From 95b95a284b77a1e4c8eeff07c2d9da9110c7e89c Mon Sep 17 00:00:00 2001
From: wangchaochaohu <wangchao66@baidu.com>
Date: Mon, 9 Dec 2019 23:41:27 -0600
Subject: [PATCH] Mean gpu optimize (#21643)

* accelerate mean op test=develop
---
 paddle/fluid/operators/mean_op.cu | 90 +++++++++++++++++++++++++++++--
 1 file changed, 87 insertions(+), 3 deletions(-)

diff --git a/paddle/fluid/operators/mean_op.cu b/paddle/fluid/operators/mean_op.cu
index 921c2e1298..f0f895c08a 100644
--- a/paddle/fluid/operators/mean_op.cu
+++ b/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>,
-- 
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