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277 lines
9.1 KiB
277 lines
9.1 KiB
/**
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* Copyright 2019 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 <string>
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#include <list>
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#include <vector>
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#include "common/common_test.h"
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#include "frontend/parallel/strategy.h"
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#include "frontend/parallel/ops_info/prelu_info.h"
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#include "frontend/parallel/device_manager.h"
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#include "frontend/parallel/step_parallel.h"
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namespace mindspore {
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namespace parallel {
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class PReLUInfo;
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using PReLUInfoPtr = std::shared_ptr<PReLUInfo>;
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PReLUInfoPtr prelu;
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PReLUInfoPtr prelu_2d;
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class TestPReLUInfo : public UT::Common {
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public:
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TestPReLUInfo() {}
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void SetUp();
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void TearDown() {}
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};
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void TestPReLUInfo::SetUp() {
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RankList dev_list;
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for (int32_t i = 0; i < 1050; i++) {
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dev_list.push_back(i);
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}
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RankList stage_map;
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stage_map.push_back(1024);
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stage_map.push_back(26);
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int32_t local_dev = 0;
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// create a new g_device_manager
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g_device_manager = std::make_shared<DeviceManager>();
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g_device_manager->Init(dev_list, local_dev, stage_map, "hccl");
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Shapes inputs_shape = {{64, 4, 8, 16}, {4}};
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Shapes outputs_shape = {{64, 4, 8, 16}};
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std::unordered_map<std::string, ValuePtr> attr;
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prelu = std::make_shared<PReLUInfo>("prelu_info", inputs_shape, outputs_shape, attr);
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Shapes inputs_shape_2d = {{1024, 4}, {4}};
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Shapes outputs_shape_2d = {{1024, 4}};
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std::unordered_map<std::string, ValuePtr> attr_2d;
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prelu_2d = std::make_shared<PReLUInfo>("prelu_info", inputs_shape_2d, outputs_shape_2d, attr_2d);
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}
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TEST_F(TestPReLUInfo, InferDevMatrixShape1) {
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Strategys inputs = {{2, 1, 8, 16}, {1}};
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StrategyPtr strategy = NewStrategy(0, inputs);
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prelu->Init(strategy);
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Shape dev_matrix_shape = prelu->dev_matrix_shape();
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Shape expect = {2, 1, 8, 16, 4};
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ASSERT_EQ(dev_matrix_shape, expect);
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}
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TEST_F(TestPReLUInfo, InferSliceShape1) {
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Strategys str = {{2, 1, 8, 16}, {1}};
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StrategyPtr strategy = NewStrategy(0, str);
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prelu->Init(strategy);
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std::vector<TensorInfo> inputs = prelu->inputs_tensor_info();
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std::vector<TensorInfo> outputs = prelu->outputs_tensor_info();
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Shape input_slice_shape_expect = {32, 4, 1, 1};
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Shape param_slice_shape_expect = {4};
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Shape output_slice_shape_expect = {32, 4, 1, 1};
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TensorInfo input_tensor_info = inputs.at(0);
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TensorInfo param_tensor_info = inputs.at(1);
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TensorInfo output_tensor_info = outputs.at(0);
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Shape input_slice_shape = input_tensor_info.slice_shape();
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Shape output_slice_shape = output_tensor_info.slice_shape();
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ASSERT_EQ(input_slice_shape, input_slice_shape_expect);
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ASSERT_EQ(output_slice_shape, output_slice_shape_expect);
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}
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TEST_F(TestPReLUInfo, GetTensorLayout1) {
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Strategys str = {{2, 1, 8, 16}, {1}};
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StrategyPtr strategy = NewStrategy(0, str);
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prelu->Init(strategy);
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std::vector<TensorInfo> inputs = prelu->inputs_tensor_info();
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std::vector<TensorInfo> outputs = prelu->outputs_tensor_info();
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TensorMap input_expect = {4, 3, 2, 1};
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TensorMap param_expect = {2};
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TensorMap output_expect = {4, 3, 2, 1};
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TensorInfo input_tensor_info = inputs.at(0);
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TensorInfo param_tensor_info = inputs.at(1);
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TensorInfo output_tensor_info = outputs.at(0);
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Map input_tensor_map = input_tensor_info.tensor_layout().origin_tensor_map();
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Map param_tensor_map = param_tensor_info.tensor_layout().origin_tensor_map();
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Map output_tensor_map = output_tensor_info.tensor_layout().origin_tensor_map();
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ASSERT_EQ(input_tensor_map.array(), input_expect);
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ASSERT_EQ(output_tensor_map.array(), output_expect);
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}
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TEST_F(TestPReLUInfo, GetMirrorOPs1) {
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Strategys str = {{2, 1, 2, 2}, {1}};
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StrategyPtr strategy = NewStrategy(0, str);
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prelu->Init(strategy);
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MirrorOps mirror_ops = prelu->mirror_ops();
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OperatorVector mirror_op = mirror_ops.at(1);
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OperatorArgs operator_args = mirror_op.at(0).second;
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std::string arg0_name = operator_args.first.at(0).first;
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ValuePtr arg0_value = operator_args.first.at(0).second;
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std::string group = arg0_value->cast<StringImmPtr>()->ToString();
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ASSERT_EQ(mirror_op.at(0).first, "_MirrorOperator");
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ASSERT_EQ(mirror_op.size(), 1);
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ASSERT_EQ(arg0_name, "group");
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}
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TEST_F(TestPReLUInfo, CheckStrategy1) {
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// Success: {{2,1,8,16},{1}}
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Strategys inputs = {{2, 1, 8, 16}};
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StrategyPtr strategy = NewStrategy(0, inputs);
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Status ret = prelu->Init(strategy);
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ASSERT_EQ(ret, FAILED);
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}
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TEST_F(TestPReLUInfo, CheckStrategy2) {
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Strategys inputs = {{2, 4, 8, 16}, {4}};
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StrategyPtr strategy = NewStrategy(0, inputs);
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Status ret = prelu->Init(strategy);
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ASSERT_EQ(ret, SUCCESS);
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}
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TEST_F(TestPReLUInfo, AutoStrategy1) {
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ASSERT_EQ(prelu->GenerateStrategies(0), Status::SUCCESS);
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std::vector<std::shared_ptr<StrategyWithCost>> sc = prelu->GetStrategyCost();
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Shapes splittable_inputs = {{1, 0, 1, 1}, {0}};
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std::vector<StrategyPtr> sp_vector;
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Shapes inputs_shape = {{64, 4, 8, 16}, {4}};
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GenerateStrategiesForIndependentInputs(0, inputs_shape, splittable_inputs, &sp_vector);
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for (auto stra : sp_vector) {
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auto stra0 = stra->GetInputDim()[0];
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auto stra1 = stra->GetInputDim()[1];
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ASSERT_EQ(stra0[1], 1);
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ASSERT_EQ(stra1[0], 1);
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}
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}
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TEST_F(TestPReLUInfo, InferDevMatrixShape_2d1) {
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Strategys inputs = {{128, 1}, {1}};
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StrategyPtr strategy = NewStrategy(0, inputs);
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prelu_2d->Init(strategy);
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Shape dev_matrix_shape = prelu_2d->dev_matrix_shape();
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Shape expect = {128, 1, 8};
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ASSERT_EQ(dev_matrix_shape, expect);
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}
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TEST_F(TestPReLUInfo, InferSliceShape_2d1) {
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Strategys str = {{128, 1}, {1}};
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StrategyPtr strategy = NewStrategy(0, str);
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prelu_2d->Init(strategy);
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std::vector<TensorInfo> inputs = prelu_2d->inputs_tensor_info();
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std::vector<TensorInfo> outputs = prelu_2d->outputs_tensor_info();
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Shape input_slice_shape_expect = {8, 4};
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Shape param_slice_shape_expect = {4};
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Shape output_slice_shape_expect = {8, 4};
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TensorInfo input_tensor_info = inputs.at(0);
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TensorInfo param_tensor_info = inputs.at(1);
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TensorInfo output_tensor_info = outputs.at(0);
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Shape input_slice_shape = input_tensor_info.slice_shape();
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Shape output_slice_shape = output_tensor_info.slice_shape();
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ASSERT_EQ(input_slice_shape, input_slice_shape_expect);
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ASSERT_EQ(output_slice_shape, output_slice_shape_expect);
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}
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TEST_F(TestPReLUInfo, GetTensorLayout_2d1) {
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Strategys str = {{128, 1}, {1}};
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StrategyPtr strategy = NewStrategy(0, str);
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prelu_2d->Init(strategy);
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std::vector<TensorInfo> inputs = prelu_2d->inputs_tensor_info();
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std::vector<TensorInfo> outputs = prelu_2d->outputs_tensor_info();
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TensorMap input_expect = {2, 1};
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TensorMap param_expect = {0};
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TensorMap output_expect = {2, 1};
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TensorInfo input_tensor_info = inputs.at(0);
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TensorInfo param_tensor_info = inputs.at(1);
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TensorInfo output_tensor_info = outputs.at(0);
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Map input_tensor_map = input_tensor_info.tensor_layout().origin_tensor_map();
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Map param_tensor_map = param_tensor_info.tensor_layout().origin_tensor_map();
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Map output_tensor_map = output_tensor_info.tensor_layout().origin_tensor_map();
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ASSERT_EQ(input_tensor_map.array(), input_expect);
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ASSERT_EQ(output_tensor_map.array(), output_expect);
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}
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TEST_F(TestPReLUInfo, GetMirrorOPs_2d1) {
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Strategys str = {{128, 1}, {1}};
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StrategyPtr strategy = NewStrategy(0, str);
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prelu_2d->Init(strategy);
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MirrorOps mirror_ops = prelu_2d->mirror_ops();
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OperatorVector mirror_op = mirror_ops.at(1);
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OperatorArgs operator_args = mirror_op.at(0).second;
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std::string arg0_name = operator_args.first.at(0).first;
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ValuePtr arg0_value = operator_args.first.at(0).second;
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std::string group = arg0_value->cast<StringImmPtr>()->ToString();
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ASSERT_EQ(mirror_op.at(0).first, "_MirrorOperator");
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ASSERT_EQ(mirror_op.size(), 1);
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ASSERT_EQ(arg0_name, "group");
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}
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TEST_F(TestPReLUInfo, CheckStrategy_2d1) {
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// Success: {{2,1,8,16},{1}}
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Strategys inputs = {{128, 1}};
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StrategyPtr strategy = NewStrategy(0, inputs);
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Status ret = prelu_2d->Init(strategy);
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ASSERT_EQ(ret, FAILED);
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}
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TEST_F(TestPReLUInfo, CheckStrategy_2d2) {
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Strategys inputs = {{128, 4}, {4}};
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StrategyPtr strategy = NewStrategy(0, inputs);
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Status ret = prelu_2d->Init(strategy);
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ASSERT_EQ(ret, SUCCESS);
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}
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TEST_F(TestPReLUInfo, AutoStrategy_2d1) {
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ASSERT_EQ(prelu_2d->GenerateStrategies(0), Status::SUCCESS);
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std::vector<std::shared_ptr<StrategyWithCost>> sc = prelu_2d->GetStrategyCost();
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Shapes splittable_inputs = {{1, 0}, {0}};
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std::vector<StrategyPtr> sp_vector;
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Shapes inputs_shape = {{1024, 4}, {4}};
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GenerateStrategiesForIndependentInputs(0, inputs_shape, splittable_inputs, &sp_vector);
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for (auto stra : sp_vector) {
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auto stra0 = stra->GetInputDim()[0];
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auto stra1 = stra->GetInputDim()[1];
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ASSERT_EQ(stra0[1], 1);
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ASSERT_EQ(stra1[0], 1);
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}
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}
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} // namespace parallel
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} // namespace mindspore
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