merge header in cc

paddle/fluid/operators/sampling_id_op.cc

@@ -12,18 +12,68 @@ 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 "paddle/fluid/operators/sampling_id_op.h"
+#include <algorithm>
+#include <iostream>
+#include <iterator>
+#include <random>
+#include <sstream>
+#include <vector>
+#include "paddle/fluid/framework/op_registry.h"
 
 namespace paddle {
 namespace operators {
 
 using Tensor = framework::Tensor;
 
+template <typename DeviceContext, typename T>
+class SamplingIdKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    const Tensor* input = context.Input<Tensor>("X");
+    const int batch_size = static_cast<int>(input->dims()[0]);
+    const int width = static_cast<int>(input->dims()[1]);
+
+    std::vector<T> ins_vector;
+    framework::TensorToVector(*input, context.device_context(), &ins_vector);
+
+    std::vector<T> ids(batch_size);
+    for (size_t i = 0; i < batch_size; ++i) {
+      double r = getRandReal();
+      int idx = width - 1;
+      for (int j = 0; j < width; ++j) {
+        if ((r -= ins_vector[i * width + j]) < 0) {
+          idx = j;
+          break;
+        }
+      }
+      ids[i] = ins_vector[i * width + idx];
+    }
+
+    std::vector<int64_t> out_dim;
+    out_dim.push_back(static_cast<int64_t>(batch_size));
+
+    Tensor* output = context.Output<Tensor>("Out");
+    output->Resize(framework::make_ddim(out_dim));
+    output->mutable_data<T>(context.GetPlace());
+    framework::TensorFromVector(ids, context.device_context(), output);
+  }
+
+ private:
+  double getRandReal() const {
+    std::random_device
+        rd;  // Will be used to obtain a seed for the random number engine
+    std::mt19937 gen(rd());  // Standard mersenne_twister_engine seeded with
+                             // rd()
+    std::uniform_real_distribution<> dis(1.0, 2.0);
+    return dis(gen);
+  }
+};
+
 class SamplingIdOp : public framework::OperatorWithKernel {
  public:
   using framework::OperatorWithKernel::OperatorWithKernel;
 
-  void InferShape(framework::InferShapeContext *ctx) const override {
+  void InferShape(framework::InferShapeContext* ctx) const override {
     PADDLE_ENFORCE(ctx->HasInput("X"),
                    "Input(X) of SamplingIdOp should not be null.");
     PADDLE_ENFORCE(ctx->HasOutput("Out"),

paddle/fluid/operators/sampling_id_op.h

@@ -1,82 +0,0 @@
-/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-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. */
-#pragma once
-
-#include <algorithm>
-#include <iostream>
-#include <iterator>
-#include <random>
-#include <sstream>
-#include <vector>
-#include "paddle/fluid/framework/lod_tensor.h"
-#include "paddle/fluid/framework/op_registry.h"
-
-namespace paddle {
-namespace operators {
-
-using Tensor = framework::Tensor;
-
-template <typename DeviceContext, typename T>
-class SamplingIdKernel : public framework::OpKernel<T> {
- public:
-  void Compute(const framework::ExecutionContext& context) const override {
-    const Tensor* input = context.Input<Tensor>("X");
-    const int batch_size = static_cast<int>(input->dims()[0]);
-    const int width = static_cast<int>(input->dims()[1]);
-
-    std::vector<T> ins_vector;
-    framework::TensorToVector(*input, context.device_context(), &ins_vector);
-
-    std::vector<T> ids(batch_size);
-    for (size_t i = 0; i < batch_size; ++i) {
-      double r = this->getRandReal();
-      int idx = width - 1;
-      for (int j = 0; j < width; ++j) {
-        if ((r -= ins_vector[i * width + j]) < 0) {
-          idx = j;
-          break;
-        }
-      }
-      ids[i] = ins_vector[i * width + idx];
-    }
-
-    std::vector<int64_t> out_dim;
-    out_dim.push_back(static_cast<int64_t>(batch_size));
-
-    Tensor* output = context.Output<Tensor>("Out");
-    output->Resize(framework::make_ddim(out_dim));
-    output->mutable_data<T>(context.GetPlace());
-    framework::TensorFromVector(ids, context.device_context(), output);
-  }
-
- private:
-  double getRandReal() const {
-    std::call_once(init_flag_, &SamplingIdKernel::getRndInstance);
-    return rnd();
-  }
-
-  static void getRndInstance() {
-    // Will be used to obtain a seed for the random number engine
-    std::random_device rd;
-    // Standard mersenne_twister_engine seeded with rd()
-    std::mt19937 gen(rd());
-    std::uniform_real_distribution<> dis(0, 1);
-    rnd = std::bind(dis, std::ref(gen));
-  }
-
-  static std::once_flag init_flag_;
-  static std::function<double()> rnd;
-};
-}  // namespace operators
-}  // namespace paddle