未验证 提交 43a3af86 编写于 作者: C chengduo 提交者: GitHub

refine sgd_op (#13626)

test=develop
上级 adae0a3b
......@@ -12,7 +12,7 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#define EIGEN_USE_GPU
#include <algorithm>
#include "paddle/fluid/operators/sgd_op.h"
#include "paddle/fluid/platform/cuda_primitives.h"
......@@ -33,22 +33,21 @@ __global__ void SGDKernel(const T* g, const T* p, const T* learning_rate,
}
}
template <typename T, int block_size>
template <typename T>
__global__ void SparseSGDFunctorKernel(const T* selected_rows,
const int64_t* rows,
const T* learning_rate, T* tensor_out,
int64_t row_numel) {
const int ty = blockIdx.y;
int tid = threadIdx.x;
selected_rows += ty * row_numel;
tensor_out += rows[ty] * row_numel;
for (int index = tid; index < row_numel; index += block_size) {
int64_t row_numel, int64_t limit) {
for (int64_t i = blockIdx.x; i < limit; i += gridDim.x) {
const T* selected_rows_ptr = selected_rows + i * row_numel;
T* tensor_out_ptr = tensor_out + rows[i] * row_numel;
for (int64_t index = threadIdx.x; index < row_numel; index += blockDim.x) {
// Since index in rows of SelectedRows can be duplicate, we have to use
// Atomic Operation to avoid concurrent write error.
paddle::platform::CudaAtomicAdd(
tensor_out + index, -1.0 * learning_rate[0] * selected_rows[index]);
tensor_out_ptr + index,
-1.0 * learning_rate[0] * selected_rows_ptr[index]);
}
}
}
} // namespace
......@@ -97,13 +96,15 @@ class SGDOpCUDAKernel : public framework::OpKernel<T> {
auto* in_data = in_value.data<T>();
auto* out_data = param_out->data<T>();
const int block_size = 256;
dim3 threads(block_size, 1);
dim3 grid(1, in_rows.size());
SparseSGDFunctorKernel<
T, 256><<<grid, threads, 0, ctx.cuda_device_context().stream()>>>(
const int kThreadsPerBlock = 256;
int thread_x = kThreadsPerBlock;
int max_threads = ctx.cuda_device_context().GetMaxPhysicalThreadCount();
int max_blocks = std::max(max_threads / kThreadsPerBlock, 1);
SparseSGDFunctorKernel<<<max_blocks, thread_x, 0,
ctx.cuda_device_context().stream()>>>(
in_data, in_rows.CUDAData(ctx.GetPlace()), learning_rate->data<T>(),
out_data, in_row_numel);
out_data, in_row_numel, in_rows.size());
} else {
PADDLE_THROW("Unsupported Variable Type of Grad");
......
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