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pkgs/xformers/_flash_attn/fused_softmax.py
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pkgs/xformers/_flash_attn/fused_softmax.py
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# [2022-10-23] Copied from https://github.com/NVIDIA/apex/blob/master/apex/transformer/functional/fused_softmax.py
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# for benchmarking.
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# We added support for seqlen=2k and seqlen=4k
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# coding=utf-8
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# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import torch
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from apex._autocast_utils import _cast_if_autocast_enabled
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from apex.transformer.enums import AttnMaskType
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from fused_softmax_lib import scaled_masked_softmax_forward, scaled_masked_softmax_backward
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from fused_softmax_lib import scaled_masked_softmax_get_batch_per_block
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from fused_softmax_lib import scaled_upper_triang_masked_softmax_forward, scaled_upper_triang_masked_softmax_backward
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class ScaledUpperTriangMaskedSoftmax(torch.autograd.Function):
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"""
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Fused operation which performs following three operations in sequence
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1. Scale the tensor.
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2. Apply upper triangular mask (typically used in gpt models).
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3. Perform softmax.
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"""
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@staticmethod
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def forward(ctx, inputs, scale):
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scale_t = torch.tensor([scale])
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softmax_results = scaled_upper_triang_masked_softmax_forward(
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inputs, scale_t[0]
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)
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ctx.save_for_backward(softmax_results, scale_t)
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return softmax_results
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@staticmethod
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def backward(ctx, output_grads):
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softmax_results, scale_t = ctx.saved_tensors
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input_grads = scaled_upper_triang_masked_softmax_backward(
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output_grads, softmax_results, scale_t[0]
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)
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return input_grads, None
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def scaled_upper_triang_masked_softmax(inputs, _, scale):
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b, np, sq, sk = inputs.size()
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assert sq == sk, "causal mask is only for self attention"
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# Reshaping input to 3D tensor (attn_batches, sq, sk)
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inputs = inputs.view(-1, sq, sk)
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args = _cast_if_autocast_enabled(inputs, scale)
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with torch.cuda.amp.autocast(enabled=False):
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probs = ScaledUpperTriangMaskedSoftmax.apply(*args)
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return probs.view(b, np, sq, sk)
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# NOTE (mkozuki): `ScaledMaskedSoftmax` somehow doesn't work well with `torch.cuda.amp.custom_fwd`.
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# Without `cast_inputs` kwarg, somehow inputs are not cast to dtype used in the autocast context.
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# So I needed to manually write two `torch.autograd.Function` inheritances.
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# Fused operation which performs following three operations in sequence
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# 1. Scale the tensor.
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# 2. Apply the mask.
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# 3. Perform softmax.
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class ScaledMaskedSoftmax(torch.autograd.Function):
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@staticmethod
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def forward(ctx, inputs, mask, scale):
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scale_t = torch.tensor([scale])
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softmax_results = scaled_masked_softmax_forward(inputs, mask, scale_t[0])
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ctx.save_for_backward(softmax_results, scale_t)
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return softmax_results
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@staticmethod
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def backward(ctx, output_grads):
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softmax_results, scale_t = ctx.saved_tensors
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input_grads = scaled_masked_softmax_backward(
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output_grads, softmax_results, scale_t[0]
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)
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return input_grads, None, None
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def scaled_masked_softmax(inputs, mask, scale):
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# input is 4D tensor (b, np, sq, sk)
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args = _cast_if_autocast_enabled(inputs, mask, scale)
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with torch.cuda.amp.autocast(enabled=False):
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return ScaledMaskedSoftmax.apply(*args)
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class FusedScaleMaskSoftmax(torch.nn.Module):
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"""
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fused operation: scaling + mask + softmax
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Arguments:
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input_in_fp16: flag to indicate if input in fp16 data format.
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input_in_bf16: flag to indicate if input in bf16 data format.
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attn_mask_type: attention mask type (pad or causal)
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scaled_masked_softmax_fusion: flag to indicate user want to use softmax fusion
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mask_func: mask function to be applied.
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softmax_in_fp32: if true, softmax in performed at fp32 precision.
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scale: scaling factor used in input tensor scaling.
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"""
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def __init__(
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self,
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input_in_fp16,
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input_in_bf16,
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attn_mask_type,
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scaled_masked_softmax_fusion,
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mask_func,
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softmax_in_fp32,
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scale,
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):
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super().__init__()
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self.input_in_fp16 = input_in_fp16
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self.input_in_bf16 = input_in_bf16
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if self.input_in_fp16 and self.input_in_bf16:
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raise RuntimeError(
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"both fp16 and bf16 flags cannot be active at the same time."
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)
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self.input_in_float16 = self.input_in_fp16 or self.input_in_bf16
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self.attn_mask_type = attn_mask_type
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self.scaled_masked_softmax_fusion = scaled_masked_softmax_fusion
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self.mask_func = mask_func
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self.softmax_in_fp32 = softmax_in_fp32
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self.scale = scale
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if not (self.scale is None or softmax_in_fp32):
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raise RuntimeError("softmax should be in fp32 when scaled")
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if self.scaled_masked_softmax_fusion:
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if self.attn_mask_type == AttnMaskType.causal:
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self.fused_softmax_func = scaled_upper_triang_masked_softmax
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elif self.attn_mask_type == AttnMaskType.padding:
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self.fused_softmax_func = scaled_masked_softmax
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else:
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raise ValueError("Invalid attn_mask_type.")
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def forward(self, input, mask):
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# [b, np, sq, sk]
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assert input.dim() == 4
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if self.is_kernel_available(mask, *input.size()):
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return self.forward_fused_softmax(input, mask)
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else:
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return self.forward_torch_softmax(input, mask)
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def is_kernel_available(self, mask, b, np, sq, sk):
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attn_batches = b * np
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if (
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self.scaled_masked_softmax_fusion # user want to fuse
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and self.input_in_float16 # input must be fp16
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and (
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self.attn_mask_type == AttnMaskType.causal
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or (self.attn_mask_type == AttnMaskType.padding and mask is not None)
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)
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and 16 < sk <= 8192 # sk must be 16 ~ 8192
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and sq % 4 == 0 # sq must be divisor of 4
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and sk % 4 == 0 # sk must be divisor of 4
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and attn_batches % 4 == 0 # np * b must be divisor of 4
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):
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if 0 <= sk <= 8192:
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batch_per_block = self.get_batch_per_block(sq, sk, b, np)
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if self.attn_mask_type == AttnMaskType.causal:
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if attn_batches % batch_per_block == 0:
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return True
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else:
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if sq % batch_per_block == 0:
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return True
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return False
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def forward_fused_softmax(self, input, mask):
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# input.shape = [b, np, sq, sk]
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scale = self.scale if self.scale is not None else 1.0
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return self.fused_softmax_func(input, mask, scale)
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def forward_torch_softmax(self, input, mask):
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if self.input_in_float16 and self.softmax_in_fp32:
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input = input.float()
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if self.scale is not None:
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input = input * self.scale
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mask_output = self.mask_func(input, mask) if mask is not None else input
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probs = torch.nn.Softmax(dim=-1)(mask_output)
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if self.input_in_float16 and self.softmax_in_fp32:
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if self.input_in_fp16:
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probs = probs.half()
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else:
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probs = probs.bfloat16()
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return probs
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@staticmethod
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def get_batch_per_block(sq, sk, b, np):
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return scaled_masked_softmax_get_batch_per_block(sq, sk, b, np)
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