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121 lines
4.3 KiB
121 lines
4.3 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/activation_info.h"
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#include "frontend/parallel/device_manager.h"
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namespace mindspore {
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namespace parallel {
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class Activation;
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class Softmax;
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using ActivationPtr = std::shared_ptr<ActivationInfo>;
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using SoftmaxPtr = std::shared_ptr<Softmax>;
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ActivationPtr act_ptr_;
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SoftmaxPtr soft_ptr_;
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class TestActivation : public UT::Common {
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public:
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TestActivation() {}
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void SetUp();
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void TearDown() {}
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};
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void TestActivation::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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ValuePtr relu = MakeValue(std::string("relu"));
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std::unordered_map<std::string, ValuePtr> relu_attr = {{"activation_type", relu}};
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ValuePtr sm = MakeValue(std::string("softmax"));
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ValuePtr axix = MakeValue(std::int32_t(2));
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std::unordered_map<std::string, ValuePtr> softmax_attr = {{"activation_type", sm}, {"axis", axix}};
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Shapes relu_inputs_shape = {{2, 4, 8, 16}};
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Shapes relu_outputs_shape = {{2, 4, 8, 16}};
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Shapes sm_inputs_shape = {{8, 8, 8, 16}};
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Shapes sm_outputs_shape = {{8, 8, 8, 16}};
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act_ptr_ = std::make_shared<ActivationInfo>("relu_info", relu_inputs_shape, relu_outputs_shape, relu_attr);
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soft_ptr_ = std::make_shared<Softmax>("softmax_info", sm_inputs_shape, sm_outputs_shape, softmax_attr);
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}
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TEST_F(TestActivation, test_activation_strategies) {
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ASSERT_EQ(act_ptr_->GenerateStrategies(0), Status::SUCCESS);
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std::vector<std::shared_ptr<StrategyWithCost>> sc = act_ptr_->GetStrategyCost();
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for (const auto& swc : sc) {
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ASSERT_NE(swc, nullptr);
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ASSERT_GT(swc->cost_list.size(), 0);
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StrategyPtr sp = swc->strategy_ptr;
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ASSERT_NE(sp, nullptr);
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Cost cost = *(swc->cost_list[0]);
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act_ptr_->InitForCostModel(sp);
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std::vector<TensorInfo> inputs_info = act_ptr_->inputs_tensor_info();
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std::vector<TensorInfo> outputs_info = act_ptr_->outputs_tensor_info();
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ASSERT_DOUBLE_EQ(act_ptr_->operator_cost()->GetComputationCost(inputs_info, outputs_info, sp->GetInputStage()),
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cost.computation_cost_);
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ASSERT_DOUBLE_EQ(act_ptr_->operator_cost()->GetCommCost(inputs_info, outputs_info, sp->GetInputStage()),
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cost.communication_cost_);
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}
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}
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TEST_F(TestActivation, test_softmax_strategies) {
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ASSERT_EQ(soft_ptr_->GenerateStrategies(0), Status::SUCCESS);
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std::vector<std::shared_ptr<StrategyWithCost>> sc = soft_ptr_->GetStrategyCost();
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for (const auto& swc : sc) {
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ASSERT_NE(swc, nullptr);
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ASSERT_GT(swc->cost_list.size(), 0);
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StrategyPtr sp = swc->strategy_ptr;
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ASSERT_NE(sp, nullptr);
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Cost cost = *(swc->cost_list[0]);
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Strategys stra = sp->GetInputDim();
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ASSERT_GT(stra.size(), 0);
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Dimensions input0_stra = stra[0];
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ASSERT_GT(input0_stra.size(), 2);
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ASSERT_EQ(input0_stra[2], 1);
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soft_ptr_->InitForCostModel(sp);
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std::vector<TensorInfo> inputs_info = soft_ptr_->inputs_tensor_info();
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std::vector<TensorInfo> outputs_info = soft_ptr_->outputs_tensor_info();
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ASSERT_DOUBLE_EQ(soft_ptr_->operator_cost()->GetComputationCost(inputs_info, outputs_info, sp->GetInputStage()),
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cost.computation_cost_);
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ASSERT_DOUBLE_EQ(soft_ptr_->operator_cost()->GetCommCost(inputs_info, outputs_info, sp->GetInputStage()),
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cost.communication_cost_);
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}
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}
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} // namespace parallel
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} // namespace mindspore
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