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135 lines
3.6 KiB
135 lines
3.6 KiB
/**
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* Copyright 2020 Huawei Technologies Co., Ltd
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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#include <vector>
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#include "common/common_test.h"
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#include "backend/kernel_compiler/common_utils.h"
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namespace mindspore {
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namespace kernel {
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class CommonUtilTest : public UT::Common {
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public:
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CommonUtilTest() = default;
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};
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TEST_F(CommonUtilTest, BucketReduceSparseGradient1) {
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// The indices is a vector and the grad is a tensor with shape (6, 2)
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/* 0
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* 0
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* 1
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* 1
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* 0
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* 3
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*/
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std::vector<int> indices{0, 0, 1, 1, 0, 3};
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/* 0 1
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* 2 3
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* 4 5
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* 6 7
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* 8 9
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* 10 11
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*/
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std::vector<float> grad;
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for (int i = 0; i < 6 * 2; i++) {
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grad.push_back(i);
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}
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std::vector<int> unique_indices(6);
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std::vector<float> summed_grad(12);
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std::vector<int> tmp_indices(6);
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std::vector<float> tmp_grad(12);
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SparseGradient unique_grad({summed_grad.data(), unique_indices.data(), 6});
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SparseGradient workspace_grad({tmp_grad.data(), tmp_indices.data(), 6});
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SparseGradient input_grad({grad.data(), indices.data(), 6});
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ReduceSparseGradientParam param;
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param.input_grad_ = &input_grad;
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param.workspace_grad_ = &workspace_grad;
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param.output_grad_ = &unique_grad;
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param.max_index_ = 6;
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param.value_stride_ = 2;
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BucketReduceSparseGradient(param);
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EXPECT_EQ(unique_grad.indices_size_, 3);
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std::vector<int> expect_indices({0, 1, 3});
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for (size_t i = 0; i < unique_grad.indices_size_; ++i) {
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EXPECT_EQ(unique_grad.indices_[i], expect_indices[i]);
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}
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/* 10 13
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* 10 12
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* 10 11
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*/
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std::vector<int> expect_value({10, 13, 10, 12, 10, 11});
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for (size_t i = 0; i < unique_grad.indices_size_ * 2; ++i) {
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EXPECT_EQ(unique_grad.value_[i], expect_value[i]);
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}
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}
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TEST_F(CommonUtilTest, BucketReduceSparseGradient2) {
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// The indices is a vector and the grad is a tensor with shape (6, 2)
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/* 0
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* 0
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* 1
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* 1
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* 0
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* 6
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*/
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std::vector<int> indices{0, 0, 1, 1, 0, 6};
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/* 0 1
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* 2 3
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* 4 5
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* 6 7
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* 8 9
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* 10 11
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*/
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std::vector<float> grad;
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for (int i = 0; i < 6 * 2; i++) {
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grad.push_back(i);
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}
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std::vector<int> unique_indices(6);
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std::vector<float> summed_grad(12);
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std::vector<int> tmp_indices(6);
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std::vector<float> tmp_grad(12);
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SparseGradient unique_grad({summed_grad.data(), unique_indices.data(), 6});
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SparseGradient workspace_grad({tmp_grad.data(), tmp_indices.data(), 6});
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SparseGradient input_grad({grad.data(), indices.data(), 6});
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ReduceSparseGradientParam param;
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param.input_grad_ = &input_grad;
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param.workspace_grad_ = &workspace_grad;
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param.output_grad_ = &unique_grad;
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param.max_index_ = 6;
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param.value_stride_ = 2;
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BucketReduceSparseGradient(param);
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EXPECT_EQ(unique_grad.indices_size_, 2);
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std::vector<int> expect_indices({0, 1});
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for (size_t i = 0; i < unique_grad.indices_size_; ++i) {
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EXPECT_EQ(unique_grad.indices_[i], expect_indices[i]);
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}
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/* 10 13
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* 10 12
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*/
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std::vector<int> expect_value({10, 13, 10, 12});
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for (size_t i = 0; i < unique_grad.indices_size_ * 2; ++i) {
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EXPECT_EQ(unique_grad.value_[i], expect_value[i]);
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}
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}
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} // namespace kernel
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} // namespace mindspore
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