# Copyright 2023 Haotian Liu # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import List, Optional, Tuple, Union import torch import torch.nn as nn import torch.nn.functional as F from torch.nn import CrossEntropyLoss from transformers import AutoConfig, AutoModelForCausalLM, \ LlamaConfig, LlamaModel, LlamaForCausalLM, \ CLIPVisionModel, CLIPImageProcessor from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast from vary.utils.constants import * from vary.model.plug.blip_process import BlipImageEvalProcessor from vary.model.vision_encoder.sam import build_sam_vit_b from transformers import OPTConfig, OPTModel, OPTForCausalLM from vary.model.plug.transforms import train_transform, test_transform class varyConfig(OPTConfig): model_type = "vary" class varyOPTModel(OPTModel): config_class = varyConfig def __init__(self, config: OPTConfig): super(varyOPTModel, self).__init__(config) self.vision_tower_2 = build_sam_vit_b() self.mm_projector = nn.Linear(1024, 768) def initialize_vision_modules( self, vision_tower, pretrained_stage1_model=None, freeze_vision_tower=False, use_im_start_end=False, vision_select_layer=-1, dtype=torch.float16, device="cuda" ): # 224*224 # image_processor do not used in opt image_processor = CLIPImageProcessor.from_pretrained('/cache/vit-large-patch14') # 1024*1024 image_processor_high = train_transform image_token_len = 256 self.config.vision_tower = vision_tower self.config.image_token_len = image_token_len self.config.use_im_start_end = True self.config.vision_select_layer = vision_select_layer self.config.freeze_vision_tower = freeze_vision_tower return dict( image_processor=image_processor, image_processor_high=image_processor_high, image_token_len=image_token_len, ) def embed_tokens(self, x): return self.get_input_embeddings()(x) def forward( self, input_ids: torch.LongTensor = None, attention_mask: Optional[torch.Tensor] = None, past_key_values: Optional[List[torch.FloatTensor]] = None, inputs_embeds: Optional[torch.FloatTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, images: Optional[torch.FloatTensor] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, BaseModelOutputWithPast]: # HACK: replace back original embeddings for LLaVA pretraining # orig_embeds_params = getattr(self, 'orig_embeds_params', None) # if orig_embeds_params is not None: # with torch.no_grad(): # self.get_input_embeddings().weight[:-self.num_new_tokens] = orig_embeds_params[:-self.num_new_tokens].data if inputs_embeds is None: inputs_embeds = self.embed_tokens(input_ids) # inputs_embeds = self.wte(input_ids) vision_tower = getattr(self, 'vision_tower_2', None) if vision_tower is not None and (input_ids.shape[1] != 1 or self.training) and images is not None: use_im_start_end = getattr(self.config, "use_im_start_end", -1) vision_select_layer = getattr(self.config, "vision_select_layer", -1) im_patch_token = getattr(self.config, "im_patch_token", -1) im_start_token = getattr(self.config, "im_start_token", -1) im_end_token = getattr(self.config, "im_end_token", -1) freeze_vision_tower = getattr(self.config, "freeze_vision_tower", False) image_features = [] for image in images: with torch.set_grad_enabled(True): cnn_feature = vision_tower(image[1]) cnn_feature = cnn_feature.flatten(2).permute(0, 2, 1) image_feature_final = cnn_feature image_features.append(image_feature_final) if type(images) is list: image_features = [self.mm_projector(image_feature) for image_feature in image_features] else: # image_features = self.mm_projector(image_features) raise NotImplementedError # dummy_image_features = torch.zeros(1024, 1664, device=inputs_embeds.device, dtype=inputs_embeds.dtype).permute(0, 2, 1).reshape(dummy_image_features.shape[0], -1, 32, 32) # VIT 1024; CNN:1024 dummy_image_features = torch.zeros(256, 1024, device=inputs_embeds.device, dtype=inputs_embeds.dtype) dummy_image_features = self.mm_projector(dummy_image_features) use_im_start_end = True new_input_embeds = [] for cur_input_ids, cur_input_embeds, cur_image_features in zip(input_ids, inputs_embeds, image_features): if (cur_input_ids == im_patch_token).sum() == 0: # multimodal LLM, but the current sample is not multimodal cur_input_embeds = cur_input_embeds + (0. * dummy_image_features).sum() new_input_embeds.append(cur_input_embeds) continue if use_im_start_end: if (cur_input_ids == im_start_token).sum() != (cur_input_ids == im_end_token).sum(): raise ValueError("The number of image start tokens and image end tokens should be the same.") image_start_tokens = torch.where(cur_input_ids == im_start_token)[0] for image_start_token_pos, per_cur_image_features in zip(image_start_tokens, cur_image_features): per_cur_image_features = per_cur_image_features.to(device=cur_input_embeds.device) num_patches = per_cur_image_features.shape[0] # print(cur_input_ids) if cur_input_ids[image_start_token_pos + num_patches + 1] != im_end_token: raise ValueError("The image end token should follow the image start token.") cur_input_embeds = torch.cat( ( cur_input_embeds[:image_start_token_pos+1], per_cur_image_features, cur_input_embeds[image_start_token_pos + num_patches + 1:] ), dim=0 ) new_input_embeds.append(cur_input_embeds) else: raise NotImplementedError inputs_embeds = torch.stack(new_input_embeds, dim=0) return super(varyOPTModel, self).forward( input_ids=None, 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 ) class varyOPTForCausalLM(OPTForCausalLM): config_class = varyConfig # supports_gradient_checkpointing = True def __init__(self, config): super(OPTForCausalLM, self).__init__(config) self.model = varyOPTModel(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_model(self): return self.model # def _set_gradient_checkpointing(self, module, value=False): # if isinstance(module, varyQwenModel): # module.gradient_checkpointing = value def forward( self, input_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[torch.Tensor] = None, encoder_attention_mask: 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, images: Optional[torch.FloatTensor] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, CausalLMOutputWithPast]: output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) 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) # print(input_ids) # print(len(images)) outputs = self.model( input_ids=input_ids, past_key_values=past_key_values, attention_mask=attention_mask, inputs_embeds=inputs_embeds, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, images=images, return_dict=return_dict ) hidden_states = outputs[0] logits = self.lm_head(hidden_states).contiguous() # logits loss = None if labels is not None: # move labels to correct device to enable model parallelism labels = labels.to(logits.device) # Shift so that tokens < n predict n shift_logits = logits[..., :-1, :].contiguous() shift_labels = labels[..., 1:].contiguous() # Flatten the tokens loss_fct = CrossEntropyLoss() loss = loss_fct(shift_logits.view(-1, self.config.vocab_size), shift_labels.view(-1)) 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, inputs_embeds=None, **kwargs ): token_type_ids = kwargs.get("token_type_ids", None) if past_key_values: input_ids = input_ids[:, -1].unsqueeze(-1) if token_type_ids is not None: token_type_ids = token_type_ids[:, -1].unsqueeze(-1) attention_mask = kwargs.get("attention_mask", None) position_ids = kwargs.get("position_ids", None) if attention_mask is not None and position_ids is None: position_ids = attention_mask.long().cumsum(-1) - 1 position_ids.masked_fill_(attention_mask == 0, 1) if past_key_values: position_ids = position_ids[:, -1].unsqueeze(-1) else: position_ids = None if inputs_embeds is not None and past_key_values is None: model_inputs = {"inputs_embeds": inputs_embeds} else: model_inputs = {"input_ids": input_ids} model_inputs.update( { "past_key_values": past_key_values, "use_cache": kwargs.get("use_cache"), "position_ids": position_ids, "attention_mask": attention_mask, "token_type_ids": token_type_ids, "images": kwargs.get("images", None), } ) return model_inputs def initialize_vision_tokenizer( self, tokenizer, freeze_lm_model=False, pretrained_stage1_model=None, device="cuda" ): config = self.get_model().config # add image patch token tokenizer.add_tokens("", special_tokens=True) self.resize_token_embeddings(len(tokenizer)) tokenizer.add_tokens(DEFAULT_IMAGE_PATCH_TOKEN, special_tokens=True) self.resize_token_embeddings(len(tokenizer)) config.im_patch_token = tokenizer.convert_tokens_to_ids(DEFAULT_IMAGE_PATCH_TOKEN) config.use_im_start_end = True # add image start token and end token if config.use_im_start_end: num_new_tokens = 2 tokenizer.add_tokens(DEFAULT_IM_START_TOKEN , special_tokens=True) tokenizer.add_tokens(DEFAULT_IM_END_TOKEN , special_tokens=True) self.resize_token_embeddings(len(tokenizer)) config.im_start_token = tokenizer.convert_tokens_to_ids(DEFAULT_IM_START_TOKEN) config.im_end_token = tokenizer.convert_tokens_to_ids(DEFAULT_IM_END_TOKEN) # config.im_start_token, config.im_end_token = 151857, 151858 if num_new_tokens > 0: input_embeddings = self.get_input_embeddings().weight.data output_embeddings = self.get_output_embeddings().weight.data input_embeddings_avg = input_embeddings[:-num_new_tokens].mean(dim=0, keepdim=True) output_embeddings_avg = output_embeddings[:-num_new_tokens].mean(dim=0, keepdim=True) input_embeddings[-num_new_tokens:] = input_embeddings_avg output_embeddings[-num_new_tokens:] = output_embeddings_avg AutoConfig.register("vary", varyConfig) AutoModelForCausalLM.register(varyConfig, varyOPTForCausalLM)