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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>
159 lines
6.3 KiB
C++
159 lines
6.3 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 aten_eval_test.cpp
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* @author wangrui39@baidu.com
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* @date Mon December 13 11:36:11 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/aten_eval.h"
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#include "poros/util/test_util.h"
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static void aten_eval_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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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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/*TEST(Converters, AppendConverterCorrectly) {
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//"aten::append.t(t[](a!) self, t(c -> *) el) -> (t[](a!))"
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const auto graph = R"IR(
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graph(%a.1 : Tensor,
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%b.1 : Tensor,
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%c.1 : Tensor):
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%12 : int = prim::Constant[value=0]()
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%x.1 : Tensor[] = prim::ListConstruct(%a.1)
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%7 : Tensor[] = aten::append(%x.1, %b.1) # test.py:26:4
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%10 : Tensor[] = aten::append(%x.1, %c.1) # test.py:27:4
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%13 : Tensor = aten::cat(%x.1, %12) # test.py:28:11
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return (%13))IR";
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std::vector<at::Tensor> input_data;
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auto input1 = at::randn({3, 4}, {at::kCUDA});
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auto input2 = at::randn({3, 4}, {at::kCUDA});
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auto input3 = at::randn({3, 4}, {at::kCUDA});
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input_data.push_back(input1);
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input_data.push_back(input2);
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input_data.push_back(input3);
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baidu::mirana::poros::AppendConverter appendConverter;
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aten_eval_test_helper(graph, input_data, &appendConverter);
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}*/
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TEST(Converters, GetitemConverterCorrectly) {
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// "aten::__getitem__.t(t[](a) list, int idx) -> (t(*))"*/
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const auto graph = R"IR(
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graph(%a.1 : Tensor,
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%b.1 : Tensor):
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%12 : int = prim::Constant[value=0]()
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%16 : int = prim::Constant[value=1]()
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%x.1 : Tensor[] = prim::ListConstruct(%a.1)
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%7 : Tensor[] = aten::append(%x.1, %b.1)
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%ret.1 : Tensor = aten::__getitem__(%x.1, %12)
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%17 : Tensor = aten::__getitem__(%x.1, %16)
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%19 : Tensor = aten::add(%ret.1, %17, %16)
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return (%19))IR";
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std::vector<at::Tensor> input_data;
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auto input1 = at::randn({3, 4}, {at::kCUDA});
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auto input2 = at::randn({3, 4}, {at::kCUDA});
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input_data.push_back(input1);
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input_data.push_back(input2);
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baidu::mirana::poros::GetitemConverter getitemconverter;
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aten_eval_test_helper(graph, input_data, &getitemconverter);
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}
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TEST(Converters, SetitemConverterCorrectly) {
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// aten::_set_item.t(t[](a!) l, int idx, t(b -> *) el) -> (t[](a!))
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const auto graph = R"IR(
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graph(%x.1 : Tensor,
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%y.1 : Tensor):
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%6 : int = prim::Constant[value=1]() # test.py:28:15
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%10 : int = prim::Constant[value=0]() # test.py:28:6
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%a.1 : Tensor[] = prim::ListConstruct(%x.1, %y.1)
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%8 : Tensor = aten::add(%x.1, %6, %6) # test.py:28:11
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%11 : Tensor[] = aten::_set_item(%a.1, %10, %8) # test.py:28:4
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%ret.1 : Tensor = aten::cat(%a.1, %10) # test.py:29:10
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return (%ret.1))IR";
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std::vector<at::Tensor> input_data;
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auto input1 = at::randn({3, 4}, {at::kCUDA});
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auto input2 = at::randn({3, 4}, {at::kCUDA});
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input_data.push_back(input1);
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input_data.push_back(input2);
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baidu::mirana::poros::SetitemConverter setitemconverter;
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aten_eval_test_helper(graph, input_data, &setitemconverter);
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}
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static void eval_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, ATenGetitemdynamicConvertsCorrectly) {
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// "aten::__getitem__.t(t[](a) list, int idx) -> (t(*))"*/
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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 = prim::Constant[value=1]()
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%3 : int = aten::__getitem__(%1, %2)
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%4 : Tensor = aten::add(%0, %3, %2)
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return (%4))IR";
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baidu::mirana::poros::GetitemConverter getitemconverter;
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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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eval_dynamic_test_helper(graph_IR, &getitemconverter, input_data, true, &prewarm_data);
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}
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