from transformers import AutoConfig, AutoModelForCausalLM, \ Qwen2Config, Qwen2Model, Qwen2ForCausalLM, \ CLIPVisionModel, CLIPImageProcessor from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast from typing import List, Optional, Tuple, Union from transformers.cache_utils import Cache, DynamicCache import torch import torch.nn as nn import torch.nn.functional as F from torch.nn import CrossEntropyLoss from GOT.utils.constants import * from GOT.model.vision_encoder.vary_b import build_vary_vit_b from GOT.model.plug.blip_process import BlipImageEvalProcessor class GOTConfig(Qwen2Config): model_type = "GOT" class GOTQwenModel(Qwen2Model): config_class = GOTConfig def __init__(self, config: Qwen2Config): super(GOTQwenModel, self).__init__(config) self.vision_tower_high = build_vary_vit_b() self.mm_projector_vary = nn.Linear(1024, 1024) 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" ): # Vary old codes, not use in GOT image_processor = BlipImageEvalProcessor(image_size=1024) # 1024*1024 image_processor_high = BlipImageEvalProcessor(image_size=1024) self.vision_tower_high = self.vision_tower_high.to(dtype=dtype, device=device) self.mm_projector_vary = self.mm_projector_vary.to(dtype=dtype, device=device) image_token_len = 256 self.config.vision_tower = vision_tower self.config.image_token_len = image_token_len # self.config.use_im_start_end = use_im_start_end 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 get_input_embeddings(self, x): # return self.wte(x) 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, 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) vision_tower_high = getattr(self, 'vision_tower_high', None) if vision_tower_high is not None and (input_ids.shape[1] != 1 or self.training) and images is not None: # if True: # assert type(images) is list, ValueError("To fit both interleave and conversation, images must be list of batches of images") # print(im) 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) im_patch_token = 151859 im_start_token = 151857 im_end_token = 151858 image_features = [] print(images.shape) for image in images: P, C, H, W = image[1].shape # with torch.set_grad_enabled(True): # # print(image[1].shape) # cnn_feature = vision_tower_high(image[1]) # cnn_feature = cnn_feature.flatten(2).permute(0, 2, 1) # 256 1024 # # image_features.append(cnn_feature) # image_features_2.append(cnn_feature) if P == 1: with torch.set_grad_enabled(False): # print(image[1].shape) cnn_feature = vision_tower_high(image[1]) cnn_feature = cnn_feature.flatten(2).permute(0, 2, 1) # 256*1024 # image_features.append(cnn_feature) # image_features_2.append(cnn_feature) image_feature = self.mm_projector_vary(cnn_feature) image_features.append(image_feature) else: image_patches = torch.unbind(image[1]) image_patches_features = [] for image_patch in image_patches: image_p = torch.stack([image_patch]) with torch.set_grad_enabled(False): cnn_feature_p = vision_tower_high(image_p) cnn_feature_p = cnn_feature_p.flatten(2).permute(0, 2, 1) image_feature_p = self.mm_projector_vary(cnn_feature_p) image_patches_features.append(image_feature_p) image_feature = torch.cat(image_patches_features, dim=1) # print(P) # print(image_feature.shape) # exit() image_features.append(image_feature) dummy_image_features_2 = torch.zeros(256, 1024, device=inputs_embeds.device, dtype=inputs_embeds.dtype) # dummy_image_features_2 = self.mm_projector_vary(dummy_image_features_2) dummy_image_features = dummy_image_features_2 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] 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(GOTQwenModel, self).forward( input_ids=None, attention_mask=attention_mask, past_key_values=past_key_values, inputs_embeds=inputs_embeds, use_cache=use_cache, position_ids = position_ids, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict ) class GOTQwenForCausalLM(Qwen2ForCausalLM): config_class = GOTConfig # supports_gradient_checkpointing = True def __init__(self, config): super(Qwen2ForCausalLM, self).__init__(config) self.model = GOTQwenModel(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_model(self): return self.model # def _set_gradient_checkpointing(self, module, value=False): # if isinstance(module, GOTQwenModel): # module.gradient_checkpointing = value # @add_start_docstrings_to_model_forward(QWEN2_INPUTS_DOCSTRING) # @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC) 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, 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)) # print(inputs_embeds) outputs = self.model( input_ids=input_ids, past_key_values=past_key_values, attention_mask=attention_mask, position_ids=position_ids, 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) logits = logits.float() # logits loss = None if labels is not None: # 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, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs ): # Omit tokens covered by past_key_values if past_key_values is not None: if isinstance(past_key_values, Cache): cache_length = past_key_values.get_seq_length() past_length = past_key_values.seen_tokens max_cache_length = past_key_values.get_max_length() else: cache_length = past_length = past_key_values[0][0].shape[2] max_cache_length = None # Keep only the unprocessed tokens: # 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where # some of the inputs are exclusively passed as part of the cache (e.g. when passing input_embeds as # input) if attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]: input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :] # 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard # input_ids based on the past_length. elif past_length < input_ids.shape[1]: input_ids = input_ids[:, past_length:] # 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens. # If we are about to go beyond the maximum cache length, we need to crop the input attention mask. if ( max_cache_length is not None and attention_mask is not None and cache_length + input_ids.shape[1] > max_cache_length ): attention_mask = attention_mask[:, -max_cache_length:] position_ids = kwargs.get("position_ids", None) if attention_mask is not None and position_ids is None: # create position_ids on the fly for batch generation position_ids = 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] :] # if `inputs_embeds` are passed, we only want to use them in the 1st generation step 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( { "position_ids": position_ids, "past_key_values": past_key_values, "use_cache": kwargs.get("use_cache"), "attention_mask": attention_mask, "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([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])[0] config.im_patch_token = 151859 config.use_im_start_end = True # add image start token and end token if config.use_im_start_end: # num_new_tokens = tokenizer.add_tokens([DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN], special_tokens=True) self.resize_token_embeddings(len(tokenizer)) # config.im_start_token, config.im_end_token = tokenizer.convert_tokens_to_ids([DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN]) config.im_start_token, config.im_end_token = 151857, 151858 AutoConfig.register("GOT", GOTConfig) AutoModelForCausalLM.register(GOTConfig, GOTQwenForCausalLM)