246 lines
8.2 KiB
Python
246 lines
8.2 KiB
Python
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from typing import List, Optional, Tuple, Union
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from .configuration_uform_gen import VLMConfig
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import torch
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import torch.nn.functional as F
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from torch.utils.checkpoint import checkpoint
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from torch import nn
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from transformers.modeling_outputs import CausalLMOutputWithPast
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from transformers.modeling_utils import PreTrainedModel
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from transformers.models.auto.modeling_auto import AutoModelForCausalLM, AutoModel
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from transformers import AutoConfig
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from transformers.utils import logging
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from .vision_encoder import VisionEncoder
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class ImageFeaturesPooler(nn.Module):
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def __init__(self, config, text_config):
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super().__init__()
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self.pooler = nn.TransformerDecoderLayer(
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config.image_encoder_hidden_size,
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config.image_pooler_num_attn_heads,
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config.image_pooler_intermediate_size,
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activation=nn.functional.silu,
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batch_first=True,
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norm_first=True,
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)
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self.image_latents = nn.Parameter(
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torch.randn(1, config.num_image_latents, config.image_encoder_hidden_size)
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* config.initializer_range**0.5
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)
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self.projection = nn.Linear(config.image_encoder_hidden_size, text_config.hidden_size)
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def forward(self, features):
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features = self.pooler(
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self.image_latents.expand(features.size(0), -1, -1), features
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)
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return self.projection(features)
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class VLMPreTrainedModel(PreTrainedModel):
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config_class = VLMConfig
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base_model_prefix = "vlm"
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supports_gradient_checkpointing = True
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_no_split_modules = []
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_skip_keys_device_placement = "past_key_values"
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def _init_weights(self, module):
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pass
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def _initialize_weights(self, module):
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pass
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class VLMForCausalLM(VLMPreTrainedModel):
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def __init__(self, config: VLMConfig):
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super().__init__(config)
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self.config = config
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self.text_config = AutoConfig.from_pretrained(
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config.text_decoder_name_or_path,
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trust_remote_code=True
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)
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self.text_decoder = AutoModelForCausalLM.from_config(
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self.text_config,
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trust_remote_code=True
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)
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self.image_encoder = VisionEncoder(
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config.image_encoder_hidden_size,
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config.image_encoder_patch_size,
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config.image_encoder_num_layers,
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config.image_encoder_num_heads,
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)
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self.image_pooler = ImageFeaturesPooler(config, self.text_config)
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def get_input_embeddings(self):
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return self.text_decoder.get_input_embeddings()
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def set_input_embeddings(self, value):
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self.text_decoder.set_input_embeddings(value)
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def get_images_embeddings(self, images):
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features = self.image_encoder(images)
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return self.image_pooler(features)
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def gather_continuous_embeddings(
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self,
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input_ids: torch.Tensor,
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word_embeddings: torch.Tensor,
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image_embeddings: torch.Tensor
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) -> torch.Tensor:
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start_indices = (input_ids == self.config.image_token_id).nonzero()[:, 1]
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embeddings = []
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for sample_idx, start_idx in enumerate(start_indices.tolist()):
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embeddings.append(
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torch.cat(
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(
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word_embeddings[sample_idx, :start_idx],
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image_embeddings[sample_idx],
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word_embeddings[sample_idx, start_idx + 1 :],
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),
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dim=0,
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)
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)
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return torch.stack(embeddings, dim=0)
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def forward(
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self,
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input_ids: torch.LongTensor = None,
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images: torch.Tensor = None,
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attention_mask: Optional[torch.Tensor] = None,
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position_ids: Optional[torch.LongTensor] = 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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use_cache: Optional[bool] = None,
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labels: Optional[torch.Tensor] = None,
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output_attentions: Optional[bool] = None,
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output_hidden_states: Optional[bool] = None,
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return_dict: Optional[bool] = None
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) -> Union[dict, Tuple, CausalLMOutputWithPast]:
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output_attentions = (
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output_attentions
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if output_attentions is not None
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else self.config.output_attentions
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)
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output_hidden_states = (
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output_hidden_states
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if output_hidden_states is not None
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else self.config.output_hidden_states
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)
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use_cache = use_cache if use_cache is not None else self.config.use_cache
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return_dict = (
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return_dict if return_dict is not None else self.config.use_return_dict
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)
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if input_ids is not None and inputs_embeds is not None:
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raise ValueError(
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"You cannot specify both input_ids and inputs_embeds at the same time"
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)
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elif input_ids is None and inputs_embeds is None:
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raise ValueError("You have to specify either input_is or inputs_embeds")
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if inputs_embeds is None and past_key_values is None:
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inputs_embeds = self.get_input_embeddings()(input_ids)
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if images is not None:
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image_embeds = self.get_images_embeddings(images)
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inputs_embeds = self.gather_continuous_embeddings(
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input_ids,
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inputs_embeds,
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image_embeds
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)
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if position_ids is None:
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seq_length = (
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inputs_embeds.shape[1]
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if inputs_embeds is not None
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else input_ids.shape[1]
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)
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past_key_values_length = 0
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if past_key_values is not None:
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past_key_values_length = past_key_values[0][0].shape[2]
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device = input_ids.device if input_ids is not None else inputs_embeds.device
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position_ids = torch.arange(
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past_key_values_length,
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seq_length + past_key_values_length,
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dtype=torch.long,
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device=device,
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)
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position_ids = position_ids.unsqueeze(0)
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outputs = self.text_decoder(
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inputs_embeds=inputs_embeds,
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input_ids=input_ids if past_key_values is not None else None,
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attention_mask=attention_mask,
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position_ids=position_ids,
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past_key_values=past_key_values,
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output_attentions=output_attentions,
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output_hidden_states=output_hidden_states,
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use_cache=use_cache,
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return_dict=return_dict,
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)
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return outputs
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def prepare_inputs_for_generation(
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self,
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input_ids,
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images=None,
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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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):
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if past_key_values:
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input_ids = input_ids[:, -1:]
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position_ids = kwargs.get("position_ids", None)
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if attention_mask is not None and position_ids is None:
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# create position_ids on the fly for batch generation
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position_ids = attention_mask.long().cumsum(-1) - 1
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position_ids.masked_fill_(attention_mask == 0, 1)
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if past_key_values:
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position_ids = position_ids[:, -1].unsqueeze(-1)
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# if `inputs_embeds` are passed, we only want to use 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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n_samples = inputs_embeds.shape[0]
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else:
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model_inputs = {"input_ids": input_ids}
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n_samples = input_ids.shape[0]
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if images is not None:
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model_inputs["images"] = images
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model_inputs.update(
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{
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"position_ids": position_ids,
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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": images if past_key_values is None else None,
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}
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)
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return model_inputs
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@classmethod
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def from_config(cls, config, **kwargs):
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return cls._from_config(config, **kwargs)
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VLMConfig.register_for_auto_class()
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VLMForCausalLM.register_for_auto_class("AutoModel")
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