From d0b601c4a8219eef669b7e530a047bf898cf4cdc Mon Sep 17 00:00:00 2001
From: Markus Kliegl <klieglmarkus@baidu.com>
Date: Wed, 15 Nov 2017 00:57:43 +0000
Subject: [PATCH] address PR feedback

---
 paddle/operators/conv_shift_op.cu | 37 +++++++++++++++++--------------
 1 file changed, 20 insertions(+), 17 deletions(-)

diff --git a/paddle/operators/conv_shift_op.cu b/paddle/operators/conv_shift_op.cu
index 2a157f457a6..95e13c38a8d 100644
--- a/paddle/operators/conv_shift_op.cu
+++ b/paddle/operators/conv_shift_op.cu
@@ -13,6 +13,7 @@
    limitations under the License. */
 
 #include "paddle/operators/conv_shift_op.h"
+#include "paddle/operators/math/math_function.h"
 #include "paddle/platform/cuda_helper.h"
 
 namespace paddle {
@@ -33,9 +34,9 @@ inline int DivUp(int x, int y) { return (x + y - 1) / y; }
 // y is fairly small. For large y, it would probably be more efficient
 // to also tile across y.
 template <typename T>
-__global__ void ConvShiftForward(const T *x, const T *y, T *out, int x_width,
-                                 int y_width, int y_half_width,
-                                 int batch_size) {
+__global__ void ConvShiftForward(const T *x, const T *y, int x_width,
+                                 int y_width, int y_half_width, int batch_size,
+                                 T *out) {
   extern __shared__ T mem[];
 
   int tx = threadIdx.x;
@@ -79,8 +80,9 @@ __global__ void ConvShiftForward(const T *x, const T *y, T *out, int x_width,
 
 // Compute x gradient - initial naive implementation with atomic add.
 template <typename T>
-__global__ void ConvShiftGradX(const T *dout, const T *y, T *dx, int x_width,
-                               int y_width, int y_half_width, int batch_size) {
+__global__ void ConvShiftGradX(const T *dout, const T *y, int x_width,
+                               int y_width, int y_half_width, int batch_size,
+                               T *dx) {
   int i = blockIdx.x * blockDim.x + threadIdx.x;  // x index
   int j = blockIdx.y;                             // y index
   int k = blockIdx.z;                             // batch index
@@ -94,8 +96,8 @@ __global__ void ConvShiftGradX(const T *dout, const T *y, T *dx, int x_width,
 
 // Compute y gradient - initial naive implementation with atomic add.
 template <typename T>
-__global__ void ConvShiftDy(const T *x, const T *dout, T *dy, int x_width,
-                            int y_width, int y_half_width, int batch_size) {
+__global__ void ConvShiftDy(const T *x, const T *dout, int x_width, int y_width,
+                            int y_half_width, int batch_size, T *dy) {
   int i = blockIdx.x * blockDim.x + threadIdx.x;  // x index
   int j = blockIdx.y;                             // y index
   int k = blockIdx.z;                             // batch index
@@ -133,7 +135,7 @@ class ConvShiftKernel<platform::GPUPlace, T> : public framework::OpKernel<T> {
     auto stream = context.cuda_device_context().stream();
 
     ConvShiftForward<T><<<grid_dim, x_per_block, mem_per_block, stream>>>(
-        x_data, y_data, out_data, x_width, y_width, y_half_width, batch_size);
+        x_data, y_data, x_width, y_width, y_half_width, batch_size, out_data);
   }
 };
 
@@ -157,7 +159,8 @@ class ConvShiftGradKernel<platform::GPUPlace, T>
     int y_width = Y->dims()[1];
     int y_half_width = (y_width - 1) / 2;
 
-    auto stream = context.cuda_device_context().stream();
+    auto &device_ctx = context.cuda_device_context();
+    math::SetConstant<platform::GPUPlace, T> zero;
 
     const int x_per_block = 256;
     int num_x_blocks = DivUp(x_width, x_per_block);
@@ -165,17 +168,17 @@ class ConvShiftGradKernel<platform::GPUPlace, T>
 
     if (dX) {
       T *dx_data = dX->mutable_data<T>(context.GetPlace());
-      cudaMemsetAsync(dx_data, 0, dX->numel() * sizeof(T), stream);
-      ConvShiftGradX<T><<<grid_dim, x_per_block, 0, stream>>>(
-          dout_data, y_data, dx_data, x_width, y_width, y_half_width,
-          batch_size);
+      zero(device_ctx, dX, static_cast<T>(0.0));
+      ConvShiftGradX<T><<<grid_dim, x_per_block, 0, device_ctx.stream()>>>(
+          dout_data, y_data, x_width, y_width, y_half_width, batch_size,
+          dx_data);
     }
     if (dY) {
       T *dy_data = dY->mutable_data<T>(context.GetPlace());
-      cudaMemsetAsync(dy_data, 0, dY->numel() * sizeof(T), stream);
-      ConvShiftDy<T><<<grid_dim, x_per_block, 0, stream>>>(
-          x_data, dout_data, dy_data, x_width, y_width, y_half_width,
-          batch_size);
+      zero(device_ctx, dY, static_cast<T>(0.0));
+      ConvShiftDy<T><<<grid_dim, x_per_block, 0, device_ctx.stream()>>>(
+          x_data, dout_data, x_width, y_width, y_half_width, batch_size,
+          dy_data);
     }
   }
 };
-- 
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