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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
4.5 KiB
C++
104 lines
4.5 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 linear_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/linear.h"
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#include "poros/util/test_util.h"
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static void linear_test_helper(const std::string& graph_IR,
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const std::vector<at::Tensor>& input_data,
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const std::vector<size_t> replace_const_index) {
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baidu::mirana::poros::PorosOptions poros_option; // default device GPU
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baidu::mirana::poros::LinearConverter linearconverter;
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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, &linearconverter,
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input_data, graph_output, poros_output, nullptr, "", replace_const_index));
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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_no_bias_graph() {
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std::string graph = R"IR(
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graph(%0 : Tensor, %1 : Tensor):
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%2 : None = prim::Constant()
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%3 : Tensor = aten::linear(%0, %1, %2)
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return (%3))IR";
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return graph;
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}
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TEST(Converters, ATenLinearNoBiasConvertsCorrectly) {
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// aten::linear(Tensor input, Tensor weight, Tensor? bias=None) -> Tensor
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const auto graph_IR = gen_no_bias_graph();
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baidu::mirana::poros::LinearConverter linearconverter;
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std::vector<at::Tensor> input_data;
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input_data.push_back(at::randn({1, 2}, {at::kCUDA}));
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input_data.push_back(at::randn({3, 2}, {at::kCUDA})); // 内部转置
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linear_test_helper(graph_IR, input_data, {});
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}
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TEST(Converters, ATenLinearNoBiasNeedPaddingConvertsCorrectly) {
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// aten::linear(Tensor input, Tensor weight, Tensor? bias=None) -> Tensor
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const auto graph_IR = gen_no_bias_graph();
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baidu::mirana::poros::LinearConverter linearconverter;
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std::vector<at::Tensor> input_data;
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input_data.push_back(at::randn({2, 64, 8}, {at::kCUDA}));
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input_data.push_back(at::randn({30, 8}, {at::kCUDA})); // 内部转置
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linear_test_helper(graph_IR, input_data, {});
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}
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TEST(Converters, ATenLinearNoBiasNeedPaddingConstWeightConvertsCorrectly) {
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// aten::linear(Tensor input, Tensor weight, Tensor? bias=None) -> Tensor
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const auto graph_IR = gen_no_bias_graph();
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baidu::mirana::poros::LinearConverter linearconverter;
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std::vector<at::Tensor> input_data;
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input_data.push_back(at::randn({2, 64, 8}, {at::kCUDA}));
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input_data.push_back(at::randn({30, 8}, {at::kCUDA})); // 内部转置
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linear_test_helper(graph_IR, input_data, {1}); //把第二个参数转换成常量
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}
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TEST(Converters, ATenLinearNoBiasNeedPaddingConstWeight2ConvertsCorrectly) {
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// aten::linear(Tensor input, Tensor weight, Tensor? bias=None) -> Tensor
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const auto graph_IR = gen_no_bias_graph();
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baidu::mirana::poros::LinearConverter linearconverter;
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std::vector<at::Tensor> input_data;
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input_data.push_back(at::randn({2, 64, 64, 8}, {at::kCUDA}));
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input_data.push_back(at::randn({30, 8}, {at::kCUDA})); // 内部转置
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linear_test_helper(graph_IR, input_data, {1}); //把第二个参数转换成常量
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}
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TEST(Converters, ATenLinearBiasConvertsCorrectly) {
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// aten::linear(Tensor input, Tensor weight, Tensor? bias=None) -> Tensor
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const auto graph_IR = R"IR(
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graph(%0 : Tensor, %1 : Tensor, %2 : Tensor):
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%3 : Tensor = aten::linear(%0, %1, %2)
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return (%3))IR";
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baidu::mirana::poros::LinearConverter linearconverter;
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std::vector<at::Tensor> input_data;
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input_data.push_back(at::randn({1, 3}, {at::kCUDA}));
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input_data.push_back(at::randn({2, 3}, {at::kCUDA}));
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input_data.push_back(at::randn({2}, {at::kCUDA}));
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linear_test_helper(graph_IR, input_data, {});
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} |