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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>
313 lines
14 KiB
C++
313 lines
14 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 shuffle_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/util/test_util.h"
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#include "poros/converter/gpu/shuffle.h"
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static void shuffle_test_helper(const std::string& graph_IR,
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baidu::mirana::poros::IConverter* converter,
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std::vector<int64_t> shape){
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std::vector<at::Tensor> input_data;
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input_data.push_back(at::randn(shape, {at::kCUDA}));
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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(graph_output[0].equal(poros_output[0]));
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}
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static void shuffle_dy_test_helper(const std::string& graph_IR,
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const std::vector<at::Tensor>& input_data,
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baidu::mirana::poros::IConverter* converter,
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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(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_double_int_graph(const std::string& op,
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const std::string& first_int,
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const std::string& second_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" + first_int + R"IR(]()
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%2 : int = prim::Constant[value=)IR" + second_int + 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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}
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std::string gen_int_list_graph(const std::string& op, const std::string& int_list) {
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return R"IR(
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graph(%0 : Tensor):
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%1 : int[] = prim::Constant[value=[)IR" + int_list + 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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std::string gen_pixel_shuffle_graph(const std::string& upscale_factor) {
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return R"IR(
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graph(%0 : Tensor):
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%1 : int = prim::Constant[value=)IR" + upscale_factor + R"IR(]()
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%2 : Tensor = aten::pixel_shuffle(%0, %1)
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return (%2))IR";
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}
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TEST(Converters, ATenTransposeConvertsCorrectly) {
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// aten::transpose.int(Tensor(a) self, int dim0, int dim1) -> Tensor(a)
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const auto graph_IR = gen_double_int_graph("transpose", "1", "2");
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baidu::mirana::poros::TransposeConverter transposeconverter;
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shuffle_test_helper(graph_IR, &transposeconverter, {2, 3, 4});
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}
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TEST(Converters, ATenTransposeNegaiveConvertsCorrectly) {
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// aten::transpose.int(Tensor(a) self, int dim0, int dim1) -> Tensor(a)
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const auto graph_IR = gen_double_int_graph("transpose", "-1", "-3");
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baidu::mirana::poros::TransposeConverter transposeconverter;
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shuffle_test_helper(graph_IR, &transposeconverter, {2, 3, 4, 5, 6});
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}
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TEST(Converters, ATenViewConvertsCorrectly) {
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// aten::view(Tensor(a) self, int[] size) -> Tensor(a)
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const auto graph_IR = gen_int_list_graph("view", "1, 6");
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baidu::mirana::poros::PermuteViewConverter permuteviewconverter;
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shuffle_test_helper(graph_IR, &permuteviewconverter, {2, 3});
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}
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TEST(Converters, ATenViewNegtiveConvertsCorrectly) {
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// aten::view(Tensor(a) self, int[] size) -> Tensor(a)
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const auto graph_IR = gen_int_list_graph("view", "-1, 8");
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baidu::mirana::poros::PermuteViewConverter permuteviewconverter;
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shuffle_test_helper(graph_IR, &permuteviewconverter, {4, 4});
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}
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TEST(Converters, ATenPermuteConvertsCorrectly) {
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// aten::permute(Tensor(a) self, int[] dims) -> Tensor(a)
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const auto graph_IR = gen_int_list_graph("permute", "1, 0");
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baidu::mirana::poros::PermuteViewConverter permuteviewconverter;
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shuffle_test_helper(graph_IR, &permuteviewconverter, {2, 3});
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}
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TEST(Converters, ATenPermute3DConvertsCorrectly) {
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// aten::permute(Tensor(a) self, int[] dims) -> Tensor(a)
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const auto graph_IR = gen_int_list_graph("permute", "1, 2, 0");
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baidu::mirana::poros::PermuteViewConverter permuteviewconverter;
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shuffle_test_helper(graph_IR, &permuteviewconverter, {1, 2, 3});
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}
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TEST(Converters, ATenPermute5DConvertsCorrectly) {
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// aten::permute(Tensor(a) self, int[] dims) -> Tensor(a)
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const auto graph_IR = gen_int_list_graph("permute", "3, 1, 0, 2, 4");
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baidu::mirana::poros::PermuteViewConverter permuteviewconverter;
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shuffle_test_helper(graph_IR, &permuteviewconverter, {2, 3, 4, 5, 1});
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}
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TEST(Converters, ATenReshapeConvertsCorrectly) {
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// aten::reshape(Tensor(a) self, int[] shape) -> Tensor(a)
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const auto graph_IR = gen_int_list_graph("reshape", "3, 2");
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baidu::mirana::poros::ReshapeConverter reshapeconverter;
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shuffle_test_helper(graph_IR, &reshapeconverter, {2, 3});
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}
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TEST(Converters, ATenReshapeNegtiveConvertsCorrectly) {
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// aten::reshape(Tensor(a) self, int[] shape) -> Tensor(a)
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const auto graph_IR = gen_int_list_graph("reshape", "-1, 8");
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baidu::mirana::poros::ReshapeConverter reshapeconverter;
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shuffle_test_helper(graph_IR, &reshapeconverter, {4, 4});
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}
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TEST(Converters, ATenFlattenConvertsCorrectly) {
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// aten::flatten.using_ints(Tensor(a) self, int start_dim=0, int end_dim=-1) -> Tensor(a)
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const auto graph_IR = gen_double_int_graph("flatten", "0", "-1");
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baidu::mirana::poros::FlattenConverter flattenconverter;
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shuffle_test_helper(graph_IR, &flattenconverter, {1, 2, 3});
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}
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TEST(Converters, ATenFlattenStartEnddimConvertsCorrectly) {
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// aten::flatten.using_ints(Tensor(a) self, int start_dim=0, int end_dim=-1) -> Tensor(a)
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const auto graph_IR = gen_double_int_graph("flatten", "1", "2");
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baidu::mirana::poros::FlattenConverter flattenconverter;
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shuffle_test_helper(graph_IR, &flattenconverter, {1, 2, 3});
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}
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TEST(Converters, ATenT1DConvertsCorrectly) {
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// aten::t(Tensor(a) self) -> Tensor(a)
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const auto graph_IR = R"IR(
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graph(%0 : Tensor):
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%1 : Tensor = aten::t(%0)
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%2 : Tensor = aten::relu(%1)
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return (%2))IR";
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baidu::mirana::poros::AtenTConverter atentConverter;
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shuffle_test_helper(graph_IR, &atentConverter, {5});
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}
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TEST(Converters, ATenT2DConvertsCorrectly) {
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// aten::t(Tensor(a) self) -> Tensor(a)
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const auto graph_IR = R"IR(
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graph(%0 : Tensor):
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%1 : Tensor = aten::t(%0)
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return (%1))IR";
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baidu::mirana::poros::AtenTConverter atentConverter;
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shuffle_test_helper(graph_IR, &atentConverter, {5, 6});
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}
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TEST(Converters, ATenPixelShuffleConvertsCorrectly) {
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// aten::pixel_shuffle(Tensor self, int upscale_factor) -> Tensor
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const auto graph_IR = gen_pixel_shuffle_graph("3");
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baidu::mirana::poros::PixelShuffleConverter pixelshuffleconverter;
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shuffle_test_helper(graph_IR, &pixelshuffleconverter, {1, 9, 4, 4});
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}
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TEST(Converters, ATenPixelShuffle3DConvertsCorrectly) {
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// aten::pixel_shuffle(Tensor self, int upscale_factor) -> Tensor
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const auto graph_IR = gen_pixel_shuffle_graph("3");
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baidu::mirana::poros::PixelShuffleConverter pixelshuffleconverter;
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shuffle_test_helper(graph_IR, &pixelshuffleconverter, {9, 5, 6});
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}
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TEST(Converters, ATenPixelShuffle5DConvertsCorrectly) {
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// aten::pixel_shuffle(Tensor self, int upscale_factor) -> Tensor
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const auto graph_IR = gen_pixel_shuffle_graph("3");
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baidu::mirana::poros::PixelShuffleConverter pixelshuffleconverter;
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shuffle_test_helper(graph_IR, &pixelshuffleconverter, {7, 8, 9, 5, 6});
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}
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static void shuffle_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, ATenViewdynamicConvertsCorrectly) {
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// aten::view(Tensor(a) self, int[] size) -> Tensor(a)
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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[] = prim::ListConstruct(%4, %3)
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%6 : Tensor = aten::view(%0, %5)
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return (%6))IR";
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baidu::mirana::poros::PermuteViewConverter permuteviewconverter;
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std::vector<at::Tensor> input_data;
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input_data.push_back(at::randn({2, 3}, {at::kCUDA}));
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std::vector<std::vector<at::Tensor>> prewarm_data = {{}, {}, {}};
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prewarm_data[0].push_back(at::randn({4, 5}, {at::kCUDA}));
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prewarm_data[1].push_back(at::randn({2, 3}, {at::kCUDA}));
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prewarm_data[2].push_back(at::randn({2, 3}, {at::kCUDA}));
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shuffle_dynamic_test_helper(graph_IR, &permuteviewconverter, input_data, true, &prewarm_data);
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}
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TEST(Converters, ATenReshapedynamicConvertsCorrectly) {
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// aten::reshape(Tensor(a) self, int[] shape) -> Tensor(a)
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const auto graph_IR = R"IR(
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graph(%0 : Tensor):
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%1 : int[] = aten::size(%0)
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%2 : int, %3 : int = prim::ListUnpack(%1)
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%4 : int[] = prim::ListConstruct(%3, %2)
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%5 : Tensor = aten::reshape(%0, %4)
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return (%5))IR";
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baidu::mirana::poros::ReshapeConverter reshapeconverter;
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std::vector<at::Tensor> input_data;
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input_data.push_back(at::randn({2, 3}, {at::kCUDA}));
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std::vector<std::vector<at::Tensor>> prewarm_data = {{}, {}, {}};
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prewarm_data[0].push_back(at::randn({4, 5}, {at::kCUDA}));
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prewarm_data[1].push_back(at::randn({2, 3}, {at::kCUDA}));
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prewarm_data[2].push_back(at::randn({2, 3}, {at::kCUDA}));
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shuffle_dynamic_test_helper(graph_IR, &reshapeconverter, input_data, true, &prewarm_data);
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}
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TEST(Converters, ATenFlattenConvertsDynamicCorrectly) {
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// aten::flatten.using_ints(Tensor(a) self, int start_dim=0, int end_dim=-1) -> Tensor(a)
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const auto graph_IR = gen_double_int_graph("flatten", "0", "2");
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std::vector<std::vector<at::Tensor>> prewarm_data = {{}, {}, {}};
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prewarm_data[0].push_back(at::randn({10, 64, 128}, {at::kCUDA}));
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prewarm_data[1].push_back(at::randn({5, 32, 64}, {at::kCUDA}));
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prewarm_data[2].push_back(at::randn({5, 32, 64}, {at::kCUDA}));
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std::vector<at::Tensor> input_data;
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input_data.push_back(at::randn({5, 32, 64}, {at::kCUDA}));
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baidu::mirana::poros::FlattenConverter flattenconverter;
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shuffle_dy_test_helper(graph_IR, input_data, &flattenconverter, true, &prewarm_data);
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}
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TEST(Converters, ATenFlattenConvertsDynamicNegStartEndCorrectly) {
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// aten::flatten.using_ints(Tensor(a) self, int start_dim=0, int end_dim=-1) -> Tensor(a)
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const auto graph_IR = gen_double_int_graph("flatten", "-3", "-2");
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std::vector<std::vector<at::Tensor>> prewarm_data = {{}, {}, {}};
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prewarm_data[0].push_back(at::randn({10, 64, 128, 32}, {at::kCUDA}));
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prewarm_data[1].push_back(at::randn({5, 32, 64, 16}, {at::kCUDA}));
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prewarm_data[2].push_back(at::randn({5, 32, 64, 16}, {at::kCUDA}));
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std::vector<at::Tensor> input_data;
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input_data.push_back(at::randn({5, 32, 64, 16}, {at::kCUDA}));
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baidu::mirana::poros::FlattenConverter flattenconverter;
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shuffle_dy_test_helper(graph_IR, input_data, &flattenconverter, true, &prewarm_data);
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}
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TEST(Converters, ATenFlattenConvertsDynamicStartEqualEndCorrectly) {
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// aten::flatten.using_ints(Tensor(a) self, int start_dim=0, int end_dim=-1) -> Tensor(a)
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const auto graph_IR = gen_double_int_graph("flatten", "1", "1");
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std::vector<std::vector<at::Tensor>> prewarm_data = {{}, {}, {}};
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prewarm_data[0].push_back(at::randn({10, 64, 128, 32}, {at::kCUDA}));
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prewarm_data[1].push_back(at::randn({5, 32, 64, 16}, {at::kCUDA}));
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prewarm_data[2].push_back(at::randn({5, 32, 64, 16}, {at::kCUDA}));
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std::vector<at::Tensor> input_data;
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input_data.push_back(at::randn({5, 32, 64, 16}, {at::kCUDA}));
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baidu::mirana::poros::FlattenConverter flattenconverter;
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shuffle_dy_test_helper(graph_IR, input_data, &flattenconverter, true, &prewarm_data);
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} |