"keep style same with uniform operators"

paddle/operators/gaussian_random_op.cc

@@ -12,42 +12,42 @@
    See the License for the specific language governing permissions and
    limitations under the License. */
 
-#include "paddle/operators/gaussian_random_op.h"
-#include "glog/logging.h"
+#include <random>
 #include "paddle/framework/op_registry.h"
 
 namespace paddle {
 namespace operators {
 
 template <typename T>
-class GaussianRandomOpKernel<platform::CPUPlace, T>
-    : public framework::OpKernel {
+class GaussianRandomKernel : public framework::OpKernel {
  public:
   void Compute(const framework::ExecutionContext& context) const override {
-    auto mean = context.op_.GetAttr<T>("mean");
-    auto std = context.op_.GetAttr<T>("std");
-    auto* output = context.Output(0)->GetMutable<framework::Tensor>();
-    T* r = output->mutable_data<T>(context.GetPlace());
-    auto ctx =
-        static_cast<const platform::CPUDeviceContext*>(context.device_context_);
-    // generator need to modify context
-    auto g = const_cast<platform::CPUDeviceContext*>(ctx)->RandGenerator();
+    T mean = static_cast<T>(context.op_.GetAttr<T>("mean"));
+    T std = static_cast<T>(context.op_.GetAttr<T>("std"));
+    auto* tensor = context.Output<framework::Tensor>(0);
+    T* data = tensor->mutable_data<T>(context.GetPlace());
+
+    // TODO(dzh): attribute does not support unsigned int.
+    // And we need a global random seed configuration.
+    int seed = context.op_.GetAttr<int>("seed");
+    if (seed == 0) {
+      seed = std::random_device()();
+    }
+    std::mt19937 g(seed);
     std::normal_distribution<T> distribution(mean, std);
-    for (int i = 0; i < framework::product(output->dims()); ++i) {
-      r[i] = distribution(g);
+    for (int i = 0; i < framework::product(tensor->dims()); ++i) {
+      data[i] = distribution(g);
     }
   }
 };
 
 class GaussianRandomOp : public framework::OperatorWithKernel {
  protected:
-  void InferShape(const framework::InferShapeContext& ctx) const override {
-    PADDLE_ENFORCE(inputs.size() == 0, "Input size of RandomOp must be zero.");
-    PADDLE_ENFORCE(outputs.size() == 1, "Output size of RandomOp must be one.");
-    PADDLE_ENFORCE(outputs[0] != nullptr,
-                   "Outputs of RandomOp must all be set.");
-    auto* tensor = ctx.Output<Tensor>(0);
-    auto dims = GetAttr(std::vector<int>("shape"));
+  void InferShape(const framework::InferShapeContext& context) const override {
+    auto* tensor = context.Output<framework::Tensor>(0);
+    auto dims = GetAttr<std::vector<int>>("dims");
+    PADDLE_ENFORCE(dims.size() > 0UL,
+                   "dims can be one int or array. dims must be set.");
     tensor->Resize(framework::make_ddim(dims));
   }
 };
@@ -57,26 +57,25 @@ class GaussianRandomOpMaker : public framework::OpProtoAndCheckerMaker {
   GaussianRandomOpMaker(framework::OpProto* proto,
                         framework::OpAttrChecker* op_checker)
       : framework::OpProtoAndCheckerMaker(proto, op_checker) {
-    AddAttr<std::vector<int>>("shape", "The shape of matrix to be randomized");
-    AddAttr<float>("mean", "mean value of random.").SetDefault(.0);
-    AddAttr<float>("std", "minimum value of random value")
-        .SetDefault(1.0)
-        .LargerThan(.0);
     AddOutput("Out", "output matrix of random op");
     AddComment(R"DOC(
-GaussianRandom Operator fill a matrix in normal distribution.
-The eqution : Out = GaussianRandom(Shape=(d0, d1, ...), Dtype, mean, std)
+GaussianRandom operator.
+Use to initialize tensor with gaussian random generator.
 )DOC");
+
+    AddAttr<std::vector<int>>("dims", "The dimension of random tensor.");
+    AddAttr<float>("mean", "mean value of random.").SetDefault(.0f);
+    AddAttr<float>("std", "minimum value of random value.").SetDefault(1.0f);
+    AddAttr<int>("seed",
+                 "Random seed of generator."
+                 "0 means use system wide seed")
+        .SetDefault(0);
   }
 };
 
 }  // namespace operators
 }  // namespace paddle
 
-REGISTER_OP(gaussian_random, paddle::operators::GaussianRandomOp,
-            paddle::operators::GaussianRandomOpMaker);
-
-typedef paddle::operators::GaussianRandomOpKernel<paddle::platform::CPUPlace,
-                                                  float>
-    GaussianRandomOpKernel_CPU_float;
-REGISTER_OP_CPU_KERNEL(gaussian_random, GaussianRandomOpKernel_CPU_float);
+namespace ops = paddle::operators;
+REGISTER_OP(gaussian_random, ops::GaussianRandomOp, ops::GaussianRandomOpMaker);
+REGISTER_OP_CPU_KERNEL(gaussian_random, ops::GaussianRandomKernel<float>);

paddle/operators/gaussian_random_op.cu

+#include <memory>
+#include <random>
+#include "paddle/platform/dynload/curand.h"
+#include "paddle/platform/gpu_info.h"
+
 #include "paddle/framework/op_registry.h"
-#include "paddle/operators/guassian_random_op.h"
 
 namespace paddle {
 namespace operators {
 
 template <typename T>
-class GaussianRandomOpKernel<platform::GPUPlace, T>
-    : public framework::OpKernel {
+class GaussianRandomKernel : public framework::OpKernel {
  public:
-  void Compute(const framework::KernelContext& context) const override {
-    auto mean = context.op_.GetAttr<T>("mean");
-    auto std = context.op_.GetAttr<T>("std");
-    auto* output = context.Output(0)->GetMutable<framework::Tensor>();
-    T* r = output->mutable_data<T>(context.GetPlace());
-    auto ctx =
-        static_cast<const platform::GPUDeviceContext*>(context.device_context_);
-    // generator need to modify context
-    auto g = const_cast<platform::GPUDeviceContext*>(ctx)->RandGenerator();
-    curandGenerateNormal(g, r, framework::product(output->dims()), mean, std);
+  void Compute(const framework::ExecutionContext& context) const override {
+    T mean = static_cast<T>(context.op_.GetAttr<T>("mean"));
+    T std = static_cast<T>(context.op_.GetAttr<T>("std"));
+    auto* tensor = context.Output<framework::Tensor>(0);
+    T* data = tensor->mutable_data<T>(context.GetPlace());
+
+    int seed = context.op_.GetAttr<int>("seed");
+    if (seed == 0) {
+      seed = std::random_device()();
+    }
+    curandGenerator_t g;
+    PADDLE_ENFORCE(platform::dynload::curandCreateGenerator(
+        &g, CURAND_RNG_PSEUDO_DEFAULT));
+    PADDLE_ENFORCE(
+        platform::dynload::curandSetPseudoRandomGeneratorSeed(g, seed));
+    // auto g = const_cast<platform::GPUDeviceContext*>(ctx)->RandGenerator();
+    curandGenerateNormal(g, data, framework::product(tensor->dims()), mean,
+                         std);
   }
 };
 
 }  // namespace operators
 }  // namespace paddle
 
-typedef paddle::operators::GaussianRandomOpKernel<paddle::platform::GPUPlace,
-                                                  float>
-    RandomOpKernel_GPU_float;
-REGISTER_OP_GPU_KERNEL(gaussian_random, GaussianRandomOpKernel_GPU_float);
\ No newline at end of file
+namespace ops = paddle::operators;
+REGISTER_OP_GPU_KERNEL(gaussian_random, ops::GaussianRandomKernel<float>);
\ No newline at end of file

paddle/operators/gaussian_random_op.h

-#pragma once
-#include <random>
-#include "glog/logging.h"
-#include "paddle/framework/eigen.h"
-#include "paddle/framework/operator.h"
-
-namespace paddle {
-namespace operators {
-
-template <typename Place, typename T>
-class GaussianRandomOpKernel : public framework::OpKernel {
-public:
-  void Compute(const framework::KernelContext& context) const override {}
-};
-
-}  // namespace operators
-}  // namespace paddle