mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-10-11 19:40:25 +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>
76 lines
2.8 KiB
C++
76 lines
2.8 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 lstm_cell_test.cpp
|
|
* @author wangrui39@baidu.com
|
|
* @date Mon December 13 11:36:11 CST 2021
|
|
* @brief
|
|
**/
|
|
#include <gflags/gflags.h>
|
|
#include <gtest/gtest.h>
|
|
|
|
#include "poros/converter/gpu/lstm_cell.h"
|
|
#include "poros/util/test_util.h"
|
|
|
|
static void linear_test_helper(const std::string& graph_IR,
|
|
const std::vector<at::Tensor>& input_data) {
|
|
baidu::mirana::poros::PorosOptions poros_option; // default device GPU
|
|
baidu::mirana::poros::LstmCellConverter lstm_cellconverter;
|
|
|
|
// 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, &lstm_cellconverter,
|
|
input_data, graph_output, poros_output));
|
|
ASSERT_EQ(1, graph_output.size());
|
|
ASSERT_EQ(1, poros_output.size());
|
|
ASSERT_TRUE(baidu::mirana::poros::testutil::almost_equal(graph_output[0], poros_output[0], 2e-6));
|
|
}
|
|
|
|
TEST(Converters, ATenlstm_cellconverterCorrectly) {
|
|
//aten::lstm_cell(Tensor input, Tensor[] hx, Tensor w_ih, Tensor w_hh, Tensor? b_ih=None, Tensor? b_hh=None) -> (Tensor, Tensor)
|
|
|
|
const auto graph = R"IR(
|
|
graph(%0 : Tensor,
|
|
%1 : Tensor,
|
|
%3 : Tensor,
|
|
%4 : Tensor,
|
|
%5 : Tensor,
|
|
%6 : Tensor,
|
|
%7 : Tensor):
|
|
%2 : Tensor[] = prim::ListConstruct(%0, %1)
|
|
%8 : Tensor, %9 : Tensor = aten::lstm_cell(%3, %2, %4, %5, %6, %7)
|
|
return (%8))IR";
|
|
|
|
std::vector<at::Tensor> input_data;
|
|
auto input = at::randn({50, 10}, {at::kCUDA});
|
|
auto h0 = at::randn({50, 20}, {at::kCUDA});
|
|
auto c0 = at::randn({50, 20}, {at::kCUDA});
|
|
auto w_ih = at::randn({4 * 20, 10}, {at::kCUDA});
|
|
auto w_hh = at::randn({4 * 20, 20}, {at::kCUDA});
|
|
auto b_ih = at::randn({4 * 20}, {at::kCUDA});
|
|
auto b_hh = at::randn({4 * 20}, {at::kCUDA});
|
|
|
|
input_data.push_back(h0);
|
|
input_data.push_back(c0);
|
|
input_data.push_back(input);
|
|
input_data.push_back(w_ih);
|
|
input_data.push_back(w_hh);
|
|
input_data.push_back(b_ih);
|
|
input_data.push_back(b_hh);
|
|
|
|
linear_test_helper(graph, input_data);
|
|
}
|