image-match/python/py/Lib/site-packages/transformers/models/colpali/modeling_colpali.py

# Copyright 2024 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
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"""PyTorch ColPali model"""

from dataclasses import dataclass

import torch
from torch import nn

from transformers import AutoModel

from ... import initialization as init
from ...cache_utils import Cache
from ...modeling_utils import PreTrainedModel
from ...processing_utils import Unpack
from ...utils import ModelOutput, TransformersKwargs, auto_docstring, can_return_tuple
from .configuration_colpali import ColPaliConfig


@auto_docstring
class ColPaliPreTrainedModel(PreTrainedModel):
    config: ColPaliConfig
    base_model_prefix = "model"
    input_modalities = ("image", "text")
    _no_split_modules = []
    _supports_sdpa = True
    _supports_flash_attn = True
    _supports_flex_attn = True

    @torch.no_grad()
    def _init_weights(self, module):
        std = (
            self.config.initializer_range
            if hasattr(self.config, "initializer_range")
            else self.config.vlm_config.text_config.initializer_range
        )

        if isinstance(module, (nn.Linear, nn.Conv2d)):
            init.normal_(module.weight, mean=0.0, std=std)
            if module.bias is not None:
                init.zeros_(module.bias)
        elif isinstance(module, nn.Embedding):
            init.normal_(module.weight, mean=0.0, std=std)
            # Here we need the check explicitly, as we slice the weight in the `zeros_` call, so it looses the flag
            if module.padding_idx is not None and not getattr(module.weight, "_is_hf_initialized", False):
                init.zeros_(module.weight[module.padding_idx])


@dataclass
@auto_docstring(
    custom_intro="""
    Base class for ColPali embeddings output.
    """
)
class ColPaliForRetrievalOutput(ModelOutput):
    r"""
    loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
        Language modeling loss (for next-token prediction).
    embeddings (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
        The embeddings of the model.
    past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
        It is a [`~cache_utils.Cache`] instance. For more details, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache).

        Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
        `past_key_values` input) to speed up sequential decoding.
    image_hidden_states (`torch.FloatTensor`, *optional*):
        A `torch.FloatTensor` of size `(batch_size, num_images, sequence_length, hidden_size)`.
        image_hidden_states of the model produced by the vision encoder after projecting last hidden state.
    """

    loss: torch.FloatTensor | None = None
    embeddings: torch.Tensor | None = None
    past_key_values: Cache | None = None
    hidden_states: tuple[torch.FloatTensor] | None = None
    attentions: tuple[torch.FloatTensor] | None = None
    image_hidden_states: torch.FloatTensor | None = None


@auto_docstring(
    custom_intro="""
    The ColPali architecture leverages VLMs to construct efficient multi-vector embeddings directly
    from document images (“screenshots”) for document retrieval. The model is trained to maximize the similarity
    between these document embeddings and the corresponding query embeddings, using the late interaction method
    introduced in ColBERT.

    Using ColPali removes the need for potentially complex and brittle layout recognition and OCR pipelines with a
    single model that can take into account both the textual and visual content (layout, charts, etc.) of a document.

    ColPali is part of the ColVision model family, which was first introduced in the following paper:
    [*ColPali: Efficient Document Retrieval with Vision Language Models*](https://huggingface.co/papers/2407.01449).
    """
)
class ColPaliForRetrieval(ColPaliPreTrainedModel):
    base_model_prefix = "vlm"

    def __init__(self, config: ColPaliConfig):
        super().__init__(config)
        self.config = config
        self.vocab_size = config.vlm_config.text_config.vocab_size

        self.vlm = AutoModel.from_config(config.vlm_config)

        self.embedding_dim = self.config.embedding_dim
        self.embedding_proj_layer = nn.Linear(
            self.config.vlm_config.text_config.hidden_size,
            self.embedding_dim,
        )

        self.post_init()

    @can_return_tuple
    @auto_docstring
    def forward(
        self,
        input_ids: torch.LongTensor | None = None,
        pixel_values: torch.FloatTensor | None = None,
        attention_mask: torch.Tensor | None = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> ColPaliForRetrievalOutput:
        if pixel_values is not None:
            pixel_values = pixel_values.to(dtype=self.dtype)
        output_hidden_states = kwargs.pop("output_hidden_states", None)
        if output_hidden_states is None:
            output_hidden_states = self.config.output_hidden_states

        vlm_output = self.vlm(
            input_ids=input_ids,
            attention_mask=attention_mask,
            pixel_values=pixel_values,
            output_hidden_states=True,
            **kwargs,
        )
        vlm_hidden_states = vlm_output.hidden_states if output_hidden_states else None
        vlm_image_hidden_states = vlm_output.image_hidden_states if pixel_values is not None else None

        last_hidden_states = vlm_output[0]  # (batch_size, sequence_length, hidden_size)
        proj_dtype = self.embedding_proj_layer.weight.dtype
        embeddings = self.embedding_proj_layer(last_hidden_states.to(proj_dtype))  # (batch_size, sequence_length, dim)

        # L2 normalization
        embeddings = embeddings / embeddings.norm(dim=-1, keepdim=True)  # (batch_size, sequence_length, dim)

        if attention_mask is not None:
            embeddings = embeddings * attention_mask.unsqueeze(-1)  # (batch_size, sequence_length, dim)

        return ColPaliForRetrievalOutput(
            embeddings=embeddings,
            past_key_values=vlm_output.past_key_values,
            hidden_states=vlm_hidden_states,
            attentions=vlm_output.attentions,
            image_hidden_states=vlm_image_hidden_states,
        )


__all__ = [
    "ColPaliForRetrieval",
    "ColPaliPreTrainedModel",
]