mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-10-13 12:23:55 +08:00
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>
186 lines
7.7 KiB
C++
186 lines
7.7 KiB
C++
// Copyright (c) 2022 Baidu, Inc. All Rights Reserved.
|
|
//
|
|
// Licensed under the Apache License, Version 2.0 (the "License");
|
|
// you may not use this file except in compliance with the License.
|
|
// You may obtain a copy of the License at
|
|
//
|
|
// http://www.apache.org/licenses/LICENSE-2.0
|
|
//
|
|
// Unless required by applicable law or agreed to in writing, software
|
|
// distributed under the License is distributed on an "AS IS" BASIS,
|
|
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
// See the License for the specific language governing permissions and
|
|
// limitations under the License.
|
|
|
|
/**
|
|
* @file stack_test.cpp
|
|
* @author tianshaoqing@baidu.com
|
|
* @date Wed Sep 27 11:24:21 CST 2021
|
|
* @brief
|
|
**/
|
|
#include <gflags/gflags.h>
|
|
#include <gtest/gtest.h>
|
|
|
|
#include "poros/converter/gpu/stack.h"
|
|
#include "poros/util/test_util.h"
|
|
|
|
static void stack_test_helper(const std::string& graph_IR,
|
|
std::vector<int64_t> shape1 = {5},
|
|
std::vector<int64_t> shape2 = {5},
|
|
bool Triple_inputs = false,
|
|
std::vector<int64_t> shape3 = {5}){
|
|
std::vector<at::Tensor> input_data;
|
|
input_data.push_back(at::randn(shape1, {at::kCUDA}));
|
|
input_data.push_back(at::randn(shape2, {at::kCUDA}));
|
|
if (Triple_inputs){
|
|
input_data.push_back(at::randn(shape3, {at::kCUDA}));
|
|
}
|
|
baidu::mirana::poros::PorosOptions poros_option; // default device GPU
|
|
baidu::mirana::poros::StackConverter stackconverter;
|
|
// 运行原图与engine获取结果
|
|
std::vector<at::Tensor> graph_output;
|
|
std::vector<at::Tensor> poros_output;
|
|
ASSERT_TRUE(baidu::mirana::poros::testutil::run_graph_and_poros(graph_IR, poros_option, &stackconverter,
|
|
input_data, graph_output, poros_output));
|
|
|
|
ASSERT_EQ(1, graph_output.size());
|
|
ASSERT_EQ(1, poros_output.size());
|
|
ASSERT_TRUE(graph_output[0].equal(poros_output[0]));
|
|
}
|
|
|
|
static std::string gen_double_inputs_stack_graph(const std::string& dim) {
|
|
return R"IR(
|
|
graph(%0 : Tensor, %1 : Tensor):
|
|
%2 : Tensor[] = prim::ListConstruct(%0, %1)
|
|
%3 : int = prim::Constant[value=)IR" + dim + R"IR(]()
|
|
%4 : Tensor = aten::stack(%2, %3)
|
|
return (%4))IR";
|
|
}
|
|
|
|
static std::string gen_triple_inputs_stack_graph(const std::string& dim) {
|
|
return R"IR(
|
|
graph(%0 : Tensor, %1 : Tensor, %2 : Tensor):
|
|
%3 : Tensor[] = prim::ListConstruct(%0, %1, %2)
|
|
%4 : int = prim::Constant[value=)IR" + dim + R"IR(]()
|
|
%5 : Tensor = aten::stack(%3, %4)
|
|
return (%5))IR";
|
|
}
|
|
|
|
TEST(Converters, ATenStackDoubleTensorConvertsCorrectly) {
|
|
// aten::stack(Tensor[] tensors, int dim=0) -> Tensor
|
|
const auto graph_IR = gen_double_inputs_stack_graph("0");
|
|
stack_test_helper(graph_IR);
|
|
}
|
|
|
|
TEST(Converters, ATenStackDoubleTensoroneDimConvertsCorrectly) {
|
|
// aten::stack(Tensor[] tensors, int dim=0) -> Tensor
|
|
const auto graph_IR = gen_double_inputs_stack_graph("1");
|
|
stack_test_helper(graph_IR, {5, 3}, {5, 3});
|
|
}
|
|
|
|
TEST(Converters, ATenStackTripleTensorConvertsCorrectly) {
|
|
// aten::stack(Tensor[] tensors, int dim=0) -> Tensor
|
|
const auto graph_IR = gen_triple_inputs_stack_graph("2");
|
|
stack_test_helper(graph_IR, {5, 2, 3}, {5, 2, 3}, true, {5, 2, 3});
|
|
}
|
|
|
|
TEST(Converters, ATenVstackDoubleTensorConvertsCorrectly) {
|
|
// aten::vstack(Tensor[] tensors) -> Tensor
|
|
const auto graph_IR = R"IR(
|
|
graph(%0 : Tensor, %1 : Tensor):
|
|
%2 : Tensor[] = prim::ListConstruct(%0, %1)
|
|
%3 : Tensor = aten::vstack(%2)
|
|
return (%3))IR";
|
|
stack_test_helper(graph_IR, {3, 1}, {3, 1});
|
|
}
|
|
|
|
TEST(Converters, ATenVstackTripleTensorConvertsCorrectly) {
|
|
// aten::vstack(Tensor[] tensors) -> Tensor
|
|
const auto graph_IR = R"IR(
|
|
graph(%0 : Tensor, %1 : Tensor, %2 : Tensor):
|
|
%3 : Tensor[] = prim::ListConstruct(%0, %1, %2)
|
|
%4 : Tensor = aten::vstack(%3)
|
|
return (%4))IR";
|
|
stack_test_helper(graph_IR, {5, 2, 3}, {5, 2, 3}, true, {5, 2, 3});
|
|
}
|
|
|
|
|
|
static void stack_dy_test_helper(const std::string& graph_IR,
|
|
const std::vector<at::Tensor>& input_data,
|
|
bool is_dynamic = false,
|
|
std::vector<std::vector<at::Tensor>>* prewarm_data = nullptr) {
|
|
baidu::mirana::poros::StackConverter stackconverter;
|
|
|
|
baidu::mirana::poros::PorosOptions poros_option; // default device GPU
|
|
poros_option.is_dynamic = is_dynamic;
|
|
// 运行原图与engine获取结果
|
|
std::vector<at::Tensor> graph_output;
|
|
std::vector<at::Tensor> poros_output;
|
|
ASSERT_TRUE(baidu::mirana::poros::testutil::run_graph_and_poros(graph_IR, poros_option, &stackconverter,
|
|
input_data, graph_output, poros_output, prewarm_data));
|
|
|
|
ASSERT_EQ(1, graph_output.size());
|
|
ASSERT_EQ(1, poros_output.size());
|
|
ASSERT_TRUE(graph_output[0].equal(poros_output[0]));
|
|
}
|
|
|
|
TEST(Converters, ATenStackDoubleTensorDynamicTestConvertsCorrectly) {
|
|
// aten::stack(Tensor[] tensors, int dim=0) -> Tensor
|
|
const auto graph_IR = gen_double_inputs_stack_graph("2");
|
|
|
|
std::vector<std::vector<at::Tensor>> prewarm_data = {{}, {}, {}};
|
|
prewarm_data[0].push_back(at::randn({10, 5, 3, 3}, {at::kCUDA}));
|
|
prewarm_data[0].push_back(at::randn({10, 5, 3, 3}, {at::kCUDA}));
|
|
prewarm_data[1].push_back(at::randn({5, 5, 3, 3}, {at::kCUDA}));
|
|
prewarm_data[1].push_back(at::randn({5, 5, 3, 3}, {at::kCUDA}));
|
|
prewarm_data[2].push_back(at::randn({5, 5, 3, 3}, {at::kCUDA}));
|
|
prewarm_data[2].push_back(at::randn({5, 5, 3, 3}, {at::kCUDA}));
|
|
|
|
std::vector<at::Tensor> input_data;
|
|
input_data.push_back(at::randn({5, 5, 3, 3}, {at::kCUDA}));
|
|
input_data.push_back(at::randn({5, 5, 3, 3}, {at::kCUDA}));
|
|
|
|
stack_dy_test_helper(graph_IR, input_data, true, &prewarm_data);
|
|
}
|
|
|
|
TEST(Converters, ATenStackDoubleTensorDynamicNegDimTestConvertsCorrectly) {
|
|
// aten::stack(Tensor[] tensors, int dim=0) -> Tensor
|
|
const auto graph_IR = gen_double_inputs_stack_graph("-2");
|
|
|
|
std::vector<std::vector<at::Tensor>> prewarm_data = {{}, {}, {}};
|
|
prewarm_data[0].push_back(at::randn({10, 5, 3, 3}, {at::kCUDA}));
|
|
prewarm_data[0].push_back(at::randn({10, 5, 3, 3}, {at::kCUDA}));
|
|
prewarm_data[1].push_back(at::randn({5, 5, 3, 3}, {at::kCUDA}));
|
|
prewarm_data[1].push_back(at::randn({5, 5, 3, 3}, {at::kCUDA}));
|
|
prewarm_data[2].push_back(at::randn({5, 5, 3, 3}, {at::kCUDA}));
|
|
prewarm_data[2].push_back(at::randn({5, 5, 3, 3}, {at::kCUDA}));
|
|
|
|
std::vector<at::Tensor> input_data;
|
|
input_data.push_back(at::randn({5, 5, 3, 3}, {at::kCUDA}));
|
|
input_data.push_back(at::randn({5, 5, 3, 3}, {at::kCUDA}));
|
|
|
|
stack_dy_test_helper(graph_IR, input_data, true, &prewarm_data);
|
|
}
|
|
|
|
TEST(Converters, ATenVStackDoubleTensorDynamicTestConvertsCorrectly) {
|
|
// aten::vstack(Tensor[] tensors) -> Tensor
|
|
const auto graph_IR = R"IR(
|
|
graph(%0 : Tensor, %1 : Tensor):
|
|
%3 : Tensor[] = prim::ListConstruct(%0, %1)
|
|
%4 : Tensor = aten::vstack(%3)
|
|
return (%4))IR";
|
|
|
|
std::vector<std::vector<at::Tensor>> prewarm_data = {{}, {}, {}};
|
|
prewarm_data[0].push_back(at::randn({10, 5, 3, 3}, {at::kCUDA}));
|
|
prewarm_data[0].push_back(at::randn({10, 5, 3, 3}, {at::kCUDA}));
|
|
prewarm_data[1].push_back(at::randn({5, 5, 3, 3}, {at::kCUDA}));
|
|
prewarm_data[1].push_back(at::randn({5, 5, 3, 3}, {at::kCUDA}));
|
|
prewarm_data[2].push_back(at::randn({5, 5, 3, 3}, {at::kCUDA}));
|
|
prewarm_data[2].push_back(at::randn({5, 5, 3, 3}, {at::kCUDA}));
|
|
|
|
std::vector<at::Tensor> input_data;
|
|
input_data.push_back(at::randn({5, 5, 3, 3}, {at::kCUDA}));
|
|
input_data.push_back(at::randn({5, 5, 3, 3}, {at::kCUDA}));
|
|
|
|
stack_dy_test_helper(graph_IR, input_data, true, &prewarm_data);
|
|
} |