# 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 email.mime import image import os from abc import ABC, abstractmethod import torch import torch.nn as nn from .multimodal_encoder.builder import build_adapter_module, build_vision_tower, build_Qformer from .multimodal_projector.builder import build_vision_projector from llava.constants import IGNORE_INDEX, MM_TOKEN_INDEX, DEFAULT_IMAGE_PATCH_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN, DEFAULT_VIDEO_PATCH_TOKEN, DEFAULT_VIDEO_START_TOKEN, DEFAULT_VIDEO_END_TOKEN from llava.mm_utils import get_anyres_image_grid_shape from llava.utils import master_print import tensorrt as trt import pycuda.driver as cuda import pycuda.autoinit import subprocess import torch.onnx 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) if getattr(config, "qformer_model_path", None): self.Qformer, self.ln_vision, self.query_tokens = build_Qformer( config.num_query_token, self.vision_tower.hidden_size) self.frame_position_encoding = nn.Embedding( config.max_num_segments, self.Qformer.config.hidden_size ) if getattr(config, "adapter_module_name", None): self.adapter_module = build_adapter_module(config, self.vision_tower.hidden_size) if 'unpad' in getattr(config, 'mm_patch_merge_type', ''): self.image_newline = nn.Parameter( torch.empty(config.hidden_size, dtype=self.dtype) ) # Prepare TRT # self.trt_logger = trt.Logger(trt.Logger.WARNING) # self.trt_runtime = trt.Runtime(self.trt_logger) # trt.init_libnvinfer_plugins(None, "") # nvidia_smi_output = subprocess.check_output(["nvidia-smi", "-L"]).decode() # gpu_info = nvidia_smi_output.split(":")[1].split("(")[0].strip() # print(gpu_info) # if "A10" in gpu_info: # vit_tagging_path = "./a10/vit.trt" # elif "A30" in gpu_info: # vit_tagging_path = "./a30/vit.trt" # else: # assert False,logging.info("just support in A10,A30") # exit() # with open(vit_tagging_path, 'rb') as f: # engine_data_vit = f.read() # self.vit_tag_trt_engine = self.trt_runtime.deserialize_cuda_engine(engine_data_vit) # self.vit_tag_trt_context = self.vit_tag_trt_engine.create_execution_context() # self.stream = cuda.Stream() # TRT Implementation code stops at self.stream, proceed to the next part 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_adapter_module(self): adapter_module = getattr(self, 'adapter_module', None) if type(adapter_module) is list: adapter_module = adapter_module[0] return adapter_module def get_qformer(self): qformer = getattr(self, 'Qformer', None) if type(qformer) is list: qformer = qformer[0] return qformer def get_ln_vision(self): ln_vision = getattr(self, 'ln_vision', None) if type(ln_vision) is list: ln_vision = ln_vision[0] return ln_vision def get_query_tokens(self): query_tokens = getattr(self, 'query_tokens', None) if type(query_tokens) is list: query_tokens = query_tokens[0] return query_tokens def get_frame_position_encoding(self): frame_position_encoding = getattr(self, 'frame_position_encoding', None) if type(frame_position_encoding) is list: frame_position_encoding = frame_position_encoding[0] return frame_position_encoding 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 image_grid_pinpoints = model_args.image_grid_pinpoints self.config.mm_vision_tower = vision_tower self.config.img_size = model_args.img_size self.config.drop_path_rate = model_args.drop_path_rate self.config.vit_precision = model_args.vit_precision self.config.vit_model_path = model_args.vit_model_path self.config.num_query_token = model_args.num_query_token self.config.qformer_model_path = model_args.qformer_model_path self.config.adapter_module_name = model_args.adapter_module_name self.config.adapter_module_path = model_args.adapter_module_path self.config.max_num_segments = model_args.max_num_segments self.config.pretrain_mm_mlp_adapter = pretrain_mm_mlp_adapter # TODO: FSDP training is not ready 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_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 self.config.image_grid_pinpoints = image_grid_pinpoints if getattr(model_args, "qformer_model_path", None): if self.get_qformer() is None: self.Qformer, self.ln_vision, self.query_tokens = build_Qformer( model_args.num_query_token, self.vision_tower.hidden_size) self.frame_position_encoding = nn.Embedding( model_args.max_num_segments, self.Qformer.config.hidden_size ) self.config.mm_hidden_size = self.Qformer.config.hidden_size # self.Qformer = self.Qformer.to(torch.bfloat16) if model_args.qformer_model_path != 'from_scratch': self.load_pretrained_qformer(model_args.qformer_model_path) if getattr(model_args, 'adapter_module_name', None): if self.get_adapter_module() is None: self.adapter_module = build_adapter_module(self.config, self.vision_tower.hidden_size) self.adapter_module.load_model() self.config.mm_hidden_size = self.adapter_module.output_dim 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} def get_variable_frame_encoding_w(model_weights, load_weights): model_len = model_weights.shape[0] load_weights = {'.'.join(k.split('.')[1:]): v for k, v in load_weights.items()} load_len = load_weights['frame_position_encoding.weight'].shape[0] if model_len == load_len: return get_w(load_weights, 'frame_position_encoding') elif model_len < load_len: value = load_weights['frame_position_encoding.weight'][:model_len] return {'weight': value} else: value = model_weights.clone().cpu() value[:load_len] = load_weights['frame_position_encoding.weight'] return {'weight': value} self.mm_projector.load_state_dict(get_w(mm_projector_weights, 'mm_projector')) if self.get_frame_position_encoding(): self.frame_position_encoding.load_state_dict(get_variable_frame_encoding_w(self.frame_position_encoding.weight, mm_projector_weights)) master_print(f"Loaded pretrained parameters from {pretrain_mm_mlp_adapter}") def load_pretrained_qformer(self, model_path): if os.path.isfile(model_path): checkpoint = torch.load(model_path, map_location="cpu") else: raise RuntimeError("checkpoint path is invalid") if 'projector.bin' in model_path: state_dict = {} match_keys = ['Qformer', 'query_tokens'] for k, v in checkpoint.items(): flag = False for match_key in match_keys: if match_key in k: flag = True break if flag: state_dict[k.replace('model.', '')] = v else: state_dict = checkpoint["model"] msg = self.load_state_dict(state_dict, strict=False) master_print(f"Loaded Qformer from {model_path}") # master_print(msg) # return msg 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 the image (height, width). 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 class LlavaMetaForCausalLM(ABC): @abstractmethod def get_model(self): pass def get_vision_tower(self): return self.get_model().get_vision_tower() def get_adapter_module(self): return self.get_model().get_adapter_module() def get_ln_vision(self): return self.get_model().get_ln_vision() def get_qformer(self): return self.get_model().get_qformer() def get_query_tokens(self): return self.get_model().get_query_tokens() def get_frame_position_encoding(self): return self.get_model().get_frame_position_encoding() def encode_images(self, images): # Uncomment below to get normal output without tensorrt image_features = self.get_vision_tower()(images) #return image_features #print(image_features.shape) #print(images.shape) #exit() #print(images.shape) #exit() #-------------------- VIT CONVERSION START -------------------------- # import torch.onnx # # Initialize the model, define the input, and export to ONNX # model = self.get_model().get_vision_tower().half() # device = next(model.parameters()).device # # Move all buffers and constants to the correct device # model.to(device) # # Ensure all buffers are on the same device # # for param in model.parameters(): # # param.data = param.data.to(device) # for buffer in model.buffers(): # buffer.data = buffer.data.to(device) # # Modify any control flow that uses tensors # # For example, in the model's forward method, ensure that any tensor used in control flow is converted to int # # Create a dummy input tensor with the same shape as the input tensor you will use in your application # dummy_input = torch.randn(10, 3, 224, 224, device=device, dtype=next(model.parameters()).dtype).half() # # Export the model # onnx_path = "vit.onnx" # torch.onnx.export( # model, # dummy_input, # onnx_path, # export_params=True, # #opset_version=10, # do_constant_folding=False, # Disable constant folding, need to do this in order to get onnx file. # input_names=['input'], # output_names=['output'], # dynamic_axes={'input' : {0 : 'batch_size'}, 'output' : {0 : 'batch_size'}} # ) # print(images.shape) # exit() #--------------------- VIT CONVERSION ENDS HERE ---------------------- # Get the device of the model's parameters # device = torch.device('cuda:0') # # Initialize the model, define the input, and export to ONNX # model = self.get_model().get_vision_tower() # model = model.to(device) # # Create a dummy input tensor with the same shape as the input tensor you will use in your application # dummy_input = torch.randn(10, 3, 224, 224).to(device) # # Export the model # onnx_path = "simple_model.onnx" # torch.onnx.export( # model, # dummy_input, # onnx_path, # export_params=True, # opset_version=10, # do_constant_folding=True, # input_names=['input'], # output_names=['output'], # dynamic_axes={'input' : {0 : 'batch_size'}, 'output' : {0 : 'batch_size'}}) # #print(images.shape) # exit() if self.get_qformer(): image_features = self.get_ln_vision()(image_features) query_tokens = self.get_query_tokens() query_tokens = query_tokens.expand(image_features.shape[0], -1, -1) attn_mask = torch.ones(image_features.size()[:-1], dtype=torch.long).to(image_features.device) dtype_ = self.get_vision_tower().dtype # print(dtype_) image_features = self.qformer_fusion( query_tokens.to(dtype_), image_features.to(dtype_), attn_mask ).to(images.dtype) # image_features = self.get_model().mm_projector(image_features) return image_features def qformer_fusion(self, query_tokens, features, attn_mask=None): qformer = self.get_qformer() query_output = qformer.bert( query_embeds=query_tokens, encoder_hidden_states=features, encoder_attention_mask=attn_mask, return_dict=True ) return query_output.last_hidden_state 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 # image: list(B) of tensor[1, 3, 336, 336] # video: list(B) of tensor[N, 3, 336, 336] # video_any_res: list(B) of tensor[N, P, 3, 336, 336] 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] # video any res if images[0].ndim == 5: concat_images = torch.cat([image.flatten(0, 1) for image in images], dim=0) split_sizes = [image.shape[0:2] for image in images] else: concat_images = torch.cat([image for image in images], dim=0) split_sizes = [image.shape[0] for image in images] image_features = self.encode_images(concat_images) # add frame encoding then projector if images[0].ndim == 5: frame_ids = [] for split_size in split_sizes: frame_ids.append(torch.tensor([idx for idx in range(split_size[0]) for _ in range(split_size[1])], \ dtype=torch.long, device=image_features.device)) else: frame_ids = [torch.arange(split_size, dtype=torch.long, device=image_features.device) for split_size in split_sizes] frame_ids = torch.concat(frame_ids) frame_position_encoding = self.get_frame_position_encoding() if frame_position_encoding: frame_embeddings = frame_position_encoding(frame_ids).unsqueeze(-2) image_features += frame_embeddings # TODO: add fusion model, rewrite this part in the future adapter_module = self.get_adapter_module() if adapter_module: image_features = adapter_module(image_features, frame_ids) image_features = self.get_model().mm_projector(image_features) if images[0].ndim == 5: split_sizes = [split_size[0] * split_size[1] for split_size in split_sizes] image_features = torch.split(image_features, split_sizes, dim=0) if adapter_module: # image_features = [image_features[i].view(images[i].shape[0], images[i].shape[1], -1) for i in range(image_features.shape[0])] image_features = [x.view(im.shape[0], -1, x.shape[2]) for x, im in zip(image_features, images)] image_features = adapter_module.compress_token_per_img(image_features) 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) # if getattr(self.config, 'tune_mm_mlp_adapter', False) and getattr(self.config, 'mm_use_start_end', False): # raise NotImplementedError # TODO: Currently, all the embed_token will bu update when tune_mm_mlp_adapter = True && mm_use_start_end = True # 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 == MM_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 == MM_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 = [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_patch_token: tokenizer.add_tokens([DEFAULT_IMAGE_PATCH_TOKEN, DEFAULT_VIDEO_PATCH_TOKEN], special_tokens=True) self.resize_token_embeddings(len(tokenizer)) if model_args.mm_use_start_end: num_new_tokens = tokenizer.add_tokens([DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN, DEFAULT_VIDEO_START_TOKEN, DEFAULT_VIDEO_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 if 'gemma' in model_args.model_name_or_path: # gemma use the same embedding for input and output pass else: for p in self.get_output_embeddings().parameters(): p.requires_grad = False if model_args.pretrain_mm_mlp_adapter: # raise NotImplementedError mm_projector_weights = torch.load(model_args.pretrain_mm_mlp_adapter, map_location='cpu') mm_projector_weights = {'.'.join(k.split('.')[1:]): v for k, v in mm_projector_weights.items()} # embed_tokens_weight = mm_projector_weights['embed_tokens.weight'] # input_embeddings[:] = embed_tokens_weight # if 'gemma' in model_args.model_name_or_path: # output_embeddings[:] = embed_tokens_weight assert num_new_tokens == 4 # 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_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