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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>
410 lines
18 KiB
C++
410 lines
18 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 mul_div_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/mul_div.h"
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#include "poros/util/test_util.h"
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static void mul_div_test_helper(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::randn(shape2, {at::kCUDA}));
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}
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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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std::string gen_mul_div_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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std::string gen_mul_div_scalar_graph(const std::string& op, const std::string& scalar) {
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return 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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}
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TEST(Converters, ATenMulConvertsCorrectly) {
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// aten::mul.Tensor(Tensor self, Tensor other) -> Tensor
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const auto graph_IR = gen_mul_div_tensor_graph("mul");
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baidu::mirana::poros::MulConverter mulconverter;
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mul_div_test_helper(graph_IR, &mulconverter, false);
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mul_div_test_helper(graph_IR, &mulconverter, false, {3, 4}, {4});
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mul_div_test_helper(graph_IR, &mulconverter, false, {4}, {3, 4});
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mul_div_test_helper(graph_IR, &mulconverter, false, {4, 1}, {1, 4});
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mul_div_test_helper(graph_IR, &mulconverter, false, {3, 4, 3}, {4, 3});
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mul_div_test_helper(graph_IR, &mulconverter, false, {4, 3}, {3, 4, 3});
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}
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TEST(Converters, ATenMulScalarConvertsCorrectly) {
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// aten::mul.Scalar(Tensor self, Scalar other) -> Tensor
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const auto graph_IR = gen_mul_div_scalar_graph("mul", "2.4");
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baidu::mirana::poros::MulConverter mulconverter;
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mul_div_test_helper(graph_IR, &mulconverter, true);
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mul_div_test_helper(graph_IR, &mulconverter, true, {3, 4, 3});
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}
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TEST(Converters, ATenMul_ConvertsCorrectly) {
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// aten::mul_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)
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const auto graph_IR = gen_mul_div_tensor_graph("mul_");
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baidu::mirana::poros::MulConverter mulconverter;
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mul_div_test_helper(graph_IR, &mulconverter, false);
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mul_div_test_helper(graph_IR, &mulconverter, false, {3, 4}, {4});
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mul_div_test_helper(graph_IR, &mulconverter, false, {3, 4, 3}, {4, 3});
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}
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TEST(Converters, ATenMul_ScalarConvertsCorrectly) {
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// aten::mul_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)
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const auto graph_IR = gen_mul_div_scalar_graph("mul_", "2.4");
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baidu::mirana::poros::MulConverter mulconverter;
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mul_div_test_helper(graph_IR, &mulconverter, true);
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mul_div_test_helper(graph_IR, &mulconverter, true, {3, 4, 3});
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}
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TEST(Converters, ATenDivConvertsCorrectly) {
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// aten::div.Tensor(Tensor self, Tensor other) -> Tensor
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const auto graph_IR = gen_mul_div_tensor_graph("div");
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baidu::mirana::poros::DivConverter divconverter;
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mul_div_test_helper(graph_IR, &divconverter, false);
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mul_div_test_helper(graph_IR, &divconverter, false, {3, 4}, {4});
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mul_div_test_helper(graph_IR, &divconverter, false, {4}, {3, 4});
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mul_div_test_helper(graph_IR, &divconverter, false, {4, 1}, {1, 4});
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mul_div_test_helper(graph_IR, &divconverter, false, {3, 4, 3}, {4, 3});
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mul_div_test_helper(graph_IR, &divconverter, false, {4, 3}, {3, 4, 3});
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}
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TEST(Converters, ATenDivScalarConvertsCorrectly) {
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// aten::div.Scalar(Tensor self, Scalar other) -> (Tensor)
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const auto graph_IR = gen_mul_div_scalar_graph("div", "2.4");
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baidu::mirana::poros::DivConverter divconverter;
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mul_div_test_helper(graph_IR, &divconverter, true);
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mul_div_test_helper(graph_IR, &divconverter, true, {3, 4, 3});
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}
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TEST(Converters, ATenDiv_ConvertsCorrectly) {
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// aten::div_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)
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const auto graph_IR = gen_mul_div_tensor_graph("div_");
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baidu::mirana::poros::DivConverter divconverter;
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mul_div_test_helper(graph_IR, &divconverter, false);
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mul_div_test_helper(graph_IR, &divconverter, false, {3, 4}, {4});
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mul_div_test_helper(graph_IR, &divconverter, false, {3, 4, 3}, {4, 3});
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}
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TEST(Converters, ATenDiv_ScalarConvertsCorrectly) {
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// aten::div_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)
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const auto graph_IR = gen_mul_div_scalar_graph("div_", "2.4");
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baidu::mirana::poros::DivConverter divconverter;
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mul_div_test_helper(graph_IR, &divconverter, true);
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mul_div_test_helper(graph_IR, &divconverter, true, {3, 4, 3});
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}
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TEST(Converters, ATenDivIntDivideIntConvertsCorrectly) {
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// aten::div.Tensor(Tensor self, Tensor other) -> Tensor
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const auto graph_IR = gen_mul_div_tensor_graph("div");
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auto options_pyt_int = torch::TensorOptions().device(torch::kCUDA, 0).dtype(torch::kInt);
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std::vector<at::Tensor> input_data;
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input_data.push_back(torch::tensor({14}, options_pyt_int));
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input_data.push_back(torch::tensor({2}, options_pyt_int));
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baidu::mirana::poros::DivConverter divconverter;
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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, &divconverter,
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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, ATenDivFloatDivideIntConvertsCorrectly) {
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// aten::div.Scalar(Tensor self, Scalar other) -> (Tensor)
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const auto graph_IR = R"IR(
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graph(%0 : Tensor):
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%1 : int = prim::Constant[value=3]()
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%2 : Tensor = aten::div(%0, %1)
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return (%2))IR";
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auto options_pyt_float = torch::TensorOptions().device(torch::kCUDA, 0).dtype(torch::kFloat);
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std::vector<at::Tensor> input_data;
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input_data.push_back(torch::tensor({15.3}, options_pyt_float));
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baidu::mirana::poros::DivConverter divconverter;
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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, &divconverter,
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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, ATenDivIntDivideFloatConvertsCorrectly) {
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// aten::div.Scalar(Tensor self, Scalar other) -> (Tensor)
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const auto graph_IR = gen_mul_div_scalar_graph("div", "2.4");
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auto options_pyt_int = torch::TensorOptions().device(torch::kCUDA, 0).dtype(torch::kInt);
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std::vector<at::Tensor> input_data;
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input_data.push_back(torch::tensor({15}, options_pyt_int));
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baidu::mirana::poros::DivConverter divconverter;
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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, &divconverter,
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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, ATenRemainderConvertsCorrectly) {
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// aten::remainder.Tensor(Tensor self, Tensor other) -> Tensor
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const auto graph_IR = gen_mul_div_tensor_graph("remainder");
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baidu::mirana::poros::RemainderConverter remainder;
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mul_div_test_helper(graph_IR, &remainder, false);
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mul_div_test_helper(graph_IR, &remainder, false, {3, 4}, {4});
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mul_div_test_helper(graph_IR, &remainder, false, {4}, {3, 4});
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mul_div_test_helper(graph_IR, &remainder, false, {4, 1}, {1, 4});
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mul_div_test_helper(graph_IR, &remainder, false, {3, 4, 3}, {4, 3});
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mul_div_test_helper(graph_IR, &remainder, false, {4, 3}, {3, 4, 3});
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}
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TEST(Converters, ATenRemainderScalarConvertsCorrectly) {
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// aten::remainder.Scalar(Tensor self, Scalar other) -> Tensor
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const auto graph_IR = gen_mul_div_scalar_graph("remainder", "-0.4");
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baidu::mirana::poros::RemainderConverter remainder;
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mul_div_test_helper(graph_IR, &remainder, true);
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mul_div_test_helper(graph_IR, &remainder, true, {3, 4, 3});
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}
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static void mul_div_dynamic_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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bool is_dynamic = false,
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std::vector<std::vector<at::Tensor>>* prewarm_data = nullptr) {
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baidu::mirana::poros::PorosOptions poros_option; // default device GPU
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poros_option.is_dynamic = is_dynamic;
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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, prewarm_data));
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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(graph_output[0].equal(poros_output[0]));
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}
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TEST(Converters, ATenMulIntdynamicConvertsCorrectly) {
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// aten::mul.int(int a, int b) -> (int)
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const auto graph_IR = R"IR(
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graph(%0 : Tensor):
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%1 : int = prim::Constant[value=0]()
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%2 : int = prim::Constant[value=1]()
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%3 : int = aten::size(%0, %1)
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%4 : int = aten::size(%0, %2)
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%5 : int = aten::mul(%3, %4)
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%6 : Tensor = aten::add(%0, %5, %2)
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return (%6))IR";
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baidu::mirana::poros::MulConverter mulconverter;
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std::vector<at::Tensor> input_data;
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input_data.push_back(at::zeros({2, 3}, {at::kCUDA}).to(at::ScalarType::Int));
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std::vector<std::vector<at::Tensor>> prewarm_data = {{}, {}, {}};
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prewarm_data[0].push_back(at::zeros({4, 5}, {at::kCUDA}).to(at::ScalarType::Int));
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prewarm_data[1].push_back(at::zeros({2, 3}, {at::kCUDA}).to(at::ScalarType::Int));
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prewarm_data[2].push_back(at::zeros({2, 3}, {at::kCUDA}).to(at::ScalarType::Int));
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mul_div_dynamic_test_helper(graph_IR, &mulconverter, input_data, true, &prewarm_data);
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}
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TEST(Converters, ATenDivIntdynamicConvertsCorrectly) {
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// aten::div.int(int a, int b) -> (float)
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const auto graph_IR = R"IR(
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graph(%0 : Tensor):
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%1 : int = prim::Constant[value=0]()
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%2 : int = prim::Constant[value=1]()
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%3 : int = aten::size(%0, %1)
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%4 : int = aten::size(%0, %2)
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%5 : float = aten::div(%3, %4)
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%6 : Tensor = aten::add(%0, %5, %2)
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return (%6))IR";
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baidu::mirana::poros::DivConverter divconverter;
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std::vector<at::Tensor> input_data;
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input_data.push_back(at::zeros({4, 5}, {at::kCUDA}));
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std::vector<std::vector<at::Tensor>> prewarm_data = {{}, {}, {}};
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prewarm_data[0].push_back(at::zeros({10, 8}, {at::kCUDA}));
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prewarm_data[1].push_back(at::zeros({4, 5}, {at::kCUDA}));
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prewarm_data[2].push_back(at::zeros({4, 5}, {at::kCUDA}));
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mul_div_dynamic_test_helper(graph_IR, &divconverter, input_data, true, &prewarm_data);
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}
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TEST(Converters, ATenDivNegIntdynamicConvertsCorrectly) {
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// aten::div.int(int a, int b) -> (float)
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const auto graph_IR = R"IR(
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graph(%0 : Tensor):
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%1 : int = prim::Constant[value=0]()
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%2 : int = prim::Constant[value=1]()
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%3 : int = aten::size(%0, %1)
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%4 : int = aten::size(%0, %2)
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%34 : int = prim::Constant[value=100]()
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%35 : int = aten::sub(%3, %34)
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%5 : float = aten::div(%35, %4)
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%6 : Tensor = aten::add(%0, %5, %2)
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return (%6))IR";
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baidu::mirana::poros::DivConverter divconverter;
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std::vector<at::Tensor> input_data;
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input_data.push_back(at::zeros({4, 5}, {at::kCUDA}));
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std::vector<std::vector<at::Tensor>> prewarm_data = {{}, {}, {}};
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prewarm_data[0].push_back(at::zeros({10, 8}, {at::kCUDA}));
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prewarm_data[1].push_back(at::zeros({4, 5}, {at::kCUDA}));
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prewarm_data[2].push_back(at::zeros({4, 5}, {at::kCUDA}));
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mul_div_dynamic_test_helper(graph_IR, &divconverter, input_data, true, &prewarm_data);
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}
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TEST(Converters, ATenFloordivIntdynamicConvertsCorrectly) {
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// aten::floordiv.int(int a, int b) -> (int)
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const auto graph_IR = R"IR(
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graph(%0 : Tensor):
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%1 : int = prim::Constant[value=0]()
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%2 : int = prim::Constant[value=1]()
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%3 : int = aten::size(%0, %1)
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%4 : int = aten::size(%0, %2)
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%5 : int = aten::floordiv(%3, %4)
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%6 : Tensor = aten::add(%0, %5, %2)
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return (%6))IR";
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baidu::mirana::poros::FloordivConverter floordivconverter;
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std::vector<at::Tensor> input_data;
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input_data.push_back(at::zeros({10, 4}, {at::kCUDA}).to(at::ScalarType::Int));
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std::vector<std::vector<at::Tensor>> prewarm_data = {{}, {}, {}};
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prewarm_data[0].push_back(at::zeros({12, 5}, {at::kCUDA}).to(at::ScalarType::Int));
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prewarm_data[1].push_back(at::zeros({10, 4}, {at::kCUDA}).to(at::ScalarType::Int));
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prewarm_data[2].push_back(at::zeros({10, 4}, {at::kCUDA}).to(at::ScalarType::Int));
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mul_div_dynamic_test_helper(graph_IR, &floordivconverter, input_data, true, &prewarm_data);
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}
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TEST(Converters, ATenFloordivNegIntdynamicConvertsCorrectly) {
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// aten::floordiv.int(int a, int b) -> (int)
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const auto graph_IR = R"IR(
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graph(%0 : Tensor):
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%1 : int = prim::Constant[value=0]()
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%2 : int = prim::Constant[value=1]()
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%3 : int = aten::size(%0, %1)
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%4 : int = aten::size(%0, %2)
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%34 : int = prim::Constant[value=100]()
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%35 : int = aten::sub(%3, %34)
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%5 : int = aten::floordiv(%35, %4)
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%6 : Tensor = aten::add(%0, %5, %2)
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return (%6))IR";
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baidu::mirana::poros::FloordivConverter floordivconverter;
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std::vector<at::Tensor> input_data;
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input_data.push_back(at::zeros({10, 4}, {at::kCUDA}).to(at::ScalarType::Int));
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std::vector<std::vector<at::Tensor>> prewarm_data = {{}, {}, {}};
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prewarm_data[0].push_back(at::zeros({12, 5}, {at::kCUDA}).to(at::ScalarType::Int));
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prewarm_data[1].push_back(at::zeros({10, 4}, {at::kCUDA}).to(at::ScalarType::Int));
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prewarm_data[2].push_back(at::zeros({10, 4}, {at::kCUDA}).to(at::ScalarType::Int));
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|
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mul_div_dynamic_test_helper(graph_IR, &floordivconverter, input_data, true, &prewarm_data);
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}
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TEST(Converters, ATenRoundToZeroFloordivIntdynamicConvertsCorrectly) {
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// aten::__round_to_zero_floordiv(int a, int b) -> (int)
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const auto graph_IR = R"IR(
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graph(%0 : Tensor):
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%1 : int = prim::Constant[value=0]()
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%2 : int = prim::Constant[value=1]()
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%3 : int = aten::size(%0, %1)
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%4 : int = aten::size(%0, %2)
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%5 : int = aten::__round_to_zero_floordiv(%3, %4)
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%6 : Tensor = aten::add(%0, %5, %2)
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return (%6))IR";
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baidu::mirana::poros::FloordivConverter floordivconverter;
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std::vector<at::Tensor> input_data;
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input_data.push_back(at::zeros({10, 4}, {at::kCUDA}).to(at::ScalarType::Int));
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|
|
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std::vector<std::vector<at::Tensor>> prewarm_data = {{}, {}, {}};
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|
prewarm_data[0].push_back(at::zeros({12, 5}, {at::kCUDA}).to(at::ScalarType::Int));
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prewarm_data[1].push_back(at::zeros({10, 4}, {at::kCUDA}).to(at::ScalarType::Int));
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|
prewarm_data[2].push_back(at::zeros({10, 4}, {at::kCUDA}).to(at::ScalarType::Int));
|
|
|
|
mul_div_dynamic_test_helper(graph_IR, &floordivconverter, input_data, true, &prewarm_data);
|
|
}
|
|
|
|
TEST(Converters, ATenRoundToZeroFloordivNegIntdynamicConvertsCorrectly) {
|
|
// aten::__round_to_zero_floordiv(int a, int b) -> (int)
|
|
const auto graph_IR = R"IR(
|
|
graph(%0 : Tensor):
|
|
%1 : int = prim::Constant[value=0]()
|
|
%2 : int = prim::Constant[value=1]()
|
|
%3 : int = aten::size(%0, %1)
|
|
%4 : int = aten::size(%0, %2)
|
|
%34 : int = prim::Constant[value=100]()
|
|
%35 : int = aten::sub(%3, %34)
|
|
%5 : int = aten::__round_to_zero_floordiv(%35, %4)
|
|
%6 : Tensor = aten::add(%0, %5, %2)
|
|
return (%6))IR";
|
|
baidu::mirana::poros::FloordivConverter floordivconverter;
|
|
std::vector<at::Tensor> input_data;
|
|
input_data.push_back(at::zeros({10, 4}, {at::kCUDA}).to(at::ScalarType::Int));
|
|
|
|
std::vector<std::vector<at::Tensor>> prewarm_data = {{}, {}, {}};
|
|
prewarm_data[0].push_back(at::zeros({12, 5}, {at::kCUDA}).to(at::ScalarType::Int));
|
|
prewarm_data[1].push_back(at::zeros({10, 4}, {at::kCUDA}).to(at::ScalarType::Int));
|
|
prewarm_data[2].push_back(at::zeros({10, 4}, {at::kCUDA}).to(at::ScalarType::Int));
|
|
|
|
mul_div_dynamic_test_helper(graph_IR, &floordivconverter, input_data, true, &prewarm_data);
|
|
} |