mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-10-12 12:00:30 +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>
228 lines
9.7 KiB
C++
228 lines
9.7 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 activation_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/activation.h"
|
|
#include "poros/util/test_util.h"
|
|
|
|
static void activation_test_helper(const std::string& graph_IR,
|
|
baidu::mirana::poros::IConverter* converter,
|
|
std::vector<int64_t> shape1 = {5},
|
|
bool single_input = true,
|
|
std::vector<int64_t> shape2 = {5}){
|
|
std::vector<at::Tensor> input_data;
|
|
input_data.push_back(at::randn(shape1, {at::kCUDA}));
|
|
if (!single_input){
|
|
input_data.push_back(at::randn(shape2, {at::kCUDA}));
|
|
}
|
|
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(1, graph_output.size());
|
|
ASSERT_EQ(1, poros_output.size());
|
|
std::string gelu_node_str("aten::gelu");
|
|
if (converter->node_kind()[0].toQualString() == gelu_node_str){
|
|
// NOTE: The official tensorrt plugin applies the Gelu activation x * Phi(x), where Phi is the Gaussian cdf,
|
|
// approximated by: 0.5 * (1 + tanh(sqrt(2 / M_PI) * (x + 0.044715 * x^3))) and the pytorch uses
|
|
// c10::cuda::compat::normcdf to compute Phi(x). So there's a difference here and therefore the threshold is slightly
|
|
// higher than other ops. One in ten runs will give you an out of normal threshold result.
|
|
ASSERT_TRUE(baidu::mirana::poros::testutil::almost_equal(graph_output[0], poros_output[0], 5e-2));
|
|
}else{
|
|
ASSERT_TRUE(baidu::mirana::poros::testutil::almost_equal(graph_output[0], poros_output[0], 2e-6));
|
|
}
|
|
}
|
|
|
|
static std::string gen_single_node_graph(const std::string& op) {
|
|
return R"IR(
|
|
graph(%0 : Tensor):
|
|
%1 : Tensor = aten::)IR" + op + R"IR((%0)
|
|
return (%1))IR";
|
|
}
|
|
|
|
static std::string gen_hardtanh_graph(const std::string& op,
|
|
const std::string& min_val,
|
|
const std::string& max_val) {
|
|
return R"IR(
|
|
graph(%0 : Tensor):
|
|
%1 : float = prim::Constant[value=)IR" + min_val + R"IR(]()
|
|
%2 : float = prim::Constant[value=)IR" + max_val + R"IR(]()
|
|
%3 : Tensor = aten::)IR" + op + R"IR((%0, %1, %2)
|
|
return (%3))IR";
|
|
}
|
|
static std::string gen_leakyrelu_graph(const std::string& op, const std::string& negative_slope) {
|
|
return R"IR(
|
|
graph(%0 : Tensor):
|
|
%1 : float = prim::Constant[value=)IR" + negative_slope + R"IR(]()
|
|
%2 : Tensor = aten::)IR" + op + R"IR((%0, %1)
|
|
return (%2))IR";
|
|
}
|
|
|
|
static std::string gen_elu_graph(const std::string& alpha) {
|
|
return R"IR(
|
|
graph(%0 : Tensor):
|
|
%1 : float = prim::Constant[value=)IR" + alpha + R"IR(]()
|
|
%2 : int = prim::Constant[value=1]()
|
|
%3 : Tensor = aten::elu(%0, %1, %2, %2)
|
|
return (%3))IR";
|
|
}
|
|
|
|
TEST(Converters, ATenReluConvertsCorrectly) {
|
|
// aten::relu(Tensor self) -> Tensor
|
|
const auto graph_IR = gen_single_node_graph("relu");
|
|
baidu::mirana::poros::ActivationConverter activationconverter;
|
|
activation_test_helper(graph_IR, &activationconverter);
|
|
}
|
|
|
|
TEST(Converters, ATenRelu_ConvertsCorrectly) {
|
|
// aten::relu_(Tensor(a!) self) -> Tensor(a!)
|
|
const auto graph_IR = gen_single_node_graph("relu_");
|
|
baidu::mirana::poros::ActivationConverter activationconverter;
|
|
activation_test_helper(graph_IR, &activationconverter);
|
|
}
|
|
|
|
TEST(Converters, ATenRelu6ConvertsCorrectly) {
|
|
// aten::relu6(Tensor self) -> Tensor
|
|
const auto graph_IR = gen_single_node_graph("relu6");
|
|
baidu::mirana::poros::ActivationConverter activationconverter;
|
|
activation_test_helper(graph_IR, &activationconverter);
|
|
}
|
|
|
|
TEST(Converters, ATenRelu6_ConvertsCorrectly) {
|
|
// aten::relu6_(Tensor(a!) self) -> Tensor(a!)
|
|
const auto graph_IR = gen_single_node_graph("relu6_");
|
|
baidu::mirana::poros::ActivationConverter activationconverter;
|
|
activation_test_helper(graph_IR, &activationconverter);
|
|
}
|
|
|
|
TEST(Converters, ATenSigmoidConvertsCorrectly) {
|
|
// aten::sigmoid(Tensor self) -> Tensor
|
|
const auto graph_IR = gen_single_node_graph("sigmoid");
|
|
baidu::mirana::poros::ActivationConverter activationconverter;
|
|
activation_test_helper(graph_IR, &activationconverter);
|
|
}
|
|
|
|
TEST(Converters, ATenSigmoid_ConvertsCorrectly) {
|
|
// aten::sigmoid_(Tensor(a!) self) -> Tensor(a!)
|
|
const auto graph_IR = gen_single_node_graph("sigmoid_");
|
|
baidu::mirana::poros::ActivationConverter activationconverter;
|
|
activation_test_helper(graph_IR, &activationconverter);
|
|
}
|
|
|
|
TEST(Converters, ATenTanhConvertsCorrectly) {
|
|
// aten::tanh(Tensor self) -> Tensor"
|
|
const auto graph_IR = gen_single_node_graph("tanh");
|
|
baidu::mirana::poros::ActivationConverter activationconverter;
|
|
activation_test_helper(graph_IR, &activationconverter);
|
|
}
|
|
|
|
TEST(Converters, ATenTanh_ConvertsCorrectly) {
|
|
// aten::tanh_(Tensor(a!) self) -> Tensor(a!)
|
|
const auto graph_IR = gen_single_node_graph("tanh_");
|
|
baidu::mirana::poros::ActivationConverter activationconverter;
|
|
activation_test_helper(graph_IR, &activationconverter);
|
|
}
|
|
|
|
TEST(Converters, ATenGeluConvertsCorrectly) {
|
|
std::string graph_IR_str;
|
|
// aten::gelu schema changed in torch-1.12
|
|
if (TORCH_VERSION_MAJOR < 2 && TORCH_VERSION_MINOR < 12) {
|
|
// aten::gelu(Tensor self) -> Tensor
|
|
graph_IR_str = gen_single_node_graph("gelu");
|
|
} else {
|
|
// aten::gelu(Tensor self, *, str approximate='none') -> Tensor
|
|
graph_IR_str = R"IR(
|
|
graph(%0 : Tensor):
|
|
%approximate : str = prim::Constant[value="tanh"]()
|
|
%1 : Tensor = aten::gelu(%0, %approximate)
|
|
return (%1))IR";
|
|
}
|
|
const auto graph_IR = graph_IR_str;
|
|
baidu::mirana::poros::GeluActivationConverter geluactivationconverter;
|
|
activation_test_helper(graph_IR, &geluactivationconverter, {10});
|
|
}
|
|
|
|
TEST(Converters, ATenLeakyreluConvertsCorrectly) {
|
|
// aten::leaky_relu(Tensor self, Scalar negative_slope=0.01) -> Tensor
|
|
const auto graph_IR = gen_leakyrelu_graph("leaky_relu", "0.01");
|
|
baidu::mirana::poros::ActivationConverter activationconverter;
|
|
activation_test_helper(graph_IR, &activationconverter);
|
|
}
|
|
|
|
TEST(Converters, ATenLeakyreluNegSlopeConvertsCorrectly) {
|
|
// aten::leaky_relu(Tensor self, Scalar negative_slope=0.01) -> Tensor
|
|
const auto graph_IR = gen_leakyrelu_graph("leaky_relu", "0.05");
|
|
baidu::mirana::poros::ActivationConverter activationconverter;
|
|
activation_test_helper(graph_IR, &activationconverter);
|
|
}
|
|
|
|
TEST(Converters, ATenHardtanhConvertsCorrectly) {
|
|
// aten::hardtanh(Tensor self, Scalar min_val=-1, Scalar max_val=1) -> Tensor
|
|
const auto graph_IR = gen_hardtanh_graph("hardtanh", "-1.0", "1.0");
|
|
baidu::mirana::poros::ActivationConverter activationconverter;
|
|
activation_test_helper(graph_IR, &activationconverter);
|
|
}
|
|
|
|
TEST(Converters, ATenHardtanhMinvalMaxvalConvertsCorrectly) {
|
|
// aten::hardtanh(Tensor self, Scalar min_val=-1, Scalar max_val=1) -> Tensor
|
|
const auto graph_IR = gen_hardtanh_graph("hardtanh", "-3.5", "2.5");
|
|
baidu::mirana::poros::ActivationConverter activationconverter;
|
|
activation_test_helper(graph_IR, &activationconverter);
|
|
}
|
|
|
|
TEST(Converters, ATenHardtanh_ConvertsCorrectly) {
|
|
// aten::hardtanh_(Tensor(a!) self, Scalar min_val=-1, Scalar max_val=1) -> Tensor(a!)
|
|
const auto graph_IR = gen_hardtanh_graph("hardtanh_", "-1.0", "1.0");
|
|
baidu::mirana::poros::ActivationConverter activationconverter;
|
|
activation_test_helper(graph_IR, &activationconverter);
|
|
}
|
|
|
|
TEST(Converters, ATenHardtanh_MinvalMaxvalConvertsCorrectly) {
|
|
// aten::hardtanh_(Tensor(a!) self, Scalar min_val=-1, Scalar max_val=1) -> Tensor(a!)
|
|
const auto graph_IR = gen_hardtanh_graph("hardtanh_", "-2.1", "3.8");
|
|
baidu::mirana::poros::ActivationConverter activationconverter;
|
|
activation_test_helper(graph_IR, &activationconverter);
|
|
}
|
|
|
|
TEST(Converters, ATenEluConvertsCorrectly) {
|
|
// aten::elu(Tensor self, Scalar alpha=1, Scalar scale=1, Scalar input_scale=1) -> Tensor
|
|
const auto graph_IR = gen_elu_graph("1.0");
|
|
baidu::mirana::poros::ActivationConverter activationconverter;
|
|
activation_test_helper(graph_IR, &activationconverter);
|
|
}
|
|
|
|
TEST(Converters, ATenEluAlphaConvertsCorrectly) {
|
|
// aten::elu(Tensor self, Scalar alpha=1, Scalar scale=1, Scalar input_scale=1) -> Tensor
|
|
const auto graph_IR = gen_elu_graph("3.4");
|
|
baidu::mirana::poros::ActivationConverter activationconverter;
|
|
activation_test_helper(graph_IR, &activationconverter);
|
|
}
|
|
|
|
TEST(Converters, ATenEluNegAlphaConvertsCorrectly) {
|
|
// aten::elu(Tensor self, Scalar alpha=1, Scalar scale=1, Scalar input_scale=1) -> Tensor
|
|
const auto graph_IR = gen_elu_graph("-2.1");
|
|
baidu::mirana::poros::ActivationConverter activationconverter;
|
|
activation_test_helper(graph_IR, &activationconverter);
|
|
} |