239 lines
10 KiB
Python
239 lines
10 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 typing import List, Optional, Union
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import torch
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import torch.nn as nn
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from transformers import PreTrainedModel
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DEFAULT_IMAGE_TOKEN = '<image>'
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DEFAULT_IMAGE_PATCH_TOKEN = '<im_patch>'
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DEFAULT_IM_START_TOKEN = '<im_start>'
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DEFAULT_IM_END_TOKEN = '<im_end>'
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class LlavaLlamaForCausalLM(PreTrainedModel):
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def __init__(self,
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vision_encoder,
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lang_encoder,
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mm_hidden_size,
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use_im_start_end=True,
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use_mm_proj=True,
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im_start_token: Optional[int] = None,
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im_end_token: Optional[int] = None,
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im_patch_token: Optional[int] = None,
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mm_vision_select_layer: int = -1):
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super().__init__(lang_encoder.config)
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self.vision_tower = vision_encoder
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self.lang_encoder = lang_encoder
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self.use_im_start_end = use_im_start_end
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self.im_start_token = im_start_token
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self.im_end_token = im_end_token
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self.im_patch_token = im_patch_token
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self.mm_hidden_size = mm_hidden_size
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self.mm_vision_select_layer = mm_vision_select_layer
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self.lang_hidden_size = lang_encoder.config.hidden_size
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if use_mm_proj and not hasattr(lang_encoder.model, 'mm_projector'):
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mm_projector = nn.Linear(self.mm_hidden_size,
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self.lang_hidden_size)
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self.lang_encoder.model.add_module('mm_projector', mm_projector)
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elif not use_mm_proj:
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self.lang_encoder.model.add_module('mm_projector', nn.Identity())
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self.post_init()
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def forward(
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self,
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input_ids: torch.LongTensor = None,
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attention_mask: Optional[torch.Tensor] = None,
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past_key_values: Optional[List[torch.FloatTensor]] = None,
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inputs_embeds: Optional[torch.FloatTensor] = None,
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labels: Optional[torch.LongTensor] = None,
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use_cache: Optional[bool] = None,
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output_attentions: Optional[bool] = None,
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output_hidden_states: Optional[bool] = None,
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images: Optional[torch.FloatTensor] = None,
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return_dict: Optional[bool] = None,
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):
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output_attentions = (
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output_attentions if output_attentions is not None else
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self.config.output_attentions)
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output_hidden_states = (
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output_hidden_states if output_hidden_states is not None else
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self.config.output_hidden_states)
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return_dict = (
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return_dict
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if return_dict is not None else self.config.use_return_dict)
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# decoder outputs consists of
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# (dec_features, layer_state, dec_hidden, dec_attn)
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if inputs_embeds is None:
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inputs_embeds = self.lang_encoder.model.embed_tokens(input_ids)
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inputs_embeds = self.forward_vision_tower(input_ids, inputs_embeds,
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images)
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return self.lang_encoder(
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input_ids=None,
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attention_mask=attention_mask,
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past_key_values=past_key_values,
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inputs_embeds=inputs_embeds,
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use_cache=use_cache,
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output_attentions=output_attentions,
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output_hidden_states=output_hidden_states,
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return_dict=return_dict,
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labels=labels,
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)
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def prepare_inputs_for_generation(self,
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input_ids,
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past_key_values=None,
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attention_mask=None,
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inputs_embeds=None,
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**kwargs):
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if past_key_values:
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input_ids = input_ids[:, -1:]
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# if `inputs_embeds` are passed, we only want to use
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# them in the 1st generation step
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if inputs_embeds is not None and past_key_values is None:
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model_inputs = {'inputs_embeds': inputs_embeds}
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else:
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model_inputs = {'input_ids': input_ids}
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model_inputs.update({
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'past_key_values': past_key_values,
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'use_cache': kwargs.get('use_cache'),
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'attention_mask': attention_mask,
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'images': kwargs.get('images', None),
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})
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return model_inputs
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def forward_vision_tower(
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self,
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input_ids: torch.LongTensor,
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inputs_embeds: torch.FloatTensor,
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images: Union[torch.FloatTensor, list, None] = None,
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):
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if self.use_im_start_end:
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assert self.im_start_token is not None
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assert self.im_end_token is not None
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if images is not None:
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assert self.im_patch_token is not None
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if self.vision_tower is None or images is None or (
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input_ids.shape[1] == 1 and not self.training):
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return inputs_embeds
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with torch.no_grad():
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if isinstance(images, (list, tuple)):
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# variable length images
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image_features = []
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for image in images:
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feats = self.vision_tower(image.unsqueeze(0))
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image_feature = feats[self.mm_vision_select_layer][:, 1:]
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image_features.append(image_feature)
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else:
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feats = self.vision_tower(images)
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image_features = feats[self.mm_vision_select_layer][:, 1:]
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mm_projector = self.lang_encoder.model.mm_projector
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if isinstance(images, (list, tuple)):
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image_features = [
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mm_projector(image_feature)[0]
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for image_feature in image_features
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]
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else:
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image_features = mm_projector(image_features)
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dummy_image_features = torch.zeros(
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256, 1024, device=inputs_embeds.device, dtype=inputs_embeds.dtype)
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dummy_image_features = mm_projector(dummy_image_features)
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new_input_embeds = []
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cur_image_idx = 0
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for cur_input_ids, cur_input_embeds in zip(input_ids, inputs_embeds):
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if (cur_input_ids != self.im_patch_token).all():
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# multimodal LLM, but the current sample is not multimodal
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cur_input_embeds = cur_input_embeds + (
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0. * dummy_image_features).sum()
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new_input_embeds.append(cur_input_embeds)
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cur_image_idx += 1
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continue
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if self.use_im_start_end:
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cur_image_features = image_features[cur_image_idx]
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num_patches = cur_image_features.shape[0]
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if (cur_input_ids == self.im_start_token).sum() != (
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cur_input_ids == self.im_end_token).sum():
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raise ValueError('The number of image start tokens and '
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'image end tokens should be the same.')
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image_start_tokens = torch.where(
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cur_input_ids == self.im_start_token)[0]
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for image_start_token_pos in image_start_tokens:
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cur_image_features = image_features[cur_image_idx].to(
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device=cur_input_embeds.device)
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num_patches = cur_image_features.shape[0]
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if cur_input_ids[image_start_token_pos + num_patches +
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1] != self.im_end_token:
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raise ValueError('The image end token should follow '
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'the image start token.')
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cur_new_input_embeds = torch.cat(
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(cur_input_embeds[:image_start_token_pos + 1],
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cur_image_features,
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cur_input_embeds[image_start_token_pos + num_patches +
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1:]),
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dim=0)
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cur_image_idx += 1
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new_input_embeds.append(cur_new_input_embeds)
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else:
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cur_image_features = image_features[cur_image_idx]
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num_patches = cur_image_features.shape[0]
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if (cur_input_ids == self.im_patch_token).sum() != num_patches:
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print(f'Debug: num_patches: {num_patches}')
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raise ValueError(
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'The number of image patch tokens should '
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'be the same as the number of image patches.')
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masked_indices = torch.where(
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cur_input_ids == self.im_patch_token)[0]
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mask_index_start = masked_indices[0]
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if (masked_indices != torch.arange(
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mask_index_start,
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mask_index_start + num_patches,
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device=masked_indices.device,
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dtype=masked_indices.dtype)).any():
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raise ValueError(
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'The image patch tokens should be consecutive.')
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cur_new_input_embeds = torch.cat(
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(cur_input_embeds[:mask_index_start], cur_image_features,
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cur_input_embeds[mask_index_start + num_patches:]),
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dim=0)
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new_input_embeds.append(cur_new_input_embeds)
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cur_image_idx += 1
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inputs_embeds = torch.stack(new_input_embeds, dim=0)
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return inputs_embeds
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@staticmethod
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def _reorder_cache(past_key_values, beam_idx):
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reordered_past = ()
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for layer_past in past_key_values:
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reordered_past += (tuple(
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past_state.index_select(0, beam_idx)
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for past_state in layer_past), )
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return reordered_past
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