# 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 .builders import build_vision_tower, build_vision_projector # from .constants import IGNORE_INDEX, IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_PATCH_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN import pdb ############################################################################# # builders ############################################################################# ################################################################### import torch import torch.nn as nn import re class IdentityMap(nn.Module): def __init__(self): super().__init__() def forward(self, x, *args, **kwargs): return x @property def config(self): return {"mm_projector_type": 'identity'} class SimpleResBlock(nn.Module): def __init__(self, channels): super().__init__() self.pre_norm = nn.LayerNorm(channels) self.proj = nn.Sequential( nn.Linear(channels, channels), nn.GELU(), nn.Linear(channels, channels) ) def forward(self, x): x = self.pre_norm(x) return x + self.proj(x) def build_vision_projector(config, delay_load=False, **kwargs): projector_type = getattr(config, 'mm_projector_type', 'linear') if projector_type == 'linear': return nn.Linear(config.mm_hidden_size, config.hidden_size) mlp_gelu_match = re.match(r'^mlp(\d+)x_gelu$', projector_type) if mlp_gelu_match: mlp_depth = int(mlp_gelu_match.group(1)) modules = [nn.Linear(config.mm_hidden_size, config.hidden_size)] for _ in range(1, mlp_depth): modules.append(nn.GELU()) modules.append(nn.Linear(config.hidden_size, config.hidden_size)) return nn.Sequential(*modules) if projector_type == 'identity': return IdentityMap() raise ValueError(f'Unknown projector type: {projector_type}') ################################################################### ################################################################### import os from transformers import CLIPVisionModel, CLIPImageProcessor, CLIPVisionConfig from transformers import AutoModel class CLIPVisionTower(nn.Module): def __init__(self, vision_tower, args, delay_load=False): super().__init__() self.is_loaded = False self.vision_tower_name = vision_tower self.select_layer = args.mm_vision_select_layer self.select_feature = getattr(args, 'mm_vision_select_feature', 'patch') if not delay_load: self.load_model() else: self.cfg_only = CLIPVisionConfig.from_pretrained(self.vision_tower_name) def load_model(self): print(f'loading vision model from {self.vision_tower_name}') self.image_processor = CLIPImageProcessor.from_pretrained(self.vision_tower_name) if 'clip' in self.vision_tower_name.lower(): self.vision_tower = CLIPVisionModel.from_pretrained(self.vision_tower_name) elif 'internvit' in self.vision_tower_name.lower(): self.vision_tower = AutoModel.from_pretrained(self.vision_tower_name, trust_remote_code=True) else: raise ValueError(f'Please implement the loading of vision encoder here') self.vision_tower.requires_grad_(False) self.is_loaded = True def feature_select(self, image_forward_outs): image_features = image_forward_outs.hidden_states[self.select_layer] if self.select_feature == 'patch': image_features = image_features[:, 1:] elif self.select_feature == 'cls_patch': image_features = image_features else: raise ValueError(f'Unexpected select feature: {self.select_feature}') return image_features @torch.no_grad() def forward(self, images): if type(images) is list: image_features = [] for image in images: image_forward_out = self.vision_tower(image.to(device=self.device, dtype=self.dtype).unsqueeze(0), output_hidden_states=True) image_feature = self.feature_select(image_forward_out).to(image.dtype) image_features.append(image_feature) else: image_forward_outs = self.vision_tower(images.to(device=self.device, dtype=self.dtype), output_hidden_states=True) image_features = self.feature_select(image_forward_outs).to(images.dtype) return image_features @property def dummy_feature(self): return torch.zeros(1, self.hidden_size, device=self.device, dtype=self.dtype) @property def dtype(self): return self.vision_tower.dtype @property def device(self): return self.vision_tower.device @property def config(self): if self.is_loaded: return self.vision_tower.config else: return self.cfg_only @property def hidden_size(self): return self.config.hidden_size @property def num_patches(self): return (self.config.image_size // self.config.patch_size) ** 2 def build_vision_tower(vision_tower_cfg, **kwargs): vision_tower = getattr(vision_tower_cfg, 'mm_vision_tower', getattr(vision_tower_cfg, 'vision_tower', None)) is_absolute_path_exists = os.path.exists(vision_tower) if is_absolute_path_exists or vision_tower.startswith("openai") or vision_tower.startswith("laion"): return CLIPVisionTower(vision_tower, args=vision_tower_cfg, **kwargs) raise ValueError(f'Unknown vision tower: {vision_tower}') ############################################################################# # builders ############################################################################# ############################################################################# # constants ############################################################################# CONTROLLER_HEART_BEAT_EXPIRATION = 30 WORKER_HEART_BEAT_INTERVAL = 15 LOGDIR = "." # Model Constants IGNORE_INDEX = -100 IMAGE_TOKEN_INDEX = -200 DEFAULT_IMAGE_TOKEN = "" DEFAULT_IMAGE_PATCH_TOKEN = "" DEFAULT_IM_START_TOKEN = "" DEFAULT_IM_END_TOKEN = "" IMAGE_PLACEHOLDER = "" ############################################################################# # constants ############################################################################# 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 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 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} self.mm_projector.load_state_dict(get_w(mm_projector_weights, 'mm_projector')) class LlavaMetaForCausalLM(ABC): @abstractmethod def get_model(self): pass @abstractmethod def get_tokenizer(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_new( self, input_ids: list[torch.tensor], position_ids, attention_mask: list[torch.tensor], past_key_values, labels, images ): vision_tower = self.get_vision_tower() if not self.training: # TODO: check this out!! # pdb.set_trace() 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: if attention_mask is None: # only happen for qwen at inference # raise ValueError(f'should not be here except for Qwen!') return input_ids, None, attention_mask, past_key_values, None, labels 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 # ####################### this block must be optimized! ####################### # 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).to(self.device) for x in image_features] # else: # image_features = self.encode_images(images).to(self.device) # ####################### this block must be optimized! ####################### # ####################### optimized ####################### if getattr(self, 'cached_image_features', None) is None: # this attribute should be cleared in bot.clear_history() 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).to(self.device) for x in image_features] else: image_features = self.encode_images(images).to(self.device) self.cached_image_features = image_features image_features = self.cached_image_features # ####################### optimized ####################### # 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) attention_mask = [torch.tensor([1]*l).to(input_ids).bool() for l in map(len, [ip for ip in input_ids])] else: # attention_mask = attention_mask.bool() attention_mask = [att.bool() for att in attention_mask] # 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.tensor([IGNORE_INDEX]*l).to(input_ids) for l in map(len, [ip for ip in input_ids])] # labels = torch.full_like(input_ids, IGNORE_INDEX) else: labels = [cur_labels[cur_attention_mask] for cur_labels, cur_attention_mask in zip(labels, attention_mask)] # remove the padding using attention_mask -- TODO: double check # pdb.set_trace() input_ids = [cur_input_ids[cur_attention_mask] for cur_input_ids, cur_attention_mask in zip(input_ids, 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: ############### FIXME ############### if cur_image_idx > len(image_features)-1: cur_image_idx = len(image_features)-1 print(f'warning: {input_ids}') ############### FIXME ############### 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 = [] # you have 10 images, but you have 11 placeholders 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: ############### FIXME ############### if cur_image_idx > len(image_features)-1: cur_image_idx = len(image_features)-1 print(f'warning: {input_ids}') ############### FIXME ############### 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 = 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=torch.bool, device=attention_mask[0].device) position_ids = torch.zeros((batch_size, max_len), dtype=torch.long, device=attention_mask[0].device) for i, (cur_new_embed, cur_new_labels) in enumerate(zip(new_input_embeds, new_labels)): cur_len = cur_new_embed.shape[0] # print(f'cur_len[{i}]before padding: {cur_len}') # if i==0: # print(f"{getattr(self.config, 'tokenizer_padding_side', 'right')} {self.get_tokenizer().padding_side}") if getattr(self.config, 'tokenizer_padding_side', 'right') == "left": # checked, this is correct # if self.get_tokenizer().padding_side == '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) attention_mask = attention_mask.to(dtype=torch.bool) 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