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| # 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 abc import ABC, abstractmethod | |
| import torch | |
| import torch.nn as nn | |
| from .multimodal_encoder.builder import build_vision_tower | |
| from .multimodal_projector.builder import build_vision_projector | |
| from llava.constants import IGNORE_INDEX, IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_PATCH_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN | |
| class LlavaMetaModel: | |
| def __init__(self, config): | |
| super(LlavaMetaModel, self).__init__(config) | |
| if hasattr(config, "mm_vision_tower"): | |
| self.vision_tower = build_vision_tower(config, delay_load=True) | |
| self.mm_projector = build_vision_projector(config) | |
| def get_vision_tower(self): | |
| vision_tower = getattr(self, 'vision_tower', None) | |
| if type(vision_tower) is list: | |
| vision_tower = vision_tower[0] | |
| return vision_tower | |
| def initialize_vision_modules(self, model_args, fsdp=None): | |
| vision_tower = model_args.vision_tower | |
| mm_vision_select_layer = model_args.mm_vision_select_layer | |
| mm_vision_select_feature = model_args.mm_vision_select_feature | |
| pretrain_mm_mlp_adapter = model_args.pretrain_mm_mlp_adapter | |
| self.config.mm_vision_tower = vision_tower | |
| vision_tower = build_vision_tower(model_args) | |
| if fsdp is not None and len(fsdp) > 0: | |
| self.vision_tower = [vision_tower] | |
| else: | |
| self.vision_tower = vision_tower | |
| self.config.use_mm_proj = True | |
| self.config.mm_projector_type = getattr(model_args, 'mm_projector_type', 'linear') | |
| self.config.mm_hidden_size = vision_tower.hidden_size | |
| self.config.mm_vision_select_layer = mm_vision_select_layer | |
| self.config.mm_vision_select_feature = mm_vision_select_feature | |
| self.mm_projector = build_vision_projector(self.config) | |
| if pretrain_mm_mlp_adapter is not None: | |
| mm_projector_weights = torch.load(pretrain_mm_mlp_adapter, map_location='cpu') | |
| def get_w(weights, keyword): | |
| return {k.split(keyword + '.')[1]: v for k, v in weights.items() if keyword in k} | |
| self.mm_projector.load_state_dict(get_w(mm_projector_weights, 'mm_projector')) | |
| class LlavaMetaForCausalLM(ABC): | |
| def get_model(self): | |
| pass | |
| def get_vision_tower(self): | |
| return self.get_model().get_vision_tower() | |
| def encode_images(self, images): | |
| image_features = self.get_model().get_vision_tower()(images) | |
| image_features = self.get_model().mm_projector(image_features) | |
| return image_features | |
| def prepare_inputs_labels_for_multimodal( | |
| self, input_ids, attention_mask, past_key_values, labels, images | |
| ): | |
| vision_tower = self.get_vision_tower() | |
| if vision_tower is None or images is None or input_ids.shape[1] == 1: | |
| if past_key_values is not None and vision_tower is not None and images is not None and input_ids.shape[1] == 1: | |
| attention_mask = torch.ones((attention_mask.shape[0], past_key_values[-1][-1].shape[-2] + 1), dtype=attention_mask.dtype, device=attention_mask.device) | |
| return input_ids, attention_mask, past_key_values, None, labels | |
| if type(images) is list or images.ndim == 5: | |
| concat_images = torch.cat([image for image in images], dim=0) | |
| image_features = self.encode_images(concat_images) | |
| split_sizes = [image.shape[0] for image in images] | |
| image_features = torch.split(image_features, split_sizes, dim=0) | |
| image_features = [x.flatten(0, 1) for x in image_features] | |
| else: | |
| image_features = self.encode_images(images) | |
| new_input_embeds = [] | |
| new_labels = [] if labels is not None else None | |
| cur_image_idx = 0 | |
| for batch_idx, cur_input_ids in enumerate(input_ids): | |
| if (cur_input_ids == IMAGE_TOKEN_INDEX).sum() == 0: | |
| # multimodal LLM, but the current sample is not multimodal | |
| # FIXME: this is a hacky fix, for deepspeed zero3 to work | |
| half_len = cur_input_ids.shape[0] // 2 | |
| cur_image_features = image_features[cur_image_idx] | |
| cur_input_embeds_1 = self.get_model().embed_tokens(cur_input_ids[:half_len]) | |
| cur_input_embeds_2 = self.get_model().embed_tokens(cur_input_ids[half_len:]) | |
| cur_input_embeds = torch.cat([cur_input_embeds_1, cur_image_features[0:0], cur_input_embeds_2], dim=0) | |
| new_input_embeds.append(cur_input_embeds) | |
| if labels is not None: | |
| new_labels.append(labels[batch_idx]) | |
| cur_image_idx += 1 | |
| continue | |
| image_token_indices = torch.where(cur_input_ids == IMAGE_TOKEN_INDEX)[0] | |
| cur_new_input_embeds = [] | |
| if labels is not None: | |
| cur_labels = labels[batch_idx] | |
| cur_new_labels = [] | |
| assert cur_labels.shape == cur_input_ids.shape | |
| while image_token_indices.numel() > 0: | |
| cur_image_features = image_features[cur_image_idx] | |
| image_token_start = image_token_indices[0] | |
| if getattr(self.config, 'tune_mm_mlp_adapter', False) and getattr(self.config, 'mm_use_im_start_end', False): | |
| cur_new_input_embeds.append(self.get_model().embed_tokens(cur_input_ids[:image_token_start-1]).detach()) | |
| cur_new_input_embeds.append(self.get_model().embed_tokens(cur_input_ids[image_token_start-1:image_token_start])) | |
| cur_new_input_embeds.append(cur_image_features) | |
| cur_new_input_embeds.append(self.get_model().embed_tokens(cur_input_ids[image_token_start+1:image_token_start+2])) | |
| if labels is not None: | |
| cur_new_labels.append(cur_labels[:image_token_start]) | |
| cur_new_labels.append(torch.full((cur_image_features.shape[0],), IGNORE_INDEX, device=labels.device, dtype=labels.dtype)) | |
| cur_new_labels.append(cur_labels[image_token_start:image_token_start+1]) | |
| cur_labels = cur_labels[image_token_start+2:] | |
| else: | |
| cur_new_input_embeds.append(self.get_model().embed_tokens(cur_input_ids[:image_token_start])) | |
| cur_new_input_embeds.append(cur_image_features) | |
| if labels is not None: | |
| cur_new_labels.append(cur_labels[:image_token_start]) | |
| cur_new_labels.append(torch.full((cur_image_features.shape[0],), IGNORE_INDEX, device=labels.device, dtype=labels.dtype)) | |
| cur_labels = cur_labels[image_token_start+1:] | |
| cur_image_idx += 1 | |
| if getattr(self.config, 'tune_mm_mlp_adapter', False) and getattr(self.config, 'mm_use_im_start_end', False): | |
| cur_input_ids = cur_input_ids[image_token_start+2:] | |
| else: | |
| cur_input_ids = cur_input_ids[image_token_start+1:] | |
| image_token_indices = torch.where(cur_input_ids == IMAGE_TOKEN_INDEX)[0] | |
| if cur_input_ids.numel() > 0: | |
| if getattr(self.config, 'tune_mm_mlp_adapter', False) and getattr(self.config, 'mm_use_im_start_end', False): | |
| cur_new_input_embeds.append(self.get_model().embed_tokens(cur_input_ids).detach()) | |
| else: | |
| cur_new_input_embeds.append(self.get_model().embed_tokens(cur_input_ids)) | |
| if labels is not None: | |
| cur_new_labels.append(cur_labels) | |
| cur_new_input_embeds = [x.to(device=self.device) for x in cur_new_input_embeds] | |
| cur_new_input_embeds = torch.cat(cur_new_input_embeds, dim=0) | |
| new_input_embeds.append(cur_new_input_embeds) | |
| if labels is not None: | |
| cur_new_labels = torch.cat(cur_new_labels, dim=0) | |
| new_labels.append(cur_new_labels) | |
| if any(x.shape != new_input_embeds[0].shape for x in new_input_embeds): | |
| max_len = max(x.shape[0] for x in new_input_embeds) | |
| new_input_embeds_align = [] | |
| for cur_new_embed in new_input_embeds: | |
| cur_new_embed = torch.cat((cur_new_embed, torch.zeros((max_len - cur_new_embed.shape[0], cur_new_embed.shape[1]), dtype=cur_new_embed.dtype, device=cur_new_embed.device)), dim=0) | |
| new_input_embeds_align.append(cur_new_embed) | |
| new_input_embeds = torch.stack(new_input_embeds_align, dim=0) | |
| if labels is not None: | |
| new_labels_align = [] | |
| _new_labels = new_labels | |
| for cur_new_label in new_labels: | |
| cur_new_label = torch.cat((cur_new_label, torch.full((max_len - cur_new_label.shape[0],), IGNORE_INDEX, dtype=cur_new_label.dtype, device=cur_new_label.device)), dim=0) | |
| new_labels_align.append(cur_new_label) | |
| new_labels = torch.stack(new_labels_align, dim=0) | |
| if attention_mask is not None: | |
| new_attention_mask = [] | |
| for cur_attention_mask, cur_new_labels, cur_new_labels_align in zip(attention_mask, _new_labels, new_labels): | |
| new_attn_mask_pad_left = torch.full((cur_new_labels.shape[0] - labels.shape[1],), True, dtype=attention_mask.dtype, device=attention_mask.device) | |
| new_attn_mask_pad_right = torch.full((cur_new_labels_align.shape[0] - cur_new_labels.shape[0],), False, dtype=attention_mask.dtype, device=attention_mask.device) | |
| cur_new_attention_mask = torch.cat((new_attn_mask_pad_left, cur_attention_mask, new_attn_mask_pad_right), dim=0) | |
| new_attention_mask.append(cur_new_attention_mask) | |
| attention_mask = torch.stack(new_attention_mask, dim=0) | |
| assert attention_mask.shape == new_labels.shape | |
| else: | |
| new_input_embeds = torch.stack(new_input_embeds, dim=0) | |
| if labels is not None: | |
| new_labels = torch.stack(new_labels, dim=0) | |
| if attention_mask is not None: | |
| new_attn_mask_pad_left = torch.full((attention_mask.shape[0], new_input_embeds.shape[1] - input_ids.shape[1]), True, dtype=attention_mask.dtype, device=attention_mask.device) | |
| attention_mask = torch.cat((new_attn_mask_pad_left, attention_mask), dim=1) | |
| assert attention_mask.shape == new_input_embeds.shape[:2] | |
| return None, attention_mask, past_key_values, new_input_embeds, new_labels | |
| def initialize_vision_tokenizer(self, model_args, tokenizer): | |
| if model_args.mm_use_im_patch_token: | |
| tokenizer.add_tokens([DEFAULT_IMAGE_PATCH_TOKEN], special_tokens=True) | |
| self.resize_token_embeddings(len(tokenizer)) | |
| if model_args.mm_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)) | |
| 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 | |
| if model_args.tune_mm_mlp_adapter: | |
| for p in self.get_input_embeddings().parameters(): | |
| p.requires_grad = True | |
| for p in self.get_output_embeddings().parameters(): | |
| p.requires_grad = False | |
| if model_args.pretrain_mm_mlp_adapter: | |
| mm_projector_weights = torch.load(model_args.pretrain_mm_mlp_adapter, map_location='cpu') | |
| embed_tokens_weight = mm_projector_weights['model.embed_tokens.weight'] | |
| assert num_new_tokens == 2 | |
| if input_embeddings.shape == embed_tokens_weight.shape: | |
| input_embeddings[-num_new_tokens:] = embed_tokens_weight[-num_new_tokens:] | |
| elif embed_tokens_weight.shape[0] == num_new_tokens: | |
| input_embeddings[-num_new_tokens:] = embed_tokens_weight | |
| else: | |
| raise ValueError(f"Unexpected embed_tokens_weight shape. Pretrained: {embed_tokens_weight.shape}. Current: {input_embeddings.shape}. Numer of new tokens: {num_new_tokens}.") | |
| elif model_args.mm_use_im_patch_token: | |
| if model_args.tune_mm_mlp_adapter: | |
| for p in self.get_input_embeddings().parameters(): | |
| p.requires_grad = False | |
| for p in self.get_output_embeddings().parameters(): | |
| p.requires_grad = False | |