/* 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. */

#include <gtest/gtest.h>
#include "paddle/fluid/memory/memcpy.h"
#include "paddle/fluid/memory/memory.h"
#include "paddle/fluid/platform/hostdevice.h"
#include "paddle/fluid/platform/transform.h"

template <typename T>
class Scale {
 public:
  explicit Scale(const T& scale) : scale_(scale) {}
  HOSTDEVICE T operator()(const T& a) const { return a * scale_; }

 private:
  T scale_;
};

template <typename T>
class Multiply {
 public:
  HOSTDEVICE T operator()(const T& a, const T& b) const { return a * b; }
};

using paddle::memory::Alloc;
using paddle::memory::Copy;

using paddle::platform::CPUPlace;
using paddle::platform::CUDAPlace;
using paddle::platform::CPUDeviceContext;
using paddle::platform::CUDADeviceContext;

using paddle::platform::Transform;

TEST(Transform, CPUUnary) {
  CPUDeviceContext ctx;
  float buf[4] = {0.1, 0.2, 0.3, 0.4};
  Transform<CPUDeviceContext> trans;
  trans(ctx, buf, buf + 4, buf, Scale<float>(10));
  for (int i = 0; i < 4; ++i) {
    ASSERT_NEAR(buf[i], static_cast<float>(i + 1), 1e-5);
  }
}

TEST(Transform, GPUUnary) {
  CUDAPlace gpu0(0);
  CUDADeviceContext ctx(gpu0);
  float cpu_buf[4] = {0.1, 0.2, 0.3, 0.4};
  auto gpu_allocation = Alloc(gpu0, sizeof(float) * 4);
  float* gpu_buf = static_cast<float*>(gpu_allocation->ptr());
  Copy(gpu0, gpu_buf, CPUPlace(), cpu_buf, sizeof(cpu_buf), ctx.stream());
  Transform<CUDADeviceContext> trans;
  trans(ctx, gpu_buf, gpu_buf + 4, gpu_buf, Scale<float>(10));
  ctx.Wait();
  Copy(CPUPlace(), cpu_buf, gpu0, gpu_buf, sizeof(cpu_buf), ctx.stream());
  for (int i = 0; i < 4; ++i) {
    ASSERT_NEAR(cpu_buf[i], static_cast<float>(i + 1), 1e-5);
  }
}

TEST(Transform, CPUBinary) {
  int buf[4] = {1, 2, 3, 4};
  Transform<CPUDeviceContext> trans;
  CPUDeviceContext ctx;
  trans(ctx, buf, buf + 4, buf, buf, Multiply<int>());
  for (int i = 0; i < 4; ++i) {
    ASSERT_EQ((i + 1) * (i + 1), buf[i]);
  }
}

TEST(Transform, GPUBinary) {
  int buf[4] = {1, 2, 3, 4};
  CUDAPlace gpu0(0);
  CUDADeviceContext ctx(gpu0);
  auto gpu_allocation = Alloc(gpu0, sizeof(buf));
  int* gpu_buf = static_cast<int*>(gpu_allocation->ptr());
  Copy(gpu0, gpu_buf, CPUPlace(), buf, sizeof(buf), ctx.stream());
  Transform<CUDADeviceContext> trans;
  trans(ctx, gpu_buf, gpu_buf + 4, gpu_buf, gpu_buf, Multiply<int>());
  ctx.Wait();
  Copy(CPUPlace(), buf, gpu0, gpu_buf, sizeof(buf), ctx.stream());
  for (int i = 0; i < 4; ++i) {
    ASSERT_EQ((i + 1) * (i + 1), buf[i]);
  }
}