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

colossalai/kernel/cuda_native/scaled_softmax.py

@@ -28,9 +28,7 @@ class ScaledUpperTriangMaskedSoftmax(torch.autograd.Function):
             raise RuntimeError('ScaledUpperTriangMaskedSoftmax requires cuda extensions')
 
         scale_t = torch.tensor([scale])
-        softmax_results = colossal_scaled_upper_triang_masked_softmax.forward(
-            inputs, scale_t[0]
-        )
+        softmax_results = colossal_scaled_upper_triang_masked_softmax.forward(inputs, scale_t[0])
 
         ctx.save_for_backward(softmax_results, scale_t)
         return softmax_results
@@ -43,9 +41,7 @@ class ScaledUpperTriangMaskedSoftmax(torch.autograd.Function):
             raise RuntimeError('ScaledUpperTriangMaskedSoftmax requires cuda extensions')
 
         softmax_results, scale_t = ctx.saved_tensors
-        input_grads = colossal_scaled_upper_triang_masked_softmax.backward(
-            output_grads, softmax_results, scale_t[0]
-        )
+        input_grads = colossal_scaled_upper_triang_masked_softmax.backward(output_grads, softmax_results, scale_t[0])
 
         return input_grads, None
 
@@ -81,9 +77,7 @@ class ScaledMaskedSoftmax(torch.autograd.Function):
 
         softmax_results, scale_t = ctx.saved_tensors
 
-        input_grads = colossal_scaled_masked_softmax.backward(
-            output_grads, softmax_results, scale_t[0]
-        )
+        input_grads = colossal_scaled_masked_softmax.backward(output_grads, softmax_results, scale_t[0])
         return input_grads, None, None
 
 
@@ -114,9 +108,8 @@ class FusedScaleMaskSoftmax(nn.Module):
         super(FusedScaleMaskSoftmax, self).__init__()
         self.input_in_fp16 = input_in_fp16
         self.input_in_bf16 = input_in_bf16
-        assert not (
-            self.input_in_fp16 and self.input_in_bf16
-        ), "both fp16 and bf16 flags cannot be active at the same time."
+        assert not (self.input_in_fp16
+                    and self.input_in_bf16), "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.attn_mask_type = attn_mask_type
         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.scale = scale
 
-        assert (
-            self.scale is None or softmax_in_fp32
-        ), "softmax should be in fp32 when scaled"
+        assert (self.scale is None or softmax_in_fp32), "softmax should be in fp32 when scaled"
 
     def forward(self, input, mask):
         # [b, np, sq, sk]
@@ -140,14 +131,13 @@ class FusedScaleMaskSoftmax(nn.Module):
     def is_kernel_available(self, mask, b, np, sq, sk):
         attn_batches = b * np
 
-        if (
-            self.scaled_masked_softmax_fusion  # user want to fuse
-            and self.input_in_float16  # input must be fp16
-            and mask is not None  # mask tensor must not be None
-            and 16 < sk <= 2048  # sk must be 16 ~ 2048
-            and sq % 4 == 0  # sq must be divisor of 4
-            and attn_batches % 4 == 0  # np * b must be divisor of 4
-        ):
+        if (self.scaled_masked_softmax_fusion    # user want to fuse
+                and self.input_in_float16    # input must be fp16
+                and mask is not None    # mask tensor must not be None
+                and 16 < sk <= 2048    # sk must be 16 ~ 2048
+                and sq % 4 == 0    # sq must be divisor of 4
+                and attn_batches % 4 == 0    # np * b must be divisor of 4
+           ):
             if 0 <= sk <= 2048:
                 batch_per_block = self.get_batch_per_block(sq, sk, b, np)