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d38aa4560c
* add poros to fastdeploy * update readme * update readme & add license for all files * update benchmark * update copyright for some files Co-authored-by: tianjinjin <tianjinjin@baidu.com>
201 lines
8.2 KiB
C++
201 lines
8.2 KiB
C++
// Copyright (c) 2022 Baidu, Inc. All Rights Reserved.
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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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* @file logical_test.cpp
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* @author Lin Xiao Chun (linxiaochun@baidu.com)
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* @date 2022-02-17 18:32:15
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* @brief
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**/
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#include <gflags/gflags.h>
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#include <gtest/gtest.h>
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#include "poros/converter/gpu/logical.h"
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#include "poros/util/test_util.h"
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enum InputTypeEnum {
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TYPE_A = 0, // [4]*[4]
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TYPE_B, // [2,2]*[2,2]
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TYPE_C, // [4]*[true]
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TYPE_D, //broadcasting [1,3,2]*[2]
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TYPE_E, //broadcasting [2,3,4]*[3,4]
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};
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static std::vector<at::Tensor> get_input_data(const InputTypeEnum input_type) {
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std::vector<at::Tensor> input_data;
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auto options_pyt = torch::TensorOptions().device(torch::kCUDA, 0).dtype(torch::kBool);
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switch (input_type) {
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case TYPE_A: // [4]*[4]
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input_data.push_back(torch::tensor({false, true, false, true}, options_pyt));
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input_data.push_back(torch::tensor({false, true, true, true}, options_pyt));
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break;
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case TYPE_B:// [2,2]*[2,2]
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input_data.push_back(torch::tensor({{false, true},
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{false, true}}, options_pyt));
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input_data.push_back(torch::tensor({{false, true},
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{true, true}}, options_pyt));
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break;
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case TYPE_C:// [4]*[1]
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input_data.push_back(torch::tensor({false, true, false, true}, options_pyt));
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input_data.push_back(torch::tensor({true}, options_pyt));
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break;
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case TYPE_D://broadcasting [1,3,2]*[2]
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input_data.push_back(torch::tensor({{{true, true}, {false, true}, {false, false}}}, options_pyt));
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input_data.push_back(torch::tensor({false, true}, options_pyt));
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break;
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case TYPE_E://broadcasting [2,3,4]*[3,4]
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input_data.push_back(torch::tensor({
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{{false, true, false, true}, {false, true, false, false},
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{true, true, true, true}},
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{{false, true, false, false}, {true, true, true, true},
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{false, true, false, true}}
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}, options_pyt));
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input_data.push_back(torch::tensor({{false, true, false, true},
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{false, true, false, false},
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{true, true, true, true}}, options_pyt));
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break;
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}
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return input_data;
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}
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static void and_test_helper(const std::string &graph_IR,
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baidu::mirana::poros::IConverter *converter,
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const InputTypeEnum input_type) {
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auto input_data = get_input_data(input_type);
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baidu::mirana::poros::PorosOptions poros_option; // default device GPU
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// 运行原图与engine获取结果
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std::vector<at::Tensor> graph_output;
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std::vector<at::Tensor> poros_output;
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ASSERT_TRUE(baidu::mirana::poros::testutil::run_graph_and_poros(graph_IR, poros_option, converter,
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input_data, graph_output, poros_output));
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ASSERT_EQ(1, graph_output.size());
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ASSERT_EQ(1, poros_output.size());
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ASSERT_TRUE(baidu::mirana::poros::testutil::almost_equal(graph_output[0], poros_output[0], 2e-6));
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}
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static std::string gen_and_or_tensor_graph(const std::string &op) {
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return R"IR(
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graph(%0 : Tensor, %1 : Tensor):
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%2 : Tensor = aten::)IR" + op + R"IR((%0, %1)
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return (%2))IR";
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}
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static std::string gen_not_tensor_graph(const std::string &op) {
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return R"IR(
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graph(%0 : Tensor):
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%2 : Tensor = aten::)IR" + op + R"IR((%0)
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return (%2))IR";
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}
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TEST(Converters, ATenLogicalAndConvertsCorrectly) {
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const auto graph_IR = gen_and_or_tensor_graph("__and__");
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baidu::mirana::poros::AndConverter converter;
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and_test_helper(graph_IR, &converter, TYPE_A);
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and_test_helper(graph_IR, &converter, TYPE_B);
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and_test_helper(graph_IR, &converter, TYPE_C);
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and_test_helper(graph_IR, &converter, TYPE_D);
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and_test_helper(graph_IR, &converter, TYPE_E);
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}
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TEST(Converters, ATenLogicalBitwiseAndConvertsCorrectly) {
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const auto graph_IR = gen_and_or_tensor_graph("bitwise_and");
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baidu::mirana::poros::AndConverter converter;
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and_test_helper(graph_IR, &converter, TYPE_A);
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and_test_helper(graph_IR, &converter, TYPE_B);
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and_test_helper(graph_IR, &converter, TYPE_C);
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and_test_helper(graph_IR, &converter, TYPE_D);
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and_test_helper(graph_IR, &converter, TYPE_E);
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}
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TEST(Converters, ATenLogicalOrConvertsCorrectly) {
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const auto graph_IR = gen_and_or_tensor_graph("__or__");
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baidu::mirana::poros::OrConverter converter;
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and_test_helper(graph_IR, &converter, TYPE_A);
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and_test_helper(graph_IR, &converter, TYPE_B);
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and_test_helper(graph_IR, &converter, TYPE_C);
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and_test_helper(graph_IR, &converter, TYPE_D);
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and_test_helper(graph_IR, &converter, TYPE_E);
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}
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TEST(Converters, ATenLogicalBitwiseOrConvertsCorrectly) {
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const auto graph_IR = gen_and_or_tensor_graph("bitwise_or");
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baidu::mirana::poros::OrConverter converter;
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and_test_helper(graph_IR, &converter, TYPE_A);
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and_test_helper(graph_IR, &converter, TYPE_B);
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and_test_helper(graph_IR, &converter, TYPE_C);
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and_test_helper(graph_IR, &converter, TYPE_D);
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and_test_helper(graph_IR, &converter, TYPE_E);
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}
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TEST(Converters, ATenLogicalXOrConvertsCorrectly) {
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const auto graph_IR = gen_and_or_tensor_graph("__xor__");
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baidu::mirana::poros::XorConverter converter;
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and_test_helper(graph_IR, &converter, TYPE_A);
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and_test_helper(graph_IR, &converter, TYPE_B);
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and_test_helper(graph_IR, &converter, TYPE_C);
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and_test_helper(graph_IR, &converter, TYPE_D);
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and_test_helper(graph_IR, &converter, TYPE_E);
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}
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TEST(Converters, ATenLogicalBitwiseXOrConvertsCorrectly) {
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const auto graph_IR = gen_and_or_tensor_graph("bitwise_xor");
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baidu::mirana::poros::XorConverter converter;
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and_test_helper(graph_IR, &converter, TYPE_A);
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and_test_helper(graph_IR, &converter, TYPE_B);
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and_test_helper(graph_IR, &converter, TYPE_C);
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and_test_helper(graph_IR, &converter, TYPE_D);
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and_test_helper(graph_IR, &converter, TYPE_E);
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}
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static void not_test_helper(const std::string &graph_IR,
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baidu::mirana::poros::IConverter *converter,
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const InputTypeEnum input_type) {
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auto input_data = get_input_data(input_type);
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input_data.pop_back(); // only need one input, pop out the last one
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baidu::mirana::poros::PorosOptions poros_option; // default device GPU
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// 运行原图与engine获取结果
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std::vector<at::Tensor> graph_output;
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std::vector<at::Tensor> poros_output;
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ASSERT_TRUE(baidu::mirana::poros::testutil::run_graph_and_poros(graph_IR, poros_option, converter,
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input_data, graph_output, poros_output));
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ASSERT_EQ(1, graph_output.size());
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ASSERT_EQ(1, poros_output.size());
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ASSERT_TRUE(baidu::mirana::poros::testutil::almost_equal(graph_output[0], poros_output[0], 2e-6));
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}
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TEST(Converters, ATenLogicalBitwiseNotConvertsCorrectly) {
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const auto graph_IR = gen_not_tensor_graph("bitwise_not");
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baidu::mirana::poros::NotConverter converter;
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not_test_helper(graph_IR, &converter, TYPE_A);
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not_test_helper(graph_IR, &converter, TYPE_B);
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not_test_helper(graph_IR, &converter, TYPE_C);
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not_test_helper(graph_IR, &converter, TYPE_D);
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not_test_helper(graph_IR, &converter, TYPE_E);
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}
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//}
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