256 lines
14 KiB
Python
256 lines
14 KiB
Python
# Copyright 2023 Haotian Liu
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from abc import ABC, abstractmethod
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import torch
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import torch.nn as nn
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from .multimodal_encoder.builder import build_vision_tower
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from .multimodal_projector.builder import build_vision_projector
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from llava.constants import IGNORE_INDEX, IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_PATCH_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN
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class LlavaMetaModel:
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def __init__(self, config):
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super(LlavaMetaModel, self).__init__(config)
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if hasattr(config, "mm_vision_tower"):
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self.vision_tower = build_vision_tower(config, delay_load=True)
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self.mm_projector = build_vision_projector(config)
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def get_vision_tower(self):
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vision_tower = getattr(self, 'vision_tower', None)
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if type(vision_tower) is list:
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vision_tower = vision_tower[0]
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return vision_tower
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def initialize_vision_modules(self, model_args, fsdp=None):
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vision_tower = model_args.vision_tower
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mm_vision_select_layer = model_args.mm_vision_select_layer
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mm_vision_select_feature = model_args.mm_vision_select_feature
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pretrain_mm_mlp_adapter = model_args.pretrain_mm_mlp_adapter
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self.config.mm_vision_tower = vision_tower
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if self.get_vision_tower() is None:
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vision_tower = build_vision_tower(model_args)
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if fsdp is not None and len(fsdp) > 0:
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self.vision_tower = [vision_tower]
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else:
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self.vision_tower = vision_tower
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else:
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if fsdp is not None and len(fsdp) > 0:
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vision_tower = self.vision_tower[0]
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else:
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vision_tower = self.vision_tower
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vision_tower.load_model()
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self.config.use_mm_proj = True
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self.config.mm_projector_type = getattr(model_args, 'mm_projector_type', 'linear')
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self.config.mm_hidden_size = vision_tower.hidden_size
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self.config.mm_vision_select_layer = mm_vision_select_layer
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self.config.mm_vision_select_feature = mm_vision_select_feature
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if getattr(self, 'mm_projector', None) is None:
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self.mm_projector = build_vision_projector(self.config)
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if pretrain_mm_mlp_adapter is not None:
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mm_projector_weights = torch.load(pretrain_mm_mlp_adapter, map_location='cpu')
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def get_w(weights, keyword):
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return {k.split(keyword + '.')[1]: v for k, v in weights.items() if keyword in k}
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self.mm_projector.load_state_dict(get_w(mm_projector_weights, 'mm_projector'))
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class LlavaMetaForCausalLM(ABC):
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@abstractmethod
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def get_model(self):
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pass
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def get_vision_tower(self):
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return self.get_model().get_vision_tower()
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def encode_images(self, images):
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image_features = self.get_model().get_vision_tower()(images)
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image_features = self.get_model().mm_projector(image_features)
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return image_features
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def prepare_inputs_labels_for_multimodal(
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self, input_ids, attention_mask, past_key_values, labels, images
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):
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vision_tower = self.get_vision_tower()
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if vision_tower is None or images is None or input_ids.shape[1] == 1:
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if past_key_values is not None and vision_tower is not None and images is not None and input_ids.shape[1] == 1:
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attention_mask = torch.ones((attention_mask.shape[0], past_key_values[-1][-1].shape[-2] + 1), dtype=attention_mask.dtype, device=attention_mask.device)
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return input_ids, attention_mask, past_key_values, None, labels
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if type(images) is list or images.ndim == 5:
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concat_images = torch.cat([image for image in images], dim=0)
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image_features = self.encode_images(concat_images)
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split_sizes = [image.shape[0] for image in images]
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image_features = torch.split(image_features, split_sizes, dim=0)
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image_features = [x.flatten(0, 1) for x in image_features]
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else:
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image_features = self.encode_images(images)
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new_input_embeds = []
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new_labels = [] if labels is not None else None
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cur_image_idx = 0
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for batch_idx, cur_input_ids in enumerate(input_ids):
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if (cur_input_ids == IMAGE_TOKEN_INDEX).sum() == 0:
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# multimodal LLM, but the current sample is not multimodal
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# FIXME: this is a hacky fix, for deepspeed zero3 to work
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half_len = cur_input_ids.shape[0] // 2
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cur_image_features = image_features[cur_image_idx]
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cur_input_embeds_1 = self.get_model().embed_tokens(cur_input_ids[:half_len])
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cur_input_embeds_2 = self.get_model().embed_tokens(cur_input_ids[half_len:])
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cur_input_embeds = torch.cat([cur_input_embeds_1, cur_image_features[0:0], cur_input_embeds_2], dim=0)
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new_input_embeds.append(cur_input_embeds)
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if labels is not None:
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new_labels.append(labels[batch_idx])
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cur_image_idx += 1
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continue
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image_token_indices = torch.where(cur_input_ids == IMAGE_TOKEN_INDEX)[0]
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cur_new_input_embeds = []
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if labels is not None:
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cur_labels = labels[batch_idx]
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cur_new_labels = []
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assert cur_labels.shape == cur_input_ids.shape
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while image_token_indices.numel() > 0:
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cur_image_features = image_features[cur_image_idx]
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image_token_start = image_token_indices[0]
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if getattr(self.config, 'tune_mm_mlp_adapter', False) and getattr(self.config, 'mm_use_im_start_end', False):
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cur_new_input_embeds.append(self.get_model().embed_tokens(cur_input_ids[:image_token_start-1]).detach())
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cur_new_input_embeds.append(self.get_model().embed_tokens(cur_input_ids[image_token_start-1:image_token_start]))
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cur_new_input_embeds.append(cur_image_features)
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cur_new_input_embeds.append(self.get_model().embed_tokens(cur_input_ids[image_token_start+1:image_token_start+2]))
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if labels is not None:
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cur_new_labels.append(cur_labels[:image_token_start])
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cur_new_labels.append(torch.full((cur_image_features.shape[0],), IGNORE_INDEX, device=labels.device, dtype=labels.dtype))
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cur_new_labels.append(cur_labels[image_token_start:image_token_start+1])
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cur_labels = cur_labels[image_token_start+2:]
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else:
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cur_new_input_embeds.append(self.get_model().embed_tokens(cur_input_ids[:image_token_start]))
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cur_new_input_embeds.append(cur_image_features)
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if labels is not None:
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cur_new_labels.append(cur_labels[:image_token_start])
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cur_new_labels.append(torch.full((cur_image_features.shape[0],), IGNORE_INDEX, device=labels.device, dtype=labels.dtype))
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cur_labels = cur_labels[image_token_start+1:]
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cur_image_idx += 1
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if getattr(self.config, 'tune_mm_mlp_adapter', False) and getattr(self.config, 'mm_use_im_start_end', False):
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cur_input_ids = cur_input_ids[image_token_start+2:]
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else:
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cur_input_ids = cur_input_ids[image_token_start+1:]
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image_token_indices = torch.where(cur_input_ids == IMAGE_TOKEN_INDEX)[0]
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if cur_input_ids.numel() > 0:
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if getattr(self.config, 'tune_mm_mlp_adapter', False) and getattr(self.config, 'mm_use_im_start_end', False):
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cur_new_input_embeds.append(self.get_model().embed_tokens(cur_input_ids).detach())
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else:
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cur_new_input_embeds.append(self.get_model().embed_tokens(cur_input_ids))
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if labels is not None:
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cur_new_labels.append(cur_labels)
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cur_new_input_embeds = [x.to(device=self.device) for x in cur_new_input_embeds]
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cur_new_input_embeds = torch.cat(cur_new_input_embeds, dim=0)
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new_input_embeds.append(cur_new_input_embeds)
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if labels is not None:
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cur_new_labels = torch.cat(cur_new_labels, dim=0)
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new_labels.append(cur_new_labels)
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if any(x.shape != new_input_embeds[0].shape for x in new_input_embeds):
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max_len = max(x.shape[0] for x in new_input_embeds)
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new_input_embeds_align = []
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for cur_new_embed in new_input_embeds:
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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)
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new_input_embeds_align.append(cur_new_embed)
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new_input_embeds = torch.stack(new_input_embeds_align, dim=0)
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if labels is not None:
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new_labels_align = []
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_new_labels = new_labels
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for cur_new_label in new_labels:
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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)
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new_labels_align.append(cur_new_label)
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new_labels = torch.stack(new_labels_align, dim=0)
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if attention_mask is not None:
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new_attention_mask = []
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for cur_attention_mask, cur_new_labels, cur_new_labels_align in zip(attention_mask, _new_labels, new_labels):
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new_attn_mask_pad_left = torch.full((cur_new_labels.shape[0] - labels.shape[1],), True, dtype=attention_mask.dtype, device=attention_mask.device)
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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)
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cur_new_attention_mask = torch.cat((new_attn_mask_pad_left, cur_attention_mask, new_attn_mask_pad_right), dim=0)
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new_attention_mask.append(cur_new_attention_mask)
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attention_mask = torch.stack(new_attention_mask, dim=0)
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assert attention_mask.shape == new_labels.shape
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else:
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new_input_embeds = torch.stack(new_input_embeds, dim=0)
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if labels is not None:
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new_labels = torch.stack(new_labels, dim=0)
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if attention_mask is not None:
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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)
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attention_mask = torch.cat((new_attn_mask_pad_left, attention_mask), dim=1)
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assert attention_mask.shape == new_input_embeds.shape[:2]
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return None, attention_mask, past_key_values, new_input_embeds, new_labels
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def initialize_vision_tokenizer(self, model_args, tokenizer):
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if model_args.mm_use_im_patch_token:
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tokenizer.add_tokens([DEFAULT_IMAGE_PATCH_TOKEN], special_tokens=True)
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self.resize_token_embeddings(len(tokenizer))
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if model_args.mm_use_im_start_end:
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num_new_tokens = tokenizer.add_tokens([DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN], special_tokens=True)
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self.resize_token_embeddings(len(tokenizer))
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if num_new_tokens > 0:
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input_embeddings = self.get_input_embeddings().weight.data
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output_embeddings = self.get_output_embeddings().weight.data
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input_embeddings_avg = input_embeddings[:-num_new_tokens].mean(
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dim=0, keepdim=True)
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output_embeddings_avg = output_embeddings[:-num_new_tokens].mean(
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dim=0, keepdim=True)
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input_embeddings[-num_new_tokens:] = input_embeddings_avg
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output_embeddings[-num_new_tokens:] = output_embeddings_avg
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if model_args.tune_mm_mlp_adapter:
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for p in self.get_input_embeddings().parameters():
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p.requires_grad = True
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for p in self.get_output_embeddings().parameters():
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p.requires_grad = False
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if model_args.pretrain_mm_mlp_adapter:
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mm_projector_weights = torch.load(model_args.pretrain_mm_mlp_adapter, map_location='cpu')
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embed_tokens_weight = mm_projector_weights['model.embed_tokens.weight']
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assert num_new_tokens == 2
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if input_embeddings.shape == embed_tokens_weight.shape:
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input_embeddings[-num_new_tokens:] = embed_tokens_weight[-num_new_tokens:]
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elif embed_tokens_weight.shape[0] == num_new_tokens:
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input_embeddings[-num_new_tokens:] = embed_tokens_weight
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else:
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raise ValueError(f"Unexpected embed_tokens_weight shape. Pretrained: {embed_tokens_weight.shape}. Current: {input_embeddings.shape}. Numer of new tokens: {num_new_tokens}.")
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elif model_args.mm_use_im_patch_token:
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if model_args.tune_mm_mlp_adapter:
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for p in self.get_input_embeddings().parameters():
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p.requires_grad = False
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for p in self.get_output_embeddings().parameters():
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p.requires_grad = False
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