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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>
233 lines
10 KiB
C++
233 lines
10 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 expand_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/expand.h"
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#include "poros/util/test_util.h"
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static void expand_test_helper(const std::string& graph_IR,
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baidu::mirana::poros::IConverter* converter,
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bool singleInput,
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std::vector<int64_t> shape1 = {3, 1},
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std::vector<int64_t> shape2 = {3, 1}){
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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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if (!singleInput){
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input_data.push_back(at::randn(shape2, {at::kCUDA}));
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}
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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::almostEqual(graph_output[0], poros_output[0], 2e-6));
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ASSERT_TRUE(graph_output[0].equal(poros_output[0]));
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}
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static std::string gen_expand_graph(const std::string& size, const std::string& implicit) {
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return R"IR(
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graph(%0 : Tensor):
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%1 : int[] = prim::Constant[value=[)IR" + size + R"IR(]]()
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%2 : bool = prim::Constant[value=)IR" + implicit + R"IR(]()
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%3 : Tensor = aten::expand(%0, %1, %2)
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return (%3))IR";
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}
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static std::string gen_repeat_graph(const std::string& size) {
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return R"IR(
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graph(%0 : Tensor):
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%1 : int[] = prim::Constant[value=[)IR" + size + R"IR(]]()
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%2 : Tensor = aten::repeat(%0, %1)
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return (%2))IR";
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}
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TEST(Converters, ATenExpandSameDimConvertsCorrectly) {
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// aten::expand(Tensor(a) self, int[] size, *, bool implicit=False) -> Tensor(a)
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const auto graph_IR = gen_expand_graph("3, 4", "0");
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baidu::mirana::poros::ExpandConverter expandconverter;
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expand_test_helper(graph_IR, &expandconverter, true);
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}
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TEST(Converters, ATenExpandTileConvertsCorrectly) {
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// aten::expand(Tensor(a) self, int[] size, *, bool implicit=False) -> Tensor(a)
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// 若%2参数个数大于%1,则expand从后向前对齐
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// [3,1] [2,3,4] -> [2,3,4]
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// [3,1] [1,3,4] -> [1,3,4]
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// [3,1] [3,-1,4] -> [3,3,4]
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const auto graph_IR = gen_expand_graph("2, 3, 4", "0");
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baidu::mirana::poros::ExpandConverter expandconverter;
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expand_test_helper(graph_IR, &expandconverter, true);
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}
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TEST(Converters, ATenExpandTileLastConvertsCorrectly) {
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// aten::expand(Tensor(a) self, int[] size, *, bool implicit=False) -> Tensor(a)
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const auto graph_IR = gen_expand_graph("1, 3, 4", "0");
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baidu::mirana::poros::ExpandConverter expandconverter;
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expand_test_helper(graph_IR, &expandconverter, true);
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}
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TEST(Converters, ATenExpandNegativeSizeConvertsCorrectly) {
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// aten::expand(Tensor(a) self, int[] size, *, bool implicit=False) -> Tensor(a)
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// 1 means not changing the size of that dimension
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const auto graph_IR = gen_expand_graph("3, -1, 4", "0");
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baidu::mirana::poros::ExpandConverter expandconverter;
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expand_test_helper(graph_IR, &expandconverter, true);
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}
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TEST(Converters, ATenRepeatConvertsCorrectly) {
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// aten::repeat(Tensor self, int[] repeats) -> Tensor
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// output shape计算方法:参数向后对齐(如果%1与%2维度不同的话,同expand)，依次相乘
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// [3,1] [4,2] -> [12,2]
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// [2,3,2] [2,2,2] -> [4,6,4]
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// [3,1] [1,3,2] -> [1,9,2]
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const auto graph_IR = gen_repeat_graph("4, 2");
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baidu::mirana::poros::RepeatConverter repeatconverter;
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expand_test_helper(graph_IR, &repeatconverter, true);
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}
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TEST(Converters, ATenRepeat3dConvertsCorrectly) {
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// aten::repeat(Tensor self, int[] repeats) -> Tensor
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const auto graph_IR = gen_repeat_graph("2, 2, 2");
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baidu::mirana::poros::RepeatConverter repeatconverter;
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expand_test_helper(graph_IR, &repeatconverter, true, {2, 3, 2});
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}
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TEST(Converters, ATenRepeatExtraDimsConvertsCorrectly) {
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// aten::repeat(Tensor self, int[] repeats) -> Tensor
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const auto graph_IR = gen_repeat_graph("1, 3, 2");
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baidu::mirana::poros::RepeatConverter repeatconverter;
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expand_test_helper(graph_IR, &repeatconverter, true);
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}
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static void expand_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, ATenExpandFromSizedynamicConvertsCorrectly) {
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// aten::expand(Tensor(a) self, int[] size, *, bool implicit=False) -> Tensor(a)
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const auto graph_IR = R"IR(
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graph(%0 : Tensor, %1 : Tensor):
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%2 : int = prim::Constant[value=-1]()
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%3 : int[] = aten::size(%0)
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%B.1 : int, %H.1 : int, %W.1 : int, %C.1 : int = prim::ListUnpack(%3)
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%4 : int[] = prim::ListConstruct(%B.1, %2, %C.1)
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%5 : Tensor = aten::reshape(%0, %4)
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%6 : int[] = aten::size(%5)
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%B.2 : int, %N.2 : int, %C.2 : int = prim::ListUnpack(%6)
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%7 : int[] = prim::ListConstruct(%B.2, %2, %2)
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%8 : bool = prim::Constant[value=0]()
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%9 : Tensor = aten::expand(%1, %7, %8)
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return (%9))IR";
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baidu::mirana::poros::ExpandConverter expandconverter;
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std::vector<at::Tensor> input_data;
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input_data.push_back(at::randn({2, 24, 24, 512}, {at::kCUDA}));
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input_data.push_back(at::randn({1, 1, 512}, {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, 24, 24, 512}, {at::kCUDA}));
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prewarm_data[0].push_back(at::randn({1, 1, 512}, {at::kCUDA}));
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prewarm_data[1].push_back(at::randn({2, 24, 24, 512}, {at::kCUDA}));
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prewarm_data[1].push_back(at::randn({1, 1, 512}, {at::kCUDA}));
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prewarm_data[2].push_back(at::randn({2, 24, 24, 512}, {at::kCUDA}));
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prewarm_data[2].push_back(at::randn({1, 1, 512}, {at::kCUDA}));
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expand_dynamic_test_helper(graph_IR, &expandconverter, input_data, true, &prewarm_data);
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}
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/*aten::expand_as(Tensor(a) self, Tensor other) -> Tensor(a)*/
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static std::string gen_expand_as_graph() {
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return R"IR(
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graph(%0 : Tensor, %1 : Tensor):
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%3 : Tensor = aten::expand_as(%0, %1)
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return (%3))IR";
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}
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TEST(Converters, ATenExpandAsConvertsCorrectly) {
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/*aten::expand_as(Tensor(a) self, Tensor other) -> Tensor(a)*/
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const auto graph_IR = gen_expand_as_graph();
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baidu::mirana::poros::ExpandConverter expandconverter;
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std::vector<at::Tensor> input_data;
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input_data.push_back(at::randn({1, 1, 512}, {at::kCUDA}));
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input_data.push_back(at::randn({2, 24, 1, 512}, {at::kCUDA}));
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expand_dynamic_test_helper(graph_IR, &expandconverter, input_data);
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}
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TEST(Converters, ATenExpandAsDynamicConvertsCorrectly) {
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/*aten::expand_as(Tensor(a) self, Tensor other) -> Tensor(a)*/
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const auto graph_IR = gen_expand_as_graph();
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baidu::mirana::poros::ExpandConverter expandconverter;
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std::vector<at::Tensor> input_data;
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input_data.push_back(at::randn({1, 1, 512}, {at::kCUDA}));
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input_data.push_back(at::randn({2, 24, 1, 512}, {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({1, 1, 512}, {at::kCUDA}));
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prewarm_data[0].push_back(at::randn({4, 24, 1, 512}, {at::kCUDA}));
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prewarm_data[1].push_back(at::randn({1, 1, 512}, {at::kCUDA}));
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prewarm_data[1].push_back(at::randn({2, 24, 1, 512}, {at::kCUDA}));
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prewarm_data[2].push_back(at::randn({1, 1, 512}, {at::kCUDA}));
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prewarm_data[2].push_back(at::randn({2, 24, 1, 512}, {at::kCUDA}));
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expand_dynamic_test_helper(graph_IR, &expandconverter, input_data, true, &prewarm_data);
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}
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TEST(Converters, ATenExpandAsDynamicMoreConvertsCorrectly) {
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/*aten::expand_as(Tensor(a) self, Tensor other) -> Tensor(a)*/
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const auto graph_IR = gen_expand_as_graph();
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baidu::mirana::poros::ExpandConverter expandconverter;
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std::vector<at::Tensor> input_data;
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input_data.push_back(at::randn({24, 1, 512}, {at::kCUDA}));
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input_data.push_back(at::randn({4, 24, 1, 512}, {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({24, 1, 512}, {at::kCUDA}));
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prewarm_data[0].push_back(at::randn({4, 24, 1, 512}, {at::kCUDA}));
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prewarm_data[1].push_back(at::randn({2, 1, 512}, {at::kCUDA}));
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prewarm_data[1].push_back(at::randn({4, 2, 1, 512}, {at::kCUDA}));
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prewarm_data[2].push_back(at::randn({4, 1, 512}, {at::kCUDA}));
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prewarm_data[2].push_back(at::randn({4, 4, 1, 512}, {at::kCUDA}));
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expand_dynamic_test_helper(graph_IR, &expandconverter, input_data, true, &prewarm_data);
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} |