From 2ea15fc9618b82f8003ea6df57a6446fc117282a Mon Sep 17 00:00:00 2001
From: Zhang Zheng <32410583+ZzSean@users.noreply.github.com>
Date: Fri, 11 Feb 2022 15:53:18 +0800
Subject: [PATCH] Optimize performance of softmax_bwd when axis!=-1 (#38609)

* Optimize performance of softmax_bwd when axis!=-1

* fix

* fix

* fix

* fix
---
 paddle/fluid/operators/softmax_cudnn_op.cu.h | 62 ++++++++++++++++++++
 1 file changed, 62 insertions(+)

diff --git a/paddle/fluid/operators/softmax_cudnn_op.cu.h b/paddle/fluid/operators/softmax_cudnn_op.cu.h
index 85bc3946bae..dc5166f4f99 100644
--- a/paddle/fluid/operators/softmax_cudnn_op.cu.h
+++ b/paddle/fluid/operators/softmax_cudnn_op.cu.h
@@ -584,6 +584,43 @@ __global__ void NormalSoftmaxForward(T* output, const T* input, int high_dim,
   }
 }
 
+template <typename T, typename AccT,
+          template <typename, typename> class Functor>
+__global__ void NormalSoftmaxBackward(T* input_grad, const T* output_grad,
+                                      const T* output, int high_dim,
+                                      int mid_dim, int low_dim) {
+  using kMode = kps::details::ReduceMode;
+  const int high_stride = mid_dim * low_dim;
+  const int mid_stride = low_dim;
+  for (int high_id = blockIdx.y; high_id < high_dim; high_id += gridDim.y) {
+    for (int low_id = blockIdx.x * blockDim.x + threadIdx.x; low_id < low_dim;
+         low_id += blockDim.x * gridDim.x) {
+      const int grad_offset = high_id * high_stride + low_id;
+
+      // 1. reduce sum
+      AccT sum = 0;
+      for (int mid_id = threadIdx.y; mid_id < mid_dim; mid_id += blockDim.y) {
+        int data_offset = grad_offset + mid_id * mid_stride;
+        sum += static_cast<AccT>(output_grad[data_offset]) *
+               static_cast<AccT>(output[data_offset]);
+      }
+      if (blockDim.y > 1) {
+        kps::Reduce<AccT, 1, 1, 1, kps::AddFunctor<AccT>, kMode::kGlobalMode>(
+            &sum, &sum, kps::AddFunctor<AccT>(), false);
+      }
+
+      // 2. (log)softmax backward
+      Functor<AccT, T> functor(sum);
+      for (int mid_id = threadIdx.y; mid_id < mid_dim; mid_id += blockDim.y) {
+        int data_offset = grad_offset + mid_id * mid_stride;
+        input_grad[data_offset] =
+            functor(static_cast<AccT>(output_grad[data_offset]),
+                    static_cast<AccT>(output[data_offset]));
+      }
+    }
+  }
+}
+
 template <typename T, bool LogMode = false>
 void LaunchNormalSoftmaxForward(const platform::CUDADeviceContext& dev_ctx,
                                 T* output_data, const T* input_data,
@@ -603,6 +640,28 @@ void LaunchNormalSoftmaxForward(const platform::CUDADeviceContext& dev_ctx,
   }
 }
 
+template <typename T, bool LogMode = false>
+void LaunchNormalSoftmaxBackward(const platform::CUDADeviceContext& dev_ctx,
+                                 T* input_grad_data, const T* output_grad_data,
+                                 const T* output_data, int high_dim,
+                                 int mid_dim, int low_dim) {
+  using AccT = typename details::MPTypeTrait<T>::Type;
+  dim3 grid, block;
+  GetLaunchConfig(high_dim, mid_dim, low_dim, &grid, &block);
+  if (LogMode) {
+    NormalSoftmaxBackward<
+        T, AccT,
+        LogSoftmaxBackwardFunctor><<<grid, block, 0, dev_ctx.stream()>>>(
+        input_grad_data, output_grad_data, output_data, high_dim, mid_dim,
+        low_dim);
+  } else {
+    NormalSoftmaxBackward<
+        T, AccT, SoftmaxBackwardFunctor><<<grid, block, 0, dev_ctx.stream()>>>(
+        input_grad_data, output_grad_data, output_data, high_dim, mid_dim,
+        low_dim);
+  }
+}
+
 template <typename T, bool LogMode = false>
 void SoftmaxForwardCUDAKernelDriver(const platform::CUDADeviceContext& dev_ctx,
                                     const Tensor& x, const int input_axis,
@@ -741,6 +800,9 @@ void SoftmaxBackwardCUDAKernelDriver(const platform::CUDADeviceContext& dev_ctx,
           blocks, threads, dev_ctx, dx_data, dout.data<T>(), out.data<T>(), N,
           dim, dim, kDimLog2);
     }
+  } else if (D > 1) {
+    LaunchNormalSoftmaxBackward<T, LogMode>(dev_ctx, dx_data, dout.data<T>(),
+                                            out.data<T>(), N, dim, D);
   } else {
     ScopedTensorDescriptor desc;
     std::vector<int> tensor_dims = {N, dim, D, 1};
-- 
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