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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>
104 lines
4.7 KiB
C++
104 lines
4.7 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 matrix_multiply_test.cpp
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* @author tianjinjin@baidu.com
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* @date Tue Sep 14 18:19:00 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/matrix_multiply.h"
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#include "poros/util/test_util.h"
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static void matrix_multiply_test_helper(const std::string& graph_IR,
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baidu::mirana::poros::IConverter* converter,
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std::vector<int64_t> shape1,
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std::vector<int64_t> shape2,
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bool tripleinputs = false,
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std::vector<int64_t> shape3 = {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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input_data.push_back(at::randn(shape2, {at::kCUDA}));
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if (tripleinputs){
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input_data.push_back(at::randn(shape3, {at::kCUDA}));
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}
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baidu::mirana::poros::PorosOptions poros_option; // default device GPU
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c10::ShowLogInfoToStderr();
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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, ATenMatmulConvertersCorrectly) {
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// aten::matmul(Tensor self, Tensor other) -> Tensor
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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::matmul(%0, %1)
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return (%2))IR";
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baidu::mirana::poros::MatmulConverter matmulconverter;
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matrix_multiply_test_helper(graph_IR, &matmulconverter, {3}, {3});
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matrix_multiply_test_helper(graph_IR, &matmulconverter, {1, 1536}, {1536, 2});
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matrix_multiply_test_helper(graph_IR, &matmulconverter, {3}, {3, 512});
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matrix_multiply_test_helper(graph_IR, &matmulconverter, {512}, {512, 3});
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matrix_multiply_test_helper(graph_IR, &matmulconverter, {512, 3}, {3});
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matrix_multiply_test_helper(graph_IR, &matmulconverter, {1, 30, 1024}, {1024});
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matrix_multiply_test_helper(graph_IR, &matmulconverter, {1, 30, 1024}, {1024, 214});
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matrix_multiply_test_helper(graph_IR, &matmulconverter, {8}, {512, 8, 10});
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matrix_multiply_test_helper(graph_IR, &matmulconverter, {254, 8}, {512, 8, 10});
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matrix_multiply_test_helper(graph_IR, &matmulconverter, {10, 3, 512}, {10, 512, 214});
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matrix_multiply_test_helper(graph_IR, &matmulconverter, {10, 1, 24, 224}, {7, 224, 5});
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}
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TEST(Converters, ATenBmmConvertersCorrectly) {
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// aten::bmm(Tensor self, Tensor mat2) -> Tensor
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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::bmm(%0, %1)
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return (%2))IR";
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baidu::mirana::poros::BmmConverter bmmconverter;
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matrix_multiply_test_helper(graph_IR, &bmmconverter, {10, 3, 4}, {10, 4, 5});
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}
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static std::string gen_addmm_graph(const std::string& beta, const std::string& alpha) {
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return R"IR(
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graph(%0 : Tensor, %1 : Tensor, %2 : Tensor):
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%3 : float = prim::Constant[value=)IR" + beta + R"IR(]()
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%4 : float = prim::Constant[value=)IR" + alpha + R"IR(]()
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%5 : Tensor = aten::addmm(%0, %1, %2, %3, %4)
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return (%5))IR";
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}
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TEST(Converters, ATenAddmmConvertersCorrectly) {
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// aten::addmm(Tensor self, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1) -> Tensor
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const auto graph_IR = gen_addmm_graph("1.0", "1.0");
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baidu::mirana::poros::AddmmConverter addmmconverter;
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matrix_multiply_test_helper(graph_IR, &addmmconverter, {2, 3}, {2, 3}, true, {3, 3});
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matrix_multiply_test_helper(graph_IR, &addmmconverter, {3}, {2, 3}, true, {3, 3});
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}
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TEST(Converters, ATenAddmmBetaAlphaConvertersCorrectly) {
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// aten::addmm(Tensor self, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1) -> Tensor
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const auto graph_IR = gen_addmm_graph("3.3", "2.2");
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baidu::mirana::poros::AddmmConverter addmmconverter;
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matrix_multiply_test_helper(graph_IR, &addmmconverter, {2, 3}, {2, 3}, true, {3, 3});
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} |