Mean gpu optimize (#21643)

* accelerate mean op test=develop

paddle/fluid/operators/mean_op.cu

@@ -11,15 +11,99 @@ distributed under the License is distributed on an "AS IS" BASIS,
 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. */
+#include "cub/cub.cuh"
 #include "paddle/fluid/operators/mean_op.h"
+#include "paddle/fluid/platform/cuda_primitives.h"
 #include "paddle/fluid/platform/float16.h"
 
+namespace paddle {
+namespace operators {
+
+template <typename T>
+struct DivideFunctor {
+  HOSTDEVICE explicit inline DivideFunctor(int n)
+      : n_inv(static_cast<T>(1.0 / n)) {}
+
+  HOSTDEVICE inline T operator()(const T& x) const { return x * n_inv; }
+
+ private:
+  T n_inv;
+};
+
+template <typename T>
+__global__ void MeanRunKernel(const T in_data, T* out_data, int N) {
+  int idx = blockDim.x * blockIdx.x + threadIdx.x;
+  for (; idx < N; idx += blockDim.x * gridDim.x) {
+    out_data[idx] = in_data / (static_cast<T>(N));
+  }
+}
+
+template <typename DeviceContext, typename T>
+class MeanCUDAKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    auto* input = context.Input<Tensor>("X");
+    auto* output = context.Output<Tensor>("Out");
+
+    output->mutable_data<T>(context.GetPlace());
+    auto size_prob = input->numel();
+    const T* in_data = input->data<T>();
+    T* out_data = output->mutable_data<T>(context.GetPlace());
+    auto stream = context.cuda_device_context().stream();
+
+    DivideFunctor<T> transformer(size_prob);
+    cub::TransformInputIterator<T, DivideFunctor<T>, const T*> trans_x(
+        in_data, transformer);
+    size_t temp_storage_bytes = 0;
+
+    auto err = cub::DeviceReduce::Sum(nullptr, temp_storage_bytes, trans_x,
+                                      out_data, size_prob, stream);
+    PADDLE_ENFORCE_CUDA_SUCCESS(err,
+                                "MeanOP failed to get reduce workspace size",
+                                cudaGetErrorString(err));
+    framework::Tensor tmp;
+    auto* temp_storage = tmp.mutable_data<uint8_t>(
+        framework::make_ddim({static_cast<int64_t>(temp_storage_bytes)}),
+        context.GetPlace());
+    err = cub::DeviceReduce::Sum(temp_storage, temp_storage_bytes, trans_x,
+                                 out_data, size_prob, stream);
+    PADDLE_ENFORCE_CUDA_SUCCESS(err, "MeanOP failed to run reduce computation",
+                                cudaGetErrorString(err));
+  }
+};
+
+template <typename DeviceContext, typename T>
+class MeanCUDAGradKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    auto OG = context.Input<Tensor>(framework::GradVarName("Out"));
+    PADDLE_ENFORCE_EQ(
+        OG->numel(), 1,
+        platform::errors::InvalidArgument(
+            "Mean Gradient Input Tensor len should be 1. But received %d",
+            OG->numel()));
+    auto IG = context.Output<Tensor>(framework::GradVarName("X"));
+    IG->mutable_data<T>(context.GetPlace());
+
+    T in_data = OG[0];
+    auto size_prob = IG->numel();
+    auto out_data = IG->data<T>();
+    int threads = 512;
+    int grid = (size_prob + threads - 1) / threads;
+    auto stream = context.cuda_device_context().stream();
+    MeanRunKernel<T><<<grid, threads, 0, stream>>>(in_data, out_data,
+                                                   size_prob);
+  }
+};
+}  // namespace operators
+}  // namespace paddle
+
 namespace ops = paddle::operators;
 namespace plat = paddle::platform;
 REGISTER_OP_CUDA_KERNEL(
-    mean, ops::MeanKernel<paddle::platform::CUDADeviceContext, float>,
-    ops::MeanKernel<paddle::platform::CUDADeviceContext, double>,
-    ops::MeanKernel<paddle::platform::CUDADeviceContext, plat::float16>);
+    mean, ops::MeanCUDAKernel<paddle::platform::CUDADeviceContext, float>,
+    ops::MeanCUDAKernel<paddle::platform::CUDADeviceContext, double>,
+    ops::MeanCUDAKernel<paddle::platform::CUDADeviceContext, plat::float16>);
 REGISTER_OP_CUDA_KERNEL(
     mean_grad, ops::MeanGradKernel<paddle::platform::CUDADeviceContext, float>,
     ops::MeanGradKernel<paddle::platform::CUDADeviceContext, double>,