455 lines
20 KiB
Python
455 lines
20 KiB
Python
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# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
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# This file was automatically generated from src/transformers/models/vipllava/modular_vipllava.py.
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# Do NOT edit this file manually as any edits will be overwritten by the generation of
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# the file from the modular. If any change should be done, please apply the change to the
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# modular_vipllava.py file directly. One of our CI enforces this.
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# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
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# coding=utf-8
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# Copyright 2023 the 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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from dataclasses import dataclass
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from typing import Optional, Union
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import torch
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from torch import nn
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from ...activations import ACT2FN
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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 BaseModelOutputWithPast, ModelOutput
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from ...modeling_utils import PreTrainedModel
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from ...utils import auto_docstring, can_return_tuple, is_torchdynamo_compiling
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from ..auto import AutoModel
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from .configuration_vipllava import VipLlavaConfig
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@dataclass
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@auto_docstring(
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custom_intro="""
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Base class for VipLlava outputs, with hidden states and attentions.
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"""
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)
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class VipLlavaModelOutputWithPast(BaseModelOutputWithPast):
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r"""
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past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
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Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
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`(batch_size, num_heads, sequence_length, embed_size_per_head)`)
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Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
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`past_key_values` input) to speed up sequential decoding.
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image_hidden_states (`torch.FloatTensor`, *optional*):
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A `torch.FloatTensor` of size `(batch_size, num_images, sequence_length, hidden_size)`.
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image_hidden_states of the model produced by the vision encoder and after projecting the last hidden state.
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"""
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image_hidden_states: Optional[torch.FloatTensor] = None
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@dataclass
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@auto_docstring(
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custom_intro="""
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Base class for VipLlava causal language model (or autoregressive) outputs.
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"""
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)
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class VipLlavaCausalLMOutputWithPast(ModelOutput):
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r"""
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loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
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Language modeling loss (for next-token prediction).
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logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
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Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
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past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
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Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
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`(batch_size, num_heads, sequence_length, embed_size_per_head)`)
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Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
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`past_key_values` input) to speed up sequential decoding.
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image_hidden_states (`torch.FloatTensor`, *optional*):
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A `torch.FloatTensor` of size `(batch_size, num_images, sequence_length, hidden_size)`.
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image_hidden_states of the model produced by the vision encoder and after projecting the last hidden state.
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"""
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loss: Optional[torch.FloatTensor] = None
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logits: Optional[torch.FloatTensor] = None
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past_key_values: Optional[list[torch.FloatTensor]] = None
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hidden_states: Optional[tuple[torch.FloatTensor]] = None
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attentions: Optional[tuple[torch.FloatTensor]] = None
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image_hidden_states: Optional[torch.FloatTensor] = None
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class VipLlavaMultiModalProjector(nn.Module):
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def __init__(self, config: VipLlavaConfig):
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super().__init__()
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num_feature_layers = 1 if isinstance(config.vision_feature_layers, int) else len(config.vision_feature_layers)
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self.projector_layernorm = nn.LayerNorm(
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num_feature_layers * config.vision_config.hidden_size, eps=config.projector_layernorm_eps
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)
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self.linear_1 = nn.Linear(
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num_feature_layers * config.vision_config.hidden_size,
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config.text_config.hidden_size,
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bias=True,
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)
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self.act = ACT2FN[config.projector_hidden_act]
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self.linear_2 = nn.Linear(config.text_config.hidden_size, config.text_config.hidden_size, bias=True)
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def forward(self, hidden_states):
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hidden_states = self.projector_layernorm(hidden_states)
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hidden_states = self.linear_1(hidden_states)
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hidden_states = self.act(hidden_states)
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hidden_states = self.linear_2(hidden_states)
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return hidden_states
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@auto_docstring
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class VipLlavaPreTrainedModel(PreTrainedModel):
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config: VipLlavaConfig
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base_model_prefix = ""
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supports_gradient_checkpointing = True
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_skip_keys_device_placement = "past_key_values"
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_supports_flash_attn = True
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_supports_sdpa = True
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_can_compile_fullgraph = True
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_supports_flex_attn = True
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_supports_attention_backend = True
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@auto_docstring(
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custom_intro="""
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The VipLlava 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 VipLlavaModel(VipLlavaPreTrainedModel):
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_checkpoint_conversion_mapping = {"language_model.model": "language_model"}
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def __init__(self, config: VipLlavaConfig):
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super().__init__(config)
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self.vision_tower = AutoModel.from_config(config.vision_config)
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self.multi_modal_projector = VipLlavaMultiModalProjector(config)
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self.language_model = AutoModel.from_config(config.text_config)
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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 get_image_features(
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self, pixel_values: torch.FloatTensor, vision_feature_layers: Optional[Union[int, list[int]]] = None
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):
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"""
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Obtains image last hidden states from the vision tower and apply multimodal projection.
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Args:
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pixel_values (`torch.FloatTensor]` of shape `(batch_size, channels, height, width)`)
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The tensors corresponding to the input images.
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vision_feature_layers (`Union[int, list[int]]`):
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The vision feature layer, or the list of indexes of the layers to select
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the vision feature.
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Returns:
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image_features (`torch.Tensor`): Image feature tensor of shape `(num_images, image_length, embed_dim)`).
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"""
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vision_feature_layers = (
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vision_feature_layers if vision_feature_layers is not None else self.config.vision_feature_layers
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)
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image_outputs = self.vision_tower(pixel_values, output_hidden_states=True)
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# If multiple feature layers are provided (which is usually the case)
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# then the image features are concatenated after the CLS is removed.
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if isinstance(vision_feature_layers, int):
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image_features = image_outputs.hidden_states[vision_feature_layers][:, 1:]
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else:
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# Usually, we select the features from index 1: the layers -2, -5, -8, -11 and 6
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image_features = [image_outputs.hidden_states[index][:, 1:] for index in vision_feature_layers]
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image_features = torch.cat(image_features, dim=-1)
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image_features = self.multi_modal_projector(image_features)
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return image_features
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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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pixel_values: torch.FloatTensor = 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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vision_feature_layers: Optional[Union[int, list[int]]] = 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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cache_position: Optional[torch.LongTensor] = None,
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**lm_kwargs,
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) -> Union[tuple, VipLlavaModelOutputWithPast]:
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r"""
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vision_feature_layers (`Union[int, list[int]]`, *optional*):
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The vision feature layer, or the list of indexes of the layers to select
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the vision feature.
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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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return_dict = return_dict if return_dict is not None else self.config.use_return_dict
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vision_feature_layers = (
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vision_feature_layers if vision_feature_layers is not None else self.config.vision_feature_layers
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)
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if (input_ids is None) ^ (inputs_embeds is not None):
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raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
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if inputs_embeds is None:
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inputs_embeds = self.get_input_embeddings()(input_ids)
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if pixel_values is not None:
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image_features = self.get_image_features(
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pixel_values=pixel_values, vision_feature_layers=vision_feature_layers
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)
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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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n_image_tokens = (special_image_mask).sum()
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special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
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if not is_torchdynamo_compiling() and inputs_embeds[special_image_mask].numel() != image_features.numel():
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n_image_features = image_features.shape[0] * image_features.shape[1]
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raise ValueError(
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f"Image features and image tokens do not match: tokens: {n_image_tokens}, features {n_image_features}"
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)
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image_features = image_features.to(inputs_embeds.device, inputs_embeds.dtype)
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inputs_embeds = inputs_embeds.masked_scatter(special_image_mask, image_features)
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outputs = self.language_model(
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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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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=True,
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cache_position=cache_position,
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**lm_kwargs,
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)
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output = VipLlavaModelOutputWithPast(
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last_hidden_state=outputs.last_hidden_state,
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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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image_hidden_states=image_features if pixel_values is not None else None,
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)
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return output if return_dict else output.to_tuple()
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@auto_docstring(
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custom_intro="""
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The VIPLLAVA model which consists of a vision backbone and a language model.
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"""
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)
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class VipLlavaForConditionalGeneration(VipLlavaPreTrainedModel, GenerationMixin):
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_checkpoint_conversion_mapping = {
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"^language_model.model": "model.language_model",
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"^vision_tower": "model.vision_tower",
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"^multi_modal_projector": "model.multi_modal_projector",
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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: VipLlavaConfig):
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super().__init__(config)
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self.model = VipLlavaModel(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 get_output_embeddings(self) -> nn.Module:
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return self.lm_head
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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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def get_image_features(
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self, pixel_values: torch.FloatTensor, vision_feature_layers: Optional[Union[int, list[int]]] = None
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):
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return self.model.get_image_features(pixel_values=pixel_values, vision_feature_layers=vision_feature_layers)
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# Make modules available throught conditional class for BC
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@property
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def language_model(self):
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return self.model.language_model
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@property
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def vision_tower(self):
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return self.model.vision_tower
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@property
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def multi_modal_projector(self):
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return self.model.multi_modal_projector
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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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pixel_values: torch.FloatTensor = 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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vision_feature_layers: Optional[Union[int, list[int]]] = 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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return_dict: Optional[bool] = None,
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cache_position: Optional[torch.LongTensor] = None,
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logits_to_keep: Union[int, torch.Tensor] = 0,
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**lm_kwargs,
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) -> Union[tuple, VipLlavaCausalLMOutputWithPast]:
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r"""
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vision_feature_layers (`Union[int, list[int]]`, *optional*):
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The vision feature layer, or the list of indexes of the layers to select
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the vision feature.
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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.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.vocab_size]`.
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Example:
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```python
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>>> import torch
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>>> from PIL import Image
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>>> import requests
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>>> from transformers import AutoProcessor, VipLlavaForConditionalGeneration
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>>> model = VipLlavaForConditionalGeneration.from_pretrained("llava-hf/vip-llava-7b-hf", device_map="auto", torch_dtype=torch.float16)
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>>> processor = AutoProcessor.from_pretrained("llava-hf/vip-llava-7b-hf")
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>>> prompt = "A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions.###Human: <image>\n{}###Assistant:"
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>>> question = "Can you please describe this image?"
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>>> prompt = prompt.format(question)
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>>> url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/compel-neg.png"
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>>> image = Image.open(requests.get(url, stream=True).raw)
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>>> inputs = processor(text=text, images=image, return_tensors="pt").to(0, torch.float16)
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>>> # Generate
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>>> generate_ids = model.generate(**inputs, max_new_tokens=20)
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>>> processor.decode(generate_ids[0][len(inputs["input_ids"][0]):], skip_special_tokens=True)
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The image features a brown and white cat sitting on a green surface, with a red ball in its
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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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return_dict = return_dict if return_dict is not None else self.config.use_return_dict
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vision_feature_layers = (
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vision_feature_layers if vision_feature_layers is not None else self.config.vision_feature_layers
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)
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outputs = self.model(
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input_ids=input_ids,
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pixel_values=pixel_values,
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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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inputs_embeds=inputs_embeds,
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use_cache=use_cache,
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vision_feature_layers=vision_feature_layers,
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output_attentions=output_attentions,
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output_hidden_states=output_hidden_states,
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return_dict=True,
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cache_position=cache_position,
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**lm_kwargs,
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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(logits=logits, labels=labels, vocab_size=self.config.text_config.vocab_size)
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return VipLlavaCausalLMOutputWithPast(
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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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image_hidden_states=outputs.image_hidden_states,
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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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inputs_embeds=None,
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pixel_values=None,
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attention_mask=None,
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cache_position=None,
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logits_to_keep=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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inputs_embeds=inputs_embeds,
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attention_mask=attention_mask,
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cache_position=cache_position,
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logits_to_keep=logits_to_keep,
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**kwargs,
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)
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if cache_position[0] == 0:
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# If we're in cached decoding stage, pixel values should be None because input ids do not contain special image token anymore
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# Otherwise we need pixel values to be passed to model
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model_inputs["pixel_values"] = pixel_values
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return model_inputs
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__all__ = ["VipLlavaModel", "VipLlavaForConditionalGeneration", "VipLlavaPreTrainedModel"]
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