Source code for torch.nn.attention.bias
"""Defines bias subclasses that work with scaled_dot_product_attention"""
from enum import auto, IntEnum
from typing import Optional
from warnings import warn
import torch
from torch.backends.cuda import (
can_use_efficient_attention,
can_use_flash_attention,
SDPAParams,
)
from torch.nn.attention import _raise_kernel_warnings
from torch.nn.attention._utils import (
_calculate_scale,
_input_requires_grad,
_postprocess_flash_output,
_validate_sdpa_input,
)
from torch.nn.functional import scaled_dot_product_attention
__all__ = ["causal_upper_left", "causal_lower_right", "CausalVariant", "CausalBias"]
torch._dynamo.allow_in_graph(can_use_flash_attention)
torch._dynamo.allow_in_graph(can_use_efficient_attention)
torch._dynamo.allow_in_graph(SDPAParams)
[docs]class CausalVariant(IntEnum):
r"""
Enum for causal variants used in attention mechanisms.
Defines two types of causal biases:
`UPPER_LEFT`: Represents upper-left triangular bias for standard causal attention.
The equivalent pytorch code for constructing this bias is:
.. code-block:: python
torch.tril(torch.ones(size, dtype=torch.bool))
For instance, with `shape=(3,4)`, the materialized bias tensor will be:
.. code-block:: text
[[1, 0, 0, 0],
[1, 1, 0, 0],
[1, 1, 1, 0]]
`LOWER_RIGHT`: Represents lower-right triangular bias, the include values are aligned to the lower
right corner of the matrix.
The equivalent pytorch code for constructing this bias is:
.. code-block:: python
diagonal_offset = size[1] - size[0]
torch.tril(
torch.ones(size, dtype=torch.bool),
diagonal=diagonal_offset,
)
For instance, with `shape=(3,4)`, the materialized bias tensor will be:
.. code-block:: text
[[1, 1, 0, 0],
[1, 1, 1, 0],
[1, 1, 1, 1]]
Note that these variants are equivalent to each other when the sequence lengths of the query and key/value
tensors are equal since the triangular matrix is square.
.. warning:: This enum is a prototype and subject to change.
"""
UPPER_LEFT = auto()
LOWER_RIGHT = auto()
[docs]class CausalBias(torch.Tensor):
"""
A bias representing causal attention patterns. For an overview of the bias structure, see the :class:`CausalVariant` enum.
This class is used for defining causal (triangular) attention biases. For construing the bias, there exist
two factory functions: :func:`causal_upper_left` and :func:`causal_lower_right`.
Example:
.. code-block:: python
from torch.nn.attention.bias import causal_lower_right
bsz, num_heads, seqlen_q, seqlen_kv, head_dim = 32, 8, 4, 12, 8
# Create a lower-right causal bias
attn_bias = causal_lower_right(seqlen_q, seqlen_kv)
q = torch.randn(bsz, num_heads, seqlen_q, head_dim, device="cuda", dtype=torch.float16)
k = torch.randn(bsz, num_heads, seqlen_kv, head_dim, device="cuda", dtype=torch.float16)
v = torch.randn(bsz, num_heads, seqlen_kv, head_dim, device="cuda", dtype=torch.float16)
out = F.scaled_dot_product_attention(q, k, v, attn_bias)
.. warning:: This class is a prototype and subject to change.
"""
def __init__(self, variant: CausalVariant, seq_len_q: int, seq_len_kv: int):
"""
Initializes the CausalBias instance with a specified variant and sequence lengths.
Args:
variant (CausalVariant): The type of causal bias to use (either UPPER_LEFT or LOWER_RIGHT).
seq_len_q (int): The sequence length of the query tensor.
seq_len_kv (int): The sequence length of the key/value tensor.
Raises a warning if the LOWER_RIGHT variant is used with seq_len_q > seq_len_kv, as it may produce NaNs.
"""
assert isinstance(variant, CausalVariant)
self.variant = variant
self.seq_len_q = seq_len_q
self.seq_len_kv = seq_len_kv
if seq_len_q > seq_len_kv and variant == CausalVariant.LOWER_RIGHT:
warn(
"Lower right causal bias will produce NaNs in the output when seq_len_q > seq_len_kv!"
)
def _upper_left(self, device: torch.device) -> torch.Tensor:
"""Upper left causal bias"""
return torch.tril(
torch.ones(self.seq_len_q, self.seq_len_kv, device=device, dtype=torch.bool)
)
def _lower_right(self, device: torch.device) -> torch.Tensor:
"""Lower right causal bias"""
diagonal_offset = self.seq_len_kv - self.seq_len_q
return torch.tril(
torch.ones(
self.seq_len_q, self.seq_len_kv, device=device, dtype=torch.bool
),
diagonal=diagonal_offset,
)
def _materialize(self, device: Optional[torch.device] = None) -> torch.Tensor:
"""
Materializes the causal bias into a tensor form.
Depending on the variant, this method generates either an upper-left or lower-right
triangular matrix to represent the causal bias.
Args:
device (Optional[torch.device]): The device on which to create the tensor. Defaults to CPU.
Returns:
torch.Tensor: The materialized bias tensor.
"""
if device is None:
device = torch.device("cpu")
if self.variant == CausalVariant.UPPER_LEFT:
return self._upper_left(device)
elif self.variant == CausalVariant.LOWER_RIGHT:
return self._lower_right(device)
@staticmethod
def _dispatch(
query: torch.Tensor,
key: torch.Tensor,
value: torch.Tensor,
attn_mask: "CausalBias",
dropout_p: float = 0.0,
is_causal: bool = False,
scale: Optional[float] = None,
) -> torch.Tensor:
r"""
Handles the logic for computing attention with the specified causal bias.
Args:
query (Tensor): Query tensor; shape :math:`(N, ..., L, E)`.
key (Tensor): Key tensor; shape :math:`(N, ..., S, E)`.
value (Tensor): Value tensor; shape :math:`(N, ..., S, Ev)`.
attn_mask (CausalBias): The type of causal attention to apply.
A boolean mask where a value of True indicates that the element *should* take part in attention.
A float mask of the same type as query, key, value that is added to the attention score.
dropout_p (float): Dropout probability; if greater than 0.0, dropout is applied
is_causal (bool): If true, assumes upper left causal attention masking and errors if both attn_mask and is_causal
are set.
scale (optional float): Scaling factor applied prior to softmax. If None, the default value is set
to :math:`\frac{1}{\sqrt{E}}`.
Returns:
output (Tensor): Attention output; shape :math:`(N, ..., L, Ev)`.
Raises:
ValueError: If the causal bias variant is not a CausalVariant type.
"""
if is_causal:
raise ValueError("CausalBias should not be used with causal=True")
if (
attn_mask.seq_len_q == attn_mask.seq_len_kv
or attn_mask.variant == CausalVariant.UPPER_LEFT
):
return scaled_dot_product_attention(
query,
key,
value,
attn_mask=None,
dropout_p=dropout_p,
is_causal=True,
scale=scale,
)
elif attn_mask.variant == CausalVariant.LOWER_RIGHT:
_validate_sdpa_input(query, key, value, None, dropout_p, is_causal, scale)
sdpa_params = SDPAParams(query, key, value, None, dropout_p, is_causal)
if can_use_flash_attention(sdpa_params):
needs_padding = query.size(-1) % 8 != 0
og_head_size = query.size(-1)
og_scale = _calculate_scale(og_head_size, scale)
if needs_padding:
query = torch.nn.functional.pad(query, (0, 8 - query.size(-1) % 8))
key = torch.nn.functional.pad(key, (0, 8 - key.size(-1) % 8))
value = torch.nn.functional.pad(value, (0, 8 - value.size(-1) % 8))
out = torch.ops.aten._scaled_dot_product_flash_attention(
query,
key,
value,
dropout_p,
is_causal=True, # TODO: Flash accepts causal = True and for this particular op it means lower right
return_debug_mask=False,
scale=og_scale,
)[0]
return _postprocess_flash_output(out, og_head_size)
if can_use_efficient_attention(sdpa_params):
compute_log_sumexp = False
if _input_requires_grad(query, key, value):
compute_log_sumexp = True
return torch.ops.aten._efficient_attention_forward(
query.transpose(1, 2),
key.transpose(1, 2),
value.transpose(1, 2),
bias=None,
cu_seqlens_q=None,
cu_seqlens_k=None,
max_seqlen_q=None,
max_seqlen_k=None,
dropout_p=dropout_p,
custom_mask_type=int(attn_mask.variant),
compute_log_sumexp=compute_log_sumexp,
scale=scale,
causal_diagonal=None,
seqlen_k=None,
)[0].transpose(1, 2)
else:
_raise_kernel_warnings(sdpa_params)
# We cant use efficient attention the only support for lower right is via materialization
return scaled_dot_product_attention(
query,
key,
value,
attn_mask=attn_mask._materialize(query.device),
dropout_p=dropout_p,
is_causal=False,
scale=scale,
)
else:
raise ValueError(
f"CausalBias.variant must be a CausalVariant type, but found: {attn_mask.variant}"
)
@classmethod
def __torch_function__(cls, func, types, args=(), kwargs=None):
"""Defines the behavior of torch.nn.functional.scaled_dot_product_attention when the attn_bias is an AttnBias"""
if kwargs is None:
kwargs = {}
if func != torch.nn.functional.scaled_dot_product_attention:
raise NotImplementedError(
"CausalBias only supports scaled_dot_product_attention"
)
return cls._dispatch(*args, **kwargs)
def __repr__(self):
return self._materialize().__repr__()
[docs]def causal_upper_left(*size) -> CausalBias:
"""
Creates an upper-left triangular causal bias.
This function generates a upper-left triangular matrix to represent causal attention bias with a
diagonal offset set so that the inclusive values are aligned to the upper left corner of the matrix.
This equivalent to the `is_causal=True` argument in `scaled_dot_product_attention`.
The equivalent pytorch code for constructing this bias is:
.. code-block:: python
torch.tril(torch.ones(size, dtype=torch.bool))
For instance, with `shape=(3,4)`, the materialized bias tensor will be:
.. code-block:: text
[[1, 0, 0, 0],
[1, 1, 0, 0],
[1, 1, 1, 0]]
Args:
size: The size of the bias matrix.
Returns:
CausalBias: The UPPER_LEFT triangular causal bias variant.
"""
assert len(size) == 2, "causal_upper_left only supports 2D tensors"
seq_len_q, seq_len_kv = size
return CausalBias(CausalVariant.UPPER_LEFT, seq_len_q, seq_len_kv)
[docs]def causal_lower_right(*size) -> CausalBias:
"""
Creates a lower-right triangular causal bias.
This function generates a lower-right triangular matrix to represent causal attention bias with a
diagonal offset set so that the inclusive values are aligned to the lower right corner of the matrix.
The equivalent pytorch code for constructing this bias is:
.. code-block:: python
diagonal_offset = size[1] - size[0]
torch.tril(
torch.ones(size, dtype=torch.bool),
diagonal=diagonal_offset,
)
For instance, with `shape=(3,4)`, the materialized bias tensor will be:
.. code-block:: text
[[1, 1, 0, 0],
[1, 1, 1, 0],
[1, 1, 1, 1]]
Args:
size: The size of the bias matrix.
Returns:
CausalBias: The LOWER_RIGHT triangular causal bias variant.
"""
assert len(size) == 2, "causal_lower_right only supports 2D tensors"
seq_len_q, seq_len_kv = size
return CausalBias(CausalVariant.LOWER_RIGHT, seq_len_q, seq_len_kv)
