modeling_attn_mask_utils.py

# Copyright 2023 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 dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import torch


@dataclass
class AttentionMaskConverter:
    """
    A utility attention mask class that allows one to:
        - Create a causal 4d mask
        - Create a causal 4d mask with slided window
        - Convert a 2d attention mask (batch_size, query_length) to a 4d attention mask (batch_size, 1, query_length,
          key_value_length) that can be multiplied with attention scores

    Examples:

    ```python
    >>> import torch
    >>> from transformers.modeling_attn_mask_utils import AttentionMaskConverter

    >>> converter = AttentionMaskConverter(True)
    >>> converter.to_4d(torch.tensor([[0, 0, 0, 1, 1]]), 5, key_value_length=5, dtype=torch.float32)
    tensor([[[[-3.4028e+38, -3.4028e+38, -3.4028e+38, -3.4028e+38, -3.4028e+38],
            [-3.4028e+38, -3.4028e+38, -3.4028e+38, -3.4028e+38, -3.4028e+38],
            [-3.4028e+38, -3.4028e+38, -3.4028e+38, -3.4028e+38, -3.4028e+38],
            [-3.4028e+38, -3.4028e+38, -3.4028e+38,  0.0000e+00, -3.4028e+38],
            [-3.4028e+38, -3.4028e+38, -3.4028e+38,  0.0000e+00,  0.0000e+00]]]])
    ```

    Parameters:
        is_causal (`bool`):
            Whether the attention mask should be a uni-directional (causal) or bi-directional mask.

        sliding_window (`int`, *optional*):
            Optionally, the sliding window masks can be created if `sliding_window` is defined to a positive integer.
    """

    is_causal: bool
    sliding_window: int

    def __init__(self, is_causal: bool, sliding_window: Optional[int] = None):
        self.is_causal = is_causal
        self.sliding_window = sliding_window

        if self.sliding_window is not None and self.sliding_window <= 0:
            raise ValueError(
                f"Make sure that when passing `sliding_window` that its value is a strictly positive integer, not `{self.sliding_window}`"
            )

    def to_causal_4d(
        self,
        batch_size: int,
        query_length: int,
        key_value_length: int,
        dtype: torch.dtype,
        device: Union[torch.device, "str"] = "cpu",
    ) -> Optional[torch.Tensor]:
        """
        Creates a causal 4D mask of (bsz, head_dim=1, query_length, key_value_length) shape and adds large negative
        bias to upper right hand triangular matrix (causal mask).
        """
        if not self.is_causal:
            raise ValueError(f"Please use `to_causal_4d` only if {self.__class__} has `is_causal` set to True.")

        # If shape is not cached, create a new causal mask and cache it
        input_shape = (batch_size, query_length)
        past_key_values_length = key_value_length - query_length

        # create causal mask
        # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
        causal_4d_mask = None
        if input_shape[-1] > 1 or self.sliding_window is not None:
            causal_4d_mask = self._make_causal_mask(
                input_shape,
                dtype,
                device=device,
                past_key_values_length=past_key_values_length,
                sliding_window=self.sliding_window,
            )

        return causal_4d_mask

    def to_4d(
        self,
        attention_mask_2d: torch.Tensor,
        query_length: int,
        dtype: torch.dtype,
        key_value_length: Optional[int] = None,
    ) -> torch.Tensor:
        """
        Converts 2D attention mask to 4D attention mask by expanding mask to (bsz, head_dim=1, query_length,
        key_value_length) shape and by adding a large negative bias to not-attended positions. If attention_mask is
        causal, a causal mask will be added.
        """
        input_shape = (attention_mask_2d.shape[0], query_length)

        # create causal mask
        # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
        causal_4d_mask = None
        if (input_shape[-1] > 1 or self.sliding_window is not None) and self.is_causal:
            if key_value_length is None:
                raise ValueError(
                    "This attention mask converter is causal. Make sure to pass `key_value_length` to correctly create a causal mask."
                )

            past_key_values_length = key_value_length - query_length
            causal_4d_mask = self._make_causal_mask(
                input_shape,
                dtype,
                device=attention_mask_2d.device,
                past_key_values_length=past_key_values_length,
                sliding_window=self.sliding_window,
            )
        elif self.sliding_window is not None:
            raise NotImplementedError("Sliding window is currently only implemented for causal masking")

        # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
        expanded_attn_mask = self._expand_mask(attention_mask_2d, dtype, tgt_len=input_shape[-1]).to(
            attention_mask_2d.device
        )

        if causal_4d_mask is not None:
            expanded_attn_mask = causal_4d_mask.masked_fill(expanded_attn_mask.bool(), torch.finfo(dtype).min)

        # expanded_attn_mask + causal_4d_mask can cause some overflow
        expanded_4d_mask = expanded_attn_mask

        return expanded_4d_mask

    @staticmethod
    def _make_causal_mask(
        input_ids_shape: torch.Size,
        dtype: torch.dtype,
        device: torch.device,
        past_key_values_length: int = 0,
        sliding_window: Optional[int] = None,
    ):
        """
        Make causal mask used for bi-directional self-attention.
        """
        bsz, tgt_len = input_ids_shape
        mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min, device=device)
        mask_cond = torch.arange(mask.size(-1), device=device)
        mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)

        mask = mask.to(dtype)

        if past_key_values_length > 0:
            mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1)

        # add lower triangular sliding window mask if necessary
        if sliding_window is not None:
            diagonal = past_key_values_length - sliding_window - 1

            context_mask = torch.tril(torch.ones_like(mask, dtype=torch.bool), diagonal=diagonal)
            mask.masked_fill_(context_mask, torch.finfo(dtype).min)

        return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)

    @staticmethod
    def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
        """
        Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
        """
        bsz, src_len = mask.size()
        tgt_len = tgt_len if tgt_len is not None else src_len

        expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)

        inverted_mask = 1.0 - expanded_mask

        return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)

    @staticmethod
    def _unmask_unattended(
        expanded_mask: torch.FloatTensor,
        min_dtype: float,
    ):
        # fmt: off
        """
        Attend to all tokens in masked rows from the expanded attention mask, for example the relevant first rows when
        using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path.
        Details: https://github.com/pytorch/pytorch/issues/110213

        `expanded_mask` is [bsz, num_masks, tgt_seq_len, src_seq_len] or [bsz, tgt_seq_len, src_seq_len].
        `attention_mask` is [bsz, src_seq_len].

        The dimension num_masks of `expanded_mask` is most often 1, but it can also be the number of heads in the case of alibi attention bias.

        For example, if `expanded_mask` is (e.g. here left-padding case)
        ```
        [[[[0, 0, 0],
           [0, 0, 0],
           [0, 0, 1]]],
         [[[1, 0, 0],
           [1, 1, 0],
           [1, 1, 1]]],
         [[[0, 0, 0],
           [0, 1, 0],
           [0, 1, 1]]]]
        ```
        then the modified `expanded_mask` will be
        ```
        [[[[1, 1, 1],   <-- modified
           [1, 1, 1],   <-- modified
           [0, 0, 1]]],
         [[[1, 0, 0],
           [1, 1, 0],
           [1, 1, 1]]],
         [[[1, 1, 1],   <-- modified
           [0, 1, 0],
           [0, 1, 1]]]]
        ```
        """
        # fmt: on
        if expanded_mask.dtype == torch.bool:
            raise ValueError(
                "AttentionMaskConverter._unmask_unattended expects a float `expanded_mask`, got a BoolTensor."
            )

        return expanded_mask.mul(~torch.all(expanded_mask == min_dtype, dim=-1, keepdim=True))

    @staticmethod
    def _ignore_causal_mask_sdpa(
        attention_mask: Optional[torch.Tensor],
        inputs_embeds: torch.Tensor,
        past_key_values_length: int,
        sliding_window: Optional[int] = None,
        is_training: bool = False,
    ) -> bool:
        """
        Detects whether the optional user-specified attention_mask & the automatically created causal mask can be ignored in case PyTorch's SDPA is used, rather relying on SDPA's `is_causal` argument.

        In case no token is masked in the `attention_mask` argument, if `query_length == 1` or
        `key_value_length == query_length`, we rather rely on SDPA `is_causal` argument to use causal/non-causal masks,
        allowing to dispatch to the flash attention kernel (that can otherwise not be used if a custom `attn_mask` is passed).
        """

        _, query_length = inputs_embeds.shape[0], inputs_embeds.shape[1]
        key_value_length = query_length + past_key_values_length

        is_tracing = (
            torch.jit.is_tracing()
            or isinstance(inputs_embeds, torch.fx.Proxy)
            or (hasattr(torch, "_dynamo") and torch._dynamo.is_compiling())
        )

        ignore_causal_mask = False

        if attention_mask is None:
            # TODO: When tracing with TorchDynamo with fullgraph=True, the model is recompiled depending on the input shape, thus SDPA's `is_causal` argument is rightfully updated (see https://gist.github.com/fxmarty/1313f39037fc1c112508989628c57363). However, when using `torch.export` or
            # or `torch.onnx.dynamo_export`, we must pass an example input, and `is_causal` behavior is hard-coded. If a user exports a model with q_len > 1, the exported model will hard-code `is_causal=True` which is in general wrong (see https://github.com/pytorch/pytorch/issues/108108).
            # Thus, we only set `ignore_causal_mask = True` if the model is set to training.
            #
            # Besides, jit.trace can not handle the `q_len > 1` condition for `is_causal` (`TypeError: scaled_dot_product_attention(): argument 'is_causal' must be bool, not Tensor`).
            if (
                (is_training or not is_tracing)
                and (query_length == 1 or key_value_length == query_length)
                and (sliding_window is None or key_value_length < sliding_window)
            ):
                ignore_causal_mask = True
        elif sliding_window is None or key_value_length < sliding_window:
            if len(attention_mask.shape) == 4:
                return False
            elif (is_training or not is_tracing) and torch.all(attention_mask == 1):
                if query_length == 1 or key_value_length == query_length:
                    # For query_length == 1, causal attention and bi-directional attention are the same.
                    ignore_causal_mask = True

                # Unfortunately, for query_length > 1 and key_value_length != query_length, we cannot generally ignore the attention mask, as SDPA causal mask generation
                # may be wrong. We will set `is_causal=False` in SDPA and rely on Transformers attention_mask instead, hence not setting it to None here.
                # Reference: https://github.com/pytorch/pytorch/issues/108108
                # TODO: maybe revisit this with https://github.com/pytorch/pytorch/pull/114823 in PyTorch 2.3.

        return ignore_causal_mask


def _prepare_4d_causal_attention_mask(
    attention_mask: Optional[torch.Tensor],
    input_shape: Union[torch.Size, Tuple, List],
    inputs_embeds: torch.Tensor,
    past_key_values_length: int,
    sliding_window: Optional[int] = None,
):
    """
    Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape
    `(batch_size, key_value_length)`

    Args:
        attention_mask (`torch.Tensor` or `None`):
            A 2D attention mask of shape `(batch_size, key_value_length)`
        input_shape (`tuple(int)` or `list(int)` or `torch.Size`):
            The input shape should be a tuple that defines `(batch_size, query_length)`.
        inputs_embeds (`torch.Tensor`):
            The embedded inputs as a torch Tensor.
        past_key_values_length (`int`):
            The length of the key value cache.
        sliding_window (`int`, *optional*):
            If the model uses windowed attention, a sliding window should be passed.
    """
    attn_mask_converter = AttentionMaskConverter(is_causal=True, sliding_window=sliding_window)

    key_value_length = input_shape[-1] + past_key_values_length

    # 4d mask is passed through the layers
    if attention_mask is not None and len(attention_mask.shape) == 2:
        attention_mask = attn_mask_converter.to_4d(
            attention_mask, input_shape[-1], key_value_length=key_value_length, dtype=inputs_embeds.dtype
        )
    elif attention_mask is not None and len(attention_mask.shape) == 4:
        expected_shape = (input_shape[0], 1, input_shape[1], key_value_length)
        if tuple(attention_mask.shape) != expected_shape:
            raise ValueError(
                f"Incorrect 4D attention_mask shape: {tuple(attention_mask.shape)}; expected: {expected_shape}."
            )
        else:
            # if the 4D mask has correct shape - invert it and fill with negative infinity
            inverted_mask = 1.0 - attention_mask
            attention_mask = inverted_mask.masked_fill(
                inverted_mask.to(torch.bool), torch.finfo(inputs_embeds.dtype).min
            )
    else:
        attention_mask = attn_mask_converter.to_causal_4d(
            input_shape[0], input_shape[-1], key_value_length, dtype=inputs_embeds.dtype, device=inputs_embeds.device
        )

    return attention_mask


# Adapted from _prepare_4d_causal_attention_mask
def _prepare_4d_causal_attention_mask_for_sdpa(
    attention_mask: Optional[torch.Tensor],
    input_shape: Union[torch.Size, Tuple, List],
    inputs_embeds: torch.Tensor,
    past_key_values_length: int,
    sliding_window: Optional[int] = None,
):
    """
    Prepares the correct `attn_mask` argument to be used by `torch.nn.functional.scaled_dot_product_attention`.

    In case no token is masked in the `attention_mask` argument, we simply set it to `None` for the cases `query_length == 1` and
    `key_value_length == query_length`, and rely instead on SDPA `is_causal` argument to use causal/non-causal masks,
    allowing to dispatch to the flash attention kernel (that can otherwise not be used if a custom `attn_mask` is passed).
    """
    attn_mask_converter = AttentionMaskConverter(is_causal=True, sliding_window=sliding_window)

    key_value_length = input_shape[-1] + past_key_values_length

    # torch.jit.trace, symbolic_trace and torchdynamo with fullgraph=True are unable to capture the controlflow `is_causal=attention_mask is None and q_len > 1`
    # used as an SDPA argument. We keep compatibility with these tracing tools by always using SDPA's `attn_mask` argument in case we are tracing.
    # TODO: For dynamo, rather use a check on fullgraph=True once this is possible (https://github.com/pytorch/pytorch/pull/120400).
    is_tracing = (
        torch.jit.is_tracing()
        or isinstance(inputs_embeds, torch.fx.Proxy)
        or (hasattr(torch, "_dynamo") and torch._dynamo.is_compiling())
    )

    ignore_causal_mask = AttentionMaskConverter._ignore_causal_mask_sdpa(
        attention_mask=attention_mask,
        inputs_embeds=inputs_embeds,
        past_key_values_length=past_key_values_length,
        sliding_window=sliding_window,
    )

    if ignore_causal_mask:
        expanded_4d_mask = None
    elif attention_mask is None:
        expanded_4d_mask = attn_mask_converter.to_causal_4d(
            input_shape[0], input_shape[-1], key_value_length, dtype=inputs_embeds.dtype, device=inputs_embeds.device
        )
    else:
        if attention_mask.dim() == 4:
            # in this case we assume that the mask comes already in inverted form and requires no inversion or slicing
            if attention_mask.max() != 0:
                raise ValueError("Custom 4D attention mask should be passed in inverted form with max==0`")
            expanded_4d_mask = attention_mask
        else:
            expanded_4d_mask = attn_mask_converter.to_4d(
                attention_mask,
                input_shape[-1],
                dtype=inputs_embeds.dtype,
                key_value_length=key_value_length,
            )

        # Attend to all tokens in masked rows from the causal_mask, for example the relevant first rows when
        # using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path.
        # Details: https://github.com/pytorch/pytorch/issues/110213
        if not is_tracing and expanded_4d_mask.device.type == "cuda":
            expanded_4d_mask = AttentionMaskConverter._unmask_unattended(
                expanded_4d_mask, min_dtype=torch.finfo(inputs_embeds.dtype).min
            )

    return expanded_4d_mask


def _prepare_4d_attention_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
    """
    Creates a non-causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape
    `(batch_size, key_value_length)`

    Args:
        mask (`torch.Tensor` or `None`):
            A 2D attention mask of shape `(batch_size, key_value_length)`
        dtype (`torch.dtype`):
            The torch dtype the created mask shall have.
        tgt_len (`int`):
            The target length or query length the created mask shall have.
    """
    return AttentionMaskConverter._expand_mask(mask=mask, dtype=dtype, tgt_len=tgt_len)


def _prepare_4d_attention_mask_for_sdpa(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
    """
    Creates a non-causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape
    `(batch_size, key_value_length)`

    Args:
        mask (`torch.Tensor` or `None`):
            A 2D attention mask of shape `(batch_size, key_value_length)`
        dtype (`torch.dtype`):
            The torch dtype the created mask shall have.
        tgt_len (`int`):
            The target length or query length the created mask shall have.
    """
    batch_size, key_value_length = mask.shape
    tgt_len = tgt_len if tgt_len is not None else key_value_length

    # torch.jit.trace, symbolic_trace and torchdynamo with fullgraph=True are unable to capture the controlflow `is_causal=attention_mask is None and q_len > 1`
    # used as an SDPA argument. We keep compatibility with these tracing tools by always using SDPA's `attn_mask` argument in case we are tracing.
    # TODO: For dynamo, rather use a check on fullgraph=True once this is possible (https://github.com/pytorch/pytorch/pull/120400).
    is_tracing = (
        torch.jit.is_tracing()
        or isinstance(mask, torch.fx.Proxy)
        or (hasattr(torch, "_dynamo") and torch._dynamo.is_compiling())
    )

    if not is_tracing and torch.all(mask == 1):
        if tgt_len == 1:
            # For query_length == 1, causal attention and bi-directional attention are the same.
            return None
        elif key_value_length == tgt_len:
            return None
        else:
            # Unfortunately, for query_length > 1 and key_value_length != query_length, we can not generally ignore the attention mask, as SDPA causal mask generation
            # may be wrong. We will set is_causal=False in SDPA and rely on Transformers attention_mask instead, hence not setting it to None here.
            # Reference: https://github.com/pytorch/pytorch/issues/108108
            return AttentionMaskConverter._expand_mask(mask=mask, dtype=dtype, tgt_len=tgt_len)
    else:
        return AttentionMaskConverter._expand_mask(mask=mask, dtype=dtype, tgt_len=tgt_len)


def _create_4d_causal_attention_mask(
    input_shape: Union[torch.Size, Tuple, List],
    dtype: torch.dtype,
    device: torch.device,
    past_key_values_length: int = 0,
    sliding_window: Optional[int] = None,
) -> Optional[torch.Tensor]:
    """
    Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)`

    Args:
        input_shape (`tuple(int)` or `list(int)` or `torch.Size`):
            The input shape should be a tuple that defines `(batch_size, query_length)`.
        dtype (`torch.dtype`):
            The torch dtype the created mask shall have.
        device (`int`):
            The torch device the created mask shall have.
        sliding_window (`int`, *optional*):
            If the model uses windowed attention, a sliding window should be passed.
    """
    attn_mask_converter = AttentionMaskConverter(is_causal=True, sliding_window=sliding_window)

    key_value_length = past_key_values_length + input_shape[-1]
    attention_mask = attn_mask_converter.to_causal_4d(
        input_shape[0], input_shape[-1], key_value_length, dtype=dtype, device=device
    )

    return attention_mask