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FastDeploy/poros/unittest/converter/lstm_test.cpp
kiddyjinjin d38aa4560c [Backend]add poros to fastdeploy (#671) 
* 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>
2022-11-28 14:08:18 +08:00

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// 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.h"
#include "poros/util/test_util.h"
static void lstm_test_helper(const std::string& graph_IR,
const std::vector<at::Tensor>& input_data,
baidu::mirana::poros::IConverter* converter) {
baidu::mirana::poros::PorosOptions poros_option; // default device GPU
// 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, converter,
input_data, graph_output, poros_output));
ASSERT_EQ(3, graph_output.size());
ASSERT_EQ(3, poros_output.size());
ASSERT_TRUE(baidu::mirana::poros::testutil::almost_equal(graph_output[0], poros_output[0], 2e-6));
ASSERT_TRUE(baidu::mirana::poros::testutil::almost_equal(graph_output[1], poros_output[1], 2e-6));
ASSERT_TRUE(baidu::mirana::poros::testutil::almost_equal(graph_output[2], poros_output[2], 2e-6));
}
TEST(Converters, ATenlstmconverterCorrectly) {
// aten::lstm.input(Tensor input, Tensor[] hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional, bool batch_first) -> (Tensor, Tensor, Tensor)
// num_layers = 1
// bidirectional = false
// batch_first = false
const auto graph = R"IR(
graph( %0 : Tensor,
%1 : Tensor,
%2 : Tensor,
%3 : Tensor,
%4 : Tensor,
%5 : Tensor,
%6 : Tensor):
%11 : bool = prim::Constant[value=1]()
%12 : bool = prim::Constant[value=0]()
%13 : int = prim::Constant[value=1]()
%14 : float = prim::Constant[value=0.0]()
%15 : Tensor[] = prim::ListConstruct(%0, %1)
%16 : Tensor[] = prim::ListConstruct(%3, %4, %5, %6)
%17 : Tensor, %18 : Tensor, %19 : Tensor = aten::lstm(%2, %15, %16, %11, %13, %14, %12, %12, %12)
return (%17, %18, %19))IR";
/*const auto graph = R"IR(
graph( %0 : Tensor,
%1 : Tensor,
%2 : Tensor,
%3 : Tensor,
%4 : Tensor,
%5 : Tensor,
%6 : Tensor):
%11 : bool = prim::Constant[value=1]()
%12 : bool = prim::Constant[value=0]()
%13 : int = prim::Constant[value=1]()
%14 : float = prim::Constant[value=0.0]()
%15 : Tensor[] = prim::ListConstruct(%0, %1)
%16 : Tensor[] = prim::ListConstruct(%3, %4, %5, %6)
%17 : Tensor, %18 : Tensor, %19 : Tensor = aten::lstm(%2, %15, %16, %11, %13, %14, %12, %12, %12)
return (%17, %18, %19))IR";*/
std::vector<at::Tensor> input_data;
auto input = at::randn({1, 5, 1}, {at::kCUDA});
auto h0 = at::randn({1, 5, 2}, {at::kCUDA});
auto c0 = at::randn({1, 5, 2}, {at::kCUDA});
auto w1 = at::randn({8, 1}, {at::kCUDA});
auto w2 = at::randn({8, 2}, {at::kCUDA});
auto w3 = at::randn({8}, {at::kCUDA});
auto w4 = at::randn({8}, {at::kCUDA});
input_data.push_back(h0);
input_data.push_back(c0);
input_data.push_back(input);
input_data.push_back(w1);
input_data.push_back(w2);
input_data.push_back(w3);
input_data.push_back(w4);
baidu::mirana::poros::LstmConverter lstmconverter;
lstm_test_helper(graph, input_data, &lstmconverter);
}
TEST(Converters, ATenlstmconverterBidirectionalCorrectly) {
// aten::lstm.input(Tensor input, Tensor[] hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional, bool batch_first) -> (Tensor, Tensor, Tensor)
// num_layers = 1
// bidirectional = true
// batch_first = true
const auto graph = R"IR(
graph( %0 : Tensor,
%1 : Tensor,
%2 : Tensor,
%3 : Tensor,
%4 : Tensor,
%5 : Tensor,
%6 : Tensor,
%7 : Tensor,
%8 : Tensor,
%9 : Tensor,
%10 : Tensor):
%11 : bool = prim::Constant[value=1]()
%12 : bool = prim::Constant[value=0]()
%13 : int = prim::Constant[value=1]()
%14 : float = prim::Constant[value=0.0]()
%15 : Tensor[] = prim::ListConstruct(%0, %1)
%16 : Tensor[] = prim::ListConstruct(%3, %4, %5, %6, %7, %8, %9, %10)
%17 : Tensor, %18 : Tensor, %19 : Tensor = aten::lstm(%2, %15, %16, %11, %13, %14, %12, %11, %11)
return (%17, %18, %19))IR";
std::vector<at::Tensor> input_data;
auto input = at::randn({50, 7, 10}, {at::kCUDA});
auto h0 = at::randn({2, 50, 20}, {at::kCUDA});
auto c0 = at::randn({2, 50, 20}, {at::kCUDA});
auto w1 = at::randn({80, 10}, {at::kCUDA});
auto w2 = at::randn({80, 20}, {at::kCUDA});
auto w3 = at::randn({80}, {at::kCUDA});
auto w4 = at::randn({80}, {at::kCUDA});
auto r_w1 = at::randn({80, 10}, {at::kCUDA});
auto r_w2 = at::randn({80, 20}, {at::kCUDA});
auto r_w3 = at::randn({80}, {at::kCUDA});
auto r_w4 = at::randn({80}, {at::kCUDA});
input_data.push_back(h0);
input_data.push_back(c0);
input_data.push_back(input);
input_data.push_back(w1);
input_data.push_back(w2);
input_data.push_back(w3);
input_data.push_back(w4);
input_data.push_back(r_w1);
input_data.push_back(r_w2);
input_data.push_back(r_w3);
input_data.push_back(r_w4);
baidu::mirana::poros::LstmConverter lstmconverter;
lstm_test_helper(graph, input_data, &lstmconverter);
}
TEST(Converters, ATenlstmconverterNumlayerCorrectly) {
// aten::lstm.input(Tensor input, Tensor[] hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional, bool batch_first) -> (Tensor, Tensor, Tensor)
// num_layers > 1
// bidirectional = false
// batch_first = true
const auto graph = R"IR(
graph( %0 : Tensor,
%1 : Tensor,
%2 : Tensor,
%3 : Tensor,
%4 : Tensor,
%5 : Tensor,
%6 : Tensor,
%7 : Tensor,
%8 : Tensor,
%9 : Tensor,
%10 : Tensor):
%11 : bool = prim::Constant[value=1]()
%12 : bool = prim::Constant[value=0]()
%13 : int = prim::Constant[value=2]()
%14 : float = prim::Constant[value=0.0]()
%15 : Tensor[] = prim::ListConstruct(%0, %1)
%16 : Tensor[] = prim::ListConstruct(%3, %4, %5, %6, %7, %8, %9, %10)
%17 : Tensor, %18 : Tensor, %19 : Tensor = aten::lstm(%2, %15, %16, %11, %13, %14, %12, %12, %11)
return (%17, %18, %19))IR";
std::vector<at::Tensor> input_data;
auto input = at::randn({50, 7, 10}, {at::kCUDA});
auto h0 = at::randn({2, 50, 20}, {at::kCUDA});
auto c0 = at::randn({2, 50, 20}, {at::kCUDA});
auto num1_w1 = at::randn({80, 10}, {at::kCUDA});
auto num1_w2 = at::randn({80, 20}, {at::kCUDA});
auto num1_w3 = at::randn({80}, {at::kCUDA});
auto num1_w4 = at::randn({80}, {at::kCUDA});
auto num2_w1 = at::randn({80, 20}, {at::kCUDA});
auto num2_w2 = at::randn({80, 20}, {at::kCUDA});
auto num2_w3 = at::randn({80}, {at::kCUDA});
auto num2_w4 = at::randn({80}, {at::kCUDA});
input_data.push_back(h0);
input_data.push_back(c0);
input_data.push_back(input);
input_data.push_back(num1_w1);
input_data.push_back(num1_w2);
input_data.push_back(num1_w3);
input_data.push_back(num1_w4);
input_data.push_back(num2_w1);
input_data.push_back(num2_w2);
input_data.push_back(num2_w3);
input_data.push_back(num2_w4);
baidu::mirana::poros::LstmConverter lstmconverter;
lstm_test_helper(graph, input_data, &lstmconverter);
}
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