# 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 ola_vlm.constants import IGNORE_INDEX, IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_PATCH_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN from ola_vlm.mm_utils import get_anyres_image_grid_shape import numpy as np def build_mlp(in_hidden_size, hidden_size): modules = [nn.Linear(in_hidden_size, hidden_size)] modules.append(nn.GELU()) modules.append(nn.Linear(hidden_size, hidden_size)) return nn.Sequential(*modules) class LlavaMetaModel: def __init__(self, config): super(LlavaMetaModel, self).__init__(config) self.num_task_tokens = 0 if hasattr(config, "mm_vision_tower"): self.vision_tower = build_vision_tower(config, delay_load=False) 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) ) if hasattr(config, 'num_task_tokens'): self.initialize_special_tokens(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 get_task_mlp(self): mlp_task = getattr(self, 'mlp_task', None) return mlp_task def get_special_tokens(self): depth_tokens = getattr(self, 'special_depth_tokens', None) seg_tokens = getattr(self, 'special_seg_tokens', None) gen_tokens = getattr(self, 'special_gen_tokens', None) return depth_tokens, seg_tokens, gen_tokens def initialize_special_tokens(self, config): self.num_task_tokens = config.num_task_tokens task_token_format = getattr(config, "task_token_format", "emb") self.task_token_format = task_token_format self.is_sample_tokens = getattr(config, "sample_tokens", False) self.aux_tokens = config.aux_mode self.token_order = config.aux_mode.split("-") if self.num_task_tokens > 0: if "depth" in config.aux_mode: self.special_depth_tokens = nn.Parameter( torch.randn( config.image_depth["num_tokens"], config.hidden_size ) ) if "seg" in config.aux_mode: self.special_seg_tokens = nn.Parameter( torch.randn( config.image_seg["num_tokens"], config.hidden_size ) ) if "gen" in config.aux_mode: self.special_gen_tokens = nn.Parameter( torch.randn( config.num_task_tokens, config.hidden_size ) ) 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 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 PIL image (width, height). 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 def unpad_prep_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 PIL image (width, height). 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: mode = "height" 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] mode = "width" return unpadded_tensor, mode, padding class LlavaMetaForCausalLM(ABC): @abstractmethod def get_model(self): pass def get_vision_tower(self): return self.get_model().get_vision_tower() @property def depth_tokens(self): return self.get_model().get_special_tokens()[0] @property def seg_tokens(self): return self.get_model().get_special_tokens()[1] @property def gen_tokens(self): return self.get_model().get_special_tokens()[2] @property def num_task_tokens(self): return self.get_model().num_task_tokens @property def task_token_format(self): return self.get_model().task_token_format @property def aux_tokens(self): return self.get_model().aux_tokens @property def token_order(self): return self.get_model().token_order @property def is_sample_tokens(self): return self.get_model().is_sample_tokens def append_special_tokens(self, cur_new_input_embeds, cur_image_features, cur_labels, cur_new_labels): if self.num_task_tokens == 0: return cur_new_input_embeds, cur_new_labels def _get_tokens(self, tokens): if self.task_token_format == "text": tk_weights = self.get_model().embed_tokens(tokens.to(cur_image_features.device)) elif self.task_token_format == "emb": tk_weights = tokens elif self.task_token_format == "expand_emb": tk_weights = tokens.view(self.num_task_tokens, tokens.shape[0] // self.num_task_tokens, tokens.shape[1]) tk_weights = tk_weights.mean(dim=1) else: raise ValueError(f"Unexpected task_token_format: {self.task_token_format}") return tk_weights token_types = { "depth": self.depth_tokens, "seg": self.seg_tokens, "gen": self.gen_tokens } for token_type in self.token_order: tk_weights = None if token_type in self.aux_tokens: if token_type == "depth" and token_types["depth"] is not None: tk_weights = _get_tokens(self, token_types["depth"]) elif token_type == "seg" and token_types["seg"] is not None: tk_weights = _get_tokens(self, token_types["seg"]) elif token_type == "gen" and token_types["gen"] is not None: tk_weights = ( self.get_model().embed_tokens(self.gen_tokens.to(cur_image_features.device)) if self.task_token_format == "text" else self.gen_tokens ) if tk_weights is not None: cur_new_input_embeds.append(tk_weights) cur_new_labels.append(torch.full((tk_weights.shape[0],), IGNORE_INDEX, device=cur_labels.device, dtype=cur_labels.dtype)) return cur_new_input_embeds, cur_new_labels def encode_images(self, images): image_features = self.get_model().get_vision_tower()(images).to(images.dtype).to(images.device) 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, cur_new_labels = self.append_special_tokens( cur_new_input_embeds, cur_image_features, cur_labels, cur_new_labels, ) 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.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