# 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 math import re import time import torch import torch.nn as nn from ola_vlm.model.llava_one.multimodal_encoder.builder import build_vision_tower from ola_vlm.model.llava_one.multimodal_resampler.builder import build_vision_resampler from ola_vlm.model.llava_one.multimodal_projector.builder import build_vision_projector from ola_vlm.constants import IGNORE_INDEX, IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_PATCH_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN from ola_vlm.mm_utils import get_anyres_image_grid_shape # from ola_vlm.utils import rank0_print import random class LlavaMetaModel: def __init__(self, config): super(LlavaMetaModel, self).__init__(config) if hasattr(config, "mm_vision_tower"): delay_load = getattr(config, "delay_load", False) self.vision_tower = build_vision_tower(config, delay_load=delay_load) 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) if "unpad" in getattr(config, "mm_patch_merge_type", ""): self.image_newline = nn.Parameter(torch.empty(config.hidden_size, dtype=self.dtype)) 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 mm_patch_merge_type = model_args.mm_patch_merge_type self.config.mm_vision_tower = vision_tower self.config.vision_tower_pretrained = getattr(model_args, "vision_tower_pretrained", "") 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) 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 self.config.mm_patch_merge_type = mm_patch_merge_type if not hasattr(self.config, 'add_faster_video'): if model_args.add_faster_video: embed_std = 1 / torch.sqrt(torch.tensor(self.config.hidden_size, dtype=self.dtype)) self.faster_token = nn.Parameter( torch.randn(self.config.hidden_size, dtype=self.dtype) * embed_std ) if getattr(self, "mm_projector", None) is None: self.mm_projector = build_vision_projector(self.config, vision_cfg=vision_tower.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} incompatible_keys = self.mm_projector.load_state_dict(get_w(mm_projector_weights, "mm_projector")) # rank0_print(f"Loaded mm projector weights from {pretrain_mm_mlp_adapter}. Incompatible keys: {incompatible_keys}") incompatible_keys = self.vision_resampler.load_state_dict(get_w(mm_projector_weights, "vision_resampler"), strict=False) # rank0_print(f"Loaded vision resampler weights from {pretrain_mm_mlp_adapter}. Incompatible keys: {incompatible_keys}") 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:] # Compute aspect ratios original_aspect_ratio = original_width / original_height current_aspect_ratio = current_width / current_height # Determine padding size and direction if original_aspect_ratio > current_aspect_ratio: # Padding was added to the height 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: # Padding was added to the width 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_2dPool(self, image_feature, stride=2): height = width = self.get_vision_tower().num_patches_per_side num_frames, num_tokens, num_dim = image_feature.shape image_feature = image_feature.view(num_frames, height, width, -1) image_feature = image_feature.permute(0, 3, 1, 2).contiguous() # image_feature = nn.functional.max_pool2d(image_feature, self.config.mm_spatial_pool_stride) if self.config.mm_spatial_pool_mode == "average": image_feature = nn.functional.avg_pool2d(image_feature, stride) elif self.config.mm_spatial_pool_mode == "max": image_feature = nn.functional.max_pool2d(image_feature, stride) elif self.config.mm_spatial_pool_mode == "bilinear": height, width = image_feature.shape[2:] scaled_shape = [math.ceil(height / stride), math.ceil(width / stride)] image_feature = nn.functional.interpolate(image_feature, size=scaled_shape, mode='bilinear') else: raise ValueError(f"Unexpected mm_spatial_pool_mode: {self.config.mm_spatial_pool_mode}") image_feature = image_feature.permute(0, 2, 3, 1) image_feature = image_feature.view(num_frames, -1, num_dim) return image_feature def encode_images(self, images): image_features = self.get_model().get_vision_tower()(images) # image_features = self.get_model().vision_resampler(image_features, images=images) image_features = self.get_model().mm_projector(image_features) return image_features def encode_multimodals(self, videos_or_images, video_idx_in_batch, split_sizes=None): videos_or_images_features = self.get_model().get_vision_tower()(videos_or_images) per_videos_or_images_features = torch.split(videos_or_images_features, split_sizes, dim=0) # tuple, (dim_1, 576, 4096) all_videos_or_images_features = [] all_faster_video_features = [] cur_mm_spatial_pool_stride = self.config.mm_spatial_pool_stride for idx, feat in enumerate(per_videos_or_images_features): feat = self.get_model().mm_projector(feat) faster_video_feature = 0 slower_img_feat = 0 if idx in video_idx_in_batch and cur_mm_spatial_pool_stride > 1: slower_img_feat = self.get_2dPool(feat,cur_mm_spatial_pool_stride) if self.config.add_faster_video: cur_mm_spatial_pool_stride = cur_mm_spatial_pool_stride * 2 faster_video_feature = self.get_2dPool(feat,cur_mm_spatial_pool_stride) if slower_img_feat != 0: all_videos_or_images_features.append(slower_img_feat) else: all_videos_or_images_features.append(feat) all_faster_video_features.append(faster_video_feature) return all_videos_or_images_features,all_faster_video_features def add_token_per_grid(self, image_feature): resize_h = int(math.sqrt(image_feature.shape[1])) num_frames = image_feature.shape[0] feature_dim = image_feature.shape[-1] image_feature = image_feature.view(num_frames, 1, resize_h, resize_h, -1) image_feature = image_feature.permute(4, 0, 2, 1, 3).contiguous() image_feature = image_feature.flatten(1, 2).flatten(2, 3) image_feature = torch.cat((image_feature, self.model.image_newline[:, None, None].expand(*image_feature.shape[:-1], 1).to(image_feature.device)), dim=-1) if getattr(self.config, "add_faster_video", False): # import pdb; pdb.set_trace() # (3584, 832, 14) -> (3584, 64, 13, 14) image_feature = image_feature.view(feature_dim, num_frames,resize_h, -1) # (3584, 64, 13, 14) -> (64, 13, 14, 3584) image_feature = image_feature.permute(1, 2, 3, 0).contiguous() # (64, 13, 14, 3584) -> (64, 13*14, 3584) image_feature = image_feature.flatten(1, 2) # import pdb; pdb.set_trace() return image_feature # import pdb; pdb.set_trace() image_feature = image_feature.flatten(1, 2).transpose(0, 1) return image_feature def add_token_per_frame(self, image_feature): image_feature = image_feature.permute(2, 0, 1).contiguous() 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.permute(1, 2, 0).contiguous() return image_feature 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() # rank_print(modalities) 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, None modalities = ["image"] * len(images) if isinstance(modalities, str): modalities = [modalities] # import pdb; pdb.set_trace() 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_idx_in_batch = [] for _ in range(len(modalities)): if modalities[_] == "video": video_idx_in_batch.append(_) images_list = [] for image in images: if image.ndim == 4: images_list.append(image) else: images_list.append(image.unsqueeze(0)) concat_images = torch.cat([image for image in images_list], dim=0) split_sizes = [image.shape[0] for image in images_list] encoded_image_features = self.encode_images(concat_images) # image_features,all_faster_video_features = self.encode_multimodals(concat_images, video_idx_in_batch, split_sizes) # This is a list, each element is [num_images, patch * patch, dim] # rank_print(f"Concat images : {concat_images.shape}") encoded_image_features = torch.split(encoded_image_features, split_sizes) image_features = [] for idx, image_feat in enumerate(encoded_image_features): if idx in video_idx_in_batch: image_features.append(self.get_2dPool(image_feat)) else: image_features.append(image_feat) # image_features = self.encode_multimodals(concat_images, video_idx_in_batch, split_sizes) # rank_print(f"Encoded image feats : {[x.shape for x in image_features]}") # image_features = torch.split(image_features, split_sizes, dim=0) mm_patch_merge_type = getattr(self.config, "mm_patch_merge_type", "flat") image_aspect_ratio = getattr(self.config, "image_aspect_ratio", "square") mm_newline_position = getattr(self.config, "mm_newline_position", "one_token") 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): # FIXME: now assume the image is square, and split to 2x2 patches # num_patches = h * w, where h = w = sqrt(num_patches) # currently image_feature is a tensor of shape (4, num_patches, hidden_size) # we want to first unflatten it to (2, 2, h, w, hidden_size) # rank0_print("At least we are reaching here") # import pdb; pdb.set_trace() if image_idx in video_idx_in_batch: # video operations # rank0_print("Video") if mm_newline_position == "grid": # Grid-wise image_feature = self.add_token_per_grid(image_feature) if getattr(self.config, "add_faster_video", False): faster_video_feature = self.add_token_per_grid(all_faster_video_features[image_idx]) # Add a token for each frame concat_slow_fater_token = [] # import pdb; pdb.set_trace() for _ in range(image_feature.shape[0]): if _ % self.config.faster_token_stride == 0: concat_slow_fater_token.append(torch.cat((image_feature[_], self.model.faster_token[None].to(image_feature.device)), dim=0)) else: concat_slow_fater_token.append(torch.cat((faster_video_feature[_], self.model.faster_token[None].to(image_feature.device)), dim=0)) # import pdb; pdb.set_trace() image_feature = torch.cat(concat_slow_fater_token) # print("!!!!!!!!!!!!") new_image_features.append(image_feature) elif mm_newline_position == "frame": # Frame-wise image_feature = self.add_token_per_frame(image_feature) new_image_features.append(image_feature.flatten(0, 1)) elif mm_newline_position == "one_token": # one-token image_feature = image_feature.flatten(0, 1) 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) elif mm_newline_position == "no_token": new_image_features.append(image_feature.flatten(0, 1)) else: raise ValueError(f"Unexpected mm_newline_position: {mm_newline_position}") elif image_feature.shape[0] > 1: # multi patches and multi images operations # rank0_print("Single-images") 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 "anyres_max" in image_aspect_ratio: matched_anyres_max_num_patches = re.match(r"anyres_max_(\d+)", image_aspect_ratio) if matched_anyres_max_num_patches: max_num_patches = int(matched_anyres_max_num_patches.group(1)) if image_aspect_ratio == "anyres" or "anyres_max" in image_aspect_ratio: if hasattr(self.get_vision_tower(), "image_size"): vision_tower_image_size = self.get_vision_tower().image_size else: raise ValueError("vision_tower_image_size is not found in the vision tower.") try: num_patch_width, num_patch_height = get_anyres_image_grid_shape(image_sizes[image_idx], self.config.image_grid_pinpoints, vision_tower_image_size) except Exception as e: print(f"Error: {e}") num_patch_width, num_patch_height = 2, 2 image_feature = image_feature.view(num_patch_height, num_patch_width, height, width, -1) else: image_feature = image_feature.view(2, 2, height, width, -1) if "maxpool2x2" 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 = nn.functional.max_pool2d(image_feature, 2) image_feature = image_feature.flatten(1, 2).transpose(0, 1) elif "unpad" in mm_patch_merge_type and "anyres_max" in image_aspect_ratio and matched_anyres_max_num_patches: unit = image_feature.shape[2] 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]) c, h, w = image_feature.shape times = math.sqrt(h * w / (max_num_patches * unit**2)) if times > 1.1: image_feature = image_feature[None] image_feature = nn.functional.interpolate(image_feature, [int(h // times), int(w // times)], mode="bilinear")[0] 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) elif "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) if "nobase" in mm_patch_merge_type: pass else: image_feature = torch.cat((base_image_feature, image_feature), dim=0) new_image_features.append(image_feature) else: # single image operations image_feature = image_feature[0] if "unpad" in mm_patch_merge_type: image_feature = torch.cat((image_feature, self.model.image_newline[None]), 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) # 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 # rank_print(f"Total images : {len(image_features)}") # 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 # rank_print("Inserting Images embedding") for batch_idx, cur_input_ids in enumerate(input_ids): num_images = (cur_input_ids == IMAGE_TOKEN_INDEX).sum() # rank0_print(num_images) 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 = [] 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: try: cur_image_features = image_features[cur_image_idx] except IndexError: cur_image_features = image_features[cur_image_idx - 1] 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] # import pdb; pdb.set_trace() 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) # rank_print("Finishing Inserting") new_input_embeds = [x[:tokenizer_model_max_length] for x, modality in zip(new_input_embeds, modalities)] new_labels = [x[:tokenizer_model_max_length] for x, modality in zip(new_labels, modalities)] # TODO: Hard code for control loss spike # if tokenizer_model_max_length is not None: # new_input_embeds = [x[:4096] if modality != "video" else x[:tokenizer_model_max_length] for x, modality in zip(new_input_embeds, modalities)] # new_labels = [x[:4096] if modality != "video" else x[:tokenizer_model_max_length] for x, modality in zip(new_labels, modalities)] # 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) # rank0_print("Prepare pos id") 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) # rank0_print("tokenizer padding") 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 if getattr(self.config, "use_pos_skipping", False) and self.training: position_ids = torch.arange(new_input_embeds.size(1), device=new_input_embeds.device).unsqueeze(0).to(new_input_embeds.device) split_position = random.randint(0, new_input_embeds.size(1)) left_add = random.randint(0, self.config.pos_skipping_range) right_add = random.randint(left_add, self.config.pos_skipping_range) position_ids[:, :split_position] += left_add position_ids[:, split_position:] += right_add # import pdb; pdb.set_trace() # rank0_print("Finish preparing") return None, position_ids, attention_mask, past_key_values, new_input_embeds, new_labels, None 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