提交 d0b601c4 编写于 作者: M Markus Kliegl

address PR feedback

上级 42dd5da0
......@@ -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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