from shared.utils.phase_progress import text_encoding_progress 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 from .llava.modeling_llava import LlavaForConditionalGeneration from shared.utils import files_locator as fl 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] text_encoder_path = fl.locate_folder(text_encoder_path) 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] tokenizer_path = fl.locate_folder(tokenizer_path) 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}") from mmgp import offload if "llm" in text_encoder_type: if "i2v" in text_encoder_type: self.model= offload.fast_load_transformers_model(self.model_path, modelClass= LlavaForConditionalGeneration, writable_tensors=False) else: self.model= offload.fast_load_transformers_model(self.model_path, modelPrefix="language_model", forcedConfigPath = fl.locate_file("llava-llama-3-8b/config.json"), writable_tensors=False) self.model.final_layer_norm = self.model.model.norm else: self.model= offload.fast_load_transformers_model(fl.locate_file("clip_vit_large_patch14/model.safetensors"), ignore_unused_weights= True, modelClass=CLIPTextModel, forcedConfigPath = fl.locate_file("clip_vit_large_patch14/text_config.json"), writable_tensors=False) self.model.final_layer_norm = self.model.text_model.final_layer_norm 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' % name elif isinstance(text, str): text = text + '\nThe %s looks like' % 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: with text_encoding_progress(self.model.language_model.model.layers if "i2v" in self.text_encoder_type else self.model.model.layers if "llm" in self.text_encoder_type else self.model.text_model.encoder.layers, prompt_count=batch_encoding["input_ids"].shape[0]): 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: with text_encoding_progress(self.model.language_model.model.layers if "i2v" in self.text_encoder_type else self.model.model.layers if "llm" in self.text_encoder_type else self.model.text_model.encoder.layers, prompt_count=batch_encoding["input_ids"].shape[0]): 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 ) with text_encoding_progress(self.model.language_model.model.layers if "i2v" in self.text_encoder_type else self.model.model.layers if "llm" in self.text_encoder_type else self.model.text_model.encoder.layers, prompt_count=batch_encoding["input_ids"].shape[0]): 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, )