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from dataclasses import dataclass
from typing import Optional, Tuple
from copy import deepcopy
import torch
import torch.nn as nn
from transformers import (
CLIPTextModel,
CLIPTokenizer,
AutoTokenizer,
AutoModel,
LlavaForConditionalGeneration,
CLIPImageProcessor,
)
from transformers.utils import ModelOutput
from ..constants import TEXT_ENCODER_PATH, TOKENIZER_PATH
from ..constants import PRECISION_TO_TYPE
def use_default(value, default):
return value if value is not None else default
def load_text_encoder(
text_encoder_type,
text_encoder_precision=None,
text_encoder_path=None,
device=None,
):
if text_encoder_path is None:
text_encoder_path = TEXT_ENCODER_PATH[text_encoder_type]
if text_encoder_type == "clipL":
text_encoder = CLIPTextModel.from_pretrained(text_encoder_path)
text_encoder.final_layer_norm = text_encoder.text_model.final_layer_norm
elif text_encoder_type == "llm":
text_encoder = AutoModel.from_pretrained(
text_encoder_path, low_cpu_mem_usage=True
)
text_encoder.final_layer_norm = text_encoder.norm
elif text_encoder_type == "llm-i2v":
text_encoder = LlavaForConditionalGeneration.from_pretrained(
text_encoder_path, low_cpu_mem_usage=True
)
else:
raise ValueError(f"Unsupported text encoder type: {text_encoder_type}")
# from_pretrained will ensure that the model is in eval mode.
if text_encoder_precision is not None:
text_encoder = text_encoder.to(dtype=PRECISION_TO_TYPE[text_encoder_precision])
text_encoder.requires_grad_(False)
if device is not None:
text_encoder = text_encoder.to(device)
return text_encoder, text_encoder_path
def load_tokenizer(
tokenizer_type, tokenizer_path=None, padding_side="right"
):
if tokenizer_path is None:
tokenizer_path = TOKENIZER_PATH[tokenizer_type]
processor = None
if tokenizer_type == "clipL":
tokenizer = CLIPTokenizer.from_pretrained(tokenizer_path, max_length=77)
elif tokenizer_type == "llm":
tokenizer = AutoTokenizer.from_pretrained(
tokenizer_path, padding_side=padding_side
)
elif tokenizer_type == "llm-i2v":
tokenizer = AutoTokenizer.from_pretrained(
tokenizer_path, padding_side=padding_side
)
processor = CLIPImageProcessor.from_pretrained(tokenizer_path)
else:
raise ValueError(f"Unsupported tokenizer type: {tokenizer_type}")
return tokenizer, tokenizer_path, processor
@dataclass
class TextEncoderModelOutput(ModelOutput):
"""
Base class for model's outputs that also contains a pooling of the last hidden states.
Args:
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.
attention_mask (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in ``[0, 1]``:
hidden_states_list (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed):
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.
text_outputs (`list`, *optional*, returned when `return_texts=True` is passed):
List of decoded texts.
"""
hidden_state: torch.FloatTensor = None
attention_mask: Optional[torch.LongTensor] = None
hidden_states_list: Optional[Tuple[torch.FloatTensor, ...]] = None
text_outputs: Optional[list] = None
class TextEncoder(nn.Module):
def __init__(
self,
text_encoder_type: str,
max_length: int,
text_encoder_precision: Optional[str] = None,
text_encoder_path: Optional[str] = None,
tokenizer_type: Optional[str] = None,
tokenizer_path: Optional[str] = None,
output_key: Optional[str] = None,
use_attention_mask: bool = True,
i2v_mode: bool = False,
input_max_length: Optional[int] = None,
prompt_template: Optional[dict] = None,
prompt_template_video: Optional[dict] = None,
hidden_state_skip_layer: Optional[int] = None,
apply_final_norm: bool = False,
reproduce: bool = False,
device=None,
# image_embed_interleave (int): The number of times to interleave the image and text embeddings. Defaults to 2.
image_embed_interleave=2,
):
super().__init__()
self.text_encoder_type = text_encoder_type
self.max_length = max_length
self.precision = text_encoder_precision
self.model_path = text_encoder_path
self.tokenizer_type = (
tokenizer_type if tokenizer_type is not None else text_encoder_type
)
self.tokenizer_path = (
tokenizer_path if tokenizer_path is not None else None # text_encoder_path
)
self.use_attention_mask = use_attention_mask
if prompt_template_video is not None:
assert (
use_attention_mask is True
), "Attention mask is True required when training videos."
self.input_max_length = (
input_max_length if input_max_length is not None else max_length
)
self.prompt_template = prompt_template
self.prompt_template_video = prompt_template_video
self.hidden_state_skip_layer = hidden_state_skip_layer
self.apply_final_norm = apply_final_norm
self.i2v_mode = i2v_mode
self.reproduce = reproduce
self.image_embed_interleave = image_embed_interleave
self.use_template = self.prompt_template is not None
if self.use_template:
assert (
isinstance(self.prompt_template, dict)
and "template" in self.prompt_template
), f"`prompt_template` must be a dictionary with a key 'template', got {self.prompt_template}"
assert "{}" in str(self.prompt_template["template"]), (
"`prompt_template['template']` must contain a placeholder `{}` for the input text, "
f"got {self.prompt_template['template']}"
)
self.use_video_template = self.prompt_template_video is not None
if self.use_video_template:
if self.prompt_template_video is not None:
assert (
isinstance(self.prompt_template_video, dict)
and "template" in self.prompt_template_video
), f"`prompt_template_video` must be a dictionary with a key 'template', got {self.prompt_template_video}"
assert "{}" in str(self.prompt_template_video["template"]), (
"`prompt_template_video['template']` must contain a placeholder `{}` for the input text, "
f"got {self.prompt_template_video['template']}"
)
if "t5" in text_encoder_type:
self.output_key = output_key or "last_hidden_state"
elif "clip" in text_encoder_type:
self.output_key = output_key or "pooler_output"
elif "llm" in text_encoder_type or "glm" in text_encoder_type:
self.output_key = output_key or "last_hidden_state"
else:
raise ValueError(f"Unsupported text encoder type: {text_encoder_type}")
if "llm" in text_encoder_type:
from mmgp import offload
forcedConfigPath= None if "i2v" in text_encoder_type else "ckpts/llava-llama-3-8b/config.json"
self.model= offload.fast_load_transformers_model(self.model_path, modelPrefix="language_model" if forcedConfigPath != None else None, forcedConfigPath=forcedConfigPath)
if forcedConfigPath != None:
self.model.final_layer_norm = self.model.model.norm
else:
self.model, self.model_path = load_text_encoder(
text_encoder_type=self.text_encoder_type,
text_encoder_precision=self.precision,
text_encoder_path=self.model_path,
device=device,
)
self.dtype = self.model.dtype
self.device = self.model.device
self.tokenizer, self.tokenizer_path, self.processor = load_tokenizer(
tokenizer_type=self.tokenizer_type,
tokenizer_path=self.tokenizer_path,
padding_side="right",
)
def __repr__(self):
return f"{self.text_encoder_type} ({self.precision} - {self.model_path})"
@staticmethod
def apply_text_to_template(text, template, prevent_empty_text=True):
"""
Apply text to template.
Args:
text (str): Input text.
template (str or list): Template string or list of chat conversation.
prevent_empty_text (bool): If Ture, we will prevent the user text from being empty
by adding a space. Defaults to True.
"""
if isinstance(template, str):
# Will send string to tokenizer. Used for llm
return template.format(text)
else:
raise TypeError(f"Unsupported template type: {type(template)}")
def text2tokens(self, text, data_type="image", name = None):
"""
Tokenize the input text.
Args:
text (str or list): Input text.
"""
tokenize_input_type = "str"
if self.use_template:
if data_type == "image":
prompt_template = self.prompt_template["template"]
elif data_type == "video":
prompt_template = self.prompt_template_video["template"]
else:
raise ValueError(f"Unsupported data type: {data_type}")
if isinstance(text, (list, tuple)):
text = [
self.apply_text_to_template(one_text, prompt_template)
for one_text in text
]
if isinstance(text[0], list):
tokenize_input_type = "list"
elif isinstance(text, str):
text = self.apply_text_to_template(text, prompt_template)
if isinstance(text, list):
tokenize_input_type = "list"
else:
raise TypeError(f"Unsupported text type: {type(text)}")
kwargs = dict(truncation=True, max_length=self.max_length, padding="max_length", return_tensors="pt")
if self.text_encoder_type == "llm-i2v" and name != None: #llava-llama-3-8b
if isinstance(text, list):
for i in range(len(text)):
text[i] = text[i] + '\nThe %s looks like<image>' % name
elif isinstance(text, str):
text = text + '\nThe %s looks like<image>' % name
else:
raise NotImplementedError
kwargs = dict(
truncation=True,
max_length=self.max_length,
padding="max_length",
return_tensors="pt",
)
if tokenize_input_type == "str":
return self.tokenizer(
text,
return_length=False,
return_overflowing_tokens=False,
return_attention_mask=True,
**kwargs,
)
elif tokenize_input_type == "list":
return self.tokenizer.apply_chat_template(
text,
add_generation_prompt=True,
tokenize=True,
return_dict=True,
**kwargs,
)
else:
raise ValueError(f"Unsupported tokenize_input_type: {tokenize_input_type}")
def encode(
self,
batch_encoding,
use_attention_mask=None,
output_hidden_states=False,
do_sample=None,
hidden_state_skip_layer=None,
return_texts=False,
data_type="image",
semantic_images=None,
device=None,
):
"""
Args:
batch_encoding (dict): Batch encoding from tokenizer.
use_attention_mask (bool): Whether to use attention mask. If None, use self.use_attention_mask.
Defaults to None.
output_hidden_states (bool): Whether to output hidden states. If False, return the value of
self.output_key. If True, return the entire output. If set self.hidden_state_skip_layer,
output_hidden_states will be set True. Defaults to False.
do_sample (bool): Whether to sample from the model. Used for Decoder-Only LLMs. Defaults to None.
When self.produce is False, do_sample is set to True by default.
hidden_state_skip_layer (int): Number of hidden states to hidden_state_skip_layer. 0 means the last layer.
If None, self.output_key will be used. Defaults to None.
hidden_state_skip_layer (PIL.Image): The reference images for i2v models.
image_embed_interleave (int): The number of times to interleave the image and text embeddings. Defaults to 2.
return_texts (bool): Whether to return the decoded texts. Defaults to False.
"""
device = self.model.device if device is None else device
use_attention_mask = use_default(use_attention_mask, self.use_attention_mask)
hidden_state_skip_layer = use_default(
hidden_state_skip_layer, self.hidden_state_skip_layer
)
do_sample = use_default(do_sample, not self.reproduce)
if not self.i2v_mode:
attention_mask = (
batch_encoding["attention_mask"].to(device)
if use_attention_mask
else None
)
if 'pixel_value_llava' in batch_encoding:
outputs = self.model(
input_ids=batch_encoding["input_ids"].to(self.model.device),
attention_mask=attention_mask,
pixel_values=batch_encoding["pixel_value_llava"].to(self.model.device),
output_hidden_states=output_hidden_states or hidden_state_skip_layer is not None)
else:
outputs = self.model(
input_ids=batch_encoding["input_ids"].to(self.model.device),
attention_mask=attention_mask,
output_hidden_states=output_hidden_states or hidden_state_skip_layer is not None,)
if hidden_state_skip_layer is not None:
last_hidden_state = outputs.hidden_states[
-(hidden_state_skip_layer + 1)
]
# Real last hidden state already has layer norm applied. So here we only apply it
# for intermediate layers.
if hidden_state_skip_layer > 0 and self.apply_final_norm:
last_hidden_state = self.model.final_layer_norm(last_hidden_state)
else:
last_hidden_state = outputs[self.output_key]
# Remove hidden states of instruction tokens, only keep prompt tokens.
if self.use_template:
if data_type == "image":
crop_start = self.prompt_template.get("crop_start", -1)
elif data_type == "video":
crop_start = self.prompt_template_video.get("crop_start", -1)
else:
raise ValueError(f"Unsupported data type: {data_type}")
if crop_start > 0:
last_hidden_state = last_hidden_state[:, crop_start:]
attention_mask = (
attention_mask[:, crop_start:] if use_attention_mask else None
)
if output_hidden_states:
return TextEncoderModelOutput(
last_hidden_state, attention_mask, outputs.hidden_states
)
return TextEncoderModelOutput(last_hidden_state, attention_mask)
else:
image_outputs = self.processor(semantic_images, return_tensors="pt")[
"pixel_values"
].to(device)
attention_mask = (
batch_encoding["attention_mask"].to(device)
if use_attention_mask
else None
)
outputs = self.model(
input_ids=batch_encoding["input_ids"].to(device),
attention_mask=attention_mask,
output_hidden_states=output_hidden_states
or hidden_state_skip_layer is not None,
pixel_values=image_outputs,
)
if hidden_state_skip_layer is not None:
last_hidden_state = outputs.hidden_states[
-(hidden_state_skip_layer + 1)
]
# Real last hidden state already has layer norm applied. So here we only apply it
# for intermediate layers.
if hidden_state_skip_layer > 0 and self.apply_final_norm:
last_hidden_state = self.model.final_layer_norm(last_hidden_state)
else:
last_hidden_state = outputs[self.output_key]
if self.use_template:
if data_type == "video":
crop_start = self.prompt_template_video.get("crop_start", -1)
text_crop_start = (
crop_start
- 1
+ self.prompt_template_video.get("image_emb_len", 576)
)
image_crop_start = self.prompt_template_video.get(
"image_emb_start", 5
)
image_crop_end = self.prompt_template_video.get(
"image_emb_end", 581
)
batch_indices, last_double_return_token_indices = torch.where(
batch_encoding["input_ids"]
== self.prompt_template_video.get("double_return_token_id", 271)
)
if last_double_return_token_indices.shape[0] == 3:
# in case the prompt is too long
last_double_return_token_indices = torch.cat(
(
last_double_return_token_indices,
torch.tensor([batch_encoding["input_ids"].shape[-1]]),
)
)
batch_indices = torch.cat((batch_indices, torch.tensor([0])))
last_double_return_token_indices = (
last_double_return_token_indices.reshape(
batch_encoding["input_ids"].shape[0], -1
)[:, -1]
)
batch_indices = batch_indices.reshape(
batch_encoding["input_ids"].shape[0], -1
)[:, -1]
assistant_crop_start = (
last_double_return_token_indices
- 1
+ self.prompt_template_video.get("image_emb_len", 576)
- 4
)
assistant_crop_end = (
last_double_return_token_indices
- 1
+ self.prompt_template_video.get("image_emb_len", 576)
)
attention_mask_assistant_crop_start = (
last_double_return_token_indices - 4
)
attention_mask_assistant_crop_end = last_double_return_token_indices
else:
raise ValueError(f"Unsupported data type: {data_type}")
text_last_hidden_state = []
text_attention_mask = []
image_last_hidden_state = []
image_attention_mask = []
for i in range(batch_encoding["input_ids"].shape[0]):
text_last_hidden_state.append(
torch.cat(
[
last_hidden_state[
i, text_crop_start : assistant_crop_start[i].item()
],
last_hidden_state[i, assistant_crop_end[i].item() :],
]
)
)
text_attention_mask.append(
torch.cat(
[
attention_mask[
i,
crop_start : attention_mask_assistant_crop_start[
i
].item(),
],
attention_mask[
i, attention_mask_assistant_crop_end[i].item() :
],
]
)
if use_attention_mask
else None
)
image_last_hidden_state.append(
last_hidden_state[i, image_crop_start:image_crop_end]
)
image_attention_mask.append(
torch.ones(image_last_hidden_state[-1].shape[0])
.to(last_hidden_state.device)
.to(attention_mask.dtype)
if use_attention_mask
else None
)
text_last_hidden_state = torch.stack(text_last_hidden_state)
text_attention_mask = torch.stack(text_attention_mask)
image_last_hidden_state = torch.stack(image_last_hidden_state)
image_attention_mask = torch.stack(image_attention_mask)
if semantic_images is not None and 0 < self.image_embed_interleave < 6:
image_last_hidden_state = image_last_hidden_state[
:, ::self.image_embed_interleave, :
]
image_attention_mask = image_attention_mask[
:, ::self.image_embed_interleave
]
assert (
text_last_hidden_state.shape[0] == text_attention_mask.shape[0]
and image_last_hidden_state.shape[0]
== image_attention_mask.shape[0]
)
last_hidden_state = torch.cat(
[image_last_hidden_state, text_last_hidden_state], dim=1
)
attention_mask = torch.cat(
[image_attention_mask, text_attention_mask], dim=1
)
if output_hidden_states:
return TextEncoderModelOutput(
last_hidden_state,
attention_mask,
hidden_states_list=outputs.hidden_states,
)
return TextEncoderModelOutput(last_hidden_state, attention_mask)
def forward(
self,
text,
use_attention_mask=None,
output_hidden_states=False,
do_sample=False,
hidden_state_skip_layer=None,
return_texts=False,
):
batch_encoding = self.text2tokens(text)
return self.encode(
batch_encoding,
use_attention_mask=use_attention_mask,
output_hidden_states=output_hidden_states,
do_sample=do_sample,
hidden_state_skip_layer=hidden_state_skip_layer,
return_texts=return_texts,
)
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