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[NFC] polish colossalai/kernel/cuda_native/scaled_softmax.py code style (#955)

pull/997/head
Wangbo Zhao(黑色枷锁) 2022-05-13 22:16:46 +08:00 committed by binmakeswell
parent f6970ef8b1
commit 8ca2a85682
1 changed files with 13 additions and 23 deletions
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36
colossalai/kernel/cuda_native/scaled_softmax.py
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@ -28,9 +28,7 @@ class ScaledUpperTriangMaskedSoftmax(torch.autograd.Function):
raise RuntimeError('ScaledUpperTriangMaskedSoftmax requires cuda extensions') raise RuntimeError('ScaledUpperTriangMaskedSoftmax requires cuda extensions')
scale_t = torch.tensor([scale]) scale_t = torch.tensor([scale])
softmax_results = colossal_scaled_upper_triang_masked_softmax.forward( softmax_results = colossal_scaled_upper_triang_masked_softmax.forward(inputs, scale_t[0])
inputs, scale_t[0]
)
ctx.save_for_backward(softmax_results, scale_t) ctx.save_for_backward(softmax_results, scale_t)
return softmax_results return softmax_results
@ -43,9 +41,7 @@ class ScaledUpperTriangMaskedSoftmax(torch.autograd.Function):
raise RuntimeError('ScaledUpperTriangMaskedSoftmax requires cuda extensions') raise RuntimeError('ScaledUpperTriangMaskedSoftmax requires cuda extensions')
softmax_results, scale_t = ctx.saved_tensors softmax_results, scale_t = ctx.saved_tensors
input_grads = colossal_scaled_upper_triang_masked_softmax.backward( input_grads = colossal_scaled_upper_triang_masked_softmax.backward(output_grads, softmax_results, scale_t[0])
output_grads, softmax_results, scale_t[0]
)
return input_grads, None return input_grads, None
@ -81,9 +77,7 @@ class ScaledMaskedSoftmax(torch.autograd.Function):
softmax_results, scale_t = ctx.saved_tensors softmax_results, scale_t = ctx.saved_tensors
input_grads = colossal_scaled_masked_softmax.backward( input_grads = colossal_scaled_masked_softmax.backward(output_grads, softmax_results, scale_t[0])
output_grads, softmax_results, scale_t[0]
)
return input_grads, None, None return input_grads, None, None
@ -114,9 +108,8 @@ class FusedScaleMaskSoftmax(nn.Module):
super(FusedScaleMaskSoftmax, self).__init__() super(FusedScaleMaskSoftmax, self).__init__()
self.input_in_fp16 = input_in_fp16 self.input_in_fp16 = input_in_fp16
self.input_in_bf16 = input_in_bf16 self.input_in_bf16 = input_in_bf16
assert not ( assert not (self.input_in_fp16
self.input_in_fp16 and self.input_in_bf16 and self.input_in_bf16), "both fp16 and bf16 flags cannot be active at the same time."
), "both fp16 and bf16 flags cannot be active at the same time."
self.input_in_float16 = self.input_in_fp16 or self.input_in_bf16 self.input_in_float16 = self.input_in_fp16 or self.input_in_bf16
self.attn_mask_type = attn_mask_type self.attn_mask_type = attn_mask_type
self.scaled_masked_softmax_fusion = scaled_masked_softmax_fusion self.scaled_masked_softmax_fusion = scaled_masked_softmax_fusion
@ -124,9 +117,7 @@ class FusedScaleMaskSoftmax(nn.Module):
self.softmax_in_fp32 = softmax_in_fp32 self.softmax_in_fp32 = softmax_in_fp32
self.scale = scale self.scale = scale
assert ( assert (self.scale is None or softmax_in_fp32), "softmax should be in fp32 when scaled"
self.scale is None or softmax_in_fp32
), "softmax should be in fp32 when scaled"
def forward(self, input, mask): def forward(self, input, mask):
# [b, np, sq, sk] # [b, np, sq, sk]
@ -140,14 +131,13 @@ class FusedScaleMaskSoftmax(nn.Module):
def is_kernel_available(self, mask, b, np, sq, sk): def is_kernel_available(self, mask, b, np, sq, sk):
attn_batches = b * np attn_batches = b * np
if ( if (self.scaled_masked_softmax_fusion # user want to fuse
self.scaled_masked_softmax_fusion # user want to fuse and self.input_in_float16 # input must be fp16
and self.input_in_float16 # input must be fp16 and mask is not None # mask tensor must not be None
and mask is not None # mask tensor must not be None and 16 < sk <= 2048 # sk must be 16 ~ 2048
and 16 < sk <= 2048 # sk must be 16 ~ 2048 and sq % 4 == 0 # sq must be divisor of 4
and sq % 4 == 0 # sq must be divisor of 4 and attn_batches % 4 == 0 # np * b must be divisor of 4
and attn_batches % 4 == 0 # np * b must be divisor of 4 ):
):
if 0 <= sk <= 2048: if 0 <= sk <= 2048:
batch_per_block = self.get_batch_per_block(sq, sk, b, np) batch_per_block = self.get_batch_per_block(sq, sk, b, np)