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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>
305 lines
13 KiB
C++
305 lines
13 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 element_wise_test.cpp
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* @author tianshaoqing@baidu.com
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* @date Wed Sep 27 11:24:21 CST 2021
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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/element_wise.h"
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#include "poros/util/test_util.h"
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static void poros_test_helper(const std::string& graph_IR,
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baidu::mirana::poros::IConverter* converter,
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const std::vector<at::Tensor>& input_data){
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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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std::string pow_node_name("aten::pow");
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if(converter->node_kind()[0].toQualString() == pow_node_name){
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ASSERT_TRUE(baidu::mirana::poros::testutil::almost_equal(graph_output[0], poros_output[0], 2e-6));
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}else{
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ASSERT_TRUE(graph_output[0].equal(poros_output[0]));
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}
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}
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static void pow_test_examples(const std::string& graph_IR,
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baidu::mirana::poros::IConverter* converter,
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bool singleInput,
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std::vector<int64_t> shape1 = {5},
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std::vector<int64_t> shape2 = {5}){
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std::vector<at::Tensor> input_data;
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input_data.push_back(at::randn(shape1, {at::kCUDA}));
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if (!singleInput){
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input_data.push_back(at::randint(-5, 5, shape2, {at::kCUDA}));
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}
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poros_test_helper(graph_IR, converter, input_data);
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}
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TEST(Converters, ATenPowTensorConvertsCorrectly) {
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// aten::pow.Tensor_Tensor(Tensor self, Tensor exponent) -> Tensor
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const auto graph_IR = R"IR(
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graph(%1 : Tensor, %2 : Tensor):
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%3 : Tensor = aten::pow(%1, %2)
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return (%3))IR";
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baidu::mirana::poros::PowOrFloordivideConverter poworfloordivideconverter;
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pow_test_examples(graph_IR, &poworfloordivideconverter, false);
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pow_test_examples(graph_IR, &poworfloordivideconverter, false, {3, 4}, {4});
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pow_test_examples(graph_IR, &poworfloordivideconverter, false, {4}, {3, 4});
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pow_test_examples(graph_IR, &poworfloordivideconverter, false, {3, 4, 3}, {4, 3});
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pow_test_examples(graph_IR, &poworfloordivideconverter, false, {4, 3}, {3, 4, 3});
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}
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TEST(Converters, ATenPowScalarConvertsCorrectly) {
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// aten::pow.Tensor_Scalar(Tensor self, Scalar exponent) -> Tensor
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const auto graph_IR = R"IR(
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graph(%1 : Tensor):
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%2 : float = prim::Constant[value=2.0]()
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%3 : Tensor = aten::pow(%1, %2)
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return (%3))IR";
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baidu::mirana::poros::PowOrFloordivideConverter poworfloordivideconverter;
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pow_test_examples(graph_IR, &poworfloordivideconverter, true);
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pow_test_examples(graph_IR, &poworfloordivideconverter, true, {3, 4});
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}
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static void elementwise_tensor_test_examples(const std::string& op,
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baidu::mirana::poros::IConverter* converter){
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const auto graph_IR = 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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std::vector<at::Tensor> input_data;
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input_data.push_back(at::randn({2, 2}, {at::kCUDA}));
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input_data.push_back(at::randn({2, 2}, {at::kCUDA}));
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poros_test_helper(graph_IR, converter, input_data);
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input_data.clear();
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input_data.push_back(at::randn({2, 2}, {at::kCUDA}));
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input_data.push_back(at::randn({2, 2}, {at::kCUDA}));
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input_data[0][0][0] = 2.5;
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input_data[1][0][0] = 2.5;
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poros_test_helper(graph_IR, converter, input_data);
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input_data.clear();
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input_data.push_back(at::randn({3, 4, 3}, {at::kCUDA}));
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input_data.push_back(at::randn({4, 3}, {at::kCUDA}));
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input_data[0][0][0][0] = 2.5;
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input_data[1][0][0] = 2.5;
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poros_test_helper(graph_IR, converter, input_data);
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input_data.clear();
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input_data.push_back(at::randn({4, 3}, {at::kCUDA}));
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input_data.push_back(at::randn({3, 4, 3}, {at::kCUDA}));
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input_data[0][0][0] = 2.5;
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input_data[1][0][0][0] = 2.5;
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poros_test_helper(graph_IR, converter, input_data);
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}
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static void elementwise_scalar_test_examples(const std::string& op,
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const std::string& scalar,
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baidu::mirana::poros::IConverter* converter){
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const auto graph_IR = R"IR(
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graph(%0 : Tensor):
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%1 : float = prim::Constant[value=)IR" + scalar + R"IR(]()
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%2 : Tensor = aten::)IR" + op + R"IR((%0, %1)
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return (%2))IR";
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std::vector<at::Tensor> input_data;
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input_data.push_back(at::randn({2, 2}, {at::kCUDA}));
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poros_test_helper(graph_IR, converter, input_data);
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input_data.clear();
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input_data.push_back(at::randn({2, 2}, {at::kCUDA}));
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input_data[0][0][0] = 2.5;
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poros_test_helper(graph_IR, converter, input_data);
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input_data.clear();
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input_data.push_back(at::randn({1}, {at::kCUDA}));
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input_data[0][0] = 2.5;
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poros_test_helper(graph_IR, converter, input_data);
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}
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TEST(Converters, ATenEqualTensorConvertsCorrectly) {
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// aten::eq.Tensor(Tensor self, Tensor other) -> Tensor
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baidu::mirana::poros::EqualOrNotequalConverter equalorbotequalconverter;
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elementwise_tensor_test_examples("eq", &equalorbotequalconverter);
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}
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TEST(Converters, ATenEqualScalarConvertsCorrectly) {
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// aten::eq.Scalar(Tensor self, Scalar other) -> Tensor
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baidu::mirana::poros::EqualOrNotequalConverter equalorbotequalconverter;
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elementwise_scalar_test_examples("eq", "2.5",&equalorbotequalconverter);
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}
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TEST(Converters, ATenNotEqualTensorConvertsCorrectly) {
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// aten::ne.Tensor(Tensor self, Tensor other) -> Tensor
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baidu::mirana::poros::EqualOrNotequalConverter equalorbotequalconverter;
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elementwise_tensor_test_examples("ne", &equalorbotequalconverter);
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}
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TEST(Converters, ATenNotEqualScalarConvertsCorrectly) {
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// aten::ne.Scalar(Tensor self, Scalar other) -> Tensor
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baidu::mirana::poros::EqualOrNotequalConverter equalorbotequalconverter;
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elementwise_scalar_test_examples("ne", "2.5", &equalorbotequalconverter);
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}
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TEST(Converters, ATenGtTensorConvertsCorrectly) {
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// aten::gt.Tensor(Tensor self, Tensor other) -> Tensor
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baidu::mirana::poros::GreaterOrLessConverter greaterorlessconverter;
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elementwise_tensor_test_examples("gt", &greaterorlessconverter);
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}
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TEST(Converters, ATenGtScalarConvertsCorrectly) {
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// aten::gt.Scalar(Tensor self, Scalar other) -> Tensor
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baidu::mirana::poros::GreaterOrLessConverter greaterorlessconverter;
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elementwise_scalar_test_examples("gt", "2.5", &greaterorlessconverter);
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}
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TEST(Converters, ATenLtTensorConvertsCorrectly) {
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// aten::lt.Tensor(Tensor self, Tensor other) -> Tensor
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baidu::mirana::poros::GreaterOrLessConverter greaterorlessconverter;
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elementwise_tensor_test_examples("lt", &greaterorlessconverter);
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}
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TEST(Converters, ATenLtScalarConvertsCorrectly) {
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// aten::lt.Scalar(Tensor self, Scalar other) -> Tensor
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baidu::mirana::poros::GreaterOrLessConverter greaterorlessconverter;
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elementwise_scalar_test_examples("lt", "2.5", &greaterorlessconverter);
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}
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TEST(Converters, ATenGeTensorConvertsCorrectly) {
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// aten::ge.Tensor(Tensor self, Tensor other) -> Tensor
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baidu::mirana::poros::GreaterOrLessConverter greaterorlessconverter;
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elementwise_tensor_test_examples("ge", &greaterorlessconverter);
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}
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TEST(Converters, ATenGeScalarConvertsCorrectly) {
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// aten::ge.Scalar(Tensor self, Scalar other) -> Tensor
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baidu::mirana::poros::GreaterOrLessConverter greaterorlessconverter;
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elementwise_scalar_test_examples("ge", "2.5", &greaterorlessconverter);
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}
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TEST(Converters, ATenLeTensorConvertsCorrectly) {
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// aten::le.Tensor(Tensor self, Tensor other) -> Tensor
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baidu::mirana::poros::GreaterOrLessConverter greaterorlessconverter;
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elementwise_tensor_test_examples("le", &greaterorlessconverter);
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}
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TEST(Converters, ATenLeScalarConvertsCorrectly) {
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// aten::le.Scalar(Tensor self, Scalar other) -> Tensor
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baidu::mirana::poros::GreaterOrLessConverter greaterorlessconverter;
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elementwise_scalar_test_examples("le", "2.5", &greaterorlessconverter);
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}
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static std::string gen_clamp_graph(const std::string& op,
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const std::string& min_val,
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const std::string& max_val){
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if (op == "clamp"){
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std::string min_val_IR;
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std::string max_val_IR;
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if (min_val.empty()){
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min_val_IR = "None = prim::Constant()";
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}else{
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min_val_IR = "float = prim::Constant[value=" + min_val + "]()";
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}
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if (max_val.empty()){
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max_val_IR = "None = prim::Constant()";
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}else{
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max_val_IR = "float = prim::Constant[value=" + max_val + "]()";
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}
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return R"IR(
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graph(%0 : Tensor):
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%1 : )IR" + min_val_IR + R"IR(
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%2 : )IR" + max_val_IR + R"IR(
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%3 : Tensor = aten::)IR" + op + R"IR((%0, %1, %2)
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return (%3))IR";
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}else if (op == "clamp_min"){
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return R"IR(
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graph(%0 : Tensor):
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%1 : float = prim::Constant[value=)IR" + min_val + R"IR(]()
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%2 : Tensor = aten::)IR" + op + R"IR((%0, %1)
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return (%2))IR";
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}else if (op == "clamp_max"){
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return R"IR(
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graph(%0 : Tensor):
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%1 : float = prim::Constant[value=)IR" + max_val + R"IR(]()
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%2 : Tensor = aten::)IR" + op + R"IR((%0, %1)
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return (%2))IR";
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}else{
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return "";
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}
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}
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TEST(Converters, ATenClampMinConvertsCorrectly) {
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// aten::clamp(Tensor self, Scalar? min=None, Scalar? max=None) -> Tensor
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const auto graph_IR = gen_clamp_graph("clamp", "1.5", "");
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baidu::mirana::poros::ClampConverter clampconverter;
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std::vector<at::Tensor> input_data;
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input_data.push_back(at::randn({10}, {at::kCUDA}));
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poros_test_helper(graph_IR, &clampconverter, input_data);
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}
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TEST(Converters, ATenClampMaxConvertsCorrectly) {
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// aten::clamp(Tensor self, Scalar? min=None, Scalar? max=None) -> Tensor
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const auto graph_IR = gen_clamp_graph("clamp", "", "0.5");
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baidu::mirana::poros::ClampConverter clampconverter;
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std::vector<at::Tensor> input_data;
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input_data.push_back(at::randn({10}, {at::kCUDA}));
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poros_test_helper(graph_IR, &clampconverter, input_data);
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}
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TEST(Converters, ATenClampMinMaxConvertsCorrectly) {
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// aten::clamp(Tensor self, Scalar? min=None, Scalar? max=None) -> Tensor
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const auto graph_IR = gen_clamp_graph("clamp", "-0.5", "0.5");
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baidu::mirana::poros::ClampConverter clampconverter;
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std::vector<at::Tensor> input_data;
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input_data.push_back(at::randn({10}, {at::kCUDA}));
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poros_test_helper(graph_IR, &clampconverter, input_data);
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}
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TEST(Converters, ATenClampMaximumConvertsCorrectly) {
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// aten::clamp_max(Tensor self, Scalar max) -> Tensor
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const auto graph_IR = gen_clamp_graph("clamp_max", "", "0.5");
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baidu::mirana::poros::ClampConverter clampconverter;
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std::vector<at::Tensor> input_data;
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input_data.push_back(at::randn({10}, {at::kCUDA}));
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poros_test_helper(graph_IR, &clampconverter, input_data);
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}
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TEST(Converters, ATenClampMinimumConvertsCorrectly) {
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// aten::clamp_min(Tensor self, Scalar min) -> Tensor
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const auto graph_IR = gen_clamp_graph("clamp_min", "-0.5", "");
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baidu::mirana::poros::ClampConverter clampconverter;
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std::vector<at::Tensor> input_data;
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input_data.push_back(at::randn({10}, {at::kCUDA}));
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poros_test_helper(graph_IR, &clampconverter, input_data);
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}
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TEST(Converters, ATenClampMinGtMaxConvertsCorrectly) {
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// aten::clamp_min(Tensor self, Scalar min) -> Tensor
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const auto graph_IR = gen_clamp_graph("clamp", "0.5", "-0.5");
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baidu::mirana::poros::ClampConverter clampconverter;
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std::vector<at::Tensor> input_data;
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input_data.push_back(at::randn({10}, {at::kCUDA}));
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poros_test_helper(graph_IR, &clampconverter, input_data);
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} |