from abc import ABC, abstractmethod import torch from .speech_encoder.builder import build_speech_encoder from .speech_projector.builder import build_speech_projector from ola.constants import IGNORE_INDEX, SPEECH_TOKEN_INDEX from ola.utils import lengths_to_padding_mask from .multimodal_encoder.builder import build_vision_tower from .multimodal_resampler.builder import build_vision_resampler from .multimodal_projector.builder import build_vision_projector from ola.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_PATCH_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN class OlaMetaModel: def __init__(self, config): super(OlaMetaModel, self).__init__(config) if hasattr(config, "speech_encoder"): self.speech_encoder = build_speech_encoder(config) self.speech_projector = build_speech_projector(config) if hasattr(config, "mm_vision_tower"): self.vision_tower = build_vision_tower(config, delay_load=True) self.vision_resampler = build_vision_resampler(config, vision_tower=self.vision_tower) self.mm_projector = build_vision_projector(config, vision_cfg=self.vision_tower.config) def get_speech_encoder(self): speech_encoder = getattr(self, 'speech_encoder', None) if type(speech_encoder) is list: speech_encoder = speech_encoder[0] return speech_encoder 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_speech_modules(self, model_args, fsdp=None): self.config.speech_encoder = getattr(model_args, "speech_encoder", None) self.config.speech_encoder_type = getattr(model_args, "speech_encoder_type", None) self.config.speech_projector_type = getattr(model_args, 'speech_projector_type', 'linear') self.config.speech_encoder_ds_rate = getattr(model_args, 'speech_encoder_ds_rate', 5) self.config.speech_encoder_hidden_size = getattr(model_args, 'speech_encoder_hidden_size', 1280) if self.get_speech_encoder() is None: speech_encoder = build_speech_encoder(self.config) if fsdp is not None and len(fsdp) > 0: self.speech_encoder = [speech_encoder] else: self.speech_encoder = speech_encoder if getattr(self, 'speech_projector', None) is None: self.speech_projector = build_speech_projector(self.config) else: # In case it is frozen by LoRA for p in self.speech_projector.parameters(): p.requires_grad = True if model_args.pretrain_speech_projector is not None: pretrain_speech_projector_weights = torch.load(model_args.pretrain_speech_projector, map_location='cpu') def get_w(weights, keyword): return {k.split(keyword + '.')[1]: v for k, v in weights.items() if keyword in k} print('Loading pretrain speech projector weights') msg = self.speech_projector.load_state_dict(get_w(pretrain_speech_projector_weights, 'speech_projector'), strict=False) print(msg) 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) vision_resampler = build_vision_resampler(model_args, vision_tower=vision_tower) ## Get the mm_spatial_pool_mode and mm_spatial_pool_stride for k, v in vision_resampler.config.items(): setattr(self.config, k, v) if fsdp is not None and len(fsdp) > 0: self.vision_tower = [vision_tower] self.vision_resampler = [vision_resampler] else: self.vision_tower = vision_tower self.vision_resampler = vision_resampler else: if fsdp is not None and len(fsdp) > 0: vision_resampler = self.vision_resampler[0] vision_tower = self.vision_tower[0] else: vision_resampler = self.vision_resampler vision_tower = self.vision_tower vision_tower.load_model() # In case it is frozen by LoRA for p in self.vision_resampler.parameters(): p.requires_grad = True self.config.use_mm_proj = True self.config.mm_projector_type = getattr(model_args, 'mm_projector_type', 'linear') self.config.mm_hidden_size = getattr(vision_resampler, '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, vision_cfg=vision_tower.config) else: 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')) print('Loading pretrain mm projector weights') incompatible_keys = self.vision_resampler.load_state_dict(get_w(mm_projector_weights, 'vision_resampler'), strict=False) print(incompatible_keys) class OlaMetaForCausalLM(ABC): @abstractmethod def get_model(self): pass def get_speech_encoder(self): return self.get_model().get_speech_encoder() def get_vision_tower(self): return self.get_model().get_vision_tower() def get_speech_projector(self): return self.get_model().speech_projector def encode_speech(self, speech, speech_lengths, speech_wav): # import pdb; pdb.set_trace() speech_encoder_type = self.config.speech_encoder_type speech_encoder = self.get_speech_encoder() if "whisper" in speech_encoder_type.lower(): encoder_outs = speech_encoder(speech.permute(0, 2, 1)) speech_lengths = (speech_lengths + 1) // 2 else: encoder_outs = speech_encoder(speech.permute(0, 2, 1), raw_wav=speech_wav) speech_lengths = (speech_lengths + 1) // 2 speech_projector_type = self.config.speech_projector_type speech_projector = self.get_speech_projector() if speech_projector_type == "linear": encoder_outs = speech_projector(encoder_outs) speech_lengths = speech_lengths // speech_projector.k else: raise ValueError(f'Unknown speech projector: {speech_projector_type}') # speech_features = [encoder_outs[i, :speech_lengths[i]] for i in range(len(encoder_outs))] return encoder_outs def prepare_inputs_labels_for_speech_vision_text( self, input_ids, position_ids, attention_mask, past_key_values, labels, speech, speech_lengths, speech_chunks, speech_wav, images, modalities, image_sizes=None, images_highres=None ): speech_encoder = self.get_speech_encoder() vision_tower = self.get_vision_tower() if speech_encoder is None or input_ids.shape[1] == 1: return input_ids, position_ids, attention_mask, past_key_values, None, labels if vision_tower is None or input_ids.shape[1] == 1: return input_ids, position_ids, attention_mask, past_key_values, None, labels # encode speech if not isinstance(speech, list): speech = torch.split(speech, speech_chunks.tolist(), dim=0) speech_lengths = torch.split(speech_lengths, speech_chunks.tolist(), dim=0) speech_wav = torch.split(speech_wav, speech_chunks.tolist(), dim=0) speech_features = [] for idx in range(len(speech)): speech_features.append(self.encode_speech(speech[idx], speech_lengths[idx], speech_wav[idx])) # encode vision if isinstance(modalities, str): modalities = [modalities] video_idx_in_batch = [] for modal in range(len(modalities)): if 'video' in modalities[modal]: video_idx_in_batch.append(modal) # Fix training with deepspeed zero3 num_modality = len(modalities) # try: # world_size = dist.get_world_size() # tensor_in = torch.zeros(1, dtype=torch.int64, device=images[0].device).fill_(num_modality) # tensor_out = torch.zeros(world_size, dtype=torch.int64, device=images[0].device) # dist.all_gather_into_tensor(tensor_out, tensor_in) # max_num_modality = tensor_out.max().item() # except: # max_num_modality = num_modality aimg = images[-1] lowres_img = [] for idx, img_feat in enumerate(images): if idx in video_idx_in_batch: img_feat = aimg.new(1, 3, 128, 128).fill_(0) lowres_img.append(img_feat) # Fix training with deepspeed zero3 # if max_num_modality > num_modality: # for _ in range(max_num_modality - num_modality): # lowres_img.append(aimg.new(1, 3, 64, 64).fill_(0)) # images_highres.append(aimg.new(1, 3, 64, 64).fill_(0)) # modalities.append('image') lowres_img_features, lowres_img_sizes = self.get_model().get_vision_tower()(lowres_img) highres_img_features = [] highres_img_sizes = [] for idx, img_feat in enumerate(images_highres): if img_feat.ndim == 5: img_feat = img_feat.squeeze(1) highres_img_feature, highres_img_size = self.get_model().get_vision_tower()(img_feat) highres_img_features.append(highres_img_feature) highres_img_sizes.append(highres_img_size) image_features = [] for idx in range(len(modalities)): img_feat = self.get_model().mm_projector(lowres_img_features[idx], lowres_img_sizes[idx], highres_img_features[idx], highres_img_sizes[idx], modalities[idx]) image_features.append(img_feat.flatten(0, 1)) # if max_num_modality > num_modality: # image_features = image_features[:num_modality] # modalities = modalities[:num_modality] _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_speech_idx = 0 cur_image_idx = 0 for batch_idx, cur_input_ids in enumerate(input_ids): num_speech = (cur_input_ids == SPEECH_TOKEN_INDEX).sum() num_images = (cur_input_ids == IMAGE_TOKEN_INDEX).sum() num_speech_images = (cur_input_ids == IMAGE_TOKEN_INDEX).sum() + (cur_input_ids == SPEECH_TOKEN_INDEX).sum() if num_speech_images == 0: cur_speech_features = speech_features[cur_speech_idx] cur_images_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_speech_features[0:0], cur_images_features[0:0]], dim=0) new_input_embeds.append(cur_input_embeds) new_labels.append(labels[batch_idx]) cur_speech_idx += 1 cur_image_idx += 1 continue speech_image_token_indices = [-1] + torch.where((cur_input_ids == SPEECH_TOKEN_INDEX) | (cur_input_ids == IMAGE_TOKEN_INDEX))[0].tolist() + [cur_input_ids.shape[0]] cur_input_ids_nospeech_image = [] cur_labels = labels[batch_idx] cur_labels_nospeech_image = [] for i in range(len(speech_image_token_indices) - 1): cur_input_ids_nospeech_image.append(cur_input_ids[speech_image_token_indices[i]+1:speech_image_token_indices[i+1]]) cur_labels_nospeech_image.append(cur_labels[speech_image_token_indices[i]+1:speech_image_token_indices[i+1]]) split_sizes = [x.shape[0] for x in cur_labels_nospeech_image] cur_input_embeds = self.get_model().embed_tokens(torch.cat(cur_input_ids_nospeech_image)) cur_input_embeds_no_speech_image = torch.split(cur_input_embeds, split_sizes, dim=0) cur_new_input_embeds = [] cur_new_labels = [] for i in range(num_speech_images + 1): cur_new_input_embeds.append(cur_input_embeds_no_speech_image[i]) cur_new_labels.append(cur_labels_nospeech_image[i]) if i < num_speech_images: if i < num_images: cur_images_features = image_features[cur_image_idx] cur_image_idx += 1 cur_new_input_embeds.append(cur_images_features) cur_new_labels.append(torch.full((cur_images_features.shape[0],), IGNORE_INDEX, device=cur_labels.device, dtype=cur_labels.dtype)) else: cur_speech_features = speech_features[cur_speech_idx] cur_speech_idx += 1 cur_new_input_embeds.append(cur_speech_features) cur_new_labels.append(torch.full((cur_speech_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) if num_images == 0: cur_new_input_embeds = torch.cat([cur_new_input_embeds, image_features[cur_image_idx][0:0]], dim=0) cur_image_idx += 1 if num_speech == 0: cur_new_input_embeds = torch.cat([cur_new_input_embeds, speech_features[cur_speech_idx][0:0]], dim=0) cur_speech_idx += 1 new_input_embeds.append(cur_new_input_embeds) new_labels.append(cur_new_labels) # Truncate sequences to max length as speech features 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