# 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. # ------------------------------------------------------------------------ # Modified from LLaVA (https://github.com/haotian-liu/LLaVA) # Copyright 2024 Yanwei Li # ------------------------------------------------------------------------ from abc import ABC, abstractmethod import torch import torch.nn as nn import torch.nn.functional as F import json import os import transformers import safetensors from transformers.deepspeed import is_deepspeed_zero3_enabled import deepspeed from .multimodal_encoder.builder import build_vision_tower, build_vision_tower_aux from .multimodal_projector.builder import build_vision_projector from mgm.constants import (IGNORE_INDEX, IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_PATCH_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN) IS_NEW_TRANSFORMERS = transformers.__version__ >= "4.34.0" class MGMMetaModel: def __init__(self, config): super(MGMMetaModel, 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 hasattr(config, "mm_vision_tower_aux"): self.vision_tower_aux = build_vision_tower_aux(config, delay_load=True) 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_vision_tower_aux(self): vision_tower_aux = getattr(self, 'vision_tower_aux', None) if type(vision_tower_aux) is list: vision_tower_aux = vision_tower_aux[0] return vision_tower_aux def initialize_vision_modules(self, model_args, fsdp=None): vision_tower = model_args.vision_tower vision_tower_aux = model_args.vision_tower_aux 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 self.config.mm_vision_tower_aux = vision_tower_aux 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() if vision_tower_aux is not None: if self.get_vision_tower_aux() is None: vision_tower_aux = build_vision_tower_aux(model_args) if fsdp is not None and len(fsdp) > 0: self.vision_tower_aux = [vision_tower_aux] else: self.vision_tower_aux = vision_tower_aux else: if fsdp is not None and len(fsdp) > 0: vision_tower_aux = self.vision_tower_aux[0] else: vision_tower_aux = self.vision_tower_aux vision_tower_aux.load_model() self.config.mm_hidden_size_aux = vision_tower_aux.hidden_size 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_hidden_size = 3072 self.config.mm_hidden_size_uni = vision_tower.hidden_size self.config.mm_vision_select_layer = mm_vision_select_layer self.config.mm_vision_select_feature = mm_vision_select_feature if getattr(self, 'mm_projector', None) is None: self.mm_projector = build_vision_projector(self.config) 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} if 'model' in mm_projector_weights.keys(): mm_projector_weights = mm_projector_weights['model'] if is_deepspeed_zero3_enabled(): if len(mm_projector_weights) > 0: with deepspeed.zero.GatheredParameters(mm_projector_weights, modifier_rank=0): if torch.distributed.get_rank() == 0: self.mm_projector.load_state_dict(mm_projector_weights) else: status = self.mm_projector.load_state_dict(mm_projector_weights, strict=False) print('missing_keys:', status.missing_keys) else: if is_deepspeed_zero3_enabled(): named_parameters = get_w(mm_projector_weights, 'mm_projector') if len(named_parameters) > 0: with deepspeed.zero.GatheredParameters(named_parameters, modifier_rank=0): if torch.distributed.get_rank() == 0: self.mm_projector.load_state_dict(named_parameters) else: status = self.mm_projector.load_state_dict(get_w(mm_projector_weights, 'mm_projector'), strict=False) print('missing_keys:', status.missing_keys) self.mm_projector = self.mm_projector.to(device='cuda') def initialize_uni_modules(self, model_args, for_eval=False): pretrain_mm_mlp_adapter = getattr(model_args, "pretrain_mm_mlp_adapter", None) self.config.image_size_aux = getattr(model_args, 'image_size_aux', 320) self.config.optimize_vision_tower = getattr(model_args, 'optimize_vision_tower', False) self.config.optimize_vision_tower_aux = getattr(model_args, 'optimize_vision_tower_aux', False) self.vlm_uni_query_projector = nn.Sequential(nn.LayerNorm(self.config.mm_hidden_size_uni), nn.Linear(self.config.mm_hidden_size_uni, self.config.mm_hidden_size_uni)) self.vlm_uni_aux_projector = nn.Sequential(nn.LayerNorm(self.config.mm_hidden_size_aux), nn.Linear(self.config.mm_hidden_size_aux, self.config.mm_hidden_size_uni)) self.vlm_uni_val_projector = nn.Sequential(nn.LayerNorm(self.config.mm_hidden_size_aux), nn.Linear(self.config.mm_hidden_size_aux, self.config.mm_hidden_size_uni)) if pretrain_mm_mlp_adapter is not None: projector_weights = torch.load(pretrain_mm_mlp_adapter, map_location='cpu') else: trainable_module = ['vlm_uni', 'vision_fpn', 'vision_stages'] if hasattr(model_args, 'model_name_or_path'): model_save_path = model_args.model_name_or_path else: model_save_path = model_args.model_path model_idx_path = getattr(model_args, 'model_path', model_save_path) if IS_NEW_TRANSFORMERS: try: weight_file = json.load(open(os.path.join(model_idx_path, 'model.safetensors.index.json'), 'r'))['weight_map'] except: weight_file = json.load(open(os.path.join(model_idx_path, 'pytorch_model.bin.index.json'), 'r'))['weight_map'] else: weight_file = json.load(open(os.path.join(model_idx_path, 'pytorch_model.bin.index.json'), 'r'))['weight_map'] model_path = set([weight_file[_key] for _key in weight_file if any([_module in _key for _module in trainable_module])]) projector_weights = {} for _model in model_path: if not IS_NEW_TRANSFORMERS: projector_weights.update(torch.load(os.path.join(model_idx_path, _model), map_location='cpu')) else: with safetensors.safe_open(os.path.join(model_idx_path, _model), framework="pt", device='cpu') as f: for _key in f.keys(): projector_weights.update({_key: f.get_tensor(_key)}) if len(projector_weights) == 0: return def get_w(weights, keyword, main_module, sub_module): if getattr(main_module, sub_module, None) is None: return pretrain_weight = {k.split(keyword + '.')[1]: v for k, v in weights.items() if keyword + '.' in k} if len(pretrain_weight) == 0: return if is_deepspeed_zero3_enabled(): named_parameters = [v for k, v in getattr(main_module, sub_module).named_parameters()] if len(named_parameters) > 0: # because zero3 puts placeholders in model params, this context # manager gathers (unpartitions) the params of the current layer, then loads from # the state dict and then re-partitions them again with deepspeed.zero.GatheredParameters(named_parameters, modifier_rank=0): if torch.distributed.get_rank() == 0: getattr(main_module, sub_module).load_state_dict(pretrain_weight) with deepspeed.zero.GatheredParameters(self.mm_projector[0].weight, modifier_rank=None): weight_type = self.mm_projector[0].weight.dtype device_type = self.mm_projector[0].weight.device else: weight_type = self.mm_projector[0].weight.dtype device_type = self.mm_projector[0].weight.device getattr(main_module, sub_module).load_state_dict(pretrain_weight) if weight_type == torch.uint8 or weight_type == torch.int8 or weight_type == torch.int16: weight_type = torch.float16 getattr(main_module, sub_module).to(device=device_type, dtype=weight_type) print(f"Loading {sub_module} weights...") # load pretrained weights get_w(projector_weights, 'vision_tower.vision_tower', self.vision_tower, 'vision_tower') # load pretrained weights if self.config.optimize_vision_tower_aux: # not optimize vision stem, just used to check get_w(projector_weights, 'vision_tower_aux.vision_stem', self.vision_tower_aux, 'vision_stem') get_w(projector_weights, 'vision_tower_aux.vision_stages', self.vision_tower_aux, 'vision_stages') get_w(projector_weights, 'vlm_uni_query_projector', self, 'vlm_uni_query_projector') get_w(projector_weights, 'vlm_uni_aux_projector', self, 'vlm_uni_aux_projector') get_w(projector_weights, 'vlm_uni_val_projector', self, 'vlm_uni_val_projector') class MGMMetaForCausalLM(ABC): @abstractmethod def get_model(self): pass def get_vision_tower(self): return self.get_model().get_vision_tower() def get_vision_tower_aux(self): return self.get_model().get_vision_tower_aux() def encode_images(self, images, images_aux=None, is_video=False): image_grid = getattr(self.config, 'image_grid', 1) image_global = getattr(self.config, 'image_global', False) if image_grid > 1: batch_size = images.shape[0] if image_global: global_images = images[:, -1:].flatten(0,1).contiguous() grid_images = images[:, :-1].flatten(0,1).contiguous() images = torch.cat([grid_images, global_images], dim=0) else: images = images.flatten(0,1).contiguous() image_features, image_forward_outs = self.get_model().get_vision_tower()(images) if image_global: # false image_feat_global = image_features[-len(global_images):] image_features = image_features[:len(grid_images)] if images_aux is not None: image_aux_features_raw = self.get_model().get_vision_tower_aux()(images_aux).to( dtype=image_features.dtype, device=image_features.device) if image_global: image_aux_features_global = F.interpolate(image_aux_features_raw.float(), scale_factor=1/image_grid, mode='bilinear', align_corners=False).to(dtype=image_aux_features_raw.dtype) image_feat_global, image_aux_feat_global = self.unified_resampler(image_feat_global, image_aux_features_global) if image_grid > 1: image_aux_features_raw = image_aux_features_raw.reshape(*image_aux_features_raw.shape[:2], image_grid, image_aux_features_raw.shape[-2]//image_grid, image_grid, image_aux_features_raw.shape[-1]//image_grid) image_aux_features_raw = image_aux_features_raw.permute(0, 2, 4, 1, 3, 5).flatten(1,2).flatten(0,1).contiguous() image_features, image_aux_features = self.unified_resampler(image_features, image_aux_features_raw) if image_grid > 1: image_features = image_features.reshape(batch_size, image_grid**2, *image_features.shape[1:]) image_features = image_features.flatten(1,2).contiguous() image_aux_features = image_aux_features.reshape(batch_size, image_grid**2, *image_aux_features.shape[1:]) image_aux_features = image_aux_features.flatten(1,2).contiguous() # add global features, [global, local] if image_global: image_features = torch.cat([image_feat_global, image_features], dim=1) image_aux_features = torch.cat([image_aux_feat_global, image_aux_features], dim=1) # token generation image_features = image_features + image_aux_features # dense connector image_features_1 = [] image_features_2 = [] for i in range(0, 12): image_features_1.append(image_forward_outs.hidden_states[i][:, 1:].to(image_features.dtype)) image_features_1 = torch.stack(image_features_1, dim=0) image_features_1 = torch.sum(image_features_1, dim=0) / 12 for i in range(12, 24): image_features_2.append(image_forward_outs.hidden_states[i][:, 1:].to(image_features.dtype)) image_features_2 = torch.stack(image_features_2, dim=0) image_features_2 = torch.sum(image_features_2, dim=0) / 12 image_features = torch.cat([image_features, image_features_1, image_features_2], dim=-1) ## dense connector end # process image features after token generation image_features = self.get_model().mm_projector(image_features) return image_features def unified_resampler(self, images, images_aux): # patchwise with square images patch_num = int(images.shape[1]**0.5) patch_size = images_aux.shape[-1]//patch_num # within patch attention images_aux = images_aux.permute(0,2,3,1) images_aux = images_aux.reshape(len(images_aux), patch_num, patch_size, patch_num, patch_size, images_aux.shape[-1]) images_aux = images_aux.permute(0,1,3,2,4,5) images_aux = images_aux.reshape(len(images_aux), patch_num**2, patch_size**2, images_aux.shape[-1]).contiguous() # token attention embed_query = self.get_model().vlm_uni_query_projector(images) embed_aux = self.get_model().vlm_uni_aux_projector(images_aux) embed_value = self.get_model().vlm_uni_val_projector(images_aux) embed_att = embed_query[:,:,None] @ (embed_aux.transpose(-1,-2) / (embed_aux.shape[-1]**0.5)) embed_att = embed_att.nan_to_num() embed_feat = (embed_att.softmax(-1) @ embed_value).mean(2) return images, embed_feat def prepare_inputs_labels_for_multimodal( self, input_ids, position_ids, attention_mask, past_key_values, labels, images=None, images_aux=None, ): 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: target_shape = past_key_values[-1][-1].shape[-2] + 1 attention_mask = torch.cat((attention_mask, torch.ones( (attention_mask.shape[0], target_shape - attention_mask.shape[1]), dtype=attention_mask.dtype, device=attention_mask.device )), dim=1) position_ids = torch.sum(attention_mask, dim=1).unsqueeze(-1) - 1 return input_ids, position_ids, attention_mask, past_key_values, None, labels if isinstance(images, list): images = torch.stack(images, dim=0) if isinstance(images_aux, list): images_aux = torch.stack(images_aux, dim=0) image_features = self.encode_images(images, images_aux) # 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 -- TODO: double check 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 = [] max_pos_id = 0 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]) max_pos_id += cur_input_embeds_no_im[i].shape[0] 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)) max_pos_id += cur_image_features.shape[0] cur_new_input_embeds = [x.to(device=cur_input_embeds.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