343 lines
10 KiB
Python
343 lines
10 KiB
Python
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
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#
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# This source code is licensed under the BSD license found in the
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# LICENSE file in the root directory of this source tree.
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import logging
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import math
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from contextlib import nullcontext
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from functools import lru_cache
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from typing import Optional, Union
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import torch
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from xformers import _has_cpp_library, _is_triton_available
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from xformers.components.attention.attention_mask import AttentionMask
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if _has_cpp_library:
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from ._sputnik_sparse import SparseCS
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if _is_triton_available():
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from xformers.triton.softmax import softmax as triton_softmax
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from xformers.triton.utils import gpu_capabilities_older_than_70
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_is_blocksparse_available = (
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_is_triton_available() and not gpu_capabilities_older_than_70()
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)
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if _is_blocksparse_available:
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from xformers.components.attention.blocksparse import BlockSparseAttention
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logger = logging.getLogger("xformers")
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def _create_random_sparsity(matrix, sparsity, divisible_by=4):
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assert matrix.ndim == 3
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keep = torch.rand_like(matrix[0], dtype=torch.float32) > sparsity
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nonzero = torch.nonzero(keep)
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nnz = nonzero.shape[0]
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# NOTE: need to make it a multiple of 4 for sputnik
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nonzero = nonzero[: (nnz - nnz % divisible_by)]
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i, j = nonzero.unbind(1)
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output = torch.zeros_like(matrix)
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bdim = torch.arange(matrix.shape[0], device=matrix.device)[:, None]
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output[bdim, i, j] = matrix[bdim, i, j]
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return output
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def _broadcast_batch(mask, batch_size):
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if mask.ndim == 3:
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return mask
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assert mask.ndim == 2
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mask = mask.coalesce()
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values = mask.values()
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indices = mask.indices()
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nnz = len(values)
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# strategy: repeat the indices and append the extra batch dimension to the indices
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indices = indices.repeat(1, batch_size)
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# now create the batch indices
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batch_indices = torch.arange(batch_size, device=indices.device)
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batch_indices = batch_indices[:, None].expand(batch_size, nnz).flatten()
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# put them together
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indices = torch.cat([batch_indices[None, :], indices], dim=0)
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# now repeat the values
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values = values.repeat(batch_size)
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size = (batch_size,) + mask.shape
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return torch.sparse_coo_tensor(indices, values, size)
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def _matmul_with_mask(
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a: torch.Tensor,
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b: torch.Tensor,
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mask: Optional[Union[torch.Tensor, "SparseCS"]],
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) -> torch.Tensor:
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if mask is None:
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return a @ b
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if _has_cpp_library and mask.dtype == torch.bool:
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if isinstance(mask, SparseCS):
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return mask.matmul_with_mask(a, b)
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if mask.is_sparse:
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# perform broadcasting if needed
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mask = _broadcast_batch(mask, a.shape[0])
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# coalesced is not implemented for bool tensors, so need to cast
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mask = mask.to(dtype=a.dtype) # type: ignore # mypy is missing the catch above
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return torch.ops.xformers.matmul_with_mask(a, b, mask)
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# Non optimized codepath
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if _has_cpp_library:
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assert not isinstance(mask, SparseCS)
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att = a @ b
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if mask.dtype == torch.bool:
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assert not isinstance(mask, SparseCS)
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if mask.ndim == 2:
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mask = mask.unsqueeze(0).expand(att.shape[0], -1, -1)
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# mask is presumed false == ignore
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att[~mask] = float("-inf")
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else:
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# mask is presumed additive
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# repeat if batch sizes don't match
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if (
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not isinstance(mask, SparseCS)
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and mask.ndim == 3
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and mask.shape[0] != att.shape[0]
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and (att.shape[0] % mask.shape[0]) == 0
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):
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repeat_factor = att.shape[0] // mask.shape[0]
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mask = mask.repeat([repeat_factor, 1, 1])
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logger.info("Mismatched batch dimensions for mask, repeating mask.")
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att += mask
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return att
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def _softmax(a: torch.Tensor, causal: bool = False) -> torch.Tensor:
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if _has_cpp_library and isinstance(a, SparseCS):
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return a.softmax()
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if a.is_sparse:
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return torch.sparse.softmax(a, dim=a.ndim - 1)
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if _is_triton_available():
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return triton_softmax(a, mask=None, causal=causal)
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else:
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return torch.softmax(a, dim=a.ndim - 1)
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if _has_cpp_library:
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class SparseBMM(torch.autograd.Function):
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@staticmethod
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def forward(ctx, a, b):
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a = a.coalesce()
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r = torch.bmm(a, b)
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ctx.save_for_backward(a, b)
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return r
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@staticmethod
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def backward(ctx, grad):
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a, b = ctx.saved_tensors
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# gradients w.r.t. a
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ga = None
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if ctx.needs_input_grad[0]:
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ga = torch.ops.xformers.matmul_with_mask(grad, b.transpose(-2, -1), a)
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# gradients w.r.t. b
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gb = None
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if ctx.needs_input_grad[1]:
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gb = a.transpose(1, 2).bmm(grad)
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return ga, gb
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def _sparse_bmm(a: torch.Tensor, b: torch.Tensor) -> torch.Tensor:
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"""
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Batch matrix multiply between a sparse matrix and a dense matrix
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"""
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assert a.ndim == b.ndim == 3
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assert a.shape[0] == b.shape[0]
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assert a.shape[2] == b.shape[1]
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return SparseBMM.apply(a, b)
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def bmm(a: torch.Tensor, b: torch.Tensor) -> torch.Tensor:
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if _has_cpp_library:
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if isinstance(a, SparseCS):
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return a.spmm(b)
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if a.is_sparse:
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return _sparse_bmm(a, b)
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return a @ b
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def _apply_dropout(att, dropout):
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if dropout is None:
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return att
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# Dropout chokes on sparse tensors
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if _has_cpp_library:
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if isinstance(att, SparseCS):
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values = att.values.clone()
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values = dropout(values)
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att = SparseCS.wrap(
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att.shape,
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values,
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att.row_indices,
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att.row_offsets,
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att.column_indices,
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att._transp_info,
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)
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elif att.is_sparse:
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att = att.coalesce()
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values = att.values().clone() # protect against in-place dropout
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values = dropout(values)
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att = torch.sparse_coo_tensor(att.indices(), values, att.shape)
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else:
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# Simple dense case
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att = dropout(att)
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return att
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# Non optimized vanilla dropout
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att = dropout(att)
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return att
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def scaled_query_key_softmax(
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q: torch.Tensor,
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k: torch.Tensor,
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att_mask: Optional[Union[AttentionMask, "SparseCS", torch.Tensor]],
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) -> torch.Tensor:
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# TODO assume we have (N, S, hs) instead of (B, nh, S, hs), with N = B x nh
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# this is needed due to limitations in sparse_bmm for now
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# Self-attend: (N, S, hs) x (N, hs, S) -> (N, S, S)
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q = q / math.sqrt(k.size(-1))
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# Matmul with mask
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if att_mask is not None and isinstance(att_mask, AttentionMask):
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# Additive mask
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mask: Optional[Union[SparseCS, torch.Tensor]] = att_mask.values
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else:
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mask = att_mask
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att = _matmul_with_mask(q, k.transpose(-2, -1), mask)
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# Softmax to get the attention probabilities
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is_causal = isinstance(att_mask, AttentionMask) and att_mask.is_causal
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att = _softmax(att, causal=is_causal)
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return att
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if _is_blocksparse_available:
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# 128 is default maxsize
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@lru_cache(maxsize=128)
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def _retrieve_blocksparse(
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num_heads: int, seq_len: int, block_size: int
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) -> BlockSparseAttention:
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# Checks if blocksparse object exists in cache
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blocks = seq_len // block_size
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layout_fill = torch.ones((num_heads, blocks, blocks), dtype=torch.long)
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return BlockSparseAttention(
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layout=layout_fill, block_size=block_size, causal=True
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)
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def blocksparse_attention(
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q: torch.Tensor,
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k: torch.Tensor,
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v: torch.Tensor,
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dropout: Optional[torch.nn.Module] = None,
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block_size: int = 128,
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) -> torch.Tensor:
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orig_dim = q.dim()
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seq_len = q.shape[-2]
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# Layout head dimension: 1 or batch size (q.shape[0])
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layout_heads = 1
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# TODO perhaps add functionality to pad qkv if sequence length is not divisible by block size?
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assert seq_len % block_size == 0, "Sequence length must be divisible by block size"
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if orig_dim == 3:
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# Reshape from (N, S, hs) to (B, nh, S, hs) where N = B x nh, hs = D / nh
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# Assuming num_heads = 1, (N, S, hs) to (B, 1, S, hs)
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if layout_heads == 1:
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q = q.unsqueeze(1)
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k = k.unsqueeze(1)
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v = v.unsqueeze(1)
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else:
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q = q.unsqueeze(0)
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k = k.unsqueeze(0)
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v = v.unsqueeze(0)
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blocksparse_attention = _retrieve_blocksparse(layout_heads, seq_len, block_size)
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# Dropout is a no-op in evaluation mode
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if isinstance(dropout, torch.nn.Dropout):
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blocksparse_attention.attn_drop = dropout
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else:
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blocksparse_attention.attn_drop = torch.nn.Dropout(0.0)
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att = blocksparse_attention(q, k, v)
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# Reshape attention (B, nh, S, hs) back to (N, S, hs)
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if orig_dim == 3:
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return att.flatten(0, 1)
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return att
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def scaled_dot_product_attention(
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q: torch.Tensor,
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k: torch.Tensor,
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v: torch.Tensor,
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att_mask: Optional[Union[AttentionMask, "SparseCS", torch.Tensor]],
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dropout: Optional[torch.nn.Module] = None,
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block_size: int = 128,
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) -> torch.Tensor:
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autocast_disabled = (
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_has_cpp_library
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and isinstance(att_mask, SparseCS)
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or (att_mask is not None and att_mask.is_sparse)
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)
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seq_len = q.shape[-2]
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# switch if:
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# causal is required but mask is not sparse
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# fp16 or under amp context
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# sequence length is divisible by block size
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# same seq len for K and Q
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switch_to_blocksparse = (
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_is_blocksparse_available
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and (att_mask is not None and not att_mask.is_sparse)
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and (isinstance(att_mask, AttentionMask) and att_mask.is_causal)
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and (q.dtype == torch.float16 or torch.is_autocast_enabled())
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and not seq_len % block_size
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and q.shape[-2] == k.shape[-2]
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)
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if switch_to_blocksparse:
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logger.info("Switching causal attention to Triton blocksparse...")
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return blocksparse_attention(q, k, v, dropout, block_size)
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with torch.cuda.amp.autocast(enabled=False) if autocast_disabled else nullcontext(): # type: ignore
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if autocast_disabled:
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q, k, v = q.float(), k.float(), v.float()
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att = scaled_query_key_softmax(q, k, att_mask=att_mask)
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# Optional dropout, could be part of the masking in the future
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att = _apply_dropout(att, dropout)
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# Get to the predicted values, for all heads
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# y = att @ v # (N, S, S) x (N, S, hs) -> (N, S, hs)
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y = bmm(att, v)
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return y
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