# 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_image_tower, build_video_tower from .multimodal_projector.builder import build_vision_projector from llava.constants import IGNORE_INDEX, X_TOKEN_INDEX, DEFAULT_X_PATCH_TOKEN, DEFAULT_X_START_TOKEN, DEFAULT_X_END_TOKEN class LlavaMetaModel: def __init__(self, config): super(LlavaMetaModel, self).__init__(config) if hasattr(config, "mm_image_tower"): self.image_tower = build_image_tower(config, delay_load=True) self.mm_projector = build_vision_projector(config) if hasattr(config, "mm_video_tower"): self.video_tower = build_video_tower(config, delay_load=True) self.mm_projector = build_vision_projector(config) def get_image_tower(self): image_tower = getattr(self, 'image_tower', None) if type(image_tower) is list: image_tower = image_tower[0] return image_tower def get_video_tower(self): video_tower = getattr(self, 'video_tower', None) if type(video_tower) is list: video_tower = video_tower[0] return video_tower def initialize_image_modules(self, model_args, fsdp=None): image_tower = model_args.image_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_image_tower = image_tower image_tower = build_image_tower(model_args) if fsdp is not None and len(fsdp) > 0: self.image_tower = [image_tower] else: self.image_tower = image_tower self.config.use_mm_proj = True self.config.mm_projector_type = getattr(model_args, 'mm_projector_type', 'linear') self.config.mm_hidden_size = image_tower.hidden_size self.config.mm_vision_select_layer = mm_vision_select_layer self.config.mm_vision_select_feature = mm_vision_select_feature self.mm_projector = build_vision_projector(self.config) 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 initialize_video_modules(self, model_args, fsdp=None): video_tower = model_args.video_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_video_tower = video_tower video_tower = build_video_tower(model_args) if fsdp is not None and len(fsdp) > 0: self.video_tower = [video_tower] else: self.video_tower = video_tower self.config.use_mm_proj = True self.config.mm_projector_type = getattr(model_args, 'mm_projector_type', 'linear') self.config.mm_hidden_size = video_tower.hidden_size self.config.mm_vision_select_layer = mm_vision_select_layer self.config.mm_vision_select_feature = mm_vision_select_feature self.mm_projector = build_vision_projector(self.config) 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 def get_image_tower(self): return self.get_model().get_image_tower() def get_video_tower(self): return self.get_model().get_video_tower() def get_all_tower(self, keys): tower = {key: getattr(self, f'get_{key}_tower') for key in keys} return tower def encode_images(self, images): image_features = self.get_model().get_image_tower()(images) image_features = self.get_model().mm_projector(image_features) return image_features def encode_videos(self, videos): video_features = self.get_model().get_video_tower()(videos) video_features = self.get_model().mm_projector(video_features) return video_features # # def prepare_inputs_labels_for_multimodal( # self, input_ids, attention_mask, past_key_values, labels, images # ): # 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: # attention_mask = torch.ones((attention_mask.shape[0], past_key_values[-1][-1].shape[-2] + 1), dtype=attention_mask.dtype, device=attention_mask.device) # return input_ids, attention_mask, past_key_values, None, labels # # 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) for x in image_features] # else: # image_features = self.encode_images(images) # # new_input_embeds = [] # new_labels = [] if labels is not None else None # cur_image_idx = 0 # for batch_idx, cur_input_ids in enumerate(input_ids): # if (cur_input_ids == IMAGE_TOKEN_INDEX).sum() == 0: # # multimodal LLM, but the current sample is not multimodal # # FIXME: this is a hacky fix, for deepspeed zero3 to work # half_len = cur_input_ids.shape[0] // 2 # cur_image_features = image_features[cur_image_idx] # cur_input_embeds_1 = self.get_model().embed_tokens(cur_input_ids[:half_len]) # cur_input_embeds_2 = self.get_model().embed_tokens(cur_input_ids[half_len:]) # cur_input_embeds = torch.cat([cur_input_embeds_1, cur_image_features[0:0], cur_input_embeds_2], dim=0) # new_input_embeds.append(cur_input_embeds) # if labels is not None: # new_labels.append(labels[batch_idx]) # cur_image_idx += 1 # continue # image_token_indices = torch.where(cur_input_ids == IMAGE_TOKEN_INDEX)[0] # 把中间的imgtoken的位置找到 # cur_new_input_embeds = [] # if labels is not None: # cur_labels = labels[batch_idx] # cur_new_labels = [] # assert cur_labels.shape == cur_input_ids.shape # while image_token_indices.numel() > 0: # cur_image_features = image_features[cur_image_idx] # image_token_start = image_token_indices[0] # if getattr(self.config, 'tune_mm_mlp_adapter', False) and getattr(self.config, 'mm_use_im_start_end', False): # cur_new_input_embeds.append(self.get_model().embed_tokens(cur_input_ids[:image_token_start-1]).detach()) # cur_new_input_embeds.append(self.get_model().embed_tokens(cur_input_ids[image_token_start-1:image_token_start])) # cur_new_input_embeds.append(cur_image_features) # cur_new_input_embeds.append(self.get_model().embed_tokens(cur_input_ids[image_token_start+1:image_token_start+2])) # if labels is not None: # cur_new_labels.append(cur_labels[:image_token_start]) # cur_new_labels.append(torch.full((cur_image_features.shape[0],), IGNORE_INDEX, device=labels.device, dtype=labels.dtype)) # cur_new_labels.append(cur_labels[image_token_start:image_token_start+1]) # cur_labels = cur_labels[image_token_start+2:] # else: # cur_new_input_embeds.append(self.get_model().embed_tokens(cur_input_ids[:image_token_start])) # imgtoken之前的text拿出来，好像都是模板套话 # cur_new_input_embeds.append(cur_image_features) # if labels is not None: # cur_new_labels.append(cur_labels[:image_token_start]) # cur_new_labels.append(torch.full((cur_image_features.shape[0],), IGNORE_INDEX, device=labels.device, dtype=labels.dtype)) # cur_labels = cur_labels[image_token_start+1:] # cur_image_idx += 1 # if getattr(self.config, 'tune_mm_mlp_adapter', False) and getattr(self.config, 'mm_use_im_start_end', False): # cur_input_ids = cur_input_ids[image_token_start+2:] # else: # cur_input_ids = cur_input_ids[image_token_start+1:] # imgtoken之后的text拿出来，是真的question # image_token_indices = torch.where(cur_input_ids == IMAGE_TOKEN_INDEX)[0] # if cur_input_ids.numel() > 0: # if getattr(self.config, 'tune_mm_mlp_adapter', False) and getattr(self.config, 'mm_use_im_start_end', False): # cur_new_input_embeds.append(self.get_model().embed_tokens(cur_input_ids).detach()) # else: # cur_new_input_embeds.append(self.get_model().embed_tokens(cur_input_ids)) # if labels is not None: # cur_new_labels.append(cur_labels) # cur_new_input_embeds = [x.to(device=self.device) for x in cur_new_input_embeds] # 前面text+图片+后面question # cur_new_input_embeds = torch.cat(cur_new_input_embeds, dim=0) # new_input_embeds.append(cur_new_input_embeds) # if labels is not None: # cur_new_labels = torch.cat(cur_new_labels, dim=0) # new_labels.append(cur_new_labels) # # if any(x.shape != new_input_embeds[0].shape for x in new_input_embeds): # max_len = max(x.shape[0] for x in new_input_embeds) # # new_input_embeds_align = [] # for cur_new_embed in new_input_embeds: # cur_new_embed = torch.cat((cur_new_embed, torch.zeros((max_len - cur_new_embed.shape[0], cur_new_embed.shape[1]), dtype=cur_new_embed.dtype, device=cur_new_embed.device)), dim=0) # new_input_embeds_align.append(cur_new_embed) # new_input_embeds = torch.stack(new_input_embeds_align, dim=0) # # if labels is not None: # new_labels_align = [] # _new_labels = new_labels # for cur_new_label in new_labels: # cur_new_label = torch.cat((cur_new_label, torch.full((max_len - cur_new_label.shape[0],), IGNORE_INDEX, dtype=cur_new_label.dtype, device=cur_new_label.device)), dim=0) # new_labels_align.append(cur_new_label) # new_labels = torch.stack(new_labels_align, dim=0) # # if attention_mask is not None: # new_attention_mask = [] # for cur_attention_mask, cur_new_labels, cur_new_labels_align in zip(attention_mask, _new_labels, new_labels): # new_attn_mask_pad_left = torch.full((cur_new_labels.shape[0] - labels.shape[1],), True, dtype=attention_mask.dtype, device=attention_mask.device) # new_attn_mask_pad_right = torch.full((cur_new_labels_align.shape[0] - cur_new_labels.shape[0],), False, dtype=attention_mask.dtype, device=attention_mask.device) # cur_new_attention_mask = torch.cat((new_attn_mask_pad_left, cur_attention_mask, new_attn_mask_pad_right), dim=0) # new_attention_mask.append(cur_new_attention_mask) # attention_mask = torch.stack(new_attention_mask, dim=0) # assert attention_mask.shape == new_labels.shape # else: # new_input_embeds = torch.stack(new_input_embeds, dim=0) # if labels is not None: # new_labels = torch.stack(new_labels, dim=0) # # if attention_mask is not None: # new_attn_mask_pad_left = torch.full((attention_mask.shape[0], new_input_embeds.shape[1] - input_ids.shape[1]), True, dtype=attention_mask.dtype, device=attention_mask.device) # attention_mask = torch.cat((new_attn_mask_pad_left, attention_mask), dim=1) # assert attention_mask.shape == new_input_embeds.shape[:2] # # return None, 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 def prepare_inputs_labels_for_multimodal( self, input_ids, attention_mask, past_key_values, labels, X_modalities ): ''' X_modalities [ [img_feature, img_feature, video_feature, audio_feature], ['image', 'image', 'video', 'audio'] ] ''' Xs, keys = X_modalities all_tower = self.get_all_tower(set(keys)) if len(keys) > 0 else None # print(2.5) if all_tower is None or X_modalities[0][0] is None or input_ids.shape[1] == 1: if past_key_values is not None and all_tower is not None and Xs is not None and input_ids.shape[1] == 1: attention_mask = torch.ones((attention_mask.shape[0], past_key_values[-1][-1].shape[-2] + 1), dtype=attention_mask.dtype, device=attention_mask.device) return input_ids, attention_mask, past_key_values, None, labels # 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) for x in image_features] # else: print(keys) X_features = [getattr(self, f'encode_{key}s')(X.unsqueeze(0)) for X, key in zip(Xs, keys)] # expand to get batchsize # X_features = [] # # print(2.5, *[i.shape for i in Xs], keys) # for X, key in zip(Xs, keys): # temp_X = X.unsqueeze(0) # # print(2.6) # # fn = getattr(self, f'encode_{key}s') # if key == 'image': # out = self.encode_images(temp_X) # # print(2.65, 'image', out.shape) # elif key == 'video': # out = self.encode_videos(temp_X) # # print(2.65, 'video', out.shape) # else: # raise NameError(f'{key}') # # print(2.8, out.shape) # X_features.append(out) X_features = [x.flatten(0, 1) for x in X_features] # print([[j, i.shape] for i, j in zip(X_features, keys)]) new_input_embeds = [] new_labels = [] if labels is not None else None cur_X_idx = 0 # print(2.9, input_ids.shape) for batch_idx, cur_input_ids in enumerate(input_ids): # print(333333) if (torch.any(torch.stack([cur_input_ids == X_TOKEN_INDEX[key.upper()] for key in keys]), dim=0)).sum() == 0: # multimodal LLM, but the current sample is not multimodal # FIXME: this is a hacky fix, for deepspeed zero3 to work half_len = cur_input_ids.shape[0] // 2 cur_X_features = X_features[cur_X_idx] cur_input_embeds_1 = self.get_model().embed_tokens(cur_input_ids[:half_len]) cur_input_embeds_2 = self.get_model().embed_tokens(cur_input_ids[half_len:]) cur_input_embeds = torch.cat([cur_input_embeds_1, cur_X_features[0:0], cur_input_embeds_2], dim=0) new_input_embeds.append(cur_input_embeds) if labels is not None: new_labels.append(labels[batch_idx]) cur_X_idx += 1 ############## 注意这里跳过了，如果一个sample是一个modal，那么就跳过1个全zero的modal，如果一个sample对应多个modal，这里的训练逻辑不对！！！ ###### 但似乎不影响1个sample的inference ###### 一个text对应视频和图片，直接走下边了。只有1个text，传入none或者1个/2个全zero都无所谓，反正没有下一个数据了。 continue X_token_indices = torch.where(torch.any(torch.stack([cur_input_ids == X_TOKEN_INDEX[key.upper()] for key in keys]), dim=0))[0] # 把中间的imgtoken的位置找到 cur_new_input_embeds = [] if labels is not None: cur_labels = labels[batch_idx] cur_new_labels = [] assert cur_labels.shape == cur_input_ids.shape # print(4444444444) while X_token_indices.numel() > 0: cur_X_features = X_features[cur_X_idx] X_token_start = X_token_indices[0] if getattr(self.config, 'tune_mm_mlp_adapter', False) and getattr(self.config, 'mm_use_x_start_end', False): cur_new_input_embeds.append(self.get_model().embed_tokens(cur_input_ids[:X_token_start-1]).detach()) cur_new_input_embeds.append(self.get_model().embed_tokens(cur_input_ids[X_token_start-1:X_token_start])) cur_new_input_embeds.append(cur_X_features) cur_new_input_embeds.append(self.get_model().embed_tokens(cur_input_ids[X_token_start+1:X_token_start+2])) if labels is not None: cur_new_labels.append(cur_labels[:X_token_start]) cur_new_labels.append(torch.full((cur_X_features.shape[0],), IGNORE_INDEX, device=labels.device, dtype=labels.dtype)) cur_new_labels.append(cur_labels[X_token_start:X_token_start+1]) cur_labels = cur_labels[X_token_start+2:] else: cur_new_input_embeds.append(self.get_model().embed_tokens(cur_input_ids[:X_token_start])) # imgtoken之前的text拿出来，好像都是模板套话 cur_new_input_embeds.append(cur_X_features) if labels is not None: cur_new_labels.append(cur_labels[:X_token_start]) cur_new_labels.append(torch.full((cur_X_features.shape[0],), IGNORE_INDEX, device=labels.device, dtype=labels.dtype)) cur_labels = cur_labels[X_token_start+1:] cur_X_idx += 1 if getattr(self.config, 'tune_mm_mlp_adapter', False) and getattr(self.config, 'mm_use_x_start_end', False): cur_input_ids = cur_input_ids[X_token_start+2:] else: cur_input_ids = cur_input_ids[X_token_start+1:] # imgtoken之后的text拿出来，是真的question X_token_indices = torch.where(torch.any(torch.stack([cur_input_ids == X_TOKEN_INDEX[key.upper()] for key in keys]), dim=0))[0] # print(55555555555555555) if cur_input_ids.numel() > 0: if getattr(self.config, 'tune_mm_mlp_adapter', False) and getattr(self.config, 'mm_use_x_start_end', False): cur_new_input_embeds.append(self.get_model().embed_tokens(cur_input_ids).detach()) else: cur_new_input_embeds.append(self.get_model().embed_tokens(cur_input_ids)) if labels is not None: cur_new_labels.append(cur_labels) cur_new_input_embeds = [x.to(device=self.device) for x in cur_new_input_embeds] # 前面text+图片+后面question cur_new_input_embeds = torch.cat(cur_new_input_embeds, dim=0) new_input_embeds.append(cur_new_input_embeds) if labels is not None: cur_new_labels = torch.cat(cur_new_labels, dim=0) new_labels.append(cur_new_labels) if any(x.shape != new_input_embeds[0].shape for x in new_input_embeds): max_len = max(x.shape[0] for x in new_input_embeds) new_input_embeds_align = [] for cur_new_embed in new_input_embeds: cur_new_embed = torch.cat((cur_new_embed, torch.zeros((max_len - cur_new_embed.shape[0], cur_new_embed.shape[1]), dtype=cur_new_embed.dtype, device=cur_new_embed.device)), dim=0) new_input_embeds_align.append(cur_new_embed) new_input_embeds = torch.stack(new_input_embeds_align, dim=0) if labels is not None: new_labels_align = [] _new_labels = new_labels for cur_new_label in new_labels: cur_new_label = torch.cat((cur_new_label, torch.full((max_len - cur_new_label.shape[0],), IGNORE_INDEX, dtype=cur_new_label.dtype, device=cur_new_label.device)), dim=0) new_labels_align.append(cur_new_label) new_labels = torch.stack(new_labels_align, dim=0) if attention_mask is not None: new_attention_mask = [] for cur_attention_mask, cur_new_labels, cur_new_labels_align in zip(attention_mask, _new_labels, new_labels): new_attn_mask_pad_left = torch.full((cur_new_labels.shape[0] - labels.shape[1],), True, dtype=attention_mask.dtype, device=attention_mask.device) new_attn_mask_pad_right = torch.full((cur_new_labels_align.shape[0] - cur_new_labels.shape[0],), False, dtype=attention_mask.dtype, device=attention_mask.device) cur_new_attention_mask = torch.cat((new_attn_mask_pad_left, cur_attention_mask, new_attn_mask_pad_right), dim=0) new_attention_mask.append(cur_new_attention_mask) attention_mask = torch.stack(new_attention_mask, dim=0) assert attention_mask.shape == new_labels.shape else: new_input_embeds = torch.stack(new_input_embeds, dim=0) if labels is not None: new_labels = torch.stack(new_labels, dim=0) if attention_mask is not None: new_attn_mask_pad_left = torch.full((attention_mask.shape[0], new_input_embeds.shape[1] - input_ids.shape[1]), True, dtype=attention_mask.dtype, device=attention_mask.device) attention_mask = torch.cat((new_attn_mask_pad_left, attention_mask), dim=1) assert attention_mask.shape == new_input_embeds.shape[:2] return None, attention_mask, past_key_values, new_input_embeds, new_labels def initialize_X_tokenizer(self, model_args, tokenizer): if model_args.mm_use_x_patch_token: for x in model_args.X: tokenizer.add_tokens([DEFAULT_X_PATCH_TOKEN[x.upper()]], special_tokens=True) # tokenizer.add_tokens([DEFAULT_IMAGE_PATCH_TOKEN], special_tokens=True) self.resize_token_embeddings(len(tokenizer)) if model_args.mm_use_x_start_end: num_new_tokens = 0 for x in model_args.X: num_new_tokens += tokenizer.add_tokens([DEFAULT_X_START_TOKEN[x.upper()], DEFAULT_X_END_TOKEN[x.upper()]], 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_x_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