Merge pull request #5558 from mkliegl/conv_shift_fix_camel_case

conv shift op: change to CamelCase & fix bug

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 {
@@ -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;
@@ -62,25 +63,26 @@ __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();
 
-  // Compute dot product of sx[tx:tx + y_width] and sy.
-  T sum = 0;
-  for (int j = 0; j < y_width; ++j) {
-    sum += sx[tx + j] * sy[j];
-  }
+  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) {
+      sum += sx[tx + j] * sy[j];
+    }
 
-  // Save to out[k, i].
-  out[k * x_width + i] = sum;
+    // 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);
     }
   }
 };