/** * Copyright 2019 Huawei Technologies Co., Ltd * * 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 #include #include #include "common/common_test.h" #include "frontend/parallel/strategy.h" #include "frontend/parallel/ops_info/reshape_info.h" #include "frontend/parallel/device_manager.h" #include "frontend/parallel/step_parallel.h" namespace mindspore { namespace parallel { class ReshapeInfo; using ReshapeInfoPtr = std::shared_ptr; ReshapeInfoPtr reshape; class TestReshapeInfo : public UT::Common { public: TestReshapeInfo() {} void SetUp(); void TearDown() {} }; void TestReshapeInfo::SetUp() { RankList dev_list; for (int32_t i = 0; i < 34; i++) { dev_list.push_back(i); } RankList stage_map; stage_map.push_back(32); stage_map.push_back(2); int32_t local_dev = 0; // create a new g_device_manager g_device_manager = std::make_shared(); g_device_manager->Init(dev_list, local_dev, stage_map, "hccl"); std::unordered_map attr; Shapes inputs_shape = {{32, 512, 7, 7}}; Shapes outputs_shape = {{32, 25088}}; std::vector axis = {32, 25088}; ValuePtr val0; ValuePtr val1 = MakeValue(axis); std::vector val = {val0, val1}; reshape = std::make_shared("reshape_info", inputs_shape, outputs_shape, attr); reshape->set_input_value(val); } TEST_F(TestReshapeInfo, InferDevMatrixShape1) { Strategys inputs = {{4, 1, 1, 1}}; StrategyPtr strategy = NewStrategy(0, inputs); reshape->Init(strategy); Shape dev_matrix_shape = reshape->dev_matrix_shape(); Shape expect = {8, 4}; ASSERT_EQ(dev_matrix_shape, expect); } TEST_F(TestReshapeInfo, InferDevMatrixShape2) { Strategys inputs = {{32, 1, 1, 1}}; StrategyPtr strategy = NewStrategy(0, inputs); reshape->Init(strategy); Shape dev_matrix_shape = reshape->dev_matrix_shape(); Shape expect = {32}; ASSERT_EQ(dev_matrix_shape, expect); } TEST_F(TestReshapeInfo, InferSliceShape1) { Strategys str = {{4, 1, 1, 1}}; StrategyPtr strategy = NewStrategy(0, str); reshape->Init(strategy); std::vector inputs = reshape->inputs_tensor_info(); std::vector outputs = reshape->outputs_tensor_info(); Shape input_slice_shape_expect = {8, 512, 7, 7}; Shape output_slice_shape_expect = {8, 25088}; TensorInfo input_tensor_info = inputs.at(0); TensorInfo output_tensor_info = outputs.at(0); Shape input_slice_shape = input_tensor_info.slice_shape(); Shape output_slice_shape = output_tensor_info.slice_shape(); ASSERT_EQ(input_slice_shape, input_slice_shape_expect); ASSERT_EQ(output_slice_shape, output_slice_shape_expect); } TEST_F(TestReshapeInfo, InferSliceShape2) { Strategys str = {{32, 1, 1, 1}}; StrategyPtr strategy = NewStrategy(0, str); reshape->Init(strategy); std::vector inputs = reshape->inputs_tensor_info(); std::vector outputs = reshape->outputs_tensor_info(); Shape input_slice_shape_expect = {1, 512, 7, 7}; Shape output_slice_shape_expect = {1, 25088}; TensorInfo input_tensor_info = inputs.at(0); TensorInfo output_tensor_info = outputs.at(0); Shape input_slice_shape = input_tensor_info.slice_shape(); Shape output_slice_shape = output_tensor_info.slice_shape(); ASSERT_EQ(input_slice_shape, input_slice_shape_expect); ASSERT_EQ(output_slice_shape, output_slice_shape_expect); } TEST_F(TestReshapeInfo, GetTensorLayout1) { Strategys str = {{4, 1, 1, 1}}; StrategyPtr strategy = NewStrategy(0, str); reshape->Init(strategy); std::vector inputs = reshape->inputs_tensor_info(); std::vector outputs = reshape->outputs_tensor_info(); TensorMap input_expect = {0, -1, -1, -1}; TensorMap output_expect = {0, -1}; TensorInfo input_tensor_info = inputs.at(0); TensorInfo output_tensor_info = outputs.at(0); Map input_tensor_map = input_tensor_info.tensor_layout().origin_tensor_map(); Map output_tensor_map = output_tensor_info.tensor_layout().origin_tensor_map(); ASSERT_EQ(input_tensor_map.array(), input_expect); ASSERT_EQ(output_tensor_map.array(), output_expect); } TEST_F(TestReshapeInfo, GetTensorLayout2) { Strategys str = {{32, 1, 1, 1}}; StrategyPtr strategy = NewStrategy(0, str); reshape->Init(strategy); std::vector inputs = reshape->inputs_tensor_info(); std::vector outputs = reshape->outputs_tensor_info(); TensorMap input_expect = {0, -1, -1, -1}; TensorMap output_expect = {0, -1}; TensorInfo input_tensor_info = inputs.at(0); TensorInfo output_tensor_info = outputs.at(0); Map input_tensor_map = input_tensor_info.tensor_layout().origin_tensor_map(); Map output_tensor_map = output_tensor_info.tensor_layout().origin_tensor_map(); ASSERT_EQ(input_tensor_map.array(), input_expect); ASSERT_EQ(output_tensor_map.array(), output_expect); } TEST_F(TestReshapeInfo, GetForwardOp1) { Strategys inputs = {{4, 1, 1, 1}}; StrategyPtr strategy = NewStrategy(0, inputs); reshape->Init(strategy); OperatorVector forward_op = reshape->forward_op(); size_t size = forward_op.size(); ASSERT_EQ(size, 0); } TEST_F(TestReshapeInfo, GetMirrorOPs1) { Strategys inputs = {{4, 1, 1, 1}}; StrategyPtr strategy = NewStrategy(0, inputs); reshape->Init(strategy); MirrorOps mirror_ops = reshape->mirror_ops(); size_t size = mirror_ops.size(); ASSERT_EQ(size, 2); } TEST_F(TestReshapeInfo, CheckStrategy1) { Strategys inputs = {{1, 4, 8}}; StrategyPtr strategy = NewStrategy(0, inputs); Status ret = reshape->Init(strategy); ASSERT_EQ(ret, FAILED); } TEST_F(TestReshapeInfo, CheckStrategy2) { Strategys inputs = {{2, 4, 8}, {2, 4, 8}}; StrategyPtr strategy = NewStrategy(0, inputs); Status ret = reshape->Init(strategy); ASSERT_EQ(ret, FAILED); } TEST_F(TestReshapeInfo, CheckStrategy3) { Strategys inputs = {{4, 1, 1, 1}}; StrategyPtr strategy = NewStrategy(0, inputs); Status ret = reshape->Init(strategy); ASSERT_EQ(ret, SUCCESS); } } // namespace parallel } // namespace mindspore