feature_vector_length: int, attention_mask: torch.LongTensor, add_adapter=None
):
# Effectively attention_mask.sum(-1), but not inplace to be able to run
# on inference mode.
non_padded_lengths = attention_mask.cumsum(dim=-1)[:, -1]
output_lengths = self._get_feat_extract_output_lengths(non_padded_lengths, add_adapter=add_adapter)
output_lengths = output_lengths.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
WAV2VEC2_CONFORMER_START_DOCSTRING = r"""
Wav2Vec2Conformer was proposed in [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech
Representations](https://arxiv.org/abs/2006.11477) by Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael
Auli.
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 [nn.Module](https://pytorch.org/docs/stable/nn.html#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 ([`Wav2Vec2ConformerConfig`]): 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.
"""
WAV2VEC2_CONFORMER_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
[wav2vec2-conformer-rel-pos-large](https://huggingface.co/facebook/wav2vec2-conformer-rel-pos-large),
`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 Wav2Vec2Conformer Model transformer outputting raw hidden-states without any specific head on top.",
WAV2VEC2_CONFORMER_START_DOCSTRING,
)
class Wav2Vec2ConformerModel(Wav2Vec2ConformerPreTrainedModel):
def __init__(self, config: Wav2Vec2ConformerConfig):
super().__init__(config)
self.config = config
self.feature_extractor = Wav2Vec2ConformerFeatureEncoder(config)
self.feature_projection = Wav2Vec2ConformerFeatureProjection(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_())
self.encoder = Wav2Vec2ConformerEncoder(config)
self.adapter = Wav2Vec2ConformerAdapter(config) if config.add_adapter else None
# Initialize weights and apply final processing
self.post_init()
# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2Model.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.feature_extractor._freeze_parameters()
# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2Model._mask_hidden_states
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(WAV2VEC2_CONFORMER_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=Wav2Vec2BaseModelOutput,
config_class=_CONFIG_FOR_DOC,
modality="audio",
expected_output=_EXPECTED_OUTPUT_SHAPE,
)
# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2Model.forward with wav2vec2->wav2vec2_conformer
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, Wav2Vec2BaseModelOutput]:
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, add_adapter=False
)
hidden_states, extract_features = self.feature_projection(extract_features)
hidden_states = self._mask_hidden_states(
hidden_states, mask_time_indices=mask_time_indices, attention_mask=attention_mask
)
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 self.adapter is not None:
hidden_states = self.adapter(hidden_states)
if not return_dict:
return (hidden_states, extract_features) + encoder_outputs[1:]
return Wav2Vec2BaseModelOutput(
last_hidden_state=hidden_states,
extract_features=extract_features,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
)
@add_start_docstrings(
"""Wav2Vec2Conformer Model with a quantizer and `VQ` head on top.""", WAV2VEC2_CONFORMER_START_DOCSTRING
)
class Wav2Vec2ConformerForPreTraining(Wav2Vec2ConformerPreTrainedModel):
# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForPreTraining.__init__ with Wav2Vec2->Wav2Vec2Conformer,wav2vec2->wav2vec2_conformer
def __init__(self, config: Wav2Vec2ConformerConfig):
super().__init__(config)
self.wav2vec2_conformer = Wav2Vec2ConformerModel(config)
self.dropout_features = nn.Dropout(config.feat_quantizer_dropout)
self.quantizer = Wav2Vec2ConformerGumbelVectorQuantizer(config)
self.project_hid = nn.Linear(config.hidden_size, config.proj_codevector_dim)
self.project_q = nn.Linear(config.codevector_dim, config.proj_codevector_dim)
# Initialize weights and apply final processing
self.post_init()
# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForPreTraining.set_gumbel_temperature
def set_gumbel_temperature(self, temperature: int):
"""
Set the Gumbel softmax temperature to a given value. Only necessary for training
"""
self.quantizer.temperature = temperature
# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForPreTraining.freeze_feature_encoder with wav2vec2->wav2vec2_conformer
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.wav2vec2_conformer.feature_extractor._freeze_parameters()
@staticmethod
# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForPreTraining.compute_contrastive_logits
def compute_contrastive_logits(
target_features: torch.FloatTensor,
negative_features: torch.FloatTensor,
predicted_features: torch.FloatTensor,
temperature: int = 0.1,
):
"""
Compute logits for contrastive loss based using cosine similarity as the distance measure between
`[positive_feature, negative_features]` and `[predicted_features]`. Additionally, temperature can be applied.
"""
target_features = torch.cat([target_features, negative_features], dim=0)
logits = torch.cosine_similarity(predicted_features.float(), target_features.float(), dim=-1).type_as(
target_features
)
# apply temperature
logits = logits / temperature
return logits
@add_start_docstrings_to_model_forward(WAV2VEC2_CONFORMER_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=Wav2Vec2ConformerForPreTrainingOutput, config_class=_CONFIG_FOR_DOC)
# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForPreTraining.forward with Wav2Vec2->Wav2Vec2Conformer,wav2vec2->wav2vec2_conformer,wav2vec2_conformer-base->wav2vec2-conformer-rel-pos-large
def forward(
self,
input_values: Optional[torch.Tensor],
attention_mask: Optional[torch.Tensor] = None,
mask_time_indices: Optional[torch.BoolTensor] = None,
sampled_negative_indices: Optional[torch.BoolTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, Wav2Vec2ConformerForPreTrainingOutput]:
r"""
mask_time_indices (`torch.BoolTensor` of shape `(batch_size, sequence_length)`, *optional*):
Indices to mask extracted features for contrastive loss. When in training mode, model learns to predict
masked extracted features in *config.proj_codevector_dim* space.
sampled_negative_indices (`torch.BoolTensor` of shape `(batch_size, sequence_length, num_negatives)`, *optional*):
Indices indicating which quantized target vectors are used as negative sampled vectors in contrastive loss.
Required input for pre-training.
Returns:
Example:
```python
>>> import torch
>>> from transformers import AutoFeatureExtractor, Wav2Vec2ConformerForPreTraining
>>> from transformers.models.wav2vec2_conformer.modeling_wav2vec2_conformer import _compute_mask_indices, _sample_negative_indices
>>> from datasets import load_dataset
>>> feature_extractor = AutoFeatureExtractor.from_pretrained("facebook/wav2vec2-conformer-rel-pos-large")
>>> model = Wav2Vec2ConformerForPreTraining.from_pretrained("facebook/wav2vec2-conformer-rel-pos-large")
>>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
>>> input_values = feature_extractor(ds[0]["audio"]["array"], return_tensors="pt").input_values # Batch size 1
>>> # compute masked indices
>>> batch_size, raw_sequence_length = input_values.shape
>>> sequence_length = model._get_feat_extract_output_lengths(raw_sequence_length).item()
>>> mask_time_indices = _compute_mask_indices(
... shape=(batch_size, sequence_length), mask_prob=0.2, mask_length=2
... )
>>> sampled_negative_indices = _sample_negative_indices(
... features_shape=(batch_size, sequence_length),
... num_negatives=model.config.num_negatives,
... mask_time_indices=mask_time_indices,
... )
>>> mask_time_indices = torch.tensor(data=mask_time_indices, device=input_values.device, dtype=torch.long)
>>> sampled_negative_indices = torch.tensor(
... data=sampled_negative_indices, device=input_values.device, dtype=torch.long
... )
>>> with torch.no_grad():
... outputs = model(input_values, mask_time_indices=mask_time_indices)
>>> # compute cosine similarity between predicted (=projected_states) and target (=projected_quantized_states)
>>> cosine_sim = torch.cosine_similarity(outputs.projected_states, outputs.projected_quantized_states, dim=-1)
>>> # show that cosine similarity is much higher than random
>>> cosine_sim[mask_time_indices.to(torch.bool)].mean() > 0.5
tensor(True)
>>> # for contrastive loss training model should be put into train mode
>>> model = model.train()
>>> loss = model(
... input_values, mask_time_indices=mask_time_indices, sampled_negative_indices=sampled_negative_indices
... ).loss
```"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if mask_time_indices is not None:
mask_time_indices = mask_time_indices.to(torch.bool)
outputs = self.wav2vec2_conformer(
input_values,
attention_mask=attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
mask_time_indices=mask_time_indices,
return_dict=return_dict,
)
# 1. project all transformed features (including masked) to final vq dim
transformer_features = self.project_hid(outputs[0])
# 2. quantize all (unmasked) extracted features and project to final vq dim
extract_features = self.dropout_features(outputs[1])
if attention_mask is not None:
# compute reduced attention_mask correponding to feature vectors
attention_mask = self._get_feature_vector_attention_mask(
extract_features.shape[1], attention_mask, add_adapter=False
)
quantized_features, codevector_perplexity = self.quantizer(
extract_features, mask_time_indices=mask_time_indices
)
quantized_features = quantized_features.to(self.project_q.weight.dtype)
quantized_features = self.project_q(quantized_features)
loss = contrastive_loss = diversity_loss = None
if sampled_negative_indices is not None:
batch_size, sequence_length, hidden_size = quantized_features.shape
# for training, we sample negatives
# 3. sample K negatives (distractors) quantized states for contrastive loss
# if attention_mask is passed, make sure that padded feature vectors cannot be sampled
# sample negative quantized vectors BTC => (BxT)C
negative_quantized_features = quantized_features.view(-1, hidden_size)[
sampled_negative_indices.long().view(-1)
]
negative_quantized_features = negative_quantized_features.view(
batch_size, sequence_length, -1, hidden_size
).permute(2, 0, 1, 3)
# 4. compute logits, corresponding to `logs = sim(c_t, [q_t, \sim{q}_t]) / \kappa`
# of equation (3) in https://arxiv.org/pdf/2006.11477.pdf
logits = self.compute_contrastive_logits(
quantized_features[None, :],
negative_quantized_features,
transformer_features,
self.config.contrastive_logits_temperature,
)
# 5. if a negative vector is identical to the positive (i.e. when codebook utilization is low),
# its cosine similarity will be masked
neg_is_pos = (quantized_features == negative_quantized_features).all(-1)
if neg_is_pos.any():
logits[1:][neg_is_pos] = float("-inf")
# 6. compute contrastive loss \mathbf{L}_m = cross_entropy(logs) =
# -log(exp(sim(c_t, q_t)/\kappa) / \sum_{\sim{q}} exp(sim(c_t, \sim{q})/\kappa))
logits = logits.transpose(0, 2).reshape(-1, logits.size(0))
target = ((1 - mask_time_indices.long()) * -100).transpose(0, 1).flatten()
contrastive_loss = nn.functional.cross_entropy(logits.float(), target, reduction="sum")
# 7. compute diversity loss: \mathbf{L}_d
num_codevectors = self.config.num_codevectors_per_group * self.config.num_codevector_groups
diversity_loss = ((num_codevectors - codevector_perplexity) / num_codevectors) * mask_time_indices.sum()
# 8. \mathbf{L} = \mathbf{L}_m + \alpha * \mathbf{L}_d
loss = contrastive_loss + self.config.diversity_loss_weight * diversity_loss
if not return_dict:
if loss is not None:
return (loss, transformer_features, quantized_features, codevector_perplexity) + outputs[2:]
return (transformer_features, quantized_features, codevector_perplexity) + outputs[2:]
return Wav2Vec2ConformerForPreTrainingOutput(
loss=loss,
projected_states=transformer_features,
projected_quantized_states=quantized_features,
codevector_perplexity=codevector_perplexity,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
contrastive_loss=contrastive_loss,
diversity_loss=diversity_loss,
)
@add_start_docstrings(
"""Wav2Vec2Conformer Model with a `language modeling` head on top for Connectionist Temporal Classification (CTC).""",
WAV2VEC2_CONFORMER_START_DOCSTRING,
)
class Wav2Vec2ConformerForCTC(Wav2Vec2ConformerPreTrainedModel):
# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForCTC.__init__ with Wav2Vec2->Wav2Vec2Conformer,wav2vec2->wav2vec2_conformer
def __init__(self, config, target_lang: Optional[str] = None):
super().__init__(config)
self.wav2vec2_conformer = Wav2Vec2ConformerModel(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: `Wav2Vec2ConformerForCTC.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()
# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForCTC.freeze_feature_encoder with wav2vec2->wav2vec2_conformer
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.wav2vec2_conformer.feature_extractor._freeze_parameters()
@add_start_docstrings_to_model_forward(WAV2VEC2_CONFORMER_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,
)
# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForCTC.forward with Wav2Vec2->Wav2Vec2Conformer,wav2vec2->wav2vec2_conformer
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.wav2vec2_conformer(
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
)
@add_start_docstrings(
"""
Wav2Vec2Conformer Model with a sequence classification head on top (a linear layer over the pooled output) for
tasks like SUPERB Keyword Spotting.
""",
WAV2VEC2_CONFORMER_START_DOCSTRING,
)
class Wav2Vec2ConformerForSequenceClassification(Wav2Vec2ConformerPreTrainedModel):
# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForSequenceClassification.__init__ with Wav2Vec2->Wav2Vec2Conformer,wav2vec2->wav2vec2_conformer
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 Wav2Vec2Conformer adapters (config.add_adapter=True)"
)
self.wav2vec2_conformer = Wav2Vec2ConformerModel(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()
# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForSequenceClassification.freeze_feature_encoder with wav2vec2->wav2vec2_conformer
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.wav2vec2_conformer.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.wav2vec2_conformer.parameters():
param.requires_grad = False
@add_start_docstrings_to_model_forward(WAV2VEC2_CONFORMER_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=SequenceClassifierOutput,
config_class=_CONFIG_FOR_DOC,
modality="audio",
)
# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForSequenceClassification.forward with Wav2Vec2->Wav2Vec2Conformer,wav2vec2->wav2vec2_conformer,WAV_2_VEC_2->WAV2VEC2_CONFORMER
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.wav2vec2_conformer(
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,
)
@add_start_docstrings(
"""
Wav2Vec2Conformer Model with a frame classification head on top for tasks like Speaker Diarization.
""",
WAV2VEC2_CONFORMER_START_DOCSTRING,
)
class Wav2Vec2ConformerForAudioFrameClassification(Wav2Vec2ConformerPreTrainedModel):
# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForAudioFrameClassification.__init__ with Wav2Vec2->Wav2Vec2Conformer,wav2vec2->wav2vec2_conformer,WAV_2_VEC_2->WAV2VEC2_CONFORMER
def __init__(self, config):
super().__init__(config)
if hasattr(config, "add_adapter") and config.add_adapter:
raise ValueError(
"Audio frame classification does not support the use of Wav2Vec2Conformer adapters (config.add_adapter=True)"
)
self.wav2vec2_conformer = Wav2Vec2ConformerModel(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.classifier = nn.Linear(config.hidden_size, config.num_labels)
self.num_labels = config.num_labels
self.init_weights()
# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForAudioFrameClassification.freeze_feature_encoder with wav2vec2->wav2vec2_conformer
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.wav2vec2_conformer.feature_extractor._freeze_parameters()
# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForAudioFrameClassification.freeze_base_model with wav2vec2->wav2vec2_conformer
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.wav2vec2_conformer.parameters():
param.requires_grad = False
@add_start_docstrings_to_model_forward(WAV2VEC2_CONFORMER_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TokenClassifierOutput,
config_class=_CONFIG_FOR_DOC,
modality="audio",
)
# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForAudioFrameClassification.forward with wav2vec2->wav2vec2_conformer
def forward(
self,
input_values: Optional[torch.Tensor],
attention_mask: Optional[torch.Tensor] = None,
labels: Optional[torch.Tensor] = 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,)`, *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.wav2vec2_conformer(
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]
logits = self.classifier(hidden_states)
loss = None
if labels is not None:
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.view(-1, self.num_labels), torch.argmax(labels.view(-1, self.num_labels), axis=1))
if not return_dict:
output = (logits,) + outputs[_HIDDEN_STATES_START_POSITION:]
return output
return TokenClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
# Copied from transformers.models.wav2vec2.modeling_wav2vec2.AMSoftmaxLoss
class AMSoftmaxLoss(nn.Module):
def __init__(self, input_dim, num_labels, scale=30.0, margin=0.4):
super(AMSoftmaxLoss, self).__init__()
self.scale = scale
self.margin = margin
self.num_labels = num_labels
self.weight = nn.Parameter(torch.randn(input_dim, num_labels), requires_grad=True)
self.loss = nn.CrossEntropyLoss()
def forward(self, hidden_states, labels):
labels = labels.flatten()
weight = nn.functional.normalize(self.weight, dim=0)
hidden_states = nn.functional.normalize(hidden_states, dim=1)
cos_theta = torch.mm(hidden_states, weight)
psi = cos_theta - self.margin
onehot = nn.functional.one_hot(labels, self.num_labels)
logits = self.scale * torch.where(onehot.bool(), psi, cos_theta)
loss = self.loss(logits, labels)
return loss
# Copied from transformers.models.wav2vec2.modeling_wav2vec2.TDNNLayer
class TDNNLayer(nn.Module):
def __init__(self, config, layer_id=0):
super().__init__()
self.in_conv_dim = config.tdnn_dim[layer_id - 1] if layer_id > 0 else config.tdnn_dim[layer_id]
self.out_conv_dim = config.tdnn_dim[layer_id]
self.kernel_size = config.tdnn_kernel[layer_id]
self.dilation = config.tdnn_dilation[layer_id]
self.kernel = nn.Linear(self.in_conv_dim * self.kernel_size, self.out_conv_dim)
self.activation = nn.ReLU()
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
if is_peft_available():
from peft.tuners.lora import LoraLayer
if isinstance(self.kernel, LoraLayer):
warnings.warn(
"Detected LoRA on TDNNLayer. LoRA weights won't be applied due to optimization. "
"You should exclude TDNNLayer from LoRA's target modules.",
)
# for backward compatibility, we keep nn.Linear but call F.conv1d for speed up
hidden_states = hidden_states.transpose(1, 2)
weight = self.kernel.weight.view(self.out_conv_dim, self.kernel_size, self.in_conv_dim).transpose(1, 2)
hidden_states = nn.functional.conv1d(hidden_states, weight, self.kernel.bias, dilation=self.dilation)
hidden_states = hidden_states.transpose(1, 2)
hidden_states = self.activation(hidden_states)
return hidden_states
@add_start_docstrings(
"""
Wav2Vec2Conformer Model with an XVector feature extraction head on top for tasks like Speaker Verification.
""",
WAV2VEC2_CONFORMER_START_DOCSTRING,
)
class Wav2Vec2ConformerForXVector(Wav2Vec2ConformerPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.wav2vec2_conformer = Wav2Vec2ConformerModel(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.tdnn_dim[0])
tdnn_layers = [TDNNLayer(config, i) for i in range(len(config.tdnn_dim))]
self.tdnn = nn.ModuleList(tdnn_layers)
self.feature_extractor = nn.Linear(config.tdnn_dim[-1] * 2, config.xvector_output_dim)
self.classifier = nn.Linear(config.xvector_output_dim, config.xvector_output_dim)
self.objective = AMSoftmaxLoss(config.xvector_output_dim, config.num_labels)
self.init_weights()
# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForXVector.freeze_feature_encoder with wav2vec2->wav2vec2_conformer
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.wav2vec2_conformer.feature_extractor._freeze_parameters()
# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForXVector.freeze_base_model with wav2vec2->wav2vec2_conformer
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.wav2vec2_conformer.parameters():
param.requires_grad = False
# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForXVector._get_tdnn_output_lengths with wav2vec2->wav2vec2_conformer
def _get_tdnn_output_lengths(self, input_lengths: Union[torch.LongTensor, int]):
"""
Computes the output length of the TDNN 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 in self.config.tdnn_kernel:
input_lengths = _conv_out_length(input_lengths, kernel_size, 1)
return input_lengths
@add_start_docstrings_to_model_forward(WAV2VEC2_CONFORMER_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=XVectorOutput,
config_class=_CONFIG_FOR_DOC,
modality="audio",
)
# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForXVector.forward with Wav2Vec2->Wav2Vec2Conformer,wav2vec2->wav2vec2_conformer,WAV_2_VEC_2->WAV2VEC2_CONFORMER
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, XVectorOutput]:
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.wav2vec2_conformer(
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)
for tdnn_layer in self.tdnn:
hidden_states = tdnn_layer(hidden_states)
# Statistic Pooling
if attention_mask is None:
mean_features = hidden_states.mean(dim=1)
std_features = hidden_states.std(dim=1)
else:
feat_extract_output_lengths = self._get_feat_extract_output_lengths(attention_mask.sum(dim=1))
tdnn_output_lengths = self._get_tdnn_output_lengths(feat_extract_output_lengths)
mean_features = []
std_features = []
for i, length in enumerate(tdnn_output_lengths):
mean_features.append(hidden_states[i, :length].mean(dim=0))
std_features.append(hidden_states[i, :length].std(dim=0))
mean_features = torch.stack(mean_features)
std_features = torch.stack(std_features)
statistic_pooling = torch.cat([mean_features, std_features], dim=-1)
output_embeddings = self.feature_extractor(statistic_pooling)
logits = self.classifier(output_embeddings)
loss = None
if labels is not None:
loss = self.objective(logits, labels)
if not return_dict:
output = (logits, output_embeddings) + outputs[_HIDDEN_STATES_START_POSITION:]
return ((loss,) + output) if loss is not None else output
return XVectorOutput(
loss=loss,
logits=logits,
embeddings=output_embeddings,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
__all__ = [
"Wav2Vec2ConformerForAudioFrameClassification",
"Wav2Vec2ConformerForCTC",
"Wav2Vec2ConformerForPreTraining",
"Wav2Vec2ConformerForSequenceClassification",
"Wav2Vec2ConformerForXVector",
"Wav2Vec2ConformerModel",
"Wav2Vec2ConformerPreTrainedModel",
]
| transformers/src/transformers/models/wav2vec2_conformer/modeling_wav2vec2_conformer.py/0 | {
"file_path": "transformers/src/transformers/models/wav2vec2_conformer/modeling_wav2vec2_conformer.py",
"repo_id": "transformers",
"token_count": 41006
} |
# coding=utf-8
# Copyright 2022 The OpenAI 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.
"""Flax whisper model."""
import math
import random
from functools import partial
from typing import Optional, Tuple
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict, freeze, unfreeze
from flax.linen import combine_masks, make_causal_mask
from flax.linen import partitioning as nn_partitioning
from flax.linen.attention import dot_product_attention_weights
from flax.traverse_util import flatten_dict, unflatten_dict
from jax import lax
from jax.random import PRNGKey
from ...generation.flax_logits_process import FlaxWhisperTimeStampLogitsProcessor
from ...modeling_flax_outputs import (
FlaxBaseModelOutput,
FlaxBaseModelOutputWithPastAndCrossAttentions,
FlaxCausalLMOutputWithCrossAttentions,
FlaxSeq2SeqLMOutput,
FlaxSeq2SeqModelOutput,
FlaxSequenceClassifierOutput,
)
from ...modeling_flax_utils import (
ACT2FN,
FlaxPreTrainedModel,
append_call_sample_docstring,
append_replace_return_docstrings,
overwrite_call_docstring,
)
from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
from .configuration_whisper import WhisperConfig
logger = logging.get_logger(__name__)
_CHECKPOINT_FOR_DOC = "openai/whisper-tiny"
_CONFIG_FOR_DOC = "WhisperConfig"
remat = nn_partitioning.remat
def sinusoidal_embedding_init(key, shape, dtype=jnp.float_) -> jax.Array:
"""Returns sinusoids for positional embedding"""
length, channels = shape
if channels % 2 != 0:
raise ValueError(
f"Number of channels has to be divisible by 2 for sinusoidal positional embeddings, got {channels} channels."
)
log_timescale_increment = math.log(10000) / (channels // 2 - 1)
inv_timescales = jnp.exp(-log_timescale_increment * jnp.arange(channels // 2))
scaled_time = jnp.arange(length).reshape(-1, 1) * inv_timescales.reshape(1, -1)
return jnp.concatenate([jnp.sin(scaled_time), jnp.cos(scaled_time)], axis=1).astype(dtype)
WHISPER_START_DOCSTRING = r"""
This model inherits from [`FlaxPreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its models (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 ([`WhisperConfig`]): 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`].
"""
WHISPER_INPUTS_DOCSTRING = r"""
Args:
input_features (`numpy.ndarray` of shape `(batch_size, feature_size, sequence_length)`):
Float values mel features extracted from the raw speech waveform. Raw speech waveform 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_features`, the
[`WhisperFeatureExtractor`] should be used for extracting the features, padding and conversion into a
tensor of type `numpy.ndarray`. See [`~WhisperFeatureExtractor.__call__`]
attention_mask (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*):
Whisper does not support masking of the `input_features`, this argument is preserved for compatibility, but
is not used. By default the silence in the input log mel spectrogram are ignored.
decoder_input_ids (`numpy.ndarray` of shape `(batch_size, target_sequence_length)`, *optional*):
Indices of decoder input sequence tokens in the vocabulary. Indices can be obtained using
[`WhisperTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details.
[What are decoder input IDs?](../glossary#decoder-input-ids) Whisper uses the `decoder_start_token_id` as
the starting token for `decoder_input_ids` generation.
decoder_attention_mask (`numpy.ndarray` 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. If you want to change padding behavior, you should modify to your needs. See diagram 1
in [the paper](https://arxiv.org/abs/1910.13461) for more information on the default strategy.
position_ids (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*):
Whisper does not use `position_ids` in the encoder as `input_features` is always the same size and doesn't
use masking, but this argument is preserved for compatibility. By default the silence in the input log mel
spectrogram are ignored.
decoder_position_ids (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*):
Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the
range `[0, config.max_position_embeddings - 1]`.
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.
"""
WHISPER_ENCODE_INPUTS_DOCSTRING = r"""
Args:
input_features (`numpy.ndarray` of shape `(batch_size, feature_size, sequence_length)`):
Float values mel features extracted from the raw speech waveform. Raw speech waveform 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_features`, the
[`WhisperFeatureExtractor`] should be used for extracting the mel features, padding and conversion into a
tensor of type `numpy.ndarray`. See [`~WhisperFeatureExtractor.__call__`].
attention_mask (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*):
Whisper does not support masking of the `input_features`, this argument is preserved for compatibility, but
is not used. By default the silence in the input log mel spectrogram are ignored.
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.
"""
WHISPER_DECODE_INPUTS_DOCSTRING = r"""
Args:
decoder_input_ids (`numpy.ndarray` of shape `(batch_size, target_sequence_length)`):
Indices of decoder input sequence tokens in the vocabulary. Indices can be obtained using
[`WhisperTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details.
[What are decoder input IDs?](../glossary#decoder-input-ids)
encoder_outputs (`tuple(tuple(numpy.ndarray)`):
Tuple consists of (`last_hidden_state`, *optional*: `hidden_states`, *optional*: `attentions`)
`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.
encoder_attention_mask (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*):
Whisper does not support masking of the `input_features`, this argument is preserved for compatibility,
but it is not used. By default the silence in the input log mel spectrogram are ignored.
decoder_attention_mask (`numpy.ndarray` 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. If you want to change padding behavior, you should modify to your needs. See diagram 1
in [the paper](https://arxiv.org/abs/1910.13461) for more information on the default strategy.
decoder_position_ids (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*):
Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the
range `[0, config.max_position_embeddings - 1]`.
past_key_values (`Dict[str, numpy.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.
"""
class FlaxWhisperAttention(nn.Module):
config: WhisperConfig
embed_dim: int
num_heads: int
dropout: float = 0.0
causal: bool = False
bias: bool = True
dtype: jnp.dtype = jnp.float32
def setup(self) -> None:
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}"
f" and `num_heads`: {self.num_heads})."
)
dense = partial(
nn.Dense,
self.embed_dim,
dtype=self.dtype,
kernel_init=jax.nn.initializers.normal(self.config.init_std),
)
self.q_proj = dense(use_bias=self.bias)
self.k_proj = dense(use_bias=False)
self.v_proj = dense(use_bias=self.bias)
self.out_proj = dense(use_bias=self.bias)
if self.causal:
self.causal_mask = make_causal_mask(
jnp.ones((1, self.config.max_target_positions), dtype="bool"), dtype="bool"
)
def __call__(
self,
hidden_states: jnp.ndarray,
key_value_states: Optional[jnp.ndarray] = None,
attention_mask: Optional[jnp.ndarray] = None,
init_cache: bool = False,
deterministic: bool = True,
) -> Tuple[jnp.ndarray]:
is_cross_attention = key_value_states is not None
batch_size = hidden_states.shape[0]
query_states = self.q_proj(hidden_states)
if is_cross_attention:
key_states = self.k_proj(key_value_states)
value_states = self.v_proj(key_value_states)
else:
key_states = self.k_proj(hidden_states)
value_states = self.v_proj(hidden_states)
query_states = self._split_heads(query_states)
key_states = self._split_heads(key_states)
value_states = self._split_heads(value_states)
if self.causal:
query_length, key_length = query_states.shape[1], key_states.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]
causal_mask = jnp.broadcast_to(causal_mask, (batch_size,) + causal_mask.shape[1:])
# combine masks if needed
if attention_mask is not None and self.causal:
attention_mask = jnp.broadcast_to(jnp.expand_dims(attention_mask, axis=(-3, -2)), causal_mask.shape)
attention_mask = combine_masks(attention_mask, causal_mask)
elif self.causal:
attention_mask = causal_mask
elif attention_mask is not None:
attention_mask = jnp.expand_dims(attention_mask, axis=(-3, -2))
# During fast autoregressive decoding, we feed one position at a time,
# and cache the keys and values step by step.
if self.causal and (self.has_variable("cache", "cached_key") or init_cache):
key_states, value_states, attention_mask = self._concatenate_to_cache(
key_states, value_states, query_states, attention_mask
)
# Convert the boolean attention mask to an attention bias.
if attention_mask is not None:
# attention mask in the form of attention bias
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),
)
else:
attention_bias = None
dropout_rng = None
if not deterministic and self.dropout > 0.0:
dropout_rng = self.make_rng("dropout")
attn_weights = dot_product_attention_weights(
query_states,
key_states,
bias=attention_bias,
dropout_rng=dropout_rng,
dropout_rate=self.dropout,
broadcast_dropout=True,
deterministic=deterministic,
dtype=self.dtype,
precision=None,
)
attn_output = jnp.einsum("...hqk,...khd->...qhd", attn_weights, value_states)
attn_output = self._merge_heads(attn_output)
attn_output = self.out_proj(attn_output)
return attn_output, attn_weights
def _split_heads(self, hidden_state) -> jnp.ndarray:
return hidden_state.reshape(hidden_state.shape[:2] + (self.num_heads, self.head_dim))
def _merge_heads(self, hidden_state) -> jnp.ndarray:
return hidden_state.reshape(hidden_state.shape[:2] + (self.embed_dim,))
@nn.compact
def _concatenate_to_cache(self, key, value, query, attention_mask) -> Tuple[jnp.ndarray, jnp.ndarray, jnp.ndarray]:
# 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
# Copied from transformers.models.mbart.modeling_flax_mbart.FlaxMBartEncoderLayer with MBart->Whisper
class FlaxWhisperEncoderLayer(nn.Module):
config: WhisperConfig
dtype: jnp.dtype = jnp.float32
def setup(self) -> None:
self.embed_dim = self.config.d_model
self.self_attn = FlaxWhisperAttention(
config=self.config,
embed_dim=self.embed_dim,
num_heads=self.config.encoder_attention_heads,
dropout=self.config.attention_dropout,
dtype=self.dtype,
)
self.self_attn_layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05)
self.dropout_layer = nn.Dropout(rate=self.config.dropout)
self.activation_fn = ACT2FN[self.config.activation_function]
self.activation_dropout_layer = nn.Dropout(rate=self.config.activation_dropout)
self.fc1 = nn.Dense(
self.config.encoder_ffn_dim,
dtype=self.dtype,
kernel_init=jax.nn.initializers.normal(self.config.init_std),
)
self.fc2 = nn.Dense(
self.embed_dim, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.init_std)
)
self.final_layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05)
def __call__(
self,
hidden_states: jnp.ndarray,
attention_mask: jnp.ndarray,
output_attentions: bool = True,
deterministic: bool = True,
) -> Tuple[jnp.ndarray]:
residual = hidden_states
hidden_states = self.self_attn_layer_norm(hidden_states)
hidden_states, attn_weights = self.self_attn(hidden_states=hidden_states, attention_mask=attention_mask)
hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic)
hidden_states = residual + hidden_states
residual = hidden_states
hidden_states = self.final_layer_norm(hidden_states)
hidden_states = self.activation_fn(self.fc1(hidden_states))
hidden_states = self.activation_dropout_layer(hidden_states, deterministic=deterministic)
hidden_states = self.fc2(hidden_states)
hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic)
hidden_states = residual + hidden_states
outputs = (hidden_states,)
if output_attentions:
outputs += (attn_weights,)
return outputs
class FlaxWhisperEncoderLayerCollection(nn.Module):
config: WhisperConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
gradient_checkpointing: bool = False
def setup(self):
if self.gradient_checkpointing:
FlaxWhisperEncoderCheckpointLayer = remat(FlaxWhisperEncoderLayer, static_argnums=(2, 3))
self.layers = [
FlaxWhisperEncoderCheckpointLayer(self.config, name=str(i), dtype=self.dtype)
for i in range(self.config.encoder_layers)
]
else:
self.layers = [
FlaxWhisperEncoderLayer(self.config, name=str(i), dtype=self.dtype)
for i in range(self.config.encoder_layers)
]
self.layerdrop = self.config.encoder_layerdrop
def __call__(
self,
hidden_states,
attention_mask,
deterministic: bool = True,
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 encoder_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 = random.uniform(0, 1)
if not deterministic and (dropout_probability < self.layerdrop): # skip the layer
layer_outputs = (None, None)
else:
layer_outputs = encoder_layer(
hidden_states,
attention_mask,
output_attentions,
deterministic,
)
hidden_states = layer_outputs[0]
if output_attentions:
all_attentions = all_attentions + (layer_outputs[1],)
if output_hidden_states:
all_hidden_states += (hidden_states,)
outputs = (hidden_states, all_hidden_states, all_attentions)
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=all_hidden_states, attentions=all_attentions
)
# Copied from transformers.models.mbart.modeling_flax_mbart.FlaxMBartDecoderLayer with MBart->Whisper
class FlaxWhisperDecoderLayer(nn.Module):
config: WhisperConfig
dtype: jnp.dtype = jnp.float32
def setup(self) -> None:
self.embed_dim = self.config.d_model
self.self_attn = FlaxWhisperAttention(
config=self.config,
embed_dim=self.embed_dim,
num_heads=self.config.decoder_attention_heads,
dropout=self.config.attention_dropout,
causal=True,
dtype=self.dtype,
)
self.dropout_layer = nn.Dropout(rate=self.config.dropout)
self.activation_fn = ACT2FN[self.config.activation_function]
self.activation_dropout_layer = nn.Dropout(rate=self.config.activation_dropout)
self.self_attn_layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05)
self.encoder_attn = FlaxWhisperAttention(
config=self.config,
embed_dim=self.embed_dim,
num_heads=self.config.decoder_attention_heads,
dropout=self.config.attention_dropout,
dtype=self.dtype,
)
self.encoder_attn_layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05)
self.fc1 = nn.Dense(
self.config.decoder_ffn_dim,
dtype=self.dtype,
kernel_init=jax.nn.initializers.normal(self.config.init_std),
)
self.fc2 = nn.Dense(
self.embed_dim, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.init_std)
)
self.final_layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05)
def __call__(
self,
hidden_states: jnp.ndarray,
attention_mask: jnp.ndarray,
encoder_hidden_states: Optional[jnp.ndarray] = None,
encoder_attention_mask: Optional[jnp.ndarray] = None,
init_cache: bool = False,
output_attentions: bool = True,
deterministic: bool = True,
) -> Tuple[jnp.ndarray]:
residual = hidden_states
hidden_states = self.self_attn_layer_norm(hidden_states)
# Self Attention
hidden_states, self_attn_weights = self.self_attn(
hidden_states=hidden_states, attention_mask=attention_mask, init_cache=init_cache
)
hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic)
hidden_states = residual + hidden_states
# Cross-Attention Block
cross_attn_weights = None
if encoder_hidden_states is not None:
residual = hidden_states
hidden_states = self.encoder_attn_layer_norm(hidden_states)
hidden_states, cross_attn_weights = self.encoder_attn(
hidden_states=hidden_states,
key_value_states=encoder_hidden_states,
attention_mask=encoder_attention_mask,
)
hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic)
hidden_states = residual + hidden_states
# Fully Connected
residual = hidden_states
hidden_states = self.final_layer_norm(hidden_states)
hidden_states = self.activation_fn(self.fc1(hidden_states))
hidden_states = self.activation_dropout_layer(hidden_states, deterministic=deterministic)
hidden_states = self.fc2(hidden_states)
hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic)
hidden_states = residual + hidden_states
outputs = (hidden_states,)
if output_attentions:
outputs += (self_attn_weights, cross_attn_weights)
return outputs
class FlaxWhisperDecoderLayerCollection(nn.Module):
config: WhisperConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
gradient_checkpointing: bool = False
def setup(self):
if self.gradient_checkpointing:
FlaxWhisperDecoderCheckpointLayer = remat(FlaxWhisperDecoderLayer, static_argnums=(4, 5, 6))
self.layers = [
FlaxWhisperDecoderCheckpointLayer(self.config, name=str(i), dtype=self.dtype)
for i in range(self.config.decoder_layers)
]
else:
self.layers = [
FlaxWhisperDecoderLayer(self.config, name=str(i), dtype=self.dtype)
for i in range(self.config.decoder_layers)
]
self.layerdrop = self.config.decoder_layerdrop
def __call__(
self,
hidden_states,
attention_mask,
encoder_hidden_states: Optional[jnp.ndarray] = None,
encoder_attention_mask: Optional[jnp.ndarray] = None,
deterministic: bool = True,
init_cache: bool = False,
output_attentions: bool = False,
output_hidden_states: bool = False,
return_dict: bool = True,
):
# 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
for decoder_layer in self.layers:
if output_hidden_states:
all_hidden_states += (hidden_states,)
# add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
dropout_probability = random.uniform(0, 1)
if not deterministic and (dropout_probability < self.layerdrop):
layer_outputs = (None, None, None)
else:
layer_outputs = decoder_layer(
hidden_states,
attention_mask,
encoder_hidden_states,
encoder_attention_mask,
init_cache,
output_attentions,
deterministic,
)
hidden_states = layer_outputs[0]
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,)
outputs = [hidden_states, all_hidden_states, all_self_attns, all_cross_attentions]
if not return_dict:
return tuple(v for v in outputs if v is not None)
return FlaxBaseModelOutputWithPastAndCrossAttentions(
last_hidden_state=hidden_states,
hidden_states=all_hidden_states,
attentions=all_self_attns,
cross_attentions=all_cross_attentions,
)
class FlaxWhisperEncoder(nn.Module):
config: WhisperConfig
dtype: jnp.dtype = jnp.float32
gradient_checkpointing: bool = False
def setup(self) -> None:
self.conv1 = nn.Conv(
self.config.d_model,
kernel_size=(3,),
padding=1,
kernel_init=jax.nn.initializers.normal(self.config.init_std),
dtype=self.dtype,
)
self.conv2 = nn.Conv(
self.config.d_model,
kernel_size=(3,),
strides=2,
padding=1,
kernel_init=jax.nn.initializers.normal(self.config.init_std),
dtype=self.dtype,
)
self.dropout_layer = nn.Dropout(rate=self.config.dropout)
self.layers = FlaxWhisperEncoderLayerCollection(
self.config,
dtype=self.dtype,
gradient_checkpointing=self.gradient_checkpointing,
)
self.embed_positions = nn.Embed(
self.config.max_source_positions,
self.config.d_model,
dtype=self.dtype,
embedding_init=sinusoidal_embedding_init,
)
self.layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05)
def __call__(
self,
input_features: jnp.ndarray,
output_attentions: bool = False,
output_hidden_states: bool = False,
return_dict: bool = True,
deterministic: bool = True,
) -> Tuple[jnp.ndarray]:
if input_features.shape[1:] != (self.config.num_mel_bins, self.config.max_source_positions * 2):
raise ValueError(
"input_features.shape[1:], must be equal to (self.config.num_mel_bins,"
f" self.config.max_source_positions * 2) (got {input_features.shape[1:]}, but should be"
f" ({self.config.num_mel_bins}, {self.config.max_source_positions * 2}))"
)
input_features = input_features.transpose(0, 2, 1)
hidden_states = jax.nn.gelu(self.conv1(input_features), approximate=False)
hidden_states = jax.nn.gelu(self.conv2(hidden_states), approximate=False)
embed_positions = self.embed_positions(jnp.arange(self.config.max_source_positions))
# freeze the sinusoidal embeddings by stopping the back-prop
embed_positions = jax.lax.stop_gradient(embed_positions)
hidden_states = hidden_states + embed_positions
hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic)
outputs = self.layers(
hidden_states,
attention_mask=None,
deterministic=deterministic,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
last_hidden_states = outputs[0]
last_hidden_states = self.layer_norm(last_hidden_states)
# update the last element in `hidden_states` after applying `layernorm` above
hidden_states = None
if output_hidden_states:
hidden_states = outputs[1]
hidden_states = hidden_states[:-1] + (last_hidden_states,)
if not return_dict:
outputs = (last_hidden_states, hidden_states) + (outputs[2:] if output_hidden_states else outputs[1:])
return tuple(v for v in outputs if v is not None)
return FlaxBaseModelOutput(
last_hidden_state=last_hidden_states,
hidden_states=hidden_states,
attentions=outputs.attentions,
)
class FlaxWhisperDecoder(nn.Module):
config: WhisperConfig
dtype: jnp.dtype = jnp.float32
gradient_checkpointing: bool = False
def setup(self) -> None:
self.embed_tokens = nn.Embed(self.config.vocab_size, self.config.d_model, dtype=self.dtype)
self.embed_positions = nn.Embed(self.config.max_target_positions, self.config.d_model, dtype=self.dtype)
self.layers = FlaxWhisperDecoderLayerCollection(
self.config, dtype=self.dtype, gradient_checkpointing=self.gradient_checkpointing
)
self.dropout_layer = nn.Dropout(rate=self.config.dropout)
self.layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-5)
def __call__(
self,
input_ids: jnp.ndarray,
attention_mask: jnp.ndarray,
position_ids: jnp.ndarray,
encoder_hidden_states: Optional[jnp.ndarray] = None,
init_cache: bool = False,
output_attentions: bool = False,
output_hidden_states: bool = False,
return_dict: bool = True,
deterministic: bool = True,
) -> Tuple[jnp.ndarray]:
input_embeds = self.embed_tokens(input_ids)
position_embeds = self.embed_positions(position_ids)
hidden_states = input_embeds + position_embeds
hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic)
outputs = self.layers(
hidden_states,
attention_mask=attention_mask,
encoder_hidden_states=encoder_hidden_states,
deterministic=deterministic,
init_cache=init_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
last_hidden_states = outputs[0]
last_hidden_states = self.layer_norm(last_hidden_states)
# update the last element in `hidden_states` after applying `layernorm` above
hidden_states = None
if output_hidden_states:
hidden_states = outputs[1]
hidden_states = hidden_states[:-1] + (last_hidden_states,)
if not return_dict:
outputs = (last_hidden_states, hidden_states) + (outputs[2:] if output_hidden_states else outputs[1:])
return tuple(v for v in outputs if v is not None)
return FlaxBaseModelOutputWithPastAndCrossAttentions(
last_hidden_state=last_hidden_states,
hidden_states=hidden_states,
attentions=outputs.attentions,
cross_attentions=outputs.cross_attentions,
)
class FlaxWhisperModule(nn.Module):
config: WhisperConfig
dtype: jnp.dtype = jnp.float32
gradient_checkpointing: bool = False
def setup(self) -> None:
self.encoder = FlaxWhisperEncoder(
self.config, dtype=self.dtype, gradient_checkpointing=self.gradient_checkpointing
)
self.decoder = FlaxWhisperDecoder(
self.config, dtype=self.dtype, gradient_checkpointing=self.gradient_checkpointing
)
def __call__(
self,
input_features: jnp.ndarray,
decoder_input_ids: jnp.ndarray,
decoder_attention_mask: jnp.ndarray,
decoder_position_ids: jnp.ndarray,
output_attentions: bool = False,
output_hidden_states: bool = False,
return_dict: bool = True,
deterministic: bool = True,
):
encoder_outputs = self.encoder(
input_features,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
deterministic=deterministic,
)
decoder_outputs = self.decoder(
input_ids=decoder_input_ids,
attention_mask=decoder_attention_mask,
position_ids=decoder_position_ids,
encoder_hidden_states=encoder_outputs[0],
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
deterministic=deterministic,
)
if not return_dict:
return decoder_outputs + encoder_outputs
return FlaxSeq2SeqModelOutput(
last_hidden_state=decoder_outputs.last_hidden_state,
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,
)
def _get_encoder_module(self):
return self.encoder
def _get_decoder_module(self):
return self.decoder
class FlaxWhisperPreTrainedModel(FlaxPreTrainedModel):
config_class = WhisperConfig
base_model_prefix: str = "model"
main_input_name = "input_features"
module_class: nn.Module = None
def __init__(
self,
config: WhisperConfig,
input_shape: Tuple[int] = None,
seed: int = 0,
dtype: jnp.dtype = jnp.float32,
_do_init: bool = True,
gradient_checkpointing: bool = False,
**kwargs,
):
module = self.module_class(config=config, dtype=dtype, gradient_checkpointing=gradient_checkpointing, **kwargs)
if input_shape is None:
input_shape = (1, config.num_mel_bins, 2 * config.max_source_positions)
super().__init__(config, module, input_shape=input_shape, seed=seed, dtype=dtype, _do_init=_do_init)
def enable_gradient_checkpointing(self):
self._module = self.module_class(
config=self.config,
dtype=self.dtype,
gradient_checkpointing=True,
)
def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple, params: FrozenDict = None) -> FrozenDict:
# init input tensors
input_features = jnp.zeros(input_shape, dtype="f4")
input_features = input_features.at[(..., -1)].set(self.config.eos_token_id)
decoder_input_ids = jnp.zeros((input_shape[0], 1), dtype="i4")
decoder_attention_mask = jnp.ones_like(decoder_input_ids)
batch_size, sequence_length = decoder_input_ids.shape
decoder_position_ids = jnp.broadcast_to(jnp.arange(sequence_length)[None, :], (batch_size, sequence_length))
params_rng, dropout_rng = jax.random.split(rng)
rngs = {"params": params_rng, "dropout": dropout_rng}
random_params = self.module.init(
rngs,
input_features=input_features,
decoder_input_ids=decoder_input_ids,
decoder_attention_mask=decoder_attention_mask,
decoder_position_ids=decoder_position_ids,
)["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
# Copied from transformers.models.bart.modeling_flax_bart.FlaxBartPreTrainedModel.init_cache with Bart->Whisper
def init_cache(self, batch_size, max_length, encoder_outputs):
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.
encoder_outputs (`Union[FlaxBaseModelOutput, tuple(tuple(jnp.ndarray)]`):
`encoder_outputs` consists of (`last_hidden_state`, *optional*: `hidden_states`, *optional*:
`attentions`). `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.
"""
# init input variables to retrieve cache
decoder_input_ids = jnp.ones((batch_size, max_length), dtype="i4")
decoder_attention_mask = jnp.ones_like(decoder_input_ids)
decoder_position_ids = jnp.broadcast_to(
jnp.arange(jnp.atleast_2d(decoder_input_ids).shape[-1]), decoder_input_ids.shape
)
def _decoder_forward(module, decoder_input_ids, decoder_attention_mask, decoder_position_ids, **kwargs):
decoder_module = module._get_decoder_module()
return decoder_module(
decoder_input_ids,
decoder_attention_mask,
decoder_position_ids,
**kwargs,
)
init_variables = self.module.init(
jax.random.PRNGKey(0),
decoder_input_ids=decoder_input_ids,
decoder_attention_mask=decoder_attention_mask,
decoder_position_ids=decoder_position_ids,
encoder_hidden_states=encoder_outputs[0],
init_cache=True,
method=_decoder_forward, # we only need to call the decoder to init the cache
)
return unfreeze(init_variables["cache"])
@add_start_docstrings(WHISPER_ENCODE_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=FlaxBaseModelOutput, config_class=WhisperConfig)
def encode(
self,
input_features: jnp.ndarray,
attention_mask: Optional[jnp.ndarray] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
train: bool = False,
params: dict = None,
dropout_rng: PRNGKey = None,
**kwargs,
):
r"""
Returns:
Example:
```python
>>> from transformers import WhisperProcessor, FlaxWhisperForConditionalGeneration
>>> from datasets import load_dataset
>>> processor = WhisperProcessor.from_pretrained("openai/whisper-tiny.en")
>>> model = FlaxWhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny.en", from_pt=True)
>>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
>>> inputs = processor(ds[0]["audio"]["array"], return_tensors="np")
>>> input_features = inputs.input_features
>>> encoder_outputs = model.encode(input_features=input_features)
```"""
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
# Handle any PRNG if needed
rngs = {}
if dropout_rng is not None:
rngs["dropout"] = dropout_rng
def _encoder_forward(module, input_features, **kwargs):
encode_module = module._get_encoder_module()
return encode_module(input_features, **kwargs)
return self.module.apply(
{"params": params or self.params},
input_features=jnp.array(input_features, dtype="f4"),
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
deterministic=not train,
rngs=rngs,
method=_encoder_forward,
)
@add_start_docstrings(WHISPER_DECODE_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=FlaxBaseModelOutputWithPastAndCrossAttentions, config_class=WhisperConfig)
def decode(
self,
decoder_input_ids,
encoder_outputs,
encoder_attention_mask: Optional[jnp.ndarray] = None,
decoder_attention_mask: Optional[jnp.ndarray] = None,
decoder_position_ids: Optional[jnp.ndarray] = None,
past_key_values: dict = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
train: bool = False,
params: dict = None,
dropout_rng: PRNGKey = None,
):
r"""
Returns:
Example:
```python
>>> from transformers import WhisperProcessor, FlaxWhisperForConditionalGeneration
>>> from datasets import load_dataset
>>> import jax.numpy as jnp
>>> processor = WhisperProcessor.from_pretrained("openai/whisper-tiny.en")
>>> model = FlaxWhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny.en", from_pt=True)
>>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
>>> input_features = processor(ds[0]["audio"]["array"], return_tensors="np").input_features
>>> encoder_outputs = model.encode(input_features=input_features)
>>> decoder_start_token_id = model.config.decoder_start_token_id
>>> decoder_input_ids = jnp.ones((input_features.shape[0], 1), dtype="i4") * decoder_start_token_id
>>> outputs = model.decode(decoder_input_ids, encoder_outputs)
>>> last_decoder_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.return_dict
encoder_hidden_states = encoder_outputs[0]
batch_size, sequence_length = decoder_input_ids.shape
if decoder_position_ids is None:
if past_key_values is not None:
raise ValueError("Make sure to provide `decoder_position_ids` when passing `past_key_values`.")
if decoder_attention_mask is not None:
decoder_position_ids = (decoder_attention_mask.cumsum(-1) * decoder_attention_mask) - 1
else:
decoder_position_ids = jnp.broadcast_to(
jnp.arange(sequence_length)[None, :], (batch_size, sequence_length)
)
if decoder_attention_mask is None:
decoder_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 FlaxWhisperAttention module
if past_key_values:
inputs["cache"] = past_key_values
mutable = ["cache"]
else:
mutable = False
def _decoder_forward(module, decoder_input_ids, decoder_attention_mask, decoder_position_ids, **kwargs):
decoder_module = module._get_decoder_module()
return decoder_module(
input_ids=decoder_input_ids,
attention_mask=decoder_attention_mask,
position_ids=decoder_position_ids,
**kwargs,
)
outputs = self.module.apply(
inputs,
decoder_input_ids=jnp.array(decoder_input_ids, dtype="i4"),
decoder_attention_mask=jnp.array(decoder_attention_mask, dtype="i4"),
decoder_position_ids=jnp.array(decoder_position_ids, dtype="i4"),
encoder_hidden_states=encoder_hidden_states,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
deterministic=not train,
rngs=rngs,
mutable=mutable,
method=_decoder_forward,
)
# add updated cache to model output
if past_key_values is not None and return_dict:
outputs, past = outputs
outputs["past_key_values"] = unfreeze(past["cache"])
return outputs
elif past_key_values is not None and not return_dict:
outputs, past = outputs
outputs = outputs[:1] + (unfreeze(past["cache"]),) + outputs[1:]
return outputs
@add_start_docstrings_to_model_forward(WHISPER_INPUTS_DOCSTRING)
def __call__(
self,
input_features: jnp.ndarray,
decoder_input_ids: jnp.ndarray,
attention_mask: Optional[jnp.ndarray] = None,
decoder_attention_mask: Optional[jnp.ndarray] = None,
position_ids: Optional[jnp.ndarray] = None,
decoder_position_ids: Optional[jnp.ndarray] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
train: bool = False,
params: dict = None,
dropout_rng: PRNGKey = 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
# prepare decoder inputs
if decoder_position_ids is None:
if decoder_attention_mask is not None:
decoder_position_ids = (decoder_attention_mask.cumsum(-1) * decoder_attention_mask) - 1
else:
batch_size, sequence_length = decoder_input_ids.shape
decoder_position_ids = jnp.broadcast_to(
jnp.arange(sequence_length)[None, :], (batch_size, sequence_length)
)
if decoder_attention_mask is None:
decoder_attention_mask = jnp.ones_like(decoder_input_ids)
# Handle any PRNG if needed
rngs = {"dropout": dropout_rng} if dropout_rng is not None else {}
return self.module.apply(
{"params": params or self.params},
input_features=jnp.array(input_features, dtype="f4"),
decoder_input_ids=jnp.array(decoder_input_ids, dtype="i4"),
decoder_attention_mask=jnp.array(decoder_attention_mask, dtype="i4"),
decoder_position_ids=jnp.array(decoder_position_ids, dtype="i4"),
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
deterministic=not train,
rngs=rngs,
)
@add_start_docstrings(
"The bare Whisper Model transformer outputting raw hidden-states without any specific head on top.",
WHISPER_START_DOCSTRING,
)
class FlaxWhisperModel(FlaxWhisperPreTrainedModel):
config: WhisperConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
module_class = FlaxWhisperModule
append_call_sample_docstring(FlaxWhisperModel, _CHECKPOINT_FOR_DOC, FlaxSeq2SeqModelOutput, _CONFIG_FOR_DOC)
class FlaxWhisperForConditionalGenerationModule(nn.Module):
config: WhisperConfig
dtype: jnp.dtype = jnp.float32
gradient_checkpointing: bool = False
def setup(self) -> None:
self.model = FlaxWhisperModule(
config=self.config, dtype=self.dtype, gradient_checkpointing=self.gradient_checkpointing
)
self.lm_head = nn.Dense(
self.config.vocab_size,
use_bias=False,
dtype=self.dtype,
kernel_init=jax.nn.initializers.normal(self.config.init_std),
)
def _get_encoder_module(self):
return self.model.encoder
def _get_decoder_module(self):
return self.model.decoder
def __call__(
self,
input_features,
decoder_input_ids,
decoder_attention_mask: jnp.ndarray = None,
decoder_position_ids: jnp.ndarray = None,
position_ids: jnp.ndarray = None,
attention_mask: jnp.ndarray = None,
output_attentions: bool = False,
output_hidden_states: bool = False,
return_dict: bool = True,
deterministic: bool = True,
):
outputs = self.model(
input_features=input_features,
decoder_input_ids=decoder_input_ids,
decoder_attention_mask=decoder_attention_mask,
decoder_position_ids=decoder_position_ids,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
deterministic=deterministic,
)
hidden_states = outputs[0]
if self.config.tie_word_embeddings:
shared_embedding = self.model.decoder.embed_tokens.variables["params"]["embedding"]
lm_logits = self.lm_head.apply({"params": {"kernel": shared_embedding.T}}, hidden_states)
else:
lm_logits = self.lm_head(hidden_states)
if not return_dict:
output = (lm_logits,) + outputs[1:]
return output
return FlaxSeq2SeqLMOutput(
logits=lm_logits,
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,
)
@add_start_docstrings("The Whisper Model with a language modeling head.", WHISPER_START_DOCSTRING)
class FlaxWhisperForConditionalGeneration(FlaxWhisperPreTrainedModel):
module_class = FlaxWhisperForConditionalGenerationModule
dtype: jnp.dtype = jnp.float32
@add_start_docstrings(WHISPER_DECODE_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=FlaxCausalLMOutputWithCrossAttentions, config_class=WhisperConfig)
def decode(
self,
decoder_input_ids,
encoder_outputs,
encoder_attention_mask: Optional[jnp.ndarray] = None,
decoder_attention_mask: Optional[jnp.ndarray] = None,
decoder_position_ids: Optional[jnp.ndarray] = None,
past_key_values: dict = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
train: bool = False,
params: dict = None,
dropout_rng: PRNGKey = None,
):
r"""
Returns:
Example:
```python
>>> from transformers import WhisperProcessor, FlaxWhisperForConditionalGeneration
>>> from datasets import load_dataset
>>> processor = WhisperProcessor.from_pretrained("openai/whisper-tiny.en")
>>> model = FlaxWhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny.en", from_pt=True)
>>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
>>> inputs = processor(ds[0]["audio"]["array"], return_tensors="np")
>>> input_features = inputs.input_features
>>> encoder_outputs = model.encode(input_features=input_features)
>>> decoder_start_token_id = model.config.decoder_start_token_id
>>> decoder_input_ids = jnp.ones((inputs.input_ids.shape[0], 1), dtype="i4") * decoder_start_token_id
>>> outputs = model.decode(decoder_input_ids, encoder_outputs)
>>> last_decoder_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.return_dict
encoder_hidden_states = encoder_outputs[0]
batch_size, sequence_length = decoder_input_ids.shape
if decoder_position_ids is None:
if past_key_values is not None:
raise ValueError("Make sure to provide `decoder_position_ids` when passing `past_key_values`.")
if decoder_attention_mask is not None:
decoder_position_ids = (decoder_attention_mask.cumsum(-1) * decoder_attention_mask) - 1
else:
decoder_position_ids = jnp.broadcast_to(
jnp.arange(sequence_length)[None, :], (batch_size, sequence_length)
)
if decoder_attention_mask is None:
decoder_attention_mask = jnp.ones((batch_size, sequence_length), dtype="i4")
# 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 FlaxWhisperAttention module
if past_key_values:
inputs["cache"] = past_key_values
mutable = ["cache"]
else:
mutable = False
def _decoder_forward(module, decoder_input_ids, decoder_attention_mask, decoder_position_ids, **kwargs):
decoder_module = module._get_decoder_module()
outputs = decoder_module(
input_ids=decoder_input_ids,
attention_mask=decoder_attention_mask,
position_ids=decoder_position_ids,
**kwargs,
)
hidden_states = outputs[0]
if self.config.tie_word_embeddings:
shared_embedding = module.model.decoder.embed_tokens.variables["params"]["embedding"]
lm_logits = module.lm_head.apply({"params": {"kernel": shared_embedding.T}}, hidden_states)
else:
lm_logits = module.lm_head(hidden_states)
return lm_logits, outputs
outputs = self.module.apply(
inputs,
decoder_input_ids=jnp.array(decoder_input_ids, dtype="i4"),
decoder_attention_mask=jnp.array(decoder_attention_mask, dtype="i4"),
decoder_position_ids=jnp.array(decoder_position_ids, dtype="i4"),
encoder_hidden_states=encoder_hidden_states,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
deterministic=not train,
rngs=rngs,
mutable=mutable,
method=_decoder_forward,
)
if past_key_values is None:
lm_logits, decoder_outputs = outputs
else:
(lm_logits, decoder_outputs), past = outputs
if return_dict:
outputs = FlaxCausalLMOutputWithCrossAttentions(
logits=lm_logits,
hidden_states=decoder_outputs.hidden_states,
attentions=decoder_outputs.attentions,
cross_attentions=decoder_outputs.cross_attentions,
)
else:
outputs = (lm_logits,) + decoder_outputs[1:]
# add updated cache to model output
if past_key_values is not None and return_dict:
outputs["past_key_values"] = unfreeze(past["cache"])
return outputs
elif past_key_values is not None and not return_dict:
outputs = outputs[:1] + (unfreeze(past["cache"]),) + outputs[1:]
return outputs
def generate(
self,
input_features,
generation_config=None,
logits_processor=None,
return_timestamps=None,
task=None,
language=None,
is_multilingual=None,
**kwargs,
):
if generation_config is None:
generation_config = self.generation_config
if return_timestamps is not None:
generation_config.return_timestamps = return_timestamps
if task is not None:
generation_config.task = task
if is_multilingual is not None:
generation_config.is_multilingual = is_multilingual
if language is not None:
generation_config.language = language
if kwargs is not None and "decoder_input_ids" in kwargs:
decoder_input_length = len(kwargs["decoder_input_ids"])
else:
decoder_input_length = 1
forced_decoder_ids = []
if hasattr(generation_config, "is_multilingual") and generation_config.is_multilingual:
if hasattr(generation_config, "language"):
forced_decoder_ids.append((1, generation_config.lang_to_id[generation_config.language]))
else:
forced_decoder_ids.append((1, None))
if hasattr(generation_config, "task"):
forced_decoder_ids.append((2, generation_config.task_to_id[generation_config.task]))
else:
forced_decoder_ids.append((2, generation_config.task_to_id["transcribe"]))
if (
hasattr(generation_config, "return_timestamps") and generation_config.return_timestamps
) or return_timestamps:
logits_processor = [
FlaxWhisperTimeStampLogitsProcessor(generation_config, self.config, decoder_input_length)
]
else:
if forced_decoder_ids and forced_decoder_ids[-1][0] != generation_config.no_timestamps_token_id:
idx = forced_decoder_ids[-1][0] + 1 if forced_decoder_ids else 1
forced_decoder_ids.append((idx, generation_config.no_timestamps_token_id))
if len(forced_decoder_ids) > 0:
generation_config.forced_decoder_ids = forced_decoder_ids
return super().generate(
input_features,
generation_config,
logits_processor=logits_processor,
**kwargs,
)
def prepare_inputs_for_generation(
self,
decoder_input_ids,
max_length,
attention_mask: Optional[jax.Array] = None,
decoder_attention_mask: Optional[jax.Array] = None,
encoder_outputs=None,
**kwargs,
):
# initializing the cache
batch_size, seq_length = decoder_input_ids.shape
past_key_values = self.init_cache(batch_size, max_length, encoder_outputs)
# 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 the decoder 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 decoder_attention_mask is not None:
position_ids = decoder_attention_mask.cumsum(-1) - 1
extended_attention_mask = lax.dynamic_update_slice(extended_attention_mask, decoder_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,
"encoder_outputs": encoder_outputs,
"encoder_attention_mask": attention_mask,
"decoder_attention_mask": extended_attention_mask,
"decoder_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["decoder_position_ids"] = model_kwargs["decoder_position_ids"][:, -1:] + 1
return model_kwargs
FLAX_WHISPER_CONDITIONAL_GENERATION_DOCSTRING = r"""
Returns:
Transcription example:
```python
>>> from transformers import WhisperProcessor, FlaxWhisperForConditionalGeneration
>>> from datasets import load_dataset
>>> processor = WhisperProcessor.from_pretrained("openai/whisper-tiny.en")
>>> model = FlaxWhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny.en", from_pt=True)
>>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
>>> inputs = processor(ds[0]["audio"]["array"], return_tensors="np")
>>> input_features = inputs.input_features
>>> generated_ids = model.generate(input_ids=input_features)
>>> transcription = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
>>> transcription
' Mr. Quilter is the apostle of the middle classes, and we are glad to welcome his gospel.'
```
"""
overwrite_call_docstring(
FlaxWhisperForConditionalGeneration, WHISPER_INPUTS_DOCSTRING + FLAX_WHISPER_CONDITIONAL_GENERATION_DOCSTRING
)
append_replace_return_docstrings(
FlaxWhisperForConditionalGeneration, output_type=FlaxSeq2SeqLMOutput, config_class=_CONFIG_FOR_DOC
)
class FlaxWhisperForAudioClassificationModule(nn.Module):
config: WhisperConfig
dtype: jnp.dtype = jnp.float32
gradient_checkpointing: bool = False
def setup(self) -> None:
self.encoder = FlaxWhisperEncoder(
config=self.config, dtype=self.dtype, gradient_checkpointing=self.gradient_checkpointing
)
self.config.is_encoder_decoder = False
num_layers = self.config.num_hidden_layers + 1
if self.config.use_weighted_layer_sum:
self.layer_weights = jnp.repeat(1 / num_layers, num_layers)
self.projector = nn.Dense(self.config.classifier_proj_size, dtype=self.dtype)
self.classifier = nn.Dense(self.config.num_labels, dtype=self.dtype)
def __call__(
self,
input_features,
encoder_outputs=None,
output_attentions=None,
output_hidden_states: bool = True,
return_dict: bool = 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 encoder_outputs is None:
encoder_outputs = self.encoder(
input_features,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
if self.config.use_weighted_layer_sum:
hidden_states = jnp.stack(encoder_outputs, axis=1)
norm_weights = jax.nn.softmax(self.layer_weights, axis=-1)
hidden_states = jnp.sum(hidden_states * jnp.reshape(norm_weights, [-1, 1, 1]), axis=1)
else:
hidden_states = encoder_outputs[0]
hidden_states = self.projector(hidden_states)
pooled_output = jnp.mean(hidden_states, axis=1)
logits = self.classifier(pooled_output)
if not return_dict:
return (logits,) + encoder_outputs[1:]
return FlaxSequenceClassifierOutput(
logits=logits,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
)
@add_start_docstrings("The Whisper Model with an audio classification head on top.", WHISPER_START_DOCSTRING)
class FlaxWhisperForAudioClassification(FlaxWhisperPreTrainedModel):
module_class = FlaxWhisperForAudioClassificationModule
dtype: jnp.dtype = jnp.float32
def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple, params: FrozenDict = None) -> FrozenDict:
# init input tensors
input_features = jnp.zeros(input_shape, dtype="f4")
input_features = input_features.at[(..., -1)].set(self.config.eos_token_id)
params_rng, dropout_rng = jax.random.split(rng)
rngs = {"params": params_rng, "dropout": dropout_rng}
random_params = self.module.init(
rngs,
input_features=input_features,
)["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
@add_start_docstrings_to_model_forward(WHISPER_INPUTS_DOCSTRING)
def __call__(
self,
input_features: jnp.ndarray,
attention_mask: Optional[jnp.ndarray] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
train: bool = False,
params: dict = None,
dropout_rng: PRNGKey = None,
**kwargs,
):
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
# Handle any PRNG if needed
rngs = {}
if dropout_rng is not None:
rngs["dropout"] = dropout_rng
return self.module.apply(
{"params": params or self.params},
input_features=jnp.array(input_features, dtype="f4"),
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
rngs=rngs,
)
FLAX_WHISPER_AUDIO_CLASSIFICATION_DOCSTRING = r"""
Returns:
Transcription example:
```python
>>> import jax.numpy as jnp
>>> from transformers import AutoFeatureExtractor, FlaxWhisperForAudioClassification
>>> from datasets import load_dataset
>>> feature_extractor = AutoFeatureExtractor.from_pretrained("sanchit-gandhi/whisper-medium-fleurs-lang-id")
>>> model = FlaxWhisperForAudioClassification.from_pretrained(
... "sanchit-gandhi/whisper-medium-fleurs-lang-id", from_pt=True
... )
>>> ds = load_dataset("google/fleurs", "all", split="validation", streaming=True, trust_remote_code=True)
>>> sample = next(iter(ds))
>>> inputs = feature_extractor(
... sample["audio"]["array"], sampling_rate=sample["audio"]["sampling_rate"], return_tensors="np"
... )
>>> input_features = inputs.input_features
>>> logits = model(input_features).logits
>>> predicted_class_ids = jnp.argmax(logits).item()
>>> predicted_label = model.config.id2label[predicted_class_ids]
>>> predicted_label
'af_za'
```
"""
overwrite_call_docstring(
FlaxWhisperForAudioClassification, WHISPER_INPUTS_DOCSTRING + FLAX_WHISPER_AUDIO_CLASSIFICATION_DOCSTRING
)
append_replace_return_docstrings(
FlaxWhisperForAudioClassification, output_type=FlaxSequenceClassifierOutput, config_class=_CONFIG_FOR_DOC
)
__all__ = [
"FlaxWhisperForConditionalGeneration",
"FlaxWhisperModel",
"FlaxWhisperPreTrainedModel",
"FlaxWhisperForAudioClassification",
]
| transformers/src/transformers/models/whisper/modeling_flax_whisper.py/0 | {
"file_path": "transformers/src/transformers/models/whisper/modeling_flax_whisper.py",
"repo_id": "transformers",
"token_count": 32322
} |
# coding=utf-8
# Copyright 2021 The Fairseq Authors 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 XGLM model."""
import math
from typing import List, Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from ...activations import ACT2FN
from ...generation import GenerationMixin
from ...modeling_attn_mask_utils import _prepare_4d_attention_mask, _prepare_4d_causal_attention_mask
from ...modeling_outputs import BaseModelOutputWithPastAndCrossAttentions, CausalLMOutputWithCrossAttentions
from ...modeling_utils import PreTrainedModel
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_xglm import XGLMConfig
logger = logging.get_logger(__name__)
_CHECKPOINT_FOR_DOC = "facebook/xglm-564M"
_CONFIG_FOR_DOC = "XGLMConfig"
XGLM_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 ([`XGLMConfig`]):
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.
"""
XGLM_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)
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)
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 `(num_layers, 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 `(num_layers, 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))`, *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.
"""
# Copied from transformers.models.bart.modeling_bart.BartScaledWordEmbedding with Bart->XGLM
class XGLMScaledWordEmbedding(nn.Embedding):
"""
This module overrides nn.Embeddings' forward by multiplying with embeddings scale.
"""
def __init__(self, num_embeddings: int, embedding_dim: int, padding_idx: int, embed_scale: Optional[float] = 1.0):
super().__init__(num_embeddings, embedding_dim, padding_idx)
self.embed_scale = embed_scale
def forward(self, input_ids: torch.Tensor):
return super().forward(input_ids) * self.embed_scale
class XGLMSinusoidalPositionalEmbedding(nn.Module):
"""This module produces sinusoidal positional embeddings of any length."""
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)
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
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, position_ids: torch.Tensor = None, past_key_values_length: int = 0):
bsz, seq_len = position_ids.size()
position_ids += self.offset
# Expand embeddings if needed. `position_ids.max()` is NOT used to keep torch.fx compatibility.
max_pos = 2 + seq_len + past_key_values_length
if max_pos > self.weights.size(0):
self.make_weights(max_pos, 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()
class XGLMAttention(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,
):
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)
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
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 = 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.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()}"
)
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 = torch.max(
attn_weights, torch.tensor(torch.finfo(attn_weights.dtype).min, device=attn_weights.device)
)
attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
# upcast to fp32 if the weights are in fp16. Please see https://github.com/huggingface/transformers/pull/17437
if attn_weights.dtype == torch.float16:
attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(torch.float16)
else:
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 aross 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 XGLMDecoderLayer(nn.Module):
def __init__(self, config: XGLMConfig):
super().__init__()
self.embed_dim = config.d_model
self.self_attn = XGLMAttention(
embed_dim=self.embed_dim,
num_heads=config.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
if config.add_cross_attention:
self.encoder_attn = XGLMAttention(
embed_dim=self.embed_dim,
num_heads=config.attention_heads,
dropout=config.attention_dropout,
is_decoder=True,
)
self.encoder_attn_layer_norm = nn.LayerNorm(self.embed_dim)
self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
self.fc1 = nn.Linear(self.embed_dim, config.ffn_dim)
self.fc2 = nn.Linear(config.ffn_dim, self.embed_dim)
self.final_layer_norm = nn.LayerNorm(self.embed_dim)
# Copied from transformers.models.mbart.modeling_mbart.MBartDecoderLayer.forward
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,
) -> torch.Tensor:
"""
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
hidden_states = self.self_attn_layer_norm(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
# Cross-Attention Block
cross_attn_present_key_value = None
cross_attn_weights = None
if encoder_hidden_states is not None:
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,
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
# 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)
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
outputs = (hidden_states,)
if output_attentions:
outputs += (self_attn_weights, cross_attn_weights)
if use_cache:
outputs += (present_key_value,)
return outputs
class XGLMPreTrainedModel(PreTrainedModel):
config_class = XGLMConfig
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = ["XGLMDecoderLayer"]
def _init_weights(self, module):
std = self.config.init_std
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_()
@add_start_docstrings(
"The bare XGLM Model transformer outputting raw hidden-states without any specific head on top.",
XGLM_START_DOCSTRING,
)
class XGLMModel(XGLMPreTrainedModel):
"""
Transformer decoder consisting of *config.num_layers* layers. Each layer is a [`XGLMDecoderLayer`]
Args:
config: XGLMConfig
embed_tokens (nn.Embedding): output embedding
"""
def __init__(self, config: XGLMConfig, embed_tokens: Optional[nn.Embedding] = None):
super().__init__(config)
self.dropout = config.dropout
self.layerdrop = config.layerdrop
self.padding_idx = config.pad_token_id
self.max_target_positions = config.max_position_embeddings
embed_scale = math.sqrt(config.d_model) if config.scale_embedding else 1.0
if embed_tokens is not None:
self.embed_tokens = embed_tokens
else:
self.embed_tokens = XGLMScaledWordEmbedding(
config.vocab_size, config.d_model, self.padding_idx, embed_scale=embed_scale
)
self.embed_positions = XGLMSinusoidalPositionalEmbedding(
config.max_position_embeddings,
config.d_model,
config.pad_token_id,
)
self.layers = nn.ModuleList([XGLMDecoderLayer(config) for _ in range(config.num_layers)])
self.layer_norm = nn.LayerNorm(config.d_model)
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
@add_start_docstrings_to_model_forward(XGLM_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=BaseModelOutputWithPastAndCrossAttentions,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: 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,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple[torch.Tensor], 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
# retrieve input_ids and inputs_embeds
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])
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[0][0].shape[2] if past_key_values is not None else 0
if position_ids is None:
position_ids = torch.arange(
past_key_values_length,
input_shape[-1] + past_key_values_length,
dtype=torch.long,
device=input_ids.device if input_ids is not None else inputs_embeds.device,
)
position_ids = position_ids.unsqueeze(0)
if inputs_embeds is None:
inputs_embeds = self.embed_tokens(input_ids)
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 = inputs_embeds + self.embed_positions(position_ids, past_key_values_length)
hidden_states = nn.functional.dropout(hidden_states, p=float(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],)
hidden_states = self.layer_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 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,
)
def xglm_cross_entropy_loss(
logits,
labels,
num_items_in_batch: int = None,
ignore_index: int = -100,
pad_token_id: int = -100,
vocab_size: int = None,
):
"""
Loss function for XGLM that takes into account `num_items_in_batch`
"""
shift_labels = labels.new_zeros(labels.shape)
shift_labels[:, :-1] = labels[:, 1:].clone()
shift_labels[:, -1] = pad_token_id
# move labels to correct device to enable model parallelism
labels = labels.float().to(logits.device)
logits = logits.view(-1, vocab_size).float()
shift_labels = shift_labels.view(-1)
reduction = "sum" if num_items_in_batch is not None else "mean"
loss = nn.functional.cross_entropy(logits, shift_labels, ignore_index=ignore_index, reduction=reduction)
if reduction == "sum":
loss = loss / num_items_in_batch
return loss
@add_start_docstrings(
"""
The XGLM Model transformer with a language modeling head on top (linear layer with weights tied to the input
embeddings).
""",
XGLM_START_DOCSTRING,
)
class XGLMForCausalLM(XGLMPreTrainedModel, GenerationMixin):
base_model_prefix = "model"
_tied_weights_keys = ["lm_head.weight"]
def __init__(self, config):
super().__init__(config)
self.model = XGLMModel(config)
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
# Initialize weights and apply final processing
self.post_init()
self._loss_function = xglm_cross_entropy_loss
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
@add_start_docstrings_to_model_forward(XGLM_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=CausalLMOutputWithCrossAttentions,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: 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,
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,
) -> Union[Tuple[torch.Tensor], CausalLMOutputWithCrossAttentions]:
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]`.
"""
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,
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,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
logits = self.lm_head(outputs[0])
loss = None
if labels is not None:
loss = self.loss_function(
logits,
labels,
vocab_size=self.config.vocab_size,
pad_token_id=self.config.pad_token_id,
**kwargs,
)
if not return_dict:
output = (logits,) + outputs[1:]
return (loss,) + output if loss is not None else output
return CausalLMOutputWithCrossAttentions(
loss=loss,
logits=logits,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
cross_attentions=outputs.cross_attentions,
)
@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__ = ["XGLMForCausalLM", "XGLMModel", "XGLMPreTrainedModel"]
| transformers/src/transformers/models/xglm/modeling_xglm.py/0 | {
"file_path": "transformers/src/transformers/models/xglm/modeling_xglm.py",
"repo_id": "transformers",
"token_count": 16864
} |
# 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.
"""YOLOS model configuration"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
logger = logging.get_logger(__name__)
class YolosConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`YolosModel`]. It is used to instantiate a YOLOS
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 YOLOS
[hustvl/yolos-base](https://huggingface.co/hustvl/yolos-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:
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 (`List[int]`, *optional*, defaults to `[512, 864]`):
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.
num_detection_tokens (`int`, *optional*, defaults to 100):
The number of detection tokens.
use_mid_position_embeddings (`bool`, *optional*, defaults to `True`):
Whether to use the mid-layer position encodings.
auxiliary_loss (`bool`, *optional*, defaults to `False`):
Whether auxiliary decoding losses (loss at each decoder layer) are to be used.
class_cost (`float`, *optional*, defaults to 1):
Relative weight of the classification error in the Hungarian matching cost.
bbox_cost (`float`, *optional*, defaults to 5):
Relative weight of the L1 error of the bounding box coordinates in the Hungarian matching cost.
giou_cost (`float`, *optional*, defaults to 2):
Relative weight of the generalized IoU loss of the bounding box in the Hungarian matching cost.
bbox_loss_coefficient (`float`, *optional*, defaults to 5):
Relative weight of the L1 bounding box loss in the object detection loss.
giou_loss_coefficient (`float`, *optional*, defaults to 2):
Relative weight of the generalized IoU loss in the object detection loss.
eos_coefficient (`float`, *optional*, defaults to 0.1):
Relative classification weight of the 'no-object' class in the object detection loss.
Example:
```python
>>> from transformers import YolosConfig, YolosModel
>>> # Initializing a YOLOS hustvl/yolos-base style configuration
>>> configuration = YolosConfig()
>>> # Initializing a model (with random weights) from the hustvl/yolos-base style configuration
>>> model = YolosModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "yolos"
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=[512, 864],
patch_size=16,
num_channels=3,
qkv_bias=True,
num_detection_tokens=100,
use_mid_position_embeddings=True,
auxiliary_loss=False,
class_cost=1,
bbox_cost=5,
giou_cost=2,
bbox_loss_coefficient=5,
giou_loss_coefficient=2,
eos_coefficient=0.1,
**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.num_detection_tokens = num_detection_tokens
self.use_mid_position_embeddings = use_mid_position_embeddings
self.auxiliary_loss = auxiliary_loss
# 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.eos_coefficient = eos_coefficient
class YolosOnnxConfig(OnnxConfig):
torch_onnx_minimum_version = version.parse("1.11")
@property
def inputs(self) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
]
)
@property
def atol_for_validation(self) -> float:
return 1e-4
@property
def default_onnx_opset(self) -> int:
return 12
__all__ = ["YolosConfig", "YolosOnnxConfig"]
| transformers/src/transformers/models/yolos/configuration_yolos.py/0 | {
"file_path": "transformers/src/transformers/models/yolos/configuration_yolos.py",
"repo_id": "transformers",
"token_count": 2940
} |
# Copyright 2021 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.
import warnings
from collections import defaultdict
from typing import TYPE_CHECKING, Dict, Optional, Union
import numpy as np
import requests
from ..tokenization_utils import PreTrainedTokenizer
from ..utils import is_torch_available, is_torchaudio_available, logging
from .audio_utils import ffmpeg_read
from .base import ChunkPipeline
if TYPE_CHECKING:
from pyctcdecode import BeamSearchDecoderCTC
from ..feature_extraction_sequence_utils import SequenceFeatureExtractor
from ..modeling_utils import PreTrainedModel
logger = logging.get_logger(__name__)
if is_torch_available():
import torch
from ..models.auto.modeling_auto import MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES
def rescale_stride(stride, ratio):
"""
Rescales the stride values from audio space to tokens/logits space.
(160_000, 16_000, 16_000) -> (2000, 200, 200) for instance.
"""
# Shape is [B, SEQ] for tokens
# [B, SEQ, V] for logits
new_strides = []
for input_n, left, right in stride:
token_n = int(round(input_n * ratio))
left = int(round(left / input_n * token_n))
right = int(round(right / input_n * token_n))
new_stride = (token_n, left, right)
new_strides.append(new_stride)
return new_strides
def chunk_iter(inputs, feature_extractor, chunk_len, stride_left, stride_right, dtype=None):
inputs_len = inputs.shape[0]
step = chunk_len - stride_left - stride_right
for chunk_start_idx in range(0, inputs_len, step):
chunk_end_idx = chunk_start_idx + chunk_len
chunk = inputs[chunk_start_idx:chunk_end_idx]
processed = feature_extractor(chunk, sampling_rate=feature_extractor.sampling_rate, return_tensors="pt")
if dtype is not None:
processed = processed.to(dtype=dtype)
_stride_left = 0 if chunk_start_idx == 0 else stride_left
is_last = chunk_end_idx >= inputs_len
_stride_right = 0 if is_last else stride_right
chunk_len = chunk.shape[0]
stride = (chunk_len, _stride_left, _stride_right)
if chunk.shape[0] > _stride_left:
yield {"is_last": is_last, "stride": stride, **processed}
if is_last:
break
def _fast_find_longest_common_sequence(sequence_left, sequence_right):
seq_len_left = len(sequence_left)
seq_len_right = len(sequence_right)
counter = [[0] * (seq_len_right + 1) for _ in range(seq_len_left + 1)]
longest = 0
for i in range(seq_len_left):
for j in range(seq_len_right):
if sequence_left[i] == sequence_right[j]:
previous_counter = counter[i][j] + 1
counter[i + 1][j + 1] = previous_counter
if previous_counter > longest:
longest = previous_counter
counter = np.array(counter)
# we return the idx of the first element of the longest common sequence in the left sequence
index_left = np.argwhere(counter == longest)[-1][0] - longest if longest != 0 else -1
index_right = np.argwhere(counter == longest)[-1][1] - longest if longest != 0 else -1
return index_left, index_right, longest
def _find_longest_common_sequence(sequences, tokenizer):
# TODO Use a faster algorithm this can probably be done in O(n)
# using suffix array.
# It might be tedious to do because of fault tolerance.
# We actually have a really good property which is that the total sequence
# MUST be those subsequences in order.
# Also the algorithm should be more tolerant to errors.
sequence = [tok_id for tok_id in sequences[0][0].tolist() if tok_id not in tokenizer.all_special_ids]
for new_seq in sequences[1:]:
new_sequence = [tok_id for tok_id in new_seq[0].tolist() if tok_id not in tokenizer.all_special_ids]
index = 0
max_ = 0.0
for i in range(1, len(new_sequence) + 1):
# epsilon to favor long perfect matches
eps = i / 10000.0
matches = np.sum(np.array(sequence[-i:]) == np.array(new_sequence[:i]))
matching = matches / i + eps
if matches > 1 and matching > max_:
index = i
max_ = matching
sequence.extend(new_sequence[index:])
return np.array(sequence)
class AutomaticSpeechRecognitionPipeline(ChunkPipeline):
"""
Pipeline that aims at extracting spoken text contained within some audio.
The input can be either a raw waveform or a audio file. In case of the audio file, ffmpeg should be installed for
to support multiple audio formats
Example:
```python
>>> from transformers import pipeline
>>> transcriber = pipeline(model="openai/whisper-base")
>>> transcriber("https://huggingface.co/datasets/Narsil/asr_dummy/resolve/main/1.flac")
{'text': ' He hoped there would be stew for dinner, turnips and carrots and bruised potatoes and fat mutton pieces to be ladled out in thick, peppered flour-fatten sauce.'}
```
Learn more about the basics of using a pipeline in the [pipeline tutorial](../pipeline_tutorial)
Arguments:
model ([`PreTrainedModel`] or [`TFPreTrainedModel`]):
The model that will be used by the pipeline to make predictions. This needs to be a model inheriting from
[`PreTrainedModel`] for PyTorch and [`TFPreTrainedModel`] for TensorFlow.
feature_extractor ([`SequenceFeatureExtractor`]):
The feature extractor that will be used by the pipeline to encode waveform for the model.
tokenizer ([`PreTrainedTokenizer`]):
The tokenizer that will be used by the pipeline to encode data for the model. This object inherits from
[`PreTrainedTokenizer`].
decoder (`pyctcdecode.BeamSearchDecoderCTC`, *optional*):
[PyCTCDecode's
BeamSearchDecoderCTC](https://github.com/kensho-technologies/pyctcdecode/blob/2fd33dc37c4111417e08d89ccd23d28e9b308d19/pyctcdecode/decoder.py#L180)
can be passed for language model boosted decoding. See [`Wav2Vec2ProcessorWithLM`] for more information.
chunk_length_s (`float`, *optional*, defaults to 0):
The input length for in each chunk. If `chunk_length_s = 0` then chunking is disabled (default).
<Tip>
For more information on how to effectively use `chunk_length_s`, please have a look at the [ASR chunking
blog post](https://huggingface.co/blog/asr-chunking).
</Tip>
stride_length_s (`float`, *optional*, defaults to `chunk_length_s / 6`):
The length of stride on the left and right of each chunk. Used only with `chunk_length_s > 0`. This enables
the model to *see* more context and infer letters better than without this context but the pipeline
discards the stride bits at the end to make the final reconstitution as perfect as possible.
<Tip>
For more information on how to effectively use `stride_length_s`, please have a look at the [ASR chunking
blog post](https://huggingface.co/blog/asr-chunking).
</Tip>
framework (`str`, *optional*):
The framework to use, either `"pt"` for PyTorch or `"tf"` for TensorFlow. The specified framework must be
installed. If no framework is specified, will default to the one currently installed. If no framework is
specified and both frameworks are installed, will default to the framework of the `model`, or to PyTorch if
no model is provided.
device (Union[`int`, `torch.device`], *optional*):
Device ordinal for CPU/GPU supports. Setting this to `None` will leverage CPU, a positive will run the
model on the associated CUDA device id.
torch_dtype (Union[`int`, `torch.dtype`], *optional*):
The data-type (dtype) of the computation. Setting this to `None` will use float32 precision. Set to
`torch.float16` or `torch.bfloat16` to use half-precision in the respective dtypes.
"""
def __init__(
self,
model: "PreTrainedModel",
feature_extractor: Union["SequenceFeatureExtractor", str] = None,
tokenizer: Optional[PreTrainedTokenizer] = None,
decoder: Optional[Union["BeamSearchDecoderCTC", str]] = None,
device: Union[int, "torch.device"] = None,
torch_dtype: Optional[Union[str, "torch.dtype"]] = None,
**kwargs,
):
# set the model type so we can check we have the right pre- and post-processing parameters
if model.config.model_type == "whisper":
self.type = "seq2seq_whisper"
elif model.__class__.__name__ in MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES.values():
self.type = "seq2seq"
elif (
feature_extractor._processor_class
and feature_extractor._processor_class.endswith("WithLM")
and decoder is not None
):
self.decoder = decoder
self.type = "ctc_with_lm"
else:
self.type = "ctc"
super().__init__(model, tokenizer, feature_extractor, device=device, torch_dtype=torch_dtype, **kwargs)
def __call__(
self,
inputs: Union[np.ndarray, bytes, str],
**kwargs,
):
"""
Transcribe the audio sequence(s) given as inputs to text. See the [`AutomaticSpeechRecognitionPipeline`]
documentation for more information.
Args:
inputs (`np.ndarray` or `bytes` or `str` or `dict`):
The inputs is either :
- `str` that is either the filename of a local audio file, or a public URL address to download the
audio file. The file will be read at the correct sampling rate to get the waveform using
*ffmpeg*. This requires *ffmpeg* to be installed on the system.
- `bytes` it is supposed to be the content of an audio file and is interpreted by *ffmpeg* in the
same way.
- (`np.ndarray` of shape (n, ) of type `np.float32` or `np.float64`)
Raw audio at the correct sampling rate (no further check will be done)
- `dict` form can be used to pass raw audio sampled at arbitrary `sampling_rate` and let this
pipeline do the resampling. The dict must be in the format `{"sampling_rate": int, "raw":
np.array}` with optionally a `"stride": (left: int, right: int)` than can ask the pipeline to
treat the first `left` samples and last `right` samples to be ignored in decoding (but used at
inference to provide more context to the model). Only use `stride` with CTC models.
return_timestamps (*optional*, `str` or `bool`):
Only available for pure CTC models (Wav2Vec2, HuBERT, etc) and the Whisper model. Not available for
other sequence-to-sequence models.
For CTC models, timestamps can take one of two formats:
- `"char"`: the pipeline will return timestamps along the text for every character in the text. For
instance, if you get `[{"text": "h", "timestamp": (0.5, 0.6)}, {"text": "i", "timestamp": (0.7,
0.9)}]`, then it means the model predicts that the letter "h" was spoken after `0.5` and before
`0.6` seconds.
- `"word"`: the pipeline will return timestamps along the text for every word in the text. For
instance, if you get `[{"text": "hi ", "timestamp": (0.5, 0.9)}, {"text": "there", "timestamp":
(1.0, 1.5)}]`, then it means the model predicts that the word "hi" was spoken after `0.5` and
before `0.9` seconds.
For the Whisper model, timestamps can take one of two formats:
- `"word"`: same as above for word-level CTC timestamps. Word-level timestamps are predicted
through the *dynamic-time warping (DTW)* algorithm, an approximation to word-level timestamps
by inspecting the cross-attention weights.
- `True`: the pipeline will return timestamps along the text for *segments* of words in the text.
For instance, if you get `[{"text": " Hi there!", "timestamp": (0.5, 1.5)}]`, then it means the
model predicts that the segment "Hi there!" was spoken after `0.5` and before `1.5` seconds.
Note that a segment of text refers to a sequence of one or more words, rather than individual
words as with word-level timestamps.
generate_kwargs (`dict`, *optional*):
The dictionary of ad-hoc parametrization of `generate_config` to be used for the generation call. For a
complete overview of generate, check the [following
guide](https://huggingface.co/docs/transformers/en/main_classes/text_generation).
Return:
`Dict`: A dictionary with the following keys:
- **text** (`str`): The recognized text.
- **chunks** (*optional(, `List[Dict]`)
When using `return_timestamps`, the `chunks` will become a list containing all the various text
chunks identified by the model, *e.g.* `[{"text": "hi ", "timestamp": (0.5, 0.9)}, {"text":
"there", "timestamp": (1.0, 1.5)}]`. The original full text can roughly be recovered by doing
`"".join(chunk["text"] for chunk in output["chunks"])`.
"""
return super().__call__(inputs, **kwargs)
def _sanitize_parameters(
self,
chunk_length_s=None,
stride_length_s=None,
ignore_warning=None,
decoder_kwargs=None,
return_timestamps=None,
return_language=None,
generate_kwargs=None,
max_new_tokens=None,
):
# No parameters on this pipeline right now
preprocess_params = {}
if chunk_length_s is not None:
if self.type == "seq2seq" and not ignore_warning:
logger.warning(
"Using `chunk_length_s` is very experimental with seq2seq models. The results will not necessarily"
" be entirely accurate and will have caveats. More information:"
" https://github.com/huggingface/transformers/pull/20104. Ignore this warning with pipeline(...,"
" ignore_warning=True)"
)
preprocess_params["chunk_length_s"] = chunk_length_s
if stride_length_s is not None:
preprocess_params["stride_length_s"] = stride_length_s
forward_params = defaultdict(dict)
if max_new_tokens is not None:
warnings.warn(
"`max_new_tokens` is deprecated and will be removed in version 4.49 of Transformers. To remove this warning, pass `max_new_tokens` as a key inside `generate_kwargs` instead.",
FutureWarning,
)
forward_params["max_new_tokens"] = max_new_tokens
if generate_kwargs is not None:
if max_new_tokens is not None and "max_new_tokens" in generate_kwargs:
raise ValueError(
"`max_new_tokens` is defined both as an argument and inside `generate_kwargs` argument, please use"
" only 1 version"
)
forward_params.update(generate_kwargs)
postprocess_params = {}
if decoder_kwargs is not None:
postprocess_params["decoder_kwargs"] = decoder_kwargs
if return_timestamps is not None:
# Check whether we have a valid setting for return_timestamps and throw an error before we perform a forward pass
if self.type == "seq2seq" and return_timestamps:
raise ValueError("We cannot return_timestamps yet on non-CTC models apart from Whisper!")
if self.type == "ctc_with_lm" and return_timestamps != "word":
raise ValueError("CTC with LM can only predict word level timestamps, set `return_timestamps='word'`")
if self.type == "ctc" and return_timestamps not in ["char", "word"]:
raise ValueError(
"CTC can either predict character level timestamps, or word level timestamps. "
"Set `return_timestamps='char'` or `return_timestamps='word'` as required."
)
if self.type == "seq2seq_whisper" and return_timestamps == "char":
raise ValueError(
"Whisper cannot return `char` timestamps, only word level or segment level timestamps. "
"Use `return_timestamps='word'` or `return_timestamps=True` respectively."
)
forward_params["return_timestamps"] = return_timestamps
postprocess_params["return_timestamps"] = return_timestamps
if return_language is not None:
if self.type != "seq2seq_whisper":
raise ValueError("Only Whisper can return language for now.")
postprocess_params["return_language"] = return_language
if self.assistant_model is not None:
forward_params["assistant_model"] = self.assistant_model
if self.assistant_tokenizer is not None:
forward_params["tokenizer"] = self.tokenizer
forward_params["assistant_tokenizer"] = self.assistant_tokenizer
return preprocess_params, forward_params, postprocess_params
def preprocess(self, inputs, chunk_length_s=0, stride_length_s=None):
if isinstance(inputs, str):
if inputs.startswith("http://") or inputs.startswith("https://"):
# We need to actually check for a real protocol, otherwise it's impossible to use a local file
# like http_huggingface_co.png
inputs = requests.get(inputs).content
else:
with open(inputs, "rb") as f:
inputs = f.read()
if isinstance(inputs, bytes):
inputs = ffmpeg_read(inputs, self.feature_extractor.sampling_rate)
stride = None
extra = {}
if isinstance(inputs, dict):
stride = inputs.pop("stride", None)
# Accepting `"array"` which is the key defined in `datasets` for
# better integration
if not ("sampling_rate" in inputs and ("raw" in inputs or "array" in inputs)):
raise ValueError(
"When passing a dictionary to AutomaticSpeechRecognitionPipeline, the dict needs to contain a "
'"raw" key containing the numpy array representing the audio and a "sampling_rate" key, '
"containing the sampling_rate associated with that array"
)
_inputs = inputs.pop("raw", None)
if _inputs is None:
# Remove path which will not be used from `datasets`.
inputs.pop("path", None)
_inputs = inputs.pop("array", None)
in_sampling_rate = inputs.pop("sampling_rate")
extra = inputs
inputs = _inputs
if in_sampling_rate != self.feature_extractor.sampling_rate:
if is_torchaudio_available():
from torchaudio import functional as F
else:
raise ImportError(
"torchaudio is required to resample audio samples in AutomaticSpeechRecognitionPipeline. "
"The torchaudio package can be installed through: `pip install torchaudio`."
)
inputs = F.resample(
torch.from_numpy(inputs), in_sampling_rate, self.feature_extractor.sampling_rate
).numpy()
ratio = self.feature_extractor.sampling_rate / in_sampling_rate
else:
ratio = 1
if stride is not None:
if stride[0] + stride[1] > inputs.shape[0]:
raise ValueError("Stride is too large for input")
# Stride needs to get the chunk length here, it's going to get
# swallowed by the `feature_extractor` later, and then batching
# can add extra data in the inputs, so we need to keep track
# of the original length in the stride so we can cut properly.
stride = (inputs.shape[0], int(round(stride[0] * ratio)), int(round(stride[1] * ratio)))
if not isinstance(inputs, np.ndarray):
raise TypeError(f"We expect a numpy ndarray as input, got `{type(inputs)}`")
if len(inputs.shape) != 1:
raise ValueError("We expect a single channel audio input for AutomaticSpeechRecognitionPipeline")
if chunk_length_s:
if stride_length_s is None:
stride_length_s = chunk_length_s / 6
if isinstance(stride_length_s, (int, float)):
stride_length_s = [stride_length_s, stride_length_s]
# XXX: Carefuly, this variable will not exist in `seq2seq` setting.
# Currently chunking is not possible at this level for `seq2seq` so
# it's ok.
align_to = getattr(self.model.config, "inputs_to_logits_ratio", 1)
chunk_len = int(round(chunk_length_s * self.feature_extractor.sampling_rate / align_to) * align_to)
stride_left = int(round(stride_length_s[0] * self.feature_extractor.sampling_rate / align_to) * align_to)
stride_right = int(round(stride_length_s[1] * self.feature_extractor.sampling_rate / align_to) * align_to)
if chunk_len < stride_left + stride_right:
raise ValueError("Chunk length must be superior to stride length")
for item in chunk_iter(
inputs, self.feature_extractor, chunk_len, stride_left, stride_right, self.torch_dtype
):
yield {**item, **extra}
else:
if self.type == "seq2seq_whisper" and inputs.shape[0] > self.feature_extractor.n_samples:
processed = self.feature_extractor(
inputs,
sampling_rate=self.feature_extractor.sampling_rate,
truncation=False,
padding="longest",
return_tensors="pt",
return_attention_mask=True,
)
else:
if self.type == "seq2seq_whisper" and stride is None:
processed = self.feature_extractor(
inputs,
sampling_rate=self.feature_extractor.sampling_rate,
return_tensors="pt",
return_token_timestamps=True,
return_attention_mask=True,
)
extra["num_frames"] = processed.pop("num_frames")
else:
processed = self.feature_extractor(
inputs,
sampling_rate=self.feature_extractor.sampling_rate,
return_tensors="pt",
return_attention_mask=True,
)
if self.torch_dtype is not None:
processed = processed.to(dtype=self.torch_dtype)
if stride is not None:
if self.type == "seq2seq":
raise ValueError("Stride is only usable with CTC models, try removing it !")
processed["stride"] = stride
yield {"is_last": True, **processed, **extra}
def _forward(self, model_inputs, return_timestamps=False, **generate_kwargs):
attention_mask = model_inputs.pop("attention_mask", None)
stride = model_inputs.pop("stride", None)
num_frames = model_inputs.pop("num_frames", None)
is_last = model_inputs.pop("is_last")
if stride is not None and num_frames is not None:
raise ValueError("num_frames must be used only when stride is None")
if self.type in {"seq2seq", "seq2seq_whisper"}:
# Consume values so we can let extra information flow freely through
# the pipeline (important for `partial` in microphone)
if "input_features" in model_inputs:
inputs = model_inputs.pop("input_features")
elif "input_values" in model_inputs:
inputs = model_inputs.pop("input_values")
else:
raise ValueError(
"Seq2Seq speech recognition model requires either a "
f"`input_features` or `input_values` key, but only has {model_inputs.keys()}"
)
# custom processing for Whisper timestamps and word-level timestamps
if return_timestamps and self.type == "seq2seq_whisper":
generate_kwargs["return_timestamps"] = return_timestamps
if return_timestamps == "word":
generate_kwargs["return_token_timestamps"] = True
generate_kwargs["return_segments"] = True
if stride is not None:
if isinstance(stride, tuple):
generate_kwargs["num_frames"] = stride[0] // self.feature_extractor.hop_length
else:
generate_kwargs["num_frames"] = [s[0] // self.feature_extractor.hop_length for s in stride]
else:
generate_kwargs["num_frames"] = num_frames
# User-defined `generation_config` passed to the pipeline call take precedence
if "generation_config" not in generate_kwargs:
generate_kwargs["generation_config"] = self.generation_config
tokens = self.model.generate(
inputs=inputs,
attention_mask=attention_mask,
**generate_kwargs,
)
# whisper longform generation stores timestamps in "segments"
if return_timestamps == "word" and self.type == "seq2seq_whisper":
if "segments" not in tokens:
out = {"tokens": tokens["sequences"], "token_timestamps": tokens["token_timestamps"]}
else:
token_timestamps = [
torch.cat([segment["token_timestamps"] for segment in segment_list])
for segment_list in tokens["segments"]
]
out = {"tokens": tokens["sequences"], "token_timestamps": token_timestamps}
else:
out = {"tokens": tokens}
if self.type == "seq2seq_whisper":
if stride is not None:
out["stride"] = stride
else:
inputs = {
self.model.main_input_name: model_inputs.pop(self.model.main_input_name),
"attention_mask": attention_mask,
}
outputs = self.model(**inputs)
logits = outputs.logits
if self.type == "ctc_with_lm":
out = {"logits": logits}
else:
out = {"tokens": logits.argmax(dim=-1)}
if stride is not None:
# Send stride to `postprocess`.
# it needs to be handled there where
# the pieces are to be concatenated.
ratio = 1 / self.model.config.inputs_to_logits_ratio
if isinstance(stride, tuple):
out["stride"] = rescale_stride([stride], ratio)[0]
else:
out["stride"] = rescale_stride(stride, ratio)
# Leftover
extra = model_inputs
return {"is_last": is_last, **out, **extra}
def postprocess(
self, model_outputs, decoder_kwargs: Optional[Dict] = None, return_timestamps=None, return_language=None
):
# Optional return types
optional = {}
final_items = []
key = "logits" if self.type == "ctc_with_lm" else "tokens"
stride = None
for outputs in model_outputs:
if self.framework == "pt" and outputs[key].dtype in (torch.bfloat16, torch.float16):
items = outputs[key].to(torch.float32).numpy()
else:
items = outputs[key].numpy()
stride = outputs.get("stride", None)
if stride is not None and self.type in {"ctc", "ctc_with_lm"}:
total_n, left, right = stride
# Total_n might be < logits.shape[1]
# because of padding, that's why
# we need to reconstruct this information
# This won't work with left padding (which doesn't exist right now)
right_n = total_n - right
items = items[:, left:right_n]
final_items.append(items)
if stride and self.type == "seq2seq":
items = _find_longest_common_sequence(final_items, self.tokenizer)
elif self.type == "seq2seq_whisper":
time_precision = self.feature_extractor.chunk_length / self.model.config.max_source_positions
# Send the chunking back to seconds, it's easier to handle in whisper
sampling_rate = self.feature_extractor.sampling_rate
for output in model_outputs:
if "stride" in output:
chunk_len, stride_left, stride_right = output["stride"]
# Go back in seconds
chunk_len /= sampling_rate
stride_left /= sampling_rate
stride_right /= sampling_rate
output["stride"] = chunk_len, stride_left, stride_right
text, optional = self.tokenizer._decode_asr(
model_outputs,
return_timestamps=return_timestamps,
return_language=return_language,
time_precision=time_precision,
)
else:
items = np.concatenate(final_items, axis=1)
items = items.squeeze(0)
if self.type == "ctc_with_lm":
if decoder_kwargs is None:
decoder_kwargs = {}
beams = self.decoder.decode_beams(items, **decoder_kwargs)
text = beams[0][0]
if return_timestamps:
# Simply cast from pyctcdecode format to wav2vec2 format to leverage
# pre-existing code later
chunk_offset = beams[0][2]
offsets = []
for word, (start_offset, end_offset) in chunk_offset:
offsets.append({"word": word, "start_offset": start_offset, "end_offset": end_offset})
elif self.type != "seq2seq_whisper":
skip_special_tokens = self.type != "ctc"
text = self.tokenizer.decode(items, skip_special_tokens=skip_special_tokens)
if return_timestamps:
offsets = self.tokenizer.decode(
items, skip_special_tokens=skip_special_tokens, output_char_offsets=True
)["char_offsets"]
if return_timestamps == "word":
offsets = self.tokenizer._get_word_offsets(offsets, self.tokenizer.replace_word_delimiter_char)
if return_timestamps and self.type not in {"seq2seq", "seq2seq_whisper"}:
chunks = []
for item in offsets:
start = item["start_offset"] * self.model.config.inputs_to_logits_ratio
start /= self.feature_extractor.sampling_rate
stop = item["end_offset"] * self.model.config.inputs_to_logits_ratio
stop /= self.feature_extractor.sampling_rate
chunks.append({"text": item[return_timestamps], "timestamp": (start, stop)})
optional["chunks"] = chunks
extra = defaultdict(list)
for output in model_outputs:
output.pop("tokens", None)
output.pop("logits", None)
output.pop("is_last", None)
output.pop("stride", None)
output.pop("token_timestamps", None)
for k, v in output.items():
extra[k].append(v)
return {"text": text, **optional, **extra}
def _find_timestamp_sequence(sequences, tokenizer, feature_extractor, max_source_positions):
"""
Computes the final sequences by merging the end of the nth sequence with the beginning of the n+1th sequence. Since
`WhisperForConditionalGeneration` produces the timestamps pairwise, we filter the consecutive timestamps and only
iterate over them. We keep track of the `time` which indicates the actual starting time of the chunk that is
processed. We need to make sure to offset the timestamps tokens by the `time` in order for the tokenizer to
properly compute the final `offset`.
"""
# index of the first timestamp token
timestamp_begin = tokenizer.convert_tokens_to_ids("<|notimestamps|>") + 1
items = []
# approximation of the token to time ratio : ~0.2seconds
time_precision = feature_extractor.chunk_length / max_source_positions
time = 0
for seq_idx, item in enumerate(sequences):
sequence, stride = item
if isinstance(sequence, list):
sequence = np.array(sequence)
chunk_len, stride_left, stride_right = stride
sequence = sequence.squeeze(0)
# get rid of the `forced_decoder_idx` that are use to parametrize the generation
begin_idx = np.where(sequence == timestamp_begin)[0][0] if timestamp_begin in sequence else 0
sequence = sequence[begin_idx:]
timestamp_tokens = sequence >= timestamp_begin
if seq_idx != 0 and sum(timestamp_tokens) > 0:
consecutive = np.where(timestamp_tokens[:-1] & timestamp_tokens[1:])[0] + 1
last_timestamp = np.where(timestamp_tokens)[0][-1]
consecutive = np.append(consecutive, last_timestamp) if last_timestamp not in consecutive else consecutive
time -= stride_left + stride_right
offset = int((time / feature_extractor.sampling_rate) / time_precision)
overlap_time = int((stride_left / feature_extractor.sampling_rate) / time_precision)
# relevant timestamps are in the overlapping part
relevant_timestamp = np.where(sequence[consecutive] >= timestamp_begin + overlap_time)[0]
if relevant_timestamp.shape[0] > 0:
relevant_timestamp = (
consecutive[relevant_timestamp[0] - 1] if relevant_timestamp[0] > 0 else consecutive[0]
)
# if a big stride is used, we need to check some of the previous items for the best overlap
best_match = 0
sliced_sequence = []
for idx, previous_sequence in enumerate(reversed(items)):
previous_tokens = previous_sequence[1:-1]
if previous_sequence[0] < (timestamp_begin + offset - overlap_time) and idx != 0:
break # the previous sequence is too far in the past
if len(previous_tokens) > 0:
# find the longest common sequence between the overlapping parts
index_left, index_right, match_length = _fast_find_longest_common_sequence(
sequence[1:relevant_timestamp], previous_tokens
)
# don't do anything if only 1 token was matched
if match_length > 1 and match_length > best_match:
best_match = match_length
best_idx = idx
end_of_curr_sequence_idx = (
np.where(sequence[index_left + 1 :] >= timestamp_begin)[0][0] + 1
)
end_of_curr_sequence_idx = end_of_curr_sequence_idx + 1 + index_left
# if all the tokens are matched, suffix
if index_left == 0 and match_length == len(previous_tokens):
sliced_sequence = np.insert(
sequence[index_left + 1 : end_of_curr_sequence_idx], 0, previous_sequence[0]
)
sliced_sequence[-1] = previous_sequence[-1]
# if part of the previous sequence is not taken
elif index_left >= 0:
sliced_sequence = sequence[index_left + 1 : end_of_curr_sequence_idx]
# let's insert the missing part of the previous sequence
previous_slice = (
previous_sequence[: index_right + 1] if index_right > 0 else [previous_sequence[0]]
)
sliced_sequence = np.insert(sliced_sequence, 0, previous_slice)
sliced_sequence[-1] += offset
if len(sliced_sequence) > 0:
items[len(items) - best_idx - 1] = sliced_sequence
items = items[: len(items) - best_idx]
sequence = sequence[end_of_curr_sequence_idx:]
# sequence might have changed
timestamp_tokens = sequence >= timestamp_begin
consecutive = np.where(timestamp_tokens[:-1] & timestamp_tokens[1:])[0] + 1
if sum(timestamp_tokens) > 0:
last_timestamp = np.where(timestamp_tokens)[0][-1]
consecutive = (
np.append(consecutive, last_timestamp + 1) if last_timestamp not in consecutive else consecutive
)
if len(consecutive) > 0:
last_slice = 0
for current_slice in consecutive:
actual_offset = items[-1][-1] if seq_idx != 0 or last_slice != 0 else sequence[0]
sliced_tokens = sequence[last_slice:current_slice]
duration = sliced_tokens[-1] - sliced_tokens[0]
sliced_tokens[0] = actual_offset
sliced_tokens[-1] = actual_offset + duration
items.append(sliced_tokens)
last_slice = current_slice
time += chunk_len
result = []
for i in range(len(items)):
result += items[i].tolist()
return result
| transformers/src/transformers/pipelines/automatic_speech_recognition.py/0 | {
"file_path": "transformers/src/transformers/pipelines/automatic_speech_recognition.py",
"repo_id": "transformers",
"token_count": 17909
} |
import collections
import types
import numpy as np
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
requires_backends,
)
from .base import ArgumentHandler, Dataset, Pipeline, PipelineException, build_pipeline_init_args
if is_torch_available():
import torch
from ..models.auto.modeling_auto import (
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING_NAMES,
)
if is_tf_available():
import tensorflow as tf
from ..models.auto.modeling_tf_auto import (
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING_NAMES,
)
class TableQuestionAnsweringArgumentHandler(ArgumentHandler):
"""
Handles arguments for the TableQuestionAnsweringPipeline
"""
def __call__(self, table=None, query=None, **kwargs):
# Returns tqa_pipeline_inputs of shape:
# [
# {"table": pd.DataFrame, "query": List[str]},
# ...,
# {"table": pd.DataFrame, "query" : List[str]}
# ]
requires_backends(self, "pandas")
import pandas as pd
if table is None:
raise ValueError("Keyword argument `table` cannot be None.")
elif query is None:
if isinstance(table, dict) and table.get("query") is not None and table.get("table") is not None:
tqa_pipeline_inputs = [table]
elif isinstance(table, list) and len(table) > 0:
if not all(isinstance(d, dict) for d in table):
raise ValueError(
f"Keyword argument `table` should be a list of dict, but is {(type(d) for d in table)}"
)
if table[0].get("query") is not None and table[0].get("table") is not None:
tqa_pipeline_inputs = table
else:
raise ValueError(
"If keyword argument `table` is a list of dictionaries, each dictionary should have a `table`"
f" and `query` key, but only dictionary has keys {table[0].keys()} `table` and `query` keys."
)
elif Dataset is not None and isinstance(table, Dataset) or isinstance(table, types.GeneratorType):
return table
else:
raise ValueError(
"Invalid input. Keyword argument `table` should be either of type `dict` or `list`, but "
f"is {type(table)})"
)
else:
tqa_pipeline_inputs = [{"table": table, "query": query}]
for tqa_pipeline_input in tqa_pipeline_inputs:
if not isinstance(tqa_pipeline_input["table"], pd.DataFrame):
if tqa_pipeline_input["table"] is None:
raise ValueError("Table cannot be None.")
tqa_pipeline_input["table"] = pd.DataFrame(tqa_pipeline_input["table"])
return tqa_pipeline_inputs
@add_end_docstrings(build_pipeline_init_args(has_tokenizer=True))
class TableQuestionAnsweringPipeline(Pipeline):
"""
Table Question Answering pipeline using a `ModelForTableQuestionAnswering`. This pipeline is only available in
PyTorch.
Example:
```python
>>> from transformers import pipeline
>>> oracle = pipeline(model="google/tapas-base-finetuned-wtq")
>>> table = {
... "Repository": ["Transformers", "Datasets", "Tokenizers"],
... "Stars": ["36542", "4512", "3934"],
... "Contributors": ["651", "77", "34"],
... "Programming language": ["Python", "Python", "Rust, Python and NodeJS"],
... }
>>> oracle(query="How many stars does the transformers repository have?", table=table)
{'answer': 'AVERAGE > 36542', 'coordinates': [(0, 1)], 'cells': ['36542'], 'aggregator': 'AVERAGE'}
```
Learn more about the basics of using a pipeline in the [pipeline tutorial](../pipeline_tutorial)
This tabular question answering pipeline can currently be loaded from [`pipeline`] using the following task
identifier: `"table-question-answering"`.
The models that this pipeline can use are models that have been fine-tuned on a tabular question answering task.
See the up-to-date list of available models on
[huggingface.co/models](https://huggingface.co/models?filter=table-question-answering).
"""
default_input_names = "table,query"
def __init__(self, args_parser=TableQuestionAnsweringArgumentHandler(), *args, **kwargs):
super().__init__(*args, **kwargs)
self._args_parser = args_parser
if self.framework == "tf":
mapping = TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING_NAMES.copy()
mapping.update(TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES)
else:
mapping = MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING_NAMES.copy()
mapping.update(MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES)
self.check_model_type(mapping)
self.aggregate = bool(getattr(self.model.config, "aggregation_labels", None)) and bool(
getattr(self.model.config, "num_aggregation_labels", None)
)
self.type = "tapas" if hasattr(self.model.config, "aggregation_labels") else None
def batch_inference(self, **inputs):
return self.model(**inputs)
def sequential_inference(self, **inputs):
"""
Inference used for models that need to process sequences in a sequential fashion, like the SQA models which
handle conversational query related to a table.
"""
if self.framework == "pt":
all_logits = []
all_aggregations = []
prev_answers = None
batch_size = inputs["input_ids"].shape[0]
input_ids = inputs["input_ids"].to(self.device)
attention_mask = inputs["attention_mask"].to(self.device)
token_type_ids = inputs["token_type_ids"].to(self.device)
token_type_ids_example = None
for index in range(batch_size):
# If sequences have already been processed, the token type IDs will be created according to the previous
# answer.
if prev_answers is not None:
prev_labels_example = token_type_ids_example[:, 3] # shape (seq_len,)
model_labels = np.zeros_like(prev_labels_example.cpu().numpy()) # shape (seq_len,)
token_type_ids_example = token_type_ids[index] # shape (seq_len, 7)
for i in range(model_labels.shape[0]):
segment_id = token_type_ids_example[:, 0].tolist()[i]
col_id = token_type_ids_example[:, 1].tolist()[i] - 1
row_id = token_type_ids_example[:, 2].tolist()[i] - 1
if row_id >= 0 and col_id >= 0 and segment_id == 1:
model_labels[i] = int(prev_answers[(col_id, row_id)])
token_type_ids_example[:, 3] = torch.from_numpy(model_labels).type(torch.long).to(self.device)
input_ids_example = input_ids[index]
attention_mask_example = attention_mask[index] # shape (seq_len,)
token_type_ids_example = token_type_ids[index] # shape (seq_len, 7)
outputs = self.model(
input_ids=input_ids_example.unsqueeze(0),
attention_mask=attention_mask_example.unsqueeze(0),
token_type_ids=token_type_ids_example.unsqueeze(0),
)
logits = outputs.logits
if self.aggregate:
all_aggregations.append(outputs.logits_aggregation)
all_logits.append(logits)
dist_per_token = torch.distributions.Bernoulli(logits=logits)
probabilities = dist_per_token.probs * attention_mask_example.type(torch.float32).to(
dist_per_token.probs.device
)
coords_to_probs = collections.defaultdict(list)
for i, p in enumerate(probabilities.squeeze().tolist()):
segment_id = token_type_ids_example[:, 0].tolist()[i]
col = token_type_ids_example[:, 1].tolist()[i] - 1
row = token_type_ids_example[:, 2].tolist()[i] - 1
if col >= 0 and row >= 0 and segment_id == 1:
coords_to_probs[(col, row)].append(p)
prev_answers = {key: np.array(coords_to_probs[key]).mean() > 0.5 for key in coords_to_probs}
logits_batch = torch.cat(tuple(all_logits), 0)
return (logits_batch,) if not self.aggregate else (logits_batch, torch.cat(tuple(all_aggregations), 0))
else:
all_logits = []
all_aggregations = []
prev_answers = None
batch_size = inputs["input_ids"].shape[0]
input_ids = inputs["input_ids"]
attention_mask = inputs["attention_mask"]
token_type_ids = inputs["token_type_ids"].numpy()
token_type_ids_example = None
for index in range(batch_size):
# If sequences have already been processed, the token type IDs will be created according to the previous
# answer.
if prev_answers is not None:
prev_labels_example = token_type_ids_example[:, 3] # shape (seq_len,)
model_labels = np.zeros_like(prev_labels_example, dtype=np.int32) # shape (seq_len,)
token_type_ids_example = token_type_ids[index] # shape (seq_len, 7)
for i in range(model_labels.shape[0]):
segment_id = token_type_ids_example[:, 0].tolist()[i]
col_id = token_type_ids_example[:, 1].tolist()[i] - 1
row_id = token_type_ids_example[:, 2].tolist()[i] - 1
if row_id >= 0 and col_id >= 0 and segment_id == 1:
model_labels[i] = int(prev_answers[(col_id, row_id)])
token_type_ids_example[:, 3] = model_labels
input_ids_example = input_ids[index]
attention_mask_example = attention_mask[index] # shape (seq_len,)
token_type_ids_example = token_type_ids[index] # shape (seq_len, 7)
outputs = self.model(
input_ids=np.expand_dims(input_ids_example, axis=0),
attention_mask=np.expand_dims(attention_mask_example, axis=0),
token_type_ids=np.expand_dims(token_type_ids_example, axis=0),
)
logits = outputs.logits
if self.aggregate:
all_aggregations.append(outputs.logits_aggregation)
all_logits.append(logits)
probabilities = tf.math.sigmoid(tf.cast(logits, tf.float32)) * tf.cast(
attention_mask_example, tf.float32
)
coords_to_probs = collections.defaultdict(list)
token_type_ids_example = token_type_ids_example
for i, p in enumerate(tf.squeeze(probabilities).numpy().tolist()):
segment_id = token_type_ids_example[:, 0].tolist()[i]
col = token_type_ids_example[:, 1].tolist()[i] - 1
row = token_type_ids_example[:, 2].tolist()[i] - 1
if col >= 0 and row >= 0 and segment_id == 1:
coords_to_probs[(col, row)].append(p)
prev_answers = {key: np.array(coords_to_probs[key]).mean() > 0.5 for key in coords_to_probs}
logits_batch = tf.concat(tuple(all_logits), 0)
return (logits_batch,) if not self.aggregate else (logits_batch, tf.concat(tuple(all_aggregations), 0))
def __call__(self, *args, **kwargs):
r"""
Answers queries according to a table. The pipeline accepts several types of inputs which are detailed below:
- `pipeline(table, query)`
- `pipeline(table, [query])`
- `pipeline(table=table, query=query)`
- `pipeline(table=table, query=[query])`
- `pipeline({"table": table, "query": query})`
- `pipeline({"table": table, "query": [query]})`
- `pipeline([{"table": table, "query": query}, {"table": table, "query": query}])`
The `table` argument should be a dict or a DataFrame built from that dict, containing the whole table:
Example:
```python
data = {
"actors": ["brad pitt", "leonardo di caprio", "george clooney"],
"age": ["56", "45", "59"],
"number of movies": ["87", "53", "69"],
"date of birth": ["7 february 1967", "10 june 1996", "28 november 1967"],
}
```
This dictionary can be passed in as such, or can be converted to a pandas DataFrame:
Example:
```python
import pandas as pd
table = pd.DataFrame.from_dict(data)
```
Args:
table (`pd.DataFrame` or `Dict`):
Pandas DataFrame or dictionary that will be converted to a DataFrame containing all the table values.
See above for an example of dictionary.
query (`str` or `List[str]`):
Query or list of queries that will be sent to the model alongside the table.
sequential (`bool`, *optional*, defaults to `False`):
Whether to do inference sequentially or as a batch. Batching is faster, but models like SQA require the
inference to be done sequentially to extract relations within sequences, given their conversational
nature.
padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):
Activates and controls padding. Accepts the following values:
- `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single
sequence if provided).
- `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
acceptable input length for the model if that argument is not provided.
- `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different
lengths).
truncation (`bool`, `str` or [`TapasTruncationStrategy`], *optional*, defaults to `False`):
Activates and controls truncation. Accepts the following values:
- `True` or `'drop_rows_to_fit'`: Truncate to a maximum length specified with the argument `max_length`
or to the maximum acceptable input length for the model if that argument is not provided. This will
truncate row by row, removing rows from the table.
- `False` or `'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths
greater than the model maximum admissible input size).
Return:
A dictionary or a list of dictionaries containing results: Each result is a dictionary with the following
keys:
- **answer** (`str`) -- The answer of the query given the table. If there is an aggregator, the answer will
be preceded by `AGGREGATOR >`.
- **coordinates** (`List[Tuple[int, int]]`) -- Coordinates of the cells of the answers.
- **cells** (`List[str]`) -- List of strings made up of the answer cell values.
- **aggregator** (`str`) -- If the model has an aggregator, this returns the aggregator.
"""
pipeline_inputs = self._args_parser(*args, **kwargs)
results = super().__call__(pipeline_inputs, **kwargs)
if len(results) == 1:
return results[0]
return results
def _sanitize_parameters(self, sequential=None, padding=None, truncation=None, **kwargs):
preprocess_params = {}
if padding is not None:
preprocess_params["padding"] = padding
if truncation is not None:
preprocess_params["truncation"] = truncation
forward_params = {}
if sequential is not None:
forward_params["sequential"] = sequential
if self.assistant_model is not None:
forward_params["assistant_model"] = self.assistant_model
if self.assistant_tokenizer is not None:
forward_params["tokenizer"] = self.tokenizer
forward_params["assistant_tokenizer"] = self.assistant_tokenizer
return preprocess_params, forward_params, {}
def preprocess(self, pipeline_input, sequential=None, padding=True, truncation=None):
if truncation is None:
if self.type == "tapas":
truncation = "drop_rows_to_fit"
else:
truncation = "do_not_truncate"
table, query = pipeline_input["table"], pipeline_input["query"]
if table.empty:
raise ValueError("table is empty")
if query is None or query == "":
raise ValueError("query is empty")
inputs = self.tokenizer(table, query, return_tensors=self.framework, truncation=truncation, padding=padding)
inputs["table"] = table
return inputs
def _forward(self, model_inputs, sequential=False, **generate_kwargs):
table = model_inputs.pop("table")
if self.type == "tapas":
if sequential:
outputs = self.sequential_inference(**model_inputs)
else:
outputs = self.batch_inference(**model_inputs)
else:
# User-defined `generation_config` passed to the pipeline call take precedence
if "generation_config" not in generate_kwargs:
generate_kwargs["generation_config"] = self.generation_config
outputs = self.model.generate(**model_inputs, **generate_kwargs)
model_outputs = {"model_inputs": model_inputs, "table": table, "outputs": outputs}
return model_outputs
def postprocess(self, model_outputs):
inputs = model_outputs["model_inputs"]
table = model_outputs["table"]
outputs = model_outputs["outputs"]
if self.type == "tapas":
if self.aggregate:
logits, logits_agg = outputs[:2]
predictions = self.tokenizer.convert_logits_to_predictions(inputs, logits, logits_agg)
answer_coordinates_batch, agg_predictions = predictions
aggregators = {i: self.model.config.aggregation_labels[pred] for i, pred in enumerate(agg_predictions)}
no_agg_label_index = self.model.config.no_aggregation_label_index
aggregators_prefix = {
i: aggregators[i] + " > " for i, pred in enumerate(agg_predictions) if pred != no_agg_label_index
}
else:
logits = outputs[0]
predictions = self.tokenizer.convert_logits_to_predictions(inputs, logits)
answer_coordinates_batch = predictions[0]
aggregators = {}
aggregators_prefix = {}
answers = []
for index, coordinates in enumerate(answer_coordinates_batch):
cells = [table.iat[coordinate] for coordinate in coordinates]
aggregator = aggregators.get(index, "")
aggregator_prefix = aggregators_prefix.get(index, "")
answer = {
"answer": aggregator_prefix + ", ".join(cells),
"coordinates": coordinates,
"cells": [table.iat[coordinate] for coordinate in coordinates],
}
if aggregator:
answer["aggregator"] = aggregator
answers.append(answer)
if len(answer) == 0:
raise PipelineException("Empty answer")
else:
answers = [{"answer": answer} for answer in self.tokenizer.batch_decode(outputs, skip_special_tokens=True)]
return answers if len(answers) > 1 else answers[0]
| transformers/src/transformers/pipelines/table_question_answering.py/0 | {
"file_path": "transformers/src/transformers/pipelines/table_question_answering.py",
"repo_id": "transformers",
"token_count": 9381
} |
# 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 abc import ABC, abstractmethod
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Union
from ..utils import is_torch_available
from ..utils.quantization_config import QuantizationConfigMixin
if TYPE_CHECKING:
from ..modeling_utils import PreTrainedModel
if is_torch_available():
import torch
class HfQuantizer(ABC):
"""
Abstract class of the HuggingFace quantizer. Supports for now quantizing HF transformers models for inference and/or quantization.
This class is used only for transformers.PreTrainedModel.from_pretrained and cannot be easily used outside the scope of that method
yet.
Attributes
quantization_config (`transformers.utils.quantization_config.QuantizationConfigMixin`):
The quantization config that defines the quantization parameters of your model that you want to quantize.
modules_to_not_convert (`List[str]`, *optional*):
The list of module names to not convert when quantizing the model.
required_packages (`List[str]`, *optional*):
The list of required pip packages to install prior to using the quantizer
requires_calibration (`bool`):
Whether the quantization method requires to calibrate the model before using it.
requires_parameters_quantization (`bool`):
Whether the quantization method requires to create a new Parameter. For example, for bitsandbytes, it is
required to create a new xxxParameter in order to properly quantize the model.
"""
requires_calibration = False
required_packages = None
requires_parameters_quantization = False
def __init__(self, quantization_config: QuantizationConfigMixin, **kwargs):
self.quantization_config = quantization_config
# -- Handle extra kwargs below --
self.modules_to_not_convert = kwargs.pop("modules_to_not_convert", [])
self.pre_quantized = kwargs.pop("pre_quantized", True)
if not self.pre_quantized and self.requires_calibration:
raise ValueError(
f"The quantization method {quantization_config.quant_method} does require the model to be pre-quantized."
f" You explicitly passed `pre_quantized=False` meaning your model weights are not quantized. Make sure to "
f"pass `pre_quantized=True` while knowing what you are doing."
)
def update_torch_dtype(self, torch_dtype: "torch.dtype") -> "torch.dtype":
"""
Some quantization methods require to explicitly set the dtype of the model to a
target dtype. You need to override this method in case you want to make sure that behavior is
preserved
Args:
torch_dtype (`torch.dtype`):
The input dtype that is passed in `from_pretrained`
"""
return torch_dtype
def update_device_map(self, device_map: Optional[Dict[str, Any]]) -> Optional[Dict[str, Any]]:
"""
Override this method if you want to pass a override the existing device map with a new
one. E.g. for bitsandbytes, since `accelerate` is a hard requirement, if no device_map is
passed, the device_map is set to `"auto"``
Args:
device_map (`Union[dict, str]`, *optional*):
The device_map that is passed through the `from_pretrained` method.
"""
return device_map
def adjust_target_dtype(self, torch_dtype: "torch.dtype") -> "torch.dtype":
"""
Override this method if you want to adjust the `target_dtype` variable used in `from_pretrained`
to compute the device_map in case the device_map is a `str`. E.g. for bitsandbytes we force-set `target_dtype`
to `torch.int8` and for 4-bit we pass a custom enum `accelerate.CustomDtype.int4`.
Args:
torch_dtype (`torch.dtype`, *optional*):
The torch_dtype that is used to compute the device_map.
"""
return torch_dtype
def update_missing_keys(self, model, missing_keys: List[str], prefix: str) -> List[str]:
"""
Override this method if you want to adjust the `missing_keys`.
Args:
missing_keys (`List[str]`, *optional*):
The list of missing keys in the checkpoint compared to the state dict of the model
"""
return missing_keys
def update_expected_keys(self, model, expected_keys: List[str], loaded_keys: List[str]) -> List[str]:
"""
Override this method if you want to adjust the `update_expected_keys`.
Args:
expected_keys (`List[str]`, *optional*):
The list of the expected keys in the initialized model.
loaded_keys (`List[str]`, *optional*):
The list of the loaded keys in the checkpoint.
"""
return expected_keys
def get_special_dtypes_update(self, model, torch_dtype: "torch.dtype") -> Dict[str, "torch.dtype"]:
"""
returns dtypes for modules that are not quantized - used for the computation of the device_map in case
one passes a str as a device_map. The method will use the `modules_to_not_convert` that is modified
in `_process_model_before_weight_loading`.
Args:
model (`~transformers.PreTrainedModel`):
The model to quantize
torch_dtype (`torch.dtype`):
The dtype passed in `from_pretrained` method.
"""
return {
name: torch_dtype
for name, _ in model.named_parameters()
if any(m in name for m in self.modules_to_not_convert)
}
def adjust_max_memory(self, max_memory: Dict[str, Union[int, str]]) -> Dict[str, Union[int, str]]:
"""adjust max_memory argument for infer_auto_device_map() if extra memory is needed for quantization"""
return max_memory
def check_quantized_param(
self,
model: "PreTrainedModel",
param_value: "torch.Tensor",
param_name: str,
state_dict: Dict[str, Any],
**kwargs,
) -> bool:
"""
checks if a loaded state_dict component is part of quantized param + some validation; only defined if
requires_parameters_quantization == True for quantization methods that require to create a new parameters
for quantization.
"""
return False
def create_quantized_param(self, *args, **kwargs) -> "torch.nn.Parameter":
"""
takes needed components from state_dict and creates quantized param; only applicable if
requires_parameters_quantization == True
"""
if not self.requires_parameters_quantization:
raise AttributeError(
f"`.create_quantized_param()` method is not supported by quantizer class {self.__class__.__name__}."
)
def validate_environment(self, *args, **kwargs):
"""
This method is used to potentially check for potential conflicts with arguments that are
passed in `from_pretrained`. You need to define it for all future quantizers that are integrated with transformers.
If no explicit check are needed, simply return nothing.
"""
return
def preprocess_model(self, model: "PreTrainedModel", **kwargs):
"""
Setting model attributes and/or converting model before weights loading. At this point
the model should be initialized on the meta device so you can freely manipulate the skeleton
of the model in order to replace modules in-place. Make sure to override the abstract method `_process_model_before_weight_loading`.
Args:
model (`~transformers.PreTrainedModel`):
The model to quantize
kwargs (`dict`, *optional*):
The keyword arguments that are passed along `_process_model_before_weight_loading`.
"""
model.is_quantized = True
model.quantization_method = self.quantization_config.quant_method
return self._process_model_before_weight_loading(model, **kwargs)
def postprocess_model(self, model: "PreTrainedModel", **kwargs):
"""
Post-process the model post weights loading.
Make sure to override the abstract method `_process_model_after_weight_loading`.
Args:
model (`~transformers.PreTrainedModel`):
The model to quantize
kwargs (`dict`, *optional*):
The keyword arguments that are passed along `_process_model_after_weight_loading`.
"""
return self._process_model_after_weight_loading(model, **kwargs)
def dequantize(self, model):
"""
Potentially dequantize the model to retrive the original model, with some loss in accuracy / performance.
Note not all quantization schemes support this.
"""
model = self._dequantize(model)
# Delete quantizer and quantization config
del model.hf_quantizer
del model.config.quantization_config
del model.config._pre_quantization_dtype
model.is_quantized = False
return model
def _dequantize(self, model):
raise NotImplementedError(
f"{self.quantization_config.quant_method} has no implementation of `dequantize`, please raise an issue on GitHub."
)
@property
def is_qat_trainable(self) -> bool:
"""Flag indicating whether the quantized model can carry out quantization aware training"""
return False
@abstractmethod
def _process_model_before_weight_loading(self, model, **kwargs): ...
@abstractmethod
def _process_model_after_weight_loading(self, model, **kwargs): ...
@abstractmethod
def is_serializable(self, safe_serialization=None): ...
@property
@abstractmethod
def is_trainable(self): ...
| transformers/src/transformers/quantizers/base.py/0 | {
"file_path": "transformers/src/transformers/quantizers/base.py",
"repo_id": "transformers",
"token_count": 3939
} |
from typing import Optional
import requests
from huggingface_hub import Discussion, HfApi, get_repo_discussions
from .utils import cached_file, http_user_agent, logging
logger = logging.get_logger(__name__)
def previous_pr(api: HfApi, model_id: str, pr_title: str, token: str) -> Optional["Discussion"]:
main_commit = api.list_repo_commits(model_id, token=token)[0].commit_id
for discussion in get_repo_discussions(repo_id=model_id, token=token):
if discussion.title == pr_title and discussion.status == "open" and discussion.is_pull_request:
commits = api.list_repo_commits(model_id, revision=discussion.git_reference, token=token)
if main_commit == commits[1].commit_id:
return discussion
return None
def spawn_conversion(token: str, private: bool, model_id: str):
logger.info("Attempting to convert .bin model on the fly to safetensors.")
safetensors_convert_space_url = "https://safetensors-convert.hf.space"
sse_url = f"{safetensors_convert_space_url}/call/run"
def start(_sse_connection):
for line in _sse_connection.iter_lines():
line = line.decode()
if line.startswith("event:"):
status = line[7:]
logger.debug(f"Safetensors conversion status: {status}")
if status == "complete":
return
elif status == "heartbeat":
logger.debug("Heartbeat")
else:
logger.debug(f"Unknown status {status}")
else:
logger.debug(line)
data = {"data": [model_id, private, token]}
result = requests.post(sse_url, stream=True, json=data).json()
event_id = result["event_id"]
with requests.get(f"{sse_url}/{event_id}", stream=True) as sse_connection:
try:
logger.debug("Spawning safetensors automatic conversion.")
start(sse_connection)
except Exception as e:
logger.warning(f"Error during conversion: {repr(e)}")
def get_conversion_pr_reference(api: HfApi, model_id: str, **kwargs):
private = api.model_info(model_id).private
logger.info("Attempting to create safetensors variant")
pr_title = "Adding `safetensors` variant of this model"
token = kwargs.get("token")
# This looks into the current repo's open PRs to see if a PR for safetensors was already open. If so, it
# returns it. It checks that the PR was opened by the bot and not by another user so as to prevent
# security breaches.
pr = previous_pr(api, model_id, pr_title, token=token)
if pr is None or (not private and pr.author != "SFconvertbot"):
spawn_conversion(token, private, model_id)
pr = previous_pr(api, model_id, pr_title, token=token)
else:
logger.info("Safetensors PR exists")
sha = f"refs/pr/{pr.num}"
return sha
def auto_conversion(pretrained_model_name_or_path: str, ignore_errors_during_conversion=False, **cached_file_kwargs):
try:
api = HfApi(token=cached_file_kwargs.get("token"), headers={"user-agent": http_user_agent()})
sha = get_conversion_pr_reference(api, pretrained_model_name_or_path, **cached_file_kwargs)
if sha is None:
return None, None
cached_file_kwargs["revision"] = sha
del cached_file_kwargs["_commit_hash"]
# This is an additional HEAD call that could be removed if we could infer sharded/non-sharded from the PR
# description.
sharded = api.file_exists(
pretrained_model_name_or_path,
"model.safetensors.index.json",
revision=sha,
token=cached_file_kwargs.get("token"),
)
filename = "model.safetensors.index.json" if sharded else "model.safetensors"
resolved_archive_file = cached_file(pretrained_model_name_or_path, filename, **cached_file_kwargs)
return resolved_archive_file, sha, sharded
except Exception as e:
if not ignore_errors_during_conversion:
raise e
| transformers/src/transformers/safetensors_conversion.py/0 | {
"file_path": "transformers/src/transformers/safetensors_conversion.py",
"repo_id": "transformers",
"token_count": 1701
} |
# Copyright 2020 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.
import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union
from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings
logger = logging.getLogger(__name__)
@dataclass
@add_start_docstrings(TrainingArguments.__doc__)
class Seq2SeqTrainingArguments(TrainingArguments):
"""
Args:
predict_with_generate (`bool`, *optional*, defaults to `False`):
Whether to use generate to calculate generative metrics (ROUGE, BLEU).
generation_max_length (`int`, *optional*):
The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default to the
`max_length` value of the model configuration.
generation_num_beams (`int`, *optional*):
The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default to the
`num_beams` value of the model configuration.
generation_config (`str` or `Path` or [`~generation.GenerationConfig`], *optional*):
Allows to load a [`~generation.GenerationConfig`] from the `from_pretrained` method. This can be either:
- a string, the *model id* of a pretrained model configuration hosted inside a model repo on
huggingface.co.
- a path to a *directory* containing a configuration file saved using the
[`~GenerationConfig.save_pretrained`] method, e.g., `./my_model_directory/`.
- a [`~generation.GenerationConfig`] object.
"""
sortish_sampler: bool = field(default=False, metadata={"help": "Whether to use SortishSampler or not."})
predict_with_generate: bool = field(
default=False, metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."}
)
generation_max_length: Optional[int] = field(
default=None,
metadata={
"help": (
"The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default "
"to the `max_length` value of the model configuration."
)
},
)
generation_num_beams: Optional[int] = field(
default=None,
metadata={
"help": (
"The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default "
"to the `num_beams` value of the model configuration."
)
},
)
generation_config: Optional[Union[str, Path, GenerationConfig]] = field(
default=None,
metadata={
"help": "Model id, file path or url pointing to a GenerationConfig json file, to use during prediction."
},
)
def to_dict(self):
"""
Serializes this instance while replace `Enum` by their values and `GenerationConfig` by dictionaries (for JSON
serialization support). It obfuscates the token values by removing their value.
"""
# filter out fields that are defined as field(init=False)
d = super().to_dict()
for k, v in d.items():
if isinstance(v, GenerationConfig):
d[k] = v.to_dict()
return d
| transformers/src/transformers/training_args_seq2seq.py/0 | {
"file_path": "transformers/src/transformers/training_args_seq2seq.py",
"repo_id": "transformers",
"token_count": 1437
} |
# This file is autogenerated by the command `make fix-copies`, do not edit.
from ..utils import DummyObject, requires_backends
class AlbertTokenizer(metaclass=DummyObject):
_backends = ["sentencepiece"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["sentencepiece"])
class BarthezTokenizer(metaclass=DummyObject):
_backends = ["sentencepiece"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["sentencepiece"])
class BartphoTokenizer(metaclass=DummyObject):
_backends = ["sentencepiece"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["sentencepiece"])
class BertGenerationTokenizer(metaclass=DummyObject):
_backends = ["sentencepiece"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["sentencepiece"])
class BigBirdTokenizer(metaclass=DummyObject):
_backends = ["sentencepiece"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["sentencepiece"])
class CamembertTokenizer(metaclass=DummyObject):
_backends = ["sentencepiece"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["sentencepiece"])
class CodeLlamaTokenizer(metaclass=DummyObject):
_backends = ["sentencepiece"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["sentencepiece"])
class CpmTokenizer(metaclass=DummyObject):
_backends = ["sentencepiece"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["sentencepiece"])
class DebertaV2Tokenizer(metaclass=DummyObject):
_backends = ["sentencepiece"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["sentencepiece"])
class ErnieMTokenizer(metaclass=DummyObject):
_backends = ["sentencepiece"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["sentencepiece"])
class XLMProphetNetTokenizer(metaclass=DummyObject):
_backends = ["sentencepiece"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["sentencepiece"])
class FNetTokenizer(metaclass=DummyObject):
_backends = ["sentencepiece"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["sentencepiece"])
class GemmaTokenizer(metaclass=DummyObject):
_backends = ["sentencepiece"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["sentencepiece"])
class GPTSw3Tokenizer(metaclass=DummyObject):
_backends = ["sentencepiece"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["sentencepiece"])
class LayoutXLMTokenizer(metaclass=DummyObject):
_backends = ["sentencepiece"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["sentencepiece"])
class LlamaTokenizer(metaclass=DummyObject):
_backends = ["sentencepiece"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["sentencepiece"])
class M2M100Tokenizer(metaclass=DummyObject):
_backends = ["sentencepiece"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["sentencepiece"])
class MarianTokenizer(metaclass=DummyObject):
_backends = ["sentencepiece"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["sentencepiece"])
class MBartTokenizer(metaclass=DummyObject):
_backends = ["sentencepiece"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["sentencepiece"])
class MBart50Tokenizer(metaclass=DummyObject):
_backends = ["sentencepiece"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["sentencepiece"])
class MLukeTokenizer(metaclass=DummyObject):
_backends = ["sentencepiece"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["sentencepiece"])
class MT5Tokenizer(metaclass=DummyObject):
_backends = ["sentencepiece"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["sentencepiece"])
class NllbTokenizer(metaclass=DummyObject):
_backends = ["sentencepiece"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["sentencepiece"])
class PegasusTokenizer(metaclass=DummyObject):
_backends = ["sentencepiece"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["sentencepiece"])
class PLBartTokenizer(metaclass=DummyObject):
_backends = ["sentencepiece"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["sentencepiece"])
class ReformerTokenizer(metaclass=DummyObject):
_backends = ["sentencepiece"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["sentencepiece"])
class RemBertTokenizer(metaclass=DummyObject):
_backends = ["sentencepiece"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["sentencepiece"])
class SeamlessM4TTokenizer(metaclass=DummyObject):
_backends = ["sentencepiece"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["sentencepiece"])
class SiglipTokenizer(metaclass=DummyObject):
_backends = ["sentencepiece"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["sentencepiece"])
class Speech2TextTokenizer(metaclass=DummyObject):
_backends = ["sentencepiece"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["sentencepiece"])
class SpeechT5Tokenizer(metaclass=DummyObject):
_backends = ["sentencepiece"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["sentencepiece"])
class T5Tokenizer(metaclass=DummyObject):
_backends = ["sentencepiece"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["sentencepiece"])
class UdopTokenizer(metaclass=DummyObject):
_backends = ["sentencepiece"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["sentencepiece"])
class XGLMTokenizer(metaclass=DummyObject):
_backends = ["sentencepiece"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["sentencepiece"])
class XLMRobertaTokenizer(metaclass=DummyObject):
_backends = ["sentencepiece"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["sentencepiece"])
class XLNetTokenizer(metaclass=DummyObject):
_backends = ["sentencepiece"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["sentencepiece"])
| transformers/src/transformers/utils/dummy_sentencepiece_objects.py/0 | {
"file_path": "transformers/src/transformers/utils/dummy_sentencepiece_objects.py",
"repo_id": "transformers",
"token_count": 2512
} |
# coding=utf-8
# Copyright 2020 Hugging Face
#
# 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 os
import re
import time
from typing import Optional
import IPython.display as disp
from ..trainer_callback import TrainerCallback
from ..trainer_utils import IntervalStrategy, has_length
def format_time(t):
"Format `t` (in seconds) to (h):mm:ss"
t = int(t)
h, m, s = t // 3600, (t // 60) % 60, t % 60
return f"{h}:{m:02d}:{s:02d}" if h != 0 else f"{m:02d}:{s:02d}"
def html_progress_bar(value, total, prefix, label, width=300):
# docstyle-ignore
return f"""
<div>
{prefix}
<progress value='{value}' max='{total}' style='width:{width}px; height:20px; vertical-align: middle;'></progress>
{label}
</div>
"""
def text_to_html_table(items):
"Put the texts in `items` in an HTML table."
html_code = """<table border="1" class="dataframe">\n"""
html_code += """ <thead>\n <tr style="text-align: left;">\n"""
for i in items[0]:
html_code += f" <th>{i}</th>\n"
html_code += " </tr>\n </thead>\n <tbody>\n"
for line in items[1:]:
html_code += " <tr>\n"
for elt in line:
elt = f"{elt:.6f}" if isinstance(elt, float) else str(elt)
html_code += f" <td>{elt}</td>\n"
html_code += " </tr>\n"
html_code += " </tbody>\n</table><p>"
return html_code
class NotebookProgressBar:
"""
A progress par for display in a notebook.
Class attributes (overridden by derived classes)
- **warmup** (`int`) -- The number of iterations to do at the beginning while ignoring `update_every`.
- **update_every** (`float`) -- Since calling the time takes some time, we only do it every presumed
`update_every` seconds. The progress bar uses the average time passed up until now to guess the next value
for which it will call the update.
Args:
total (`int`):
The total number of iterations to reach.
prefix (`str`, *optional*):
A prefix to add before the progress bar.
leave (`bool`, *optional*, defaults to `True`):
Whether or not to leave the progress bar once it's completed. You can always call the
[`~utils.notebook.NotebookProgressBar.close`] method to make the bar disappear.
parent ([`~notebook.NotebookTrainingTracker`], *optional*):
A parent object (like [`~utils.notebook.NotebookTrainingTracker`]) that spawns progress bars and handle
their display. If set, the object passed must have a `display()` method.
width (`int`, *optional*, defaults to 300):
The width (in pixels) that the bar will take.
Example:
```python
import time
pbar = NotebookProgressBar(100)
for val in range(100):
pbar.update(val)
time.sleep(0.07)
pbar.update(100)
```"""
warmup = 5
update_every = 0.2
def __init__(
self,
total: int,
prefix: Optional[str] = None,
leave: bool = True,
parent: Optional["NotebookTrainingTracker"] = None,
width: int = 300,
):
self.total = total
self.prefix = "" if prefix is None else prefix
self.leave = leave
self.parent = parent
self.width = width
self.last_value = None
self.comment = None
self.output = None
self.value = None
self.label = None
if "VSCODE_PID" in os.environ:
self.update_every = 0.5 # Adjusted for smooth updated as html rending is slow on VS Code
# This is the only adjustment required to optimize training html rending
def update(self, value: int, force_update: bool = False, comment: str = None):
"""
The main method to update the progress bar to `value`.
Args:
value (`int`):
The value to use. Must be between 0 and `total`.
force_update (`bool`, *optional*, defaults to `False`):
Whether or not to force and update of the internal state and display (by default, the bar will wait for
`value` to reach the value it predicted corresponds to a time of more than the `update_every` attribute
since the last update to avoid adding boilerplate).
comment (`str`, *optional*):
A comment to add on the left of the progress bar.
"""
self.value = value
if comment is not None:
self.comment = comment
if self.last_value is None:
self.start_time = self.last_time = time.time()
self.start_value = self.last_value = value
self.elapsed_time = self.predicted_remaining = None
self.first_calls = self.warmup
self.wait_for = 1
self.update_bar(value)
elif value <= self.last_value and not force_update:
return
elif force_update or self.first_calls > 0 or value >= min(self.last_value + self.wait_for, self.total):
if self.first_calls > 0:
self.first_calls -= 1
current_time = time.time()
self.elapsed_time = current_time - self.start_time
# We could have value = self.start_value if the update is called twixe with the same start value.
if value > self.start_value:
self.average_time_per_item = self.elapsed_time / (value - self.start_value)
else:
self.average_time_per_item = None
if value >= self.total:
value = self.total
self.predicted_remaining = None
if not self.leave:
self.close()
elif self.average_time_per_item is not None:
self.predicted_remaining = self.average_time_per_item * (self.total - value)
self.update_bar(value)
self.last_value = value
self.last_time = current_time
if (self.average_time_per_item is None) or (self.average_time_per_item == 0):
self.wait_for = 1
else:
self.wait_for = max(int(self.update_every / self.average_time_per_item), 1)
def update_bar(self, value, comment=None):
spaced_value = " " * (len(str(self.total)) - len(str(value))) + str(value)
if self.elapsed_time is None:
self.label = f"[{spaced_value}/{self.total} : < :"
elif self.predicted_remaining is None:
self.label = f"[{spaced_value}/{self.total} {format_time(self.elapsed_time)}"
else:
self.label = (
f"[{spaced_value}/{self.total} {format_time(self.elapsed_time)} <"
f" {format_time(self.predicted_remaining)}"
)
if self.average_time_per_item == 0:
self.label += ", +inf it/s"
else:
self.label += f", {1/self.average_time_per_item:.2f} it/s"
self.label += "]" if self.comment is None or len(self.comment) == 0 else f", {self.comment}]"
self.display()
def display(self):
self.html_code = html_progress_bar(self.value, self.total, self.prefix, self.label, self.width)
if self.parent is not None:
# If this is a child bar, the parent will take care of the display.
self.parent.display()
return
if self.output is None:
self.output = disp.display(disp.HTML(self.html_code), display_id=True)
else:
self.output.update(disp.HTML(self.html_code))
def close(self):
"Closes the progress bar."
if self.parent is None and self.output is not None:
self.output.update(disp.HTML(""))
class NotebookTrainingTracker(NotebookProgressBar):
"""
An object tracking the updates of an ongoing training with progress bars and a nice table reporting metrics.
Args:
num_steps (`int`): The number of steps during training. column_names (`List[str]`, *optional*):
The list of column names for the metrics table (will be inferred from the first call to
[`~utils.notebook.NotebookTrainingTracker.write_line`] if not set).
"""
def __init__(self, num_steps, column_names=None):
super().__init__(num_steps)
self.inner_table = None if column_names is None else [column_names]
self.child_bar = None
def display(self):
self.html_code = html_progress_bar(self.value, self.total, self.prefix, self.label, self.width)
if self.inner_table is not None:
self.html_code += text_to_html_table(self.inner_table)
if self.child_bar is not None:
self.html_code += self.child_bar.html_code
if self.output is None:
self.output = disp.display(disp.HTML(self.html_code), display_id=True)
else:
self.output.update(disp.HTML(self.html_code))
def write_line(self, values):
"""
Write the values in the inner table.
Args:
values (`Dict[str, float]`): The values to display.
"""
if self.inner_table is None:
self.inner_table = [list(values.keys()), list(values.values())]
else:
columns = self.inner_table[0]
for key in values.keys():
if key not in columns:
columns.append(key)
self.inner_table[0] = columns
if len(self.inner_table) > 1:
last_values = self.inner_table[-1]
first_column = self.inner_table[0][0]
if last_values[0] != values[first_column]:
# write new line
self.inner_table.append([values[c] if c in values else "No Log" for c in columns])
else:
# update last line
new_values = values
for c in columns:
if c not in new_values.keys():
new_values[c] = last_values[columns.index(c)]
self.inner_table[-1] = [new_values[c] for c in columns]
else:
self.inner_table.append([values[c] for c in columns])
def add_child(self, total, prefix=None, width=300):
"""
Add a child progress bar displayed under the table of metrics. The child progress bar is returned (so it can be
easily updated).
Args:
total (`int`): The number of iterations for the child progress bar.
prefix (`str`, *optional*): A prefix to write on the left of the progress bar.
width (`int`, *optional*, defaults to 300): The width (in pixels) of the progress bar.
"""
self.child_bar = NotebookProgressBar(total, prefix=prefix, parent=self, width=width)
return self.child_bar
def remove_child(self):
"""
Closes the child progress bar.
"""
self.child_bar = None
self.display()
class NotebookProgressCallback(TrainerCallback):
"""
A [`TrainerCallback`] that displays the progress of training or evaluation, optimized for Jupyter Notebooks or
Google colab.
"""
def __init__(self):
self.training_tracker = None
self.prediction_bar = None
self._force_next_update = False
def on_train_begin(self, args, state, control, **kwargs):
self.first_column = "Epoch" if args.eval_strategy == IntervalStrategy.EPOCH else "Step"
self.training_loss = 0
self.last_log = 0
column_names = [self.first_column] + ["Training Loss"]
if args.eval_strategy != IntervalStrategy.NO:
column_names.append("Validation Loss")
self.training_tracker = NotebookTrainingTracker(state.max_steps, column_names)
def on_step_end(self, args, state, control, **kwargs):
epoch = int(state.epoch) if int(state.epoch) == state.epoch else f"{state.epoch:.2f}"
self.training_tracker.update(
state.global_step + 1,
comment=f"Epoch {epoch}/{state.num_train_epochs}",
force_update=self._force_next_update,
)
self._force_next_update = False
def on_prediction_step(self, args, state, control, eval_dataloader=None, **kwargs):
if not has_length(eval_dataloader):
return
if self.prediction_bar is None:
if self.training_tracker is not None:
self.prediction_bar = self.training_tracker.add_child(len(eval_dataloader))
else:
self.prediction_bar = NotebookProgressBar(len(eval_dataloader))
self.prediction_bar.update(1)
else:
self.prediction_bar.update(self.prediction_bar.value + 1)
def on_predict(self, args, state, control, **kwargs):
if self.prediction_bar is not None:
self.prediction_bar.close()
self.prediction_bar = None
def on_log(self, args, state, control, logs=None, **kwargs):
# Only for when there is no evaluation
if args.eval_strategy == IntervalStrategy.NO and "loss" in logs:
values = {"Training Loss": logs["loss"]}
# First column is necessarily Step sine we're not in epoch eval strategy
values["Step"] = state.global_step
self.training_tracker.write_line(values)
def on_evaluate(self, args, state, control, metrics=None, **kwargs):
if self.training_tracker is not None:
values = {"Training Loss": "No log", "Validation Loss": "No log"}
for log in reversed(state.log_history):
if "loss" in log:
values["Training Loss"] = log["loss"]
break
if self.first_column == "Epoch":
values["Epoch"] = int(state.epoch)
else:
values["Step"] = state.global_step
metric_key_prefix = "eval"
for k in metrics:
if k.endswith("_loss"):
metric_key_prefix = re.sub(r"\_loss$", "", k)
_ = metrics.pop("total_flos", None)
_ = metrics.pop("epoch", None)
_ = metrics.pop(f"{metric_key_prefix}_runtime", None)
_ = metrics.pop(f"{metric_key_prefix}_samples_per_second", None)
_ = metrics.pop(f"{metric_key_prefix}_steps_per_second", None)
_ = metrics.pop(f"{metric_key_prefix}_jit_compilation_time", None)
for k, v in metrics.items():
splits = k.split("_")
name = " ".join([part.capitalize() for part in splits[1:]])
if name == "Loss":
# Single dataset
name = "Validation Loss"
values[name] = v
self.training_tracker.write_line(values)
self.training_tracker.remove_child()
self.prediction_bar = None
# Evaluation takes a long time so we should force the next update.
self._force_next_update = True
def on_train_end(self, args, state, control, **kwargs):
self.training_tracker.update(
state.global_step,
comment=f"Epoch {int(state.epoch)}/{state.num_train_epochs}",
force_update=True,
)
self.training_tracker = None
| transformers/src/transformers/utils/notebook.py/0 | {
"file_path": "transformers/src/transformers/utils/notebook.py",
"repo_id": "transformers",
"token_count": 7048
} |
# Copyright 2023 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.
import itertools
import os
import subprocess
import unittest
from copy import deepcopy
from functools import partial
from parameterized import parameterized
import tests.trainer.test_trainer
from tests.trainer.test_trainer import TrainerIntegrationCommon # noqa
from transformers import is_torch_available
from transformers.testing_utils import (
TestCasePlus,
backend_device_count,
execute_subprocess_async,
mockenv_context,
require_accelerate,
require_fsdp,
require_torch_accelerator,
require_torch_multi_accelerator,
slow,
torch_device,
)
from transformers.trainer_callback import TrainerState
from transformers.trainer_utils import FSDPOption, set_seed
from transformers.utils import is_accelerate_available, is_torch_bf16_available_on_device
if is_torch_available():
from transformers.pytorch_utils import is_torch_greater_or_equal_than_2_1
from transformers.trainer import FSDP_MODEL_NAME
else:
is_torch_greater_or_equal_than_2_1 = False
# default torch.distributed port
DEFAULT_MASTER_PORT = "10999"
dtypes = ["fp16"]
if is_torch_bf16_available_on_device(torch_device):
dtypes += ["bf16"]
sharding_strategies = ["full_shard", "shard_grad_op"]
state_dict_types = ["FULL_STATE_DICT", "SHARDED_STATE_DICT"]
set_seed(42)
params = list(itertools.product(sharding_strategies, dtypes))
def get_master_port(real_launcher=False):
"""
When using a single gpu launcher emulation (i.e. not deepspeed or python -m torch.distributed)
the issue is that once the port is tied it can't be used anywhere else outside of this process,
since torch.dist doesn't free the port until the process exits. Therefore for the sake of being
able to run both emulated launcher and normal launcher tests we need 2 distinct ports.
This function will give the right port in the right context. For real launcher it'll give the
base port, for emulated launcher it'll give the base port + 1. In both cases a string is
returned.
Args:
`real_launcher`: whether a real launcher is going to be used, or the emulated one
"""
master_port_base = os.environ.get("DS_TEST_PORT", DEFAULT_MASTER_PORT)
if not real_launcher:
master_port_base = str(int(master_port_base) + 1)
return master_port_base
if is_torch_available():
from tests.trainer.test_trainer import ( # noqa
RegressionModelConfig,
RegressionPreTrainedModel,
)
# hack to restore original logging level pre #21700
get_regression_trainer = partial(tests.trainer.test_trainer.get_regression_trainer, log_level="info")
require_fsdp_version = require_fsdp
if is_accelerate_available():
from accelerate.utils.constants import (
FSDP_PYTORCH_VERSION,
FSDP_SHARDING_STRATEGY,
)
require_fsdp_version = partial(require_fsdp, min_version=FSDP_PYTORCH_VERSION)
def get_launcher(distributed=False, use_accelerate=False):
# 1. explicitly set --num_nodes=1 just in case these tests end up run on a multi-node setup
# - it won't be able to handle that
# 2. for now testing with just 2 gpus max (since some quality tests may give different
# results with mode gpus because we use very little data)
num_gpus = min(2, backend_device_count(torch_device)) if distributed else 1
master_port = get_master_port(real_launcher=True)
if use_accelerate:
return f"""accelerate launch
--num_processes {num_gpus}
--main_process_port {master_port}
--use_fsdp
--fsdp_auto_wrap_policy TRANSFORMER_BASED_WRAP
--fsdp_state_dict_type SHARDED_STATE_DICT
--fsdp_transformer_layer_cls_to_wrap BertLayer""".split()
return f"torchrun --nnodes 1 --nproc-per-node {num_gpus} --master-port {master_port}".split()
def _parameterized_custom_name_func(func, param_num, param):
# customize the test name generator function as we want both params to appear in the sub-test
# name, as by default it shows only the first param
param_based_name = parameterized.to_safe_name("_".join(str(x) for x in param.args))
return f"{func.__name__}_{param_based_name}"
@require_accelerate
@require_torch_accelerator
@require_fsdp_version
class TrainerIntegrationFSDP(TestCasePlus, TrainerIntegrationCommon):
def setUp(self):
super().setUp()
master_port = get_master_port(real_launcher=False)
self.dist_env_1_gpu = {
"MASTER_ADDR": "localhost",
"MASTER_PORT": master_port,
"RANK": "0",
"LOCAL_RANK": "0",
"WORLD_SIZE": "1",
}
self.fsdp_config = {
"backward_prefetch": "backward_pre",
"forward_prefetch": "False",
"limit_all_gathers": "False",
"use_orig_params": "True",
"sync_module_states": "True",
"cpu_ram_efficient_loading": "True",
"activation_checkpointing": "False",
"min_num_params": 1,
}
def tearDown(self):
super().tearDown()
@parameterized.expand(params, name_func=_parameterized_custom_name_func)
def test_fsdp_config(self, sharding_strategy, dtype):
output_dir = self.get_auto_remove_tmp_dir()
kwargs = {
"output_dir": output_dir,
"train_len": 128,
"save_steps": 5,
"learning_rate": 0.1,
"fsdp": f"{sharding_strategy} offload auto_wrap",
"fsdp_config": self.fsdp_config,
}
kwargs[dtype] = True
with mockenv_context(**self.dist_env_1_gpu):
trainer = get_regression_trainer(**kwargs)
self.assertEqual(trainer.args.fsdp[0], sharding_strategy)
self.assertEqual(trainer.args.fsdp[1], FSDPOption.OFFLOAD)
self.assertEqual(trainer.args.fsdp[2], FSDPOption.AUTO_WRAP)
for k, v in trainer.args.fsdp_config.items():
self.assertEqual(v, self.fsdp_config[k])
self.assertEqual(os.environ.get("ACCELERATE_USE_FSDP", "false"), "true")
@parameterized.expand(params, name_func=_parameterized_custom_name_func)
def test_fsdp_config_transformers_auto_wrap(self, sharding_strategy, dtype):
output_dir = self.get_auto_remove_tmp_dir()
fsdp_config = deepcopy(self.fsdp_config)
del fsdp_config["min_num_params"]
fsdp_config["transformer_layer_cls_to_wrap"] = "BertLayer"
kwargs = {
"output_dir": output_dir,
"train_len": 128,
"save_steps": 5,
"learning_rate": 0.1,
"fsdp": f"{sharding_strategy} offload auto_wrap",
"fsdp_config": fsdp_config,
}
kwargs[dtype] = True
prefix = "FSDP_"
with mockenv_context(**self.dist_env_1_gpu):
trainer = get_regression_trainer(**kwargs)
self.assertEqual(trainer.args.fsdp[0], sharding_strategy)
self.assertEqual(trainer.args.fsdp[1], FSDPOption.OFFLOAD)
self.assertEqual(trainer.args.fsdp[2], FSDPOption.AUTO_WRAP)
fsdp_sharding_strategy = str(FSDP_SHARDING_STRATEGY.index(sharding_strategy.upper()) + 1)
self.assertEqual(os.environ[f"{prefix}SHARDING_STRATEGY"], fsdp_sharding_strategy)
self.assertEqual(os.environ[f"{prefix}OFFLOAD_PARAMS"], "true")
self.assertEqual(os.environ[f"{prefix}AUTO_WRAP_POLICY"], "TRANSFORMER_BASED_WRAP")
self.assertEqual(
os.environ[f"{prefix}TRANSFORMER_CLS_TO_WRAP"], ",".join(fsdp_config["transformer_layer_cls_to_wrap"])
)
self.assertEqual(os.environ[f"{prefix}BACKWARD_PREFETCH"], fsdp_config["backward_prefetch"].upper())
self.assertEqual(os.environ[f"{prefix}FORWARD_PREFETCH"], fsdp_config["forward_prefetch"])
self.assertEqual(os.environ[f"{prefix}USE_ORIG_PARAMS"], fsdp_config["use_orig_params"])
self.assertEqual(os.environ[f"{prefix}SYNC_MODULE_STATES"], fsdp_config["sync_module_states"])
self.assertEqual(
os.environ[f"{prefix}CPU_RAM_EFFICIENT_LOADING"], fsdp_config["cpu_ram_efficient_loading"]
)
self.assertEqual(os.environ.get("ACCELERATE_USE_FSDP", "false"), "true")
@parameterized.expand(params, name_func=_parameterized_custom_name_func)
@require_torch_multi_accelerator
@slow
def test_basic_run(self, sharding_strategy, dtype):
launcher = get_launcher(distributed=True, use_accelerate=False)
output_dir = self.get_auto_remove_tmp_dir()
args = self.get_base_args(output_dir, 1, 50).split() + [f"--{dtype}"]
fsdp_args = ["--fsdp", f"{sharding_strategy} auto_wrap", "--fsdp_transformer_layer_cls_to_wrap", "BertLayer"]
script = [f"{self.examples_dir_str}/pytorch/text-classification/run_glue.py"]
cmd = launcher + script + args + fsdp_args
execute_subprocess_async(cmd, env=self.get_env())
@parameterized.expand(params, name_func=_parameterized_custom_name_func)
@require_torch_multi_accelerator
@slow
def test_basic_run_with_gradient_accumulation(self, sharding_strategy, dtype):
launcher = get_launcher(distributed=True, use_accelerate=False)
output_dir = self.get_auto_remove_tmp_dir()
args = self.get_base_args(output_dir, 1, 50).split() + [f"--{dtype}", "--gradient_accumulation_steps", "2"]
fsdp_args = ["--fsdp", f"{sharding_strategy} auto_wrap", "--fsdp_transformer_layer_cls_to_wrap", "BertLayer"]
script = [f"{self.examples_dir_str}/pytorch/text-classification/run_glue.py"]
cmd = launcher + script + args + fsdp_args
execute_subprocess_async(cmd, env=self.get_env())
@parameterized.expand(dtypes)
@require_torch_multi_accelerator
@slow
@unittest.skipIf(not is_torch_greater_or_equal_than_2_1, reason="This test on pytorch 2.0 takes 4 hours.")
def test_basic_run_with_cpu_offload(self, dtype):
launcher = get_launcher(distributed=True, use_accelerate=False)
output_dir = self.get_auto_remove_tmp_dir()
args = self.get_base_args(output_dir, 1, 50).split() + [f"--{dtype}", "--max_steps", "10"]
fsdp_args = ["--fsdp", "full_shard auto_wrap offload", "--fsdp_transformer_layer_cls_to_wrap", "BertLayer"]
script = [f"{self.examples_dir_str}/pytorch/text-classification/run_glue.py"]
cmd = launcher + script + args + fsdp_args
execute_subprocess_async(cmd, env=self.get_env())
@parameterized.expand(state_dict_types, name_func=_parameterized_custom_name_func)
@require_torch_multi_accelerator
@slow
def test_training_and_can_resume_normally(self, state_dict_type):
output_dir = self.get_auto_remove_tmp_dir("./xxx", after=False)
sharding_strategy = "full_shard"
use_accelerate = state_dict_type == "SHARDED_STATE_DICT"
launcher = get_launcher(True, use_accelerate=use_accelerate)
args = self.get_base_args(output_dir, 2, 25).split()
script = [f"{self.examples_dir_str}/pytorch/text-classification/run_glue.py"]
logs = self.run_cmd_and_get_logs(use_accelerate, sharding_strategy, launcher, script, args, output_dir)
# resume from ckpt
checkpoint = os.path.join(output_dir, "checkpoint-115")
resume_args = args + f"--resume_from_checkpoint {checkpoint}".split()
is_fsdp_ckpt = os.path.isdir(checkpoint) and (
# this checks the FSDP state dict when `SHARDED_STATE_DICT` is used
any(
FSDP_MODEL_NAME in folder_name
for folder_name in os.listdir(checkpoint)
if os.path.isdir(os.path.join(checkpoint, folder_name))
)
# this checks the FSDP state dict when `FULL_STATE_DICT` is used
or os.path.isfile(os.path.join(checkpoint, f"{FSDP_MODEL_NAME}.bin"))
)
self.assertTrue(is_fsdp_ckpt)
logs_resume = self.run_cmd_and_get_logs(
use_accelerate, sharding_strategy, launcher, script, resume_args, output_dir
)
for log, log1 in zip(logs, logs_resume):
if "learning_rate" in log:
self.assertAlmostEqual(log["learning_rate"], log1["learning_rate"], delta=1e-5)
@require_torch_multi_accelerator
@slow
@require_torch_accelerator
@require_fsdp
def test_fsdp_cpu_offloading(self):
try:
subprocess.run(
"accelerate launch utils/testing_scripts/fsdp_cpu_offloading.py --config utils/testing_scripts/dummy_fsdp_config.yml",
shell=True,
check=True,
)
except: # noqa
raise AssertionError("CPU offloading failed with FSDP!")
def run_cmd_and_get_logs(self, use_accelerate, sharding_strategy, launcher, script, args, output_dir):
if not use_accelerate:
fsdp_args = [
"--fsdp",
f"{sharding_strategy} auto_wrap",
"--fsdp_transformer_layer_cls_to_wrap",
"BertLayer",
]
cmd = launcher + script + args + fsdp_args
else:
fsdp_config = f"""
--fsdp_sharding_strategy {FSDP_SHARDING_STRATEGY.index(sharding_strategy.upper()) + 1}
""".split()
cmd = launcher + fsdp_config + script + args
# keep for quick debug
# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
execute_subprocess_async(cmd, env=self.get_env())
logs = TrainerState.load_from_json(os.path.join(output_dir, "trainer_state.json")).log_history
return logs
def get_base_args(self, output_dir, num_epochs, logging_steps):
return f"""
--model_name_or_path google-bert/bert-base-cased
--task_name mrpc
--output_dir {output_dir}
--overwrite_output_dir
--do_train
--max_seq_length 128
--per_device_train_batch_size 16
--learning_rate 5e-5
--num_train_epochs {num_epochs}
--lr_scheduler_type cosine
--logging_steps {logging_steps}
--save_strategy epoch
--do_eval
--eval_strategy epoch
--report_to none
"""
| transformers/tests/fsdp/test_fsdp.py/0 | {
"file_path": "transformers/tests/fsdp/test_fsdp.py",
"repo_id": "transformers",
"token_count": 6543
} |
# 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.
"""Testing suite for the PyTorch Bamba model."""
import inspect
import unittest
import pytest
from transformers import AutoTokenizer, BambaConfig, is_torch_available
from transformers.testing_utils import (
require_torch,
slow,
torch_device,
)
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
BambaForCausalLM,
BambaModel,
)
from transformers.models.bamba.modeling_bamba import (
HybridMambaAttentionDynamicCache,
)
class BambaModelTester:
def __init__(
self,
parent,
batch_size=13,
seq_length=7,
is_training=True,
use_input_mask=True,
use_labels=True,
vocab_size=99,
hidden_size=32,
num_hidden_layers=4,
num_attention_heads=4,
num_key_value_heads=2,
intermediate_size=64,
hidden_act="silu",
attention_dropout=0.0,
attn_layer_indices=None,
attn_rotary_emb=8,
max_position_embeddings=512,
type_vocab_size=16,
initializer_range=0.02,
num_labels=3,
pad_token_id=0,
mamba_n_groups=1,
mamba_n_heads=16,
mamba_d_state=16,
mamba_d_conv=4,
mamba_expand=2,
mamba_chunk_size=16,
scope=None,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_input_mask = use_input_mask
self.use_labels = use_labels
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.num_key_value_heads = num_key_value_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.attention_dropout = attention_dropout
self.attn_layer_indices = attn_layer_indices
self.attn_rotary_emb = attn_rotary_emb
self.max_position_embeddings = max_position_embeddings
self.type_vocab_size = type_vocab_size
self.initializer_range = initializer_range
self.num_labels = num_labels
self.pad_token_id = pad_token_id
self.scope = scope
self.mamba_n_groups = mamba_n_groups
self.mamba_n_heads = mamba_n_heads
self.mamba_d_state = mamba_d_state
self.mamba_d_conv = mamba_d_conv
self.mamba_expand = mamba_expand
self.mamba_chunk_size = mamba_chunk_size
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
input_mask = None
if self.use_input_mask:
input_mask = torch.tril(torch.ones_like(input_ids).to(torch_device))
token_labels = None
if self.use_labels:
token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
config = self.get_config()
return config, input_ids, input_mask, token_labels
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
config,
input_ids,
input_mask,
token_labels,
) = config_and_inputs
inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask}
return config, inputs_dict
def get_config(self):
# Fix for SDPA tests, force at least 4 layers
if self.num_hidden_layers < 4:
self.num_hidden_layers = 4
if self.attn_layer_indices is None:
d = [x for x in range(2, self.num_hidden_layers) if self.num_hidden_layers % x == 0]
if len(d) == 0:
raise ValueError("num_hidden_layers is prime, cannot automatically set attn_layer_indices.")
d = d[-1] # get the largest divisor
self.attn_layer_indices = [x + 1 for x in range(0, self.num_hidden_layers, d)]
return BambaConfig(
vocab_size=self.vocab_size,
hidden_size=self.hidden_size,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
num_key_value_heads=self.num_key_value_heads,
intermediate_size=self.intermediate_size,
hidden_act=self.hidden_act,
attention_dropout=self.attention_dropout,
attn_layer_indices=self.attn_layer_indices,
attn_rotary_emb=self.attn_rotary_emb,
max_position_embeddings=self.max_position_embeddings,
initializer_range=self.initializer_range,
pad_token_id=self.pad_token_id,
mamba_n_groups=self.mamba_n_groups,
mamba_n_heads=self.mamba_n_heads,
mamba_d_state=self.mamba_d_state,
mamba_d_conv=self.mamba_d_conv,
mamba_expand=self.mamba_expand,
mamba_chunk_size=self.mamba_chunk_size,
)
def create_and_check_model(
self,
config,
input_ids,
input_mask,
token_labels,
):
model = BambaModel(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask)
result = model(input_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
def create_and_check_for_causal_lm(
self,
config,
input_ids,
input_mask,
token_labels,
):
model = BambaForCausalLM(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, labels=token_labels)
result = model(input_ids, attention_mask=input_mask)
result = model(input_ids, labels=token_labels)
result = model(input_ids)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
def create_and_check_decoder_model_past_large_inputs(
self,
config,
input_ids,
input_mask,
token_labels,
):
# config.is_decoder = True
# config.add_cross_attention = True
model = BambaForCausalLM(config=config)
model.to(torch_device)
model.eval()
# first forward pass
# Attention: Jamba needs the cache to be initialized to return a cache!
past_key_values = HybridMambaAttentionDynamicCache(
config, input_ids.shape[0], model.dtype, device=model.device
)
outputs = model(
input_ids,
attention_mask=input_mask,
past_key_values=past_key_values,
use_cache=True,
)
past_key_values = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size)
next_mask = ids_tensor((self.batch_size, 3), vocab_size=2)
# append to next input_ids and
next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
next_attention_mask = torch.cat([input_mask, next_mask], dim=-1)
output_from_no_past = model(
next_input_ids,
attention_mask=next_attention_mask,
output_hidden_states=True,
)["hidden_states"][0]
output_from_past = model(
next_tokens,
attention_mask=next_attention_mask,
past_key_values=past_key_values,
output_hidden_states=True,
cache_position=torch.arange(
input_ids.shape[1], input_ids.shape[1] + next_tokens.shape[1], device=model.device
),
)["hidden_states"][0]
# select random slice
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach()
output_from_past_slice = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
@require_torch
class BambaModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (
(
BambaModel,
BambaForCausalLM,
)
if is_torch_available()
else ()
)
all_generative_model_classes = (BambaForCausalLM,) if is_torch_available() else ()
pipeline_model_mapping = (
{
"feature-extraction": BambaModel,
"text-generation": BambaForCausalLM,
}
if is_torch_available()
else {}
)
test_headmasking = False
test_pruning = False
fx_compatible = False
# Need to use `0.8` instead of `0.9` for `test_cpu_offload`
# This is because we are hitting edge cases with the causal_mask buffer
model_split_percents = [0.5, 0.7, 0.8]
def setUp(self):
self.model_tester = BambaModelTester(self)
self.config_tester = ConfigTester(self, config_class=BambaConfig, hidden_size=64)
def test_config(self):
self.config_tester.run_common_tests()
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
def test_for_casual_lm(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_causal_lm(*config_and_inputs)
def test_decoder_model_past_with_large_inputs(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_decoder_model_past_large_inputs(*config_and_inputs)
def test_initialization(self):
r"""
Overriding the test_initialization test as the A_log and D params of the Bamba mixer are initialized differently
"""
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
configs_no_init = _config_zero_init(config)
for model_class in self.all_model_classes:
model = model_class(config=configs_no_init)
for name, param in model.named_parameters():
if param.requires_grad:
if "A_log" in name:
A = torch.arange(1, config.mamba_n_heads + 1, dtype=torch.float32)
torch.testing.assert_close(param.data, torch.log(A), rtol=1e-5, atol=1e-5)
elif "D" in name:
D = torch.ones(config.mamba_n_heads, dtype=torch.float32)
torch.testing.assert_close(param.data, D, rtol=1e-5, atol=1e-5)
else:
self.assertIn(
((param.data.mean() * 1e9).round() / 1e9).item(),
[0.0, 1.0],
msg=f"Parameter {name} of model {model_class} seems not properly initialized",
)
def test_mismatched_shapes_have_properly_initialized_weights(self):
r"""
Overriding the test_mismatched_shapes_have_properly_initialized_weights test because A_log and D params of the
Bamba mixer are initialized differently and we tested that in test_initialization
"""
self.skipTest(reason="Cumbersome and redundant for Bamba")
def test_attention_outputs(self):
r"""
Overriding the test_attention_outputs test as the Bamba model outputs attention only for its attention layers
"""
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.return_dict = True
seq_len = getattr(self.model_tester, "seq_length", None)
encoder_seq_length = getattr(self.model_tester, "encoder_seq_length", seq_len)
encoder_key_length = getattr(self.model_tester, "key_length", encoder_seq_length)
expected_num_attentions = self.model_tester.num_hidden_layers - len(self.model_tester.attn_layer_indices)
for model_class in self.all_model_classes:
inputs_dict["output_attentions"] = True
inputs_dict["output_hidden_states"] = False
config.return_dict = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
attentions = outputs.attentions
self.assertEqual(len(attentions), expected_num_attentions)
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
config.output_attentions = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
attentions = outputs.attentions
self.assertEqual(len(attentions), expected_num_attentions)
self.assertListEqual(
list(attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length],
)
out_len = len(outputs)
# Check attention is always last and order is fine
inputs_dict["output_attentions"] = True
inputs_dict["output_hidden_states"] = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
added_hidden_states = 1
self.assertEqual(out_len + added_hidden_states, len(outputs))
self_attentions = outputs.attentions
self.assertEqual(len(self_attentions), expected_num_attentions)
self.assertListEqual(
list(self_attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length],
)
def test_batching_equivalence(self):
# need to disable the tril input mask
orig = self.model_tester.use_input_mask
self.model_tester.use_input_mask = False
super().test_batching_equivalence()
self.model_tester.use_input_mask = orig
# essentially the same test in test_utils, just adjustment for rtol for this model
@pytest.mark.generate
def test_left_padding_compatibility(self):
# NOTE: left-padding results in small numerical differences. This is expected.
# See https://github.com/huggingface/transformers/issues/25420#issuecomment-1775317535
# First, filter out models that don't support left padding
# - The model must have generative capabilities
if len(self.all_generative_model_classes) == 0:
self.skipTest(reason="No generative architecture available for this model.")
# - The model must support padding
if not self.has_attentions:
self.skipTest(reason="This model doesn't support padding.")
# - The model must be a decoder-only architecture (encoder-based architectures use right-padding)
decoder_only_classes = []
for model_class in self.all_generative_model_classes:
config, _ = self.prepare_config_and_inputs_for_generate()
if config.is_encoder_decoder:
continue
else:
decoder_only_classes.append(model_class)
if len(decoder_only_classes) == 0:
self.skipTest(reason="No decoder-only architecture available for this model.")
# - Decoder-only architectures derived from encoder-decoder models could support it in theory, but we haven't
# added support for it yet. We skip these models for now.
has_encoder_attributes = any(
attr_name
for attr_name in config.to_dict().keys()
if attr_name.startswith("encoder") and attr_name != "encoder_no_repeat_ngram_size"
)
if has_encoder_attributes:
self.skipTest(
reason="The decoder-only derived from encoder-decoder models are not expected to support left-padding."
)
# Then, test left-padding
def _prepare_model_kwargs(input_ids, attention_mask, signature):
model_kwargs = {"input_ids": input_ids, "attention_mask": attention_mask}
if "position_ids" in signature:
position_ids = torch.cumsum(attention_mask, dim=-1) - 1
position_ids.masked_fill_(attention_mask == 0, 1)
model_kwargs["position_ids"] = position_ids
if "cache_position" in signature:
cache_position = torch.arange(input_ids.shape[-1], device=torch_device)
model_kwargs["cache_position"] = cache_position
return model_kwargs
for model_class in decoder_only_classes:
config, inputs_dict = self.prepare_config_and_inputs_for_generate()
input_ids = inputs_dict["input_ids"]
# - for left padding we absolutely need to use an all ones
# attention mask, so we do not use the one in inputs_dict
attention_mask = torch.ones_like(input_ids)
model = model_class(config).to(torch_device).eval()
signature = inspect.signature(model.forward).parameters.keys()
# no cache as some models require special cache classes to be init outside forward
model.generation_config.use_cache = False
# Without padding
model_kwargs = _prepare_model_kwargs(input_ids, attention_mask, signature)
next_logits_wo_padding = model(**model_kwargs).logits[:, -1, :]
# With left-padding (length 32)
# can hardcode pad_token to be 0 as we'll do attn masking anyway
pad_token_id = (
config.get_text_config().pad_token_id if config.get_text_config().pad_token_id is not None else 0
)
pad_size = (input_ids.shape[0], 32)
padding = torch.ones(pad_size, dtype=input_ids.dtype, device=torch_device) * pad_token_id
padded_input_ids = torch.cat((padding, input_ids), dim=1)
padded_attention_mask = torch.cat((torch.zeros_like(padding), attention_mask), dim=1)
model_kwargs = _prepare_model_kwargs(padded_input_ids, padded_attention_mask, signature)
next_logits_with_padding = model(**model_kwargs).logits[:, -1, :]
# They should result in very similar logits
torch.testing.assert_close(next_logits_wo_padding, next_logits_with_padding, rtol=1e-5, atol=1e-5)
@slow
@require_torch
class BambaModelIntegrationTest(unittest.TestCase):
model = None
tokenizer = None
# This variable is used to determine which CUDA device are we using for our runners (A10 or T4)
# Depending on the hardware we get different logits / generations
cuda_compute_capability_major_version = None
@classmethod
def setUpClass(cls):
model_id = "ibm-fms/Bamba-9B"
cls.model = BambaForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, low_cpu_mem_usage=True)
cls.tokenizer = AutoTokenizer.from_pretrained(model_id)
# feels a bit forced to have to do this for the generation test
cls.tokenizer.pad_token_id = cls.model.config.pad_token_id
cls.tokenizer.padding_side = "left"
if is_torch_available() and torch.cuda.is_available():
# 8 is for A100 / A10 and 7 for T4
cls.cuda_compute_capability_major_version = torch.cuda.get_device_capability()[0]
def test_simple_generate(self):
# Key 9 for MI300, Key 8 for A100/A10, and Key 7 for T4.
#
# Note: Key 9 is currently set for MI300, but may need potential future adjustments for H100s,
# considering differences in hardware processing and potential deviations in generated text.
EXPECTED_TEXTS = {
# 7: "",
8: "<|begin_of_text|>Hey how are you doing on this lovely evening? I hope you are all having a good time.",
# 9: """,
}
self.model.to(torch_device)
input_ids = self.tokenizer("Hey how are you doing on this lovely evening?", return_tensors="pt")[
"input_ids"
].to(torch_device)
out = self.model.generate(input_ids, do_sample=False, max_new_tokens=10)
output_sentence = self.tokenizer.decode(out[0, :])
self.assertEqual(output_sentence, EXPECTED_TEXTS[self.cuda_compute_capability_major_version])
# TODO: there are significant differences in the logits across major cuda versions, which shouldn't exist
if self.cuda_compute_capability_major_version == 8:
with torch.no_grad():
logits = self.model(input_ids=input_ids, logits_to_keep=40).logits
EXPECTED_LOGITS_NO_GRAD = torch.tensor(
[
149., 142., 146., 142., 143., 144., 142., 145.,
142., 146., 144., 146., 147., 147., 148., 145.,
147., 145., 145., 145., 145., 144., 144., 144.,
144., 145., 147., 146., 144., 144., 148., 147.,
148., 147., 147., 147., 146., 146., 148., 148.
], dtype=torch.bfloat16) # fmt: skip
torch.testing.assert_close(logits[0, -1, :40].cpu(), EXPECTED_LOGITS_NO_GRAD, rtol=1e-3, atol=1)
def test_simple_batched_generate_with_padding(self):
# Key 9 for MI300, Key 8 for A100/A10, and Key 7 for T4.
#
# Note: Key 9 is currently set for MI300, but may need potential future adjustments for H100s,
# considering differences in hardware processing and potential deviations in generated text.
EXPECTED_TEXTS = {
7: [],
8: [
"<|begin_of_text|>Hey how are you doing on this lovely evening? I hope you are doing well. I am here",
"!!!<|begin_of_text|>I am late! I need to get to work! I have to get to the",
],
9: [],
}
self.model.to(torch_device)
inputs = self.tokenizer(
["Hey how are you doing on this lovely evening?", "I am late! I need to"],
padding=True,
return_tensors="pt",
).to(torch_device)
out = self.model.generate(**inputs, do_sample=False, max_new_tokens=10)
output_sentences = self.tokenizer.batch_decode(out)
self.assertEqual(output_sentences[0], EXPECTED_TEXTS[self.cuda_compute_capability_major_version][0])
self.assertEqual(output_sentences[1], EXPECTED_TEXTS[self.cuda_compute_capability_major_version][1])
# TODO: there are significant differences in the logits across major cuda versions, which shouldn't exist
if self.cuda_compute_capability_major_version == 8:
with torch.no_grad():
logits = self.model(input_ids=inputs["input_ids"]).logits
EXPECTED_LOGITS_NO_GRAD_0 = torch.tensor(
[
149., 142., 146., 142., 143., 144., 142., 145.,
142., 146., 144., 146., 147., 147., 148., 145.,
147., 145., 145., 145., 145., 144., 144., 144.,
144., 145., 147., 146., 144., 144., 148., 147.,
148., 147., 147., 147., 146., 146., 148., 148.
], dtype=torch.bfloat16) # fmt: skip
EXPECTED_LOGITS_NO_GRAD_1 = torch.tensor(
[
182., 178., 177., 174., 176., 176., 178., 178.,
177., 179., 176., 183., 180., 182., 179., 174.,
178., 176., 176., 175., 175., 175., 174., 173.,
174., 182., 180., 176., 177., 177., 180., 176.,
178., 177., 177., 175., 176., 177., 175., 177.
], dtype=torch.bfloat16) # fmt: skip
torch.testing.assert_close(logits[0, -1, :40].cpu(), EXPECTED_LOGITS_NO_GRAD_0, rtol=1e-3, atol=1)
torch.testing.assert_close(logits[1, -1, :40].cpu(), EXPECTED_LOGITS_NO_GRAD_1, rtol=1e-3, atol=1)
| transformers/tests/models/bamba/test_modeling_bamba.py/0 | {
"file_path": "transformers/tests/models/bamba/test_modeling_bamba.py",
"repo_id": "transformers",
"token_count": 11606
} |
# Copyright 2021 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.
import inspect
import unittest
import numpy as np
from transformers import BeitConfig
from transformers.testing_utils import require_flax, require_vision, slow
from transformers.utils import cached_property, is_flax_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor
if is_flax_available():
import jax
from transformers import FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling, FlaxBeitModel
if is_vision_available():
from PIL import Image
from transformers import BeitImageProcessor
class FlaxBeitModelTester:
def __init__(
self,
parent,
vocab_size=100,
batch_size=13,
image_size=30,
patch_size=2,
num_channels=3,
is_training=True,
use_labels=True,
hidden_size=32,
num_hidden_layers=2,
num_attention_heads=4,
intermediate_size=37,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
type_sequence_label_size=10,
initializer_range=0.02,
num_labels=3,
):
self.parent = parent
self.vocab_size = vocab_size
self.batch_size = batch_size
self.image_size = image_size
self.patch_size = patch_size
self.num_channels = num_channels
self.is_training = is_training
self.use_labels = use_labels
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.type_sequence_label_size = type_sequence_label_size
self.initializer_range = initializer_range
# in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
num_patches = (image_size // patch_size) ** 2
self.seq_length = num_patches + 1
def prepare_config_and_inputs(self):
pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
labels = None
if self.use_labels:
labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
config = BeitConfig(
vocab_size=self.vocab_size,
image_size=self.image_size,
patch_size=self.patch_size,
num_channels=self.num_channels,
hidden_size=self.hidden_size,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
intermediate_size=self.intermediate_size,
hidden_act=self.hidden_act,
hidden_dropout_prob=self.hidden_dropout_prob,
attention_probs_dropout_prob=self.attention_probs_dropout_prob,
is_decoder=False,
initializer_range=self.initializer_range,
)
return config, pixel_values, labels
def create_and_check_model(self, config, pixel_values, labels):
model = FlaxBeitModel(config=config)
result = model(pixel_values)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
def create_and_check_for_masked_lm(self, config, pixel_values, labels):
model = FlaxBeitForMaskedImageModeling(config=config)
result = model(pixel_values)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length - 1, self.vocab_size))
def create_and_check_for_image_classification(self, config, pixel_values, labels):
config.num_labels = self.type_sequence_label_size
model = FlaxBeitForImageClassification(config=config)
result = model(pixel_values)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.type_sequence_label_size))
# test greyscale images
config.num_channels = 1
model = FlaxBeitForImageClassification(config)
pixel_values = floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
result = model(pixel_values)
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
config,
pixel_values,
labels,
) = config_and_inputs
inputs_dict = {"pixel_values": pixel_values}
return config, inputs_dict
@require_flax
class FlaxBeitModelTest(FlaxModelTesterMixin, unittest.TestCase):
all_model_classes = (
(FlaxBeitModel, FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling) if is_flax_available() else ()
)
def setUp(self) -> None:
self.model_tester = FlaxBeitModelTester(self)
self.config_tester = ConfigTester(self, config_class=BeitConfig, has_text_modality=False, hidden_size=37)
def test_config(self):
self.config_tester.run_common_tests()
# We need to override this test because Beit's forward signature is different than text models.
def test_forward_signature(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
signature = inspect.signature(model.__call__)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
arg_names = [*signature.parameters.keys()]
expected_arg_names = ["pixel_values"]
self.assertListEqual(arg_names[:1], expected_arg_names)
# We need to override this test because Beit expects pixel_values instead of input_ids
def test_jit_compilation(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__):
prepared_inputs_dict = self._prepare_for_class(inputs_dict, model_class)
model = model_class(config)
@jax.jit
def model_jitted(pixel_values, **kwargs):
return model(pixel_values=pixel_values, **kwargs)
with self.subTest("JIT Enabled"):
jitted_outputs = model_jitted(**prepared_inputs_dict).to_tuple()
with self.subTest("JIT Disabled"):
with jax.disable_jit():
outputs = model_jitted(**prepared_inputs_dict).to_tuple()
self.assertEqual(len(outputs), len(jitted_outputs))
for jitted_output, output in zip(jitted_outputs, outputs):
self.assertEqual(jitted_output.shape, output.shape)
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
def test_for_masked_lm(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*config_and_inputs)
def test_for_image_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*config_and_inputs)
@slow
def test_model_from_pretrained(self):
for model_class_name in self.all_model_classes:
model = model_class_name.from_pretrained("microsoft/beit-base-patch16-224")
outputs = model(np.ones((1, 3, 224, 224)))
self.assertIsNotNone(outputs)
# We will verify our results on an image of cute cats
def prepare_img():
image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
return image
@require_vision
@require_flax
class FlaxBeitModelIntegrationTest(unittest.TestCase):
@cached_property
def default_image_processor(self):
return BeitImageProcessor.from_pretrained("microsoft/beit-base-patch16-224") if is_vision_available() else None
@slow
def test_inference_masked_image_modeling_head(self):
model = FlaxBeitForMaskedImageModeling.from_pretrained("microsoft/beit-base-patch16-224-pt22k")
image_processor = self.default_image_processor
image = prepare_img()
pixel_values = image_processor(images=image, return_tensors="np").pixel_values
# prepare bool_masked_pos
bool_masked_pos = np.ones((1, 196), dtype=bool)
# forward pass
outputs = model(pixel_values=pixel_values, bool_masked_pos=bool_masked_pos)
logits = outputs.logits
# verify the logits
expected_shape = (1, 196, 8192)
self.assertEqual(logits.shape, expected_shape)
expected_slice = np.array(
[[-3.2437, 0.5072, -13.9174], [-3.2456, 0.4948, -13.9401], [-3.2033, 0.5121, -13.8550]]
)
self.assertTrue(np.allclose(logits[bool_masked_pos][:3, :3], expected_slice, atol=1e-2))
@slow
def test_inference_image_classification_head_imagenet_1k(self):
model = FlaxBeitForImageClassification.from_pretrained("microsoft/beit-base-patch16-224")
image_processor = self.default_image_processor
image = prepare_img()
inputs = image_processor(images=image, return_tensors="np")
# forward pass
outputs = model(**inputs)
logits = outputs.logits
# verify the logits
expected_shape = (1, 1000)
self.assertEqual(logits.shape, expected_shape)
expected_slice = np.array([-1.2385, -1.0987, -1.0108])
self.assertTrue(np.allclose(logits[0, :3], expected_slice, atol=1e-4))
expected_class_idx = 281
self.assertEqual(logits.argmax(-1).item(), expected_class_idx)
@slow
def test_inference_image_classification_head_imagenet_22k(self):
model = FlaxBeitForImageClassification.from_pretrained("microsoft/beit-large-patch16-224-pt22k-ft22k")
image_processor = self.default_image_processor
image = prepare_img()
inputs = image_processor(images=image, return_tensors="np")
# forward pass
outputs = model(**inputs)
logits = outputs.logits
# verify the logits
expected_shape = (1, 21841)
self.assertEqual(logits.shape, expected_shape)
expected_slice = np.array([1.6881, -0.2787, 0.5901])
self.assertTrue(np.allclose(logits[0, :3], expected_slice, atol=1e-4))
expected_class_idx = 2396
self.assertEqual(logits.argmax(-1).item(), expected_class_idx)
| transformers/tests/models/beit/test_modeling_flax_beit.py/0 | {
"file_path": "transformers/tests/models/beit/test_modeling_flax_beit.py",
"repo_id": "transformers",
"token_count": 4812
} |
# Copyright 2020 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.
import unittest
from transformers import BigBirdConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax
from transformers.models.big_bird.modeling_flax_big_bird import (
FlaxBigBirdForCausalLM,
FlaxBigBirdForMaskedLM,
FlaxBigBirdForMultipleChoice,
FlaxBigBirdForPreTraining,
FlaxBigBirdForQuestionAnswering,
FlaxBigBirdForSequenceClassification,
FlaxBigBirdForTokenClassification,
FlaxBigBirdModel,
)
class FlaxBigBirdModelTester:
def __init__(
self,
parent,
batch_size=2,
seq_length=56,
is_training=True,
use_attention_mask=True,
use_token_type_ids=True,
use_labels=True,
vocab_size=99,
hidden_size=32,
num_hidden_layers=2,
num_attention_heads=2,
intermediate_size=7,
hidden_act="gelu_new",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
type_vocab_size=16,
type_sequence_label_size=2,
initializer_range=0.02,
num_choices=4,
attention_type="block_sparse",
use_bias=True,
rescale_embeddings=False,
block_size=2,
num_random_blocks=3,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_attention_mask = use_attention_mask
self.use_token_type_ids = use_token_type_ids
self.use_labels = use_labels
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.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.max_position_embeddings = max_position_embeddings
self.type_vocab_size = type_vocab_size
self.type_sequence_label_size = type_sequence_label_size
self.initializer_range = initializer_range
self.num_choices = num_choices
self.rescale_embeddings = rescale_embeddings
self.attention_type = attention_type
self.use_bias = use_bias
self.block_size = block_size
self.num_random_blocks = num_random_blocks
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
attention_mask = None
if self.use_attention_mask:
attention_mask = random_attention_mask([self.batch_size, self.seq_length])
token_type_ids = None
if self.use_token_type_ids:
token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
config = BigBirdConfig(
vocab_size=self.vocab_size,
hidden_size=self.hidden_size,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
intermediate_size=self.intermediate_size,
hidden_act=self.hidden_act,
hidden_dropout_prob=self.hidden_dropout_prob,
attention_probs_dropout_prob=self.attention_probs_dropout_prob,
max_position_embeddings=self.max_position_embeddings,
type_vocab_size=self.type_vocab_size,
is_decoder=False,
initializer_range=self.initializer_range,
attention_type=self.attention_type,
block_size=self.block_size,
num_random_blocks=self.num_random_blocks,
use_bias=self.use_bias,
rescale_embeddings=self.rescale_embeddings,
)
return config, input_ids, token_type_ids, attention_mask
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
config, input_ids, token_type_ids, attention_mask = config_and_inputs
inputs_dict = {
"input_ids": input_ids,
"token_type_ids": token_type_ids,
"attention_mask": attention_mask,
}
return config, inputs_dict
@require_flax
class FlaxBigBirdModelTest(FlaxModelTesterMixin, unittest.TestCase):
all_model_classes = (
(
FlaxBigBirdForCausalLM,
FlaxBigBirdModel,
FlaxBigBirdForPreTraining,
FlaxBigBirdForMaskedLM,
FlaxBigBirdForMultipleChoice,
FlaxBigBirdForQuestionAnswering,
FlaxBigBirdForSequenceClassification,
FlaxBigBirdForTokenClassification,
)
if is_flax_available()
else ()
)
test_attn_probs = False
test_mismatched_shapes = False
def setUp(self):
self.model_tester = FlaxBigBirdModelTester(self)
@slow
# copied from `test_modeling_flax_common` because it takes much longer than other models
def test_from_pretrained_save_pretrained(self):
super().test_from_pretrained_save_pretrained()
@slow
# copied from `test_modeling_flax_common` because it takes much longer than other models
def test_from_pretrained_with_no_automatic_init(self):
super().test_from_pretrained_with_no_automatic_init()
@slow
# copied from `test_modeling_flax_common` because it takes much longer than other models
def test_no_automatic_init(self):
super().test_no_automatic_init()
@slow
# copied from `test_modeling_flax_common` because it takes much longer than other models
def test_hidden_states_output(self):
super().test_hidden_states_output()
@slow
def test_model_from_pretrained(self):
for model_class_name in self.all_model_classes:
model = model_class_name.from_pretrained("google/bigbird-roberta-base")
self.assertIsNotNone(model)
def test_attention_outputs(self):
if self.test_attn_probs:
super().test_attention_outputs()
@slow
# copied from `test_modeling_flax_common` because it takes much longer than other models
def test_jit_compilation(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__):
prepared_inputs_dict = self._prepare_for_class(inputs_dict, model_class)
model = model_class(config)
@jax.jit
def model_jitted(input_ids, attention_mask=None, **kwargs):
return model(input_ids=input_ids, attention_mask=attention_mask, **kwargs)
with self.subTest("JIT Enabled"):
jitted_outputs = model_jitted(**prepared_inputs_dict).to_tuple()
with self.subTest("JIT Disabled"):
with jax.disable_jit():
outputs = model_jitted(**prepared_inputs_dict).to_tuple()
self.assertEqual(len(outputs), len(jitted_outputs))
for jitted_output, output in zip(jitted_outputs, outputs):
self.assertEqual(jitted_output.shape, output.shape)
# overwrite from common in order to skip the check on `attentions`
def check_pt_flax_outputs(self, fx_outputs, pt_outputs, model_class, tol=1e-5, name="outputs", attributes=None):
# `bigbird_block_sparse_attention` in `FlaxBigBird` returns `attention_probs = None`, while in PyTorch version,
# an effort was done to return `attention_probs` (yet to be verified).
if name.startswith("outputs.attentions"):
return
else:
super().check_pt_flax_outputs(fx_outputs, pt_outputs, model_class, tol, name, attributes)
| transformers/tests/models/big_bird/test_modeling_flax_big_bird.py/0 | {
"file_path": "transformers/tests/models/big_bird/test_modeling_flax_big_bird.py",
"repo_id": "transformers",
"token_count": 3786
} |
# coding=utf-8
# Copyright 2022 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.
import unittest
from datasets import load_dataset
from transformers import BloomTokenizerFast
from transformers.testing_utils import require_jinja, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class BloomTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
from_pretrained_id = "bigscience/tokenizer"
slow_tokenizer_class = None
rust_tokenizer_class = BloomTokenizerFast
tokenizer_class = BloomTokenizerFast
test_rust_tokenizer = True
test_slow_tokenizer = False
from_pretrained_vocab_key = "tokenizer_file"
special_tokens_map = {"bos_token": "<s>", "eos_token": "</s>", "unk_token": "<unk>", "pad_token": "<pad>"}
def setUp(self):
super().setUp()
tokenizer = BloomTokenizerFast.from_pretrained("bigscience/tokenizer")
tokenizer.save_pretrained(self.tmpdirname)
def get_rust_tokenizer(self, **kwargs):
kwargs.update(self.special_tokens_map)
return BloomTokenizerFast.from_pretrained(self.tmpdirname, **kwargs)
@unittest.skip(reason="This needs a slow tokenizer. Bloom does not have one!")
def test_encode_decode_with_spaces(self):
return
def test_encodings_from_sample_data(self):
"""
Assert that the created tokens are the same than the hard-coded ones
"""
tokenizer = self.get_rust_tokenizer()
INPUT_SENTENCES = ["The quick brown fox</s>", "jumps over the lazy dog</s>"]
TARGET_TOKENS = [[2175, 23714, 73173, 144252, 2], [77, 132619, 3478, 368, 109586, 35433, 2]]
computed_tokens = tokenizer.batch_encode_plus(INPUT_SENTENCES)["input_ids"]
self.assertListEqual(TARGET_TOKENS, computed_tokens)
decoded_tokens = tokenizer.batch_decode(computed_tokens)
self.assertListEqual(decoded_tokens, INPUT_SENTENCES)
def test_padding(self, max_length=6):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
# tokenizer_r.pad_token = None # Hotfixing padding = None
# Simple input
s = "This is a simple input"
s2 = ["This is a simple input 1", "This is a simple input 2"]
p = ("This is a simple input", "This is a pair")
p2 = [
("This is a simple input 1", "This is a simple input 2"),
("This is a simple pair 1", "This is a simple pair 2"),
]
# Simple input tests
try:
tokenizer_r.encode(s, max_length=max_length)
tokenizer_r.encode_plus(s, max_length=max_length)
tokenizer_r.batch_encode_plus(s2, max_length=max_length)
tokenizer_r.encode(p, max_length=max_length)
tokenizer_r.batch_encode_plus(p2, max_length=max_length)
except ValueError:
self.fail("Bloom Tokenizer should be able to deal with padding")
tokenizer_r.pad_token = None # Hotfixing padding = None
self.assertRaises(ValueError, tokenizer_r.encode, s, max_length=max_length, padding="max_length")
# Simple input
self.assertRaises(ValueError, tokenizer_r.encode_plus, s, max_length=max_length, padding="max_length")
# Simple input
self.assertRaises(
ValueError,
tokenizer_r.batch_encode_plus,
s2,
max_length=max_length,
padding="max_length",
)
# Pair input
self.assertRaises(ValueError, tokenizer_r.encode, p, max_length=max_length, padding="max_length")
# Pair input
self.assertRaises(ValueError, tokenizer_r.encode_plus, p, max_length=max_length, padding="max_length")
# Pair input
self.assertRaises(
ValueError,
tokenizer_r.batch_encode_plus,
p2,
max_length=max_length,
padding="max_length",
)
def test_encodings_from_xnli_dataset(self):
"""
Tests the tokenizer downloaded from here:
- https://huggingface.co/bigscience/tokenizer/
"""
tokenizer = self.get_rust_tokenizer()
ds = load_dataset("facebook/xnli", "all_languages", split="test", streaming=True)
sample_data = next(iter(ds))["premise"] # pick up one data
input_text = list(sample_data.values())
output_tokens = list(map(tokenizer.encode, input_text))
predicted_text = [tokenizer.decode(x, clean_up_tokenization_spaces=False) for x in output_tokens]
self.assertListEqual(predicted_text, input_text)
@require_jinja
def test_tokenization_for_chat(self):
tokenizer = self.get_rust_tokenizer()
tokenizer.chat_template = "{% for message in messages %}" "{{ message.content }}{{ eos_token }}" "{% endfor %}"
test_chats = [
[{"role": "system", "content": "You are a helpful chatbot."}, {"role": "user", "content": "Hello!"}],
[
{"role": "system", "content": "You are a helpful chatbot."},
{"role": "user", "content": "Hello!"},
{"role": "assistant", "content": "Nice to meet you."},
],
[{"role": "assistant", "content": "Nice to meet you."}, {"role": "user", "content": "Hello!"}],
]
tokenized_chats = [tokenizer.apply_chat_template(test_chat) for test_chat in test_chats]
expected_tokens = [
[5448, 1306, 267, 66799, 44799, 37143, 17, 2, 59414, 4, 2],
[5448, 1306, 267, 66799, 44799, 37143, 17, 2, 59414, 4, 2, 229126, 427, 11890, 1152, 17, 2],
[229126, 427, 11890, 1152, 17, 2, 59414, 4, 2],
]
for tokenized_chat, expected_tokens in zip(tokenized_chats, expected_tokens):
self.assertListEqual(tokenized_chat, expected_tokens)
def test_add_prefix_space_fast(self):
tokenizer_w_prefix = self.get_rust_tokenizer(add_prefix_space=True)
tokenizer_wo_prefix = self.get_rust_tokenizer(add_prefix_space=False)
tokens_w_prefix = tokenizer_w_prefix.tokenize("Hey")
tokens_wo_prefix = tokenizer_wo_prefix.tokenize("Hey")
self.assertNotEqual(tokens_w_prefix, tokens_wo_prefix)
| transformers/tests/models/bloom/test_tokenization_bloom.py/0 | {
"file_path": "transformers/tests/models/bloom/test_tokenization_bloom.py",
"repo_id": "transformers",
"token_count": 3278
} |
# 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.
"""Testing suite for the TensorFlow CLIP model."""
from __future__ import annotations
import inspect
import os
import tempfile
import unittest
from importlib import import_module
import requests
from transformers import CLIPConfig, CLIPTextConfig, CLIPVisionConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFCLIPModel, TFCLIPTextModel, TFCLIPVisionModel, TFSharedEmbeddings
from transformers.modeling_tf_utils import keras
if is_vision_available():
from PIL import Image
from transformers import CLIPProcessor
class TFCLIPVisionModelTester:
def __init__(
self,
parent,
batch_size=12,
image_size=30,
patch_size=2,
num_channels=3,
is_training=True,
hidden_size=32,
num_hidden_layers=2,
num_attention_heads=4,
intermediate_size=37,
dropout=0.1,
attention_dropout=0.1,
initializer_range=0.02,
scope=None,
):
self.parent = parent
self.batch_size = batch_size
self.image_size = image_size
self.patch_size = patch_size
self.num_channels = num_channels
self.is_training = is_training
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.dropout = dropout
self.attention_dropout = attention_dropout
self.initializer_range = initializer_range
self.scope = scope
def prepare_config_and_inputs(self):
pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
config = self.get_config()
return config, pixel_values
def get_config(self):
return CLIPVisionConfig(
image_size=self.image_size,
patch_size=self.patch_size,
num_channels=self.num_channels,
hidden_size=self.hidden_size,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
intermediate_size=self.intermediate_size,
dropout=self.dropout,
attention_dropout=self.attention_dropout,
initializer_range=self.initializer_range,
)
def create_and_check_model(self, config, pixel_values):
model = TFCLIPVisionModel(config=config)
result = model(pixel_values, training=False)
# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
image_size = (self.image_size, self.image_size)
patch_size = (self.patch_size, self.patch_size)
num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, num_patches + 1, self.hidden_size))
self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
config, pixel_values = config_and_inputs
inputs_dict = {"pixel_values": pixel_values}
return config, inputs_dict
@require_tf
class TFCLIPVisionModelTest(TFModelTesterMixin, unittest.TestCase):
"""
Here we also overwrite some of the tests of test_modeling_common.py, as CLIP does not use input_ids, inputs_embeds,
attention_mask and seq_length.
"""
all_model_classes = (TFCLIPVisionModel,) if is_tf_available() else ()
test_pruning = False
test_resize_embeddings = False
test_head_masking = False
test_onnx = False
def setUp(self):
self.model_tester = TFCLIPVisionModelTester(self)
self.config_tester = ConfigTester(self, config_class=CLIPVisionConfig, has_text_modality=False, hidden_size=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_inputs_embeds(self):
# CLIP does not use inputs_embeds
pass
def test_graph_mode_with_inputs_embeds(self):
# CLIP does not use inputs_embeds
pass
def test_model_common_attributes(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
self.assertIsInstance(model.get_input_embeddings(), (keras.layers.Layer))
x = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(x, keras.layers.Layer))
def test_forward_signature(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
signature = inspect.signature(model.call)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
arg_names = [*signature.parameters.keys()]
expected_arg_names = ["pixel_values"]
self.assertListEqual(arg_names[:1], expected_arg_names)
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
def test_attention_outputs(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.return_dict = True
# in CLIP, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
image_size = (self.model_tester.image_size, self.model_tester.image_size)
patch_size = (self.model_tester.patch_size, self.model_tester.patch_size)
num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
seq_len = num_patches + 1
for model_class in self.all_model_classes:
inputs_dict["output_attentions"] = True
inputs_dict["output_hidden_states"] = False
config.return_dict = True
model = model_class(config)
outputs = model(**self._prepare_for_class(inputs_dict, model_class), training=False)
attentions = outputs.attentions
self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
config.output_attentions = True
model = model_class(config)
outputs = model(**self._prepare_for_class(inputs_dict, model_class), training=False)
attentions = outputs.attentions
self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
out_len = len(outputs)
# Check attention is always last and order is fine
inputs_dict["output_attentions"] = True
inputs_dict["output_hidden_states"] = True
model = model_class(config)
outputs = model(**self._prepare_for_class(inputs_dict, model_class), training=False)
added_hidden_states = 1
self.assertEqual(out_len + added_hidden_states, len(outputs))
self_attentions = outputs.attentions
self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers)
self.assertListEqual(
list(self_attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads, seq_len, seq_len],
)
def test_hidden_states_output(self):
def check_hidden_states_output(inputs_dict, config, model_class):
model = model_class(config)
outputs = model(**self._prepare_for_class(inputs_dict, model_class), training=False)
hidden_states = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
expected_num_layers = getattr(
self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1
)
self.assertEqual(len(hidden_states), expected_num_layers)
# CLIP has a different seq_length
image_size = (self.model_tester.image_size, self.model_tester.image_size)
patch_size = (self.model_tester.patch_size, self.model_tester.patch_size)
num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
seq_length = num_patches + 1
self.assertListEqual(
list(hidden_states[0].shape[-2:]),
[seq_length, self.model_tester.hidden_size],
)
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
inputs_dict["output_hidden_states"] = True
check_hidden_states_output(inputs_dict, config, model_class)
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
config.output_hidden_states = True
check_hidden_states_output(inputs_dict, config, model_class)
@slow
def test_model_from_pretrained(self):
model_name = "openai/clip-vit-base-patch32"
model = TFCLIPVisionModel.from_pretrained(model_name)
self.assertIsNotNone(model)
@slow
def test_saved_model_creation_extended(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.output_hidden_states = True
config.output_attentions = True
if hasattr(config, "use_cache"):
config.use_cache = True
# in CLIP, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
image_size = (self.model_tester.image_size, self.model_tester.image_size)
patch_size = (self.model_tester.patch_size, self.model_tester.patch_size)
num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
seq_len = num_patches + 1
for model_class in self.all_model_classes:
class_inputs_dict = self._prepare_for_class(inputs_dict, model_class)
model = model_class(config)
num_out = len(model(class_inputs_dict))
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(tmpdirname, saved_model=True)
saved_model_dir = os.path.join(tmpdirname, "saved_model", "1")
model = keras.models.load_model(saved_model_dir)
outputs = model(class_inputs_dict)
output_hidden_states = outputs["hidden_states"]
output_attentions = outputs["attentions"]
# Check num outputs
self.assertEqual(len(outputs), num_out)
# Check num layers
expected_num_layers = getattr(
self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1
)
self.assertEqual(len(output_hidden_states), expected_num_layers)
self.assertEqual(len(output_attentions), self.model_tester.num_hidden_layers)
# Check attention outputs
image_size = (self.model_tester.image_size, self.model_tester.image_size)
patch_size = (self.model_tester.patch_size, self.model_tester.patch_size)
num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
seq_len = num_patches + 1
self.assertListEqual(
list(output_attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads, seq_len, seq_len],
)
# Check hidden states
self.assertListEqual(
list(output_hidden_states[0].shape[-2:]),
[seq_len, self.model_tester.hidden_size],
)
class TFCLIPTextModelTester:
def __init__(
self,
parent,
batch_size=12,
seq_length=7,
is_training=True,
use_input_mask=True,
use_labels=True,
vocab_size=99,
hidden_size=32,
num_hidden_layers=2,
num_attention_heads=4,
intermediate_size=37,
dropout=0.1,
attention_dropout=0.1,
max_position_embeddings=512,
initializer_range=0.02,
scope=None,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_input_mask = use_input_mask
self.use_labels = use_labels
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.intermediate_size = intermediate_size
self.dropout = dropout
self.attention_dropout = attention_dropout
self.max_position_embeddings = max_position_embeddings
self.initializer_range = initializer_range
self.scope = scope
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
input_mask = None
if self.use_input_mask:
input_mask = random_attention_mask([self.batch_size, self.seq_length])
# make sure the first token has attention mask `1` to ensure that, after combining the causal mask, there
# is still at least one token being attended to for each batch.
# TODO: Change `random_attention_mask` in PT/TF/Flax common test file, after a discussion with the team.
input_mask = tf.concat(
[tf.ones_like(input_mask[:, :1], dtype=input_mask.dtype), input_mask[:, 1:]], axis=-1
)
config = self.get_config()
return config, input_ids, input_mask
def get_config(self):
return CLIPTextConfig(
vocab_size=self.vocab_size,
hidden_size=self.hidden_size,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
intermediate_size=self.intermediate_size,
dropout=self.dropout,
attention_dropout=self.attention_dropout,
max_position_embeddings=self.max_position_embeddings,
initializer_range=self.initializer_range,
)
def create_and_check_model(self, config, input_ids, input_mask):
model = TFCLIPTextModel(config=config)
result = model(input_ids, attention_mask=input_mask, training=False)
result = model(input_ids, training=False)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
config, input_ids, input_mask = config_and_inputs
inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_tf
class TFCLIPTextModelTest(TFModelTesterMixin, unittest.TestCase):
all_model_classes = (TFCLIPTextModel,) if is_tf_available() else ()
test_pruning = False
test_head_masking = False
test_onnx = False
def setUp(self):
self.model_tester = TFCLIPTextModelTester(self)
self.config_tester = ConfigTester(self, config_class=CLIPTextConfig, hidden_size=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
def test_inputs_embeds(self):
# CLIP does not use inputs_embeds
pass
@slow
def test_model_from_pretrained(self):
model_name = "openai/clip-vit-base-patch32"
model = TFCLIPTextModel.from_pretrained(model_name)
self.assertIsNotNone(model)
@slow
def test_saved_model_creation_extended(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.output_hidden_states = True
config.output_attentions = True
if hasattr(config, "use_cache"):
config.use_cache = True
for model_class in self.all_model_classes:
class_inputs_dict = self._prepare_for_class(inputs_dict, model_class)
model = model_class(config)
num_out = len(model(class_inputs_dict))
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(tmpdirname, saved_model=True)
saved_model_dir = os.path.join(tmpdirname, "saved_model", "1")
model = keras.models.load_model(saved_model_dir)
outputs = model(class_inputs_dict)
output_hidden_states = outputs["hidden_states"]
output_attentions = outputs["attentions"]
# Check number of outputs
self.assertEqual(len(outputs), num_out)
# Check number of layers
expected_num_layers = getattr(
self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1
)
# Check hidden states
self.assertEqual(len(output_hidden_states), expected_num_layers)
self.assertListEqual(
list(output_hidden_states[0].shape[-2:]),
[self.model_tester.seq_length, self.model_tester.hidden_size],
)
# Check attention outputs
self.assertEqual(len(output_attentions), self.model_tester.num_hidden_layers)
seq_length = self.model_tester.seq_length
key_length = getattr(self.model_tester, "key_length", seq_length)
self.assertListEqual(
list(output_attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads, seq_length, key_length],
)
class TFCLIPModelTester:
def __init__(self, parent, is_training=True):
self.parent = parent
self.text_model_tester = TFCLIPTextModelTester(parent)
self.vision_model_tester = TFCLIPVisionModelTester(parent)
self.is_training = is_training
def prepare_config_and_inputs(self):
text_config, input_ids, attention_mask = self.text_model_tester.prepare_config_and_inputs()
vision_config, pixel_values = self.vision_model_tester.prepare_config_and_inputs()
config = self.get_config()
return config, input_ids, attention_mask, pixel_values
def get_config(self):
return CLIPConfig.from_text_vision_configs(
self.text_model_tester.get_config(), self.vision_model_tester.get_config(), projection_dim=64
)
def create_and_check_model(self, config, input_ids, attention_mask, pixel_values):
model = TFCLIPModel(config)
result = model(input_ids, pixel_values, attention_mask, training=False)
self.parent.assertEqual(
result.logits_per_image.shape, (self.vision_model_tester.batch_size, self.text_model_tester.batch_size)
)
self.parent.assertEqual(
result.logits_per_text.shape, (self.text_model_tester.batch_size, self.vision_model_tester.batch_size)
)
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
config, input_ids, attention_mask, pixel_values = config_and_inputs
inputs_dict = {
"input_ids": input_ids,
"attention_mask": attention_mask,
"pixel_values": pixel_values,
"return_loss": True,
}
return config, inputs_dict
@require_tf
class TFCLIPModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (TFCLIPModel,) if is_tf_available() else ()
pipeline_model_mapping = {"feature-extraction": TFCLIPModel} if is_tf_available() else {}
test_head_masking = False
test_pruning = False
test_resize_embeddings = False
test_attention_outputs = False
test_onnx = False
def setUp(self):
self.model_tester = TFCLIPModelTester(self)
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
# hidden_states are tested in individual model tests
def test_hidden_states_output(self):
pass
# input_embeds are tested in individual model tests
def test_inputs_embeds(self):
pass
# CLIPModel does not have input/output embeddings
def test_model_common_attributes(self):
pass
# overwrite from common since `TFCLIPModelTester` set `return_loss` to `True` and causes the preparation of
# `symbolic_inputs` failed.
def test_keras_save_load(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
# remove `return_loss` to make code work
if self.__class__.__name__ == "TFCLIPModelTest":
inputs_dict.pop("return_loss", None)
tf_main_layer_classes = {
module_member
for model_class in self.all_model_classes
for module in (import_module(model_class.__module__),)
for module_member_name in dir(module)
if module_member_name.endswith("MainLayer")
# This condition is required, since `modeling_tf_clip.py` has 3 classes whose names end with `MainLayer`.
and module_member_name[: -len("MainLayer")] == model_class.__name__[: -len("Model")]
for module_member in (getattr(module, module_member_name),)
if isinstance(module_member, type)
and keras.layers.Layer in module_member.__bases__
and getattr(module_member, "_keras_serializable", False)
}
for main_layer_class in tf_main_layer_classes:
# T5MainLayer needs an embed_tokens parameter when called without the inputs_embeds parameter
if "T5" in main_layer_class.__name__:
# Take the same values than in TFT5ModelTester for this shared layer
shared = TFSharedEmbeddings(99, 32, name="shared")
config.use_cache = inputs_dict.pop("use_cache", None)
main_layer = main_layer_class(config, embed_tokens=shared)
else:
main_layer = main_layer_class(config)
symbolic_inputs = {
name: keras.Input(tensor.shape[1:], dtype=tensor.dtype) for name, tensor in inputs_dict.items()
}
model = keras.Model(symbolic_inputs, outputs=main_layer(symbolic_inputs))
outputs = model(inputs_dict)
with tempfile.TemporaryDirectory() as tmpdirname:
filepath = os.path.join(tmpdirname, "keras_model.h5")
model.save(filepath)
if "T5" in main_layer_class.__name__:
model = keras.models.load_model(
filepath,
custom_objects={
main_layer_class.__name__: main_layer_class,
"TFSharedEmbeddings": TFSharedEmbeddings,
},
)
else:
model = keras.models.load_model(
filepath, custom_objects={main_layer_class.__name__: main_layer_class}
)
assert isinstance(model, keras.Model)
after_outputs = model(inputs_dict)
self.assert_outputs_same(after_outputs, outputs)
@slow
def test_model_from_pretrained(self):
model_name = "openai/clip-vit-base-patch32"
model = TFCLIPModel.from_pretrained(model_name)
self.assertIsNotNone(model)
@unittest.skip(reason="Currently `saved_model` doesn't work with nested outputs.")
@slow
def test_saved_model_creation(self):
pass
@unittest.skip(reason="Currently `saved_model` doesn't work with nested outputs.")
@slow
def test_saved_model_creation_extended(self):
pass
@unittest.skip(reason="`saved_model` doesn't work with nested outputs so no preparation happens.")
@slow
def test_prepare_serving_output(self):
pass
# We will verify our results on an image of cute cats
def prepare_img():
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
im = Image.open(requests.get(url, stream=True).raw)
return im
@require_vision
@require_tf
class TFCLIPModelIntegrationTest(unittest.TestCase):
@slow
def test_inference(self):
model_name = "openai/clip-vit-base-patch32"
model = TFCLIPModel.from_pretrained(model_name)
processor = CLIPProcessor.from_pretrained(model_name)
image = prepare_img()
inputs = processor(
text=["a photo of a cat", "a photo of a dog"], images=image, padding=True, return_tensors="tf"
)
outputs = model(**inputs, training=False)
# verify the logits
self.assertEqual(
outputs.logits_per_image.shape,
tf.TensorShape((inputs.pixel_values.shape[0], inputs.input_ids.shape[0])),
)
self.assertEqual(
outputs.logits_per_text.shape,
tf.TensorShape((inputs.input_ids.shape[0], inputs.pixel_values.shape[0])),
)
expected_logits = tf.constant([[24.5701, 19.3049]])
tf.debugging.assert_near(outputs.logits_per_image, expected_logits, atol=1e-3)
| transformers/tests/models/clip/test_modeling_tf_clip.py/0 | {
"file_path": "transformers/tests/models/clip/test_modeling_tf_clip.py",
"repo_id": "transformers",
"token_count": 11929
} |
# 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.
"""Testing suite for the PyTorch ConvNext model."""
import unittest
from transformers import ConvNextConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import ConvNextBackbone, ConvNextForImageClassification, ConvNextModel
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class ConvNextModelTester:
def __init__(
self,
parent,
batch_size=13,
image_size=32,
num_channels=3,
num_stages=4,
hidden_sizes=[10, 20, 30, 40],
depths=[2, 2, 3, 2],
is_training=True,
use_labels=True,
intermediate_size=37,
hidden_act="gelu",
num_labels=10,
initializer_range=0.02,
out_features=["stage2", "stage3", "stage4"],
out_indices=[2, 3, 4],
scope=None,
):
self.parent = parent
self.batch_size = batch_size
self.image_size = image_size
self.num_channels = num_channels
self.num_stages = num_stages
self.hidden_sizes = hidden_sizes
self.depths = depths
self.is_training = is_training
self.use_labels = use_labels
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.num_labels = num_labels
self.initializer_range = initializer_range
self.out_features = out_features
self.out_indices = out_indices
self.scope = scope
def prepare_config_and_inputs(self):
pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
labels = None
if self.use_labels:
labels = ids_tensor([self.batch_size], self.num_labels)
config = self.get_config()
return config, pixel_values, labels
def get_config(self):
return ConvNextConfig(
num_channels=self.num_channels,
hidden_sizes=self.hidden_sizes,
depths=self.depths,
num_stages=self.num_stages,
hidden_act=self.hidden_act,
is_decoder=False,
initializer_range=self.initializer_range,
out_features=self.out_features,
out_indices=self.out_indices,
num_labels=self.num_labels,
)
def create_and_check_model(self, config, pixel_values, labels):
model = ConvNextModel(config=config)
model.to(torch_device)
model.eval()
result = model(pixel_values)
# expected last hidden states: B, C, H // 32, W // 32
self.parent.assertEqual(
result.last_hidden_state.shape,
(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32),
)
def create_and_check_for_image_classification(self, config, pixel_values, labels):
model = ConvNextForImageClassification(config)
model.to(torch_device)
model.eval()
result = model(pixel_values, labels=labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
def create_and_check_backbone(self, config, pixel_values, labels):
model = ConvNextBackbone(config=config)
model.to(torch_device)
model.eval()
result = model(pixel_values)
# verify hidden states
self.parent.assertEqual(len(result.feature_maps), len(config.out_features))
self.parent.assertListEqual(list(result.feature_maps[0].shape), [self.batch_size, self.hidden_sizes[1], 4, 4])
# verify channels
self.parent.assertEqual(len(model.channels), len(config.out_features))
self.parent.assertListEqual(model.channels, config.hidden_sizes[1:])
# verify backbone works with out_features=None
config.out_features = None
model = ConvNextBackbone(config=config)
model.to(torch_device)
model.eval()
result = model(pixel_values)
# verify feature maps
self.parent.assertEqual(len(result.feature_maps), 1)
self.parent.assertListEqual(list(result.feature_maps[0].shape), [self.batch_size, self.hidden_sizes[-1], 1, 1])
# verify channels
self.parent.assertEqual(len(model.channels), 1)
self.parent.assertListEqual(model.channels, [config.hidden_sizes[-1]])
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
config, pixel_values, labels = config_and_inputs
inputs_dict = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class ConvNextModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
"""
Here we also overwrite some of the tests of test_modeling_common.py, as ConvNext does not use input_ids, inputs_embeds,
attention_mask and seq_length.
"""
all_model_classes = (
(
ConvNextModel,
ConvNextForImageClassification,
ConvNextBackbone,
)
if is_torch_available()
else ()
)
pipeline_model_mapping = (
{"image-feature-extraction": ConvNextModel, "image-classification": ConvNextForImageClassification}
if is_torch_available()
else {}
)
fx_compatible = True
test_pruning = False
test_resize_embeddings = False
test_head_masking = False
has_attentions = False
def setUp(self):
self.model_tester = ConvNextModelTester(self)
self.config_tester = ConfigTester(
self,
config_class=ConvNextConfig,
has_text_modality=False,
hidden_size=37,
common_properties=["num_channels", "hidden_sizes"],
)
def test_config(self):
self.config_tester.run_common_tests()
@unittest.skip(reason="ConvNext does not use inputs_embeds")
def test_inputs_embeds(self):
pass
@unittest.skip(reason="ConvNext does not support input and output embeddings")
def test_model_get_set_embeddings(self):
pass
@unittest.skip(reason="ConvNext does not use feedforward chunking")
def test_feed_forward_chunking(self):
pass
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
def test_backbone(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_backbone(*config_and_inputs)
def test_hidden_states_output(self):
def check_hidden_states_output(inputs_dict, config, model_class):
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
hidden_states = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
expected_num_stages = self.model_tester.num_stages
self.assertEqual(len(hidden_states), expected_num_stages + 1)
# ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:]),
[self.model_tester.image_size // 4, self.model_tester.image_size // 4],
)
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
inputs_dict["output_hidden_states"] = True
check_hidden_states_output(inputs_dict, config, model_class)
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
config.output_hidden_states = True
check_hidden_states_output(inputs_dict, config, model_class)
def test_for_image_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*config_and_inputs)
@slow
def test_model_from_pretrained(self):
model_name = "facebook/convnext-tiny-224"
model = ConvNextModel.from_pretrained(model_name)
self.assertIsNotNone(model)
# We will verify our results on an image of cute cats
def prepare_img():
image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
return image
@require_torch
@require_vision
class ConvNextModelIntegrationTest(unittest.TestCase):
@cached_property
def default_image_processor(self):
return AutoImageProcessor.from_pretrained("facebook/convnext-tiny-224") if is_vision_available() else None
@slow
def test_inference_image_classification_head(self):
model = ConvNextForImageClassification.from_pretrained("facebook/convnext-tiny-224").to(torch_device)
image_processor = self.default_image_processor
image = prepare_img()
inputs = image_processor(images=image, return_tensors="pt").to(torch_device)
# forward pass
with torch.no_grad():
outputs = model(**inputs)
# verify the logits
expected_shape = torch.Size((1, 1000))
self.assertEqual(outputs.logits.shape, expected_shape)
expected_slice = torch.tensor([-0.0260, -0.4739, 0.1911]).to(torch_device)
torch.testing.assert_close(outputs.logits[0, :3], expected_slice, rtol=1e-4, atol=1e-4)
@require_torch
class ConvNextBackboneTest(unittest.TestCase, BackboneTesterMixin):
all_model_classes = (ConvNextBackbone,) if is_torch_available() else ()
config_class = ConvNextConfig
has_attentions = False
def setUp(self):
self.model_tester = ConvNextModelTester(self)
| transformers/tests/models/convnext/test_modeling_convnext.py/0 | {
"file_path": "transformers/tests/models/convnext/test_modeling_convnext.py",
"repo_id": "transformers",
"token_count": 4492
} |
"""Testing suite for the Tensorflow CvT model."""
from __future__ import annotations
import inspect
import unittest
from math import floor
import numpy as np
from transformers import CvtConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFCvtForImageClassification, TFCvtModel
from transformers.modeling_tf_utils import keras
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class TFCvtConfigTester(ConfigTester):
def create_and_test_config_common_properties(self):
config = self.config_class(**self.inputs_dict)
self.parent.assertTrue(hasattr(config, "embed_dim"))
self.parent.assertTrue(hasattr(config, "num_heads"))
class TFCvtModelTester:
def __init__(
self,
parent,
batch_size=13,
image_size=64,
num_channels=3,
embed_dim=[16, 32, 48],
num_heads=[1, 2, 3],
depth=[1, 2, 10],
patch_sizes=[7, 3, 3],
patch_stride=[4, 2, 2],
patch_padding=[2, 1, 1],
stride_kv=[2, 2, 2],
cls_token=[False, False, True],
attention_drop_rate=[0.0, 0.0, 0.0],
initializer_range=0.02,
layer_norm_eps=1e-12,
is_training=True,
use_labels=True,
num_labels=2,
):
self.parent = parent
self.batch_size = batch_size
self.image_size = image_size
self.patch_sizes = patch_sizes
self.patch_stride = patch_stride
self.patch_padding = patch_padding
self.is_training = is_training
self.use_labels = use_labels
self.num_labels = num_labels
self.num_channels = num_channels
self.embed_dim = embed_dim
self.num_heads = num_heads
self.stride_kv = stride_kv
self.depth = depth
self.cls_token = cls_token
self.attention_drop_rate = attention_drop_rate
self.initializer_range = initializer_range
self.layer_norm_eps = layer_norm_eps
def prepare_config_and_inputs(self):
pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
labels = None
if self.use_labels:
# create a random int32 tensor of given shape
labels = ids_tensor([self.batch_size], self.num_labels)
config = self.get_config()
return config, pixel_values, labels
def get_config(self):
return CvtConfig(
image_size=self.image_size,
num_labels=self.num_labels,
num_channels=self.num_channels,
embed_dim=self.embed_dim,
num_heads=self.num_heads,
patch_sizes=self.patch_sizes,
patch_padding=self.patch_padding,
patch_stride=self.patch_stride,
stride_kv=self.stride_kv,
depth=self.depth,
cls_token=self.cls_token,
attention_drop_rate=self.attention_drop_rate,
initializer_range=self.initializer_range,
)
def create_and_check_model(self, config, pixel_values, labels):
model = TFCvtModel(config=config)
result = model(pixel_values, training=False)
image_size = (self.image_size, self.image_size)
height, width = image_size[0], image_size[1]
for i in range(len(self.depth)):
height = floor(((height + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1)
width = floor(((width + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.embed_dim[-1], height, width))
def create_and_check_for_image_classification(self, config, pixel_values, labels):
config.num_labels = self.num_labels
model = TFCvtForImageClassification(config)
result = model(pixel_values, labels=labels, training=False)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
config, pixel_values, labels = config_and_inputs
inputs_dict = {"pixel_values": pixel_values}
return config, inputs_dict
@require_tf
class TFCvtModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
"""
Here we also overwrite some of the tests of test_modeling_common.py, as Cvt
does not use input_ids, inputs_embeds, attention_mask and seq_length.
"""
all_model_classes = (TFCvtModel, TFCvtForImageClassification) if is_tf_available() else ()
pipeline_model_mapping = (
{"feature-extraction": TFCvtModel, "image-classification": TFCvtForImageClassification}
if is_tf_available()
else {}
)
test_pruning = False
test_resize_embeddings = False
test_head_masking = False
has_attentions = False
test_onnx = False
def setUp(self):
self.model_tester = TFCvtModelTester(self)
self.config_tester = TFCvtConfigTester(self, config_class=CvtConfig, has_text_modality=False, hidden_size=37)
def test_config(self):
self.config_tester.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
@unittest.skip(reason="Cvt does not output attentions")
def test_attention_outputs(self):
pass
@unittest.skip(reason="Cvt does not use inputs_embeds")
def test_inputs_embeds(self):
pass
@unittest.skip(reason="Cvt does not support input and output embeddings")
def test_model_common_attributes(self):
pass
@unittest.skipIf(
not is_tf_available() or len(tf.config.list_physical_devices("GPU")) == 0,
reason="TF does not support backprop for grouped convolutions on CPU.",
)
def test_dataset_conversion(self):
super().test_dataset_conversion()
@unittest.skipIf(
not is_tf_available() or len(tf.config.list_physical_devices("GPU")) == 0,
reason="TF does not support backprop for grouped convolutions on CPU.",
)
@slow
def test_keras_fit(self):
super().test_keras_fit()
@unittest.skip(reason="Get `Failed to determine best cudnn convolution algo.` error after using TF 2.12+cuda 11.8")
def test_keras_fit_mixed_precision(self):
policy = keras.mixed_precision.Policy("mixed_float16")
keras.mixed_precision.set_global_policy(policy)
super().test_keras_fit()
keras.mixed_precision.set_global_policy("float32")
def test_forward_signature(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
signature = inspect.signature(model.call)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
arg_names = [*signature.parameters.keys()]
expected_arg_names = ["pixel_values"]
self.assertListEqual(arg_names[:1], expected_arg_names)
def test_hidden_states_output(self):
def check_hidden_states_output(inputs_dict, config, model_class):
model = model_class(config)
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
hidden_states = outputs.hidden_states
expected_num_layers = len(self.model_tester.depth)
self.assertEqual(len(hidden_states), expected_num_layers)
# verify the first hidden states (first block)
self.assertListEqual(
list(hidden_states[0].shape[-3:]),
[
self.model_tester.embed_dim[0],
self.model_tester.image_size // 4,
self.model_tester.image_size // 4,
],
)
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
inputs_dict["output_hidden_states"] = True
check_hidden_states_output(inputs_dict, config, model_class)
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
config.output_hidden_states = True
check_hidden_states_output(inputs_dict, config, model_class)
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
def test_for_image_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*config_and_inputs)
@slow
def test_model_from_pretrained(self):
model_name = "microsoft/cvt-13"
model = TFCvtModel.from_pretrained(model_name)
self.assertIsNotNone(model)
# We will verify our results on an image of cute cats
def prepare_img():
image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
return image
@require_tf
@require_vision
class TFCvtModelIntegrationTest(unittest.TestCase):
@cached_property
def default_image_processor(self):
return AutoImageProcessor.from_pretrained("microsoft/cvt-13")
@slow
def test_inference_image_classification_head(self):
model = TFCvtForImageClassification.from_pretrained("microsoft/cvt-13")
image_processor = self.default_image_processor
image = prepare_img()
inputs = image_processor(images=image, return_tensors="tf")
# forward pass
outputs = model(**inputs)
# verify the logits
expected_shape = tf.TensorShape((1, 1000))
self.assertEqual(outputs.logits.shape, expected_shape)
expected_slice = tf.constant([0.9285, 0.9015, -0.3150])
self.assertTrue(np.allclose(outputs.logits[0, :3].numpy(), expected_slice, atol=1e-4))
| transformers/tests/models/cvt/test_modeling_tf_cvt.py/0 | {
"file_path": "transformers/tests/models/cvt/test_modeling_tf_cvt.py",
"repo_id": "transformers",
"token_count": 4585
} |
# coding=utf-8
# Copyright 2019 Hugging Face inc.
#
# 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 unittest
from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class DebertaTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
from_pretrained_id = "microsoft/deberta-base"
tokenizer_class = DebertaTokenizer
test_rust_tokenizer = True
rust_tokenizer_class = DebertaTokenizerFast
def setUp(self):
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
vocab = [
"l",
"o",
"w",
"e",
"r",
"s",
"t",
"i",
"d",
"n",
"\u0120",
"\u0120l",
"\u0120n",
"\u0120lo",
"\u0120low",
"er",
"\u0120lowest",
"\u0120newer",
"\u0120wider",
"[UNK]",
]
vocab_tokens = dict(zip(vocab, range(len(vocab))))
merges = ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
self.special_tokens_map = {"unk_token": "[UNK]"}
self.vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["vocab_file"])
self.merges_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["merges_file"])
with open(self.vocab_file, "w", encoding="utf-8") as fp:
fp.write(json.dumps(vocab_tokens) + "\n")
with open(self.merges_file, "w", encoding="utf-8") as fp:
fp.write("\n".join(merges))
def get_tokenizer(self, **kwargs):
kwargs.update(self.special_tokens_map)
return self.tokenizer_class.from_pretrained(self.tmpdirname, **kwargs)
def get_input_output_texts(self, tokenizer):
input_text = "lower newer"
output_text = "lower newer"
return input_text, output_text
def test_full_tokenizer(self):
tokenizer = self.get_tokenizer()
text = "lower newer"
bpe_tokens = ["l", "o", "w", "er", "\u0120", "n", "e", "w", "er"]
tokens = tokenizer.tokenize(text)
self.assertListEqual(tokens, bpe_tokens)
input_tokens = tokens + [tokenizer.unk_token]
input_bpe_tokens = [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(tokenizer.convert_tokens_to_ids(input_tokens), input_bpe_tokens)
def test_token_type_ids(self):
tokenizer = self.get_tokenizer()
tokd = tokenizer("Hello", "World")
expected_token_type_ids = [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
self.assertListEqual(tokd["token_type_ids"], expected_token_type_ids)
@slow
def test_sequence_builders(self):
tokenizer = self.tokenizer_class.from_pretrained("microsoft/deberta-base")
text = tokenizer.encode("sequence builders", add_special_tokens=False)
text_2 = tokenizer.encode("multi-sequence build", add_special_tokens=False)
encoded_text_from_decode = tokenizer.encode(
"sequence builders", add_special_tokens=True, add_prefix_space=False
)
encoded_pair_from_decode = tokenizer.encode(
"sequence builders", "multi-sequence build", add_special_tokens=True, add_prefix_space=False
)
encoded_sentence = tokenizer.build_inputs_with_special_tokens(text)
encoded_pair = tokenizer.build_inputs_with_special_tokens(text, text_2)
assert encoded_sentence == encoded_text_from_decode
assert encoded_pair == encoded_pair_from_decode
@slow
def test_tokenizer_integration(self):
tokenizer_classes = [self.tokenizer_class]
if self.test_rust_tokenizer:
tokenizer_classes.append(self.rust_tokenizer_class)
for tokenizer_class in tokenizer_classes:
tokenizer = tokenizer_class.from_pretrained("microsoft/deberta-base")
sequences = [
"ALBERT: A Lite BERT for Self-supervised Learning of Language Representations",
"ALBERT incorporates two parameter reduction techniques",
"The first one is a factorized embedding parameterization. By decomposing the large vocabulary"
" embedding matrix into two small matrices, we separate the size of the hidden layers from the size of"
" vocabulary embedding.",
]
encoding = tokenizer(sequences, padding=True)
decoded_sequences = [tokenizer.decode(seq, skip_special_tokens=True) for seq in encoding["input_ids"]]
# fmt: off
expected_encoding = {
'input_ids': [
[1, 2118, 11126, 565, 35, 83, 25191, 163, 18854, 13, 12156, 12, 16101, 25376, 13807, 9, 22205, 27893, 1635, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 2118, 11126, 565, 24536, 80, 43797, 4878, 7373, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 133, 78, 65, 16, 10, 3724, 1538, 33183, 11303, 43797, 1938, 4, 870, 24165, 29105, 5, 739, 32644, 33183, 11303, 36173, 88, 80, 650, 7821, 45940, 6, 52, 2559, 5, 1836, 9, 5, 7397, 13171, 31, 5, 1836, 9, 32644, 33183, 11303, 4, 2]
],
'token_type_ids': [
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
],
'attention_mask': [
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
]
}
# fmt: on
expected_decoded_sequence = [
"ALBERT: A Lite BERT for Self-supervised Learning of Language Representations",
"ALBERT incorporates two parameter reduction techniques",
"The first one is a factorized embedding parameterization. By decomposing the large vocabulary"
" embedding matrix into two small matrices, we separate the size of the hidden layers from the size of"
" vocabulary embedding.",
]
self.assertDictEqual(encoding.data, expected_encoding)
for expected, decoded in zip(expected_decoded_sequence, decoded_sequences):
self.assertEqual(expected, decoded)
| transformers/tests/models/deberta/test_tokenization_deberta.py/0 | {
"file_path": "transformers/tests/models/deberta/test_tokenization_deberta.py",
"repo_id": "transformers",
"token_count": 3812
} |
# coding=utf-8
# Copyright 2020 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 transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow
from ..bert.test_tokenization_bert import BertTokenizationTest
@require_tokenizers
class DistilBertTokenizationTest(BertTokenizationTest):
tokenizer_class = DistilBertTokenizer
rust_tokenizer_class = DistilBertTokenizerFast
test_rust_tokenizer = True
from_pretrained_id = "distilbert/distilbert-base-uncased"
@slow
def test_sequence_builders(self):
tokenizer = DistilBertTokenizer.from_pretrained("distilbert-base-uncased")
text = tokenizer.encode("sequence builders", add_special_tokens=False)
text_2 = tokenizer.encode("multi-sequence build", add_special_tokens=False)
encoded_sentence = tokenizer.build_inputs_with_special_tokens(text)
encoded_pair = tokenizer.build_inputs_with_special_tokens(text, text_2)
assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_2 + [
tokenizer.sep_token_id
]
| transformers/tests/models/distilbert/test_tokenization_distilbert.py/0 | {
"file_path": "transformers/tests/models/distilbert/test_tokenization_distilbert.py",
"repo_id": "transformers",
"token_count": 599
} |
# 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.
"""Testing suite for the PyTorch Hiera model."""
import math
import unittest
from typing import Dict, List, Tuple
from transformers import HieraConfig
from transformers.testing_utils import (
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import (
cached_property,
is_torch_available,
is_vision_available,
)
from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import HieraBackbone, HieraForImageClassification, HieraForPreTraining, HieraModel
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class HieraModelTester:
def __init__(
self,
parent,
batch_size=13,
image_size=[64, 64],
mlp_ratio=1.0,
num_channels=3,
depths=[1, 1, 1, 1],
patch_stride=[4, 4],
patch_size=[7, 7],
patch_padding=[3, 3],
masked_unit_size=[8, 8],
num_heads=[1, 1, 1, 1],
embed_dim_multiplier=2.0,
is_training=True,
use_labels=True,
embed_dim=8,
hidden_act="gelu",
decoder_hidden_size=2,
decoder_depth=1,
decoder_num_heads=1,
initializer_range=0.02,
scope=None,
type_sequence_label_size=10,
):
self.parent = parent
self.batch_size = batch_size
self.image_size = image_size
self.mlp_ratio = mlp_ratio
self.num_channels = num_channels
self.depths = depths
self.patch_stride = patch_stride
self.patch_size = patch_size
self.patch_padding = patch_padding
self.masked_unit_size = masked_unit_size
self.num_heads = num_heads
self.embed_dim_multiplier = embed_dim_multiplier
self.is_training = is_training
self.use_labels = use_labels
self.embed_dim = embed_dim
self.hidden_act = hidden_act
self.decoder_hidden_size = decoder_hidden_size
self.decoder_depth = decoder_depth
self.decoder_num_heads = decoder_num_heads
self.initializer_range = initializer_range
self.scope = scope
self.type_sequence_label_size = type_sequence_label_size
def prepare_config_and_inputs(self):
pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]])
labels = None
if self.use_labels:
labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
config = self.get_config()
return config, pixel_values, labels
def get_config(self):
return HieraConfig(
embed_dim=self.embed_dim,
image_size=self.image_size,
patch_stride=self.patch_stride,
patch_size=self.patch_size,
patch_padding=self.patch_padding,
masked_unit_size=self.masked_unit_size,
mlp_ratio=self.mlp_ratio,
num_channels=self.num_channels,
depths=self.depths,
num_heads=self.num_heads,
embed_dim_multiplier=self.embed_dim_multiplier,
hidden_act=self.hidden_act,
decoder_hidden_size=self.decoder_hidden_size,
decoder_depth=self.decoder_depth,
decoder_num_heads=self.decoder_num_heads,
initializer_range=self.initializer_range,
)
def create_and_check_model(self, config, pixel_values, labels):
model = HieraModel(config=config)
model.to(torch_device)
model.eval()
result = model(pixel_values)
tokens_spatial_shape = [i // s for i, s in zip(self.image_size, config.patch_stride)]
expected_seq_len = math.prod(tokens_spatial_shape) // math.prod(config.query_stride) ** (config.num_query_pool)
expected_dim = int(config.embed_dim * config.embed_dim_multiplier ** (len(config.depths) - 1))
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, expected_seq_len, expected_dim))
def create_and_check_backbone(self, config, pixel_values, labels):
model = HieraBackbone(config=config)
model.to(torch_device)
model.eval()
result = model(pixel_values)
# verify hidden states
self.parent.assertEqual(len(result.feature_maps), len(config.out_features))
num_patches = config.image_size[0] // config.patch_stride[0] // config.masked_unit_size[0]
self.parent.assertListEqual(
list(result.feature_maps[0].shape), [self.batch_size, model.channels[0], num_patches, num_patches]
)
# verify channels
self.parent.assertEqual(len(model.channels), len(config.out_features))
# verify backbone works with out_features=None
config.out_features = None
model = HieraBackbone(config=config)
model.to(torch_device)
model.eval()
result = model(pixel_values)
# verify feature maps
self.parent.assertEqual(len(result.feature_maps), 1)
self.parent.assertListEqual(
list(result.feature_maps[0].shape), [self.batch_size, model.channels[-1], num_patches, num_patches]
)
# verify channels
self.parent.assertEqual(len(model.channels), 1)
def create_and_check_for_pretraining(self, config, pixel_values, labels):
model = HieraForPreTraining(config=config)
model.to(torch_device)
model.eval()
result = model(pixel_values)
pred_stride = config.patch_stride[-1] * (config.query_stride[-1] ** config.num_query_pool)
num_patches = self.image_size[0] // pred_stride
self.parent.assertEqual(
result.logits.shape, (self.batch_size, num_patches**2, self.num_channels * pred_stride**2)
)
# test greyscale images
config.num_channels = 1
model = HieraForPreTraining(config)
model.to(torch_device)
model.eval()
pixel_values = floats_tensor([self.batch_size, 1, self.image_size[0], self.image_size[0]])
result = model(pixel_values)
self.parent.assertEqual(result.logits.shape, (self.batch_size, num_patches**2, pred_stride**2))
def create_and_check_for_image_classification(self, config, pixel_values, labels):
config.num_labels = self.type_sequence_label_size
model = HieraForImageClassification(config)
model.to(torch_device)
model.eval()
result = model(pixel_values, labels=labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.type_sequence_label_size))
# test greyscale images
config.num_channels = 1
model = HieraForImageClassification(config)
model.to(torch_device)
model.eval()
pixel_values = floats_tensor([self.batch_size, 1, self.image_size[0], self.image_size[0]])
result = model(pixel_values)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.type_sequence_label_size))
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
config,
pixel_values,
labels,
) = config_and_inputs
inputs_dict = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class HieraModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
"""
Here we also overwrite some of the tests of test_modeling_common.py, as Hiera does not use input_ids, inputs_embeds,
attention_mask and seq_length.
"""
all_model_classes = (
(
HieraModel,
HieraBackbone,
HieraForImageClassification,
HieraForPreTraining,
)
if is_torch_available()
else ()
)
pipeline_model_mapping = (
{"image-feature-extraction": HieraModel, "image-classification": HieraForImageClassification}
if is_torch_available()
else {}
)
fx_compatible = True
test_pruning = False
test_resize_embeddings = False
test_head_masking = False
def setUp(self):
self.model_tester = HieraModelTester(self)
self.config_tester = ConfigTester(self, config_class=HieraConfig, has_text_modality=False)
def test_config(self):
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
# Overriding as Hiera `get_input_embeddings` returns HieraPatchEmbeddings
def test_model_get_set_embeddings(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
self.assertIsInstance(model.get_input_embeddings(), (nn.Module))
x = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(x, nn.Linear))
# Overriding as attention shape depends on patch_stride and mask_unit_size
def test_attention_outputs(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.return_dict = True
for model_class in self.all_model_classes:
inputs_dict["output_attentions"] = True
inputs_dict["output_hidden_states"] = False
config.return_dict = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
attentions = outputs.attentions
expected_num_attentions = len(self.model_tester.depths)
self.assertEqual(len(attentions), expected_num_attentions)
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
config.output_attentions = True
seq_len = math.prod([i // s for i, s in zip(config.image_size, config.patch_stride)])
mask_unit_area = math.prod(config.masked_unit_size)
num_windows = seq_len // mask_unit_area
if model_class.__name__ == "HieraForPreTraining":
num_windows = int(num_windows * (1 - config.mask_ratio))
seq_len = int(num_windows * mask_unit_area)
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
attentions = outputs.attentions
self.assertEqual(len(attentions), expected_num_attentions)
self.assertListEqual(
list(attentions[0].shape[-4:]),
[self.model_tester.num_heads[0], num_windows, mask_unit_area, seq_len // num_windows],
)
out_len = len(outputs)
# Check attention is always last and order is fine
inputs_dict["output_attentions"] = True
inputs_dict["output_hidden_states"] = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
# also another +1 for reshaped_hidden_states
added_hidden_states = 1 if model_class.__name__ == "HieraBackbone" else 2
self.assertEqual(out_len + added_hidden_states, len(outputs))
self_attentions = outputs.attentions
self.assertEqual(len(self_attentions), expected_num_attentions)
self.assertListEqual(
list(self_attentions[0].shape[-4:]),
[self.model_tester.num_heads[0], num_windows, mask_unit_area, seq_len // num_windows],
)
# Overriding as attention shape depends on patch_stride and mask_unit_size
def test_hidden_states_output(self):
def check_hidden_states_output(inputs_dict, config, model_class, image_size):
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
hidden_states = outputs.hidden_states
expected_num_layers = getattr(
self.model_tester, "expected_num_hidden_layers", len(self.model_tester.depths) + 1
)
self.assertEqual(len(hidden_states), expected_num_layers)
# Hiera has a different seq_length
patch_size = config.patch_stride
num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
if model_class.__name__ == "HieraForPreTraining":
mask_unit_area = math.prod(config.masked_unit_size)
num_windows = num_patches // mask_unit_area
num_windows = int(num_windows * (1 - config.mask_ratio))
num_patches = int(num_windows * mask_unit_area)
self.assertListEqual(
list(hidden_states[0].shape[-2:]),
[num_patches, self.model_tester.embed_dim],
)
if not model_class.__name__ == "HieraBackbone":
reshaped_hidden_states = outputs.reshaped_hidden_states
self.assertEqual(len(reshaped_hidden_states), expected_num_layers)
batch_size = reshaped_hidden_states[0].shape[0]
num_channels = reshaped_hidden_states[0].shape[-1]
reshaped_hidden_states = reshaped_hidden_states[0].view(batch_size, -1, num_channels)
self.assertListEqual(
list(reshaped_hidden_states.shape[-2:]),
[num_patches, self.model_tester.embed_dim],
)
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
image_size = self.model_tester.image_size
for model_class in self.all_model_classes:
inputs_dict["output_hidden_states"] = True
check_hidden_states_output(inputs_dict, config, model_class, image_size)
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
config.output_hidden_states = True
check_hidden_states_output(inputs_dict, config, model_class, image_size)
# Overriding since HieraForPreTraining outputs bool_masked_pos which has to be converted to float in the msg
def test_model_outputs_equivalence(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
def set_nan_tensor_to_zero(t):
t[t != t] = 0
return t
def check_equivalence(model, tuple_inputs, dict_inputs, additional_kwargs={}):
with torch.no_grad():
tuple_output = model(**tuple_inputs, return_dict=False, **additional_kwargs)
dict_output = model(**dict_inputs, return_dict=True, **additional_kwargs).to_tuple()
def recursive_check(tuple_object, dict_object):
if isinstance(tuple_object, (List, Tuple)):
for tuple_iterable_value, dict_iterable_value in zip(tuple_object, dict_object):
recursive_check(tuple_iterable_value, dict_iterable_value)
elif isinstance(tuple_object, Dict):
for tuple_iterable_value, dict_iterable_value in zip(
tuple_object.values(), dict_object.values()
):
recursive_check(tuple_iterable_value, dict_iterable_value)
elif tuple_object is None:
return
else:
self.assertTrue(
torch.allclose(
set_nan_tensor_to_zero(tuple_object), set_nan_tensor_to_zero(dict_object), atol=1e-5
),
msg=(
"Tuple and dict output are not equal. Difference:"
f" {torch.max(torch.abs(tuple_object.float() - dict_object.float()))}. Tuple has `nan`:"
f" {torch.isnan(tuple_object).any()} and `inf`: {torch.isinf(tuple_object)}. Dict has"
f" `nan`: {torch.isnan(dict_object).any()} and `inf`: {torch.isinf(dict_object)}."
),
)
recursive_check(tuple_output, dict_output)
for model_class in self.all_model_classes:
model = model_class(config)
model.to(torch_device)
model.eval()
additional_kwargs = {}
tuple_inputs = self._prepare_for_class(inputs_dict, model_class)
dict_inputs = self._prepare_for_class(inputs_dict, model_class)
check_equivalence(model, tuple_inputs, dict_inputs, additional_kwargs)
tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
check_equivalence(model, tuple_inputs, dict_inputs, additional_kwargs)
tuple_inputs = self._prepare_for_class(inputs_dict, model_class)
dict_inputs = self._prepare_for_class(inputs_dict, model_class)
additional_kwargs["output_hidden_states"] = True
check_equivalence(model, tuple_inputs, dict_inputs, additional_kwargs)
tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
check_equivalence(model, tuple_inputs, dict_inputs, additional_kwargs)
if self.has_attentions:
# Removing "output_hidden_states"
del additional_kwargs["output_hidden_states"]
tuple_inputs = self._prepare_for_class(inputs_dict, model_class)
dict_inputs = self._prepare_for_class(inputs_dict, model_class)
additional_kwargs["output_attentions"] = True
check_equivalence(model, tuple_inputs, dict_inputs, additional_kwargs)
tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
check_equivalence(model, tuple_inputs, dict_inputs, additional_kwargs)
tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
additional_kwargs["output_hidden_states"] = True
check_equivalence(model, tuple_inputs, dict_inputs, additional_kwargs)
@unittest.skip(reason="Hiera Transformer does not use feedforward chunking")
def test_feed_forward_chunking(self):
pass
@unittest.skip(reason="Hiera does not use inputs_embeds")
def test_inputs_embeds(self):
pass
def test_model_common_attributes(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
self.assertIsInstance(model.get_input_embeddings(), (nn.Module))
x = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(x, nn.Linear))
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
def test_backbone(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_backbone(*config_and_inputs)
def test_for_pretraining(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*config_and_inputs)
def test_for_image_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*config_and_inputs)
@slow
def test_model_from_pretrained(self):
for model_name in ["facebook/hiera-tiny-224-hf"]:
model = HieraModel.from_pretrained(model_name)
self.assertIsNotNone(model)
# We will verify our results on an image of cute cats
def prepare_img():
image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
return image
@require_torch
@require_vision
class HieraModelIntegrationTest(unittest.TestCase):
@cached_property
def default_image_processor(self):
return AutoImageProcessor.from_pretrained("facebook/hiera-tiny-224-in1k-hf") if is_vision_available() else None
@slow
def test_inference_image_classification_head(self):
model = HieraForImageClassification.from_pretrained("facebook/hiera-tiny-224-in1k-hf").to(torch_device)
image_processor = self.default_image_processor
image = prepare_img()
inputs = image_processor(images=image, return_tensors="pt").to(torch_device)
expected_pixel_values = torch.tensor(
[
[[0.2967, 0.4679, 0.4508], [0.3309, 0.4337, 0.3309], [0.3309, 0.3823, 0.3309]],
[[-1.5455, -1.4930, -1.5455], [-1.5280, -1.4755, -1.5980], [-1.5630, -1.5280, -1.4755]],
[[-0.6367, -0.4973, -0.5321], [-0.7936, -0.6715, -0.6715], [-0.8284, -0.7413, -0.5670]],
]
).to(torch_device)
torch.testing.assert_close(inputs.pixel_values[0, :3, :3, :3], expected_pixel_values, rtol=1e-4, atol=1e-4)
# forward pass
with torch.no_grad():
outputs = model(**inputs)
# verify the logits
expected_shape = torch.Size((1, 1000))
self.assertEqual(outputs.logits.shape, expected_shape)
expected_slice = torch.tensor([[0.8028, 0.2409, -0.2254, -0.3712, -0.2848]]).to(torch_device)
torch.testing.assert_close(outputs.logits[0, :5], expected_slice, rtol=1e-4, atol=1e-4)
def test_inference_interpolate_pos_encoding(self):
model = HieraModel.from_pretrained("facebook/hiera-tiny-224-hf").to(torch_device)
image_processor = AutoImageProcessor.from_pretrained(
"facebook/hiera-tiny-224-hf", size={"shortest_edge": 448}, crop_size={"height": 448, "width": 448}
)
image = prepare_img()
inputs = image_processor(images=image, return_tensors="pt")
pixel_values = inputs.pixel_values.to(torch_device)
# forward pass
with torch.no_grad():
outputs = model(pixel_values, interpolate_pos_encoding=True)
# verify the logits
expected_shape = torch.Size((1, 196, 768))
self.assertEqual(outputs.last_hidden_state.shape, expected_shape)
expected_slice = torch.tensor(
[[1.7853, 0.0690, 0.3177], [2.6853, -0.2334, 0.0889], [1.5445, -0.1515, -0.0300]]
).to(torch_device)
torch.testing.assert_close(outputs.last_hidden_state[0, :3, :3], expected_slice, rtol=1e-4, atol=1e-4)
@slow
def test_inference_for_pretraining(self):
# make random mask reproducible
torch.manual_seed(2)
model = HieraForPreTraining.from_pretrained("facebook/hiera-tiny-224-mae-hf").to(torch_device)
image_processor = self.default_image_processor
image = prepare_img()
inputs = image_processor(images=image, return_tensors="pt").to(torch_device)
config = model.config
mask_spatial_shape = [
i // s // ms for i, s, ms in zip(config.image_size, config.patch_stride, config.masked_unit_size)
]
num_windows = math.prod(mask_spatial_shape)
noise = torch.rand(1, num_windows).to(torch_device)
# forward pass
with torch.no_grad():
outputs = model(**inputs, noise=noise)
# verify the logits
expected_shape = torch.Size((1, 196, 768))
self.assertEqual(outputs.logits.shape, expected_shape)
expected_slice = torch.tensor(
[
[1.6407, 1.6506, 1.6541, 1.6617, 1.6703],
[1.9730, 1.9842, 1.9848, 1.9896, 1.9947],
[1.5949, 1.8262, 1.2602, 1.4801, 1.4448],
[1.2341, 1.7907, 0.8618, 1.5202, 1.4523],
[2.0140, 1.9846, 1.9434, 1.9019, 1.8648],
]
)
torch.testing.assert_close(outputs.logits[0, :5, :5], expected_slice.to(torch_device), rtol=1e-4, atol=1e-4)
@require_torch
class HieraBackboneTest(unittest.TestCase, BackboneTesterMixin):
all_model_classes = (HieraBackbone,) if is_torch_available() else ()
config_class = HieraConfig
def setUp(self):
self.model_tester = HieraModelTester(self)
| transformers/tests/models/hiera/test_modeling_hiera.py/0 | {
"file_path": "transformers/tests/models/hiera/test_modeling_hiera.py",
"repo_id": "transformers",
"token_count": 12056
} |
# 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.
"""Testing suite for the PyTorch LeViT model."""
import unittest
import warnings
from math import ceil, floor
from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
LevitForImageClassification,
LevitForImageClassificationWithTeacher,
LevitModel,
)
from transformers.models.auto.modeling_auto import (
MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES,
MODEL_MAPPING_NAMES,
)
if is_vision_available():
from PIL import Image
from transformers import LevitImageProcessor
class LevitConfigTester(ConfigTester):
def create_and_test_config_common_properties(self):
config = self.config_class(**self.inputs_dict)
self.parent.assertTrue(hasattr(config, "hidden_sizes"))
self.parent.assertTrue(hasattr(config, "num_attention_heads"))
class LevitModelTester:
def __init__(
self,
parent,
batch_size=13,
image_size=64,
num_channels=3,
kernel_size=3,
stride=2,
padding=1,
patch_size=16,
hidden_sizes=[16, 32, 48],
num_attention_heads=[1, 2, 3],
depths=[2, 3, 4],
key_dim=[8, 8, 8],
drop_path_rate=0,
mlp_ratio=[2, 2, 2],
attention_ratio=[2, 2, 2],
initializer_range=0.02,
is_training=True,
use_labels=True,
num_labels=2, # Check
):
self.parent = parent
self.batch_size = batch_size
self.image_size = image_size
self.num_channels = num_channels
self.kernel_size = kernel_size
self.stride = stride
self.padding = padding
self.hidden_sizes = hidden_sizes
self.num_attention_heads = num_attention_heads
self.depths = depths
self.key_dim = key_dim
self.drop_path_rate = drop_path_rate
self.patch_size = patch_size
self.attention_ratio = attention_ratio
self.mlp_ratio = mlp_ratio
self.initializer_range = initializer_range
self.down_ops = [
["Subsample", key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
["Subsample", key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
]
self.is_training = is_training
self.use_labels = use_labels
self.num_labels = num_labels
self.initializer_range = initializer_range
def prepare_config_and_inputs(self):
pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
labels = None
if self.use_labels:
labels = ids_tensor([self.batch_size], self.num_labels)
config = self.get_config()
return config, pixel_values, labels
def get_config(self):
return LevitConfig(
image_size=self.image_size,
num_channels=self.num_channels,
kernel_size=self.kernel_size,
stride=self.stride,
padding=self.padding,
patch_size=self.patch_size,
hidden_sizes=self.hidden_sizes,
num_attention_heads=self.num_attention_heads,
depths=self.depths,
key_dim=self.key_dim,
drop_path_rate=self.drop_path_rate,
mlp_ratio=self.mlp_ratio,
attention_ratio=self.attention_ratio,
initializer_range=self.initializer_range,
down_ops=self.down_ops,
)
def create_and_check_model(self, config, pixel_values, labels):
model = LevitModel(config=config)
model.to(torch_device)
model.eval()
result = model(pixel_values)
image_size = (self.image_size, self.image_size)
height, width = image_size[0], image_size[1]
for _ in range(4):
height = floor(((height + 2 * self.padding - self.kernel_size) / self.stride) + 1)
width = floor(((width + 2 * self.padding - self.kernel_size) / self.stride) + 1)
self.parent.assertEqual(
result.last_hidden_state.shape,
(self.batch_size, ceil(height / 4) * ceil(width / 4), self.hidden_sizes[-1]),
)
def create_and_check_for_image_classification(self, config, pixel_values, labels):
config.num_labels = self.num_labels
model = LevitForImageClassification(config)
model.to(torch_device)
model.eval()
result = model(pixel_values, labels=labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
config, pixel_values, labels = config_and_inputs
inputs_dict = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class LevitModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
"""
Here we also overwrite some of the tests of test_modeling_common.py, as Levit does not use input_ids, inputs_embeds,
attention_mask and seq_length.
"""
all_model_classes = (
(LevitModel, LevitForImageClassification, LevitForImageClassificationWithTeacher)
if is_torch_available()
else ()
)
pipeline_model_mapping = (
{
"image-feature-extraction": LevitModel,
"image-classification": (LevitForImageClassification, LevitForImageClassificationWithTeacher),
}
if is_torch_available()
else {}
)
test_pruning = False
test_torchscript = False
test_resize_embeddings = False
test_head_masking = False
has_attentions = False
def setUp(self):
self.model_tester = LevitModelTester(self)
self.config_tester = ConfigTester(
self, config_class=LevitConfig, has_text_modality=False, common_properties=["image_size", "num_channels"]
)
def test_config(self):
self.config_tester.run_common_tests()
@unittest.skip(reason="Levit does not use inputs_embeds")
def test_inputs_embeds(self):
pass
@unittest.skip(reason="Levit does not support input and output embeddings")
def test_model_get_set_embeddings(self):
pass
@unittest.skip(reason="Levit does not output attentions")
def test_attention_outputs(self):
pass
def test_hidden_states_output(self):
def check_hidden_states_output(inputs_dict, config, model_class):
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
hidden_states = outputs.hidden_states
expected_num_layers = len(self.model_tester.depths) + 1
self.assertEqual(len(hidden_states), expected_num_layers)
image_size = (self.model_tester.image_size, self.model_tester.image_size)
height, width = image_size[0], image_size[1]
for _ in range(4):
height = floor(
(
(height + 2 * self.model_tester.padding - self.model_tester.kernel_size)
/ self.model_tester.stride
)
+ 1
)
width = floor(
(
(width + 2 * self.model_tester.padding - self.model_tester.kernel_size)
/ self.model_tester.stride
)
+ 1
)
# verify the first hidden states (first block)
self.assertListEqual(
list(hidden_states[0].shape[-2:]),
[
height * width,
self.model_tester.hidden_sizes[0],
],
)
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
inputs_dict["output_hidden_states"] = True
check_hidden_states_output(inputs_dict, config, model_class)
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
config.output_hidden_states = True
check_hidden_states_output(inputs_dict, config, model_class)
def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels)
if return_labels:
if model_class.__name__ == "LevitForImageClassificationWithTeacher":
del inputs_dict["labels"]
return inputs_dict
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
def test_for_image_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*config_and_inputs)
# special case for LevitForImageClassificationWithTeacher model
def test_training(self):
if not self.model_tester.is_training:
self.skipTest(reason="model_tester.is_training is set to False")
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.return_dict = True
for model_class in self.all_model_classes:
# LevitForImageClassificationWithTeacher supports inference-only
if (
model_class.__name__ in MODEL_MAPPING_NAMES.values()
or model_class.__name__ == "LevitForImageClassificationWithTeacher"
):
continue
model = model_class(config)
model.to(torch_device)
model.train()
inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
loss = model(**inputs).loss
loss.backward()
def test_training_gradient_checkpointing(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
if not self.model_tester.is_training:
self.skipTest(reason="model_tester.is_training is set to False")
config.use_cache = False
config.return_dict = True
for model_class in self.all_model_classes:
if model_class.__name__ in MODEL_MAPPING_NAMES.values() or not model_class.supports_gradient_checkpointing:
continue
# LevitForImageClassificationWithTeacher supports inference-only
if model_class.__name__ == "LevitForImageClassificationWithTeacher":
continue
model = model_class(config)
model.gradient_checkpointing_enable()
model.to(torch_device)
model.train()
inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
loss = model(**inputs).loss
loss.backward()
def test_problem_types(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
problem_types = [
{"title": "multi_label_classification", "num_labels": 2, "dtype": torch.float},
{"title": "single_label_classification", "num_labels": 1, "dtype": torch.long},
{"title": "regression", "num_labels": 1, "dtype": torch.float},
]
for model_class in self.all_model_classes:
if (
model_class.__name__
not in [
*MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES.values(),
]
or model_class.__name__ == "LevitForImageClassificationWithTeacher"
):
continue
for problem_type in problem_types:
with self.subTest(msg=f"Testing {model_class} with {problem_type['title']}"):
config.problem_type = problem_type["title"]
config.num_labels = problem_type["num_labels"]
model = model_class(config)
model.to(torch_device)
model.train()
inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
if problem_type["num_labels"] > 1:
inputs["labels"] = inputs["labels"].unsqueeze(1).repeat(1, problem_type["num_labels"])
inputs["labels"] = inputs["labels"].to(problem_type["dtype"])
# This tests that we do not trigger the warning form PyTorch "Using a target size that is different
# to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
# they have the same size." which is a symptom something in wrong for the regression problem.
# See https://github.com/huggingface/transformers/issues/11780
with warnings.catch_warnings(record=True) as warning_list:
loss = model(**inputs).loss
for w in warning_list:
if "Using a target size that is different to the input size" in str(w.message):
raise ValueError(
f"Something is going wrong in the regression problem: intercepted {w.message}"
)
loss.backward()
@slow
def test_model_from_pretrained(self):
model_name = "facebook/levit-128S"
model = LevitModel.from_pretrained(model_name)
self.assertIsNotNone(model)
# We will verify our results on an image of cute cats
def prepare_img():
image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
return image
@require_torch
@require_vision
class LevitModelIntegrationTest(unittest.TestCase):
@cached_property
def default_image_processor(self):
return LevitImageProcessor.from_pretrained("facebook/levit-128S")
@slow
def test_inference_image_classification_head(self):
model = LevitForImageClassificationWithTeacher.from_pretrained("facebook/levit-128S").to(torch_device)
image_processor = self.default_image_processor
image = prepare_img()
inputs = image_processor(images=image, return_tensors="pt").to(torch_device)
# forward pass
with torch.no_grad():
outputs = model(**inputs)
# verify the logits
expected_shape = torch.Size((1, 1000))
self.assertEqual(outputs.logits.shape, expected_shape)
expected_slice = torch.tensor([1.0448, -0.3745, -1.8317]).to(torch_device)
torch.testing.assert_close(outputs.logits[0, :3], expected_slice, rtol=1e-4, atol=1e-4)
| transformers/tests/models/levit/test_modeling_levit.py/0 | {
"file_path": "transformers/tests/models/levit/test_modeling_levit.py",
"repo_id": "transformers",
"token_count": 7152
} |
# Copyright 2020 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.
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
)
from ...test_tokenization_common import TokenizerTesterMixin
SAMPLE_VOCAB = get_tests_dir("fixtures/test_sentencepiece.model")
if is_torch_available():
from transformers.models.mbart.modeling_mbart import shift_tokens_right
EN_CODE = 250004
RO_CODE = 250020
@require_sentencepiece
@require_tokenizers
class MBartTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
from_pretrained_id = "facebook/mbart-large-en-ro"
tokenizer_class = MBartTokenizer
rust_tokenizer_class = MBartTokenizerFast
test_rust_tokenizer = True
test_sentencepiece = True
def setUp(self):
super().setUp()
# We have a SentencePiece fixture for testing
tokenizer = MBartTokenizer(SAMPLE_VOCAB, keep_accents=True)
tokenizer.save_pretrained(self.tmpdirname)
def test_full_tokenizer(self):
tokenizer = MBartTokenizer(SAMPLE_VOCAB, keep_accents=True)
tokens = tokenizer.tokenize("This is a test")
self.assertListEqual(tokens, ["▁This", "▁is", "▁a", "▁t", "est"])
self.assertListEqual(
tokenizer.convert_tokens_to_ids(tokens),
[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]],
)
tokens = tokenizer.tokenize("I was born in 92000, and this is falsé.")
self.assertListEqual(
tokens,
[
SPIECE_UNDERLINE + "I",
SPIECE_UNDERLINE + "was",
SPIECE_UNDERLINE + "b",
"or",
"n",
SPIECE_UNDERLINE + "in",
SPIECE_UNDERLINE + "",
"9",
"2",
"0",
"0",
"0",
",",
SPIECE_UNDERLINE + "and",
SPIECE_UNDERLINE + "this",
SPIECE_UNDERLINE + "is",
SPIECE_UNDERLINE + "f",
"al",
"s",
"é",
".",
],
)
ids = tokenizer.convert_tokens_to_ids(tokens)
self.assertListEqual(
ids,
[
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
# ^ unk: 2 + 1 = 3 unk: 2 + 1 = 3 ^
],
)
back_tokens = tokenizer.convert_ids_to_tokens(ids)
self.assertListEqual(
back_tokens,
[
SPIECE_UNDERLINE + "I",
SPIECE_UNDERLINE + "was",
SPIECE_UNDERLINE + "b",
"or",
"n",
SPIECE_UNDERLINE + "in",
SPIECE_UNDERLINE + "",
"<unk>",
"2",
"0",
"0",
"0",
",",
SPIECE_UNDERLINE + "and",
SPIECE_UNDERLINE + "this",
SPIECE_UNDERLINE + "is",
SPIECE_UNDERLINE + "f",
"al",
"s",
"<unk>",
".",
],
)
# overwrite from test_tokenization_common to speed up test
def test_save_pretrained(self):
if not self.test_slow_tokenizer:
# as we don't have a slow version, we can't compare the outputs between slow and fast versions
self.skipTest(reason="test_slow_tokenizer is set to False")
self.tokenizers_list[0] = (self.rust_tokenizer_class, "hf-internal-testing/tiny-random-mbart", {})
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
tokenizer_p = self.tokenizer_class.from_pretrained(pretrained_name, **kwargs)
tmpdirname2 = tempfile.mkdtemp()
tokenizer_r_files = tokenizer_r.save_pretrained(tmpdirname2)
tokenizer_p_files = tokenizer_p.save_pretrained(tmpdirname2)
# Checks it save with the same files + the tokenizer.json file for the fast one
self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files))
tokenizer_r_files = tuple(f for f in tokenizer_r_files if "tokenizer.json" not in f)
self.assertSequenceEqual(tokenizer_r_files, tokenizer_p_files)
# Checks everything loads correctly in the same way
tokenizer_rp = tokenizer_r.from_pretrained(tmpdirname2)
tokenizer_pp = tokenizer_p.from_pretrained(tmpdirname2)
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(tokenizer_rp, key))
# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))
shutil.rmtree(tmpdirname2)
# Save tokenizer rust, legacy_format=True
tmpdirname2 = tempfile.mkdtemp()
tokenizer_r_files = tokenizer_r.save_pretrained(tmpdirname2, legacy_format=True)
tokenizer_p_files = tokenizer_p.save_pretrained(tmpdirname2)
# Checks it save with the same files
self.assertSequenceEqual(tokenizer_r_files, tokenizer_p_files)
# Checks everything loads correctly in the same way
tokenizer_rp = tokenizer_r.from_pretrained(tmpdirname2)
tokenizer_pp = tokenizer_p.from_pretrained(tmpdirname2)
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(tokenizer_rp, key))
shutil.rmtree(tmpdirname2)
# Save tokenizer rust, legacy_format=False
tmpdirname2 = tempfile.mkdtemp()
tokenizer_r_files = tokenizer_r.save_pretrained(tmpdirname2, legacy_format=False)
tokenizer_p_files = tokenizer_p.save_pretrained(tmpdirname2)
# Checks it saved the tokenizer.json file
self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files))
# Checks everything loads correctly in the same way
tokenizer_rp = tokenizer_r.from_pretrained(tmpdirname2)
tokenizer_pp = tokenizer_p.from_pretrained(tmpdirname2)
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(tokenizer_rp, key))
shutil.rmtree(tmpdirname2)
@unittest.skip(reason="Need to fix this after #26538")
def test_training_new_tokenizer(self):
pass
@require_torch
@require_sentencepiece
@require_tokenizers
class MBartEnroIntegrationTest(unittest.TestCase):
checkpoint_name = "facebook/mbart-large-en-ro"
src_text = [
" UN Chief Says There Is No Military Solution in Syria",
""" Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for Syria is that "there is no military solution" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.""",
]
tgt_text = [
"Şeful ONU declară că nu există o soluţie militară în Siria",
"Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei"
' pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi că noi arme nu vor'
" face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.",
]
expected_src_tokens = [8274, 127873, 25916, 7, 8622, 2071, 438, 67485, 53, 187895, 23, 51712, 2, EN_CODE]
@classmethod
def setUpClass(cls):
cls.tokenizer: MBartTokenizer = MBartTokenizer.from_pretrained(
cls.checkpoint_name, src_lang="en_XX", tgt_lang="ro_RO"
)
cls.pad_token_id = 1
return cls
def check_language_codes(self):
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ar_AR"], 250001)
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["en_EN"], 250004)
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ro_RO"], 250020)
def test_enro_tokenizer_batch_encode_plus(self):
ids = self.tokenizer.batch_encode_plus(self.src_text).input_ids[0]
self.assertListEqual(self.expected_src_tokens, ids)
def test_enro_tokenizer_decode_ignores_language_codes(self):
self.assertIn(RO_CODE, self.tokenizer.all_special_ids)
generated_ids = [RO_CODE, 884, 9019, 96, 9, 916, 86792, 36, 18743, 15596, 5, 2]
result = self.tokenizer.decode(generated_ids, skip_special_tokens=True)
expected_romanian = self.tokenizer.decode(generated_ids[1:], skip_special_tokens=True)
self.assertEqual(result, expected_romanian)
self.assertNotIn(self.tokenizer.eos_token, result)
def test_enro_tokenizer_truncation(self):
src_text = ["this is gunna be a long sentence " * 20]
assert isinstance(src_text[0], str)
desired_max_length = 10
ids = self.tokenizer(src_text, max_length=desired_max_length, truncation=True).input_ids[0]
self.assertEqual(ids[-2], 2)
self.assertEqual(ids[-1], EN_CODE)
self.assertEqual(len(ids), desired_max_length)
def test_mask_token(self):
self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "ar_AR"]), [250026, 250001])
def test_special_tokens_unaffacted_by_save_load(self):
tmpdirname = tempfile.mkdtemp()
original_special_tokens = self.tokenizer.fairseq_tokens_to_ids
self.tokenizer.save_pretrained(tmpdirname)
new_tok = MBartTokenizer.from_pretrained(tmpdirname)
self.assertDictEqual(new_tok.fairseq_tokens_to_ids, original_special_tokens)
@require_torch
def test_batch_fairseq_parity(self):
batch = self.tokenizer(self.src_text, text_target=self.tgt_text, padding=True, return_tensors="pt")
batch["decoder_input_ids"] = shift_tokens_right(batch["labels"], self.tokenizer.pad_token_id)
# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
assert batch.input_ids[1][-2:].tolist() == [2, EN_CODE]
assert batch.decoder_input_ids[1][0].tolist() == RO_CODE
assert batch.decoder_input_ids[1][-1] == 2
assert batch.labels[1][-2:].tolist() == [2, RO_CODE]
@require_torch
def test_enro_tokenizer_prepare_batch(self):
batch = self.tokenizer(
self.src_text,
text_target=self.tgt_text,
padding=True,
truncation=True,
max_length=len(self.expected_src_tokens),
return_tensors="pt",
)
batch["decoder_input_ids"] = shift_tokens_right(batch["labels"], self.tokenizer.pad_token_id)
self.assertIsInstance(batch, BatchEncoding)
self.assertEqual((2, 14), batch.input_ids.shape)
self.assertEqual((2, 14), batch.attention_mask.shape)
result = batch.input_ids.tolist()[0]
self.assertListEqual(self.expected_src_tokens, result)
self.assertEqual(2, batch.decoder_input_ids[0, -1]) # EOS
# Test that special tokens are reset
self.assertEqual(self.tokenizer.prefix_tokens, [])
self.assertEqual(self.tokenizer.suffix_tokens, [self.tokenizer.eos_token_id, EN_CODE])
def test_seq2seq_max_length(self):
batch = self.tokenizer(self.src_text, padding=True, truncation=True, max_length=3, return_tensors="pt")
targets = self.tokenizer(
text_target=self.tgt_text, padding=True, truncation=True, max_length=10, return_tensors="pt"
)
labels = targets["input_ids"]
batch["decoder_input_ids"] = shift_tokens_right(labels, self.tokenizer.pad_token_id)
self.assertEqual(batch.input_ids.shape[1], 3)
self.assertEqual(batch.decoder_input_ids.shape[1], 10)
@require_torch
def test_tokenizer_translation(self):
inputs = self.tokenizer._build_translation_inputs(
"A test", return_tensors="pt", src_lang="en_XX", tgt_lang="ar_AR"
)
self.assertEqual(
nested_simplify(inputs),
{
# A, test, EOS, en_XX
"input_ids": [[62, 3034, 2, 250004]],
"attention_mask": [[1, 1, 1, 1]],
# ar_AR
"forced_bos_token_id": 250001,
},
)
| transformers/tests/models/mbart/test_tokenization_mbart.py/0 | {
"file_path": "transformers/tests/models/mbart/test_tokenization_mbart.py",
"repo_id": "transformers",
"token_count": 6637
} |
# coding=utf-8
# Copyright 2024 Mistral 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.
"""Testing suite for the TF 2.0 Mistral model."""
import unittest
import numpy as np
from transformers import AutoTokenizer, MistralConfig, is_tf_available
from transformers.testing_utils import (
require_tf,
slow,
)
from ...generation.test_tf_utils import TFGenerationIntegrationTests
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers.models.mistral.modeling_tf_mistral import (
TFMistralForCausalLM,
TFMistralForSequenceClassification,
TFMistralModel,
)
class TFMistralModelTester:
def __init__(self, parent):
self.parent = parent
self.batch_size = 13
self.seq_length = 7
self.is_training = True
self.use_input_mask = True
self.use_token_type_ids = False
self.use_labels = True
self.vocab_size = 99
self.hidden_size = 32
self.num_hidden_layers = 2
self.num_attention_heads = 4
self.num_key_value_heads = 2
self.intermediate_size = 37
self.hidden_act = "gelu"
self.hidden_dropout_prob = 0.1
self.attention_probs_dropout_prob = 0.1
self.max_position_embeddings = 512
self.type_vocab_size = 16
self.type_sequence_label_size = 2
self.initializer_range = 0.02
self.num_labels = 3
self.num_choices = 4
self.pad_token_id = 0
self.scope = None
self.bos_token_id = self.vocab_size - 1
self.eos_token_id = self.vocab_size - 1
self.pad_token_id = self.vocab_size - 1
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
input_mask = None
if self.use_input_mask:
input_mask = random_attention_mask([self.batch_size, self.seq_length], self.vocab_size)
token_type_ids = None
if self.use_token_type_ids:
token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
sequence_labels = None
token_labels = None
choice_labels = None
if self.use_labels:
sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
choice_labels = ids_tensor([self.batch_size], self.num_choices)
config = MistralConfig(
vocab_size=self.vocab_size,
hidden_size=self.hidden_size,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
num_key_value_heads=self.num_key_value_heads,
intermediate_size=self.intermediate_size,
hidden_act=self.hidden_act,
hidden_dropout_prob=self.hidden_dropout_prob,
attention_probs_dropout_prob=self.attention_probs_dropout_prob,
max_position_embeddings=self.max_position_embeddings,
type_vocab_size=self.type_vocab_size,
is_decoder=False,
initializer_range=self.initializer_range,
pad_token_id=self.pad_token_id,
)
return (
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
)
def create_and_check_model(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = TFMistralModel(config=config)
result = model(input_ids, attention_mask=input_mask)
result = model(input_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
def create_and_check_model_as_decoder(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
):
config.add_cross_attention = True
model = TFMistralModel(config)
result = model(
input_ids,
attention_mask=input_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
)
result = model(
input_ids,
attention_mask=input_mask,
encoder_hidden_states=encoder_hidden_states,
)
result = model(input_ids, attention_mask=input_mask)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
def create_and_check_for_causal_lm(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
):
model = TFMistralForCausalLM(config=config)
result = model(input_ids, attention_mask=input_mask, labels=token_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
def create_and_check_decoder_model_past_large_inputs(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
):
config.is_decoder = True
config.add_cross_attention = True
model = TFMistralForCausalLM(config=config)
# first forward pass
outputs = model(
input_ids,
attention_mask=input_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
use_cache=True,
)
past_key_values = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size)
next_mask = ids_tensor((self.batch_size, 3), vocab_size=2)
# append to next input_ids and
next_input_ids = tf.concat([input_ids, next_tokens], axis=-1)
next_attention_mask = tf.concat([input_mask, next_mask], axis=-1)
output_from_no_past = model(
next_input_ids,
attention_mask=next_attention_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
output_hidden_states=True,
)["hidden_states"][0]
output_from_past = model(
next_tokens,
attention_mask=next_attention_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
past_key_values=past_key_values,
output_hidden_states=True,
)["hidden_states"][0]
# select random slice
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach()
output_from_past_slice = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])
# test that outputs are equal for slice
self.parent.assertTrue(np.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
) = config_and_inputs
inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_tf
class TFMistralModelTest(TFModelTesterMixin, TFGenerationIntegrationTests, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (
(TFMistralModel, TFMistralForCausalLM, TFMistralForSequenceClassification) if is_tf_available() else ()
)
all_generative_model_classes = (TFMistralForCausalLM,) if is_tf_available() else ()
pipeline_model_mapping = (
{
"feature-extraction": TFMistralModel,
"text-classification": TFMistralForSequenceClassification,
"text-generation": TFMistralForCausalLM,
"zero-shot": TFMistralForSequenceClassification,
}
if is_tf_available()
else {}
)
test_onnx = False
test_pruning = False
test_missing_keys = False
test_head_masking = False
# TODO (ydshieh): Check this. See https://app.circleci.com/pipelines/github/huggingface/transformers/79245/workflows/9490ef58-79c2-410d-8f51-e3495156cf9c/jobs/1012146
def is_pipeline_test_to_skip(
self,
pipeline_test_case_name,
config_class,
model_architecture,
tokenizer_name,
image_processor_name,
feature_extractor_name,
processor_name,
):
return True
def setUp(self):
self.model_tester = TFMistralModelTester(self)
self.config_tester = ConfigTester(self, config_class=MistralConfig, hidden_size=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
def test_model_various_embeddings(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
config_and_inputs[0].position_embedding_type = type
self.model_tester.create_and_check_model(*config_and_inputs)
def test_Mistral_sequence_classification_model(self):
config, input_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.num_labels = 3
input_ids = input_dict["input_ids"]
attention_mask = tf.not_equal(input_ids, 1)
sequence_labels = ids_tensor([self.model_tester.batch_size], self.model_tester.type_sequence_label_size)
model = TFMistralForSequenceClassification(config)
result = model(input_ids, attention_mask=attention_mask, labels=sequence_labels)
self.assertEqual(result.logits.shape, (self.model_tester.batch_size, self.model_tester.num_labels))
def test_Mistral_sequence_classification_model_for_single_label(self):
config, input_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.num_labels = 3
config.problem_type = "single_label_classification"
input_ids = input_dict["input_ids"]
attention_mask = tf.not_equal(input_ids, 1)
sequence_labels = ids_tensor([self.model_tester.batch_size], self.model_tester.type_sequence_label_size)
model = TFMistralForSequenceClassification(config)
result = model(input_ids, attention_mask=attention_mask, labels=sequence_labels)
self.assertEqual(result.logits.shape, (self.model_tester.batch_size, self.model_tester.num_labels))
def test_Mistral_sequence_classification_model_for_multi_label(self):
config, input_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.num_labels = 3
config.problem_type = "multi_label_classification"
input_ids = input_dict["input_ids"]
attention_mask = tf.not_equal(input_ids, 1)
sequence_labels = ids_tensor([self.model_tester.batch_size], self.model_tester.type_sequence_label_size)
model = TFMistralForSequenceClassification(config)
result = model(input_ids, attention_mask=attention_mask, labels=sequence_labels)
self.assertEqual(result.logits.shape, (self.model_tester.batch_size, self.model_tester.num_labels))
@unittest.skip("Mistral buffers include complex numbers, which breaks this test")
def test_save_load_fast_init_from_base(self):
pass
@unittest.skip("Mistral uses GQA on all models so the KV cache is a non standard format")
def test_past_key_values_format(self):
pass
@unittest.skip("Vocab resizing is not supported")
def test_save_load_after_resize_token_embeddings(self):
pass
@require_tf
class TFMistralIntegrationTest(unittest.TestCase):
@slow
def test_model_7b_logits(self):
input_ids = [1, 306, 4658, 278, 6593, 310, 2834, 338]
model = TFMistralForCausalLM.from_pretrained(
"hf-internal-testing/tiny-random-MistralForCausalLM", from_pt=True
)
input_ids = tf.constant([input_ids])
out = model(input_ids).logits
# Expected mean on dim = -1
EXPECTED_MEAN = tf.constant(
[[-1.281e-04, -2.869e-04, -9.989e-05, -8.995e-05, 2.494e-04, -3.083e-04, -2.672e-04, -1.239e-04]]
)
tf.debugging.assert_near(tf.reduce_mean(out, axis=-1), EXPECTED_MEAN, atol=1e-2, rtol=1e-2)
# slicing logits[0, 0, 0:30]
EXPECTED_SLICE = tf.constant([0.1033, 0.1493, -0.0041, -0.0021, -0.1686, 0.0356, 0.0812, 0.2218, -0.1257, 0.1920, 0.0929, 0.1181, 0.0111, 0.0395, -0.0064, 0.1712, -0.0751, 0.0625, -0.2409, 0.1541, -0.1271, -0.2296, -0.0099, -0.0160, 0.0311, -0.0824, -0.1518, 0.0722, 0.0187, 0.0484]) # fmt: skip
tf.debugging.assert_near(out[0, 0, :30], EXPECTED_SLICE, atol=1e-4, rtol=1e-4)
@slow
def test_model_7b_generation(self):
EXPECTED_TEXT_COMPLETION = """My favourite condiment is Werk a EgyadjustPrintfigiousPDFPHPct guns Ein motor conceti barSequ内 infrastructure millretval"""
prompt = "My favourite condiment is "
tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-MistralForCausalLM", use_fast=False)
model = TFMistralForCausalLM.from_pretrained(
"hf-internal-testing/tiny-random-MistralForCausalLM", from_pt=True
)
input_ids = tokenizer.encode(prompt, return_tensors="tf")
# greedy generation outputs
generated_ids = model.generate(input_ids, max_new_tokens=20, temperature=0)
text = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
self.assertEqual(EXPECTED_TEXT_COMPLETION, text)
| transformers/tests/models/mistral/test_modeling_tf_mistral.py/0 | {
"file_path": "transformers/tests/models/mistral/test_modeling_tf_mistral.py",
"repo_id": "transformers",
"token_count": 6855
} |
# coding=utf-8
# Copyright 2024 HuggingFace Inc.
#
# 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 itertools
import math
import os
import random
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
check_json_file_has_correct_format,
require_torch,
require_torchaudio,
)
from transformers.utils.import_utils import is_torchaudio_available
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
if is_torchaudio_available():
import torch
from transformers import MusicgenMelodyFeatureExtractor
global_rng = random.Random()
# Copied from tests.models.whisper.test_feature_extraction_whisper.floats_list
def floats_list(shape, scale=1.0, rng=None, name=None):
"""Creates a random float32 tensor"""
if rng is None:
rng = global_rng
values = []
for batch_idx in range(shape[0]):
values.append([])
for _ in range(shape[1]):
values[-1].append(rng.random() * scale)
return values
# Copied from tests.models.musicgen.test_modeling_musicgen.get_bip_bip
def get_bip_bip(bip_duration=0.125, duration=0.5, sample_rate=32000):
"""Produces a series of 'bip bip' sounds at a given frequency."""
timesteps = np.arange(int(duration * sample_rate)) / sample_rate
wav = np.cos(2 * math.pi * 440 * timesteps)
time_period = (timesteps % (2 * bip_duration)) / (2 * bip_duration)
envelope = time_period >= 0.5
return wav * envelope
@require_torch
@require_torchaudio
class MusicgenMelodyFeatureExtractionTester:
def __init__(
self,
parent,
batch_size=7,
min_seq_length=400,
max_seq_length=2000,
feature_size=12,
padding_value=0.0,
sampling_rate=4_000,
return_attention_mask=True,
):
self.parent = parent
self.batch_size = batch_size
self.min_seq_length = min_seq_length
self.max_seq_length = max_seq_length
self.seq_length_diff = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
self.padding_value = padding_value
self.sampling_rate = sampling_rate
self.return_attention_mask = return_attention_mask
self.feature_size = feature_size
self.num_chroma = feature_size
def prepare_feat_extract_dict(self):
return {
"feature_size": self.feature_size,
"padding_value": self.padding_value,
"sampling_rate": self.sampling_rate,
"return_attention_mask": self.return_attention_mask,
}
# Copied from tests.models.whisper.test_feature_extraction_whisper.WhisperFeatureExtractionTester.prepare_inputs_for_common
def prepare_inputs_for_common(self, equal_length=False, numpify=False):
def _flatten(list_of_lists):
return list(itertools.chain(*list_of_lists))
if equal_length:
speech_inputs = [floats_list((self.max_seq_length, self.feature_size)) for _ in range(self.batch_size)]
else:
# make sure that inputs increase in size
speech_inputs = [
floats_list((x, self.feature_size))
for x in range(self.min_seq_length, self.max_seq_length, self.seq_length_diff)
]
if numpify:
speech_inputs = [np.asarray(x) for x in speech_inputs]
return speech_inputs
@require_torchaudio
@require_torch
class MusicgenMelodyFeatureExtractionTest(SequenceFeatureExtractionTestMixin, unittest.TestCase):
feature_extraction_class = MusicgenMelodyFeatureExtractor if is_torchaudio_available() else None
def setUp(self):
self.feat_extract_tester = MusicgenMelodyFeatureExtractionTester(self)
# Copied from tests.models.seamless_m4t.test_feature_extraction_seamless_m4t.SeamlessM4TFeatureExtractionTest.test_feat_extract_from_and_save_pretrained
def test_feat_extract_from_and_save_pretrained(self):
feat_extract_first = self.feature_extraction_class(**self.feat_extract_dict)
with tempfile.TemporaryDirectory() as tmpdirname:
saved_file = feat_extract_first.save_pretrained(tmpdirname)[0]
check_json_file_has_correct_format(saved_file)
feat_extract_second = self.feature_extraction_class.from_pretrained(tmpdirname)
dict_first = feat_extract_first.to_dict()
dict_second = feat_extract_second.to_dict()
self.assertDictEqual(dict_first, dict_second)
# Copied from tests.models.seamless_m4t.test_feature_extraction_seamless_m4t.SeamlessM4TFeatureExtractionTest.test_feat_extract_to_json_file
def test_feat_extract_to_json_file(self):
feat_extract_first = self.feature_extraction_class(**self.feat_extract_dict)
with tempfile.TemporaryDirectory() as tmpdirname:
json_file_path = os.path.join(tmpdirname, "feat_extract.json")
feat_extract_first.to_json_file(json_file_path)
feat_extract_second = self.feature_extraction_class.from_json_file(json_file_path)
dict_first = feat_extract_first.to_dict()
dict_second = feat_extract_second.to_dict()
self.assertEqual(dict_first, dict_second)
def test_call(self):
# Tests that all call wrap to encode_plus and batch_encode_plus
feature_extractor = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
# create three inputs of length 800, 1000, and 1200
speech_inputs = [floats_list((1, x))[0] for x in range(800, 1400, 200)]
np_speech_inputs = [np.asarray(speech_input) for speech_input in speech_inputs]
# Test feature size
input_features = feature_extractor(np_speech_inputs, padding=True, return_tensors="np").input_features
self.assertTrue(input_features.ndim == 3)
self.assertTrue(input_features.shape[0] == 3)
# Ignore copy
self.assertTrue(input_features.shape[-1] == feature_extractor.feature_size)
# Test not batched input
encoded_sequences_1 = feature_extractor(speech_inputs[0], return_tensors="np").input_features
encoded_sequences_2 = feature_extractor(np_speech_inputs[0], return_tensors="np").input_features
self.assertTrue(np.allclose(encoded_sequences_1, encoded_sequences_2, atol=1e-3))
# Test batched
encoded_sequences_1 = feature_extractor(speech_inputs, return_tensors="np").input_features
encoded_sequences_2 = feature_extractor(np_speech_inputs, return_tensors="np").input_features
for enc_seq_1, enc_seq_2 in zip(encoded_sequences_1, encoded_sequences_2):
self.assertTrue(np.allclose(enc_seq_1, enc_seq_2, atol=1e-3))
# Test 2-D numpy arrays are batched.
speech_inputs = [floats_list((1, x))[0] for x in (800, 800, 800)]
np_speech_inputs = np.asarray(speech_inputs)
encoded_sequences_1 = feature_extractor(speech_inputs, return_tensors="np").input_features
encoded_sequences_2 = feature_extractor(np_speech_inputs, return_tensors="np").input_features
for enc_seq_1, enc_seq_2 in zip(encoded_sequences_1, encoded_sequences_2):
self.assertTrue(np.allclose(enc_seq_1, enc_seq_2, atol=1e-3))
@require_torchaudio
def test_call_from_demucs(self):
# Tests that all call wrap to encode_plus and batch_encode_plus
feature_extractor = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
# (batch_size, num_stems, channel_size, audio_length)
inputs = torch.rand([4, 5, 2, 44000])
# Test feature size
input_features = feature_extractor(inputs, padding=True, return_tensors="np").input_features
self.assertTrue(input_features.ndim == 3)
self.assertTrue(input_features.shape[0] == 4)
self.assertTrue(input_features.shape[-1] == feature_extractor.feature_size)
# Test single input
encoded_sequences_1 = feature_extractor(inputs[[0]], return_tensors="np").input_features
self.assertTrue(np.allclose(encoded_sequences_1[0], input_features[0], atol=1e-3))
# Copied from tests.models.whisper.test_feature_extraction_whisper.WhisperFeatureExtractionTest.test_double_precision_pad with input_features->input_features
def test_double_precision_pad(self):
import torch
feature_extractor = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
np_speech_inputs = np.random.rand(100, 32).astype(np.float64)
py_speech_inputs = np_speech_inputs.tolist()
for inputs in [py_speech_inputs, np_speech_inputs]:
np_processed = feature_extractor.pad([{"input_features": inputs}], return_tensors="np")
self.assertTrue(np_processed.input_features.dtype == np.float32)
pt_processed = feature_extractor.pad([{"input_features": inputs}], return_tensors="pt")
self.assertTrue(pt_processed.input_features.dtype == torch.float32)
def test_integration(self):
EXPECTED_INPUT_FEATURES = torch.zeros([2, 8, 12])
EXPECTED_INPUT_FEATURES[0, :6, 9] = 1
EXPECTED_INPUT_FEATURES[0, 6:, 0] = 1
EXPECTED_INPUT_FEATURES[1, :, 9] = 1
input_speech = [get_bip_bip(duration=0.5), get_bip_bip(duration=1.0)]
feature_extractor = MusicgenMelodyFeatureExtractor()
input_features = feature_extractor(input_speech, return_tensors="pt").input_features
self.assertEqual(input_features.shape, (2, 8, 12))
self.assertTrue((input_features == EXPECTED_INPUT_FEATURES).all())
| transformers/tests/models/musicgen_melody/test_feature_extraction_musicgen_melody.py/0 | {
"file_path": "transformers/tests/models/musicgen_melody/test_feature_extraction_musicgen_melody.py",
"repo_id": "transformers",
"token_count": 4122
} |
# 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.
"""Testing suite for the PyTorch PaliGemma model."""
import unittest
import requests
from transformers import (
PaliGemmaConfig,
PaliGemmaForConditionalGeneration,
PaliGemmaProcessor,
is_torch_available,
is_vision_available,
)
from transformers.testing_utils import (
cleanup,
require_read_token,
require_torch,
slow,
torch_device,
)
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
class PaliGemmaVisionText2TextModelTester:
def __init__(
self,
parent,
ignore_index=-100,
image_token_index=0,
projector_hidden_act="gelu",
seq_length=25,
vision_feature_select_strategy="default",
vision_feature_layer=-1,
projection_dim=32,
text_config={
"model_type": "gemma",
"seq_length": 128,
"is_training": True,
# "use_input_mask": True,
"use_token_type_ids": False,
"use_labels": True,
"vocab_size": 99,
"hidden_size": 32,
"num_hidden_layers": 2,
"num_attention_heads": 4,
"num_key_value_heads": 1,
"head_dim": 8,
"intermediate_size": 37,
"hidden_activation": "gelu_pytorch_tanh",
"hidden_dropout_prob": 0.1,
"attention_probs_dropout_prob": 0.1,
"max_position_embeddings": 512,
"type_vocab_size": 16,
"type_sequence_label_size": 2,
"initializer_range": 0.02,
"num_labels": 3,
"num_choices": 4,
"pad_token_id": 1,
},
is_training=True,
vision_config={
"use_labels": True,
"image_size": 20,
"patch_size": 5,
"num_image_tokens": 4,
"num_channels": 3,
"is_training": True,
"hidden_size": 32,
"projection_dim": 32,
"num_key_value_heads": 1,
"num_hidden_layers": 2,
"num_attention_heads": 4,
"intermediate_size": 37,
"dropout": 0.1,
"attention_dropout": 0.1,
"initializer_range": 0.02,
},
use_cache=False,
):
self.parent = parent
self.ignore_index = ignore_index
# `image_token_index` is set to 0 to pass "resize_embeddings" test, do not modify
self.image_token_index = image_token_index
self.projector_hidden_act = projector_hidden_act
self.vision_feature_select_strategy = vision_feature_select_strategy
self.vision_feature_layer = vision_feature_layer
self.text_config = text_config
self.vision_config = vision_config
self.seq_length = seq_length
self.projection_dim = projection_dim
self.pad_token_id = text_config["pad_token_id"]
self.num_hidden_layers = text_config["num_hidden_layers"]
self.vocab_size = text_config["vocab_size"]
self.hidden_size = text_config["hidden_size"]
self.num_attention_heads = text_config["num_attention_heads"]
self.is_training = is_training
self.batch_size = 3
self.num_channels = vision_config["num_channels"]
self.image_size = vision_config["image_size"]
self.encoder_seq_length = seq_length
self.use_cache = use_cache
def get_config(self):
return PaliGemmaConfig(
text_config=self.text_config,
vision_config=self.vision_config,
ignore_index=self.ignore_index,
image_token_index=self.image_token_index,
projector_hidden_act=self.projector_hidden_act,
projection_dim=self.projection_dim,
vision_feature_select_strategy=self.vision_feature_select_strategy,
vision_feature_layer=self.vision_feature_layer,
)
def prepare_config_and_inputs(self):
pixel_values = floats_tensor(
[
self.batch_size,
self.vision_config["num_channels"],
self.vision_config["image_size"],
self.vision_config["image_size"],
]
)
config = self.get_config()
return config, pixel_values
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
config, pixel_values = config_and_inputs
input_ids = ids_tensor([self.batch_size, self.seq_length], config.text_config.vocab_size - 1) + 1
attention_mask = input_ids.ne(self.pad_token_id).to(torch_device)
# set the 16 first tokens to be image, and ensure that no other tokens are image tokens
# do not change this unless you modified image size or patch size
input_ids[input_ids == config.image_token_index] = self.pad_token_id
input_ids[:, :16] = config.image_token_index
inputs_dict = {
"pixel_values": pixel_values,
"input_ids": input_ids,
"attention_mask": attention_mask,
"labels": input_ids,
"token_type_ids": torch.zeros_like(input_ids),
}
return config, inputs_dict
@require_torch
class PaliGemmaForConditionalGenerationModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
"""
Model tester for `PaliGemmaForConditionalGeneration`.
"""
all_model_classes = (PaliGemmaForConditionalGeneration,) if is_torch_available() else ()
all_generative_model_classes = (PaliGemmaForConditionalGeneration,) if is_torch_available() else ()
pipeline_model_mapping = {"image-text-to-text": PaliGemmaForConditionalGeneration}
fx_compatible = False
test_pruning = False
test_torchscript = False
test_head_masking = False
_is_composite = True
def setUp(self):
self.model_tester = PaliGemmaVisionText2TextModelTester(self)
self.config_tester = ConfigTester(self, config_class=PaliGemmaConfig, has_text_modality=False)
# overwrite inputs_embeds tests because we need to delete "pixel values" for LVLMs
def test_inputs_embeds(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
model.to(torch_device)
model.eval()
inputs = self._prepare_for_class(inputs_dict, model_class)
input_ids = inputs["input_ids"]
del inputs["input_ids"]
del inputs["pixel_values"]
wte = model.get_input_embeddings()
inputs["inputs_embeds"] = wte(input_ids)
with torch.no_grad():
model(**inputs)
# overwrite inputs_embeds tests because we need to delete "pixel values" for LVLMs
# while some other models require pixel_values to be present
def test_inputs_embeds_matches_input_ids(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
model.to(torch_device)
model.eval()
inputs = self._prepare_for_class(inputs_dict, model_class)
input_ids = inputs["input_ids"]
del inputs["input_ids"]
del inputs["pixel_values"]
inputs_embeds = model.get_input_embeddings()(input_ids)
with torch.no_grad():
out_ids = model(input_ids=input_ids, **inputs)[0]
out_embeds = model(inputs_embeds=inputs_embeds, **inputs)[0]
torch.testing.assert_close(out_embeds, out_ids)
# Copied from tests.models.llava.test_modeling_llava.LlavaForConditionalGenerationModelTest.test_mismatching_num_image_tokens
def test_mismatching_num_image_tokens(self):
"""
Tests that VLMs through an error with explicit message saying what is wrong
when number of images don't match number of image tokens in the text.
Also we need to test multi-image cases when one prompr has multiple image tokens.
"""
config, input_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config).to(torch_device)
_ = model(**input_dict) # successfull forward with no modifications
# remove one image but leave the image token in text
input_dict["pixel_values"] = input_dict["pixel_values"][-1:, ...]
with self.assertRaises(ValueError):
_ = model(**input_dict)
# simulate multi-image case by concatenating inputs where each has exactly one image/image-token
input_ids = input_dict["input_ids"][:1]
pixel_values = input_dict["pixel_values"][:1]
input_ids = torch.cat([input_ids, input_ids], dim=0)
# one image and two image tokens raise an error
with self.assertRaises(ValueError):
_ = model(input_ids=input_ids, pixel_values=pixel_values)
# two images and two image tokens don't raise an error
pixel_values = torch.cat([pixel_values, pixel_values], dim=0)
_ = model(input_ids=input_ids, pixel_values=pixel_values)
@unittest.skip(
reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
)
def test_training_gradient_checkpointing(self):
pass
@unittest.skip(
reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
)
def test_training_gradient_checkpointing_use_reentrant(self):
pass
@unittest.skip(
reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
)
def test_training_gradient_checkpointing_use_reentrant_false(self):
pass
@unittest.skip(reason="Some undefined behavior encountered with test versions of this model. Skip for now.")
def test_cpu_offload(self):
pass
@unittest.skip(reason="Some undefined behavior encountered with test versions of this model. Skip for now.")
def test_disk_offload_bin(self):
pass
@unittest.skip(reason="Some undefined behavior encountered with test versions of this model. Skip for now.")
def test_disk_offload_safetensors(self):
pass
@unittest.skip(reason="Some undefined behavior encountered with test versions of this model. Skip for now.")
def test_model_parallelism(self):
pass
@unittest.skip(
reason="PaliGemmma's SigLip encoder uses the same initialization scheme as the Flax original implementation"
)
def test_initialization(self):
pass
# TODO extend valid outputs to include this test @Molbap
@unittest.skip(reason="PaliGemma has currently one output format.")
def test_model_outputs_equivalence(self):
pass
# TODO fix the loss = nan in the testing configuration chosen @Molbap
@unittest.skip(reason="Edge case giving loss nan values in testing configuration.")
def test_determinism(self):
pass
@unittest.skip(reason="PaliGemma does not use feedforward chunking.")
def test_feed_forward_chunking(self):
pass
@unittest.skip(reason="PaliGemma does not support low_cpu_mem_usage.")
def test_save_load_low_cpu_mem_usage(self):
pass
@unittest.skip(reason="PaliGemma does not support low_cpu_mem_usage.")
def test_save_load_low_cpu_mem_usage_checkpoints(self):
pass
@unittest.skip(reason="PaliGemma does not support low_cpu_mem_usage.")
def test_save_load_low_cpu_mem_usage_no_safetensors(self):
pass
@unittest.skip(
reason="VLMs doen't accept inputs embeds and pixel values at the same time. So if the test passed for bacbone LM, it passes for VLM also"
)
def test_generate_from_inputs_embeds_with_static_cache(self):
pass
@unittest.skip("FlashAttention only support fp16 and bf16 data type")
def test_flash_attn_2_fp32_ln(self):
pass
@unittest.skip(
"VLMs need lots of steps to prepare images/mask correctly to get pad-free inputs. Can be tested as part of LLM test"
)
def test_flash_attention_2_padding_matches_padding_free_with_position_ids(self):
pass
# TODO (joao, raushan): fix me -- the problem is in `cache_position[0] == 0`, i.e. dynamic control flow
@unittest.skip("PaliGemma is not compatible with end-to-end generation compilation")
def test_generate_compile_model_forward(self):
pass
@slow
@require_torch
@require_read_token
class PaliGemmaForConditionalGenerationIntegrationTest(unittest.TestCase):
def setUp(self):
self.processor = PaliGemmaProcessor.from_pretrained("google/paligemma-3b-pt-224")
def tearDown(self):
cleanup(torch_device, gc_collect=True)
def test_small_model_integration_test(self):
# Let' s make sure we test the preprocessing to replace what is used
model_id = "google/paligemma-3b-pt-224"
model = PaliGemmaForConditionalGeneration.from_pretrained(model_id)
prompt = ""
image_file = (
"https://huggingface.co/datasets/hf-internal-testing/fixtures-captioning/resolve/main/cow_beach_1.png"
)
raw_image = Image.open(requests.get(image_file, stream=True).raw)
inputs = self.processor(images=raw_image, text=prompt, return_tensors="pt")
EXPECTED_INPUT_IDS = torch.tensor([[257152] * 256 + [2, 108]])
self.assertTrue(torch.equal(inputs["input_ids"], EXPECTED_INPUT_IDS))
output = model.generate(**inputs, max_new_tokens=20)
EXPECTED_DECODED_TEXT = "\ncow on the beach" # fmt: skip
self.assertEqual(
self.processor.decode(output[0], skip_special_tokens=True),
EXPECTED_DECODED_TEXT,
)
def test_small_model_integration_test_multiimage(self):
model_id = "google/paligemma-3b-ft-nlvr2-448" # checkpoint tuned for multiple images
model = PaliGemmaForConditionalGeneration.from_pretrained(model_id)
processor = PaliGemmaProcessor.from_pretrained(model_id)
prompt = "answer en There is no snowman in any of the images. Is this true or false?"
stop_sign_image = Image.open(
requests.get("https://www.ilankelman.org/stopsigns/australia.jpg", stream=True).raw
)
snow_image = Image.open(
requests.get(
"https://huggingface.co/microsoft/kosmos-2-patch14-224/resolve/main/snowman.jpg", stream=True
).raw
)
inputs = processor(text=prompt, images=[[snow_image, snow_image]], return_tensors="pt")
output = model.generate(**inputs, max_new_tokens=20)
EXPECTED_DECODED_TEXT = "answer en There is no snowman in any of the images. Is this true or false?\nFalse"
self.assertEqual(
self.processor.decode(output[0], skip_special_tokens=True),
EXPECTED_DECODED_TEXT,
)
# try another prompt with two different image this time
prompt = "answer en There is exactly one snowman. Is this true or false?"
inputs = processor(text=prompt, images=[[snow_image, stop_sign_image]], return_tensors="pt")
output = model.generate(**inputs, max_new_tokens=20)
EXPECTED_DECODED_TEXT = "answer en There is exactly one snowman. Is this true or false?\nTrue"
self.assertEqual(
self.processor.decode(output[0], skip_special_tokens=True),
EXPECTED_DECODED_TEXT,
)
def test_small_model_integration_test_paligemma_VQA(self):
# Let' s make sure we test the preprocessing to replace what is used
model_id = "google/paligemma-3b-pt-224"
model = PaliGemmaForConditionalGeneration.from_pretrained(model_id)
prompt = "answer en Where is the cow standing?"
image_file = (
"https://huggingface.co/datasets/hf-internal-testing/fixtures-captioning/resolve/main/cow_beach_1.png"
)
raw_image = Image.open(requests.get(image_file, stream=True).raw)
inputs = self.processor(images=raw_image, text=prompt, return_tensors="pt").to(torch.float16)
output = model.generate(**inputs, max_new_tokens=900, do_sample=False)
EXPECTED_DECODED_TEXT = "answer en Where is the cow standing?\nbeach" # fmt: skip
self.assertEqual(
self.processor.decode(output[0], skip_special_tokens=True),
EXPECTED_DECODED_TEXT,
)
def test_small_model_integration_test_paligemma_empty_prompt(self):
# Let' s make sure we test the preprocessing to replace what is used
model_id = "google/paligemma-3b-pt-224"
model = PaliGemmaForConditionalGeneration.from_pretrained(model_id)
prompt = ""
image_file = (
"https://huggingface.co/datasets/hf-internal-testing/fixtures-captioning/resolve/main/cow_beach_1.png"
)
raw_image = Image.open(requests.get(image_file, stream=True).raw)
inputs = self.processor(images=raw_image, text=prompt, return_tensors="pt").to(torch.float16)
output = model.generate(**inputs, max_new_tokens=900, do_sample=False)
EXPECTED_DECODED_TEXT = "\ncow on the beach" # fmt: skip
self.assertEqual(
self.processor.decode(output[0], skip_special_tokens=True),
EXPECTED_DECODED_TEXT,
)
def test_small_model_integration_test_paligemma_batched(self):
# Let' s make sure we test the preprocessing to replace what is used
model_id = "google/paligemma-3b-pt-224"
model = PaliGemmaForConditionalGeneration.from_pretrained(model_id)
prompts = [
"answer en Where is the cow standing?",
"",
]
image1 = Image.open(
requests.get(
"https://huggingface.co/datasets/hf-internal-testing/fixtures-captioning/resolve/main/cow_beach_1.png",
stream=True,
).raw
)
image2 = image1
inputs = self.processor(images=[image1, image2], text=prompts, return_tensors="pt", padding=True)
output = model.generate(**inputs, max_new_tokens=20)
EXPECTED_DECODED_TEXT = ["answer en Where is the cow standing?\nbeach", "\ncow on the beach"] # fmt: skip
self.assertEqual(self.processor.batch_decode(output, skip_special_tokens=True), EXPECTED_DECODED_TEXT)
def test_small_model_integration_test_paligemma_batched_bf16(self):
# Let' s make sure we test the preprocessing to replace what is used
model_id = "google/paligemma-3b-pt-224"
model = PaliGemmaForConditionalGeneration.from_pretrained(
model_id, revision="bfloat16", torch_dtype=torch.bfloat16
).to(torch_device)
# The first batch is longer in terms of text, the second will be padded.
prompts = [
"answer en Where is the cow standing?",
"",
]
image1 = Image.open(
requests.get(
"https://huggingface.co/datasets/hf-internal-testing/fixtures-captioning/resolve/main/cow_beach_1.png",
stream=True,
).raw
)
image2 = image1
inputs = (
self.processor(images=[image1, image2], text=prompts, return_tensors="pt", padding=True)
.to(torch.bfloat16)
.to(torch_device)
)
output = model.generate(**inputs, max_new_tokens=20)
EXPECTED_DECODED_TEXT = ["answer en Where is the cow standing?\nbeach", "\ncow on the beach"] # fmt: skip
self.assertEqual(self.processor.batch_decode(output, skip_special_tokens=True), EXPECTED_DECODED_TEXT)
def test_small_model_integration_test_paligemma_batched_f16(self):
# Let' s make sure we test the preprocessing to replace what is used
model_id = "google/paligemma-3b-pt-224"
model = PaliGemmaForConditionalGeneration.from_pretrained(
model_id, revision="float16", torch_dtype=torch.float16
).to(torch_device)
# The first batch is longer in terms of text, the second will be padded.
prompts = [
"answer en Where is the cow standing?",
"",
]
image1 = Image.open(
requests.get(
"https://huggingface.co/datasets/hf-internal-testing/fixtures-captioning/resolve/main/cow_beach_1.png",
stream=True,
).raw
)
image2 = image1
inputs = (
self.processor(images=[image1, image2], text=prompts, return_tensors="pt", padding=True)
.to(torch.float16)
.to(torch_device)
)
output = model.generate(**inputs, max_new_tokens=20)
EXPECTED_DECODED_TEXT = ["answer en Where is the cow standing?\nbeach", "\ncow on the beach"] # fmt: skip
self.assertEqual(self.processor.batch_decode(output, skip_special_tokens=True), EXPECTED_DECODED_TEXT)
def test_integration_detection_bug(self):
# this is a reproducer of https://github.com/huggingface/transformers/issues/31425 where not enough context
# impacted negatively segmentation generations.
model_id = "google/paligemma-3b-pt-224"
model = PaliGemmaForConditionalGeneration.from_pretrained(
model_id, revision="bfloat16", torch_dtype=torch.bfloat16
).to(torch_device)
prompt = ("detect shoe",)
image = Image.open(
requests.get(
"https://huggingface.co/datasets/hf-internal-testing/fixtures-captioning/resolve/main/shoe.png",
stream=True,
).raw
)
inputs = self.processor(images=image, text=prompt, return_tensors="pt").to(torch.bfloat16).to(torch_device)
output = model.generate(**inputs, max_new_tokens=20)
EXPECTED_DECODED_TEXT = "detect shoe\n<loc0051><loc0309><loc0708><loc0646> shoe" # fmt: skip
self.assertEqual(self.processor.decode(output[0], skip_special_tokens=True), EXPECTED_DECODED_TEXT)
def test_paligemma_index_error_bug(self):
# This is a reproducer of https://github.com/huggingface/transformers/pull/28032 and makes sure it does not happen anymore
# Please refer to that PR, or specifically https://github.com/huggingface/transformers/pull/28032#issuecomment-1860650043 for
# more details
model_id = "google/paligemma-3b-pt-224"
model = PaliGemmaForConditionalGeneration.from_pretrained(model_id)
# Simulate a super long prompt
prompt = "\n" * 200
image_file = (
"https://huggingface.co/datasets/hf-internal-testing/fixtures-captioning/resolve/main/cow_beach_1.png"
)
raw_image = Image.open(requests.get(image_file, stream=True).raw)
inputs = self.processor(
images=raw_image,
text=prompt,
return_tensors="pt",
).to(torch.float16)
# Make sure that `generate` works
_ = model.generate(**inputs, max_new_tokens=20)
def test_paligemma_finetuning_with_suffixes_bf16(self):
# this is a supplementary test to ensure paligemma fine-tuning that relies on token_type_ids is robust to future changes
model_id = "google/paligemma-3b-pt-224"
model = PaliGemmaForConditionalGeneration.from_pretrained(
model_id, revision="bfloat16", torch_dtype=torch.bfloat16
).to(torch_device)
# The first batch is longer in terms of text, the second will be padded.
prompts = [
"answer en Where is the cow standing?",
"",
]
suffixes = ["beach", "cow standing on the beach"]
image1 = Image.open(
requests.get(
"https://huggingface.co/datasets/hf-internal-testing/fixtures-captioning/resolve/main/cow_beach_1.png",
stream=True,
).raw
)
image2 = image1
inputs = (
self.processor(images=[image1, image2], text=prompts, suffix=suffixes, return_tensors="pt", padding=True)
.to(torch.bfloat16)
.to(torch_device)
)
expected_labels = torch.tensor(
[266 * [-100] + [54901, 1], 262 * [-100] + [14706, 9980, 611, 573, 8318, 1]]
).to(torch_device)
assert torch.equal(inputs["labels"], expected_labels)
expected_token_type_ids = torch.tensor([266 * [0] + 2 * [1], 262 * [0] + 6 * [1]]).to(torch_device)
assert torch.equal(inputs["token_type_ids"], expected_token_type_ids)
output = model(**inputs)
# check that loss does not error out
_ = output.loss
| transformers/tests/models/paligemma/test_modeling_paligemma.py/0 | {
"file_path": "transformers/tests/models/paligemma/test_modeling_paligemma.py",
"repo_id": "transformers",
"token_count": 11256
} |
# 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.
"""Testing suite for the PyTorch Perceiver model."""
import copy
import inspect
import math
import tempfile
import unittest
import warnings
from typing import Dict, List, Tuple
import numpy as np
from datasets import load_dataset
from transformers import PerceiverConfig
from transformers.testing_utils import (
IS_ROCM_SYSTEM,
require_torch,
require_torch_multi_gpu,
require_vision,
slow,
torch_device,
)
from transformers.utils import is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import (
PerceiverForImageClassificationConvProcessing,
PerceiverForImageClassificationFourier,
PerceiverForImageClassificationLearned,
PerceiverForMaskedLM,
PerceiverForMultimodalAutoencoding,
PerceiverForOpticalFlow,
PerceiverForSequenceClassification,
PerceiverModel,
PerceiverTokenizer,
)
from transformers.models.auto.modeling_auto import (
MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES,
MODEL_FOR_MASKED_LM_MAPPING_NAMES,
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES,
MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES,
MODEL_MAPPING_NAMES,
)
if is_vision_available():
from PIL import Image
from transformers import PerceiverImageProcessor
class PerceiverModelTester:
def __init__(
self,
parent,
batch_size=13,
seq_length=7,
num_channels=3,
image_size=32,
train_size=[20, 20],
num_frames=5,
audio_samples_per_frame=200,
samples_per_patch=20,
nchunks=20,
num_latents=10,
d_latents=20,
d_model=64,
num_blocks=1,
num_self_attends_per_block=2,
num_self_attention_heads=1,
num_cross_attention_heads=1,
self_attention_widening_factor=4,
cross_attention_widening_factor=4,
is_training=True,
use_input_mask=True,
use_labels=True,
vocab_size=99,
hidden_act="gelu",
attention_probs_dropout_prob=0.1,
initializer_range=0.02,
max_position_embeddings=7,
num_labels=3,
scope=None,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.num_channels = num_channels
self.image_size = image_size
self.train_size = train_size
self.num_frames = num_frames
self.audio_samples_per_frame = audio_samples_per_frame
self.samples_per_patch = samples_per_patch
self.nchunks = nchunks
self.num_latents = num_latents
self.d_latents = d_latents
self.d_model = d_model
self.num_blocks = num_blocks
self.num_self_attends_per_block = num_self_attends_per_block
self.num_self_attention_heads = num_self_attention_heads
self.num_cross_attention_heads = num_cross_attention_heads
self.self_attention_widening_factor = self_attention_widening_factor
self.cross_attention_widening_factor = cross_attention_widening_factor
self.is_training = is_training
self.use_input_mask = use_input_mask
self.use_labels = use_labels
self.vocab_size = vocab_size
self.hidden_act = hidden_act
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.initializer_range = initializer_range
self.num_labels = num_labels
self.scope = scope
# set subsampling for multimodal model (take first chunk)
image_chunk_size = np.prod((self.num_frames, self.image_size, self.image_size)) // self.nchunks
audio_chunk_size = self.num_frames * self.audio_samples_per_frame // self.samples_per_patch // self.nchunks
self.subsampling = {
"image": torch.arange(0, image_chunk_size),
"audio": torch.arange(0, audio_chunk_size),
"label": None,
}
def prepare_config_and_inputs(self, model_class=None):
config = self.get_config()
input_mask = None
sequence_labels = None
token_labels = None
if self.use_labels:
sequence_labels = ids_tensor([self.batch_size], self.num_labels)
token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
if model_class is None or model_class.__name__ == "PerceiverModel":
inputs = floats_tensor([self.batch_size, self.seq_length, config.d_model], scale=1.0)
return config, inputs, input_mask, sequence_labels, token_labels
elif model_class.__name__ in ["PerceiverForMaskedLM", "PerceiverForSequenceClassification"]:
inputs = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
# input mask is only relevant for text inputs
if self.use_input_mask:
input_mask = random_attention_mask([self.batch_size, self.seq_length])
elif model_class.__name__ == "PerceiverForImageClassificationLearned":
inputs = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
elif model_class.__name__ == "PerceiverForImageClassificationFourier":
inputs = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
elif model_class.__name__ == "PerceiverForImageClassificationConvProcessing":
inputs = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
elif model_class.__name__ == "PerceiverForOpticalFlow":
inputs = floats_tensor([self.batch_size, 2, 27, self.train_size[0], self.train_size[1]])
elif model_class.__name__ == "PerceiverForMultimodalAutoencoding":
images = torch.randn(
(self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size),
device=torch_device,
)
audio = torch.randn(
(self.batch_size, self.num_frames * self.audio_samples_per_frame, 1), device=torch_device
)
inputs = {
"image": images,
"audio": audio,
"label": torch.zeros((self.batch_size, self.num_labels), device=torch_device),
}
else:
raise ValueError(f"Model class {model_class} not supported")
return config, inputs, input_mask, sequence_labels, token_labels
def get_config(self):
return PerceiverConfig(
num_latents=self.num_latents,
d_latents=self.d_latents,
d_model=self.d_model,
qk_channels=self.d_latents,
v_channels=self.d_latents,
num_blocks=self.num_blocks,
num_self_attends_per_block=self.num_self_attends_per_block,
num_self_attention_heads=self.num_self_attention_heads,
num_cross_attention_heads=self.num_cross_attention_heads,
self_attention_widening_factor=self.self_attention_widening_factor,
cross_attention_widening_factor=self.cross_attention_widening_factor,
vocab_size=self.vocab_size,
hidden_act=self.hidden_act,
attention_probs_dropout_prob=self.attention_probs_dropout_prob,
initializer_range=self.initializer_range,
max_position_embeddings=self.max_position_embeddings,
image_size=self.image_size,
train_size=self.train_size,
num_frames=self.num_frames,
audio_samples_per_frame=self.audio_samples_per_frame,
samples_per_patch=self.samples_per_patch,
num_labels=self.num_labels,
output_num_channels=32,
_label_trainable_num_channels=16,
)
def get_pipeline_config(self):
config = self.get_config()
# Byte level vocab
config.vocab_size = 261
config.max_position_embeddings = 40
return config
def create_and_check_for_masked_lm(self, config, inputs, input_mask, sequence_labels, token_labels):
model = PerceiverForMaskedLM(config=config)
model.to(torch_device)
model.eval()
result = model(inputs, attention_mask=input_mask, labels=token_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
def create_and_check_for_sequence_classification(self, config, inputs, input_mask, sequence_labels, token_labels):
model = PerceiverForSequenceClassification(config=config)
model.to(torch_device)
model.eval()
result = model(inputs, attention_mask=input_mask, labels=sequence_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
def create_and_check_for_image_classification_learned(
self, config, inputs, input_mask, sequence_labels, token_labels
):
model = PerceiverForImageClassificationLearned(config=config)
model.to(torch_device)
model.eval()
result = model(inputs, attention_mask=input_mask, labels=sequence_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
def create_and_check_for_image_classification_fourier(
self, config, inputs, input_mask, sequence_labels, token_labels
):
model = PerceiverForImageClassificationFourier(config=config)
model.to(torch_device)
model.eval()
result = model(inputs, attention_mask=input_mask, labels=sequence_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
def create_and_check_for_image_classification_conv(
self, config, inputs, input_mask, sequence_labels, token_labels
):
model = PerceiverForImageClassificationConvProcessing(config=config)
model.to(torch_device)
model.eval()
result = model(inputs, attention_mask=input_mask, labels=sequence_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
config, inputs, input_mask, sequence_labels, token_labels = config_and_inputs
inputs_dict = {"inputs": inputs, "attention_mask": input_mask}
return config, inputs_dict
def prepare_config_and_inputs_for_model_class(self, model_class):
config_and_inputs = self.prepare_config_and_inputs(model_class)
config, inputs, input_mask, sequence_labels, token_labels = config_and_inputs
inputs_dict = {"inputs": inputs, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class PerceiverModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (
(
PerceiverModel,
PerceiverForMaskedLM,
PerceiverForImageClassificationLearned,
PerceiverForImageClassificationConvProcessing,
PerceiverForImageClassificationFourier,
PerceiverForOpticalFlow,
PerceiverForMultimodalAutoencoding,
PerceiverForSequenceClassification,
)
if is_torch_available()
else ()
)
pipeline_model_mapping = (
{
"feature-extraction": PerceiverModel,
"fill-mask": PerceiverForMaskedLM,
"image-classification": (
PerceiverForImageClassificationConvProcessing,
PerceiverForImageClassificationFourier,
PerceiverForImageClassificationLearned,
),
"text-classification": PerceiverForSequenceClassification,
"zero-shot": PerceiverForSequenceClassification,
}
if is_torch_available()
else {}
)
test_pruning = False
test_head_masking = False
test_torchscript = False
maxDiff = None
def setUp(self):
self.model_tester = PerceiverModelTester(self)
self.config_tester = ConfigTester(
self,
config_class=PerceiverConfig,
hidden_size=37,
common_properties=["d_model", "num_self_attention_heads", "num_cross_attention_heads"],
)
def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
inputs_dict = copy.deepcopy(inputs_dict)
if model_class.__name__ == "PerceiverForMultimodalAutoencoding":
inputs_dict["subsampled_output_points"] = self.model_tester.subsampling
if return_labels:
if model_class.__name__ in [
*MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES.values(),
"PerceiverForImageClassificationLearned",
"PerceiverForImageClassificationFourier",
"PerceiverForImageClassificationConvProcessing",
*MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES.values(),
]:
inputs_dict["labels"] = torch.zeros(
self.model_tester.batch_size, dtype=torch.long, device=torch_device
)
elif model_class.__name__ in [
*MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES.values(),
*MODEL_FOR_MASKED_LM_MAPPING_NAMES.values(),
]:
inputs_dict["labels"] = torch.zeros(
(self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=torch_device
)
return inputs_dict
def test_config(self):
self.config_tester.run_common_tests()
def test_for_masked_lm(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs(model_class=PerceiverForMaskedLM)
self.model_tester.create_and_check_for_masked_lm(*config_and_inputs)
def test_for_sequence_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs(model_class=PerceiverForSequenceClassification)
self.model_tester.create_and_check_for_sequence_classification(*config_and_inputs)
def test_for_image_classification_learned(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs(
model_class=PerceiverForImageClassificationLearned
)
self.model_tester.create_and_check_for_image_classification_learned(*config_and_inputs)
def test_for_image_classification_fourier(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs(
model_class=PerceiverForImageClassificationFourier
)
self.model_tester.create_and_check_for_image_classification_fourier(*config_and_inputs)
def test_for_image_classification_conv(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs(
model_class=PerceiverForImageClassificationConvProcessing
)
self.model_tester.create_and_check_for_image_classification_conv(*config_and_inputs)
def test_model_get_set_embeddings(self):
for model_class in self.all_model_classes:
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_model_class(model_class)
model = model_class(config)
# we overwrite this, as the embeddings of Perceiver are an instance of nn.Parameter
# and Perceiver doesn't support get_output_embeddings
self.assertIsInstance(model.get_input_embeddings(), (nn.Parameter))
def test_training(self):
if not self.model_tester.is_training:
self.skipTest(reason="model_tester.is_training is set to False")
for model_class in self.all_model_classes:
if model_class.__name__ in [
*MODEL_MAPPING_NAMES.values(),
"PerceiverForOpticalFlow",
"PerceiverForMultimodalAutoencoding",
]:
continue
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_model_class(model_class)
config.return_dict = True
model = model_class(config)
model.to(torch_device)
model.train()
inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
loss = model(**inputs).loss
loss.backward()
def test_forward_signature(self):
for model_class in self.all_model_classes:
config, _ = self.model_tester.prepare_config_and_inputs_for_model_class(model_class)
model = model_class(config)
signature = inspect.signature(model.forward)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
arg_names = [*signature.parameters.keys()]
expected_arg_names = ["inputs"]
self.assertListEqual(arg_names[:1], expected_arg_names)
def test_determinism(self):
for model_class in self.all_model_classes:
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_model_class(model_class)
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
inputs_dict = self._prepare_for_class(inputs_dict, model_class)
first = model(**inputs_dict)[0]
second = model(**inputs_dict)[0]
if model_class.__name__ == "PerceiverForMultimodalAutoencoding":
# model outputs a dictionary with logits per modality, let's verify each modality
for modality in first.keys():
out_1 = first[modality].cpu().numpy()
out_2 = second[modality].cpu().numpy()
out_1 = out_1[~np.isnan(out_1)]
out_2 = out_2[~np.isnan(out_2)]
max_diff = np.amax(np.abs(out_1 - out_2))
self.assertLessEqual(max_diff, 1e-5)
else:
out_1 = first.cpu().numpy()
out_2 = second.cpu().numpy()
out_1 = out_1[~np.isnan(out_1)]
out_2 = out_2[~np.isnan(out_2)]
max_diff = np.amax(np.abs(out_1 - out_2))
self.assertLessEqual(max_diff, 1e-5)
def test_attention_outputs(self):
seq_len = getattr(self.model_tester, "num_latents", None)
for model_class in self.all_model_classes:
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_model_class(model_class)
config.return_dict = True
inputs_dict["output_attentions"] = True
inputs_dict["output_hidden_states"] = False
config.return_dict = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
self_attentions = outputs.attentions
cross_attentions = outputs.cross_attentions
# check expected number of attentions depending on model class
expected_num_self_attentions = self.model_tester.num_blocks * self.model_tester.num_self_attends_per_block
if model.__class__.__name__ == "PerceiverModel":
# we expect to have 2 cross-attentions, namely one in the PerceiverEncoder, and one in PerceiverBasicDecoder
expected_num_cross_attentions = 1
else:
# we expect to have 2 cross-attentions, namely one in the PerceiverEncoder, and one in PerceiverBasicDecoder
expected_num_cross_attentions = 2
self.assertEqual(len(self_attentions), expected_num_self_attentions)
self.assertEqual(len(cross_attentions), expected_num_cross_attentions)
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
config.output_attentions = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
self_attentions = outputs.attentions
cross_attentions = outputs.cross_attentions
self.assertEqual(len(self_attentions), expected_num_self_attentions)
self.assertEqual(len(cross_attentions), expected_num_cross_attentions)
self.assertListEqual(
list(self_attentions[0].shape[-3:]),
[self.model_tester.num_self_attention_heads, seq_len, seq_len],
)
out_len = len(outputs)
# Check attention is always last and order is fine
inputs_dict["output_attentions"] = True
inputs_dict["output_hidden_states"] = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
self.assertEqual(out_len + 1, len(outputs))
self_attentions = outputs.attentions
self.assertEqual(len(self_attentions), expected_num_self_attentions)
self.assertListEqual(
list(self_attentions[0].shape[-3:]),
[self.model_tester.num_self_attention_heads, seq_len, seq_len],
)
def test_hidden_states_output(self):
def check_hidden_states_output(inputs_dict, config, model_class):
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
hidden_states = outputs.hidden_states
expected_num_layers = self.model_tester.num_blocks * self.model_tester.num_self_attends_per_block + 1
self.assertEqual(len(hidden_states), expected_num_layers)
seq_length = self.model_tester.num_latents
self.assertListEqual(
list(hidden_states[0].shape[-2:]),
[seq_length, self.model_tester.d_latents],
)
for model_class in self.all_model_classes:
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_model_class(model_class)
inputs_dict["output_hidden_states"] = True
check_hidden_states_output(inputs_dict, config, model_class)
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
config.output_hidden_states = True
check_hidden_states_output(inputs_dict, config, model_class)
def test_model_outputs_equivalence(self):
def set_nan_tensor_to_zero(t):
t[t != t] = 0
return t
def check_equivalence(model, tuple_inputs, dict_inputs, additional_kwargs={}):
with torch.no_grad():
tuple_output = model(**tuple_inputs, return_dict=False, **additional_kwargs)
dict_output = model(**dict_inputs, return_dict=True, **additional_kwargs).to_tuple()
def recursive_check(tuple_object, dict_object):
if isinstance(tuple_object, (List, Tuple)):
for tuple_iterable_value, dict_iterable_value in zip(tuple_object, dict_object):
recursive_check(tuple_iterable_value, dict_iterable_value)
elif isinstance(tuple_object, Dict):
for tuple_iterable_value, dict_iterable_value in zip(
tuple_object.values(), dict_object.values()
):
recursive_check(tuple_iterable_value, dict_iterable_value)
elif tuple_object is None:
return
else:
self.assertTrue(
torch.allclose(
set_nan_tensor_to_zero(tuple_object), set_nan_tensor_to_zero(dict_object), atol=1e-5
),
msg=(
"Tuple and dict output are not equal. Difference:"
f" {torch.max(torch.abs(tuple_object - dict_object))}. Tuple has `nan`:"
f" {torch.isnan(tuple_object).any()} and `inf`: {torch.isinf(tuple_object)}. Dict has"
f" `nan`: {torch.isnan(dict_object).any()} and `inf`: {torch.isinf(dict_object)}."
),
)
recursive_check(tuple_output, dict_output)
for model_class in self.all_model_classes:
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_model_class(model_class)
model = model_class(config)
model.to(torch_device)
model.eval()
tuple_inputs = self._prepare_for_class(inputs_dict, model_class)
dict_inputs = self._prepare_for_class(inputs_dict, model_class)
check_equivalence(model, tuple_inputs, dict_inputs)
if model_class.__name__ not in ["PerceiverForOpticalFlow", "PerceiverForMultimodalAutoencoding"]:
# optical flow + multimodal models don't support training for now
tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
check_equivalence(model, tuple_inputs, dict_inputs)
tuple_inputs = self._prepare_for_class(inputs_dict, model_class)
dict_inputs = self._prepare_for_class(inputs_dict, model_class)
check_equivalence(model, tuple_inputs, dict_inputs, {"output_hidden_states": True})
tuple_inputs = self._prepare_for_class(inputs_dict, model_class)
dict_inputs = self._prepare_for_class(inputs_dict, model_class)
check_equivalence(model, tuple_inputs, dict_inputs, {"output_attentions": True})
if model_class.__name__ not in ["PerceiverForOpticalFlow", "PerceiverForMultimodalAutoencoding"]:
# optical flow + multimodal models don't support training for now
tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
check_equivalence(model, tuple_inputs, dict_inputs, {"output_hidden_states": True})
if model_class.__name__ not in ["PerceiverForOpticalFlow", "PerceiverForMultimodalAutoencoding"]:
# optical flow + multimodal models don't support training for now
tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
check_equivalence(model, tuple_inputs, dict_inputs, {"output_attentions": True})
if model_class.__name__ not in ["PerceiverForOpticalFlow", "PerceiverForMultimodalAutoencoding"]:
# optical flow + multimodal models don't support training for now
tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
check_equivalence(
model, tuple_inputs, dict_inputs, {"output_hidden_states": True, "output_attentions": True}
)
def test_retain_grad_hidden_states_attentions(self):
# no need to test all models as different heads yield the same functionality
model_class = PerceiverForMaskedLM
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_model_class(model_class)
config.output_hidden_states = True
config.output_attentions = True
model = model_class(config)
model.to(torch_device)
inputs = self._prepare_for_class(inputs_dict, model_class)
outputs = model(**inputs)
output = outputs[0]
# Encoder-only model
hidden_states = outputs.hidden_states[0]
attentions = outputs.attentions[0]
hidden_states.retain_grad()
attentions.retain_grad()
output.flatten()[0].backward(retain_graph=True)
self.assertIsNotNone(hidden_states.grad)
self.assertIsNotNone(attentions.grad)
def test_feed_forward_chunking(self):
for model_class in self.all_model_classes:
original_config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_model_class(model_class)
torch.manual_seed(0)
config = copy.deepcopy(original_config)
model = model_class(config)
model.to(torch_device)
model.eval()
hidden_states_no_chunk = model(**self._prepare_for_class(inputs_dict, model_class))[0]
torch.manual_seed(0)
config.chunk_size_feed_forward = 1
model = model_class(config)
model.to(torch_device)
model.eval()
hidden_states_with_chunk = model(**self._prepare_for_class(inputs_dict, model_class))[0]
if model_class.__name__ == "PerceiverForMultimodalAutoencoding":
# model outputs a dictionary with logits for each modality
for modality in hidden_states_no_chunk.keys():
self.assertTrue(
torch.allclose(hidden_states_no_chunk[modality], hidden_states_with_chunk[modality], atol=1e-3)
)
else:
torch.testing.assert_close(hidden_states_no_chunk, hidden_states_with_chunk, rtol=1e-3, atol=1e-3)
def test_save_load(self):
for model_class in self.all_model_classes:
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_model_class(model_class)
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
if model_class.__name__ == "PerceiverForMultimodalAutoencoding":
for modality in outputs[0].keys():
out_2 = outputs[0][modality].cpu().numpy()
out_2[np.isnan(out_2)] = 0
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(tmpdirname)
model = model_class.from_pretrained(tmpdirname)
model.to(torch_device)
with torch.no_grad():
after_outputs = model(**self._prepare_for_class(inputs_dict, model_class))
# Make sure we don't have nans
out_1 = after_outputs[0][modality].cpu().numpy()
out_1[np.isnan(out_1)] = 0
max_diff = np.amax(np.abs(out_1 - out_2))
self.assertLessEqual(max_diff, 1e-5)
else:
out_2 = outputs[0].cpu().numpy()
out_2[np.isnan(out_2)] = 0
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(tmpdirname)
model = model_class.from_pretrained(tmpdirname)
model.to(torch_device)
with torch.no_grad():
after_outputs = model(**self._prepare_for_class(inputs_dict, model_class))
# Make sure we don't have nans
out_1 = after_outputs[0].cpu().numpy()
out_1[np.isnan(out_1)] = 0
max_diff = np.amax(np.abs(out_1 - out_2))
self.assertLessEqual(max_diff, 1e-5)
def test_correct_missing_keys(self):
if not self.test_missing_keys:
self.skipTest(reason="test_missing_keys is set to False")
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
# most Perceiver models don't have a typical head like is the case with BERT
if model_class.__name__ in [
"PerceiverForOpticalFlow",
"PerceiverForMultimodalAutoencoding",
*MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES.values(),
"PerceiverForImageClassificationLearned",
"PerceiverForImageClassificationFourier",
"PerceiverForImageClassificationConvProcessing",
*MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES.values(),
]:
continue
model = model_class(config)
base_model_prefix = model.base_model_prefix
if hasattr(model, base_model_prefix):
with tempfile.TemporaryDirectory() as temp_dir_name:
model.base_model.save_pretrained(temp_dir_name)
model, loading_info = model_class.from_pretrained(temp_dir_name, output_loading_info=True)
with self.subTest(msg=f"Missing keys for {model.__class__.__name__}"):
self.assertGreater(len(loading_info["missing_keys"]), 0)
def test_problem_types(self):
problem_types = [
{"title": "multi_label_classification", "num_labels": 2, "dtype": torch.float},
{"title": "single_label_classification", "num_labels": 1, "dtype": torch.long},
{"title": "regression", "num_labels": 1, "dtype": torch.float},
]
for model_class in self.all_model_classes:
if model_class.__name__ not in MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES.values():
continue
config, inputs, input_mask, _, _ = self.model_tester.prepare_config_and_inputs(model_class=model_class)
inputs_dict = {"inputs": inputs, "attention_mask": input_mask}
for problem_type in problem_types:
with self.subTest(msg=f"Testing {model_class} with {problem_type['title']}"):
config.problem_type = problem_type["title"]
config.num_labels = problem_type["num_labels"]
model = model_class(config)
model.to(torch_device)
model.train()
inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
if problem_type["num_labels"] > 1:
inputs["labels"] = inputs["labels"].unsqueeze(1).repeat(1, problem_type["num_labels"])
inputs["labels"] = inputs["labels"].to(problem_type["dtype"])
# This tests that we do not trigger the warning form PyTorch "Using a target size that is different
# to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
# they have the same size." which is a symptom something in wrong for the regression problem.
# See https://github.com/huggingface/transformers/issues/11780
with warnings.catch_warnings(record=True) as warning_list:
loss = model(**inputs).loss
for w in warning_list:
if "Using a target size that is different to the input size" in str(w.message):
raise ValueError(
f"Something is going wrong in the regression problem: intercepted {w.message}"
)
loss.backward()
@require_torch_multi_gpu
@unittest.skip(
reason=(
"Perceiver does not work with data parallel (DP) because of a bug in PyTorch:"
" https://github.com/pytorch/pytorch/issues/36035"
)
)
def test_multi_gpu_data_parallel_forward(self):
pass
@unittest.skip(reason="Perceiver models don't have a typical head like is the case with BERT")
def test_save_load_fast_init_from_base(self):
pass
@unittest.skip(reason="Perceiver models don't have a typical head like is the case with BERT")
def test_save_load_fast_init_to_base(self):
pass
@unittest.skip(reason="Perceiver doesn't support resize_token_embeddings")
def test_resize_tokens_embeddings(self):
pass
@unittest.skip(reason="Perceiver doesn't support resize_token_embeddings")
def test_resize_embeddings_untied(self):
pass
@unittest.skip(reason="Perceiver doesn't support inputs_embeds")
def test_inputs_embeds(self):
pass
@unittest.skip(reason="Perceiver doesn't support the AutoModel API")
def test_load_with_mismatched_shapes(self):
pass
@slow
def test_model_from_pretrained(self):
model_name = "deepmind/language-perceiver"
model = PerceiverModel.from_pretrained(model_name)
self.assertIsNotNone(model)
# We will verify our results on an image of cute cats
def prepare_img():
image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
return image
# Helper functions for optical flow integration test
def prepare_optical_flow_images():
dataset = load_dataset("hf-internal-testing/fixtures_sintel", split="test", trust_remote_code=True)
image1 = Image.open(dataset[0]["file"]).convert("RGB")
image2 = Image.open(dataset[0]["file"]).convert("RGB")
return image1, image2
def normalize(img):
return img / 255.0 * 2 - 1
def extract_image_patches(x, kernel, stride=1, dilation=1):
# Do TF 'SAME' Padding
b, c, h, w = x.shape
h2 = math.ceil(h / stride)
w2 = math.ceil(w / stride)
pad_row = (h2 - 1) * stride + (kernel - 1) * dilation + 1 - h
pad_col = (w2 - 1) * stride + (kernel - 1) * dilation + 1 - w
x = torch.nn.functional.pad(x, (pad_row // 2, pad_row - pad_row // 2, pad_col // 2, pad_col - pad_col // 2))
# Extract patches
patches = x.unfold(2, kernel, stride).unfold(3, kernel, stride)
patches = patches.permute(0, 4, 5, 1, 2, 3).contiguous()
return patches.view(b, -1, patches.shape[-2], patches.shape[-1])
@require_torch
@require_vision
class PerceiverModelIntegrationTest(unittest.TestCase):
@slow
def test_inference_masked_lm(self):
tokenizer = PerceiverTokenizer.from_pretrained("deepmind/language-perceiver")
model = PerceiverForMaskedLM.from_pretrained("deepmind/language-perceiver")
model.to(torch_device)
# prepare inputs
text = "This is an incomplete sentence where some words are missing."
encoding = tokenizer(text, padding="max_length", return_tensors="pt")
# mask " missing.".
encoding.input_ids[0, 52:61] = tokenizer.mask_token_id
inputs, input_mask = encoding.input_ids.to(torch_device), encoding.attention_mask.to(torch_device)
# forward pass
with torch.no_grad():
outputs = model(inputs=inputs, attention_mask=input_mask)
logits = outputs.logits
# verify logits
expected_shape = torch.Size((1, tokenizer.model_max_length, len(tokenizer)))
self.assertEqual(logits.shape, expected_shape)
expected_slice = torch.tensor(
[[-10.8609, -10.7651, -10.9187], [-12.1689, -11.9389, -12.1479], [-12.1518, -11.9707, -12.2073]],
device=torch_device,
)
torch.testing.assert_close(logits[0, :3, :3], expected_slice, rtol=1e-4, atol=1e-4)
expected_greedy_predictions = [38, 115, 111, 121, 121, 111, 116, 109, 52]
masked_tokens_predictions = logits[0, 52:61].argmax(dim=-1).tolist()
self.assertListEqual(expected_greedy_predictions, masked_tokens_predictions)
@slow
def test_inference_image_classification(self):
image_processor = PerceiverImageProcessor()
model = PerceiverForImageClassificationLearned.from_pretrained("deepmind/vision-perceiver-learned")
model.to(torch_device)
# prepare inputs
image = prepare_img()
inputs = image_processor(image, return_tensors="pt").pixel_values.to(torch_device)
input_mask = None
# forward pass
with torch.no_grad():
outputs = model(inputs=inputs, attention_mask=input_mask)
logits = outputs.logits
# verify logits
expected_shape = torch.Size((1, model.config.num_labels))
self.assertEqual(logits.shape, expected_shape)
expected_slice = torch.tensor([-1.1652, -0.1992, -0.7520], device=torch_device)
atol = 1e-3 if IS_ROCM_SYSTEM else 1e-4
torch.testing.assert_close(logits[0, :3], expected_slice, rtol=atol, atol=atol)
@slow
def test_inference_image_classification_fourier(self):
image_processor = PerceiverImageProcessor()
model = PerceiverForImageClassificationFourier.from_pretrained("deepmind/vision-perceiver-fourier")
model.to(torch_device)
# prepare inputs
image = prepare_img()
inputs = image_processor(image, return_tensors="pt").pixel_values.to(torch_device)
input_mask = None
# forward pass
with torch.no_grad():
outputs = model(inputs=inputs, attention_mask=input_mask)
logits = outputs.logits
# verify logits
expected_shape = torch.Size((1, model.config.num_labels))
self.assertEqual(logits.shape, expected_shape)
expected_slice = torch.tensor([-1.1295, -0.2832, 0.3226], device=torch_device)
torch.testing.assert_close(logits[0, :3], expected_slice, rtol=1e-4, atol=1e-4)
@slow
def test_inference_image_classification_conv(self):
image_processor = PerceiverImageProcessor()
model = PerceiverForImageClassificationConvProcessing.from_pretrained("deepmind/vision-perceiver-conv")
model.to(torch_device)
# prepare inputs
image = prepare_img()
inputs = image_processor(image, return_tensors="pt").pixel_values.to(torch_device)
input_mask = None
# forward pass
with torch.no_grad():
outputs = model(inputs=inputs, attention_mask=input_mask)
logits = outputs.logits
# verify logits
expected_shape = torch.Size((1, model.config.num_labels))
self.assertEqual(logits.shape, expected_shape)
expected_slice = torch.tensor([-1.1186, 0.0554, 0.0897], device=torch_device)
torch.testing.assert_close(logits[0, :3], expected_slice, rtol=1e-4, atol=1e-4)
@slow
def test_inference_optical_flow(self):
model = PerceiverForOpticalFlow.from_pretrained("deepmind/optical-flow-perceiver")
model.to(torch_device)
# prepare inputs
image1, image2 = prepare_optical_flow_images()
img1 = normalize(np.array(image1))
img2 = normalize(np.array(image1))
# stack images
img1 = torch.tensor(np.moveaxis(img1, -1, 0))
img2 = torch.tensor(np.moveaxis(img2, -1, 0))
images = torch.stack([img1, img2], dim=0)
# extract 3x3 patches
patch_size = model.config.train_size
inputs = images[..., : patch_size[0], : patch_size[1]].unsqueeze(0)
batch_size, _, C, H, W = inputs.shape
patches = extract_image_patches(inputs.view(batch_size * 2, C, H, W), kernel=3)
_, C, H, W = patches.shape
patches = patches.view(batch_size, -1, C, H, W).float()
# forward pass
with torch.no_grad():
outputs = model(inputs=patches.to(torch_device))
logits = outputs.logits
# verify logits
expected_shape = torch.Size((1, 368, 496, 2))
self.assertEqual(logits.shape, expected_shape)
expected_slice = torch.tensor(
[
[[0.0025, -0.0050], [0.0025, -0.0049], [0.0025, -0.0048]],
[[0.0026, -0.0049], [0.0026, -0.0048], [0.0026, -0.0047]],
[[0.0026, -0.0049], [0.0026, -0.0048], [0.0026, -0.0046]],
],
device=torch_device,
)
torch.testing.assert_close(logits[0, :3, :3, :3], expected_slice, rtol=1e-4, atol=1e-4)
@slow
def test_inference_interpolate_pos_encoding(self):
image_processor = PerceiverImageProcessor(size={"height": 384, "width": 384})
model = PerceiverForImageClassificationLearned.from_pretrained("deepmind/vision-perceiver-learned")
model.to(torch_device)
# prepare inputs
image = prepare_img()
inputs = image_processor(image, return_tensors="pt").pixel_values.to(torch_device)
input_mask = None
# forward pass
with torch.no_grad():
outputs = model(inputs=inputs, attention_mask=input_mask, interpolate_pos_encoding=True)
logits = outputs.logits
# verify logits
expected_shape = torch.Size((1, model.config.num_labels))
self.assertEqual(logits.shape, expected_shape)
| transformers/tests/models/perceiver/test_modeling_perceiver.py/0 | {
"file_path": "transformers/tests/models/perceiver/test_modeling_perceiver.py",
"repo_id": "transformers",
"token_count": 21277
} |
# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team, The Microsoft Research 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 copy
import tempfile
import unittest
from transformers import ProphetNetConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
ProphetNetDecoder,
ProphetNetEncoder,
ProphetNetForCausalLM,
ProphetNetForConditionalGeneration,
ProphetNetModel,
ProphetNetTokenizer,
)
from transformers.modeling_outputs import BaseModelOutput
class ProphetNetModelTester:
def __init__(
self,
parent,
vocab_size=99,
batch_size=13,
hidden_size=16,
encoder_seq_length=7,
decoder_seq_length=9,
# For common tests
is_training=True,
use_attention_mask=True,
use_labels=True,
decoder_start_token_id=0,
encoder_ffn_dim=32,
num_encoder_layers=2,
num_encoder_attention_heads=4,
decoder_ffn_dim=32,
num_decoder_layers=2,
num_decoder_attention_heads=4,
max_position_embeddings=30,
is_encoder_decoder=True,
pad_token_id=0,
bos_token_id=1,
eos_token_id=2,
ngram=2,
num_buckets=32,
relative_max_distance=128,
disable_ngram_loss=False,
scope=None,
):
self.parent = parent
self.batch_size = batch_size
self.encoder_seq_length = encoder_seq_length
self.decoder_seq_length = decoder_seq_length
# For common tests
self.seq_length = self.decoder_seq_length
self.is_training = is_training
self.use_attention_mask = use_attention_mask
self.use_labels = use_labels
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_decoder_layers
self.num_encoder_layers = num_encoder_layers
self.num_decoder_layers = num_decoder_layers
self.decoder_ffn_dim = decoder_ffn_dim
self.encoder_ffn_dim = encoder_ffn_dim
self.num_attention_heads = num_decoder_attention_heads
self.num_encoder_attention_heads = num_encoder_attention_heads
self.num_decoder_attention_heads = num_decoder_attention_heads
self.eos_token_id = eos_token_id
self.bos_token_id = bos_token_id
self.pad_token_id = pad_token_id
self.decoder_start_token_id = decoder_start_token_id
self.ngram = ngram
self.num_buckets = num_buckets
self.relative_max_distance = relative_max_distance
self.disable_ngram_loss = disable_ngram_loss
self.max_position_embeddings = max_position_embeddings
self.is_encoder_decoder = is_encoder_decoder
self.scope = None
self.decoder_key_length = decoder_seq_length
self.base_model_out_len = 7
self.num_hidden_states_types = 3 # encoder, decoder_main, decoder_ngram
self.decoder_attention_idx = 2
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.encoder_seq_length], self.vocab_size)
decoder_input_ids = ids_tensor([self.batch_size, self.decoder_seq_length], self.vocab_size)
attention_mask = None
decoder_attention_mask = None
if self.use_attention_mask:
attention_mask = ids_tensor([self.batch_size, self.encoder_seq_length], vocab_size=2)
decoder_attention_mask = ids_tensor([self.batch_size, self.decoder_seq_length], vocab_size=2)
lm_labels = None
if self.use_labels:
lm_labels = ids_tensor([self.batch_size, self.decoder_seq_length], self.vocab_size)
config = self.get_config()
return (
config,
input_ids,
decoder_input_ids,
attention_mask,
decoder_attention_mask,
lm_labels,
)
def get_config(self):
return ProphetNetConfig(
vocab_size=self.vocab_size,
hidden_size=self.hidden_size,
num_encoder_layers=self.num_encoder_layers,
num_decoder_layers=self.num_decoder_layers,
decoder_ffn_dim=self.decoder_ffn_dim,
encoder_ffn_dim=self.encoder_ffn_dim,
num_encoder_attention_heads=self.num_encoder_attention_heads,
num_decoder_attention_heads=self.num_decoder_attention_heads,
eos_token_id=self.eos_token_id,
bos_token_id=self.bos_token_id,
pad_token_id=self.pad_token_id,
decoder_start_token_id=self.decoder_start_token_id,
ngram=self.ngram,
num_buckets=self.num_buckets,
relative_max_distance=self.relative_max_distance,
disable_ngram_loss=self.disable_ngram_loss,
max_position_embeddings=self.max_position_embeddings,
is_encoder_decoder=self.is_encoder_decoder,
)
def prepare_config_and_inputs_for_decoder(self):
(
config,
input_ids,
decoder_input_ids,
attention_mask,
decoder_attention_mask,
lm_labels,
) = self.prepare_config_and_inputs()
encoder_hidden_states = floats_tensor([self.batch_size, self.encoder_seq_length, self.hidden_size])
encoder_attention_mask = ids_tensor([self.batch_size, self.encoder_seq_length], vocab_size=2)
return (
config,
decoder_input_ids,
decoder_attention_mask,
encoder_hidden_states,
encoder_attention_mask,
lm_labels,
)
def check_prepare_lm_labels_via_shift_left(
self,
config,
input_ids,
decoder_input_ids,
attention_mask,
decoder_attention_mask,
lm_labels,
):
model = ProphetNetModel(config=config)
model.to(torch_device)
model.eval()
# make sure that lm_labels are correctly padded from the right
lm_labels.masked_fill_((lm_labels == self.decoder_start_token_id), self.eos_token_id)
# add casaul pad token mask
triangular_mask = torch.tril(lm_labels.new_ones(lm_labels.shape)).logical_not()
lm_labels.masked_fill_(triangular_mask, self.pad_token_id)
decoder_input_ids = model._shift_right(lm_labels)
for i, (decoder_input_ids_slice, lm_labels_slice) in enumerate(zip(decoder_input_ids, lm_labels)):
# first item
self.parent.assertEqual(decoder_input_ids_slice[0].item(), self.decoder_start_token_id)
if i < decoder_input_ids_slice.shape[-1]:
if i < decoder_input_ids.shape[-1] - 1:
# items before diagonal
self.parent.assertListEqual(
decoder_input_ids_slice[1 : i + 1].tolist(), lm_labels_slice[:i].tolist()
)
# pad items after diagonal
if i < decoder_input_ids.shape[-1] - 2:
self.parent.assertListEqual(
decoder_input_ids_slice[i + 2 :].tolist(), lm_labels_slice[i + 1 : -1].tolist()
)
else:
# all items after square
self.parent.assertListEqual(decoder_input_ids_slice[1:].tolist(), lm_labels_slice[:-1].tolist())
def create_and_check_model(
self,
config,
input_ids,
decoder_input_ids,
attention_mask,
decoder_attention_mask,
lm_labels,
):
model = ProphetNetModel(config=config)
model.to(torch_device)
model.eval()
result = model(
input_ids=input_ids,
decoder_input_ids=decoder_input_ids,
attention_mask=attention_mask,
decoder_attention_mask=decoder_attention_mask,
)
result = model(input_ids=input_ids, decoder_input_ids=decoder_input_ids)
decoder_output = result.last_hidden_state
decoder_past = result.past_key_values
encoder_output = result.encoder_last_hidden_state
self.parent.assertEqual(encoder_output.size(), (self.batch_size, self.encoder_seq_length, self.hidden_size))
self.parent.assertEqual(decoder_output.size(), (self.batch_size, self.decoder_seq_length, self.hidden_size))
# There should be `num_layers` key value embeddings stored in decoder_past
self.parent.assertEqual(len(decoder_past), config.num_decoder_layers)
# There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
self.parent.assertEqual(len(decoder_past[0]), 4) # cross-attention + uni-directional self-attention
def create_and_check_with_lm_head(
self,
config,
input_ids,
decoder_input_ids,
attention_mask,
decoder_attention_mask,
lm_labels,
):
model = ProphetNetForConditionalGeneration(config=config).to(torch_device).eval()
outputs = model(
input_ids=input_ids,
decoder_input_ids=decoder_input_ids,
decoder_attention_mask=decoder_attention_mask,
labels=lm_labels,
)
self.parent.assertEqual(len(outputs), 5)
self.parent.assertEqual(outputs["logits"].size(), (self.batch_size, self.decoder_seq_length, self.vocab_size))
self.parent.assertEqual(outputs["loss"].size(), ())
def create_and_check_causal_lm_decoder(
self,
config,
input_ids,
decoder_input_ids,
attention_mask,
decoder_attention_mask,
lm_labels,
):
model = ProphetNetForCausalLM(config=config).to(torch_device).eval()
outputs = model(
input_ids=decoder_input_ids,
attention_mask=decoder_attention_mask,
labels=lm_labels,
)
self.parent.assertEqual(len(outputs), 4)
self.parent.assertEqual(outputs["logits"].size(), (self.batch_size, self.decoder_seq_length, self.vocab_size))
self.parent.assertEqual(outputs["loss"].size(), ())
def create_and_check_generate_with_past_key_value_states(
self,
config,
input_ids,
decoder_input_ids,
attention_mask,
decoder_attention_mask,
lm_labels,
):
model = ProphetNetForConditionalGeneration(config=config).to(torch_device).eval()
torch.manual_seed(0)
output_without_past_cache = model.generate(
input_ids[:1], num_beams=2, max_length=5, do_sample=True, use_cache=False
)
torch.manual_seed(0)
output_with_past_cache = model.generate(input_ids[:1], num_beams=2, max_length=5, do_sample=True)
self.parent.assertTrue(torch.all(output_with_past_cache == output_without_past_cache))
def create_and_check_decoder_generate_with_past_key_value_states(
self,
config,
input_ids,
decoder_input_ids,
attention_mask,
decoder_attention_mask,
lm_labels,
):
model = ProphetNetForCausalLM(config=config).to(torch_device).eval()
torch.manual_seed(0)
output_without_past_cache = model.generate(
input_ids[:1], num_beams=2, max_length=10, do_sample=True, use_cache=False
)
torch.manual_seed(0)
output_with_past_cache = model.generate(input_ids[:1], num_beams=2, max_length=10, do_sample=True)
self.parent.assertTrue(torch.all(output_with_past_cache == output_without_past_cache))
def create_and_check_model_fp16_forward(
self,
config,
input_ids,
decoder_input_ids,
attention_mask,
decoder_attention_mask,
lm_labels,
):
model = ProphetNetModel(config=config).to(torch_device).half().eval()
output = model(input_ids, decoder_input_ids=input_ids, attention_mask=attention_mask)["last_hidden_state"]
self.parent.assertFalse(torch.isnan(output).any().item())
def create_and_check_encoder_decoder_shared_weights(
self,
config,
input_ids,
decoder_input_ids,
attention_mask,
decoder_attention_mask,
lm_labels,
):
for model_class in [ProphetNetModel, ProphetNetForConditionalGeneration]:
torch.manual_seed(0)
model = model_class(config=config).to(torch_device).eval()
# load state dict copies weights but does not tie them
if model_class == ProphetNetForConditionalGeneration:
model.prophetnet.encoder.load_state_dict(model.prophetnet.decoder.state_dict(), strict=False)
else:
model.encoder.load_state_dict(model.decoder.state_dict(), strict=False)
torch.manual_seed(0)
tied_config = copy.deepcopy(config)
tied_config.tie_encoder_decoder = True
tied_model = model_class(config=tied_config).to(torch_device).eval()
model_result = model(
input_ids=input_ids,
decoder_input_ids=decoder_input_ids,
attention_mask=attention_mask,
decoder_attention_mask=decoder_attention_mask,
)
tied_model_result = tied_model(
input_ids=input_ids,
decoder_input_ids=decoder_input_ids,
attention_mask=attention_mask,
decoder_attention_mask=decoder_attention_mask,
)
# check that models has less parameters
self.parent.assertLess(
sum(p.numel() for p in tied_model.parameters()), sum(p.numel() for p in model.parameters())
)
random_slice_idx = ids_tensor((1,), model_result[0].shape[-1]).item()
# check that outputs are equal
self.parent.assertTrue(
torch.allclose(
model_result[0][0, :, random_slice_idx], tied_model_result[0][0, :, random_slice_idx], atol=1e-4
)
)
# check that outputs after saving and loading are equal
with tempfile.TemporaryDirectory() as tmpdirname:
tied_model.save_pretrained(tmpdirname)
tied_model = model_class.from_pretrained(tmpdirname)
tied_model.to(torch_device)
tied_model.eval()
# check that models has less parameters
self.parent.assertLess(
sum(p.numel() for p in tied_model.parameters()), sum(p.numel() for p in model.parameters())
)
random_slice_idx = ids_tensor((1,), model_result[0].shape[-1]).item()
tied_model_result = tied_model(
input_ids=input_ids,
decoder_input_ids=decoder_input_ids,
attention_mask=attention_mask,
decoder_attention_mask=decoder_attention_mask,
)
# check that outputs are equal
self.parent.assertTrue(
torch.allclose(
model_result[0][0, :, random_slice_idx],
tied_model_result[0][0, :, random_slice_idx],
atol=1e-4,
)
)
def check_fast_integration(
self,
config,
*args,
):
input_ids = torch.tensor([[7, 4, 78, 0, 24, 52, 43]], device=torch_device, dtype=torch.long)
decoder_input_ids = torch.tensor([[12, 62, 25, 11, 47, 15, 14]], device=torch_device, dtype=torch.long)
attention_mask = torch.tensor([[1, 1, 1, 0, 1, 0, 0]], device=torch_device, dtype=torch.long)
decoder_attention_mask = torch.tensor([[1, 1, 1, 0, 0, 1, 0]], device=torch_device, dtype=torch.long)
lm_labels = torch.tensor([[62, 25, 11, 47, 15, 14, 24]], device=torch_device, dtype=torch.long)
torch.manual_seed(0)
config.ngram = 4
model = ProphetNetForConditionalGeneration(config=config)
model.to(torch_device)
model.eval()
with torch.no_grad():
result = model(
input_ids=input_ids,
decoder_input_ids=decoder_input_ids,
attention_mask=attention_mask,
decoder_attention_mask=decoder_attention_mask,
labels=lm_labels,
)
self.parent.assertTrue(torch.allclose(result.loss, torch.tensor(4.5892, device=torch_device), atol=1e-3))
expected_logit_slice = torch.tensor(
[-0.0184, 0.0758, -0.0543, -0.0093, 0.0050, -0.0660, -0.1453], device=torch_device
)
self.parent.assertTrue(torch.allclose(result.logits[0, :, 1], expected_logit_slice, atol=1e-3))
def check_model_with_attn_mask(self, config, input_ids, decoder_input_ids, *args):
model = ProphetNetModel(config=config)
model.to(torch_device)
model.eval()
outputs_no_mask = model(input_ids=input_ids[:, :5], decoder_input_ids=decoder_input_ids[:, :5])
attention_mask = torch.ones_like(input_ids)
decoder_attention_mask = torch.ones_like(decoder_input_ids)
attention_mask[:, 5:] = 0
outputs_with_mask = model(
input_ids=input_ids,
attention_mask=attention_mask,
decoder_input_ids=decoder_input_ids,
decoder_attention_mask=decoder_attention_mask,
)
# check encoder
self.parent.assertTrue(
torch.allclose(
outputs_no_mask.encoder_last_hidden_state[0, :, 0],
outputs_with_mask.encoder_last_hidden_state[0, :5, 0],
atol=1e-3,
)
)
# check decoder
# main stream
self.parent.assertTrue(
torch.allclose(
outputs_no_mask.last_hidden_state[0, :, 0], outputs_with_mask.last_hidden_state[0, :5, 0], atol=1e-3
)
)
# predict stream
self.parent.assertTrue(
torch.allclose(
outputs_no_mask.last_hidden_state_ngram[0, :5, 0],
outputs_with_mask.last_hidden_state_ngram[0, :5, 0],
atol=1e-2,
)
)
def check_causal_lm_from_pretrained(
self, config, input_ids, decoder_input_ids, attention_mask, decoder_attention_mask, *args
):
model = ProphetNetForConditionalGeneration(config).to(torch_device).eval()
with tempfile.TemporaryDirectory() as tmp_dirname:
model.save_pretrained(tmp_dirname)
decoder = ProphetNetForCausalLM.from_pretrained(tmp_dirname).to(torch_device)
encoder_hidden_states = model.prophetnet.encoder(input_ids).last_hidden_state
model_outputs = model(
encoder_outputs=BaseModelOutput(last_hidden_state=encoder_hidden_states),
decoder_input_ids=decoder_input_ids,
)
dec_outputs = decoder(encoder_hidden_states=encoder_hidden_states, input_ids=decoder_input_ids)
self.parent.assertTrue(
torch.allclose(
model_outputs.logits[0, :5],
dec_outputs.logits[0, :5],
atol=1e-3,
)
)
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
config,
input_ids,
decoder_input_ids,
attention_mask,
decoder_attention_mask,
lm_labels,
) = config_and_inputs
inputs_dict = {
"input_ids": input_ids,
"attention_mask": attention_mask,
"decoder_input_ids": decoder_input_ids,
"decoder_attention_mask": decoder_attention_mask,
"use_cache": False,
}
return config, inputs_dict
class ProphetNetStandaloneDecoderModelTester:
def __init__(
self,
parent,
vocab_size=99,
batch_size=13,
hidden_size=16,
encoder_seq_length=7,
decoder_seq_length=7,
# For common tests
is_training=True,
is_decoder=True,
use_attention_mask=True,
add_cross_attention=False,
use_cache=False,
use_labels=True,
decoder_start_token_id=0,
encoder_ffn_dim=32,
num_encoder_layers=2,
num_encoder_attention_heads=4,
decoder_ffn_dim=32,
num_decoder_layers=2,
num_decoder_attention_heads=4,
max_position_embeddings=30,
is_encoder_decoder=False,
pad_token_id=0,
bos_token_id=1,
eos_token_id=2,
ngram=2,
num_buckets=32,
relative_max_distance=128,
disable_ngram_loss=False,
scope=None,
):
self.parent = parent
self.batch_size = batch_size
self.encoder_seq_length = encoder_seq_length
self.decoder_seq_length = decoder_seq_length
# For common tests
self.seq_length = self.decoder_seq_length
self.is_training = is_training
self.use_attention_mask = use_attention_mask
self.use_labels = use_labels
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_decoder_layers
self.num_encoder_layers = num_encoder_layers
self.num_decoder_layers = num_decoder_layers
self.decoder_ffn_dim = decoder_ffn_dim
self.encoder_ffn_dim = encoder_ffn_dim
self.num_attention_heads = num_decoder_attention_heads
self.num_encoder_attention_heads = num_encoder_attention_heads
self.num_decoder_attention_heads = num_decoder_attention_heads
self.eos_token_id = eos_token_id
self.bos_token_id = bos_token_id
self.pad_token_id = pad_token_id
self.decoder_start_token_id = decoder_start_token_id
self.ngram = ngram
self.num_buckets = num_buckets
self.relative_max_distance = relative_max_distance
self.use_cache = use_cache
self.disable_ngram_loss = disable_ngram_loss
self.max_position_embeddings = max_position_embeddings
self.add_cross_attention = add_cross_attention
self.is_encoder_decoder = is_encoder_decoder
self.scope = None
self.decoder_key_length = decoder_seq_length
self.base_model_out_len = 2
self.num_hidden_states_types = 2 # decoder_main, decoder_ngram
self.decoder_attention_idx = 1
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.encoder_seq_length], self.vocab_size)
attention_mask = None
if self.use_attention_mask:
attention_mask = ids_tensor([self.batch_size, self.encoder_seq_length], vocab_size=2)
lm_labels = None
if self.use_labels:
lm_labels = ids_tensor([self.batch_size, self.encoder_seq_length], self.vocab_size)
config = ProphetNetConfig(
vocab_size=self.vocab_size,
hidden_size=self.hidden_size,
num_encoder_layers=self.num_encoder_layers,
num_decoder_layers=self.num_decoder_layers,
decoder_ffn_dim=self.decoder_ffn_dim,
encoder_ffn_dim=self.encoder_ffn_dim,
num_encoder_attention_heads=self.num_encoder_attention_heads,
num_decoder_attention_heads=self.num_decoder_attention_heads,
eos_token_id=self.eos_token_id,
bos_token_id=self.bos_token_id,
use_cache=self.use_cache,
pad_token_id=self.pad_token_id,
decoder_start_token_id=self.decoder_start_token_id,
ngram=self.ngram,
num_buckets=self.num_buckets,
relative_max_distance=self.relative_max_distance,
disable_ngram_loss=self.disable_ngram_loss,
max_position_embeddings=self.max_position_embeddings,
add_cross_attention=self.add_cross_attention,
is_encoder_decoder=self.is_encoder_decoder,
)
return (
config,
input_ids,
attention_mask,
lm_labels,
)
def prepare_config_and_inputs_for_decoder(self):
(
config,
input_ids,
attention_mask,
lm_labels,
) = self.prepare_config_and_inputs()
encoder_hidden_states = floats_tensor([self.batch_size, self.encoder_seq_length, self.hidden_size])
encoder_attention_mask = ids_tensor([self.batch_size, self.encoder_seq_length], vocab_size=2)
return (
config,
input_ids,
attention_mask,
encoder_hidden_states,
encoder_attention_mask,
lm_labels,
)
def create_and_check_decoder_model_past(
self,
config,
input_ids,
attention_mask,
lm_labels,
):
config.use_cache = True
model = ProphetNetDecoder(config=config).to(torch_device).eval()
# first forward pass
outputs = model(input_ids, use_cache=True)
outputs_use_cache_conf = model(input_ids)
outputs_no_past = model(input_ids, use_cache=False)
self.parent.assertTrue(len(outputs) == len(outputs_use_cache_conf))
self.parent.assertTrue(len(outputs) == len(outputs_no_past) + 1)
past_key_values = outputs["past_key_values"]
# create hypothetical next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)
# append to next input_ids and
next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
output_from_no_past = model(next_input_ids)["last_hidden_state"]
output_from_past = model(next_tokens, past_key_values=past_key_values)["last_hidden_state"]
# select random slice
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
output_from_no_past_slice = output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach()
# test that outputs are equal for slice
assert torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3)
def create_and_check_decoder_model_attention_mask_past(
self,
config,
input_ids,
attention_mask,
lm_labels,
):
model = ProphetNetDecoder(config=config).to(torch_device).eval()
# create attention mask
attn_mask = torch.ones(input_ids.shape, dtype=torch.long, device=torch_device)
half_seq_length = input_ids.shape[-1] // 2
attn_mask[:, half_seq_length:] = 0
# first forward pass
past_key_values = model(input_ids, attention_mask=attn_mask, use_cache=True)["past_key_values"]
# create hypothetical next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)
# change a random masked slice from input_ids
random_seq_idx_to_change = ids_tensor((1,), half_seq_length).item() + 1
random_other_next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size).squeeze(-1)
input_ids[:, -random_seq_idx_to_change] = random_other_next_tokens
# append to next input_ids and attn_mask
next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
attn_mask = torch.cat(
[attn_mask, torch.ones((attn_mask.shape[0], 1), dtype=torch.long, device=torch_device)],
dim=1,
)
# get two different outputs
output_from_no_past = model(next_input_ids)["last_hidden_state"]
output_from_past = model(next_tokens, past_key_values=past_key_values)["last_hidden_state"]
# select random slice
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
output_from_no_past_slice = output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach()
# test that outputs are equal for slice
assert torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-2)
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
config,
input_ids,
attention_mask,
lm_labels,
) = config_and_inputs
inputs_dict = {
"input_ids": input_ids,
"attention_mask": attention_mask,
}
return config, inputs_dict
class ProphetNetStandaloneEncoderModelTester:
def __init__(
self,
parent,
vocab_size=99,
batch_size=13,
hidden_size=16,
encoder_seq_length=7,
decoder_seq_length=7,
# For common tests
is_training=True,
is_decoder=False,
use_attention_mask=True,
add_cross_attention=False,
use_cache=False,
use_labels=True,
decoder_start_token_id=0,
encoder_ffn_dim=32,
num_encoder_layers=2,
num_encoder_attention_heads=4,
decoder_ffn_dim=32,
num_decoder_layers=2,
num_decoder_attention_heads=4,
max_position_embeddings=30,
is_encoder_decoder=False,
pad_token_id=0,
bos_token_id=1,
eos_token_id=2,
num_buckets=32,
relative_max_distance=128,
disable_ngram_loss=False,
scope=None,
):
self.parent = parent
self.batch_size = batch_size
self.encoder_seq_length = encoder_seq_length
self.decoder_seq_length = decoder_seq_length
# For common tests
self.seq_length = self.decoder_seq_length
self.is_training = is_training
self.use_attention_mask = use_attention_mask
self.use_labels = use_labels
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_decoder_layers
self.num_encoder_layers = num_encoder_layers
self.num_decoder_layers = num_decoder_layers
self.decoder_ffn_dim = decoder_ffn_dim
self.encoder_ffn_dim = encoder_ffn_dim
self.num_attention_heads = num_decoder_attention_heads
self.num_encoder_attention_heads = num_encoder_attention_heads
self.num_decoder_attention_heads = num_decoder_attention_heads
self.eos_token_id = eos_token_id
self.bos_token_id = bos_token_id
self.pad_token_id = pad_token_id
self.decoder_start_token_id = decoder_start_token_id
self.num_buckets = num_buckets
self.relative_max_distance = relative_max_distance
self.use_cache = use_cache
self.disable_ngram_loss = disable_ngram_loss
self.max_position_embeddings = max_position_embeddings
self.add_cross_attention = add_cross_attention
self.is_encoder_decoder = is_encoder_decoder
self.scope = None
self.decoder_key_length = decoder_seq_length
self.base_model_out_len = 1
self.num_hidden_states_types = 1
self.decoder_attention_idx = 1
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.encoder_seq_length], self.vocab_size)
attention_mask = None
if self.use_attention_mask:
attention_mask = ids_tensor([self.batch_size, self.encoder_seq_length], vocab_size=2)
config = ProphetNetConfig(
vocab_size=self.vocab_size,
hidden_size=self.hidden_size,
num_encoder_layers=self.num_encoder_layers,
num_decoder_layers=self.num_decoder_layers,
decoder_ffn_dim=self.decoder_ffn_dim,
encoder_ffn_dim=self.encoder_ffn_dim,
num_encoder_attention_heads=self.num_encoder_attention_heads,
num_decoder_attention_heads=self.num_decoder_attention_heads,
eos_token_id=self.eos_token_id,
bos_token_id=self.bos_token_id,
use_cache=self.use_cache,
pad_token_id=self.pad_token_id,
decoder_start_token_id=self.decoder_start_token_id,
num_buckets=self.num_buckets,
relative_max_distance=self.relative_max_distance,
disable_ngram_loss=self.disable_ngram_loss,
max_position_embeddings=self.max_position_embeddings,
add_cross_attention=self.add_cross_attention,
is_encoder_decoder=self.is_encoder_decoder,
)
return (
config,
input_ids,
attention_mask,
)
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
config,
input_ids,
attention_mask,
) = config_and_inputs
inputs_dict = {
"input_ids": input_ids,
"attention_mask": attention_mask,
}
return config, inputs_dict
@require_torch
class ProphetNetModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (ProphetNetModel, ProphetNetForConditionalGeneration) if is_torch_available() else ()
all_generative_model_classes = (ProphetNetForConditionalGeneration,) if is_torch_available() else ()
pipeline_model_mapping = (
{
"feature-extraction": ProphetNetModel,
"summarization": ProphetNetForConditionalGeneration,
"text-generation": ProphetNetForCausalLM,
"text2text-generation": ProphetNetForConditionalGeneration,
"translation": ProphetNetForConditionalGeneration,
}
if is_torch_available()
else {}
)
test_pruning = False
test_resize_embeddings = False
is_encoder_decoder = True
# TODO: Fix the failed tests
def is_pipeline_test_to_skip(
self,
pipeline_test_case_name,
config_class,
model_architecture,
tokenizer_name,
image_processor_name,
feature_extractor_name,
processor_name,
):
if pipeline_test_case_name == "TextGenerationPipelineTests":
# Get `ValueError: AttributeError: 'NoneType' object has no attribute 'new_ones'` or `AssertionError`.
# `ProphetNetConfig` was never used in pipeline tests: cannot create a simple
# tokenizer.
return True
return False
def setUp(self):
self.model_tester = ProphetNetModelTester(self)
self.config_tester = ConfigTester(self, config_class=ProphetNetConfig)
def test_config(self):
self.config_tester.run_common_tests()
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
def test_lm_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_with_lm_head(*config_and_inputs)
def test_only_decoder_causal_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_causal_lm_decoder(*config_and_inputs)
def test_fast_integration(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_fast_integration(*config_and_inputs)
def test_shared_weights(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_encoder_decoder_shared_weights(*config_and_inputs)
def test_shift_labels_via_shift_left(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_prepare_lm_labels_via_shift_left(*config_and_inputs)
@unittest.skip(reason="Flaky test with no simple resolution. TODO Fix me @patrickvonplaten")
def test_decoder_model_generate(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_generate_with_past_key_value_states(*config_and_inputs)
def test_encoder_decoder_model_generate(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_decoder_generate_with_past_key_value_states(*config_and_inputs)
def test_attn_mask_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_model_with_attn_mask(*config_and_inputs)
def test_config_save(self):
config = self.model_tester.prepare_config_and_inputs()[0]
config.add_cross_attention = False
with tempfile.TemporaryDirectory() as tmp_dirname:
config.save_pretrained(tmp_dirname)
config = ProphetNetConfig.from_pretrained(tmp_dirname)
self.assertFalse(config.add_cross_attention)
def test_causal_lm_from_pretrained(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_causal_lm_from_pretrained(*config_and_inputs)
@unittest.skipIf(torch_device == "cpu", "Cant do half precision")
def test_fp16_forward(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model_fp16_forward(*config_and_inputs)
# methods overwrite method in `test_modeling_common.py`
def test_attention_outputs(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
seq_len = getattr(self.model_tester, "seq_length", None)
decoder_seq_length = getattr(self.model_tester, "decoder_seq_length", seq_len)
encoder_seq_length = getattr(self.model_tester, "encoder_seq_length", seq_len)
decoder_key_length = getattr(self.model_tester, "decoder_key_length", decoder_seq_length)
encoder_key_length = getattr(self.model_tester, "key_length", encoder_seq_length)
chunk_length = getattr(self.model_tester, "chunk_length", None)
if chunk_length is not None and hasattr(self.model_tester, "num_hashes"):
encoder_seq_length = encoder_seq_length * self.model_tester.num_hashes
for model_class in self.all_model_classes:
inputs_dict["output_attentions"] = True
inputs_dict["output_hidden_states"] = False
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
config.output_attentions = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
if chunk_length is not None:
self.assertListEqual(
list(attentions[0].shape[-4:]),
[self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length],
)
else:
self.assertListEqual(
list(attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length],
)
out_len = len(outputs)
correct_outlen = 7
# loss is at first position
if "labels" in inputs_dict:
correct_outlen += 1 # loss is added to beginning
self.assertEqual(out_len, correct_outlen)
# decoder attentions
decoder_attentions = outputs.decoder_attentions
self.assertIsInstance(decoder_attentions, (list, tuple))
self.assertEqual(len(decoder_attentions), self.model_tester.num_hidden_layers)
self.assertListEqual(
list(decoder_attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads, decoder_seq_length, decoder_key_length],
)
# cross attentions
cross_attentions = outputs.cross_attentions
self.assertIsInstance(cross_attentions, (list, tuple))
self.assertEqual(len(cross_attentions), self.model_tester.num_hidden_layers)
self.assertListEqual(
list(cross_attentions[0].shape[-3:]),
[
self.model_tester.num_attention_heads,
(self.model_tester.ngram + 1) * decoder_seq_length,
encoder_key_length,
],
)
# Check attention is always last and order is fine
inputs_dict["output_attentions"] = True
inputs_dict["output_hidden_states"] = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
if hasattr(self.model_tester, "num_hidden_states_types"):
added_hidden_states = self.model_tester.num_hidden_states_types
elif self.is_encoder_decoder:
added_hidden_states = 2
else:
added_hidden_states = 1
self.assertEqual(out_len + added_hidden_states, len(outputs))
self_attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers)
if chunk_length is not None:
self.assertListEqual(
list(self_attentions[0].shape[-4:]),
[self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length],
)
else:
self.assertListEqual(
list(self_attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length],
)
def test_retain_grad_hidden_states_attentions(self):
# decoder cannot keep gradients
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.output_hidden_states = True
config.output_attentions = True
# no need to test all models as different heads yield the same functionality
model_class = self.all_model_classes[0]
model = model_class(config)
model.to(torch_device)
inputs = self._prepare_for_class(inputs_dict, model_class)
outputs = model(**inputs)
output = outputs[0]
encoder_hidden_states = outputs.encoder_hidden_states[0]
encoder_attentions = outputs.encoder_attentions[0]
encoder_hidden_states.retain_grad()
encoder_attentions.retain_grad()
output.flatten()[0].backward(retain_graph=True)
self.assertIsNotNone(encoder_hidden_states.grad)
self.assertIsNotNone(encoder_attentions.grad)
@unittest.skip(reason="Generating with head_masking has not been implemented for ProphetNet models yet.")
def test_generate_with_head_masking(self):
pass
@require_torch
class ProphetNetStandaloneDecoderModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
all_model_classes = (ProphetNetDecoder, ProphetNetForCausalLM) if is_torch_available() else ()
all_generative_model_classes = (ProphetNetForCausalLM,) if is_torch_available() else ()
test_pruning = False
test_resize_embeddings = False
is_encoder_decoder = False
def setUp(self):
self.model_tester = ProphetNetStandaloneDecoderModelTester(self, is_training=False)
self.config_tester = ConfigTester(self, config_class=ProphetNetConfig)
def test_config(self):
self.config_tester.run_common_tests()
def test_decoder_model_past(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_decoder_model_past(*config_and_inputs)
def test_decoder_model_attn_mask_past(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_decoder_model_attention_mask_past(*config_and_inputs)
@unittest.skip(reason="Decoder cannot keep gradients")
def test_retain_grad_hidden_states_attentions(self):
return
@require_torch
class ProphetNetStandaloneEncoderModelTest(ModelTesterMixin, unittest.TestCase):
all_model_classes = (ProphetNetEncoder,) if is_torch_available() else ()
test_pruning = False
test_resize_embeddings = False
is_encoder_decoder = False
def setUp(self):
self.model_tester = ProphetNetStandaloneEncoderModelTester(self, is_training=False)
self.config_tester = ConfigTester(self, config_class=ProphetNetConfig)
def test_config(self):
self.config_tester.run_common_tests()
@require_torch
class ProphetNetModelIntegrationTest(unittest.TestCase):
@slow
def test_pretrained_checkpoint_hidden_states(self):
model = ProphetNetForConditionalGeneration.from_pretrained("microsoft/prophetnet-large-uncased")
model.to(torch_device)
# encoder-decoder outputs
encoder_ids = torch.tensor(
[
[
2871,
102,
2048,
3176,
2780,
1997,
2871,
26727,
2169,
2097,
12673,
1996,
8457,
2006,
2049,
8240,
2859,
2799,
1012,
2023,
6512,
2038,
2174,
13977,
2195,
25962,
1012,
102,
]
]
).to(torch_device)
decoder_prev_ids = torch.tensor([[102, 2129, 2116, 2372, 2024, 2006, 2169, 1997, 2122, 2048, 2780, 1029]]).to(
torch_device
)
output = model(
input_ids=encoder_ids,
attention_mask=None,
encoder_outputs=None,
decoder_input_ids=decoder_prev_ids,
)
output_predited_logits = output[0]
expected_shape = torch.Size((1, 12, 30522))
self.assertEqual(output_predited_logits.shape, expected_shape)
expected_slice = torch.tensor(
[[[-7.7729, -8.0343, -8.26001], [-7.74213, -7.8629, -8.6000], [-7.7328, -7.8269, -8.5264]]]
).to(torch_device)
# torch.testing.assert_close(output_predited_logits[:, :3, :3], expected_slice, rtol=1e-4, atol=1e-4)
assert torch.allclose(output_predited_logits[:, :3, :3], expected_slice, atol=1e-4)
# encoder outputs
encoder_outputs = model.prophetnet.encoder(encoder_ids)[0]
expected_encoder_outputs_slice = torch.tensor(
[[[-0.2526, -0.1951, -0.2185], [-0.8923, 0.2992, -0.4623], [-0.4585, 0.0165, -0.6652]]]
).to(torch_device)
expected_shape_encoder = torch.Size((1, 28, 1024))
self.assertEqual(encoder_outputs.shape, expected_shape_encoder)
# torch.testing.assert_close(encoder_outputs[:, :3, :3], expected_encoder_outputs_slice, rtol=1e-4, atol=1e-4)
assert torch.allclose(encoder_outputs[:, :3, :3], expected_encoder_outputs_slice, atol=1e-4)
# decoder outputs
decoder_outputs = model.prophetnet.decoder(decoder_prev_ids, encoder_hidden_states=encoder_outputs)
predicting_streams = decoder_outputs[1].view(1, model.config.ngram, 12, -1)
predicting_streams_logits = model.lm_head(predicting_streams)
next_first_stream_logits = predicting_streams_logits[:, 0]
# torch.testing.assert_close(next_first_stream_logits[:, :3, :3], expected_slice, rtol=1e-4, atol=1e-4)
assert torch.allclose(next_first_stream_logits[:, :3, :3], expected_slice, atol=1e-4)
@slow
def test_cnndm_inference(self):
model = ProphetNetForConditionalGeneration.from_pretrained("microsoft/prophetnet-large-uncased-cnndm")
model.config.max_length = 512
model.to(torch_device)
tokenizer = ProphetNetTokenizer.from_pretrained("microsoft/prophetnet-large-uncased-cnndm")
ARTICLE_TO_SUMMARIZE = (
"USTC was founded in Beijing by the Chinese Academy of Sciences (CAS) in September 1958. The Director of"
" CAS, Mr. Guo Moruo was appointed the first president of USTC. USTC's founding mission was to develop a"
" high-level science and technology workforce, as deemed critical for development of China's economy,"
' defense, and science and technology education. The establishment was hailed as "A Major Event in the'
' History of Chinese Education and Science." CAS has supported USTC by combining most of its institutes'
" with the departments of the university. USTC is listed in the top 16 national key universities, becoming"
" the youngest national key university.".lower()
)
input_ids = tokenizer([ARTICLE_TO_SUMMARIZE], max_length=511, return_tensors="pt").input_ids
input_ids = input_ids.to(torch_device)
summary_ids = model.generate(
input_ids, num_beams=4, length_penalty=1.0, no_repeat_ngram_size=3, early_stopping=True
)
EXPECTED_SUMMARIZE_512 = (
"us ##tc was founded by the chinese academy of sciences ( cas ) in 1958 . [X_SEP] us ##tc is listed in the"
" top 16 national key universities ."
)
generated_titles = [
" ".join(tokenizer.convert_ids_to_tokens(g, skip_special_tokens=True)) for g in summary_ids
]
self.assertListEqual(
[EXPECTED_SUMMARIZE_512],
generated_titles,
)
input_ids = tokenizer([ARTICLE_TO_SUMMARIZE], max_length=99, return_tensors="pt").input_ids
input_ids = input_ids.to(torch_device)
# actually 98 tokens are used. max_length=100 contains bos and eos.
summary_ids = model.generate(
input_ids, num_beams=4, length_penalty=1.0, no_repeat_ngram_size=3, early_stopping=True
)
EXPECTED_SUMMARIZE_100 = (
r"us ##tc was founded in beijing by the chinese academy of sciences ( cas ) in 1958 . [X_SEP] us ##tc "
"'"
" s founding mission was to develop a high - level science and technology workforce . [X_SEP]"
' establishment hailed as " a major event in the history of chinese education and science "'
)
generated_titles = [
" ".join(tokenizer.convert_ids_to_tokens(g, skip_special_tokens=True)) for g in summary_ids
]
self.assertListEqual(
[EXPECTED_SUMMARIZE_100],
generated_titles,
)
@slow
def test_question_gen_inference(self):
model = ProphetNetForConditionalGeneration.from_pretrained("microsoft/prophetnet-large-uncased-squad-qg")
model.to(torch_device)
tokenizer = ProphetNetTokenizer.from_pretrained("microsoft/prophetnet-large-uncased-squad-qg")
INPUTS = [
"Bill Gates [SEP] Microsoft was founded by Bill Gates and Paul Allen on April 4, 1975.",
"1975 [SEP] Microsoft was founded by Bill Gates and Paul Allen on April 4, 1975.",
"April 4, 1975 [SEP] Microsoft was founded by Bill Gates and Paul Allen on April 4, 1975.",
]
input_ids = tokenizer(INPUTS, truncation=True, padding=True, return_tensors="pt").input_ids
input_ids = input_ids.to(torch_device)
gen_output = model.generate(input_ids, num_beams=5, early_stopping=True)
generated_questions = tokenizer.batch_decode(gen_output, skip_special_tokens=True)
EXPECTED_QUESTIONS = [
"along with paul allen, who founded microsoft?",
"what year was microsoft founded?",
"when was microsoft founded?",
]
self.assertListEqual(
EXPECTED_QUESTIONS,
generated_questions,
)
| transformers/tests/models/prophetnet/test_modeling_prophetnet.py/0 | {
"file_path": "transformers/tests/models/prophetnet/test_modeling_prophetnet.py",
"repo_id": "transformers",
"token_count": 25727
} |
# 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.
import tempfile
import unittest
from transformers import AutoProcessor, AutoTokenizer, Qwen2AudioProcessor, WhisperFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
@require_torch
@require_torchaudio
class Qwen2AudioProcessorTest(unittest.TestCase):
def setUp(self):
self.checkpoint = "Qwen/Qwen2-Audio-7B-Instruct"
self.tmpdirname = tempfile.mkdtemp()
def test_can_load_various_tokenizers(self):
processor = Qwen2AudioProcessor.from_pretrained(self.checkpoint)
tokenizer = AutoTokenizer.from_pretrained(self.checkpoint)
self.assertEqual(processor.tokenizer.__class__, tokenizer.__class__)
def test_save_load_pretrained_default(self):
tokenizer = AutoTokenizer.from_pretrained(self.checkpoint)
processor = Qwen2AudioProcessor.from_pretrained(self.checkpoint)
feature_extractor = processor.feature_extractor
processor = Qwen2AudioProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)
processor.save_pretrained(self.tmpdirname)
processor = Qwen2AudioProcessor.from_pretrained(self.tmpdirname)
self.assertEqual(processor.tokenizer.get_vocab(), tokenizer.get_vocab())
self.assertEqual(processor.feature_extractor.to_json_string(), feature_extractor.to_json_string())
self.assertIsInstance(processor.feature_extractor, WhisperFeatureExtractor)
def test_tokenizer_integration(self):
slow_tokenizer = AutoTokenizer.from_pretrained(self.checkpoint, use_fast=False)
fast_tokenizer = AutoTokenizer.from_pretrained(self.checkpoint, from_slow=True, legacy=False)
prompt = "<|im_start|>system\nAnswer the questions.<|im_end|><|im_start|>user\n<|audio_bos|><|AUDIO|><|audio_eos|>\nWhat is it in this audio?<|im_end|><|im_start|>assistant\n"
EXPECTED_OUTPUT = [
"<|im_start|>",
"system",
"Ċ",
"Answer",
"Ġthe",
"Ġquestions",
".",
"<|im_end|>",
"<|im_start|>",
"user",
"Ċ",
"<|audio_bos|>",
"<|AUDIO|>",
"<|audio_eos|>",
"Ċ",
"What",
"Ġis",
"Ġit",
"Ġin",
"Ġthis",
"Ġaudio",
"?",
"<|im_end|>",
"<|im_start|>",
"assistant",
"Ċ",
]
print(slow_tokenizer.tokenize(prompt))
self.assertEqual(slow_tokenizer.tokenize(prompt), EXPECTED_OUTPUT)
self.assertEqual(fast_tokenizer.tokenize(prompt), EXPECTED_OUTPUT)
def test_chat_template(self):
processor = AutoProcessor.from_pretrained(self.checkpoint)
expected_prompt = "<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\nAudio 1: <|audio_bos|><|AUDIO|><|audio_eos|>\nWhat's that sound?<|im_end|>\n<|im_start|>assistant\nIt is the sound of glass shattering.<|im_end|>\n<|im_start|>user\nAudio 2: <|audio_bos|><|AUDIO|><|audio_eos|>\nHow about this one?<|im_end|>\n<|im_start|>assistant\n"
messages = [
{"role": "system", "content": "You are a helpful assistant."},
{
"role": "user",
"content": [
{
"type": "audio",
"audio_url": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen2-Audio/audio/glass-breaking-151256.mp3",
},
{"type": "text", "text": "What's that sound?"},
],
},
{"role": "assistant", "content": "It is the sound of glass shattering."},
{
"role": "user",
"content": [
{
"type": "audio",
"audio_url": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen2-Audio/audio/f2641_0_throatclearing.wav",
},
{"type": "text", "text": "How about this one?"},
],
},
]
formatted_prompt = processor.apply_chat_template(messages, add_generation_prompt=True)
self.assertEqual(expected_prompt, formatted_prompt)
| transformers/tests/models/qwen2_audio/test_processor_qwen2_audio.py/0 | {
"file_path": "transformers/tests/models/qwen2_audio/test_processor_qwen2_audio.py",
"repo_id": "transformers",
"token_count": 2277
} |
# Copyright 2020 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.
import unittest
from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
SAMPLE_VOCAB = get_tests_dir("fixtures/test_sentencepiece.model")
@require_sentencepiece
@require_tokenizers
class ReformerTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
from_pretrained_id = "google/reformer-crime-and-punishment"
tokenizer_class = ReformerTokenizer
rust_tokenizer_class = ReformerTokenizerFast
test_rust_tokenizer = True
test_seq2seq = False
test_sentencepiece = True
def setUp(self):
super().setUp()
tokenizer = ReformerTokenizer(SAMPLE_VOCAB, keep_accents=True)
tokenizer.save_pretrained(self.tmpdirname)
def test_convert_token_and_id(self):
"""Test ``_convert_token_to_id`` and ``_convert_id_to_token``."""
token = "<s>"
token_id = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(token), token_id)
self.assertEqual(self.get_tokenizer()._convert_id_to_token(token_id), token)
def test_get_vocab(self):
vocab_keys = list(self.get_tokenizer().get_vocab().keys())
self.assertEqual(vocab_keys[0], "<unk>")
self.assertEqual(vocab_keys[1], "<s>")
self.assertEqual(vocab_keys[-1], "j")
self.assertEqual(len(vocab_keys), 1_000)
def test_vocab_size(self):
self.assertEqual(self.get_tokenizer().vocab_size, 1_000)
def test_rust_and_python_full_tokenizers(self):
if not self.test_rust_tokenizer:
self.skipTest(reason="test_rust_tokenizer is set to False")
tokenizer = self.get_tokenizer()
rust_tokenizer = self.get_rust_tokenizer()
sequence = "I was born in 92000, and this is falsé."
tokens = tokenizer.tokenize(sequence)
rust_tokens = rust_tokenizer.tokenize(sequence)
self.assertListEqual(tokens, rust_tokens)
ids = tokenizer.encode(sequence, add_special_tokens=False)
rust_ids = rust_tokenizer.encode(sequence, add_special_tokens=False)
self.assertListEqual(ids, rust_ids)
rust_tokenizer = self.get_rust_tokenizer()
ids = tokenizer.encode(sequence)
rust_ids = rust_tokenizer.encode(sequence)
self.assertListEqual(ids, rust_ids)
def test_padding(self, max_length=15):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
# Simple input
s = "This is a simple input"
s2 = ["This is a simple input 1", "This is a simple input 2"]
p = ("This is a simple input", "This is a pair")
p2 = [
("This is a simple input 1", "This is a simple input 2"),
("This is a simple pair 1", "This is a simple pair 2"),
]
# Simple input tests
self.assertRaises(ValueError, tokenizer_r.encode, s, max_length=max_length, padding="max_length")
# Simple input
self.assertRaises(ValueError, tokenizer_r.encode_plus, s, max_length=max_length, padding="max_length")
# Simple input
self.assertRaises(
ValueError,
tokenizer_r.batch_encode_plus,
s2,
max_length=max_length,
padding="max_length",
)
# Pair input
self.assertRaises(ValueError, tokenizer_r.encode, p, max_length=max_length, padding="max_length")
# Pair input
self.assertRaises(ValueError, tokenizer_r.encode_plus, p, max_length=max_length, padding="max_length")
# Pair input
self.assertRaises(
ValueError,
tokenizer_r.batch_encode_plus,
p2,
max_length=max_length,
padding="max_length",
)
@unittest.skip(reason="Tokenizer has no padding token")
def test_padding_different_model_input_name(self):
pass
def test_full_tokenizer(self):
tokenizer = ReformerTokenizer(SAMPLE_VOCAB, keep_accents=True)
tokens = tokenizer.tokenize("This is a test")
self.assertListEqual(tokens, ["▁This", "▁is", "▁a", "▁t", "est"])
self.assertListEqual(
tokenizer.convert_tokens_to_ids(tokens),
[285, 46, 10, 170, 382],
)
tokens = tokenizer.tokenize("I was born in 92000, and this is falsé.")
self.assertListEqual(
tokens,
[
SPIECE_UNDERLINE + "I",
SPIECE_UNDERLINE + "was",
SPIECE_UNDERLINE + "b",
"or",
"n",
SPIECE_UNDERLINE + "in",
SPIECE_UNDERLINE + "",
"9",
"2",
"0",
"0",
"0",
",",
SPIECE_UNDERLINE + "and",
SPIECE_UNDERLINE + "this",
SPIECE_UNDERLINE + "is",
SPIECE_UNDERLINE + "f",
"al",
"s",
"é",
".",
],
)
ids = tokenizer.convert_tokens_to_ids(tokens)
self.assertListEqual(
ids,
[8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4],
)
back_tokens = tokenizer.convert_ids_to_tokens(ids)
self.assertListEqual(
back_tokens,
[
SPIECE_UNDERLINE + "I",
SPIECE_UNDERLINE + "was",
SPIECE_UNDERLINE + "b",
"or",
"n",
SPIECE_UNDERLINE + "in",
SPIECE_UNDERLINE + "",
"<unk>",
"2",
"0",
"0",
"0",
",",
SPIECE_UNDERLINE + "and",
SPIECE_UNDERLINE + "this",
SPIECE_UNDERLINE + "is",
SPIECE_UNDERLINE + "f",
"al",
"s",
"<unk>",
".",
],
)
@cached_property
def big_tokenizer(self):
return ReformerTokenizer.from_pretrained("google/reformer-crime-and-punishment")
@slow
def test_tokenization_base_easy_symbols(self):
symbols = "Hello World!"
original_tokenizer_encodings = [126, 32, 262, 152, 38, 72, 287]
self.assertListEqual(original_tokenizer_encodings, self.big_tokenizer.encode(symbols))
@slow
def test_tokenization_base_hard_symbols(self):
symbols = (
'This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'
" add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"
)
original_tokenizer_encodings = [
108,
265,
24,
111,
4,
258,
156,
35,
28,
275,
3,
259,
297,
260,
84,
4,
35,
110,
44,
8,
259,
91,
268,
21,
11,
209,
274,
109,
266,
277,
117,
86,
93,
315,
258,
278,
258,
277,
258,
0,
258,
288,
258,
319,
258,
0,
258,
0,
258,
0,
258,
0,
258,
287,
258,
315,
258,
289,
258,
278,
99,
269,
266,
262,
8,
259,
241,
4,
217,
230,
268,
266,
55,
168,
106,
75,
193,
266,
223,
27,
49,
26,
282,
25,
264,
299,
19,
26,
0,
258,
277,
117,
86,
93,
176,
183,
270,
11,
262,
42,
61,
265,
]
self.assertListEqual(original_tokenizer_encodings, self.big_tokenizer.encode(symbols))
@require_torch
@slow
def test_torch_encode_plus_sent_to_model(self):
import torch
from transformers import ReformerConfig, ReformerModel
# Build sequence
first_ten_tokens = list(self.big_tokenizer.get_vocab().keys())[:10]
sequence = " ".join(first_ten_tokens)
encoded_sequence = self.big_tokenizer.encode_plus(sequence, return_tensors="pt")
batch_encoded_sequence = self.big_tokenizer.batch_encode_plus([sequence, sequence], return_tensors="pt")
config = ReformerConfig()
# The input gets padded during training so adjust the axial position encodings from the pretrained model value of (512, 1024)
config.axial_pos_shape = encoded_sequence["input_ids"].shape
model = ReformerModel(config)
# Reformer has config.vocab_size == tokenizer.vocab_size == len(tokenizer) - 1 = 320; len(tokenizer) is 321 (including a pad token with id 320)
assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size
with torch.no_grad():
model(**encoded_sequence)
model(**batch_encoded_sequence)
@slow
def test_tokenizer_integration(self):
expected_encoding = {'input_ids': [[108, 265, 24, 111, 4, 258, 156, 7, 51, 279, 58, 7, 76, 25, 69, 278], [140, 243, 264, 134, 17, 267, 77, 263, 22, 262, 297, 258, 304, 177, 279, 266, 14, 89, 13, 35, 261, 299, 272, 137, 275, 278]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]} # fmt: skip
# This tokenizer does not know some characters like ")".
# That is the reason why we use very simple texts here.
# Also see https://github.com/huggingface/transformers/pull/11737#issuecomment-850769064
sequences = [
"This is a very simple sentence.",
"The quick brown fox jumps over the lazy dog.",
]
self.tokenizer_integration_test_util(
expected_encoding=expected_encoding,
model_name="google/reformer-crime-and-punishment",
revision="0e6c3decb8211d49bf881013425dc8b0448b3f5a",
padding=False,
sequences=sequences,
)
| transformers/tests/models/reformer/test_tokenization_reformer.py/0 | {
"file_path": "transformers/tests/models/reformer/test_tokenization_reformer.py",
"repo_id": "transformers",
"token_count": 6425
} |
# 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.
"""Testing suite for the PyTorch RT_DETR model."""
import inspect
import math
import tempfile
import unittest
from parameterized import parameterized
from transformers import (
RTDetrConfig,
RTDetrImageProcessor,
RTDetrResNetConfig,
is_torch_available,
is_vision_available,
)
from transformers.testing_utils import (
require_torch,
require_torch_accelerator,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import RTDetrForObjectDetection, RTDetrModel
if is_vision_available():
from PIL import Image
CHECKPOINT = "PekingU/rtdetr_r50vd" # TODO: replace
class RTDetrModelTester:
def __init__(
self,
parent,
batch_size=3,
is_training=True,
use_labels=True,
n_targets=3,
num_labels=10,
initializer_range=0.02,
layer_norm_eps=1e-5,
batch_norm_eps=1e-5,
# backbone
backbone_config=None,
# encoder HybridEncoder
encoder_hidden_dim=32,
encoder_in_channels=[128, 256, 512],
feat_strides=[8, 16, 32],
encoder_layers=1,
encoder_ffn_dim=64,
encoder_attention_heads=2,
dropout=0.0,
activation_dropout=0.0,
encode_proj_layers=[2],
positional_encoding_temperature=10000,
encoder_activation_function="gelu",
activation_function="silu",
eval_size=None,
normalize_before=False,
# decoder RTDetrTransformer
d_model=32,
num_queries=30,
decoder_in_channels=[32, 32, 32],
decoder_ffn_dim=64,
num_feature_levels=3,
decoder_n_points=4,
decoder_layers=2,
decoder_attention_heads=2,
decoder_activation_function="relu",
attention_dropout=0.0,
num_denoising=0,
label_noise_ratio=0.5,
box_noise_scale=1.0,
learn_initial_query=False,
anchor_image_size=None,
image_size=64,
disable_custom_kernels=True,
with_box_refine=True,
):
self.parent = parent
self.batch_size = batch_size
self.num_channels = 3
self.is_training = is_training
self.use_labels = use_labels
self.n_targets = n_targets
self.num_labels = num_labels
self.initializer_range = initializer_range
self.layer_norm_eps = layer_norm_eps
self.batch_norm_eps = batch_norm_eps
self.backbone_config = backbone_config
self.encoder_hidden_dim = encoder_hidden_dim
self.encoder_in_channels = encoder_in_channels
self.feat_strides = feat_strides
self.encoder_layers = encoder_layers
self.encoder_ffn_dim = encoder_ffn_dim
self.encoder_attention_heads = encoder_attention_heads
self.dropout = dropout
self.activation_dropout = activation_dropout
self.encode_proj_layers = encode_proj_layers
self.positional_encoding_temperature = positional_encoding_temperature
self.encoder_activation_function = encoder_activation_function
self.activation_function = activation_function
self.eval_size = eval_size
self.normalize_before = normalize_before
self.d_model = d_model
self.num_queries = num_queries
self.decoder_in_channels = decoder_in_channels
self.decoder_ffn_dim = decoder_ffn_dim
self.num_feature_levels = num_feature_levels
self.decoder_n_points = decoder_n_points
self.decoder_layers = decoder_layers
self.decoder_attention_heads = decoder_attention_heads
self.decoder_activation_function = decoder_activation_function
self.attention_dropout = attention_dropout
self.num_denoising = num_denoising
self.label_noise_ratio = label_noise_ratio
self.box_noise_scale = box_noise_scale
self.learn_initial_query = learn_initial_query
self.anchor_image_size = anchor_image_size
self.image_size = image_size
self.disable_custom_kernels = disable_custom_kernels
self.with_box_refine = with_box_refine
self.encoder_seq_length = math.ceil(self.image_size / 32) * math.ceil(self.image_size / 32)
def prepare_config_and_inputs(self):
pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
pixel_mask = torch.ones([self.batch_size, self.image_size, self.image_size], device=torch_device)
labels = None
if self.use_labels:
# labels is a list of Dict (each Dict being the labels for a given example in the batch)
labels = []
for i in range(self.batch_size):
target = {}
target["class_labels"] = torch.randint(
high=self.num_labels, size=(self.n_targets,), device=torch_device
)
target["boxes"] = torch.rand(self.n_targets, 4, device=torch_device)
labels.append(target)
config = self.get_config()
config.num_labels = self.num_labels
return config, pixel_values, pixel_mask, labels
def get_config(self):
hidden_sizes = [10, 20, 30, 40]
backbone_config = RTDetrResNetConfig(
embeddings_size=10,
hidden_sizes=hidden_sizes,
depths=[1, 1, 2, 1],
out_features=["stage2", "stage3", "stage4"],
out_indices=[2, 3, 4],
)
return RTDetrConfig.from_backbone_configs(
backbone_config=backbone_config,
encoder_hidden_dim=self.encoder_hidden_dim,
encoder_in_channels=hidden_sizes[1:],
feat_strides=self.feat_strides,
encoder_layers=self.encoder_layers,
encoder_ffn_dim=self.encoder_ffn_dim,
encoder_attention_heads=self.encoder_attention_heads,
dropout=self.dropout,
activation_dropout=self.activation_dropout,
encode_proj_layers=self.encode_proj_layers,
positional_encoding_temperature=self.positional_encoding_temperature,
encoder_activation_function=self.encoder_activation_function,
activation_function=self.activation_function,
eval_size=self.eval_size,
normalize_before=self.normalize_before,
d_model=self.d_model,
num_queries=self.num_queries,
decoder_in_channels=self.decoder_in_channels,
decoder_ffn_dim=self.decoder_ffn_dim,
num_feature_levels=self.num_feature_levels,
decoder_n_points=self.decoder_n_points,
decoder_layers=self.decoder_layers,
decoder_attention_heads=self.decoder_attention_heads,
decoder_activation_function=self.decoder_activation_function,
attention_dropout=self.attention_dropout,
num_denoising=self.num_denoising,
label_noise_ratio=self.label_noise_ratio,
box_noise_scale=self.box_noise_scale,
learn_initial_query=self.learn_initial_query,
anchor_image_size=self.anchor_image_size,
image_size=self.image_size,
disable_custom_kernels=self.disable_custom_kernels,
with_box_refine=self.with_box_refine,
)
def prepare_config_and_inputs_for_common(self):
config, pixel_values, pixel_mask, labels = self.prepare_config_and_inputs()
inputs_dict = {"pixel_values": pixel_values}
return config, inputs_dict
def create_and_check_rt_detr_model(self, config, pixel_values, pixel_mask, labels):
model = RTDetrModel(config=config)
model.to(torch_device)
model.eval()
result = model(pixel_values=pixel_values, pixel_mask=pixel_mask)
result = model(pixel_values)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.num_queries, self.d_model))
def create_and_check_rt_detr_object_detection_head_model(self, config, pixel_values, pixel_mask, labels):
model = RTDetrForObjectDetection(config=config)
model.to(torch_device)
model.eval()
result = model(pixel_values=pixel_values, pixel_mask=pixel_mask)
result = model(pixel_values)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_queries, self.num_labels))
self.parent.assertEqual(result.pred_boxes.shape, (self.batch_size, self.num_queries, 4))
result = model(pixel_values=pixel_values, pixel_mask=pixel_mask, labels=labels)
self.parent.assertEqual(result.loss.shape, ())
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_queries, self.num_labels))
self.parent.assertEqual(result.pred_boxes.shape, (self.batch_size, self.num_queries, 4))
@require_torch
class RTDetrModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (RTDetrModel, RTDetrForObjectDetection) if is_torch_available() else ()
pipeline_model_mapping = (
{"image-feature-extraction": RTDetrModel, "object-detection": RTDetrForObjectDetection}
if is_torch_available()
else {}
)
is_encoder_decoder = True
test_torchscript = False
test_pruning = False
test_head_masking = False
test_missing_keys = False
# special case for head models
def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels)
if return_labels:
if model_class.__name__ == "RTDetrForObjectDetection":
labels = []
for i in range(self.model_tester.batch_size):
target = {}
target["class_labels"] = torch.ones(
size=(self.model_tester.n_targets,), device=torch_device, dtype=torch.long
)
target["boxes"] = torch.ones(
self.model_tester.n_targets, 4, device=torch_device, dtype=torch.float
)
labels.append(target)
inputs_dict["labels"] = labels
return inputs_dict
def setUp(self):
self.model_tester = RTDetrModelTester(self)
self.config_tester = ConfigTester(
self,
config_class=RTDetrConfig,
has_text_modality=False,
common_properties=["hidden_size", "num_attention_heads"],
)
def test_config(self):
self.config_tester.run_common_tests()
def test_rt_detr_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_rt_detr_model(*config_and_inputs)
def test_rt_detr_object_detection_head_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_rt_detr_object_detection_head_model(*config_and_inputs)
@unittest.skip(reason="RTDetr does not use inputs_embeds")
def test_inputs_embeds(self):
pass
@unittest.skip(reason="RTDetr does not use test_inputs_embeds_matches_input_ids")
def test_inputs_embeds_matches_input_ids(self):
pass
@unittest.skip(reason="RTDetr does not support input and output embeddings")
def test_model_get_set_embeddings(self):
pass
@unittest.skip(reason="RTDetr does not support input and output embeddings")
def test_model_common_attributes(self):
pass
@unittest.skip(reason="RTDetr does not use token embeddings")
def test_resize_tokens_embeddings(self):
pass
@unittest.skip(reason="Feed forward chunking is not implemented")
def test_feed_forward_chunking(self):
pass
def test_attention_outputs(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.return_dict = True
for model_class in self.all_model_classes:
inputs_dict["output_attentions"] = True
inputs_dict["output_hidden_states"] = False
config.return_dict = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
attentions = outputs.encoder_attentions
self.assertEqual(len(attentions), self.model_tester.encoder_layers)
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
config.output_attentions = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
attentions = outputs.encoder_attentions
self.assertEqual(len(attentions), self.model_tester.encoder_layers)
self.assertListEqual(
list(attentions[0].shape[-3:]),
[
self.model_tester.encoder_attention_heads,
self.model_tester.encoder_seq_length,
self.model_tester.encoder_seq_length,
],
)
out_len = len(outputs)
correct_outlen = 13
# loss is at first position
if "labels" in inputs_dict:
correct_outlen += 1 # loss is added to beginning
# Object Detection model returns pred_logits and pred_boxes
if model_class.__name__ == "RTDetrForObjectDetection":
correct_outlen += 2
self.assertEqual(out_len, correct_outlen)
# decoder attentions
decoder_attentions = outputs.decoder_attentions
self.assertIsInstance(decoder_attentions, (list, tuple))
self.assertEqual(len(decoder_attentions), self.model_tester.decoder_layers)
self.assertListEqual(
list(decoder_attentions[0].shape[-3:]),
[
self.model_tester.decoder_attention_heads,
self.model_tester.num_queries,
self.model_tester.num_queries,
],
)
# cross attentions
cross_attentions = outputs.cross_attentions
self.assertIsInstance(cross_attentions, (list, tuple))
self.assertEqual(len(cross_attentions), self.model_tester.decoder_layers)
self.assertListEqual(
list(cross_attentions[0].shape[-3:]),
[
self.model_tester.decoder_attention_heads,
self.model_tester.num_feature_levels,
self.model_tester.decoder_n_points,
],
)
# Check attention is always last and order is fine
inputs_dict["output_attentions"] = True
inputs_dict["output_hidden_states"] = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
if hasattr(self.model_tester, "num_hidden_states_types"):
added_hidden_states = self.model_tester.num_hidden_states_types
else:
# RTDetr should maintin encoder_hidden_states output
added_hidden_states = 2
self.assertEqual(out_len + added_hidden_states, len(outputs))
self_attentions = outputs.encoder_attentions
self.assertEqual(len(self_attentions), self.model_tester.encoder_layers)
self.assertListEqual(
list(self_attentions[0].shape[-3:]),
[
self.model_tester.encoder_attention_heads,
self.model_tester.encoder_seq_length,
self.model_tester.encoder_seq_length,
],
)
def test_hidden_states_output(self):
def check_hidden_states_output(inputs_dict, config, model_class):
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
hidden_states = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
expected_num_layers = getattr(
self.model_tester, "expected_num_hidden_layers", len(self.model_tester.encoder_in_channels) - 1
)
self.assertEqual(len(hidden_states), expected_num_layers)
self.assertListEqual(
list(hidden_states[1].shape[-2:]),
[
self.model_tester.image_size // self.model_tester.feat_strides[-1],
self.model_tester.image_size // self.model_tester.feat_strides[-1],
],
)
if config.is_encoder_decoder:
hidden_states = outputs.decoder_hidden_states
expected_num_layers = getattr(
self.model_tester, "expected_num_hidden_layers", self.model_tester.decoder_layers + 1
)
self.assertIsInstance(hidden_states, (list, tuple))
self.assertEqual(len(hidden_states), expected_num_layers)
self.assertListEqual(
list(hidden_states[0].shape[-2:]),
[self.model_tester.num_queries, self.model_tester.d_model],
)
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
inputs_dict["output_hidden_states"] = True
check_hidden_states_output(inputs_dict, config, model_class)
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
config.output_hidden_states = True
check_hidden_states_output(inputs_dict, config, model_class)
def test_retain_grad_hidden_states_attentions(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.output_hidden_states = True
config.output_attentions = True
model_class = self.all_model_classes[0]
model = model_class(config)
model.to(torch_device)
inputs = self._prepare_for_class(inputs_dict, model_class)
outputs = model(**inputs)
# we take the first output since last_hidden_state is the first item
output = outputs[0]
encoder_hidden_states = outputs.encoder_hidden_states[0]
encoder_attentions = outputs.encoder_attentions[0]
encoder_hidden_states.retain_grad()
encoder_attentions.retain_grad()
decoder_attentions = outputs.decoder_attentions[0]
decoder_attentions.retain_grad()
cross_attentions = outputs.cross_attentions[0]
cross_attentions.retain_grad()
output.flatten()[0].backward(retain_graph=True)
self.assertIsNotNone(encoder_hidden_states.grad)
self.assertIsNotNone(encoder_attentions.grad)
self.assertIsNotNone(decoder_attentions.grad)
self.assertIsNotNone(cross_attentions.grad)
def test_forward_signature(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
signature = inspect.signature(model.forward)
arg_names = [*signature.parameters.keys()]
expected_arg_names = ["pixel_values"]
self.assertListEqual(arg_names[:1], expected_arg_names)
def test_different_timm_backbone(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
# let's pick a random timm backbone
config.backbone = "tf_mobilenetv3_small_075"
config.backbone_config = None
config.use_timm_backbone = True
config.backbone_kwargs = {"out_indices": [2, 3, 4]}
for model_class in self.all_model_classes:
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
if model_class.__name__ == "RTDetrForObjectDetection":
expected_shape = (
self.model_tester.batch_size,
self.model_tester.num_queries,
self.model_tester.num_labels,
)
self.assertEqual(outputs.logits.shape, expected_shape)
# Confirm out_indices was propogated to backbone
self.assertEqual(len(model.model.backbone.intermediate_channel_sizes), 3)
else:
# Confirm out_indices was propogated to backbone
self.assertEqual(len(model.backbone.intermediate_channel_sizes), 3)
self.assertTrue(outputs)
def test_hf_backbone(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
# Load a pretrained HF checkpoint as backbone
config.backbone = "microsoft/resnet-18"
config.backbone_config = None
config.use_timm_backbone = False
config.use_pretrained_backbone = True
config.backbone_kwargs = {"out_indices": [2, 3, 4]}
for model_class in self.all_model_classes:
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
if model_class.__name__ == "RTDetrForObjectDetection":
expected_shape = (
self.model_tester.batch_size,
self.model_tester.num_queries,
self.model_tester.num_labels,
)
self.assertEqual(outputs.logits.shape, expected_shape)
# Confirm out_indices was propogated to backbone
self.assertEqual(len(model.model.backbone.intermediate_channel_sizes), 3)
else:
# Confirm out_indices was propogated to backbone
self.assertEqual(len(model.backbone.intermediate_channel_sizes), 3)
self.assertTrue(outputs)
def test_initialization(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
configs_no_init = _config_zero_init(config)
configs_no_init.initializer_bias_prior_prob = 0.2
bias_value = -1.3863 # log_e ((1 - 0.2) / 0.2)
failed_cases = []
for model_class in self.all_model_classes:
model = model_class(config=configs_no_init)
# Skip the check for the backbone
for name, module in model.named_modules():
if module.__class__.__name__ == "RTDetrConvEncoder":
backbone_params = [f"{name}.{key}" for key in module.state_dict().keys()]
break
for name, param in model.named_parameters():
if param.requires_grad:
if ("class_embed" in name and "bias" in name) or "enc_score_head.bias" in name:
bias_tensor = torch.full_like(param.data, bias_value)
if not torch.allclose(param.data, bias_tensor, atol=1e-4):
failed_cases.append(
f"Parameter {name} of model {model_class} seems not properly initialized. "
f"Biases should be initialized to {bias_value}, got {param.data}"
)
elif (
"level_embed" in name
or "sampling_offsets.bias" in name
or "value_proj" in name
or "output_proj" in name
or "reference_points" in name
or "enc_score_head.weight" in name
or ("class_embed" in name and "weight" in name)
or name in backbone_params
):
continue
else:
mean = param.data.mean()
round_mean = (mean * 1e9).round() / 1e9
round_mean = round_mean.item()
if round_mean not in [0.0, 1.0]:
failed_cases.append(
f"Parameter {name} of model {model_class} seems not properly initialized. "
f"Mean is {round_mean}, but should be in [0, 1]"
)
message = "\n" + "\n".join(failed_cases)
self.assertTrue(not failed_cases, message)
@parameterized.expand(["float32", "float16", "bfloat16"])
@require_torch_accelerator
@slow
def test_inference_with_different_dtypes(self, torch_dtype_str):
torch_dtype = {
"float32": torch.float32,
"float16": torch.float16,
"bfloat16": torch.bfloat16,
}[torch_dtype_str]
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
model.to(torch_device).to(torch_dtype)
model.eval()
for key, tensor in inputs_dict.items():
if tensor.dtype == torch.float32:
inputs_dict[key] = tensor.to(torch_dtype)
with torch.no_grad():
_ = model(**self._prepare_for_class(inputs_dict, model_class))
@parameterized.expand(["float32", "float16", "bfloat16"])
@require_torch_accelerator
@slow
def test_inference_equivalence_for_static_and_dynamic_anchors(self, torch_dtype_str):
torch_dtype = {
"float32": torch.float32,
"float16": torch.float16,
"bfloat16": torch.bfloat16,
}[torch_dtype_str]
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
h, w = inputs_dict["pixel_values"].shape[-2:]
# convert inputs to the desired dtype
for key, tensor in inputs_dict.items():
if tensor.dtype == torch.float32:
inputs_dict[key] = tensor.to(torch_dtype)
for model_class in self.all_model_classes:
with tempfile.TemporaryDirectory() as tmpdirname:
model_class(config).save_pretrained(tmpdirname)
model_static = model_class.from_pretrained(
tmpdirname, anchor_image_size=[h, w], device_map=torch_device, torch_dtype=torch_dtype
).eval()
model_dynamic = model_class.from_pretrained(
tmpdirname, anchor_image_size=None, device_map=torch_device, torch_dtype=torch_dtype
).eval()
self.assertIsNotNone(model_static.config.anchor_image_size)
self.assertIsNone(model_dynamic.config.anchor_image_size)
with torch.no_grad():
outputs_static = model_static(**self._prepare_for_class(inputs_dict, model_class))
outputs_dynamic = model_dynamic(**self._prepare_for_class(inputs_dict, model_class))
self.assertTrue(
torch.allclose(
outputs_static.last_hidden_state, outputs_dynamic.last_hidden_state, rtol=1e-4, atol=1e-4
),
f"Max diff: {(outputs_static.last_hidden_state - outputs_dynamic.last_hidden_state).abs().max()}",
)
TOLERANCE = 1e-4
# We will verify our results on an image of cute cats
def prepare_img():
image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
return image
@require_torch
@require_vision
class RTDetrModelIntegrationTest(unittest.TestCase):
@cached_property
def default_image_processor(self):
return RTDetrImageProcessor.from_pretrained(CHECKPOINT) if is_vision_available() else None
def test_inference_object_detection_head(self):
model = RTDetrForObjectDetection.from_pretrained(CHECKPOINT).to(torch_device)
image_processor = self.default_image_processor
image = prepare_img()
inputs = image_processor(images=image, return_tensors="pt").to(torch_device)
with torch.no_grad():
outputs = model(**inputs)
expected_shape_logits = torch.Size((1, 300, model.config.num_labels))
self.assertEqual(outputs.logits.shape, expected_shape_logits)
expected_logits = torch.tensor(
[
[-4.64763879776001, -5.001153945922852, -4.978509902954102],
[-4.159348487854004, -4.703853607177734, -5.946484565734863],
[-4.437461853027344, -4.65836238861084, -6.235235691070557],
]
).to(torch_device)
expected_boxes = torch.tensor(
[
[0.1688060760498047, 0.19992263615131378, 0.21225441992282867],
[0.768376350402832, 0.41226309537887573, 0.4636859893798828],
[0.25953856110572815, 0.5483334064483643, 0.4777486026287079],
]
).to(torch_device)
torch.testing.assert_close(outputs.logits[0, :3, :3], expected_logits, rtol=1e-4, atol=1e-4)
expected_shape_boxes = torch.Size((1, 300, 4))
self.assertEqual(outputs.pred_boxes.shape, expected_shape_boxes)
torch.testing.assert_close(outputs.pred_boxes[0, :3, :3], expected_boxes, rtol=1e-4, atol=1e-4)
# verify postprocessing
results = image_processor.post_process_object_detection(
outputs, threshold=0.0, target_sizes=[image.size[::-1]]
)[0]
expected_scores = torch.tensor(
[0.9703017473220825, 0.9599503874778748, 0.9575679302215576, 0.9506784677505493], device=torch_device
)
expected_labels = [57, 15, 15, 65]
expected_slice_boxes = torch.tensor(
[
[0.13774872, 0.37821293, 640.13074, 476.21088],
[343.38132, 24.276838, 640.1404, 371.49573],
[13.225126, 54.179348, 318.98422, 472.2207],
[40.114475, 73.44104, 175.9573, 118.48469],
],
device=torch_device,
)
torch.testing.assert_close(results["scores"][:4], expected_scores, rtol=1e-4, atol=1e-4)
self.assertSequenceEqual(results["labels"][:4].tolist(), expected_labels)
torch.testing.assert_close(results["boxes"][:4], expected_slice_boxes, rtol=1e-4, atol=1e-4)
| transformers/tests/models/rt_detr/test_modeling_rt_detr.py/0 | {
"file_path": "transformers/tests/models/rt_detr/test_modeling_rt_detr.py",
"repo_id": "transformers",
"token_count": 15268
} |
# 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.
"""Testing suite for the PyTorch SeamlessM4Tv2 model."""
import copy
import tempfile
import unittest
from transformers import SeamlessM4Tv2Config, is_speech_available, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.trainer_utils import set_seed
from transformers.utils import cached_property
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import (
ModelTesterMixin,
_config_zero_init,
floats_tensor,
ids_tensor,
random_attention_mask,
)
if is_torch_available():
import torch
from transformers import (
SeamlessM4Tv2ForSpeechToSpeech,
SeamlessM4Tv2ForSpeechToText,
SeamlessM4Tv2ForTextToSpeech,
SeamlessM4Tv2ForTextToText,
SeamlessM4Tv2Model,
)
if is_speech_available():
from transformers import SeamlessM4TProcessor
class SeamlessM4Tv2ModelTester:
def __init__(
self,
parent,
input_modality="speech",
batch_size=2,
seq_length=4,
is_training=True,
use_input_mask=True,
use_token_type_ids=True,
use_labels=True,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
initializer_range=0.02,
max_new_tokens=None,
num_labels=3,
num_choices=4,
scope=None,
vocab_size=20,
t2u_vocab_size=20,
hidden_size=6,
num_hidden_layers=2,
intermediate_size=6,
max_position_embeddings=256,
encoder_layers=2,
decoder_layers=2,
encoder_ffn_dim=6,
decoder_ffn_dim=6,
t2u_encoder_layers=2,
t2u_decoder_layers=2,
t2u_encoder_ffn_dim=6,
t2u_decoder_ffn_dim=6,
num_heads=2,
vocoder_num_spkrs=5,
vocoder_num_langs=5,
upsample_initial_channel=32,
unit_embed_dim=25,
spkr_embed_dim=6,
lang_embed_dim=6,
num_conv_pos_embeddings=8,
unit_hifi_gan_vocab_size=20,
t2u_num_langs=0,
t2u_offset_tgt_lang=0,
vocoder_offset=0,
t2u_variance_predictor_hidden_dim=4,
char_vocab_size=4,
left_max_position_embeddings=2,
right_max_position_embeddings=1,
speech_encoder_chunk_size=2,
speech_encoder_left_chunk_num=1,
):
self.parent = parent
self.input_modality = input_modality
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_input_mask = use_input_mask
self.use_token_type_ids = use_token_type_ids
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.num_labels = num_labels
self.num_choices = num_choices
self.scope = scope
self.vocab_size = vocab_size
self.t2u_vocab_size = t2u_vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.intermediate_size = intermediate_size
self.max_position_embeddings = max_position_embeddings
self.encoder_layers = encoder_layers
self.decoder_layers = decoder_layers
self.encoder_ffn_dim = encoder_ffn_dim
self.decoder_ffn_dim = decoder_ffn_dim
self.t2u_encoder_layers = t2u_encoder_layers
self.t2u_decoder_layers = t2u_decoder_layers
self.t2u_encoder_ffn_dim = t2u_encoder_ffn_dim
self.t2u_decoder_ffn_dim = t2u_decoder_ffn_dim
self.num_heads = num_heads
self.num_attention_heads = num_heads
self.vocoder_num_spkrs = vocoder_num_spkrs
self.vocoder_num_langs = vocoder_num_langs
self.upsample_initial_channel = upsample_initial_channel
self.unit_embed_dim = unit_embed_dim
self.spkr_embed_dim = spkr_embed_dim
self.num_conv_pos_embeddings = num_conv_pos_embeddings
self.lang_embed_dim = lang_embed_dim
self.max_new_tokens = max_new_tokens
self.unit_hifi_gan_vocab_size = unit_hifi_gan_vocab_size
self.t2u_num_langs = t2u_num_langs
self.t2u_offset_tgt_lang = t2u_offset_tgt_lang
self.vocoder_offset = vocoder_offset
self.t2u_variance_predictor_hidden_dim = t2u_variance_predictor_hidden_dim
self.char_vocab_size = char_vocab_size
self.left_max_position_embeddings = left_max_position_embeddings
self.right_max_position_embeddings = right_max_position_embeddings
self.speech_encoder_chunk_size = speech_encoder_chunk_size
self.speech_encoder_left_chunk_num = speech_encoder_left_chunk_num
def prepare_config_and_inputs(self):
if self.input_modality == "text":
inputs = ids_tensor([self.batch_size, self.seq_length], self.vocab_size - 1)
else:
inputs = ids_tensor([self.batch_size, self.seq_length, 160], self.vocab_size - 1).float()
input_mask = None
if self.use_input_mask:
input_mask = random_attention_mask([self.batch_size, self.seq_length])
decoder_input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size - 1)
lm_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
config = self.get_config()
return config, inputs, decoder_input_ids, input_mask, lm_labels
def get_config(self):
return SeamlessM4Tv2Config(
hidden_act=self.hidden_act,
hidden_dropout_prob=self.hidden_dropout_prob,
attention_probs_dropout_prob=self.attention_probs_dropout_prob,
initializer_range=self.initializer_range,
vocab_size=self.vocab_size,
t2u_vocab_size=self.t2u_vocab_size,
hidden_size=self.hidden_size,
speech_encoder_layers=self.num_heads,
speech_encoder_intermediate_size=self.intermediate_size,
max_position_embeddings=self.max_position_embeddings,
encoder_layers=self.encoder_layers,
decoder_layers=self.decoder_layers,
encoder_ffn_dim=self.encoder_ffn_dim,
decoder_ffn_dim=self.decoder_ffn_dim,
t2u_encoder_layers=self.t2u_encoder_layers,
t2u_decoder_layers=self.t2u_decoder_layers,
t2u_encoder_ffn_dim=self.t2u_encoder_ffn_dim,
t2u_decoder_ffn_dim=self.t2u_decoder_ffn_dim,
num_attention_heads=self.num_heads,
encoder_attention_heads=self.num_heads,
decoder_attention_heads=self.num_heads,
t2u_encoder_attention_heads=self.num_heads,
t2u_decoder_attention_heads=self.num_heads,
speech_encoder_attention_heads=self.num_heads,
unit_hifigan_vocab_vise=self.t2u_vocab_size,
vocoder_num_spkrs=self.vocoder_num_spkrs,
vocoder_num_langs=self.vocoder_num_langs,
upsample_initial_channel=self.upsample_initial_channel,
unit_embed_dim=self.unit_embed_dim,
spkr_embed_dim=self.spkr_embed_dim,
num_conv_pos_embeddings=self.num_conv_pos_embeddings,
lang_embed_dim=self.lang_embed_dim,
max_new_tokens=self.max_new_tokens,
unit_hifi_gan_vocab_size=self.unit_hifi_gan_vocab_size,
t2u_num_langs=self.t2u_num_langs,
t2u_offset_tgt_lang=self.t2u_offset_tgt_lang,
vocoder_offset=self.vocoder_offset,
t2u_variance_predictor_embed_dim=self.hidden_size,
t2u_variance_predictor_hidden_dim=self.t2u_variance_predictor_hidden_dim,
char_vocab_size=self.char_vocab_size,
left_max_position_embeddings=self.left_max_position_embeddings,
right_max_position_embeddings=self.right_max_position_embeddings,
speech_encoder_chunk_size=self.speech_encoder_chunk_size,
speech_encoder_left_chunk_num=self.speech_encoder_left_chunk_num,
)
def prepare_config_and_inputs_for_decoder(self):
(
config,
input_ids,
decoder_input_ids,
input_mask,
lm_labels,
) = self.prepare_config_and_inputs()
config.is_decoder = True
encoder_hidden_states = floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
encoder_attention_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
return (
config,
input_ids,
decoder_input_ids,
input_mask,
lm_labels,
encoder_hidden_states,
encoder_attention_mask,
)
def create_and_check_model(self, config, input_ids, decoder_input_ids, input_mask, labels):
model = SeamlessM4Tv2Model(config=config)
model.to(torch_device)
model.eval()
if self.input_modality == "text":
result = model(input_ids=input_ids, attention_mask=input_mask, decoder_input_ids=decoder_input_ids)
result = model(input_ids=input_ids, decoder_input_ids=decoder_input_ids)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
else:
result = model(input_features=input_ids, attention_mask=input_mask, decoder_input_ids=decoder_input_ids)
result = model(input_features=input_ids, decoder_input_ids=decoder_input_ids)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
decoder_output = result.logits
decoder_past = result.past_key_values
encoder_output = result.encoder_last_hidden_state
if self.input_modality == "text":
seq_length = self.seq_length
else:
# if speech, expected length has been subsampled.
seq_length = model._compute_sub_sample_lengths_from_attention_mask(input_mask).max().item()
self.parent.assertEqual(encoder_output.size(), (self.batch_size, seq_length, self.hidden_size))
self.parent.assertEqual(decoder_output.size(), (self.batch_size, decoder_input_ids.shape[1], self.vocab_size))
# There should be `num_layers` key value embeddings stored in decoder_past
self.parent.assertEqual(len(decoder_past), config.decoder_layers)
# There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
self.parent.assertEqual(len(decoder_past[0]), 4)
def create_and_check_decoder_model_past_large_inputs(
self,
config,
input_ids,
decoder_input_ids,
input_mask,
lm_labels,
encoder_hidden_states,
encoder_attention_mask,
):
config.is_decoder = True
model = SeamlessM4Tv2Model(config=config)
model.to(torch_device)
model.eval()
# make sure no pad token in decoder_input_ids
decoder_input_ids = torch.clamp(decoder_input_ids, config.pad_token_id + 1)
# first forward pass
outputs = model(
input_ids, decoder_input_ids=decoder_input_ids, decoder_attention_mask=input_mask, use_cache=True
)
past_key_values = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size)
next_mask = ids_tensor((self.batch_size, 3), vocab_size=2)
# append to next input_ids and
next_input_ids = torch.cat([decoder_input_ids, next_tokens], dim=-1)
next_attention_mask = torch.cat([input_mask, next_mask], dim=-1)
output_from_no_past = model(
input_ids,
decoder_input_ids=next_input_ids,
decoder_attention_mask=next_attention_mask,
output_hidden_states=True,
)
output_from_no_past = output_from_no_past["decoder_hidden_states"][0]
output_from_past = model(
input_ids,
decoder_input_ids=next_tokens,
decoder_attention_mask=next_attention_mask,
past_key_values=past_key_values,
output_hidden_states=True,
)["decoder_hidden_states"][0]
# select random slice
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach()
output_from_past_slice = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
config,
input_ids,
decoder_input_ids,
input_mask,
lm_labels,
) = config_and_inputs
input_name = "input_ids" if self.input_modality == "text" else "input_features"
inputs_dict = {
input_name: input_ids,
"attention_mask": input_mask,
"decoder_input_ids": decoder_input_ids,
"labels": lm_labels,
}
return config, inputs_dict
@require_torch
class SeamlessM4Tv2ModelWithSpeechInputTest(ModelTesterMixin, unittest.TestCase):
is_encoder_decoder = True
fx_compatible = False
test_missing_keys = False
test_pruning = False
test_model_parallel = False
test_resize_embeddings = False
test_headmasking = False
test_torchscript = False
all_model_classes = (
(
SeamlessM4Tv2Model,
SeamlessM4Tv2ForSpeechToSpeech,
SeamlessM4Tv2ForSpeechToText,
)
if is_torch_available()
else ()
)
all_generative_model_classes = (SeamlessM4Tv2ForSpeechToText,) if is_torch_available() else ()
def setUp(self):
self.model_tester = SeamlessM4Tv2ModelTester(self, input_modality="speech")
self.config_tester = ConfigTester(self, config_class=SeamlessM4Tv2Config)
def test_config(self):
self.config_tester.run_common_tests()
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
@slow
def test_model_from_pretrained(self):
model_name = "facebook/seamless-m4t-v2-large"
model = SeamlessM4Tv2Model.from_pretrained(model_name)
self.assertIsNotNone(model)
def test_initialization(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
configs_no_init = _config_zero_init(config)
for model_class in self.all_model_classes:
model = model_class(config=configs_no_init)
for name, param in model.named_parameters():
uniform_init_parms = [
"conv",
"masked_spec_embed",
"codevectors",
"quantizer.weight_proj.weight",
"project_hid.weight",
"project_hid.bias",
"project_q.weight",
"project_q.bias",
"pos_bias_v",
"pos_bias_u",
"pointwise_conv1",
"pointwise_conv2",
"feature_projection.projection.weight",
"feature_projection.projection.bias",
"objective.weight",
"adapter",
]
if param.requires_grad:
if any(x in name for x in uniform_init_parms):
self.assertTrue(
-1.0 <= ((param.data.mean() * 1e9).round() / 1e9).item() <= 1.0,
msg=f"Parameter {name} of model {model_class} seems not properly initialized",
)
else:
self.assertIn(
((param.data.mean() * 1e9).round() / 1e9).item(),
[0.0, 1.0],
msg=f"Parameter {name} of model {model_class} seems not properly initialized",
)
@unittest.skip(reason="SeamlessM4Tv2SpeechEncoder doesn't have an embedding layer")
def test_inputs_embeds(self):
pass
@unittest.skip(reason="SeamlessM4TSpeechEncoder doesn't have an embedding layer")
def test_inputs_embeds_matches_input_ids(self):
pass
@unittest.skip(
reason="Expected missing keys serve when using SeamlessM4Tv2ForXXX.from_pretrained from a checkpoint saved by SeamlessM4Tv2Model.save_pretrained."
)
def test_model_weights_reload_no_missing_tied_weights(self):
pass
@unittest.skip(
reason="SeamlessM4Tv2Model is base class but has actually a bigger architecture than seamlessM4T task-specific models."
)
def test_save_load_fast_init_to_base(self):
pass
@unittest.skip(reason="SeamlessM4Tv2Model can takes input_ids or input_features")
def test_forward_signature(self):
pass
@unittest.skip(reason="SeamlessM4Tv2 has no base model")
def test_save_load_fast_init_from_base(self):
pass
@unittest.skip(
reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
)
def test_training_gradient_checkpointing(self):
pass
@unittest.skip(
reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
)
def test_training_gradient_checkpointing_use_reentrant(self):
pass
@unittest.skip(
reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
)
def test_training_gradient_checkpointing_use_reentrant_false(self):
pass
@unittest.skip(
reason="This architecure has tied weights by default and there is no way to remove it, check: https://github.com/huggingface/transformers/pull/31771#issuecomment-2210915245"
)
def test_load_save_without_tied_weights(self):
pass
def test_attention_outputs(self):
# expected length is subsampled so need to change a bit this test
if not self.has_attentions:
self.skipTest(reason="Model does not output attentions")
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.return_dict = True
seq_len = getattr(self.model_tester, "seq_length", None)
decoder_seq_length = getattr(self.model_tester, "decoder_seq_length", seq_len)
encoder_seq_length = getattr(self.model_tester, "encoder_seq_length", seq_len)
decoder_key_length = getattr(self.model_tester, "decoder_key_length", decoder_seq_length)
encoder_key_length = getattr(self.model_tester, "key_length", encoder_seq_length)
# no more chunk_length test
for model_class in self.all_model_classes:
inputs_dict["output_attentions"] = True
inputs_dict["output_hidden_states"] = False
config.return_dict = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
config.output_attentions = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
self.assertListEqual(
list(attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length],
)
out_len = len(outputs)
if self.is_encoder_decoder:
correct_outlen = 5
# loss is at first position
if "labels" in inputs_dict:
correct_outlen += 1 # loss is added to beginning
if "past_key_values" in outputs:
correct_outlen += 1 # past_key_values have been returned
self.assertEqual(out_len, correct_outlen)
# decoder attentions
decoder_attentions = outputs.decoder_attentions
self.assertIsInstance(decoder_attentions, (list, tuple))
self.assertEqual(len(decoder_attentions), self.model_tester.num_hidden_layers)
self.assertListEqual(
list(decoder_attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads, decoder_seq_length, decoder_key_length],
)
# cross attentions
cross_attentions = outputs.cross_attentions
self.assertIsInstance(cross_attentions, (list, tuple))
self.assertEqual(len(cross_attentions), self.model_tester.num_hidden_layers)
sub_sampled_length = (
model._compute_sub_sample_lengths_from_attention_mask(inputs_dict["attention_mask"]).max().item()
)
self.assertListEqual(
list(cross_attentions[0].shape[-3:]),
[
self.model_tester.num_attention_heads,
decoder_seq_length,
sub_sampled_length,
],
)
# Check attention is always last and order is fine
inputs_dict["output_attentions"] = True
inputs_dict["output_hidden_states"] = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
if hasattr(self.model_tester, "num_hidden_states_types"):
added_hidden_states = self.model_tester.num_hidden_states_types
elif self.is_encoder_decoder:
added_hidden_states = 2
else:
added_hidden_states = 1
self.assertEqual(out_len + added_hidden_states, len(outputs))
self_attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers)
self.assertListEqual(
list(self_attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length],
)
# TODO: @ydshieh: refer to #34968
@unittest.skip(reason="Failing on multi-gpu runner")
def test_retain_grad_hidden_states_attentions(self):
pass
@require_torch
class SeamlessM4Tv2ModelWithTextInputTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
is_encoder_decoder = True
fx_compatible = False
test_missing_keys = False
test_pruning = False
test_model_parallel = False
test_resize_embeddings = True
test_headmasking = False
test_torchscript = False
all_model_classes = (
(
SeamlessM4Tv2Model,
SeamlessM4Tv2ForTextToSpeech,
SeamlessM4Tv2ForTextToText,
)
if is_torch_available()
else ()
)
all_generative_model_classes = (SeamlessM4Tv2ForTextToText,) if is_torch_available() else ()
def setUp(self):
self.model_tester = SeamlessM4Tv2ModelTester(self, input_modality="text")
self.config_tester = ConfigTester(self, config_class=SeamlessM4Tv2Config)
def test_config(self):
self.config_tester.run_common_tests()
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
@slow
def test_model_from_pretrained(self):
model_name = "facebook/seamless-m4t-v2-large"
model = SeamlessM4Tv2Model.from_pretrained(model_name)
self.assertIsNotNone(model)
def test_initialization(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
configs_no_init = _config_zero_init(config)
for model_class in self.all_model_classes:
model = model_class(config=configs_no_init)
for name, param in model.named_parameters():
uniform_init_parms = [
"conv",
"masked_spec_embed",
"codevectors",
"quantizer.weight_proj.weight",
"project_hid.weight",
"project_hid.bias",
"project_q.weight",
"project_q.bias",
"pos_bias_v",
"pos_bias_u",
"pointwise_conv1",
"pointwise_conv2",
"feature_projection.projection.weight",
"feature_projection.projection.bias",
"objective.weight",
"adapter",
]
if param.requires_grad:
if any(x in name for x in uniform_init_parms):
self.assertTrue(
-1.0 <= ((param.data.mean() * 1e9).round() / 1e9).item() <= 1.0,
msg=f"Parameter {name} of model {model_class} seems not properly initialized",
)
else:
self.assertIn(
((param.data.mean() * 1e9).round() / 1e9).item(),
[0.0, 1.0],
msg=f"Parameter {name} of model {model_class} seems not properly initialized",
)
@unittest.skip(
reason="Expected missing keys serve when using SeamlessM4Tv2ForXXX.from_pretrained from a checkpoint saved by SeamlessM4Tv2Model.save_pretrained."
)
def test_model_weights_reload_no_missing_tied_weights(self):
pass
@unittest.skip(reason="SeamlessM4Tv2Model can take input_ids or input_features")
def test_forward_signature(self):
pass
def test_decoder_model_past_with_large_inputs(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_decoder_model_past_large_inputs(*config_and_inputs)
@unittest.skip(
reason="SeamlessM4Tv2Model is base class but has actually a bigger architecture than seamlessM4T task-specific models."
)
def test_save_load_fast_init_to_base(self):
pass
@unittest.skip(reason="SeamlessM4Tv2 has no base model")
def test_save_load_fast_init_from_base(self):
pass
@unittest.skip(
reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
)
def test_training_gradient_checkpointing(self):
pass
@unittest.skip(
reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
)
def test_training_gradient_checkpointing_use_reentrant(self):
pass
@unittest.skip(
reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
)
def test_training_gradient_checkpointing_use_reentrant_false(self):
pass
@unittest.skip(
reason="This architecure has tied weights by default and there is no way to remove it, check: https://github.com/huggingface/transformers/pull/31771#issuecomment-2210915245"
)
def test_load_save_without_tied_weights(self):
pass
@require_torch
class SeamlessM4Tv2GenerationTest(unittest.TestCase):
# test that non-standard generation works
# test generation of: SeamlessM4Tv2Model, SeamlessM4Tv2ForSpeechToSpeech, SeamlessM4Tv2ForSpeechToText, SeamlessM4Tv2ForTextToSpeech
def setUp(self):
self.speech_model_tester = SeamlessM4Tv2ModelTester(self, input_modality="speech")
self.text_model_tester = SeamlessM4Tv2ModelTester(self, input_modality="text")
self.tmpdirname = tempfile.mkdtemp()
def update_generation(self, model):
text_lang_code_to_id = {
"fra": 4,
"eng": 4,
"rus": 4,
}
speech_lang_code_to_id = {
"fra": 4,
"eng": 4,
}
id_to_text = {str(i): "a" for i in range(model.config.vocab_size)}
id_to_text["0"] = "ab"
id_to_text["1"] = "_b"
id_to_text["3"] = ","
id_to_text["4"] = "_cd"
char_to_id = {char: i for (i, char) in enumerate("abcd")}
generation_config = copy.deepcopy(model.generation_config)
generation_config.__setattr__("text_decoder_lang_to_code_id", text_lang_code_to_id)
generation_config.__setattr__("t2u_lang_code_to_id", speech_lang_code_to_id)
generation_config.__setattr__("vocoder_lang_code_to_id", speech_lang_code_to_id)
generation_config.__setattr__("id_to_text", id_to_text)
generation_config.__setattr__("char_to_id", char_to_id)
generation_config.__setattr__("eos_token_id", 0)
generation_config._from_model_config = False
model.generation_config = generation_config
def prepare_text_input(self, tgt_lang):
config, inputs, decoder_input_ids, input_mask, lm_labels = self.text_model_tester.prepare_config_and_inputs()
input_dict = {
"input_ids": inputs,
"attention_mask": input_mask,
"tgt_lang": tgt_lang,
"num_beams": 2,
"do_sample": True,
}
return config, input_dict
def prepare_speech_input(self):
config, inputs, decoder_input_ids, input_mask, lm_labels = self.speech_model_tester.prepare_config_and_inputs()
input_dict = {
"input_features": inputs,
"attention_mask": input_mask,
"tgt_lang": "fra",
"num_beams": 2,
"do_sample": True,
}
return config, input_dict
def prepare_speech_and_text_input(self):
config, inputs, decoder_input_ids, input_mask, lm_labels = self.speech_model_tester.prepare_config_and_inputs()
input_speech = {
"input_features": inputs,
"attention_mask": input_mask,
"tgt_lang": "fra",
"num_beams": 2,
"do_sample": True,
}
config, inputs, decoder_input_ids, input_mask, lm_labels = self.text_model_tester.prepare_config_and_inputs()
input_text = {
"input_ids": inputs,
"attention_mask": input_mask,
"tgt_lang": "eng",
"num_beams": 2,
"do_sample": True,
}
return config, input_speech, input_text
def factory_generation_speech_test(self, model, inputs):
set_seed(0)
output = model.generate(**inputs)
return output
def test_generation_languages(self):
config, input_text_rus = self.prepare_text_input(tgt_lang="rus")
model = SeamlessM4Tv2Model(config=config)
self.update_generation(model)
model.to(torch_device)
model.eval()
# make sure that generating speech, with a language that is only supported for text translation, raises error
with self.assertRaises(ValueError):
model.generate(**input_text_rus)
# make sure that generating text only works
model.generate(**input_text_rus, generate_speech=False)
# make sure it works for languages supported by both output modalities
config, input_text_eng = self.prepare_text_input(tgt_lang="eng")
model.generate(**input_text_eng)
model.generate(**input_text_eng, generate_speech=False)
def test_speech_generation(self):
config, input_speech, input_text = self.prepare_speech_and_text_input()
from transformers.testing_utils import set_config_for_less_flaky_test, set_model_for_less_flaky_test
set_config_for_less_flaky_test(config)
model = SeamlessM4Tv2Model(config=config)
set_model_for_less_flaky_test(model)
self.update_generation(model)
model.save_pretrained(self.tmpdirname)
model.to(torch_device)
model.eval()
output_original_text = self.factory_generation_speech_test(model, input_text)
output_original_speech = self.factory_generation_speech_test(model, input_speech)
state_dict = model.state_dict()
text_model = SeamlessM4Tv2ForTextToSpeech.from_pretrained(self.tmpdirname)
# Even if this component is loaded after `model.save_pretrained` which is after
# `set_model_for_less_flaky_test(model)`, we still need to apply `set_model_for_less_flaky_test` here as the
# `eps` attribute in the model's norm layers is not set from the config.
set_model_for_less_flaky_test(text_model)
self.update_generation(text_model)
text_model.to(torch_device)
text_model.eval()
output_text = self.factory_generation_speech_test(model, input_text)
speech_model = SeamlessM4Tv2ForSpeechToSpeech.from_pretrained(self.tmpdirname)
# Even if this component is loaded after `model.save_pretrained` which is after
# `set_model_for_less_flaky_test(model)`, we still need to apply `set_model_for_less_flaky_test` here as the
# `eps` attribute in the model's norm layers is not set from the config.
set_model_for_less_flaky_test(speech_model)
self.update_generation(speech_model)
speech_model.to(torch_device)
speech_model.eval()
for name, tensor in speech_model.state_dict().items():
right_tensor = state_dict.get(name)
self.assertEqual(tensor.tolist(), right_tensor.tolist(), f"Tensor {name}")
output_speech = self.factory_generation_speech_test(model, input_speech)
# test same text output from input text
self.assertListEqual(output_original_text[0].ravel().tolist(), output_text[0].ravel().tolist())
self.assertListEqual(output_original_text[1].ravel().tolist(), output_text[1].ravel().tolist())
# test same speech output from input text
# assertTrue because super long list makes this hang in case of failure
self.assertTrue(
output_original_speech[0].ravel().tolist() == output_speech[0].ravel().tolist(),
"Speech generated was different",
)
self.assertTrue(
output_original_speech[1].ravel().tolist() == output_speech[1].ravel().tolist(),
"Speech generated was different",
)
def test_text_generation(self):
config, input_speech, input_text = self.prepare_speech_and_text_input()
# to return speech
input_speech["generate_speech"] = False
input_text["generate_speech"] = False
model = SeamlessM4Tv2Model(config=config)
self.update_generation(model)
model.save_pretrained(self.tmpdirname)
model.to(torch_device)
model.eval()
output_original_text = self.factory_generation_speech_test(model, input_text)
output_original_speech = self.factory_generation_speech_test(model, input_speech)
# other models don't need it
input_speech.pop("generate_speech")
input_text.pop("generate_speech")
state_dict = model.state_dict()
text_model = SeamlessM4Tv2ForTextToText.from_pretrained(self.tmpdirname)
self.update_generation(text_model)
text_model.to(torch_device)
text_model.eval()
for name, tensor in text_model.state_dict().items():
right_tensor = state_dict.get(name)
self.assertEqual(tensor.tolist(), right_tensor.tolist())
output_text = self.factory_generation_speech_test(text_model, input_text)
speech_model = SeamlessM4Tv2ForSpeechToText.from_pretrained(self.tmpdirname)
for name, tensor in speech_model.state_dict().items():
right_tensor = state_dict.get(name)
self.assertEqual(tensor.tolist(), right_tensor.tolist(), f"Tensor {name}")
self.update_generation(speech_model)
speech_model.to(torch_device)
speech_model.eval()
output_speech = self.factory_generation_speech_test(speech_model, input_speech)
# test same text output from input text
self.assertListEqual(output_original_text[0].ravel().tolist(), output_text.ravel().tolist())
# test same speech output from input text
self.assertListEqual(output_original_speech[0].ravel().tolist(), output_speech.ravel().tolist())
def test_generation(self):
config, input_speech, input_text = self.prepare_speech_and_text_input()
input_speech["num_beams"] = 3
input_speech["do_sample"] = True
input_speech["temperature"] = 0.5
input_speech["num_return_sequences"] = 3
input_text["num_beams"] = 3
input_text["do_sample"] = True
input_text["temperature"] = 0.5
input_text["num_return_sequences"] = 3
for model_class in [SeamlessM4Tv2ForSpeechToSpeech, SeamlessM4Tv2ForSpeechToText, SeamlessM4Tv2Model]:
model = model_class(config=config)
self.update_generation(model)
model.to(torch_device)
model.eval()
output = model.generate(**input_speech)
output = output[0] if isinstance(output, tuple) else output
self.assertEqual(output.shape[0], 3 * input_speech["input_features"].shape[0])
for model_class in [SeamlessM4Tv2ForTextToSpeech, SeamlessM4Tv2ForTextToText, SeamlessM4Tv2Model]:
model = model_class(config=config)
self.update_generation(model)
model.to(torch_device)
model.eval()
output = model.generate(**input_text)
output = output[0] if isinstance(output, tuple) else output
self.assertEqual(output.shape[0], 3 * input_text["input_ids"].shape[0])
@require_torch
class SeamlessM4Tv2ModelIntegrationTest(unittest.TestCase):
repo_id = "facebook/seamless-m4t-v2-large"
def assertListAlmostEqual(self, list1, list2, tol=1e-4):
self.assertEqual(len(list1), len(list2))
for a, b in zip(list1, list2):
self.assertAlmostEqual(a, b, delta=tol)
@cached_property
def processor(self):
return SeamlessM4TProcessor.from_pretrained(self.repo_id)
@cached_property
def input_text(self):
# corresponds to "C'est un test." with seamlessM4T_medium checkpoint
input_ids = torch.tensor([[256026, 109, 247729, 171, 128, 6816, 247676, 3]]) # fmt: skip
input_ids = input_ids.to(torch_device)
attention_mask = torch.ones_like(input_ids).to(torch_device)
inputs = {
"attention_mask": attention_mask,
"input_ids": input_ids,
}
return inputs
@cached_property
def input_audio(self):
set_seed(0)
seq_len = 20000
sampling_rate = 16000
input_features = torch.rand((2, seq_len))
return self.processor(audios=[input_features.tolist()], sampling_rate=sampling_rate, return_tensors="pt").to(
torch_device
)
def factory_test_task(self, class1, class2, inputs, class1_kwargs, class2_kwargs):
# half-precision loading to limit GPU usage
model1 = class1.from_pretrained(self.repo_id, torch_dtype=torch.float16).to(torch_device)
model2 = class2.from_pretrained(self.repo_id, torch_dtype=torch.float16).to(torch_device)
set_seed(0)
output_1 = model1.generate(**inputs, **class1_kwargs)
set_seed(0)
output_2 = model2.generate(**inputs, **class2_kwargs)
for key in output_1:
if isinstance(output_1[key], torch.Tensor):
if len(output_1[key].shape) == 0:
self.assertEqual(output_1[key].item(), output_2[key].item())
else:
self.assertListAlmostEqual(output_1[key].squeeze().tolist(), output_2[key].squeeze().tolist())
@slow
def test_to_eng_text(self):
model = SeamlessM4Tv2Model.from_pretrained(self.repo_id).to(torch_device)
# test text - tgt lang: eng
expected_text_tokens = [3, 256022, 3080, 1, 247669, 10, 6816, 247676, 3] # fmt: skip
# fmt: off
expected_unit_tokens = [
4746,7163,8208,8208,1315,1266,4307,1119,989,9594,3007,3007,4341,5205,7631,7631,3202,4061,9092,3191,7509,1715,
5280,5280,3554,8812,8197,6366,5382,5382,7330,2758,9433,9433,6863,7510,5800,5800,5286,1948,1825,1825,3956,8724,
8724,5331,8914,9315,9315,5288,2588,8167,8787,8787,8063,6008,2621,2621,2621,5696
]
# fmt: on
expected_wav_slice = [9.485097e-04, 8.320558e-04, 7.178137e-04, 9.349979e-04, 1.121628e-03, 1.091766e-03, 1.279693e-03, 1.387754e-03, 1.296396e-03, 1.143557e-03] # fmt: skip
set_seed(0)
output = model.generate(**self.input_text, num_beams=1, tgt_lang="eng", return_intermediate_token_ids=True)
self.assertListEqual(expected_text_tokens, output.sequences.squeeze().tolist())
self.assertListEqual(
expected_unit_tokens, (output.unit_sequences - model.config.vocoder_offset).squeeze().tolist()
)
self.assertListAlmostEqual(expected_wav_slice, output.waveform.squeeze().tolist()[50:60])
# assert mean and std equality
self.assertListAlmostEqual(
[-2.349690e-04, 9.920777e-02], [output.waveform.mean().item(), output.waveform.std().item()]
)
@slow
@unittest.skip(reason="Equivalence is broken since a new update")
def test_to_swh_text(self):
model = SeamlessM4Tv2Model.from_pretrained(self.repo_id).to(torch_device)
# test text - tgt lang: swh
expected_text_tokens = [3, 256084, 109, 247729, 171, 10, 6816, 247676, 3] # fmt: skip
# fmt: off
expected_unit_tokens = [
5725,7163,7472,7472,6915,3099,3099,9921,2765,6515,6515,1374,1374,1347,8252,9854,9854,5662,2420,6600,2216,4503,
7208,6107,6107,7298,9123,6472,9663,9663,6366,6366,6445,575,3575,2052,2052,5788,5800,5800,5286,5286,1825,1825,3956,
3956,8724,8724,5331,8914,8914,9315,9315,2821,8167,8167,8787,8787,8787,8700,8700,8700,2175,2175,3196,3196,2621,1725,
1725,7507,5696
]
# fmt: on
expected_wav_slice = [3.124037e-04, 2.450471e-04, 2.286572e-04, 2.317214e-04, 2.732605e-04, 2.478790e-04, 2.704144e-04, 2.665847e-04, 2.828784e-04, 2.684390e-04] # fmt: skip
set_seed(0)
output = model.generate(**self.input_text, num_beams=1, tgt_lang="swh", return_intermediate_token_ids=True)
self.assertListEqual(expected_text_tokens, output.sequences.squeeze().tolist())
self.assertListEqual(
expected_unit_tokens, (output.unit_sequences - model.config.vocoder_offset).squeeze().tolist()
)
self.assertListAlmostEqual(expected_wav_slice, output.waveform.squeeze().tolist()[50:60])
# assert mean and std equality
self.assertListAlmostEqual(
[-2.001826e-04, 8.580012e-02], [output.waveform.mean().item(), output.waveform.std().item()]
)
@slow
def test_to_rus_speech(self):
model = SeamlessM4Tv2Model.from_pretrained(self.repo_id).to(torch_device)
# test audio - tgt lang: rus
expected_text_tokens = [3, 256074, 107, 248213, 404, 247792, 247789, 3] # fmt: skip
# fmt: off
expected_unit_tokens = [
8976,7163,6915,2728,2728,5198,3318,3318,3686,1049,9643,1200,2052,2052,8196,8196,7624,7624,7555,7555,7555,7555,
9717,9717,4869,8167,8167,8167,8053,972,9362,8167,297,297,297,3993,3993,3993,3993,4660,4660,4660,4660,4660,4660,
7962,7962,225,225,8737,4199
]
# fmt: on
expected_wav_slice = [1.415287e-03, 1.360976e-03, 1.297727e-03, 1.305321e-03, 1.352087e-03, 1.283812e-03, 1.352623e-03, 1.387384e-03, 1.449627e-03, 1.411701e-03] # fmt: skip
set_seed(0)
output = model.generate(**self.input_audio, num_beams=1, tgt_lang="rus", return_intermediate_token_ids=True)
self.assertListEqual(expected_text_tokens, output.sequences.squeeze().tolist())
self.assertListEqual(
expected_unit_tokens, (output.unit_sequences - model.config.vocoder_offset).squeeze().tolist()
)
self.assertListAlmostEqual(expected_wav_slice, output.waveform.squeeze().tolist()[50:60])
# assert mean and std equality - higher tolerance for speech
self.assertListAlmostEqual(
[-2.818016e-04, 7.169888e-02], [output.waveform.mean().item(), output.waveform.std().item()], tol=5e-4
)
@slow
def test_text_to_text_model(self):
kwargs1 = {"tgt_lang": "eng", "return_intermediate_token_ids": True, "generate_speech": False}
kwargs2 = {
"tgt_lang": "eng",
"output_hidden_states": True,
"return_dict_in_generate": True,
"output_scores": True,
}
self.factory_test_task(SeamlessM4Tv2Model, SeamlessM4Tv2ForTextToText, self.input_text, kwargs1, kwargs2)
@slow
def test_speech_to_text_model(self):
kwargs1 = {"tgt_lang": "eng", "return_intermediate_token_ids": True, "generate_speech": False}
kwargs2 = {
"tgt_lang": "eng",
"output_hidden_states": True,
"return_dict_in_generate": True,
"output_scores": True,
}
self.factory_test_task(SeamlessM4Tv2Model, SeamlessM4Tv2ForSpeechToText, self.input_audio, kwargs1, kwargs2)
@slow
def test_speech_to_speech_model(self):
kwargs1 = {"tgt_lang": "eng", "return_intermediate_token_ids": True}
self.factory_test_task(SeamlessM4Tv2Model, SeamlessM4Tv2ForSpeechToSpeech, self.input_audio, kwargs1, kwargs1)
@slow
def test_text_to_speech_model(self):
kwargs1 = {"tgt_lang": "eng", "return_intermediate_token_ids": True}
self.factory_test_task(SeamlessM4Tv2Model, SeamlessM4Tv2ForTextToSpeech, self.input_text, kwargs1, kwargs1)
| transformers/tests/models/seamless_m4t_v2/test_modeling_seamless_m4t_v2.py/0 | {
"file_path": "transformers/tests/models/seamless_m4t_v2/test_modeling_seamless_m4t_v2.py",
"repo_id": "transformers",
"token_count": 22843
} |
# 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.
"""Testing suite for the TensorFlow SwiftFormer model."""
import inspect
import unittest
from transformers import SwiftFormerConfig
from transformers.testing_utils import (
require_tf,
require_vision,
slow,
)
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFSwiftFormerForImageClassification, TFSwiftFormerModel
from transformers.modeling_tf_utils import keras
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class TFSwiftFormerModelTester:
def __init__(
self,
parent,
batch_size=1,
num_channels=3,
is_training=True,
use_labels=True,
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
image_size=224,
num_labels=2,
layer_depths=[3, 3, 6, 4],
embed_dims=[48, 56, 112, 220],
):
self.parent = parent
self.batch_size = batch_size
self.num_channels = num_channels
self.is_training = is_training
self.use_labels = use_labels
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.num_labels = num_labels
self.image_size = image_size
self.layer_depths = layer_depths
self.embed_dims = embed_dims
def prepare_config_and_inputs(self):
pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
labels = None
if self.use_labels:
labels = ids_tensor([self.batch_size], self.num_labels)
config = self.get_config()
return config, pixel_values, labels
def get_config(self):
return SwiftFormerConfig(
depths=self.layer_depths,
embed_dims=self.embed_dims,
mlp_ratio=4,
downsamples=[True, True, True, True],
hidden_act="gelu",
num_labels=self.num_labels,
down_patch_size=3,
down_stride=2,
down_pad=1,
drop_rate=0.0,
drop_path_rate=0.0,
use_layer_scale=True,
layer_scale_init_value=1e-5,
)
def create_and_check_model(self, config, pixel_values, labels):
model = TFSwiftFormerModel(config=config)
result = model(pixel_values)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.embed_dims[-1], 7, 7))
def create_and_check_for_image_classification(self, config, pixel_values, labels):
config.num_labels = self.num_labels
model = TFSwiftFormerForImageClassification(config)
result = model(pixel_values, labels=labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
model = TFSwiftFormerForImageClassification(config)
pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
result = model(pixel_values)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
def prepare_config_and_inputs_for_common(self):
(config, pixel_values, labels) = self.prepare_config_and_inputs()
inputs_dict = {"pixel_values": pixel_values}
return config, inputs_dict
@require_tf
class TFSwiftFormerModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
"""
Here we also overwrite some of the tests of test_modeling_common.py, as SwiftFormer does not use input_ids, inputs_embeds,
attention_mask and seq_length.
"""
all_model_classes = (TFSwiftFormerModel, TFSwiftFormerForImageClassification) if is_tf_available() else ()
pipeline_model_mapping = (
{"feature-extraction": TFSwiftFormerModel, "image-classification": TFSwiftFormerForImageClassification}
if is_tf_available()
else {}
)
fx_compatible = False
test_pruning = False
test_resize_embeddings = False
test_head_masking = False
has_attentions = False
test_onnx = False
from_pretrained_id = "MBZUAI/swiftformer-xs"
def setUp(self):
self.model_tester = TFSwiftFormerModelTester(self)
self.config_tester = ConfigTester(
self,
config_class=SwiftFormerConfig,
has_text_modality=False,
hidden_size=37,
num_attention_heads=12,
num_hidden_layers=12,
)
def test_config(self):
self.config_tester.run_common_tests()
@unittest.skip(reason="TFSwiftFormer does not use inputs_embeds")
def test_inputs_embeds(self):
pass
def test_model_common_attributes(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
x = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(x, keras.layers.Dense))
# Copied from transformers.tests.models.deit.test_modeling_tf_deit.py
def test_forward_signature(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
signature = inspect.signature(model.call)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
arg_names = [*signature.parameters.keys()]
expected_arg_names = ["pixel_values"]
self.assertListEqual(arg_names[:1], expected_arg_names)
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
def test_for_image_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*config_and_inputs)
@slow
def test_model_from_pretrained(self):
model = TFSwiftFormerModel.from_pretrained(self.from_pretrained_id)
self.assertIsNotNone(model)
@unittest.skip(reason="TFSwiftFormer does not output attentions")
def test_attention_outputs(self):
pass
def test_hidden_states_output(self):
def check_hidden_states_output(inputs_dict, config, model_class):
model = model_class(config)
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
hidden_states = outputs.hidden_states
expected_num_stages = 8
self.assertEqual(len(hidden_states), expected_num_stages)
# SwiftFormer's feature maps are of shape (batch_size, embed_dims, height, width)
# with the width and height being successively divided by 2, after every 2 blocks
for i in range(len(hidden_states)):
self.assertEqual(
hidden_states[i].shape,
tf.TensorShape(
[
self.model_tester.batch_size,
self.model_tester.embed_dims[i // 2],
(self.model_tester.image_size // 4) // 2 ** (i // 2),
(self.model_tester.image_size // 4) // 2 ** (i // 2),
]
),
)
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
inputs_dict["output_hidden_states"] = True
check_hidden_states_output(inputs_dict, config, model_class)
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
config.output_hidden_states = True
check_hidden_states_output(inputs_dict, config, model_class)
# We will verify our results on an image of cute cats
def prepare_img():
image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
return image
@require_tf
@require_vision
class TFSwiftFormerModelIntegrationTest(unittest.TestCase):
@cached_property
def default_image_processor(self):
return ViTImageProcessor.from_pretrained("MBZUAI/swiftformer-xs") if is_vision_available() else None
@slow
def test_inference_image_classification_head(self):
model = TFSwiftFormerForImageClassification.from_pretrained("MBZUAI/swiftformer-xs")
feature_extractor = self.default_feature_extractor
image = prepare_img()
inputs = feature_extractor(images=image, return_tensors="tf")
# forward pass
outputs = model(**inputs)
# verify the logits
expected_shape = tf.TensorShape((1, 1000))
self.assertEqual(outputs.logits.shape, expected_shape)
expected_slice = tf.constant([[-2.1703e00, 2.1107e00, -2.0811e00]])
tf.debugging.assert_near(outputs.logits[0, :3], expected_slice, atol=1e-4)
| transformers/tests/models/swiftformer/test_modeling_tf_swiftformer.py/0 | {
"file_path": "transformers/tests/models/swiftformer/test_modeling_tf_swiftformer.py",
"repo_id": "transformers",
"token_count": 4215
} |
# coding=utf-8
# Copyright 2024 HuggingFace Inc.
#
# 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 tempfile
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_torchvision, require_vision
from transformers.utils import is_torch_available, is_vision_available
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import TimmWrapperConfig, TimmWrapperImageProcessor
@require_torch
@require_vision
@require_torchvision
class TimmWrapperImageProcessingTest(unittest.TestCase):
image_processing_class = TimmWrapperImageProcessor if is_vision_available() else None
def setUp(self):
super().setUp()
self.temp_dir = tempfile.TemporaryDirectory()
config = TimmWrapperConfig.from_pretrained("timm/resnet18.a1_in1k")
config.save_pretrained(self.temp_dir.name)
def tearDown(self):
self.temp_dir.cleanup()
def test_load_from_hub(self):
image_processor = TimmWrapperImageProcessor.from_pretrained("timm/resnet18.a1_in1k")
self.assertIsInstance(image_processor, TimmWrapperImageProcessor)
def test_load_from_local_dir(self):
image_processor = TimmWrapperImageProcessor.from_pretrained(self.temp_dir.name)
self.assertIsInstance(image_processor, TimmWrapperImageProcessor)
def test_image_processor_properties(self):
image_processor = TimmWrapperImageProcessor.from_pretrained(self.temp_dir.name)
self.assertTrue(hasattr(image_processor, "data_config"))
self.assertTrue(hasattr(image_processor, "val_transforms"))
self.assertTrue(hasattr(image_processor, "train_transforms"))
def test_image_processor_call_numpy(self):
image_processor = TimmWrapperImageProcessor.from_pretrained(self.temp_dir.name)
single_image = np.random.randint(256, size=(256, 256, 3), dtype=np.uint8)
batch_images = [single_image, single_image, single_image]
# single image
pixel_values = image_processor(single_image).pixel_values
self.assertEqual(pixel_values.shape, (1, 3, 224, 224))
# batch images
pixel_values = image_processor(batch_images).pixel_values
self.assertEqual(pixel_values.shape, (3, 3, 224, 224))
def test_image_processor_call_pil(self):
image_processor = TimmWrapperImageProcessor.from_pretrained(self.temp_dir.name)
single_image = Image.fromarray(np.random.randint(256, size=(256, 256, 3), dtype=np.uint8))
batch_images = [single_image, single_image, single_image]
# single image
pixel_values = image_processor(single_image).pixel_values
self.assertEqual(pixel_values.shape, (1, 3, 224, 224))
# batch images
pixel_values = image_processor(batch_images).pixel_values
self.assertEqual(pixel_values.shape, (3, 3, 224, 224))
def test_image_processor_call_tensor(self):
image_processor = TimmWrapperImageProcessor.from_pretrained(self.temp_dir.name)
single_image = torch.from_numpy(np.random.randint(256, size=(3, 256, 256), dtype=np.uint8)).float()
batch_images = [single_image, single_image, single_image]
# single image
pixel_values = image_processor(single_image).pixel_values
self.assertEqual(pixel_values.shape, (1, 3, 224, 224))
# batch images
pixel_values = image_processor(batch_images).pixel_values
self.assertEqual(pixel_values.shape, (3, 3, 224, 224))
| transformers/tests/models/timm_wrapper/test_image_processing_timm_wrapper.py/0 | {
"file_path": "transformers/tests/models/timm_wrapper/test_image_processing_timm_wrapper.py",
"repo_id": "transformers",
"token_count": 1477
} |
# Copyright 2021 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.
import inspect
import unittest
import numpy as np
from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor
if is_flax_available():
import jax
from transformers.models.vit.modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel
class FlaxViTModelTester:
def __init__(
self,
parent,
batch_size=13,
image_size=30,
patch_size=2,
num_channels=3,
is_training=True,
use_labels=True,
hidden_size=32,
num_hidden_layers=2,
num_attention_heads=4,
intermediate_size=37,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
type_sequence_label_size=10,
initializer_range=0.02,
attn_implementation="eager",
):
self.parent = parent
self.batch_size = batch_size
self.image_size = image_size
self.patch_size = patch_size
self.num_channels = num_channels
self.is_training = is_training
self.use_labels = use_labels
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.type_sequence_label_size = type_sequence_label_size
self.initializer_range = initializer_range
self.attn_implementation = attn_implementation
# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
num_patches = (image_size // patch_size) ** 2
self.seq_length = num_patches + 1
def prepare_config_and_inputs(self):
pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
config = ViTConfig(
image_size=self.image_size,
patch_size=self.patch_size,
num_channels=self.num_channels,
hidden_size=self.hidden_size,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
intermediate_size=self.intermediate_size,
hidden_act=self.hidden_act,
hidden_dropout_prob=self.hidden_dropout_prob,
attention_probs_dropout_prob=self.attention_probs_dropout_prob,
is_decoder=False,
initializer_range=self.initializer_range,
attn_implementation=self.attn_implementation,
)
return config, pixel_values
def create_and_check_model(self, config, pixel_values):
model = FlaxViTModel(config=config)
result = model(pixel_values)
# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
image_size = (self.image_size, self.image_size)
patch_size = (self.patch_size, self.patch_size)
num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, num_patches + 1, self.hidden_size))
def create_and_check_for_image_classification(self, config, pixel_values):
config.num_labels = self.type_sequence_label_size
model = FlaxViTForImageClassification(config=config)
result = model(pixel_values)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.type_sequence_label_size))
# test greyscale images
config.num_channels = 1
model = FlaxViTForImageClassification(config)
pixel_values = floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
result = model(pixel_values)
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
config,
pixel_values,
) = config_and_inputs
inputs_dict = {"pixel_values": pixel_values}
return config, inputs_dict
@require_flax
class FlaxViTModelTest(FlaxModelTesterMixin, unittest.TestCase):
all_model_classes = (FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()
def setUp(self) -> None:
self.model_tester = FlaxViTModelTester(self)
self.config_tester = ConfigTester(self, config_class=ViTConfig, has_text_modality=False, hidden_size=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
def test_for_image_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*config_and_inputs)
# We need to override this test because ViT's forward signature is different than text models.
def test_forward_signature(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
signature = inspect.signature(model.__call__)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
arg_names = [*signature.parameters.keys()]
expected_arg_names = ["pixel_values"]
self.assertListEqual(arg_names[:1], expected_arg_names)
# We need to override this test because ViT expects pixel_values instead of input_ids
def test_jit_compilation(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__):
prepared_inputs_dict = self._prepare_for_class(inputs_dict, model_class)
model = model_class(config)
@jax.jit
def model_jitted(pixel_values, **kwargs):
return model(pixel_values=pixel_values, **kwargs)
with self.subTest("JIT Enabled"):
jitted_outputs = model_jitted(**prepared_inputs_dict).to_tuple()
with self.subTest("JIT Disabled"):
with jax.disable_jit():
outputs = model_jitted(**prepared_inputs_dict).to_tuple()
self.assertEqual(len(outputs), len(jitted_outputs))
for jitted_output, output in zip(jitted_outputs, outputs):
self.assertEqual(jitted_output.shape, output.shape)
@slow
def test_model_from_pretrained(self):
for model_class_name in self.all_model_classes:
model = model_class_name.from_pretrained("google/vit-base-patch16-224")
outputs = model(np.ones((1, 3, 224, 224)))
self.assertIsNotNone(outputs)
| transformers/tests/models/vit/test_modeling_flax_vit.py/0 | {
"file_path": "transformers/tests/models/vit/test_modeling_flax_vit.py",
"repo_id": "transformers",
"token_count": 3300
} |
# 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.
"""Testing suite for the PyTorch Wav2Vec2-BERT model."""
import tempfile
import unittest
from datasets import load_dataset
from transformers import Wav2Vec2BertConfig, is_torch_available
from transformers.testing_utils import (
is_pt_flax_cross_test,
require_torch,
require_torch_accelerator,
require_torch_fp16,
slow,
torch_device,
)
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import (
ModelTesterMixin,
_config_zero_init,
floats_tensor,
ids_tensor,
random_attention_mask,
)
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
AutoFeatureExtractor,
Wav2Vec2BertForAudioFrameClassification,
Wav2Vec2BertForCTC,
Wav2Vec2BertForSequenceClassification,
Wav2Vec2BertForXVector,
Wav2Vec2BertModel,
)
from transformers.models.wav2vec2_bert.modeling_wav2vec2_bert import (
_compute_mask_indices,
_sample_negative_indices,
)
# Copied from tests.models.wav2vec2_conformer.test_modeling_wav2vec2_conformer.Wav2Vec2ConformerModelTester with Conformer->Bert, input_values->input_features
class Wav2Vec2BertModelTester:
# Ignore copy
def __init__(
self,
parent,
batch_size=13,
seq_length=200, # speech is longer
is_training=False,
hidden_size=16,
feature_projection_input_dim=16,
num_conv_pos_embeddings=16,
num_conv_pos_embedding_groups=2,
num_hidden_layers=2,
num_attention_heads=2,
hidden_dropout_prob=0.1,
intermediate_size=20,
layer_norm_eps=1e-5,
hidden_act="gelu",
initializer_range=0.02,
mask_time_prob=0.5,
mask_time_length=2,
vocab_size=32,
do_stable_layer_norm=False,
num_adapter_layers=2,
adapter_stride=2,
tdnn_dim=(32, 32),
tdnn_kernel=(5, 3),
tdnn_dilation=(1, 2),
xvector_output_dim=32,
position_embeddings_type="relative",
scope=None,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.hidden_size = hidden_size
self.feature_projection_input_dim = feature_projection_input_dim
self.num_conv_pos_embeddings = num_conv_pos_embeddings
self.num_conv_pos_embedding_groups = num_conv_pos_embedding_groups
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.hidden_dropout_prob = hidden_dropout_prob
self.intermediate_size = intermediate_size
self.layer_norm_eps = layer_norm_eps
self.hidden_act = hidden_act
self.initializer_range = initializer_range
self.vocab_size = vocab_size
self.do_stable_layer_norm = do_stable_layer_norm
self.num_adapter_layers = num_adapter_layers
self.adapter_stride = adapter_stride
self.mask_time_prob = mask_time_prob
self.mask_time_length = mask_time_length
self.scope = scope
self.tdnn_dim = tdnn_dim
self.tdnn_kernel = tdnn_kernel
self.tdnn_dilation = tdnn_dilation
self.xvector_output_dim = xvector_output_dim
self.position_embeddings_type = position_embeddings_type
self.output_seq_length = self.seq_length
self.encoder_seq_length = self.output_seq_length
self.adapter_output_seq_length = self.output_seq_length
for _ in range(num_adapter_layers):
self.adapter_output_seq_length = (self.adapter_output_seq_length - 1) // adapter_stride + 1
# Ignore copy
def prepare_config_and_inputs(self, position_embeddings_type="relative"):
input_shape = [self.batch_size, self.seq_length, self.feature_projection_input_dim]
input_features = floats_tensor(input_shape, self.vocab_size)
attention_mask = random_attention_mask([self.batch_size, self.seq_length])
config = self.get_config(position_embeddings_type=position_embeddings_type)
return config, input_features, attention_mask
# Ignore copy
def get_config(self, position_embeddings_type="relative"):
return Wav2Vec2BertConfig(
hidden_size=self.hidden_size,
feature_projection_input_dim=self.feature_projection_input_dim,
mask_time_prob=self.mask_time_prob,
mask_time_length=self.mask_time_length,
num_conv_pos_embeddings=self.num_conv_pos_embeddings,
num_conv_pos_embedding_groups=self.num_conv_pos_embedding_groups,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
hidden_dropout_prob=self.hidden_dropout_prob,
intermediate_size=self.intermediate_size,
layer_norm_eps=self.layer_norm_eps,
do_stable_layer_norm=self.do_stable_layer_norm,
hidden_act=self.hidden_act,
initializer_range=self.initializer_range,
vocab_size=self.vocab_size,
num_adapter_layers=self.num_adapter_layers,
adapter_stride=self.adapter_stride,
tdnn_dim=self.tdnn_dim,
tdnn_kernel=self.tdnn_kernel,
tdnn_dilation=self.tdnn_dilation,
xvector_output_dim=self.xvector_output_dim,
position_embeddings_type=position_embeddings_type,
)
def create_and_check_model(self, config, input_features, attention_mask):
model = Wav2Vec2BertModel(config=config)
model.to(torch_device)
model.eval()
result = model(input_features, attention_mask=attention_mask)
self.parent.assertEqual(
result.last_hidden_state.shape, (self.batch_size, self.output_seq_length, self.hidden_size)
)
def create_and_check_model_with_adapter(self, config, input_features, attention_mask):
config.add_adapter = True
model = Wav2Vec2BertModel(config=config)
model.to(torch_device)
model.eval()
result = model(input_features, attention_mask=attention_mask)
self.parent.assertEqual(
result.last_hidden_state.shape, (self.batch_size, self.adapter_output_seq_length, self.hidden_size)
)
def create_and_check_model_with_adapter_for_ctc(self, config, input_features, attention_mask):
config.add_adapter = True
config.output_hidden_size = 2 * config.hidden_size
model = Wav2Vec2BertForCTC(config=config)
model.to(torch_device)
model.eval()
result = model(input_features, attention_mask=attention_mask)
self.parent.assertEqual(
result.logits.shape, (self.batch_size, self.adapter_output_seq_length, self.vocab_size)
)
# Ignore copy
def create_and_check_model_with_intermediate_ffn_before_adapter(self, config, input_features, attention_mask):
config.add_adapter = True
config.use_intermediate_ffn_before_adapter = True
model = Wav2Vec2BertModel(config=config)
model.to(torch_device)
model.eval()
result = model(input_features, attention_mask=attention_mask)
self.parent.assertEqual(
result.last_hidden_state.shape,
(self.batch_size, self.adapter_output_seq_length, config.output_hidden_size),
)
# also try with different adapter proj dim
config.output_hidden_size = 8
model = Wav2Vec2BertModel(config=config)
model.to(torch_device)
model.eval()
result = model(input_features, attention_mask=attention_mask)
self.parent.assertEqual(
result.last_hidden_state.shape,
(self.batch_size, self.adapter_output_seq_length, config.output_hidden_size),
)
def create_and_check_model_with_adapter_proj_dim(self, config, input_features, attention_mask):
config.add_adapter = True
config.output_hidden_size = 8
model = Wav2Vec2BertModel(config=config)
model.to(torch_device)
model.eval()
result = model(input_features, attention_mask=attention_mask)
self.parent.assertEqual(
result.last_hidden_state.shape,
(self.batch_size, self.adapter_output_seq_length, config.output_hidden_size),
)
def create_and_check_model_float16(self, config, input_features, attention_mask):
model = Wav2Vec2BertModel(config=config)
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(tmpdirname)
model = Wav2Vec2BertModel.from_pretrained(tmpdirname, torch_dtype=torch.float16)
model.to(torch_device)
model.eval()
with torch.no_grad():
result = model(input_features.type(dtype=torch.float16), attention_mask=attention_mask)
self.parent.assertEqual(
result.last_hidden_state.shape, (self.batch_size, self.output_seq_length, self.hidden_size)
)
def create_and_check_batch_inference(self, config, input_features, *args):
# test does not pass for models making use of `group_norm`
# check: https://github.com/pytorch/fairseq/issues/3227
model = Wav2Vec2BertModel(config=config)
model.to(torch_device)
model.eval()
input_features = input_features[:3]
attention_mask = torch.ones(input_features.shape, device=torch_device, dtype=torch.bool)
input_lengths = [input_features.shape[-1] // i for i in [4, 2, 1]]
# pad input
for i in range(len(input_lengths)):
input_features[i, input_lengths[i] :] = 0.0
attention_mask[i, input_lengths[i] :] = 0.0
batch_outputs = model(input_features, attention_mask=attention_mask).last_hidden_state
for i in range(input_features.shape[0]):
input_slice = input_features[i : i + 1, : input_lengths[i]]
output = model(input_slice).last_hidden_state
batch_output = batch_outputs[i : i + 1, : output.shape[1]]
self.parent.assertTrue(torch.allclose(output, batch_output, atol=1e-3))
def check_ctc_loss(self, config, input_features, *args):
model = Wav2Vec2BertForCTC(config=config)
model.to(torch_device)
# make sure that dropout is disabled
model.eval()
input_features = input_features[:3]
# Ignore copy
attention_mask = torch.ones(input_features.shape[:2], device=torch_device, dtype=torch.long)
input_lengths = [input_features.shape[1] // i for i in [4, 2, 1]]
max_length_labels = model._get_feat_extract_output_lengths(torch.tensor(input_lengths))
labels = ids_tensor((input_features.shape[0], min(max_length_labels) - 1), model.config.vocab_size)
# pad input
for i in range(len(input_lengths)):
input_features[i, input_lengths[i] :] = 0.0
attention_mask[i, input_lengths[i] :] = 0
model.config.ctc_loss_reduction = "sum"
sum_loss = model(input_features, attention_mask=attention_mask, labels=labels).loss.item()
model.config.ctc_loss_reduction = "mean"
mean_loss = model(input_features, attention_mask=attention_mask, labels=labels).loss.item()
self.parent.assertTrue(isinstance(sum_loss, float))
self.parent.assertTrue(isinstance(mean_loss, float))
def check_seq_classifier_loss(self, config, input_features, *args):
model = Wav2Vec2BertForSequenceClassification(config=config)
model.to(torch_device)
# make sure that dropout is disabled
model.eval()
input_features = input_features[:3]
# Ignore copy
attention_mask = torch.ones(input_features.shape[:2], device=torch_device, dtype=torch.long)
input_lengths = [input_features.shape[1] // i for i in [4, 2, 1]]
labels = ids_tensor((input_features.shape[0], 1), len(model.config.id2label))
# pad input
for i in range(len(input_lengths)):
input_features[i, input_lengths[i] :] = 0.0
attention_mask[i, input_lengths[i] :] = 0
masked_loss = model(input_features, attention_mask=attention_mask, labels=labels).loss.item()
unmasked_loss = model(input_features, labels=labels).loss.item()
self.parent.assertTrue(isinstance(masked_loss, float))
self.parent.assertTrue(isinstance(unmasked_loss, float))
self.parent.assertTrue(masked_loss != unmasked_loss)
def check_ctc_training(self, config, input_features, *args):
config.ctc_zero_infinity = True
model = Wav2Vec2BertForCTC(config=config)
model.to(torch_device)
model.train()
# Ignore copy
input_features = input_features[:3]
input_lengths = [input_features.shape[1] // i for i in [4, 2, 1]]
max_length_labels = model._get_feat_extract_output_lengths(torch.tensor(input_lengths))
labels = ids_tensor((input_features.shape[0], max(max_length_labels) - 2), model.config.vocab_size)
# pad input
for i in range(len(input_lengths)):
input_features[i, input_lengths[i] :] = 0.0
if max_length_labels[i] < labels.shape[-1]:
# it's important that we make sure that target lengths are at least
# one shorter than logit lengths to prevent -inf
labels[i, max_length_labels[i] - 1 :] = -100
loss = model(input_features, labels=labels).loss
self.parent.assertFalse(torch.isinf(loss).item())
loss.backward()
def check_seq_classifier_training(self, config, input_features, *args):
config.ctc_zero_infinity = True
model = Wav2Vec2BertForSequenceClassification(config=config)
model.to(torch_device)
model.train()
# freeze everything but the classification head
model.freeze_base_model()
input_features = input_features[:3]
# Ignore copy
input_lengths = [input_features.shape[1] // i for i in [4, 2, 1]]
labels = ids_tensor((input_features.shape[0], 1), len(model.config.id2label))
# pad input
for i in range(len(input_lengths)):
input_features[i, input_lengths[i] :] = 0.0
loss = model(input_features, labels=labels).loss
self.parent.assertFalse(torch.isinf(loss).item())
loss.backward()
def check_xvector_training(self, config, input_features, *args):
config.ctc_zero_infinity = True
model = Wav2Vec2BertForXVector(config=config)
model.to(torch_device)
model.train()
# freeze everything but the classification head
model.freeze_base_model()
input_features = input_features[:3]
input_lengths = [input_features.shape[-1] // i for i in [4, 2, 1]]
labels = ids_tensor((input_features.shape[0], 1), len(model.config.id2label))
# pad input
for i in range(len(input_lengths)):
input_features[i, input_lengths[i] :] = 0.0
loss = model(input_features, labels=labels).loss
self.parent.assertFalse(torch.isinf(loss).item())
loss.backward()
def check_labels_out_of_vocab(self, config, input_features, *args):
model = Wav2Vec2BertForCTC(config)
model.to(torch_device)
model.train()
input_features = input_features[:3]
input_lengths = [input_features.shape[-1] // i for i in [4, 2, 1]]
max_length_labels = model._get_feat_extract_output_lengths(torch.tensor(input_lengths))
labels = ids_tensor((input_features.shape[0], max(max_length_labels) - 2), model.config.vocab_size + 100)
with self.parent.assertRaises(ValueError):
model(input_features, labels=labels)
def prepare_config_and_inputs_for_common(self):
config, input_features, attention_mask = self.prepare_config_and_inputs()
inputs_dict = {"input_features": input_features, "attention_mask": attention_mask}
return config, inputs_dict
@require_torch
class Wav2Vec2BertModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
# Ignore copy
all_model_classes = (
(
Wav2Vec2BertForCTC,
Wav2Vec2BertModel,
Wav2Vec2BertForSequenceClassification,
Wav2Vec2BertForAudioFrameClassification,
Wav2Vec2BertForXVector,
)
if is_torch_available()
else ()
)
pipeline_model_mapping = (
{
"audio-classification": Wav2Vec2BertForSequenceClassification,
"automatic-speech-recognition": Wav2Vec2BertForCTC,
"feature-extraction": Wav2Vec2BertModel,
}
if is_torch_available()
else {}
)
test_pruning = False
test_headmasking = False
test_torchscript = False
def setUp(self):
self.model_tester = Wav2Vec2BertModelTester(self)
self.config_tester = ConfigTester(self, config_class=Wav2Vec2BertConfig, hidden_size=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
def test_model_with_relative(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs(position_embeddings_type="relative")
self.model_tester.create_and_check_model(*config_and_inputs)
# Ignore copy
def test_model_with_relative_key(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs(position_embeddings_type="relative_key")
self.model_tester.create_and_check_model(*config_and_inputs)
def test_model_with_rotary(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs(position_embeddings_type="rotary")
self.model_tester.create_and_check_model(*config_and_inputs)
def test_model_with_no_rel_pos(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs(position_embeddings_type=None)
self.model_tester.create_and_check_model(*config_and_inputs)
def test_model_with_adapter(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model_with_adapter(*config_and_inputs)
def test_model_with_adapter_for_ctc(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model_with_adapter_for_ctc(*config_and_inputs)
# Ignore copy
def test_model_with_intermediate_ffn_before_adapter(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model_with_intermediate_ffn_before_adapter(*config_and_inputs)
def test_model_with_adapter_proj_dim(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model_with_adapter_proj_dim(*config_and_inputs)
@require_torch_accelerator
@require_torch_fp16
def test_model_float16_with_relative(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs(position_embeddings_type="relative")
self.model_tester.create_and_check_model_float16(*config_and_inputs)
# Ignore copy
@require_torch_accelerator
@require_torch_fp16
def test_model_float16_with_relative_key(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs(position_embeddings_type="relative_key")
self.model_tester.create_and_check_model_float16(*config_and_inputs)
@require_torch_accelerator
@require_torch_fp16
def test_model_float16_with_rotary(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs(position_embeddings_type="rotary")
self.model_tester.create_and_check_model_float16(*config_and_inputs)
def test_ctc_loss_inference(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_ctc_loss(*config_and_inputs)
def test_seq_classifier_loss_inference(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_seq_classifier_loss(*config_and_inputs)
def test_ctc_train(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_ctc_training(*config_and_inputs)
def test_seq_classifier_train(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_seq_classifier_training(*config_and_inputs)
def test_xvector_train(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_xvector_training(*config_and_inputs)
def test_labels_out_of_vocab(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_labels_out_of_vocab(*config_and_inputs)
# Ignore copy
@unittest.skip(reason="Wav2Vec2Bert has no inputs_embeds")
def test_inputs_embeds(self):
pass
# Ignore copy
@unittest.skip(reason="`input_ids` is renamed to `input_features`")
def test_forward_signature(self):
pass
# Ignore copy
@unittest.skip(reason="Wav2Vec2Bert has no tokens embeddings")
def test_resize_tokens_embeddings(self):
pass
# Ignore copy
@unittest.skip(reason="Wav2Vec2Bert has no inputs_embeds")
def test_model_get_set_embeddings(self):
pass
# Ignore copy
@unittest.skip(reason="non-robust architecture does not exist in Flax")
@is_pt_flax_cross_test
def test_equivalence_flax_to_pt(self):
pass
# Ignore copy
@unittest.skip(reason="non-robust architecture does not exist in Flax")
@is_pt_flax_cross_test
def test_equivalence_pt_to_flax(self):
pass
def test_retain_grad_hidden_states_attentions(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.output_hidden_states = True
config.output_attentions = True
# no need to test all models as different heads yield the same functionality
model_class = self.all_model_classes[0]
model = model_class(config)
model.to(torch_device)
# set layer drop to 0
model.config.layerdrop = 0.0
input_features = inputs_dict["input_features"]
input_lengths = torch.tensor(
[input_features.shape[1] for _ in range(input_features.shape[0])], dtype=torch.long, device=torch_device
)
output_lengths = model._get_feat_extract_output_lengths(input_lengths)
labels = ids_tensor((input_features.shape[0], output_lengths[0] - 2), self.model_tester.vocab_size)
inputs_dict["attention_mask"] = torch.ones_like(inputs_dict["attention_mask"])
inputs_dict["labels"] = labels
outputs = model(**inputs_dict)
output = outputs[0]
# Encoder-/Decoder-only models
hidden_states = outputs.hidden_states[0]
attentions = outputs.attentions[0]
hidden_states.retain_grad()
attentions.retain_grad()
output.flatten()[0].backward(retain_graph=True)
self.assertIsNotNone(hidden_states.grad)
self.assertIsNotNone(attentions.grad)
def test_initialization(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
configs_no_init = _config_zero_init(config)
for model_class in self.all_model_classes:
model = model_class(config=configs_no_init)
for name, param in model.named_parameters():
uniform_init_parms = [
"conv.weight",
"conv.parametrizations.weight",
"masked_spec_embed",
"codevectors",
"quantizer.weight_proj.weight",
"project_hid.weight",
"project_hid.bias",
"project_q.weight",
"project_q.bias",
"pos_bias_v",
"pos_bias_u",
"pointwise_conv1",
"pointwise_conv2",
"feature_projection.projection.weight",
"feature_projection.projection.bias",
"objective.weight",
]
if param.requires_grad:
if any(x in name for x in uniform_init_parms):
self.assertTrue(
-1.0 <= ((param.data.mean() * 1e9).round() / 1e9).item() <= 1.0,
msg=f"Parameter {name} of model {model_class} seems not properly initialized",
)
else:
self.assertIn(
((param.data.mean() * 1e9).round() / 1e9).item(),
[0.0, 1.0],
msg=f"Parameter {name} of model {model_class} seems not properly initialized",
)
# overwrite from test_modeling_common
def _mock_init_weights(self, module):
if hasattr(module, "weight") and module.weight is not None:
module.weight.data.fill_(3)
if hasattr(module, "weight_g") and module.weight_g is not None:
module.weight_g.data.fill_(3)
if hasattr(module, "weight_v") and module.weight_v is not None:
module.weight_v.data.fill_(3)
if hasattr(module, "bias") and module.bias is not None:
module.bias.data.fill_(3)
if hasattr(module, "pos_bias_u") and module.pos_bias_u is not None:
module.pos_bias_u.data.fill_(3)
if hasattr(module, "pos_bias_v") and module.pos_bias_v is not None:
module.pos_bias_v.data.fill_(3)
if hasattr(module, "codevectors") and module.codevectors is not None:
module.codevectors.data.fill_(3)
if hasattr(module, "masked_spec_embed") and module.masked_spec_embed is not None:
module.masked_spec_embed.data.fill_(3)
# Ignore copy
@unittest.skip(reason="Kept to make #Copied from working")
def test_mask_feature_prob_ctc(self):
pass
# Ignore copy
@unittest.skip(reason="Kept to make #Copied from working")
def test_mask_time_prob_ctc(self):
pass
@unittest.skip(reason="Feed forward chunking is not implemented")
def test_feed_forward_chunking(self):
pass
@slow
def test_model_from_pretrained(self):
# Ignore copy
model = Wav2Vec2BertModel.from_pretrained("facebook/w2v-bert-2.0")
self.assertIsNotNone(model)
@require_torch
# Copied from tests.models.wav2vec2_conformer.test_modeling_wav2vec2_conformer.Wav2Vec2ConformerUtilsTest with Conformer->Bert, input_values->input_features
class Wav2Vec2BertUtilsTest(unittest.TestCase):
def test_compute_mask_indices(self):
batch_size = 4
sequence_length = 60
mask_prob = 0.5
mask_length = 1
mask = _compute_mask_indices((batch_size, sequence_length), mask_prob, mask_length)
mask = torch.from_numpy(mask).to(torch_device)
self.assertListEqual(mask.sum(axis=-1).tolist(), [mask_prob * sequence_length for _ in range(batch_size)])
def test_compute_mask_indices_low_prob(self):
# with these settings num_masked_spans=0.5, which means probabilistic rounding
# ensures that in 5 out of 10 method calls, num_masked_spans=0, and in
# the other 5 out of 10, cases num_masked_spans=1
n_trials = 100
batch_size = 4
sequence_length = 100
mask_prob = 0.05
mask_length = 10
count_dimensions_masked = 0
count_dimensions_not_masked = 0
for _ in range(n_trials):
mask = _compute_mask_indices((batch_size, sequence_length), mask_prob, mask_length)
mask = torch.from_numpy(mask).to(torch_device)
num_masks = torch.sum(mask).item()
if num_masks > 0:
count_dimensions_masked += 1
else:
count_dimensions_not_masked += 1
# as we test for at least 10 masked dimension and at least
# 10 non-masked dimension, this test could fail with probability:
# P(100 coin flips, at most 9 heads) = 1.66e-18
self.assertGreater(count_dimensions_masked, int(n_trials * 0.1))
self.assertGreater(count_dimensions_not_masked, int(n_trials * 0.1))
def test_compute_mask_indices_overlap(self):
batch_size = 4
sequence_length = 80
mask_prob = 0.5
mask_length = 4
mask = _compute_mask_indices((batch_size, sequence_length), mask_prob, mask_length)
mask = torch.from_numpy(mask).to(torch_device)
# because of overlap mask don't have to add up exactly to `mask_prob * sequence_length`, but have to be smaller or equal
for batch_sum in mask.sum(axis=-1):
self.assertTrue(int(batch_sum) <= mask_prob * sequence_length)
def test_compute_mask_indices_attn_mask_overlap(self):
batch_size = 4
sequence_length = 80
mask_prob = 0.5
mask_length = 4
attention_mask = torch.ones((batch_size, sequence_length), dtype=torch.long, device=torch_device)
attention_mask[:2, sequence_length // 2 :] = 0
mask = _compute_mask_indices(
(batch_size, sequence_length), mask_prob, mask_length, attention_mask=attention_mask
)
mask = torch.from_numpy(mask).to(torch_device)
for batch_sum in mask.sum(axis=-1):
self.assertTrue(int(batch_sum) <= mask_prob * sequence_length)
self.assertTrue(mask[:2, sequence_length // 2 :].sum() == 0)
def test_compute_mask_indices_short_audio(self):
batch_size = 4
sequence_length = 100
mask_prob = 0.05
mask_length = 10
attention_mask = torch.ones((batch_size, sequence_length), dtype=torch.long, device=torch_device)
# force one example to be heavily padded
attention_mask[0, 5:] = 0
mask = _compute_mask_indices(
(batch_size, sequence_length), mask_prob, mask_length, attention_mask=attention_mask, min_masks=2
)
# make sure that non-padded examples cannot be padded
self.assertFalse(mask[0][attention_mask[0].to(torch.bool).cpu()].any())
# Ignore copy
@unittest.skip(reason="Kept to make #Copied from working. Test a class used for pretraining, not yet supported.")
def test_compute_perplexity(self):
pass
def test_sample_negatives(self):
batch_size = 2
sequence_length = 10
hidden_size = 4
num_negatives = 3
features = (torch.arange(sequence_length * hidden_size, device=torch_device) // hidden_size).view(
sequence_length, hidden_size
) # each value in vector consits of same value
features = features[None, :].expand(batch_size, sequence_length, hidden_size).contiguous()
# sample negative indices
sampled_negative_indices = _sample_negative_indices((batch_size, sequence_length), num_negatives, None)
sampled_negative_indices = torch.from_numpy(sampled_negative_indices).to(torch_device)
negatives = features.view(-1, hidden_size)[sampled_negative_indices.long().view(-1)]
negatives = negatives.view(batch_size, sequence_length, -1, hidden_size).permute(2, 0, 1, 3)
self.assertTrue(negatives.shape == (num_negatives, batch_size, sequence_length, hidden_size))
# make sure no negatively sampled vector is actually a positive one
for negative in negatives:
self.assertTrue(((negative - features) == 0).sum() == 0.0)
# make sure that full vectors are sampled and not values of vectors => this means that `unique()` yields a single value for `hidden_size` dim
self.assertTrue(negatives.unique(dim=-1).shape, (num_negatives, batch_size, sequence_length, 1))
def test_sample_negatives_with_mask(self):
batch_size = 2
sequence_length = 10
hidden_size = 4
num_negatives = 3
# second half of last input tensor is padded
mask = torch.ones((batch_size, sequence_length), dtype=torch.long, device=torch_device)
mask[-1, sequence_length // 2 :] = 0
features = (torch.arange(sequence_length * hidden_size, device=torch_device) // hidden_size).view(
sequence_length, hidden_size
) # each value in vector consits of same value
features = features[None, :].expand(batch_size, sequence_length, hidden_size).contiguous()
# replace masked feature vectors with -100 to test that those are not sampled
features = torch.where(mask[:, :, None].expand(features.shape).bool(), features, -100)
# sample negative indices
sampled_negative_indices = _sample_negative_indices(
(batch_size, sequence_length), num_negatives, mask.cpu().numpy()
)
sampled_negative_indices = torch.from_numpy(sampled_negative_indices).to(torch_device)
negatives = features.view(-1, hidden_size)[sampled_negative_indices.long().view(-1)]
negatives = negatives.view(batch_size, sequence_length, -1, hidden_size).permute(2, 0, 1, 3)
self.assertTrue((negatives >= 0).all().item())
self.assertTrue(negatives.shape == (num_negatives, batch_size, sequence_length, hidden_size))
# make sure no negatively sampled vector is actually a positive one
for negative in negatives:
self.assertTrue(((negative - features) == 0).sum() == 0.0)
# make sure that full vectors are sampled and not values of vectors => this means that `unique()` yields a single value for `hidden_size` dim
self.assertTrue(negatives.unique(dim=-1).shape, (num_negatives, batch_size, sequence_length, 1))
@require_torch
@slow
class Wav2Vec2BertModelIntegrationTest(unittest.TestCase):
def _load_datasamples(self, num_samples):
ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
# automatic decoding with librispeech
speech_samples = ds.sort("id").filter(lambda x: x["id"] in [f"1272-141231-000{i}" for i in range(num_samples)])
speech_samples = speech_samples[:num_samples]["audio"]
return [x["array"] for x in speech_samples]
def test_inference_w2v2_bert(self):
model = Wav2Vec2BertModel.from_pretrained("facebook/w2v-bert-2.0")
model.to(torch_device)
feature_extractor = AutoFeatureExtractor.from_pretrained("facebook/w2v-bert-2.0")
input_speech = self._load_datasamples(2)
inputs = feature_extractor(input_speech, return_tensors="pt", padding=True).to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**inputs, output_attentions=True)
# fmt: off
expected_slice_0 = torch.tensor(
[[-0.0098, -0.0570, -0.1286, 0.0439, -0.1037, -0.0235],
[-0.0767, 0.0574, -0.3224, 0.0482, 0.0440, -0.0193],
[ 0.0220, -0.0878, -0.2027, -0.0028, -0.0666, 0.0721],
[ 0.0307, -0.1099, 0.0273, -0.0416, -0.0715, 0.0094],
[ 0.0758, -0.0291, 0.1084, 0.0004, -0.0751, -0.0116],
[ 0.0349, -0.0343, -0.0098, 0.0415, -0.0617, 0.0241],
[-0.0193, -0.0171, 0.1965, 0.0797, -0.0308, 0.2033],
[-0.0323, -0.0315, 0.0948, 0.0944, -0.0254, 0.1241],
[-0.0493, 0.0010, -0.1762, 0.0034, -0.0787, 0.0832],
[ 0.0043, -0.1228, -0.0739, 0.0266, -0.0337, -0.0068]]
).to(torch_device)
# fmt: on
# fmt: off
expected_slice_1 = torch.tensor(
[[-0.0348, -0.0521, -0.3036, 0.0285, -0.0715, -0.0453],
[-0.0102, 0.0114, -0.3266, 0.0027, -0.0558, 0.0038],
[ 0.0454, 0.0148, -0.2418, -0.0392, -0.0455, 0.0478],
[-0.0013, 0.0825, -0.1730, -0.0091, -0.0426, 0.0360],
[-0.0227, 0.0687, -0.1168, 0.0569, -0.0160, 0.0759],
[-0.0318, 0.0562, -0.0508, 0.0605, 0.0150, 0.0953],
[-0.0415, 0.0438, 0.0233, 0.0336, 0.0262, 0.0860],
[-0.0163, 0.0048, 0.0807, 0.0119, 0.0712, 0.0158],
[ 0.0244, -0.0145, 0.0262, -0.0237, 0.0283, -0.0125],
[-0.0587, -0.0516, -0.0368, -0.0196, 0.0307, -0.1434]]
).to(torch_device)
# fmt: on
self.assertTrue((outputs.last_hidden_state[0, 25:35, 4:10] - expected_slice_0).abs().max() <= 1e-4)
self.assertTrue((outputs.last_hidden_state[1, 25:35, 4:10] - expected_slice_1).abs().max() <= 1e-4)
self.assertAlmostEqual(outputs.last_hidden_state[1].mean().item(), 3.3123e-05)
self.assertAlmostEqual(outputs.last_hidden_state[1].std().item(), 0.1545, delta=2e-5)
self.assertListEqual(list(outputs.last_hidden_state.shape), [2, 326, 1024])
| transformers/tests/models/wav2vec2_bert/test_modeling_wav2vec2_bert.py/0 | {
"file_path": "transformers/tests/models/wav2vec2_bert/test_modeling_wav2vec2_bert.py",
"repo_id": "transformers",
"token_count": 16960
} |
# Copyright 2020 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.
import gc
import unittest
from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline
from transformers.pipelines import PipelineException
from transformers.testing_utils import (
backend_empty_cache,
is_pipeline_test,
is_torch_available,
nested_simplify,
require_tf,
require_torch,
require_torch_accelerator,
slow,
torch_device,
)
from .test_pipelines_common import ANY
@is_pipeline_test
class FillMaskPipelineTests(unittest.TestCase):
model_mapping = MODEL_FOR_MASKED_LM_MAPPING
tf_model_mapping = TF_MODEL_FOR_MASKED_LM_MAPPING
def tearDown(self):
super().tearDown()
# clean-up as much as possible GPU memory occupied by PyTorch
gc.collect()
if is_torch_available():
backend_empty_cache(torch_device)
@require_tf
def test_small_model_tf(self):
unmasker = pipeline(task="fill-mask", model="sshleifer/tiny-distilroberta-base", top_k=2, framework="tf")
outputs = unmasker("My name is <mask>")
self.assertEqual(
nested_simplify(outputs, decimals=6),
[
{"sequence": "My name is grouped", "score": 2.1e-05, "token": 38015, "token_str": " grouped"},
{"sequence": "My name is accuser", "score": 2.1e-05, "token": 25506, "token_str": " accuser"},
],
)
outputs = unmasker("The largest city in France is <mask>")
self.assertEqual(
nested_simplify(outputs, decimals=6),
[
{
"sequence": "The largest city in France is grouped",
"score": 2.1e-05,
"token": 38015,
"token_str": " grouped",
},
{
"sequence": "The largest city in France is accuser",
"score": 2.1e-05,
"token": 25506,
"token_str": " accuser",
},
],
)
outputs = unmasker("My name is <mask>", targets=[" Patrick", " Clara", " Teven"], top_k=3)
self.assertEqual(
nested_simplify(outputs, decimals=6),
[
{"sequence": "My name is Clara", "score": 2e-05, "token": 13606, "token_str": " Clara"},
{"sequence": "My name is Patrick", "score": 2e-05, "token": 3499, "token_str": " Patrick"},
{"sequence": "My name is Te", "score": 1.9e-05, "token": 2941, "token_str": " Te"},
],
)
@require_torch
def test_small_model_pt(self):
unmasker = pipeline(task="fill-mask", model="sshleifer/tiny-distilroberta-base", top_k=2, framework="pt")
outputs = unmasker("My name is <mask>")
self.assertEqual(
nested_simplify(outputs, decimals=6),
[
{"sequence": "My name is Maul", "score": 2.2e-05, "token": 35676, "token_str": " Maul"},
{"sequence": "My name isELS", "score": 2.2e-05, "token": 16416, "token_str": "ELS"},
],
)
outputs = unmasker("The largest city in France is <mask>")
self.assertEqual(
nested_simplify(outputs, decimals=6),
[
{
"sequence": "The largest city in France is Maul",
"score": 2.2e-05,
"token": 35676,
"token_str": " Maul",
},
{"sequence": "The largest city in France isELS", "score": 2.2e-05, "token": 16416, "token_str": "ELS"},
],
)
outputs = unmasker("My name is <mask>", targets=[" Patrick", " Clara", " Teven"], top_k=3)
self.assertEqual(
nested_simplify(outputs, decimals=6),
[
{"sequence": "My name is Patrick", "score": 2.1e-05, "token": 3499, "token_str": " Patrick"},
{"sequence": "My name is Te", "score": 2e-05, "token": 2941, "token_str": " Te"},
{"sequence": "My name is Clara", "score": 2e-05, "token": 13606, "token_str": " Clara"},
],
)
outputs = unmasker("My name is <mask> <mask>", top_k=2)
self.assertEqual(
nested_simplify(outputs, decimals=6),
[
[
{
"score": 2.2e-05,
"token": 35676,
"token_str": " Maul",
"sequence": "<s>My name is Maul<mask></s>",
},
{"score": 2.2e-05, "token": 16416, "token_str": "ELS", "sequence": "<s>My name isELS<mask></s>"},
],
[
{
"score": 2.2e-05,
"token": 35676,
"token_str": " Maul",
"sequence": "<s>My name is<mask> Maul</s>",
},
{"score": 2.2e-05, "token": 16416, "token_str": "ELS", "sequence": "<s>My name is<mask>ELS</s>"},
],
],
)
@require_torch_accelerator
def test_fp16_casting(self):
pipe = pipeline(
"fill-mask",
model="hf-internal-testing/tiny-random-distilbert",
device=torch_device,
framework="pt",
)
# convert model to fp16
pipe.model.half()
response = pipe("Paris is the [MASK] of France.")
# We actually don't care about the result, we just want to make sure
# it works, meaning the float16 tensor got casted back to float32
# for postprocessing.
self.assertIsInstance(response, list)
@slow
@require_torch
def test_large_model_pt(self):
unmasker = pipeline(task="fill-mask", model="distilbert/distilroberta-base", top_k=2, framework="pt")
self.run_large_test(unmasker)
@slow
@require_tf
def test_large_model_tf(self):
unmasker = pipeline(task="fill-mask", model="distilbert/distilroberta-base", top_k=2, framework="tf")
self.run_large_test(unmasker)
def run_large_test(self, unmasker):
outputs = unmasker("My name is <mask>")
self.assertEqual(
nested_simplify(outputs),
[
{"sequence": "My name is John", "score": 0.008, "token": 610, "token_str": " John"},
{"sequence": "My name is Chris", "score": 0.007, "token": 1573, "token_str": " Chris"},
],
)
outputs = unmasker("The largest city in France is <mask>")
self.assertEqual(
nested_simplify(outputs),
[
{
"sequence": "The largest city in France is Paris",
"score": 0.251,
"token": 2201,
"token_str": " Paris",
},
{
"sequence": "The largest city in France is Lyon",
"score": 0.214,
"token": 12790,
"token_str": " Lyon",
},
],
)
outputs = unmasker("My name is <mask>", targets=[" Patrick", " Clara", " Teven"], top_k=3)
self.assertEqual(
nested_simplify(outputs),
[
{"sequence": "My name is Patrick", "score": 0.005, "token": 3499, "token_str": " Patrick"},
{"sequence": "My name is Clara", "score": 0.000, "token": 13606, "token_str": " Clara"},
{"sequence": "My name is Te", "score": 0.000, "token": 2941, "token_str": " Te"},
],
)
dummy_str = "Lorem ipsum dolor sit amet, consectetur adipiscing elit," * 100
outputs = unmasker(
"My name is <mask>" + dummy_str,
tokenizer_kwargs={"truncation": True},
)
simplified = nested_simplify(outputs, decimals=4)
self.assertEqual(
[{"sequence": x["sequence"][:100]} for x in simplified],
[
{"sequence": f"My name is,{dummy_str}"[:100]},
{"sequence": f"My name is:,{dummy_str}"[:100]},
],
)
self.assertEqual(
[{k: x[k] for k in x if k != "sequence"} for x in simplified],
[
{"score": 0.2819, "token": 6, "token_str": ","},
{"score": 0.0954, "token": 46686, "token_str": ":,"},
],
)
@require_torch
def test_model_no_pad_pt(self):
unmasker = pipeline(task="fill-mask", model="sshleifer/tiny-distilroberta-base", framework="pt")
unmasker.tokenizer.pad_token_id = None
unmasker.tokenizer.pad_token = None
self.run_pipeline_test(unmasker, [])
@require_tf
def test_model_no_pad_tf(self):
unmasker = pipeline(task="fill-mask", model="sshleifer/tiny-distilroberta-base", framework="tf")
unmasker.tokenizer.pad_token_id = None
unmasker.tokenizer.pad_token = None
self.run_pipeline_test(unmasker, [])
def get_test_pipeline(
self,
model,
tokenizer=None,
image_processor=None,
feature_extractor=None,
processor=None,
torch_dtype="float32",
):
if tokenizer is None or tokenizer.mask_token_id is None:
self.skipTest(reason="The provided tokenizer has no mask token, (probably reformer or wav2vec2)")
fill_masker = FillMaskPipeline(
model=model,
tokenizer=tokenizer,
feature_extractor=feature_extractor,
image_processor=image_processor,
processor=processor,
torch_dtype=torch_dtype,
)
examples = [
f"This is another {tokenizer.mask_token} test",
]
return fill_masker, examples
def run_pipeline_test(self, fill_masker, examples):
tokenizer = fill_masker.tokenizer
model = fill_masker.model
outputs = fill_masker(
f"This is a {tokenizer.mask_token}",
)
self.assertEqual(
outputs,
[
{"sequence": ANY(str), "score": ANY(float), "token": ANY(int), "token_str": ANY(str)},
{"sequence": ANY(str), "score": ANY(float), "token": ANY(int), "token_str": ANY(str)},
{"sequence": ANY(str), "score": ANY(float), "token": ANY(int), "token_str": ANY(str)},
{"sequence": ANY(str), "score": ANY(float), "token": ANY(int), "token_str": ANY(str)},
{"sequence": ANY(str), "score": ANY(float), "token": ANY(int), "token_str": ANY(str)},
],
)
outputs = fill_masker([f"This is a {tokenizer.mask_token}"])
self.assertEqual(
outputs,
[
{"sequence": ANY(str), "score": ANY(float), "token": ANY(int), "token_str": ANY(str)},
{"sequence": ANY(str), "score": ANY(float), "token": ANY(int), "token_str": ANY(str)},
{"sequence": ANY(str), "score": ANY(float), "token": ANY(int), "token_str": ANY(str)},
{"sequence": ANY(str), "score": ANY(float), "token": ANY(int), "token_str": ANY(str)},
{"sequence": ANY(str), "score": ANY(float), "token": ANY(int), "token_str": ANY(str)},
],
)
outputs = fill_masker([f"This is a {tokenizer.mask_token}", f"Another {tokenizer.mask_token} great test."])
self.assertEqual(
outputs,
[
[
{"sequence": ANY(str), "score": ANY(float), "token": ANY(int), "token_str": ANY(str)},
{"sequence": ANY(str), "score": ANY(float), "token": ANY(int), "token_str": ANY(str)},
{"sequence": ANY(str), "score": ANY(float), "token": ANY(int), "token_str": ANY(str)},
{"sequence": ANY(str), "score": ANY(float), "token": ANY(int), "token_str": ANY(str)},
{"sequence": ANY(str), "score": ANY(float), "token": ANY(int), "token_str": ANY(str)},
],
[
{"sequence": ANY(str), "score": ANY(float), "token": ANY(int), "token_str": ANY(str)},
{"sequence": ANY(str), "score": ANY(float), "token": ANY(int), "token_str": ANY(str)},
{"sequence": ANY(str), "score": ANY(float), "token": ANY(int), "token_str": ANY(str)},
{"sequence": ANY(str), "score": ANY(float), "token": ANY(int), "token_str": ANY(str)},
{"sequence": ANY(str), "score": ANY(float), "token": ANY(int), "token_str": ANY(str)},
],
],
)
with self.assertRaises(ValueError):
fill_masker([None])
# No mask_token is not supported
with self.assertRaises(PipelineException):
fill_masker("This is")
self.run_test_top_k(model, tokenizer)
self.run_test_targets(model, tokenizer)
self.run_test_top_k_targets(model, tokenizer)
self.fill_mask_with_duplicate_targets_and_top_k(model, tokenizer)
self.fill_mask_with_multiple_masks(model, tokenizer)
def run_test_targets(self, model, tokenizer):
vocab = tokenizer.get_vocab()
targets = sorted(vocab.keys())[:2]
# Pipeline argument
fill_masker = FillMaskPipeline(model=model, tokenizer=tokenizer, targets=targets)
outputs = fill_masker(f"This is a {tokenizer.mask_token}")
self.assertEqual(
outputs,
[
{"sequence": ANY(str), "score": ANY(float), "token": ANY(int), "token_str": ANY(str)},
{"sequence": ANY(str), "score": ANY(float), "token": ANY(int), "token_str": ANY(str)},
],
)
target_ids = {vocab[el] for el in targets}
self.assertEqual({el["token"] for el in outputs}, target_ids)
processed_targets = [tokenizer.decode([x]) for x in target_ids]
self.assertEqual({el["token_str"] for el in outputs}, set(processed_targets))
# Call argument
fill_masker = FillMaskPipeline(model=model, tokenizer=tokenizer)
outputs = fill_masker(f"This is a {tokenizer.mask_token}", targets=targets)
self.assertEqual(
outputs,
[
{"sequence": ANY(str), "score": ANY(float), "token": ANY(int), "token_str": ANY(str)},
{"sequence": ANY(str), "score": ANY(float), "token": ANY(int), "token_str": ANY(str)},
],
)
target_ids = {vocab[el] for el in targets}
self.assertEqual({el["token"] for el in outputs}, target_ids)
processed_targets = [tokenizer.decode([x]) for x in target_ids]
self.assertEqual({el["token_str"] for el in outputs}, set(processed_targets))
# Score equivalence
outputs = fill_masker(f"This is a {tokenizer.mask_token}", targets=targets)
tokens = [top_mask["token_str"] for top_mask in outputs]
scores = [top_mask["score"] for top_mask in outputs]
# For some BPE tokenizers, `</w>` is removed during decoding, so `token_str` won't be the same as in `targets`.
if set(tokens) == set(targets):
unmasked_targets = fill_masker(f"This is a {tokenizer.mask_token}", targets=tokens)
target_scores = [top_mask["score"] for top_mask in unmasked_targets]
self.assertEqual(nested_simplify(scores), nested_simplify(target_scores))
# Raises with invalid
with self.assertRaises(ValueError):
outputs = fill_masker(f"This is a {tokenizer.mask_token}", targets=[])
# For some tokenizers, `""` is actually in the vocabulary and the expected error won't raised
if "" not in tokenizer.get_vocab():
with self.assertRaises(ValueError):
outputs = fill_masker(f"This is a {tokenizer.mask_token}", targets=[""])
with self.assertRaises(ValueError):
outputs = fill_masker(f"This is a {tokenizer.mask_token}", targets="")
def run_test_top_k(self, model, tokenizer):
fill_masker = FillMaskPipeline(model=model, tokenizer=tokenizer, top_k=2)
outputs = fill_masker(f"This is a {tokenizer.mask_token}")
self.assertEqual(
outputs,
[
{"sequence": ANY(str), "score": ANY(float), "token": ANY(int), "token_str": ANY(str)},
{"sequence": ANY(str), "score": ANY(float), "token": ANY(int), "token_str": ANY(str)},
],
)
fill_masker = FillMaskPipeline(model=model, tokenizer=tokenizer)
outputs2 = fill_masker(f"This is a {tokenizer.mask_token}", top_k=2)
self.assertEqual(
outputs2,
[
{"sequence": ANY(str), "score": ANY(float), "token": ANY(int), "token_str": ANY(str)},
{"sequence": ANY(str), "score": ANY(float), "token": ANY(int), "token_str": ANY(str)},
],
)
self.assertEqual(nested_simplify(outputs), nested_simplify(outputs2))
def run_test_top_k_targets(self, model, tokenizer):
vocab = tokenizer.get_vocab()
fill_masker = FillMaskPipeline(model=model, tokenizer=tokenizer)
# top_k=2, ntargets=3
targets = sorted(vocab.keys())[:3]
outputs = fill_masker(f"This is a {tokenizer.mask_token}", top_k=2, targets=targets)
# If we use the most probably targets, and filter differently, we should still
# have the same results
targets2 = [el["token_str"] for el in sorted(outputs, key=lambda x: x["score"], reverse=True)]
# For some BPE tokenizers, `</w>` is removed during decoding, so `token_str` won't be the same as in `targets`.
if set(targets2).issubset(targets):
outputs2 = fill_masker(f"This is a {tokenizer.mask_token}", top_k=3, targets=targets2)
# They should yield exactly the same result
self.assertEqual(nested_simplify(outputs), nested_simplify(outputs2))
def fill_mask_with_duplicate_targets_and_top_k(self, model, tokenizer):
fill_masker = FillMaskPipeline(model=model, tokenizer=tokenizer)
vocab = tokenizer.get_vocab()
# String duplicates + id duplicates
targets = sorted(vocab.keys())[:3]
targets = [targets[0], targets[1], targets[0], targets[2], targets[1]]
outputs = fill_masker(f"My name is {tokenizer.mask_token}", targets=targets, top_k=10)
# The target list contains duplicates, so we can't output more
# than them
self.assertEqual(len(outputs), 3)
def fill_mask_with_multiple_masks(self, model, tokenizer):
fill_masker = FillMaskPipeline(model=model, tokenizer=tokenizer)
outputs = fill_masker(
f"This is a {tokenizer.mask_token} {tokenizer.mask_token} {tokenizer.mask_token}", top_k=2
)
self.assertEqual(
outputs,
[
[
{"sequence": ANY(str), "score": ANY(float), "token": ANY(int), "token_str": ANY(str)},
{"sequence": ANY(str), "score": ANY(float), "token": ANY(int), "token_str": ANY(str)},
],
[
{"sequence": ANY(str), "score": ANY(float), "token": ANY(int), "token_str": ANY(str)},
{"sequence": ANY(str), "score": ANY(float), "token": ANY(int), "token_str": ANY(str)},
],
[
{"sequence": ANY(str), "score": ANY(float), "token": ANY(int), "token_str": ANY(str)},
{"sequence": ANY(str), "score": ANY(float), "token": ANY(int), "token_str": ANY(str)},
],
],
)
| transformers/tests/pipelines/test_pipelines_fill_mask.py/0 | {
"file_path": "transformers/tests/pipelines/test_pipelines_fill_mask.py",
"repo_id": "transformers",
"token_count": 9938
} |
# Copyright 2020 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.
import unittest
import numpy as np
from transformers import (
MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
AutoModelForTokenClassification,
AutoTokenizer,
TokenClassificationPipeline,
pipeline,
)
from transformers.pipelines import AggregationStrategy, TokenClassificationArgumentHandler
from transformers.testing_utils import (
is_pipeline_test,
is_torch_available,
nested_simplify,
require_tf,
require_torch,
require_torch_accelerator,
slow,
torch_device,
)
from .test_pipelines_common import ANY
if is_torch_available():
import torch
VALID_INPUTS = ["A simple string", ["list of strings", "A simple string that is quite a bit longer"]]
# These 2 model types require different inputs than those of the usual text models.
_TO_SKIP = {"LayoutLMv2Config", "LayoutLMv3Config"}
@is_pipeline_test
class TokenClassificationPipelineTests(unittest.TestCase):
model_mapping = MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING
tf_model_mapping = TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING
if model_mapping is not None:
model_mapping = {config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
if tf_model_mapping is not None:
tf_model_mapping = {
config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
}
def get_test_pipeline(
self,
model,
tokenizer=None,
image_processor=None,
feature_extractor=None,
processor=None,
torch_dtype="float32",
):
token_classifier = TokenClassificationPipeline(
model=model,
tokenizer=tokenizer,
feature_extractor=feature_extractor,
image_processor=image_processor,
processor=processor,
torch_dtype=torch_dtype,
)
return token_classifier, ["A simple string", "A simple string that is quite a bit longer"]
def run_pipeline_test(self, token_classifier, _):
model = token_classifier.model
tokenizer = token_classifier.tokenizer
if not tokenizer.is_fast:
return # Slow tokenizers do not return offsets mappings, so this test will fail
outputs = token_classifier("A simple string")
self.assertIsInstance(outputs, list)
n = len(outputs)
self.assertEqual(
nested_simplify(outputs),
[
{
"entity": ANY(str),
"score": ANY(float),
"start": ANY(int),
"end": ANY(int),
"index": ANY(int),
"word": ANY(str),
}
for i in range(n)
],
)
outputs = token_classifier(["list of strings", "A simple string that is quite a bit longer"])
self.assertIsInstance(outputs, list)
self.assertEqual(len(outputs), 2)
n = len(outputs[0])
m = len(outputs[1])
self.assertEqual(
nested_simplify(outputs),
[
[
{
"entity": ANY(str),
"score": ANY(float),
"start": ANY(int),
"end": ANY(int),
"index": ANY(int),
"word": ANY(str),
}
for i in range(n)
],
[
{
"entity": ANY(str),
"score": ANY(float),
"start": ANY(int),
"end": ANY(int),
"index": ANY(int),
"word": ANY(str),
}
for i in range(m)
],
],
)
self.run_aggregation_strategy(model, tokenizer)
def run_aggregation_strategy(self, model, tokenizer):
token_classifier = TokenClassificationPipeline(model=model, tokenizer=tokenizer, aggregation_strategy="simple")
self.assertEqual(token_classifier._postprocess_params["aggregation_strategy"], AggregationStrategy.SIMPLE)
outputs = token_classifier("A simple string")
self.assertIsInstance(outputs, list)
n = len(outputs)
self.assertEqual(
nested_simplify(outputs),
[
{
"entity_group": ANY(str),
"score": ANY(float),
"start": ANY(int),
"end": ANY(int),
"word": ANY(str),
}
for i in range(n)
],
)
token_classifier = TokenClassificationPipeline(model=model, tokenizer=tokenizer, aggregation_strategy="first")
self.assertEqual(token_classifier._postprocess_params["aggregation_strategy"], AggregationStrategy.FIRST)
outputs = token_classifier("A simple string")
self.assertIsInstance(outputs, list)
n = len(outputs)
self.assertEqual(
nested_simplify(outputs),
[
{
"entity_group": ANY(str),
"score": ANY(float),
"start": ANY(int),
"end": ANY(int),
"word": ANY(str),
}
for i in range(n)
],
)
token_classifier = TokenClassificationPipeline(model=model, tokenizer=tokenizer, aggregation_strategy="max")
self.assertEqual(token_classifier._postprocess_params["aggregation_strategy"], AggregationStrategy.MAX)
outputs = token_classifier("A simple string")
self.assertIsInstance(outputs, list)
n = len(outputs)
self.assertEqual(
nested_simplify(outputs),
[
{
"entity_group": ANY(str),
"score": ANY(float),
"start": ANY(int),
"end": ANY(int),
"word": ANY(str),
}
for i in range(n)
],
)
token_classifier = TokenClassificationPipeline(
model=model, tokenizer=tokenizer, aggregation_strategy="average"
)
self.assertEqual(token_classifier._postprocess_params["aggregation_strategy"], AggregationStrategy.AVERAGE)
outputs = token_classifier("A simple string")
self.assertIsInstance(outputs, list)
n = len(outputs)
self.assertEqual(
nested_simplify(outputs),
[
{
"entity_group": ANY(str),
"score": ANY(float),
"start": ANY(int),
"end": ANY(int),
"word": ANY(str),
}
for i in range(n)
],
)
with self.assertWarns(UserWarning):
token_classifier = pipeline(task="ner", model=model, tokenizer=tokenizer, grouped_entities=True)
self.assertEqual(token_classifier._postprocess_params["aggregation_strategy"], AggregationStrategy.SIMPLE)
with self.assertWarns(UserWarning):
token_classifier = pipeline(
task="ner", model=model, tokenizer=tokenizer, grouped_entities=True, ignore_subwords=True
)
self.assertEqual(token_classifier._postprocess_params["aggregation_strategy"], AggregationStrategy.FIRST)
@slow
@require_torch
def test_chunking(self):
NER_MODEL = "elastic/distilbert-base-uncased-finetuned-conll03-english"
model = AutoModelForTokenClassification.from_pretrained(NER_MODEL)
tokenizer = AutoTokenizer.from_pretrained(NER_MODEL, use_fast=True)
tokenizer.model_max_length = 10
stride = 5
sentence = (
"Hugging Face, Inc. is a French company that develops tools for building applications using machine learning. "
"The company, based in New York City was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf."
)
token_classifier = TokenClassificationPipeline(
model=model, tokenizer=tokenizer, aggregation_strategy="simple", stride=stride
)
output = token_classifier(sentence)
self.assertEqual(
nested_simplify(output),
[
{"entity_group": "ORG", "score": 0.978, "word": "hugging face, inc.", "start": 0, "end": 18},
{"entity_group": "MISC", "score": 0.999, "word": "french", "start": 24, "end": 30},
{"entity_group": "LOC", "score": 0.997, "word": "new york city", "start": 131, "end": 144},
{"entity_group": "MISC", "score": 0.999, "word": "french", "start": 168, "end": 174},
{"entity_group": "PER", "score": 0.999, "word": "clement delangue", "start": 189, "end": 205},
{"entity_group": "PER", "score": 0.999, "word": "julien chaumond", "start": 207, "end": 222},
{"entity_group": "PER", "score": 0.999, "word": "thomas wolf", "start": 228, "end": 239},
],
)
token_classifier = TokenClassificationPipeline(
model=model, tokenizer=tokenizer, aggregation_strategy="first", stride=stride
)
output = token_classifier(sentence)
self.assertEqual(
nested_simplify(output),
[
{"entity_group": "ORG", "score": 0.978, "word": "hugging face, inc.", "start": 0, "end": 18},
{"entity_group": "MISC", "score": 0.999, "word": "french", "start": 24, "end": 30},
{"entity_group": "LOC", "score": 0.997, "word": "new york city", "start": 131, "end": 144},
{"entity_group": "MISC", "score": 0.999, "word": "french", "start": 168, "end": 174},
{"entity_group": "PER", "score": 0.999, "word": "clement delangue", "start": 189, "end": 205},
{"entity_group": "PER", "score": 0.999, "word": "julien chaumond", "start": 207, "end": 222},
{"entity_group": "PER", "score": 0.999, "word": "thomas wolf", "start": 228, "end": 239},
],
)
token_classifier = TokenClassificationPipeline(
model=model, tokenizer=tokenizer, aggregation_strategy="max", stride=stride
)
output = token_classifier(sentence)
self.assertEqual(
nested_simplify(output),
[
{"entity_group": "ORG", "score": 0.978, "word": "hugging face, inc.", "start": 0, "end": 18},
{"entity_group": "MISC", "score": 0.999, "word": "french", "start": 24, "end": 30},
{"entity_group": "LOC", "score": 0.997, "word": "new york city", "start": 131, "end": 144},
{"entity_group": "MISC", "score": 0.999, "word": "french", "start": 168, "end": 174},
{"entity_group": "PER", "score": 0.999, "word": "clement delangue", "start": 189, "end": 205},
{"entity_group": "PER", "score": 0.999, "word": "julien chaumond", "start": 207, "end": 222},
{"entity_group": "PER", "score": 0.999, "word": "thomas wolf", "start": 228, "end": 239},
],
)
token_classifier = TokenClassificationPipeline(
model=model, tokenizer=tokenizer, aggregation_strategy="average", stride=stride
)
output = token_classifier(sentence)
self.assertEqual(
nested_simplify(output),
[
{"entity_group": "ORG", "score": 0.978, "word": "hugging face, inc.", "start": 0, "end": 18},
{"entity_group": "MISC", "score": 0.999, "word": "french", "start": 24, "end": 30},
{"entity_group": "LOC", "score": 0.997, "word": "new york city", "start": 131, "end": 144},
{"entity_group": "MISC", "score": 0.999, "word": "french", "start": 168, "end": 174},
{"entity_group": "PER", "score": 0.999, "word": "clement delangue", "start": 189, "end": 205},
{"entity_group": "PER", "score": 0.999, "word": "julien chaumond", "start": 207, "end": 222},
{"entity_group": "PER", "score": 0.999, "word": "thomas wolf", "start": 228, "end": 239},
],
)
@require_torch
def test_chunking_fast(self):
# Note: We cannot run the test on "conflicts" on the chunking.
# The problem is that the model is random, and thus the results do heavily
# depend on the chunking, so we cannot expect "abcd" and "bcd" to find
# the same entities. We defer to slow tests for this.
pipe = pipeline(model="hf-internal-testing/tiny-bert-for-token-classification")
sentence = "The company, based in New York City was founded in 2016 by French entrepreneurs"
results = pipe(sentence, aggregation_strategy="first")
# This is what this random model gives on the full sentence
self.assertEqual(
nested_simplify(results),
[
# This is 2 actual tokens
{"end": 39, "entity_group": "MISC", "score": 0.115, "start": 31, "word": "city was"},
{"end": 79, "entity_group": "MISC", "score": 0.115, "start": 66, "word": "entrepreneurs"},
],
)
# This will force the tokenizer to split after "city was".
pipe.tokenizer.model_max_length = 12
self.assertEqual(
pipe.tokenizer.decode(pipe.tokenizer.encode(sentence, truncation=True)),
"[CLS] the company, based in new york city was [SEP]",
)
stride = 4
results = pipe(sentence, aggregation_strategy="first", stride=stride)
self.assertEqual(
nested_simplify(results),
[
{"end": 39, "entity_group": "MISC", "score": 0.115, "start": 31, "word": "city was"},
# This is an extra entity found by this random model, but at least both original
# entities are there
{"end": 58, "entity_group": "MISC", "score": 0.115, "start": 56, "word": "by"},
{"end": 79, "entity_group": "MISC", "score": 0.115, "start": 66, "word": "entrepreneurs"},
],
)
@require_torch
@slow
def test_spanish_bert(self):
# https://github.com/huggingface/transformers/pull/4987
NER_MODEL = "mrm8488/bert-spanish-cased-finetuned-ner"
model = AutoModelForTokenClassification.from_pretrained(NER_MODEL)
tokenizer = AutoTokenizer.from_pretrained(NER_MODEL, use_fast=True)
sentence = """Consuelo Araújo Noguera, ministra de cultura del presidente Andrés Pastrana (1998.2002) fue asesinada por las Farc luego de haber permanecido secuestrada por algunos meses."""
token_classifier = pipeline("ner", model=model, tokenizer=tokenizer)
output = token_classifier(sentence)
self.assertEqual(
nested_simplify(output[:3]),
[
{"entity": "B-PER", "score": 0.999, "word": "Cons", "start": 0, "end": 4, "index": 1},
{"entity": "B-PER", "score": 0.803, "word": "##uelo", "start": 4, "end": 8, "index": 2},
{"entity": "I-PER", "score": 0.999, "word": "Ara", "start": 9, "end": 12, "index": 3},
],
)
token_classifier = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="simple")
output = token_classifier(sentence)
self.assertEqual(
nested_simplify(output[:3]),
[
{"entity_group": "PER", "score": 0.999, "word": "Cons", "start": 0, "end": 4},
{"entity_group": "PER", "score": 0.966, "word": "##uelo Araújo Noguera", "start": 4, "end": 23},
{"entity_group": "PER", "score": 1.0, "word": "Andrés Pastrana", "start": 60, "end": 75},
],
)
token_classifier = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="first")
output = token_classifier(sentence)
self.assertEqual(
nested_simplify(output[:3]),
[
{"entity_group": "PER", "score": 0.999, "word": "Consuelo Araújo Noguera", "start": 0, "end": 23},
{"entity_group": "PER", "score": 1.0, "word": "Andrés Pastrana", "start": 60, "end": 75},
{"entity_group": "ORG", "score": 0.999, "word": "Farc", "start": 110, "end": 114},
],
)
token_classifier = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="max")
output = token_classifier(sentence)
self.assertEqual(
nested_simplify(output[:3]),
[
{"entity_group": "PER", "score": 0.999, "word": "Consuelo Araújo Noguera", "start": 0, "end": 23},
{"entity_group": "PER", "score": 1.0, "word": "Andrés Pastrana", "start": 60, "end": 75},
{"entity_group": "ORG", "score": 0.999, "word": "Farc", "start": 110, "end": 114},
],
)
token_classifier = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="average")
output = token_classifier(sentence)
self.assertEqual(
nested_simplify(output[:3]),
[
{"entity_group": "PER", "score": 0.966, "word": "Consuelo Araújo Noguera", "start": 0, "end": 23},
{"entity_group": "PER", "score": 1.0, "word": "Andrés Pastrana", "start": 60, "end": 75},
{"entity_group": "ORG", "score": 0.542, "word": "Farc", "start": 110, "end": 114},
],
)
@require_torch_accelerator
@slow
def test_accelerator(self):
sentence = "This is dummy sentence"
ner = pipeline(
"token-classification",
device=torch_device,
aggregation_strategy=AggregationStrategy.SIMPLE,
)
output = ner(sentence)
self.assertEqual(nested_simplify(output), [])
@require_torch
@slow
def test_dbmdz_english(self):
# Other sentence
NER_MODEL = "dbmdz/bert-large-cased-finetuned-conll03-english"
model = AutoModelForTokenClassification.from_pretrained(NER_MODEL)
tokenizer = AutoTokenizer.from_pretrained(NER_MODEL, use_fast=True)
sentence = """Enzo works at the UN"""
token_classifier = pipeline("ner", model=model, tokenizer=tokenizer)
output = token_classifier(sentence)
self.assertEqual(
nested_simplify(output),
[
{"entity": "I-PER", "score": 0.998, "word": "En", "start": 0, "end": 2, "index": 1},
{"entity": "I-PER", "score": 0.997, "word": "##zo", "start": 2, "end": 4, "index": 2},
{"entity": "I-ORG", "score": 0.999, "word": "UN", "start": 18, "end": 20, "index": 6},
],
)
token_classifier = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="simple")
output = token_classifier(sentence)
self.assertEqual(
nested_simplify(output),
[
{"entity_group": "PER", "score": 0.997, "word": "Enzo", "start": 0, "end": 4},
{"entity_group": "ORG", "score": 0.999, "word": "UN", "start": 18, "end": 20},
],
)
token_classifier = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="first")
output = token_classifier(sentence)
self.assertEqual(
nested_simplify(output[:3]),
[
{"entity_group": "PER", "score": 0.998, "word": "Enzo", "start": 0, "end": 4},
{"entity_group": "ORG", "score": 0.999, "word": "UN", "start": 18, "end": 20},
],
)
token_classifier = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="max")
output = token_classifier(sentence)
self.assertEqual(
nested_simplify(output[:3]),
[
{"entity_group": "PER", "score": 0.998, "word": "Enzo", "start": 0, "end": 4},
{"entity_group": "ORG", "score": 0.999, "word": "UN", "start": 18, "end": 20},
],
)
token_classifier = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="average")
output = token_classifier(sentence)
self.assertEqual(
nested_simplify(output),
[
{"entity_group": "PER", "score": 0.997, "word": "Enzo", "start": 0, "end": 4},
{"entity_group": "ORG", "score": 0.999, "word": "UN", "start": 18, "end": 20},
],
)
@require_torch
@slow
def test_aggregation_strategy_byte_level_tokenizer(self):
sentence = "Groenlinks praat over Schiphol."
ner = pipeline("ner", model="FacebookAI/xlm-roberta-large-finetuned-conll02-dutch", aggregation_strategy="max")
self.assertEqual(
nested_simplify(ner(sentence)),
[
{"end": 10, "entity_group": "ORG", "score": 0.994, "start": 0, "word": "Groenlinks"},
{"entity_group": "LOC", "score": 1.0, "word": "Schiphol.", "start": 22, "end": 31},
],
)
@require_torch
def test_aggregation_strategy_no_b_i_prefix(self):
model_name = "sshleifer/tiny-dbmdz-bert-large-cased-finetuned-conll03-english"
tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)
token_classifier = pipeline(task="ner", model=model_name, tokenizer=tokenizer, framework="pt")
# Just to understand scores indexes in this test
token_classifier.model.config.id2label = {0: "O", 1: "MISC", 2: "PER", 3: "ORG", 4: "LOC"}
example = [
{
"scores": np.array([0, 0, 0, 0, 0.9968166351318359]), # fmt : skip
"index": 1,
"is_subword": False,
"word": "En",
"start": 0,
"end": 2,
},
{
"scores": np.array([0, 0, 0, 0, 0.9957635998725891]), # fmt : skip
"index": 2,
"is_subword": True,
"word": "##zo",
"start": 2,
"end": 4,
},
{
"scores": np.array([0, 0, 0, 0.9986497163772583, 0]), # fmt : skip
"index": 7,
"word": "UN",
"is_subword": False,
"start": 11,
"end": 13,
},
]
self.assertEqual(
nested_simplify(token_classifier.aggregate(example, AggregationStrategy.NONE)),
[
{"end": 2, "entity": "LOC", "score": 0.997, "start": 0, "word": "En", "index": 1},
{"end": 4, "entity": "LOC", "score": 0.996, "start": 2, "word": "##zo", "index": 2},
{"end": 13, "entity": "ORG", "score": 0.999, "start": 11, "word": "UN", "index": 7},
],
)
self.assertEqual(
nested_simplify(token_classifier.aggregate(example, AggregationStrategy.SIMPLE)),
[
{"entity_group": "LOC", "score": 0.996, "word": "Enzo", "start": 0, "end": 4},
{"entity_group": "ORG", "score": 0.999, "word": "UN", "start": 11, "end": 13},
],
)
@require_torch
def test_aggregation_strategy(self):
model_name = "sshleifer/tiny-dbmdz-bert-large-cased-finetuned-conll03-english"
tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)
token_classifier = pipeline(task="ner", model=model_name, tokenizer=tokenizer, framework="pt")
# Just to understand scores indexes in this test
self.assertEqual(
token_classifier.model.config.id2label,
{0: "O", 1: "B-MISC", 2: "I-MISC", 3: "B-PER", 4: "I-PER", 5: "B-ORG", 6: "I-ORG", 7: "B-LOC", 8: "I-LOC"},
)
example = [
{
"scores": np.array([0, 0, 0, 0, 0.9968166351318359, 0, 0, 0]), # fmt : skip
"index": 1,
"is_subword": False,
"word": "En",
"start": 0,
"end": 2,
},
{
"scores": np.array([0, 0, 0, 0, 0.9957635998725891, 0, 0, 0]), # fmt : skip
"index": 2,
"is_subword": True,
"word": "##zo",
"start": 2,
"end": 4,
},
{
"scores": np.array([0, 0, 0, 0, 0, 0.9986497163772583, 0, 0]), # fmt : skip
"index": 7,
"word": "UN",
"is_subword": False,
"start": 11,
"end": 13,
},
]
self.assertEqual(
nested_simplify(token_classifier.aggregate(example, AggregationStrategy.NONE)),
[
{"end": 2, "entity": "I-PER", "score": 0.997, "start": 0, "word": "En", "index": 1},
{"end": 4, "entity": "I-PER", "score": 0.996, "start": 2, "word": "##zo", "index": 2},
{"end": 13, "entity": "B-ORG", "score": 0.999, "start": 11, "word": "UN", "index": 7},
],
)
self.assertEqual(
nested_simplify(token_classifier.aggregate(example, AggregationStrategy.SIMPLE)),
[
{"entity_group": "PER", "score": 0.996, "word": "Enzo", "start": 0, "end": 4},
{"entity_group": "ORG", "score": 0.999, "word": "UN", "start": 11, "end": 13},
],
)
self.assertEqual(
nested_simplify(token_classifier.aggregate(example, AggregationStrategy.FIRST)),
[
{"entity_group": "PER", "score": 0.997, "word": "Enzo", "start": 0, "end": 4},
{"entity_group": "ORG", "score": 0.999, "word": "UN", "start": 11, "end": 13},
],
)
self.assertEqual(
nested_simplify(token_classifier.aggregate(example, AggregationStrategy.MAX)),
[
{"entity_group": "PER", "score": 0.997, "word": "Enzo", "start": 0, "end": 4},
{"entity_group": "ORG", "score": 0.999, "word": "UN", "start": 11, "end": 13},
],
)
self.assertEqual(
nested_simplify(token_classifier.aggregate(example, AggregationStrategy.AVERAGE)),
[
{"entity_group": "PER", "score": 0.996, "word": "Enzo", "start": 0, "end": 4},
{"entity_group": "ORG", "score": 0.999, "word": "UN", "start": 11, "end": 13},
],
)
@require_torch
def test_aggregation_strategy_example2(self):
model_name = "sshleifer/tiny-dbmdz-bert-large-cased-finetuned-conll03-english"
tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)
token_classifier = pipeline(task="ner", model=model_name, tokenizer=tokenizer, framework="pt")
# Just to understand scores indexes in this test
self.assertEqual(
token_classifier.model.config.id2label,
{0: "O", 1: "B-MISC", 2: "I-MISC", 3: "B-PER", 4: "I-PER", 5: "B-ORG", 6: "I-ORG", 7: "B-LOC", 8: "I-LOC"},
)
example = [
{
# Necessary for AVERAGE
"scores": np.array([0, 0.55, 0, 0.45, 0, 0, 0, 0, 0, 0]),
"is_subword": False,
"index": 1,
"word": "Ra",
"start": 0,
"end": 2,
},
{
"scores": np.array([0, 0, 0, 0.2, 0, 0, 0, 0.8, 0, 0]),
"is_subword": True,
"word": "##ma",
"start": 2,
"end": 4,
"index": 2,
},
{
# 4th score will have the higher average
# 4th score is B-PER for this model
# It's does not correspond to any of the subtokens.
"scores": np.array([0, 0, 0, 0.4, 0, 0, 0.6, 0, 0, 0]),
"is_subword": True,
"word": "##zotti",
"start": 11,
"end": 13,
"index": 3,
},
]
self.assertEqual(
token_classifier.aggregate(example, AggregationStrategy.NONE),
[
{"end": 2, "entity": "B-MISC", "score": 0.55, "start": 0, "word": "Ra", "index": 1},
{"end": 4, "entity": "B-LOC", "score": 0.8, "start": 2, "word": "##ma", "index": 2},
{"end": 13, "entity": "I-ORG", "score": 0.6, "start": 11, "word": "##zotti", "index": 3},
],
)
self.assertEqual(
token_classifier.aggregate(example, AggregationStrategy.FIRST),
[{"entity_group": "MISC", "score": 0.55, "word": "Ramazotti", "start": 0, "end": 13}],
)
self.assertEqual(
token_classifier.aggregate(example, AggregationStrategy.MAX),
[{"entity_group": "LOC", "score": 0.8, "word": "Ramazotti", "start": 0, "end": 13}],
)
self.assertEqual(
nested_simplify(token_classifier.aggregate(example, AggregationStrategy.AVERAGE)),
[{"entity_group": "PER", "score": 0.35, "word": "Ramazotti", "start": 0, "end": 13}],
)
@require_torch
@slow
def test_aggregation_strategy_offsets_with_leading_space(self):
sentence = "We're from New York"
model_name = "brandon25/deberta-base-finetuned-ner"
ner = pipeline("ner", model=model_name, ignore_labels=[], aggregation_strategy="max")
self.assertEqual(
nested_simplify(ner(sentence)),
[
{"entity_group": "O", "score": 1.0, "word": " We're from", "start": 0, "end": 10},
{"entity_group": "LOC", "score": 1.0, "word": " New York", "start": 10, "end": 19},
],
)
@require_torch
def test_gather_pre_entities(self):
model_name = "sshleifer/tiny-dbmdz-bert-large-cased-finetuned-conll03-english"
tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)
token_classifier = pipeline(task="ner", model=model_name, tokenizer=tokenizer, framework="pt")
sentence = "Hello there"
tokens = tokenizer(
sentence,
return_attention_mask=False,
return_tensors="pt",
truncation=True,
return_special_tokens_mask=True,
return_offsets_mapping=True,
)
offset_mapping = tokens.pop("offset_mapping").cpu().numpy()[0]
special_tokens_mask = tokens.pop("special_tokens_mask").cpu().numpy()[0]
input_ids = tokens["input_ids"].numpy()[0]
# First element in [CLS]
scores = np.array([[1, 0, 0], [0.1, 0.3, 0.6], [0.8, 0.1, 0.1]])
pre_entities = token_classifier.gather_pre_entities(
sentence,
input_ids,
scores,
offset_mapping,
special_tokens_mask,
aggregation_strategy=AggregationStrategy.NONE,
)
self.assertEqual(
nested_simplify(pre_entities),
[
{"word": "Hello", "scores": [0.1, 0.3, 0.6], "start": 0, "end": 5, "is_subword": False, "index": 1},
{
"word": "there",
"scores": [0.8, 0.1, 0.1],
"index": 2,
"start": 6,
"end": 11,
"is_subword": False,
},
],
)
@require_torch
def test_word_heuristic_leading_space(self):
model_name = "hf-internal-testing/tiny-random-deberta-v2"
tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)
token_classifier = pipeline(task="ner", model=model_name, tokenizer=tokenizer, framework="pt")
sentence = "I play the theremin"
tokens = tokenizer(
sentence,
return_attention_mask=False,
return_tensors="pt",
return_special_tokens_mask=True,
return_offsets_mapping=True,
)
offset_mapping = tokens.pop("offset_mapping").cpu().numpy()[0]
special_tokens_mask = tokens.pop("special_tokens_mask").cpu().numpy()[0]
input_ids = tokens["input_ids"].numpy()[0]
scores = np.array([[1, 0] for _ in input_ids]) # values irrelevant for heuristic
pre_entities = token_classifier.gather_pre_entities(
sentence,
input_ids,
scores,
offset_mapping,
special_tokens_mask,
aggregation_strategy=AggregationStrategy.FIRST,
)
# ensure expected tokenization and correct is_subword values
self.assertEqual(
[(entity["word"], entity["is_subword"]) for entity in pre_entities],
[("▁I", False), ("▁play", False), ("▁the", False), ("▁there", False), ("min", True)],
)
@require_tf
def test_tf_only(self):
model_name = "hf-internal-testing/tiny-random-bert-tf-only" # This model only has a TensorFlow version
# We test that if we don't specificy framework='tf', it gets detected automatically
token_classifier = pipeline(task="ner", model=model_name)
self.assertEqual(token_classifier.framework, "tf")
@require_tf
def test_small_model_tf(self):
model_name = "hf-internal-testing/tiny-bert-for-token-classification"
token_classifier = pipeline(task="token-classification", model=model_name, framework="tf")
outputs = token_classifier("This is a test !")
self.assertEqual(
nested_simplify(outputs),
[
{"entity": "I-MISC", "score": 0.115, "index": 1, "word": "this", "start": 0, "end": 4},
{"entity": "I-MISC", "score": 0.115, "index": 2, "word": "is", "start": 5, "end": 7},
],
)
@require_torch
def test_no_offset_tokenizer(self):
model_name = "hf-internal-testing/tiny-bert-for-token-classification"
tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False)
token_classifier = pipeline(task="token-classification", model=model_name, tokenizer=tokenizer, framework="pt")
outputs = token_classifier("This is a test !")
self.assertEqual(
nested_simplify(outputs),
[
{"entity": "I-MISC", "score": 0.115, "index": 1, "word": "this", "start": None, "end": None},
{"entity": "I-MISC", "score": 0.115, "index": 2, "word": "is", "start": None, "end": None},
],
)
@require_torch
def test_small_model_pt(self):
model_name = "hf-internal-testing/tiny-bert-for-token-classification"
token_classifier = pipeline(task="token-classification", model=model_name, framework="pt")
outputs = token_classifier("This is a test !")
self.assertEqual(
nested_simplify(outputs),
[
{"entity": "I-MISC", "score": 0.115, "index": 1, "word": "this", "start": 0, "end": 4},
{"entity": "I-MISC", "score": 0.115, "index": 2, "word": "is", "start": 5, "end": 7},
],
)
token_classifier = pipeline(
task="token-classification", model=model_name, framework="pt", ignore_labels=["O", "I-MISC"]
)
outputs = token_classifier("This is a test !")
self.assertEqual(
nested_simplify(outputs),
[],
)
token_classifier = pipeline(task="token-classification", model=model_name, framework="pt")
# Overload offset_mapping
outputs = token_classifier(
"This is a test !", offset_mapping=[(0, 0), (0, 1), (0, 2), (0, 0), (0, 0), (0, 0), (0, 0)]
)
self.assertEqual(
nested_simplify(outputs),
[
{"entity": "I-MISC", "score": 0.115, "index": 1, "word": "this", "start": 0, "end": 1},
{"entity": "I-MISC", "score": 0.115, "index": 2, "word": "is", "start": 0, "end": 2},
],
)
# Batch size does not affect outputs (attention_mask are required)
sentences = ["This is a test !", "Another test this is with longer sentence"]
outputs = token_classifier(sentences)
outputs_batched = token_classifier(sentences, batch_size=2)
# Batching does not make a difference in predictions
self.assertEqual(nested_simplify(outputs_batched), nested_simplify(outputs))
self.assertEqual(
nested_simplify(outputs_batched),
[
[
{"entity": "I-MISC", "score": 0.115, "index": 1, "word": "this", "start": 0, "end": 4},
{"entity": "I-MISC", "score": 0.115, "index": 2, "word": "is", "start": 5, "end": 7},
],
[],
],
)
@require_torch
def test_small_model_pt_fp16(self):
model_name = "hf-internal-testing/tiny-bert-for-token-classification"
token_classifier = pipeline(
task="token-classification", model=model_name, framework="pt", torch_dtype=torch.float16
)
outputs = token_classifier("This is a test !")
self.assertEqual(
nested_simplify(outputs),
[
{"entity": "I-MISC", "score": 0.115, "index": 1, "word": "this", "start": 0, "end": 4},
{"entity": "I-MISC", "score": 0.115, "index": 2, "word": "is", "start": 5, "end": 7},
],
)
@require_torch
def test_small_model_pt_bf16(self):
model_name = "hf-internal-testing/tiny-bert-for-token-classification"
token_classifier = pipeline(
task="token-classification", model=model_name, framework="pt", torch_dtype=torch.bfloat16
)
outputs = token_classifier("This is a test !")
self.assertEqual(
nested_simplify(outputs),
[
{"entity": "I-MISC", "score": 0.115, "index": 1, "word": "this", "start": 0, "end": 4},
{"entity": "I-MISC", "score": 0.115, "index": 2, "word": "is", "start": 5, "end": 7},
],
)
@require_torch
def test_pt_ignore_subwords_slow_tokenizer_raises(self):
model_name = "sshleifer/tiny-dbmdz-bert-large-cased-finetuned-conll03-english"
tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False)
with self.assertRaises(ValueError):
pipeline(task="ner", model=model_name, tokenizer=tokenizer, aggregation_strategy=AggregationStrategy.FIRST)
with self.assertRaises(ValueError):
pipeline(
task="ner", model=model_name, tokenizer=tokenizer, aggregation_strategy=AggregationStrategy.AVERAGE
)
with self.assertRaises(ValueError):
pipeline(task="ner", model=model_name, tokenizer=tokenizer, aggregation_strategy=AggregationStrategy.MAX)
@slow
@require_torch
def test_simple(self):
token_classifier = pipeline(task="ner", model="dslim/bert-base-NER", grouped_entities=True)
sentence = "Hello Sarah Jessica Parker who Jessica lives in New York"
sentence2 = "This is a simple test"
output = token_classifier(sentence)
output_ = nested_simplify(output)
self.assertEqual(
output_,
[
{
"entity_group": "PER",
"score": 0.996,
"word": "Sarah Jessica Parker",
"start": 6,
"end": 26,
},
{"entity_group": "PER", "score": 0.977, "word": "Jessica", "start": 31, "end": 38},
{"entity_group": "LOC", "score": 0.999, "word": "New York", "start": 48, "end": 56},
],
)
output = token_classifier([sentence, sentence2])
output_ = nested_simplify(output)
self.assertEqual(
output_,
[
[
{"entity_group": "PER", "score": 0.996, "word": "Sarah Jessica Parker", "start": 6, "end": 26},
{"entity_group": "PER", "score": 0.977, "word": "Jessica", "start": 31, "end": 38},
{"entity_group": "LOC", "score": 0.999, "word": "New York", "start": 48, "end": 56},
],
[],
],
)
class TokenClassificationArgumentHandlerTestCase(unittest.TestCase):
def setUp(self):
self.args_parser = TokenClassificationArgumentHandler()
def test_simple(self):
string = "This is a simple input"
inputs, offset_mapping = self.args_parser(string)
self.assertEqual(inputs, [string])
self.assertEqual(offset_mapping, None)
inputs, offset_mapping = self.args_parser([string, string])
self.assertEqual(inputs, [string, string])
self.assertEqual(offset_mapping, None)
inputs, offset_mapping = self.args_parser(string, offset_mapping=[(0, 1), (1, 2)])
self.assertEqual(inputs, [string])
self.assertEqual(offset_mapping, [[(0, 1), (1, 2)]])
inputs, offset_mapping = self.args_parser(
[string, string], offset_mapping=[[(0, 1), (1, 2)], [(0, 2), (2, 3)]]
)
self.assertEqual(inputs, [string, string])
self.assertEqual(offset_mapping, [[(0, 1), (1, 2)], [(0, 2), (2, 3)]])
def test_errors(self):
string = "This is a simple input"
# 2 sentences, 1 offset_mapping, args
with self.assertRaises(TypeError):
self.args_parser(string, string, offset_mapping=[[(0, 1), (1, 2)]])
# 2 sentences, 1 offset_mapping, args
with self.assertRaises(TypeError):
self.args_parser(string, string, offset_mapping=[(0, 1), (1, 2)])
# 2 sentences, 1 offset_mapping, input_list
with self.assertRaises(ValueError):
self.args_parser([string, string], offset_mapping=[[(0, 1), (1, 2)]])
# 2 sentences, 1 offset_mapping, input_list
with self.assertRaises(ValueError):
self.args_parser([string, string], offset_mapping=[(0, 1), (1, 2)])
# 1 sentences, 2 offset_mapping
with self.assertRaises(ValueError):
self.args_parser(string, offset_mapping=[[(0, 1), (1, 2)], [(0, 2), (2, 3)]])
# 0 sentences, 1 offset_mapping
with self.assertRaises(TypeError):
self.args_parser(offset_mapping=[[(0, 1), (1, 2)]])
| transformers/tests/pipelines/test_pipelines_token_classification.py/0 | {
"file_path": "transformers/tests/pipelines/test_pipelines_token_classification.py",
"repo_id": "transformers",
"token_count": 21407
} |
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