/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.

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 "PerfUtils.h"
#include "TensorCheck.h"
#include "paddle/math/Matrix.h"
#include "paddle/math/TensorAssign.h"

using paddle::BaseMatrix;
using paddle::CpuMatrix;
using paddle::GpuMatrix;
using autotest::TensorCheckEqual;
using autotest::TensorCheckErr;

typedef std::function<void(int height, int width)> testMatrixFunc;
void testMatrixCase(testMatrixFunc matrixFunc) {
  for (auto height : {1}) {
    for (auto width : {1,
                       32,
                       64,
                       128,
                       512,
                       1024,
                       4096,
                       32768,
                       65536,
                       131072,
                       262144,
                       524288,
                       1048576,
                       2097152,
                       4194304,
                       8388608}) {
      matrixFunc(height, width);
    }
  }
}

template <typename Tensor>
void testLazyAssign(int height, int width) {
  Tensor A1(height, width);
  Tensor A2(height, width);
  Tensor B(height, width);
  Tensor C(height, width);
  Tensor D(height, width);
  A1.randomizeUniform();
  B.randomizeUniform();
  C.randomizeUniform();
  D.randomizeUniform();
  A2.copyFrom(A1);

  EXPRESSION_PERFORMANCE(A1 = B + C; A1 = A1 * D;);

  EXPRESSION_PERFORMANCE(auto expr1 = A2.lazyAssign(B + C);
                         auto expr2 = A2.lazyAssign(A2 * D);
                         AssignEvaluate(expr1, expr2););

  TensorCheckErr(A1, A2);
}

TEST(lazyAssign, CPU) { testMatrixCase(testLazyAssign<CpuMatrix>); }

#ifndef PADDLE_ONLY_CPU
TEST(lazyAssign, GPU) { testMatrixCase(testLazyAssign<GpuMatrix>); }
#endif

template <typename Tensor>
void sgdUpdateTensor(
    Tensor& A, Tensor& B, Tensor& C, Tensor& D, real p1, real p2, real p3) {
  C = C * p2 - D * (B + A * p3) * p1;
  A += C;
}

void sgdUpdateLazyAssign(BaseMatrix& A,
                         BaseMatrix& B,
                         BaseMatrix& C,
                         BaseMatrix& D,
                         real p1,
                         real p2,
                         real p3) {
  auto expr1 = C.lazyAssign(C * p2 - D * (B + A * p3) * p1);
  auto expr2 = A.lazyAssign(A + C);
  AssignEvaluate(expr1, expr2);
}

template <typename Tensor>
void testSgdUpdate(int height, int width) {
  Tensor A1(height, width);
  Tensor A2(height, width);
  Tensor A3(height, width);
  A1.randomizeUniform();
  A2.copyFrom(A1);
  A3.copyFrom(A1);

  Tensor B(height, width);
  B.randomizeUniform();

  Tensor C1(height, width);
  Tensor C2(height, width);
  Tensor C3(height, width);
  C1.randomizeUniform();
  C2.copyFrom(C1);
  C3.copyFrom(C1);

  Tensor D(height, width);
  D.randomizeUniform();

  real p1 = 0.2;
  real p2 = 0.3;
  real p3 = 0.5;

  /**
   * c = p2 * c - p1 * (b + p3 * a);
   * a = a + c;
   */
  // BaseMatrix API
  EXPRESSION_PERFORMANCE(A1.sgdUpdate(B, C1, D, p1, p2, p3););

  // Tensor expression
  EXPRESSION_PERFORMANCE(sgdUpdateTensor(A2, B, C2, D, p1, p2, p3));

  // lazyAssign
  EXPRESSION_PERFORMANCE(sgdUpdateLazyAssign(A3, B, C3, D, p1, p2, p3));

  TensorCheckErr(A1, A2);
  TensorCheckErr(A1, A3);
  TensorCheckErr(C1, C2);
  TensorCheckErr(C1, C3);
}

TEST(sgdUpdate, CPU) { testMatrixCase(testSgdUpdate<CpuMatrix>); }

#ifndef PADDLE_ONLY_CPU
TEST(sgdUpdate, GPU) { testMatrixCase(testSgdUpdate<GpuMatrix>); }
#endif