venv/lib/python3.13/site-packages/transformers/modeling_layers.py

# Copyright 2025 The HuggingFace Team. All rights reserved.
#
# 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
# limitations under the License.
from functools import partial
from typing import Optional

import torch
import torch.nn as nn

from .cache_utils import Cache
from .modeling_outputs import (
    BaseModelOutputWithPast,
    QuestionAnsweringModelOutput,
    SequenceClassifierOutputWithPast,
    TokenClassifierOutput,
)
from .models.auto import AutoModel
from .processing_utils import Unpack
from .utils import TransformersKwargs, auto_docstring, can_return_tuple, logging


logger = logging.get_logger(__name__)


class GradientCheckpointingLayer(nn.Module):
    """Base class for layers with gradient checkpointing.

    This class enables gradient checkpointing functionality for a layer. By default, gradient checkpointing is disabled
    (`gradient_checkpointing = False`). When `model.set_gradient_checkpointing()` is called, gradient checkpointing is
    enabled by setting `gradient_checkpointing = True` and assigning a checkpointing function to `_gradient_checkpointing_func`.

    Important:

        When using gradient checkpointing with `use_reentrant=True`, inputs that require gradients (e.g. hidden states)
        must be passed as positional arguments (`*args`) rather than keyword arguments to properly propagate gradients.

        Example:

            ```python
            >>> # Correct - hidden_states passed as positional arg
            >>> out = self.layer(hidden_states, attention_mask=attention_mask)

            >>> # Incorrect - hidden_states passed as keyword arg
            >>> out = self.layer(hidden_states=hidden_states, attention_mask=attention_mask)
            ```
    """

    gradient_checkpointing = False

    def __call__(self, *args, **kwargs):
        if self.gradient_checkpointing and self.training:
            do_warn = False
            layer_name = self.__class__.__name__
            message = f"Caching is incompatible with gradient checkpointing in {layer_name}. Setting"

            if "use_cache" in kwargs and kwargs["use_cache"]:
                kwargs["use_cache"] = False
                message += " `use_cache=False`,"
                do_warn = True

            # different names for the same thing in different layers
            # TODO cyril: this one without `S` can be removed after deprection cycle
            if "past_key_value" in kwargs and kwargs["past_key_value"] is not None:
                kwargs["past_key_value"] = None
                message += " `past_key_value=None`,"
                do_warn = True

            if "past_key_values" in kwargs and kwargs["past_key_values"] is not None:
                kwargs["past_key_values"] = None
                message += " `past_key_values=None`,"
                do_warn = True

            if "layer_past" in kwargs and kwargs["layer_past"] is not None:
                kwargs["layer_past"] = None
                message += " `layer_past=None`,"
                do_warn = True

            # warn if anything was changed
            if do_warn:
                message = message.rstrip(",") + "."
                logger.warning_once(message)

            return self._gradient_checkpointing_func(partial(super().__call__, **kwargs), *args)
        return super().__call__(*args, **kwargs)


@auto_docstring
class GenericForSequenceClassification:
    base_model_prefix = "model"

    def __init__(self, config):
        super().__init__(config)
        self.num_labels = config.num_labels
        # Similar to `self.model = AutoModel.from_config(config)` but allows to change the base model name if needed in the child class
        setattr(self, self.base_model_prefix, AutoModel.from_config(config))
        self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)

        # Initialize weights and apply final processing
        self.post_init()

    @can_return_tuple
    @auto_docstring
    def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_values: Optional[Cache] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        labels: Optional[torch.LongTensor] = None,
        use_cache: Optional[bool] = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> SequenceClassifierOutputWithPast:
        transformer_outputs: BaseModelOutputWithPast = getattr(self, self.base_model_prefix)(
            input_ids,
            attention_mask=attention_mask,
            position_ids=position_ids,
            past_key_values=past_key_values,
            inputs_embeds=inputs_embeds,
            use_cache=use_cache,
            **kwargs,
        )
        hidden_states = transformer_outputs.last_hidden_state
        logits = self.score(hidden_states)

        if input_ids is not None:
            batch_size = input_ids.shape[0]
        else:
            batch_size = inputs_embeds.shape[0]

        if self.config.pad_token_id is None and batch_size != 1:
            raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
        if self.config.pad_token_id is None:
            last_non_pad_token = -1
        elif input_ids is not None:
            # To handle both left- and right- padding, we take the rightmost token that is not equal to pad_token_id
            non_pad_mask = (input_ids != self.config.pad_token_id).to(logits.device, torch.int32)
            token_indices = torch.arange(input_ids.shape[-1], device=logits.device, dtype=torch.int32)
            last_non_pad_token = (token_indices * non_pad_mask).argmax(-1)
        else:
            last_non_pad_token = -1
            logger.warning_once(
                f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be "
                "unexpected if using padding tokens in conjunction with `inputs_embeds.`"
            )

        pooled_logits = logits[torch.arange(batch_size, device=logits.device), last_non_pad_token]

        loss = None
        if labels is not None:
            loss = self.loss_function(logits=logits, labels=labels, pooled_logits=pooled_logits, config=self.config)

        return SequenceClassifierOutputWithPast(
            loss=loss,
            logits=pooled_logits,
            past_key_values=transformer_outputs.past_key_values,
            hidden_states=transformer_outputs.hidden_states,
            attentions=transformer_outputs.attentions,
        )


@auto_docstring
class GenericForQuestionAnswering:
    base_model_prefix = "model"

    def __init__(self, config):
        super().__init__(config)
        # Similar to `self.model = AutoModel.from_config(config)` but allows to change the base model name if needed in the child class
        setattr(self, self.base_model_prefix, AutoModel.from_config(config))
        self.qa_outputs = nn.Linear(config.hidden_size, 2)

        # Initialize weights and apply final processing
        self.post_init()

    def get_input_embeddings(self):
        return getattr(self, self.base_model_prefix).embed_tokens

    def set_input_embeddings(self, value):
        getattr(self, self.base_model_prefix).embed_tokens = value

    @can_return_tuple
    @auto_docstring
    def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_values: Optional[Cache] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        start_positions: Optional[torch.LongTensor] = None,
        end_positions: Optional[torch.LongTensor] = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> QuestionAnsweringModelOutput:
        outputs: BaseModelOutputWithPast = getattr(self, self.base_model_prefix)(
            input_ids,
            attention_mask=attention_mask,
            position_ids=position_ids,
            past_key_values=past_key_values,
            inputs_embeds=inputs_embeds,
            **kwargs,
        )

        sequence_output = outputs.last_hidden_state

        logits = self.qa_outputs(sequence_output)
        start_logits, end_logits = logits.split(1, dim=-1)
        start_logits = start_logits.squeeze(-1).contiguous()
        end_logits = end_logits.squeeze(-1).contiguous()

        loss = None
        if start_positions is not None and end_positions is not None:
            loss = self.loss_function(start_logits, end_logits, start_positions, end_positions, **kwargs)

        return QuestionAnsweringModelOutput(
            loss=loss,
            start_logits=start_logits,
            end_logits=end_logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )


@auto_docstring
class GenericForTokenClassification:
    base_model_prefix = "model"

    def __init__(self, config):
        super().__init__(config)
        self.num_labels = config.num_labels
        # Similar to `self.model = AutoModel.from_config(config)` but allows to change the base model name if needed in the child class
        setattr(self, self.base_model_prefix, AutoModel.from_config(config))
        if getattr(config, "classifier_dropout", None) is not None:
            classifier_dropout = config.classifier_dropout
        elif getattr(config, "hidden_dropout", None) is not None:
            classifier_dropout = config.hidden_dropout
        else:
            classifier_dropout = 0.1
        self.dropout = nn.Dropout(classifier_dropout)
        self.score = nn.Linear(config.hidden_size, config.num_labels)

        # Initialize weights and apply final processing
        self.post_init()

    @can_return_tuple
    @auto_docstring
    def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_values: Optional[Cache] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        labels: Optional[torch.LongTensor] = None,
        use_cache: Optional[bool] = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> TokenClassifierOutput:
        outputs: BaseModelOutputWithPast = getattr(self, self.base_model_prefix)(
            input_ids,
            attention_mask=attention_mask,
            position_ids=position_ids,
            past_key_values=past_key_values,
            inputs_embeds=inputs_embeds,
            use_cache=use_cache,
            **kwargs,
        )
        sequence_output = outputs.last_hidden_state
        sequence_output = self.dropout(sequence_output)
        logits = self.score(sequence_output)

        loss = None
        if labels is not None:
            loss = self.loss_function(logits, labels, self.config)

        return TokenClassifierOutput(
            loss=loss,
            logits=logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )