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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>
160 lines
6.7 KiB
C++
160 lines
6.7 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 constant_pad_nd_test.cpp
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* @author tianshaoqing@baidu.com
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* @date Thur Dec 2 14:29:20 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/util/test_util.h"
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#include "poros/converter/gpu/constant_pad_nd.h"
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static void constant_pad_nd_test_helper(const std::string& graph_IR,
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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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baidu::mirana::poros::ConstantPadNdConverter constantpadndconverter;
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ASSERT_TRUE(baidu::mirana::poros::testutil::run_graph_and_poros(graph_IR, poros_option, &constantpadndconverter,
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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(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_constant_pad_nd_graph(const std::string& padding_shape_str,
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const std::string& value_str,
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const bool padding_value_is_int = false) {
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if (padding_value_is_int) {
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return R"IR(
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graph(%0 : Tensor):
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%1 : int[] = prim::Constant[value=[)IR" + padding_shape_str + R"IR(]]()
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%2 : int = prim::Constant[value=)IR" + value_str + R"IR(]()
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%3 : Tensor = aten::constant_pad_nd(%0, %1, %2)
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return (%3))IR";
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} else {
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return R"IR(
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graph(%0 : Tensor):
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%1 : int[] = prim::Constant[value=[)IR" + padding_shape_str + R"IR(]]()
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%2 : float = prim::Constant[value=)IR" + value_str + R"IR(]()
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%3 : Tensor = aten::constant_pad_nd(%0, %1, %2)
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return (%3))IR";
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}
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}
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TEST(Converters, TestAtenConstantPadNdCorrectly) {
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const auto graph_IR = gen_constant_pad_nd_graph("1, 2, 3, 4", "1.5");
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std::vector<at::Tensor> input_data;
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input_data.push_back(at::randn({4, 5, 6, 7}, {at::kCUDA}));
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constant_pad_nd_test_helper(graph_IR, input_data);
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}
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TEST(Converters, TestAtenConstantPadNdLastDimCorrectly) {
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const auto graph_IR = gen_constant_pad_nd_graph("1, 2", "1.5");
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std::vector<at::Tensor> input_data;
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input_data.push_back(at::randn({4, 5, 6, 7}, {at::kCUDA}));
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constant_pad_nd_test_helper(graph_IR, input_data);
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}
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TEST(Converters, TestAtenConstantPadNdZerosPaddingDimsCorrectly) {
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const auto graph_IR = gen_constant_pad_nd_graph("0, 1, 2, 0", "1.5");
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std::vector<at::Tensor> input_data;
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input_data.push_back(at::randn({4, 5, 6, 7}, {at::kCUDA}));
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constant_pad_nd_test_helper(graph_IR, input_data);
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}
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TEST(Converters, TestAtenConstantPadNdIntCorrectly) {
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const auto graph_IR = gen_constant_pad_nd_graph("1, 2, 3, 4", "1", true);
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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::kInt);
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input_data.push_back(at::randint(0, 10, {4, 5, 6, 7}, options_pyt));
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constant_pad_nd_test_helper(graph_IR, input_data);
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}
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TEST(Converters, TestAtenConstantPadNdInputSingleDimCorrectly) {
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const auto graph_IR = gen_constant_pad_nd_graph("1, 2", "1.5");
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std::vector<at::Tensor> input_data;
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input_data.push_back(at::randn({6}, {at::kCUDA}));
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constant_pad_nd_test_helper(graph_IR, input_data);
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}
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TEST(Converters, TestAtenConstantPadNdDynamicFloatCorrectly) {
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const auto graph_IR = gen_constant_pad_nd_graph("1, 2, 3, 4", "1.5");
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std::vector<std::vector<at::Tensor>> prewarm_data = {{}, {}, {}};
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prewarm_data[0].push_back(at::randn({3, 4, 5, 6}, {at::kCUDA}));
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prewarm_data[1].push_back(at::randn({2, 3, 4, 5}, {at::kCUDA}));
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prewarm_data[2].push_back(at::randn({2, 3, 4, 5}, {at::kCUDA}));
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std::vector<at::Tensor> input_data;
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input_data.push_back(at::randn({2, 3, 4, 5}, {at::kCUDA}));
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constant_pad_nd_test_helper(graph_IR, input_data, true, &prewarm_data);
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}
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TEST(Converters, TestAtenConstantPadNdDynamicFloatTwoPaddingDimsZerosCorrectly) {
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const auto graph_IR = gen_constant_pad_nd_graph("2, 0, 0, 2", "1.5");
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std::vector<std::vector<at::Tensor>> prewarm_data = {{}, {}, {}};
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prewarm_data[0].push_back(at::randn({3, 4, 5, 6}, {at::kCUDA}));
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prewarm_data[1].push_back(at::randn({2, 3, 4, 5}, {at::kCUDA}));
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prewarm_data[2].push_back(at::randn({2, 3, 4, 5}, {at::kCUDA}));
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std::vector<at::Tensor> input_data;
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input_data.push_back(at::randn({2, 3, 4, 5}, {at::kCUDA}));
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constant_pad_nd_test_helper(graph_IR, input_data, true, &prewarm_data);
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}
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TEST(Converters, TestAtenConstantPadNdDynamicFloatSingleDimCorrectly) {
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const auto graph_IR = gen_constant_pad_nd_graph("1, 2", "1.5");
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std::vector<std::vector<at::Tensor>> prewarm_data = {{}, {}, {}};
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prewarm_data[0].push_back(at::randn({10}, {at::kCUDA}));
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prewarm_data[1].push_back(at::randn({5}, {at::kCUDA}));
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prewarm_data[2].push_back(at::randn({5}, {at::kCUDA}));
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std::vector<at::Tensor> input_data;
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input_data.push_back(at::randn({5}, {at::kCUDA}));
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constant_pad_nd_test_helper(graph_IR, input_data, true, &prewarm_data);
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}
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TEST(Converters, TestAtenConstantPadNdDynamicIntCorrectly) {
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const auto graph_IR = gen_constant_pad_nd_graph("1, 2, 3, 4", "2", true);
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std::vector<std::vector<at::Tensor>> prewarm_data = {{}, {}, {}};
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auto options_pyt = torch::TensorOptions().device(torch::kCUDA, 0).dtype(torch::kFloat);
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prewarm_data[0].push_back(at::randint(0, 10, {3, 4, 5, 6}, options_pyt));
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prewarm_data[1].push_back(at::randint(0, 10, {2, 3, 4, 5}, options_pyt));
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prewarm_data[2].push_back(at::randint(0, 10, {2, 3, 4, 5}, options_pyt));
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std::vector<at::Tensor> input_data;
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input_data.push_back(at::randint(0, 10, {2, 3, 4, 5}, {at::kCUDA}));
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constant_pad_nd_test_helper(graph_IR, input_data, true, &prewarm_data);
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} |