387 lines
17 KiB
Python
387 lines
17 KiB
Python
# coding=utf-8
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# Copyright 2023 HuggingFace Inc. team. All rights reserved.
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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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"""PyTorch Fuyu model."""
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from typing import Optional, Union
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import torch
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import torch.utils.checkpoint
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from torch import nn
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from ...cache_utils import Cache
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from ...generation import GenerationMixin
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from ...modeling_outputs import CausalLMOutputWithPast
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from ...modeling_utils import PreTrainedModel
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from ...models.auto.modeling_auto import AutoModel
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from ...utils import auto_docstring, can_return_tuple, logging
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from .configuration_fuyu import FuyuConfig
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logger = logging.get_logger(__name__)
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@auto_docstring
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class FuyuPreTrainedModel(PreTrainedModel):
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config: FuyuConfig
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base_model_prefix = "fuyu"
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supports_gradient_checkpointing = True
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_supports_attention_backend = True
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_supports_flash_attn = True
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_supports_sdpa = True
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_supports_flex_attn = 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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std = self.config.initializer_range
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if isinstance(module, nn.Linear):
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module.weight.data.normal_(mean=0.0, std=std)
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if module.bias is not None:
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module.bias.data.zero_()
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elif isinstance(module, nn.Embedding):
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module.weight.data.normal_(mean=0.0, std=std)
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if module.padding_idx is not None:
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module.weight.data[module.padding_idx].zero_()
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@auto_docstring(
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custom_intro="""
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The Fuyu model which consists of a vision backbone and a language model, without a language modeling head.
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"""
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)
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class FuyuModel(FuyuPreTrainedModel):
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_checkpoint_conversion_mapping = {"language_model.model": "language_model"}
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def __init__(self, config: FuyuConfig):
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super().__init__(config)
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self.padding_idx = config.pad_token_id
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self.vocab_size = config.text_config.vocab_size
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self.language_model = AutoModel.from_config(config.text_config)
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self.vision_embed_tokens = nn.Linear(
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config.patch_size * config.patch_size * config.num_channels, config.hidden_size
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)
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self.gradient_checkpointing = False
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# Initialize weights and apply final processing
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self.post_init()
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def get_input_embeddings(self):
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return self.language_model.get_input_embeddings()
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def set_input_embeddings(self, value):
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self.language_model.set_input_embeddings(value)
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def set_decoder(self, decoder):
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self.language_model = decoder
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def get_decoder(self):
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return self.language_model
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def gather_continuous_embeddings(
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self,
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word_embeddings: torch.Tensor,
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continuous_embeddings: list[torch.Tensor],
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image_patch_input_indices: torch.Tensor,
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) -> torch.Tensor:
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"""This function places the continuous_embeddings into the word_embeddings at the locations
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indicated by image_patch_input_indices. Different batch elements can have different numbers of continuous
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embeddings.
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Args:
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word_embeddings (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
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Tensor of word embeddings.
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continuous_embeddings (`torch.FloatTensor` of shape `(batch_size, num_patches, hidden_size)`):
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Tensor of continuous embeddings. The length of the list is the batch size. Each entry is shape
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[num_image_embeddings, hidden], and num_image_embeddings needs to match the number of non-negative
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indices in image_patch_input_indices for that batch element.
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image_patch_input_indices (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
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Tensor of indices of the image patches in the input_ids tensor.
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"""
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if not (word_embeddings.shape[0] == len(continuous_embeddings)):
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raise ValueError(
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f"Batch sizes must match! Got {len(continuous_embeddings)=} and {word_embeddings.shape[0]=}"
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)
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output_embeddings = word_embeddings.clone()
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for batch_idx in range(word_embeddings.shape[0]):
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# First, find the positions of all the non-negative values in image_patch_input_indices, those are the
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# positions in word_embeddings that we want to replace with content from continuous_embeddings.
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dst_indices = torch.nonzero(image_patch_input_indices[batch_idx] >= 0, as_tuple=True)[0]
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# Next look up those indices in image_patch_input_indices to find the indices in continuous_embeddings that we
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# want to use to replace the values in word_embeddings.
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src_indices = image_patch_input_indices[batch_idx][dst_indices]
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# Check if we have more indices than embeddings. Note that we could have fewer indices if images got truncated.
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if src_indices.shape[0] > continuous_embeddings[batch_idx].shape[0]:
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raise ValueError(
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f"Number of continuous embeddings {continuous_embeddings[batch_idx].shape=} does not match "
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f"number of continuous token ids {src_indices.shape=} in batch element {batch_idx}."
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)
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output_embeddings[batch_idx, dst_indices] = continuous_embeddings[batch_idx][src_indices].to(
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output_embeddings.device
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)
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return output_embeddings
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def get_image_features(self, pixel_values: torch.FloatTensor, **kwargs):
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"""
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Encodes images into continuous embeddings that can be forwarded to the language model.
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Args:
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pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`):
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The tensors corresponding to the input images.
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"""
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patch_embeddings = [
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self.vision_embed_tokens(patch.to(self.vision_embed_tokens.weight.dtype)).squeeze(0)
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for patch in pixel_values
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]
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return patch_embeddings
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@auto_docstring
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def forward(
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self,
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input_ids: torch.LongTensor = None,
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image_patches: torch.Tensor = None, # [batch_size, num_total_patches, patch_size_ x patch_size x num_channels ]
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image_patches_indices: 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[Cache] = None,
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inputs_embeds: Optional[torch.FloatTensor] = 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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return_dict: Optional[bool] = None,
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**kwargs,
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) -> Union[tuple, CausalLMOutputWithPast]:
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r"""
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image_patches (`torch.FloatTensor` of shape `(batch_size, num_total_patches, patch_size_ x patch_size x num_channels)`, *optional*):
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Image patches to be used as continuous embeddings. The patches are flattened and then projected to the
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hidden size of the model.
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image_patches_indices (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
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Tensor of indices of the image patches in the input_ids tensor.
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"""
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output_attentions = output_attentions if output_attentions is not None else 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 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 = return_dict if return_dict is not None else self.config.use_return_dict
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if input_ids is not None and inputs_embeds is not None:
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raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
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elif input_ids is not None:
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batch_size, seq_length = input_ids.shape
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elif inputs_embeds is not None:
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batch_size, seq_length, _ = inputs_embeds.shape
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else:
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raise ValueError("You have to specify either input_is or inputs_embeds")
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if position_ids is None:
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device = input_ids.device if input_ids is not None else inputs_embeds.device
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past_key_values_length = past_key_values.get_seq_length() if past_key_values is not None else 0
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position_ids = torch.arange(
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past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
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)
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position_ids = position_ids.unsqueeze(0)
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if inputs_embeds is None:
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inputs_embeds = self.language_model.get_input_embeddings()(input_ids)
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if image_patches is not None:
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patch_embeddings = self.get_image_features(image_patches)
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patch_embeddings = torch.cat(patch_embeddings, dim=0)
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if input_ids is None:
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special_image_mask = inputs_embeds == self.get_input_embeddings()(
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torch.tensor(self.config.image_token_id, dtype=torch.long, device=inputs_embeds.device)
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)
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special_image_mask = special_image_mask.all(-1)
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else:
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special_image_mask = input_ids == self.config.image_token_id
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special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
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patch_embeddings = patch_embeddings.to(inputs_embeds.device, inputs_embeds.dtype)
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inputs_embeds = inputs_embeds.masked_scatter(special_image_mask, patch_embeddings)
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outputs = self.language_model(
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inputs_embeds=inputs_embeds,
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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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**kwargs,
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)
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return outputs
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@auto_docstring(
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custom_intro="""
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Fuyu Model with a language modeling head on top for causal language model conditioned on image patches and text.
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"""
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)
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class FuyuForCausalLM(FuyuPreTrainedModel, GenerationMixin):
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_checkpoint_conversion_mapping = {
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"^language_model.model": "model.language_model",
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"^vision_embed_tokens": "model.vision_embed_tokens",
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"^language_model.lm_head": "lm_head",
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}
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_tied_weights_keys = ["lm_head.weight"]
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def __init__(self, config: FuyuConfig):
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super().__init__(config)
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self.model = FuyuModel(config)
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self.lm_head = nn.Linear(config.text_config.hidden_size, config.text_config.vocab_size, bias=False)
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self.post_init()
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def get_input_embeddings(self):
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return self.model.get_input_embeddings()
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def set_input_embeddings(self, value):
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self.model.set_input_embeddings(value)
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def set_decoder(self, decoder):
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self.model.set_decoder(decoder)
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def get_decoder(self):
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return self.model.get_decoder()
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@can_return_tuple
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@auto_docstring
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def forward(
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self,
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input_ids: torch.LongTensor = None,
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image_patches: torch.Tensor = None, # [batch_size, num_total_patches, patch_size_ x patch_size x num_channels ]
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image_patches_indices: 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[Cache] = 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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logits_to_keep: Optional[int] = 0,
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**kwargs,
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) -> Union[tuple, CausalLMOutputWithPast]:
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r"""
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image_patches (`torch.FloatTensor` of shape `(batch_size, num_total_patches, patch_size_ x patch_size x num_channels)`, *optional*):
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Image patches to be used as continuous embeddings. The patches are flattened and then projected to the
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hidden size of the model.
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image_patches_indices (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
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Tensor of indices of the image patches in the input_ids tensor.
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labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
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Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
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config.text_config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
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(masked), the loss is only computed for the tokens with labels in `[0, ..., config.text_config.vocab_size]`.
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Examples:
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```python
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>>> from transformers import FuyuProcessor, FuyuForCausalLM
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>>> from PIL import Image
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>>> import requests
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>>> processor = FuyuProcessor.from_pretrained("adept/fuyu-8b")
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>>> model = FuyuForCausalLM.from_pretrained("adept/fuyu-8b")
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>>> url = "https://huggingface.co/datasets/hf-internal-testing/fixtures-captioning/resolve/main/bus.png"
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>>> image = Image.open(requests.get(url, stream=True).raw)
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>>> prompt = "Generate a coco-style caption.\n"
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>>> inputs = processor(images=image, text=prompt, return_tensors="pt")
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>>> outputs = model(**inputs)
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>>> generated_ids = model.generate(**inputs, max_new_tokens=7)
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>>> generation_text = processor.batch_decode(generated_ids[:, -7:], skip_special_tokens=True)
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>>> print(generation_text[0])
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A blue bus parked on the side of a road.
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```"""
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output_attentions = output_attentions if output_attentions is not None else 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 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 = return_dict if return_dict is not None else self.config.use_return_dict
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outputs = self.model(
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input_ids=input_ids,
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image_patches=image_patches,
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image_patches_indices=image_patches_indices,
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inputs_embeds=inputs_embeds,
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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=True,
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# don't pass kwargs because Persimmon-backbone doesn't accept FA2 kwargs yet, TODO: raushan
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)
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hidden_states = outputs[0]
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# Only compute necessary logits, and do not upcast them to float if we are not computing the loss
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slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
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logits = self.lm_head(hidden_states[:, slice_indices, :])
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loss = None
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if labels is not None:
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loss = self.loss_function(
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logits=logits, labels=labels, vocab_size=self.config.text_config.vocab_size, **kwargs
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)
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return CausalLMOutputWithPast(
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loss=loss,
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logits=logits,
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past_key_values=outputs.past_key_values,
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hidden_states=outputs.hidden_states,
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attentions=outputs.attentions,
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)
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def prepare_inputs_for_generation(
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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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image_patches=None,
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image_patches_indices=None,
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**kwargs,
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):
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# Overwritten -- in specific circumstances we don't want to forward image inputs to the model
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model_inputs = super().prepare_inputs_for_generation(
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input_ids,
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past_key_values=past_key_values,
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attention_mask=attention_mask,
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inputs_embeds=inputs_embeds,
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image_patches=image_patches,
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image_patches_indices=image_patches_indices,
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**kwargs,
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)
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if past_key_values is not None:
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model_inputs["image_patches_indices"] = None
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model_inputs["image_patches"] = None
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return model_inputs
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__all__ = ["FuyuForCausalLM", "FuyuPreTrainedModel", "FuyuModel"]
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