未验证 提交 dc78f3ca 编写于 作者: C chengduo 提交者: GitHub

Merge pull request #5558 from mkliegl/conv_shift_fix_camel_case

conv shift op: change to CamelCase & fix bug
......@@ -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 {
......@@ -22,7 +23,7 @@ using framework::Tensor;
namespace {
inline int div_up(int x, int y) { return (x + y - 1) / y; }
inline int DivUp(int x, int y) { return (x + y - 1) / y; }
// Some notes on the design:
//
......@@ -33,9 +34,9 @@ inline int div_up(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 conv_shift_forward(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;
......@@ -62,11 +63,10 @@ __global__ void conv_shift_forward(const T *x, const T *y, T *out, int x_width,
if (tx < num_x) {
int load_i = (i - y_half_width + x_width) % x_width;
sx[tx] = x[k * x_width + load_i];
} else {
return;
}
__syncthreads();
if (tx < num_x) {
// Compute dot product of sx[tx:tx + y_width] and sy.
T sum = 0;
for (int j = 0; j < y_width; ++j) {
......@@ -75,12 +75,14 @@ __global__ void conv_shift_forward(const T *x, const T *y, T *out, int x_width,
// Save to out[k, i].
out[k * x_width + i] = sum;
}
}
// Compute x gradient - initial naive implementation with atomic add.
template <typename T>
__global__ void conv_shift_dx(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 conv_shift_dx(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 conv_shift_dy(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
......@@ -125,15 +127,15 @@ class ConvShiftKernel<platform::GPUPlace, T> : public framework::OpKernel<T> {
int y_half_width = (y_width - 1) / 2;
const int x_per_block = 256;
int num_x_blocks = div_up(x_width, x_per_block);
int num_x_blocks = DivUp(x_width, x_per_block);
int mem_per_block = (x_per_block + 2 * y_width) * sizeof(T);
dim3 grid_dim(num_x_blocks, batch_size);
auto stream = context.cuda_device_context().stream();
conv_shift_forward<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);
ConvShiftForward<T><<<grid_dim, x_per_block, mem_per_block, stream>>>(
x_data, y_data, x_width, y_width, y_half_width, batch_size, out_data);
}
};
......@@ -157,25 +159,26 @@ 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 = div_up(x_width, x_per_block);
int num_x_blocks = DivUp(x_width, x_per_block);
dim3 grid_dim(num_x_blocks, y_width, batch_size);
if (dX) {
T *dx_data = dX->mutable_data<T>(context.GetPlace());
cudaMemsetAsync(dx_data, 0, dX->numel() * sizeof(T), stream);
conv_shift_dx<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);
conv_shift_dy<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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