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| # Copyright 2024 Baichuan Zhou , Junlong Jia, 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 tinyllava.model.multimodal_encoder.builder import build_vision_tower | |
| from tinyllava.model.multimodal_projector.builder import build_vision_projector | |
| from tinyllava.constants import IGNORE_INDEX, IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_PATCH_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN | |
| from tinyllava.mm_utils import get_anyres_image_grid_shape | |
| 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) | |
| if 'unpad' in getattr(config, 'mm_patch_merge_type', ''): | |
| self.image_newline = nn.Parameter( | |
| torch.empty(config.hidden_size, dtype=self.dtype) | |
| ) | |
| 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 | |
| mm_patch_merge_type = model_args.mm_patch_merge_type | |
| self.config.mm_vision_tower = vision_tower | |
| if self.get_vision_tower() is None: | |
| 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 | |
| elif self.get_vision_tower().vision_tower_name != vision_tower: | |
| print(f"rebuilding vision tower! vision tower initialized from: {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 | |
| else: | |
| if fsdp is not None and len(fsdp) > 0: | |
| vision_tower = self.vision_tower[0] | |
| else: | |
| vision_tower = self.vision_tower | |
| vision_tower.load_model() | |
| 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.config.mm_patch_merge_type = mm_patch_merge_type | |
| if getattr(self, 'mm_projector', None) is None: | |
| self.mm_projector = build_vision_projector(self.config) | |
| if 'unpad' in mm_patch_merge_type: | |
| embed_std = 1 / torch.sqrt(torch.tensor(self.config.hidden_size, dtype=self.dtype)) | |
| self.image_newline = nn.Parameter( | |
| torch.randn(self.config.hidden_size, dtype=self.dtype) * embed_std | |
| ) | |
| else: | |
| # In case it is frozen by LoRA | |
| for p in self.mm_projector.parameters(): | |
| p.requires_grad = True | |
| 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')) | |
| def unpad_image(tensor, original_size): | |
| """ | |
| Unpads a PyTorch tensor of a padded and resized image. | |
| Args: | |
| tensor (torch.Tensor): The image tensor, assumed to be in CxHxW format. | |
| original_size (tuple): The original size of the image (height, width). | |
| Returns: | |
| torch.Tensor: The unpadded image tensor. | |
| """ | |
| original_width, original_height = original_size | |
| current_height, current_width = tensor.shape[1:] | |
| original_aspect_ratio = original_width / original_height | |
| current_aspect_ratio = current_width / current_height | |
| if original_aspect_ratio > current_aspect_ratio: | |
| scale_factor = current_width / original_width | |
| new_height = int(original_height * scale_factor) | |
| padding = (current_height - new_height) // 2 | |
| unpadded_tensor = tensor[:, padding:current_height - padding, :] | |
| else: | |
| scale_factor = current_height / original_height | |
| new_width = int(original_width * scale_factor) | |
| padding = (current_width - new_width) // 2 | |
| unpadded_tensor = tensor[:, :, padding:current_width - padding] | |
| return unpadded_tensor | |
| 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, position_ids, attention_mask, past_key_values, labels, | |
| images, image_sizes=None | |
| ): | |
| vision_tower = self.get_vision_tower() | |
| if vision_tower is None or images is None or input_ids.shape[1] == 1: | |
| return input_ids, position_ids, attention_mask, past_key_values, None, labels | |
| if type(images) is list or images.ndim == 5: | |
| if type(images) is list: | |
| images = [x.unsqueeze(0) if x.ndim == 3 else x for x in images] | |
| 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) | |
| mm_patch_merge_type = getattr(self.config, 'mm_patch_merge_type', 'flat') | |
| image_aspect_ratio = getattr(self.config, 'image_aspect_ratio', 'square') | |
| if mm_patch_merge_type == 'flat': | |
| image_features = [x.flatten(0, 1) for x in image_features] | |
| elif mm_patch_merge_type.startswith('spatial'): | |
| new_image_features = [] | |
| for image_idx, image_feature in enumerate(image_features): | |
| if image_feature.shape[0] > 1: | |
| base_image_feature = image_feature[0] | |
| image_feature = image_feature[1:] | |
| height = width = self.get_vision_tower().num_patches_per_side | |
| assert height * width == base_image_feature.shape[0] | |
| if image_aspect_ratio == 'anyres': | |
| num_patch_width, num_patch_height = get_anyres_image_grid_shape(image_sizes[image_idx], self.config.image_grid_pinpoints, self.get_vision_tower().config.image_size) | |
| image_feature = image_feature.view(num_patch_height, num_patch_width, height, width, -1) | |
| else: | |
| raise NotImplementedError | |
| if 'unpad' in mm_patch_merge_type: | |
| image_feature = image_feature.permute(4, 0, 2, 1, 3).contiguous() | |
| image_feature = image_feature.flatten(1, 2).flatten(2, 3) | |
| image_feature = unpad_image(image_feature, image_sizes[image_idx]) | |
| image_feature = torch.cat(( | |
| image_feature, | |
| self.model.image_newline[:, None, None].expand(*image_feature.shape[:-1], 1).to(image_feature.device) | |
| ), dim=-1) | |
| image_feature = image_feature.flatten(1, 2).transpose(0, 1) | |
| else: | |
| image_feature = image_feature.permute(0, 2, 1, 3, 4).contiguous() | |
| image_feature = image_feature.flatten(0, 3) | |
| image_feature = torch.cat((base_image_feature, image_feature), dim=0) | |
| else: | |
| image_feature = image_feature[0] | |
| if 'unpad' in mm_patch_merge_type: | |
| image_feature = torch.cat(( | |
| image_feature, | |
| self.model.image_newline[None].to(image_feature.device) | |
| ), dim=0) | |
| new_image_features.append(image_feature) | |
| image_features = new_image_features | |
| else: | |
| raise ValueError(f"Unexpected mm_patch_merge_type: {self.config.mm_patch_merge_type}") | |
| else: | |
| image_features = self.encode_images(images) | |
| # TODO: image start / end is not implemented here to support pretraining. | |
| if getattr(self.config, 'tune_mm_mlp_adapter', False) and getattr(self.config, 'mm_use_im_start_end', False): | |
| raise NotImplementedError | |
| # Let's just add dummy tensors if they do not exist, | |
| # it is a headache to deal with None all the time. | |
| # But it is not ideal, and if you have a better idea, | |
| # please open an issue / submit a PR, thanks. | |
| _labels = labels | |
| _position_ids = position_ids | |
| _attention_mask = attention_mask | |
| if attention_mask is None: | |
| attention_mask = torch.ones_like(input_ids, dtype=torch.bool) | |
| else: | |
| attention_mask = attention_mask.bool() | |
| if position_ids is None: | |
| position_ids = torch.arange(0, input_ids.shape[1], dtype=torch.long, device=input_ids.device) | |
| if labels is None: | |
| labels = torch.full_like(input_ids, IGNORE_INDEX) | |
| # remove the padding using attention_mask -- FIXME | |
| _input_ids = input_ids | |
| input_ids = [cur_input_ids[cur_attention_mask] for cur_input_ids, cur_attention_mask in zip(input_ids, attention_mask)] | |
| labels = [cur_labels[cur_attention_mask] for cur_labels, cur_attention_mask in zip(labels, attention_mask)] | |
| new_input_embeds = [] | |
| new_labels = [] | |
| cur_image_idx = 0 | |
| for batch_idx, cur_input_ids in enumerate(input_ids): | |
| num_images = (cur_input_ids == IMAGE_TOKEN_INDEX).sum() | |
| if num_images == 0: | |
| cur_image_features = image_features[cur_image_idx] | |
| cur_input_embeds_1 = self.get_model().embed_tokens(cur_input_ids) | |
| cur_input_embeds = torch.cat([cur_input_embeds_1, cur_image_features[0:0]], dim=0) | |
| new_input_embeds.append(cur_input_embeds) | |
| new_labels.append(labels[batch_idx]) | |
| cur_image_idx += 1 | |
| continue | |
| image_token_indices = [-1] + torch.where(cur_input_ids == IMAGE_TOKEN_INDEX)[0].tolist() + [cur_input_ids.shape[0]] | |
| cur_input_ids_noim = [] | |
| cur_labels = labels[batch_idx] | |
| cur_labels_noim = [] | |
| for i in range(len(image_token_indices) - 1): | |
| cur_input_ids_noim.append(cur_input_ids[image_token_indices[i]+1:image_token_indices[i+1]]) | |
| cur_labels_noim.append(cur_labels[image_token_indices[i]+1:image_token_indices[i+1]]) | |
| split_sizes = [x.shape[0] for x in cur_labels_noim] | |
| cur_input_embeds = self.get_model().embed_tokens(torch.cat(cur_input_ids_noim)) | |
| cur_input_embeds_no_im = torch.split(cur_input_embeds, split_sizes, dim=0) | |
| cur_new_input_embeds = [] | |
| cur_new_labels = [] | |
| for i in range(num_images + 1): | |
| cur_new_input_embeds.append(cur_input_embeds_no_im[i]) | |
| cur_new_labels.append(cur_labels_noim[i]) | |
| if i < num_images: | |
| cur_image_features = image_features[cur_image_idx] | |
| cur_image_idx += 1 | |
| cur_new_input_embeds.append(cur_image_features) | |
| cur_new_labels.append(torch.full((cur_image_features.shape[0],), IGNORE_INDEX, device=cur_labels.device, dtype=cur_labels.dtype)) | |
| cur_new_input_embeds = [x.to(self.device) for x in cur_new_input_embeds] | |
| cur_new_input_embeds = torch.cat(cur_new_input_embeds) | |
| cur_new_labels = torch.cat(cur_new_labels) | |
| new_input_embeds.append(cur_new_input_embeds) | |
| new_labels.append(cur_new_labels) | |
| # Truncate sequences to max length as image embeddings can make the sequence longer | |
| tokenizer_model_max_length = getattr(self.config, 'tokenizer_model_max_length', None) | |
| if tokenizer_model_max_length is not None: | |
| new_input_embeds = [x[:tokenizer_model_max_length] for x in new_input_embeds] | |
| new_labels = [x[:tokenizer_model_max_length] for x in new_labels] | |
| # Combine them | |
| max_len = max(x.shape[0] for x in new_input_embeds) | |
| batch_size = len(new_input_embeds) | |
| new_input_embeds_padded = [] | |
| new_labels_padded = torch.full((batch_size, max_len), IGNORE_INDEX, dtype=new_labels[0].dtype, device=new_labels[0].device) | |
| attention_mask = torch.zeros((batch_size, max_len), dtype=attention_mask.dtype, device=attention_mask.device) | |
| position_ids = torch.zeros((batch_size, max_len), dtype=position_ids.dtype, device=position_ids.device) | |
| for i, (cur_new_embed, cur_new_labels) in enumerate(zip(new_input_embeds, new_labels)): | |
| cur_len = cur_new_embed.shape[0] | |
| if getattr(self.config, 'tokenizer_padding_side', 'right') == "left": | |
| new_input_embeds_padded.append(torch.cat(( | |
| torch.zeros((max_len - cur_len, cur_new_embed.shape[1]), dtype=cur_new_embed.dtype, device=cur_new_embed.device), | |
| cur_new_embed | |
| ), dim=0)) | |
| if cur_len > 0: | |
| new_labels_padded[i, -cur_len:] = cur_new_labels | |
| attention_mask[i, -cur_len:] = True | |
| position_ids[i, -cur_len:] = torch.arange(0, cur_len, dtype=position_ids.dtype, device=position_ids.device) | |
| else: | |
| new_input_embeds_padded.append(torch.cat(( | |
| cur_new_embed, | |
| torch.zeros((max_len - cur_len, cur_new_embed.shape[1]), dtype=cur_new_embed.dtype, device=cur_new_embed.device) | |
| ), dim=0)) | |
| if cur_len > 0: | |
| new_labels_padded[i, :cur_len] = cur_new_labels | |
| attention_mask[i, :cur_len] = True | |
| position_ids[i, :cur_len] = torch.arange(0, cur_len, dtype=position_ids.dtype, device=position_ids.device) | |
| new_input_embeds = torch.stack(new_input_embeds_padded, dim=0) | |
| if _labels is None: | |
| new_labels = None | |
| else: | |
| new_labels = new_labels_padded | |
| if _attention_mask is None: | |
| attention_mask = None | |
| else: | |
| attention_mask = attention_mask.to(dtype=_attention_mask.dtype) | |
| if _position_ids is None: | |
| position_ids = None | |
| return None, position_ids, 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.tune_embed_tokens: | |
| for p in self.get_input_embeddings().parameters(): | |
| p.requires_grad = True | |
| for p in self.get_output_embeddings().parameters(): | |
| p.requires_grad = False | |
| print("Set input embeddings to trainable") | |
| 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 | |
| if model_args.pretrain_mm_mlp_adapter: | |
| mm_projector_weights = torch.load(model_args.pretrain_mm_mlp_adapter, map_location="cpu") | |
| if 'model.embed_tokens.weight' in mm_projector_weights.keys(): | |
| def get_w(weights, keyword): | |
| return {k.split(keyword + '.')[1]: v for k, v in weights.items() if keyword in k} | |
| self.get_model().embed_tokens.load_state_dict(get_w(mm_projector_weights, 'model.embed_tokens')) | |