tests/cpp/operator/krprod_test.cc - master - githubmirror/incubator-mxnet

tests/cpp/operator/krprod_test.cc
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mozga
[master][clang-format] Re-format cc. .h. .cu files; cond. (#20704)
19 ноя 2021, 11:27
19 ноя 2021, 11:271add250
/*
 * Licensed to the Apache Software Foundation (ASF) under one
 * or more contributor license agreements.  See the NOTICE file
 * distributed with this work for additional information
 * regarding copyright ownership.  The ASF licenses this file
 * to you 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.
 */

/*!
 *  \file krprod_test.cc
 *  \brief Test Khatri-Rao product
 *  \author Jencir Lee
 */
#include <vector>
#include <random>
#include <cmath>
#include "gtest/gtest.h"
#include "operator/contrib/krprod.h"

namespace mxnet {
namespace op {

using namespace mshadow;
using namespace mshadow::expr;
using DType = double;

#define EXPECT_DOUBLE_EQ_MATRIX(expected, actual)                  \
  {                                                                \
    for (int i = 0; i < static_cast<int>(actual.size(0)); ++i)     \
      for (int j = 0; j < static_cast<int>(actual.size(1)); ++j)   \
        EXPECT_LE(std::abs(actual[i][j] - expected[i][j]), 1e-10); \
  }

TEST(row_wise_kronecker, OneInputMatrix) {
  // Input matrices of shape (2, 4) which is also the expected result
  DType mat[8]{1, 2, 3, 4, 5, 6, 7, 8};

  // Make input tensors
  std::vector<Tensor<cpu, 2, DType> > ts_arr;
  ts_arr.emplace_back(mat, Shape2(2, 4), 4, nullptr);

  // Compute Khatri-Rao product
  Tensor<cpu, 2, DType> result(Shape2(2, 4));
  AllocSpace(&result);
  row_wise_kronecker(result, ts_arr);

  // Check against expected result
  EXPECT_DOUBLE_EQ_MATRIX(ts_arr[0], result);

  FreeSpace(&result);
}

TEST(row_wise_kronecker, TwoInputMatrices) {
  // Input matrices of shape (2, 3) and (2, 4)
  DType mat1[6]{1, 2, 3, 4, 5, 6};
  DType mat2[8]{1, 2, 3, 4, 5, 6, 7, 8};

  // Expect result of shape (2, 12)
  DType expected[24]{1,  2,  3,  4,  2,  4,  6,  8,  3,  6,  9,  12,
                     20, 24, 28, 32, 25, 30, 35, 40, 30, 36, 42, 48};

  // Make input tensors
  std::vector<Tensor<cpu, 2, DType> > ts_arr;
  ts_arr.emplace_back(mat1, Shape2(2, 3), 3, nullptr);
  ts_arr.emplace_back(mat2, Shape2(2, 4), 4, nullptr);

  // Compute Khatri-Rao product
  Tensor<cpu, 2, DType> result(Shape2(2, 12));
  AllocSpace(&result);
  row_wise_kronecker(result, ts_arr);

  // Check against expected result
  Tensor<cpu, 2, DType> ts_expected(expected, Shape2(2, 12), 12, nullptr);
  EXPECT_DOUBLE_EQ_MATRIX(ts_expected, result);

  FreeSpace(&result);
}

TEST(row_wise_kronecker, TwoInputMatrices2) {
  // Input matrices of shape (2, 3) and (2, 1)
  DType mat1[6]{1, 2, 3, 4, 5, 6};
  DType mat2[2]{1, 2};

  // Expect result of shape (2, 3)
  DType expected[6]{1, 2, 3, 8, 10, 12};

  // Make input tensors
  std::vector<Tensor<cpu, 2, DType> > ts_arr;
  ts_arr.emplace_back(mat1, Shape2(2, 3), 3, nullptr);
  ts_arr.emplace_back(mat2, Shape2(2, 1), 1, nullptr);

  // Compute Khatri-Rao product
  Tensor<cpu, 2, DType> result(Shape2(2, 3));
  AllocSpace(&result);
  row_wise_kronecker(result, ts_arr);

  // Check against expected result
  Tensor<cpu, 2, DType> ts_expected(expected, Shape2(2, 3), 3, nullptr);
  EXPECT_DOUBLE_EQ_MATRIX(ts_expected, result);

  FreeSpace(&result);
}

TEST(row_wise_kronecker, ThreeInputMatrices) {
  std::default_random_engine generator;
  std::uniform_int_distribution<int> distribution(1, 6);

  Tensor<cpu, 2, DType> in1(Shape2(3, 4)), in2(Shape2(3, 2)), in3(Shape2(3, 3)), kr12(Shape2(3, 8)),
      kr13(Shape2(3, 24)), result(Shape2(3, 24));
  AllocSpace(&in1);
  AllocSpace(&in2);
  AllocSpace(&in3);
  AllocSpace(&kr12);
  AllocSpace(&kr13);
  AllocSpace(&result);

  std::vector<Tensor<cpu, 2, DType> > ts_arr{in1, in2, in3};
  for (auto& in : ts_arr) {
    for (int i = 0; i < static_cast<int>(in.size(0)); ++i)
      for (int j = 0; j < static_cast<int>(in.size(1)); ++j)
        in[i][j] = distribution(generator);
  }

  row_wise_kronecker(kr12, {in1, in2});
  row_wise_kronecker(kr13, {kr12, in3});
  row_wise_kronecker(result, ts_arr);
  EXPECT_DOUBLE_EQ_MATRIX(kr13, result);

  for (auto& in : ts_arr)
    FreeSpace(&in);
  FreeSpace(&kr12);
  FreeSpace(&kr13);
  FreeSpace(&result);
}

TEST(row_wise_kronecker, ThreeInputMatrices2) {
  std::default_random_engine generator;
  std::uniform_int_distribution<int> distribution(1, 6);

  Tensor<cpu, 2, DType> in1(Shape2(3, 4)), in2(Shape2(3, 1)), in3(Shape2(3, 3)), kr12(Shape2(3, 4)),
      kr13(Shape2(3, 12)), result(Shape2(3, 12));
  AllocSpace(&in1);
  AllocSpace(&in2);
  AllocSpace(&in3);
  AllocSpace(&kr12);
  AllocSpace(&kr13);
  AllocSpace(&result);

  std::vector<Tensor<cpu, 2, DType> > ts_arr{in1, in2, in3};
  for (auto& in : ts_arr) {
    for (int i = 0; i < static_cast<int>(in.size(0)); ++i)
      for (int j = 0; j < static_cast<int>(in.size(1)); ++j)
        in[i][j] = distribution(generator);
  }

  row_wise_kronecker(kr12, {in1, in2});
  row_wise_kronecker(kr13, {kr12, in3});
  row_wise_kronecker(result, ts_arr);
  EXPECT_DOUBLE_EQ_MATRIX(kr13, result);

  for (auto& in : ts_arr)
    FreeSpace(&in);
  FreeSpace(&kr12);
  FreeSpace(&kr13);
  FreeSpace(&result);
}

TEST(row_wise_kronecker, ThreeInputMatrices3) {
  std::default_random_engine generator;
  std::uniform_int_distribution<int> distribution(1, 6);

  Tensor<cpu, 2, DType> in1(Shape2(3, 1)), in2(Shape2(3, 4)), in3(Shape2(3, 3)), kr12(Shape2(3, 4)),
      kr13(Shape2(3, 12)), result(Shape2(3, 12));
  AllocSpace(&in1);
  AllocSpace(&in2);
  AllocSpace(&in3);
  AllocSpace(&kr12);
  AllocSpace(&kr13);
  AllocSpace(&result);

  std::vector<Tensor<cpu, 2, DType> > ts_arr{in1, in2, in3};
  for (auto& in : ts_arr) {
    for (int i = 0; i < static_cast<int>(in.size(0)); ++i)
      for (int j = 0; j < static_cast<int>(in.size(1)); ++j)
        in[i][j] = distribution(generator);
  }

  row_wise_kronecker(kr12, {in1, in2});
  row_wise_kronecker(kr13, {kr12, in3});
  row_wise_kronecker(result, ts_arr);
  EXPECT_DOUBLE_EQ_MATRIX(kr13, result);

  for (auto& in : ts_arr)
    FreeSpace(&in);
  FreeSpace(&kr12);
  FreeSpace(&kr13);
  FreeSpace(&result);
}

TEST(row_wise_kronecker, FourInputMatrices) {
  std::default_random_engine generator;
  std::uniform_int_distribution<int> distribution(1, 6);

  Tensor<cpu, 2, DType> in1(Shape2(3, 47)), in2(Shape2(3, 1)), in3(Shape2(3, 5)),
      in4(Shape2(3, 2173)), kr12(Shape2(3, 47)), kr13(Shape2(3, 47 * 5)),
      kr14(Shape2(3, 47 * 5 * 2173)), result(Shape2(3, 47 * 5 * 2173));
  AllocSpace(&in1);
  AllocSpace(&in2);
  AllocSpace(&in3);
  AllocSpace(&in4);
  AllocSpace(&kr12);
  AllocSpace(&kr13);
  AllocSpace(&kr14);
  AllocSpace(&result);

  std::vector<Tensor<cpu, 2, DType> > ts_arr{in1, in2, in3, in4};
  for (auto& in : ts_arr) {
    for (int i = 0; i < static_cast<int>(in.size(0)); ++i)
      for (int j = 0; j < static_cast<int>(in.size(1)); ++j)
        in[i][j] = distribution(generator);
  }

  row_wise_kronecker(kr12, {in1, in2});
  row_wise_kronecker(kr13, {kr12, in3});
  row_wise_kronecker(kr14, {kr13, in4});
  row_wise_kronecker(result, ts_arr);
  EXPECT_DOUBLE_EQ_MATRIX(kr14, result);

  for (auto& in : ts_arr)
    FreeSpace(&in);
  FreeSpace(&kr12);
  FreeSpace(&kr13);
  FreeSpace(&kr14);
  FreeSpace(&result);
}

#if MXNET_USE_LAPACK == 1
TEST(khatri_rao, OneInputMatrix) {
  // Input matrices of shape (2, 4) which is also the expected result
  DType mat[8]{1, 2, 3, 4, 5, 6, 7, 8};

  // Make input tensors
  std::vector<Tensor<cpu, 2, DType> > ts_arr;
  ts_arr.emplace_back(mat, Shape2(2, 4), 4, nullptr);

  // Compute Khatri-Rao product
  Tensor<cpu, 2, DType> result(Shape2(2, 4));
  AllocSpace(&result);
  khatri_rao(result, ts_arr);

  // Check against expected result
  EXPECT_DOUBLE_EQ_MATRIX(ts_arr[0], result);

  FreeSpace(&result);
}

TEST(khatri_rao, TwoInputMatrices) {
  // Input matrices of shape (3, 2) and (4, 2)
  DType mat1[6]{1, 4, 2, 5, 3, 6};
  DType mat2[8]{1, 5, 2, 6, 3, 7, 4, 8};

  // Expect result of shape (12, 2)
  DType expected[24]{1, 20, 2, 24, 3, 28, 4, 32, 2, 25, 4,  30,
                     6, 35, 8, 40, 3, 30, 6, 36, 9, 42, 12, 48};

  // Make input tensors
  std::vector<Tensor<cpu, 2, DType> > ts_arr;
  ts_arr.emplace_back(mat1, Shape2(3, 2), 2, nullptr);
  ts_arr.emplace_back(mat2, Shape2(4, 2), 2, nullptr);

  // Compute Khatri-Rao product
  Tensor<cpu, 2, DType> result(Shape2(12, 2));
  AllocSpace(&result);
  khatri_rao(result, ts_arr);

  // Check against expected result
  Tensor<cpu, 2, DType> ts_expected(expected, Shape2(12, 2), 2, nullptr);
  EXPECT_DOUBLE_EQ_MATRIX(ts_expected, result);

  FreeSpace(&result);
}

TEST(khatri_rao, ThreeInputMatrices) {
  std::default_random_engine generator;
  std::uniform_int_distribution<int> distribution(1, 6);

  Tensor<cpu, 2, DType> in1(Shape2(4, 3)), in2(Shape2(2, 3)), in3(Shape2(3, 3)), kr12(Shape2(8, 3)),
      kr13(Shape2(24, 3)), result(Shape2(24, 3));
  AllocSpace(&in1);
  AllocSpace(&in2);
  AllocSpace(&in3);
  AllocSpace(&kr12);
  AllocSpace(&kr13);
  AllocSpace(&result);

  std::vector<Tensor<cpu, 2, DType> > ts_arr{in1, in2, in3};
  for (auto& in : ts_arr) {
    for (int i = 0; i < static_cast<int>(in.size(0)); ++i)
      for (int j = 0; j < static_cast<int>(in.size(1)); ++j)
        in[i][j] = distribution(generator);
  }

  khatri_rao(kr12, {in1, in2});
  khatri_rao(kr13, {kr12, in3});
  khatri_rao(result, ts_arr);
  EXPECT_DOUBLE_EQ_MATRIX(kr13, result);

  for (auto& in : ts_arr)
    FreeSpace(&in);
  FreeSpace(&kr12);
  FreeSpace(&kr13);
  FreeSpace(&result);
}

TEST(inv_khatri_rao, OneInputMatrixTransposed) {
  DType mat[8]{1, 2, 3, 4, 5, 6, 7, 8};

  // Make input tensors
  std::vector<Tensor<cpu, 2, DType> > ts_arr;
  ts_arr.emplace_back(mat, Shape2(2, 4), 4, nullptr);

  // Compute inverse Khatri-Rao product
  Tensor<cpu, 2, DType> inv_kr(Shape2(2, 4));
  AllocSpace(&inv_kr);
  inv_khatri_rao(inv_kr, ts_arr, true);

  // Check against expected result
  Tensor<cpu, 2, DType> actual_dot(Shape2(2, 4));
  AllocSpace(&actual_dot);
  actual_dot = implicit_dot(implicit_dot(inv_kr, ts_arr[0].T()), inv_kr);
  EXPECT_DOUBLE_EQ_MATRIX(inv_kr, actual_dot);

  FreeSpace(&inv_kr);
  FreeSpace(&actual_dot);
}

TEST(inv_khatri_rao, TwoInputMatrices) {
  // Input matrices of shape (3, 2) and (4, 2)
  DType mat1[6]{1, 4, 2, 5, 3, 6};
  DType mat2[8]{1, 5, 2, 6, 3, 7, 4, 8};

  // Make input tensors
  std::vector<Tensor<cpu, 2, DType> > ts_arr;
  ts_arr.emplace_back(mat1, Shape2(3, 2), 2, nullptr);
  ts_arr.emplace_back(mat2, Shape2(4, 2), 2, nullptr);

  // Compute inverse Khatri-Rao product
  Tensor<cpu, 2, DType> inv_kr(Shape2(2, 12)), kr(Shape2(12, 2));
  AllocSpace(&inv_kr);
  AllocSpace(&kr);
  inv_khatri_rao(inv_kr, ts_arr, false);
  khatri_rao(kr, ts_arr);

  // Check against expected result
  Tensor<cpu, 2, DType> actual_dot(Shape2(2, 12));
  AllocSpace(&actual_dot);
  actual_dot = implicit_dot(implicit_dot(inv_kr, kr), inv_kr);
  EXPECT_DOUBLE_EQ_MATRIX(inv_kr, actual_dot);

  FreeSpace(&inv_kr);
  FreeSpace(&kr);
  FreeSpace(&actual_dot);
}

TEST(inv_khatri_rao, TwoInputMatricesTransposed) {
  // Transposed input matrices of shape (2, 3) and (2, 4)
  DType mat1[6]{1, 2, 3, 4, 5, 6};
  DType mat2[8]{1, 2, 3, 4, 5, 6, 7, 8};

  // Make input tensors
  std::vector<Tensor<cpu, 2, DType> > ts_arr;
  ts_arr.emplace_back(mat1, Shape2(2, 3), 3, nullptr);
  ts_arr.emplace_back(mat2, Shape2(2, 4), 4, nullptr);

  // Compute invser Khatri-Rao product
  Tensor<cpu, 2, DType> inv_kr(Shape2(2, 12)), kr_t(Shape2(2, 12));
  AllocSpace(&inv_kr);
  AllocSpace(&kr_t);
  inv_khatri_rao(inv_kr, ts_arr, true);
  row_wise_kronecker(kr_t, ts_arr);

  // Check against expected result
  Tensor<cpu, 2, DType> actual_dot(Shape2(2, 12));
  AllocSpace(&actual_dot);
  actual_dot = implicit_dot(implicit_dot(inv_kr, kr_t.T()), inv_kr);
  EXPECT_DOUBLE_EQ_MATRIX(inv_kr, actual_dot);

  FreeSpace(&inv_kr);
  FreeSpace(&kr_t);
  FreeSpace(&actual_dot);
}

TEST(inv_khatri_rao, ThreeInputMatricesTranposed) {
  // Randomly initialise the transposed input matrices
  std::default_random_engine generator;
  std::uniform_int_distribution<int> distribution(1, 6);

  Tensor<cpu, 2, DType> in1(Shape2(3, 4)), in2(Shape2(3, 2)), in3(Shape2(3, 3));
  AllocSpace(&in1);
  AllocSpace(&in2);
  AllocSpace(&in3);

  std::vector<Tensor<cpu, 2, DType> > ts_arr{in1, in2, in3};
  for (auto& in : ts_arr) {
    for (int i = 0; i < static_cast<int>(in.size(0)); ++i)
      for (int j = 0; j < static_cast<int>(in.size(1)); ++j)
        in[i][j] = distribution(generator);
  }

  // Compute inv_kr & kr
  Tensor<cpu, 2, DType> inv_kr(Shape2(3, 24)), kr_t(Shape2(3, 24));
  AllocSpace(&inv_kr);
  AllocSpace(&kr_t);

  inv_khatri_rao(inv_kr, ts_arr, true);
  row_wise_kronecker(kr_t, ts_arr);

  // Check dot result
  Tensor<cpu, 2, DType> actual_dot(Shape2(3, 24));
  AllocSpace(&actual_dot);
  actual_dot = implicit_dot(implicit_dot(inv_kr, kr_t.T()), inv_kr);
  EXPECT_DOUBLE_EQ_MATRIX(inv_kr, actual_dot);

  for (auto& in : ts_arr)
    FreeSpace(&in);
  FreeSpace(&inv_kr);
  FreeSpace(&kr_t);
  FreeSpace(&actual_dot);
}
#endif  // MXNET_USE_LAPACK == 1
}  // namespace op
}  // namespace mxnet