mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-10-13 04:13:58 +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>
546 lines
26 KiB
C++
546 lines
26 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 generate_test.cpp
|
||
* @author tianshaoqing@baidu.com
|
||
* @date Tue Nov 23 12:26:28 CST 2021
|
||
* @brief
|
||
**/
|
||
#include <gflags/gflags.h>
|
||
#include <gtest/gtest.h>
|
||
|
||
#include "poros/converter/gpu/generate.h"
|
||
#include "poros/util/test_util.h"
|
||
|
||
static void generate_dy_test_helper(const std::string& graph_IR,
|
||
baidu::mirana::poros::IConverter* converter,
|
||
const std::vector<at::Tensor>& input_data,
|
||
bool is_dynamic = false,
|
||
std::vector<std::vector<at::Tensor>>* prewarm_data = nullptr) {
|
||
|
||
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, converter,
|
||
input_data, graph_output, poros_output, prewarm_data));
|
||
|
||
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, ATenZeroslikeConvertsCorrectly) {
|
||
// aten::zeros_like(Tensor self, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor
|
||
const auto graph_IR = R"IR(
|
||
graph(%0 : Tensor):
|
||
%1 : None = prim::Constant()
|
||
%zerosout : Tensor = aten::zeros_like(%0, %1, %1, %1, %1, %1)
|
||
return (%zerosout))IR";
|
||
std::vector<at::Tensor> input_data;
|
||
input_data.push_back(at::randn({5, 6, 7}, {at::kCUDA}));
|
||
baidu::mirana::poros::ZerosLikeConverter zeroslikeconverter;
|
||
generate_dy_test_helper(graph_IR, &zeroslikeconverter, input_data);
|
||
}
|
||
|
||
TEST(Converters, ATenZeroslikeDtypeConvertsCorrectly) {
|
||
// aten::zeros_like(Tensor self, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor
|
||
// scalar type index in aten and support situation ('o' is support and 'x' is not support):
|
||
// uint8_t -> 0 x
|
||
// int8_t -> 1 x
|
||
// int16_t -> 2 x
|
||
// int -> 3 o
|
||
// int64_t -> 4 x
|
||
// Half -> 5 o
|
||
// float -> 6 o
|
||
// bool -> 11 x
|
||
const auto graph_IR = R"IR(
|
||
graph(%0 : Tensor):
|
||
%1 : None = prim::Constant()
|
||
%2 : int = prim::Constant[value=3]()
|
||
%zerosout : Tensor = aten::zeros_like(%0, %2, %1, %1, %1, %1)
|
||
return (%zerosout))IR";
|
||
std::vector<at::Tensor> input_data;
|
||
input_data.push_back(at::randn({5, 6, 7}, {at::kCUDA}));
|
||
baidu::mirana::poros::ZerosLikeConverter zeroslikeconverter;
|
||
generate_dy_test_helper(graph_IR, &zeroslikeconverter, input_data);
|
||
}
|
||
|
||
TEST(Converters, ATenZeroslikeDynamicConvertsCorrectly) {
|
||
// aten::zeros_like(Tensor self, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor
|
||
const auto graph_IR = R"IR(
|
||
graph(%0 : Tensor):
|
||
%1 : None = prim::Constant()
|
||
%zerosout : Tensor = aten::zeros_like(%0, %1, %1, %1, %1, %1)
|
||
return (%zerosout))IR";
|
||
std::vector<std::vector<at::Tensor>> prewarm_data = {{}, {}, {}};
|
||
prewarm_data[0].push_back(at::randn({5, 6, 7}, {at::kCUDA}));
|
||
prewarm_data[1].push_back(at::randn({4, 5, 6}, {at::kCUDA}));
|
||
prewarm_data[2].push_back(at::randn({4, 5, 6}, {at::kCUDA}));
|
||
|
||
std::vector<at::Tensor> input_data;
|
||
input_data.push_back(at::randn({4, 5, 6}, {at::kCUDA}));
|
||
baidu::mirana::poros::ZerosLikeConverter zeroslikeconverter;
|
||
generate_dy_test_helper(graph_IR, &zeroslikeconverter, input_data, true, &prewarm_data);
|
||
}
|
||
|
||
TEST(Converters, ATenZeroslikeDynamicDtypeConvertsCorrectly) {
|
||
// aten::zeros_like(Tensor self, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor
|
||
const auto graph_IR = R"IR(
|
||
graph(%0 : Tensor):
|
||
%1 : None = prim::Constant()
|
||
%2 : int = prim::Constant[value=5]()
|
||
%zerosout : Tensor = aten::zeros_like(%0, %2, %1, %1, %1, %1)
|
||
return (%zerosout))IR";
|
||
std::vector<std::vector<at::Tensor>> prewarm_data = {{}, {}, {}};
|
||
prewarm_data[0].push_back(at::randn({5, 6, 7}, {at::kCUDA}));
|
||
prewarm_data[1].push_back(at::randn({4, 5, 6}, {at::kCUDA}));
|
||
prewarm_data[2].push_back(at::randn({4, 5, 6}, {at::kCUDA}));
|
||
|
||
std::vector<at::Tensor> input_data;
|
||
input_data.push_back(at::randn({4, 5, 6}, {at::kCUDA}));
|
||
baidu::mirana::poros::ZerosLikeConverter zeroslikeconverter;
|
||
generate_dy_test_helper(graph_IR, &zeroslikeconverter, input_data, true, &prewarm_data);
|
||
}
|
||
|
||
TEST(Converters, ATenZerosDynamicConvertsCorrectly) {
|
||
// aten::zeros(int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
|
||
const auto graph_IR = R"IR(
|
||
graph(%0 : Tensor):
|
||
%1 : int[] = aten::size(%0)
|
||
%2 : None = prim::Constant()
|
||
%3 : Device = prim::Constant[value="cuda"]()
|
||
%4 : Tensor = aten::zeros(%1, %2, %2, %3, %2)
|
||
return (%4))IR";
|
||
std::vector<std::vector<at::Tensor>> prewarm_data = {{}, {}, {}};
|
||
prewarm_data[0].push_back(at::randn({5, 6, 7}, {at::kCUDA}));
|
||
prewarm_data[1].push_back(at::randn({4, 5, 6}, {at::kCUDA}));
|
||
prewarm_data[2].push_back(at::randn({4, 5, 6}, {at::kCUDA}));
|
||
|
||
std::vector<at::Tensor> input_data;
|
||
input_data.push_back(at::randn({4, 5, 6}, {at::kCUDA}));
|
||
baidu::mirana::poros::ZerosConverter ZerosConverter;
|
||
generate_dy_test_helper(graph_IR, &ZerosConverter, input_data, true, &prewarm_data);
|
||
}
|
||
|
||
TEST(Converters, ATenZerosDynamicDtypeConvertsCorrectly) {
|
||
// aten::zeros(int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
|
||
const auto graph_IR = R"IR(
|
||
graph(%0 : Tensor):
|
||
%1 : int[] = aten::size(%0)
|
||
%2 : None = prim::Constant()
|
||
%3 : Device = prim::Constant[value="cuda"]()
|
||
%4 : int = prim::Constant[value=3]()
|
||
%5 : Tensor = aten::zeros(%1, %4, %2, %3, %2)
|
||
return (%5))IR";
|
||
std::vector<std::vector<at::Tensor>> prewarm_data = {{}, {}, {}};
|
||
prewarm_data[0].push_back(at::randn({5, 6, 7}, {at::kCUDA}));
|
||
prewarm_data[1].push_back(at::randn({4, 5, 6}, {at::kCUDA}));
|
||
prewarm_data[2].push_back(at::randn({4, 5, 6}, {at::kCUDA}));
|
||
|
||
std::vector<at::Tensor> input_data;
|
||
input_data.push_back(at::randn({4, 5, 6}, {at::kCUDA}));
|
||
baidu::mirana::poros::ZerosConverter ZerosConverter;
|
||
generate_dy_test_helper(graph_IR, &ZerosConverter, input_data, true, &prewarm_data);
|
||
}
|
||
|
||
TEST(Converters, ATenOnesDynamicConvertsCorrectly) {
|
||
// aten::ones(int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
|
||
const auto graph_IR = R"IR(
|
||
graph(%0 : Tensor):
|
||
%1 : int[] = aten::size(%0)
|
||
%2 : None = prim::Constant()
|
||
%3 : Device = prim::Constant[value="cuda"]()
|
||
%4 : Tensor = aten::ones(%1, %2, %2, %3, %2)
|
||
return (%4))IR";
|
||
|
||
std::vector<std::vector<at::Tensor>> prewarm_data = {{}, {}, {}};
|
||
prewarm_data[0].push_back(at::randn({5, 6, 7}, {at::kCUDA}));
|
||
prewarm_data[1].push_back(at::randn({4, 5, 6}, {at::kCUDA}));
|
||
prewarm_data[2].push_back(at::randn({4, 5, 6}, {at::kCUDA}));
|
||
|
||
std::vector<at::Tensor> input_data;
|
||
input_data.push_back(at::randn({4, 5, 6}, {at::kCUDA}));
|
||
baidu::mirana::poros::OnesConverter onesconverter;
|
||
generate_dy_test_helper(graph_IR, &onesconverter, input_data, true, &prewarm_data);
|
||
}
|
||
|
||
TEST(Converters, ATenOnesDynamicDtypeConvertsCorrectly) {
|
||
// aten::ones(int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
|
||
const auto graph_IR = R"IR(
|
||
graph(%0 : Tensor):
|
||
%1 : int[] = aten::size(%0)
|
||
%2 : None = prim::Constant()
|
||
%3 : Device = prim::Constant[value="cuda"]()
|
||
%4 : int = prim::Constant[value=5]()
|
||
%5 : Tensor = aten::ones(%1, %4, %2, %3, %2)
|
||
return (%5))IR";
|
||
std::vector<std::vector<at::Tensor>> prewarm_data = {{}, {}, {}};
|
||
prewarm_data[0].push_back(at::randn({5, 6, 7}, {at::kCUDA}));
|
||
prewarm_data[1].push_back(at::randn({4, 5, 6}, {at::kCUDA}));
|
||
prewarm_data[2].push_back(at::randn({4, 5, 6}, {at::kCUDA}));
|
||
|
||
std::vector<at::Tensor> input_data;
|
||
input_data.push_back(at::randn({4, 5, 6}, {at::kCUDA}));
|
||
baidu::mirana::poros::OnesConverter onesconverter;
|
||
generate_dy_test_helper(graph_IR, &onesconverter, input_data, true, &prewarm_data);
|
||
}
|
||
|
||
TEST(Converters, ATenFullDynamicDtypeConvertsCorrectly) {
|
||
// aten::full(int[] size, Scalar fill_value, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
|
||
const auto graph_IR = R"IR(
|
||
graph(%0 : Tensor):
|
||
%1 : int[] = aten::size(%0)
|
||
%2 : None = prim::Constant()
|
||
%3 : Device = prim::Constant[value="cuda"]()
|
||
%4 : int = prim::Constant[value=6]()
|
||
%5 : Tensor = aten::full(%1, %4, %4, %2, %3, %2)
|
||
return (%5))IR";
|
||
std::vector<std::vector<at::Tensor>> prewarm_data = {{}, {}, {}};
|
||
prewarm_data[0].push_back(at::randn({5, 6, 7}, {at::kCUDA}));
|
||
prewarm_data[1].push_back(at::randn({4, 5, 6}, {at::kCUDA}));
|
||
prewarm_data[2].push_back(at::randn({4, 5, 6}, {at::kCUDA}));
|
||
|
||
std::vector<at::Tensor> input_data;
|
||
input_data.push_back(at::randn({4, 5, 6}, {at::kCUDA}));
|
||
baidu::mirana::poros::FullConverter fullconverter;
|
||
generate_dy_test_helper(graph_IR, &fullconverter, input_data, true, &prewarm_data);
|
||
}
|
||
|
||
TEST(Converters, ATenArangeDynamicDtypeConvertsCorrectly) {
|
||
// aten::arange(Scalar end, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
|
||
const auto graph_IR = R"IR(
|
||
graph(%0 : Tensor):
|
||
%1 : int = prim::Constant[value=1]()
|
||
%2 : int = aten::size(%0, %1)
|
||
%3 : None = prim::Constant()
|
||
%4 : Device = prim::Constant[value="cuda"]()
|
||
%5 : int = prim::Constant[value=3]()
|
||
%6 : Tensor = aten::arange(%2, %5, %3, %4, %3)
|
||
return (%6))IR";
|
||
std::vector<std::vector<at::Tensor>> prewarm_data = {{}, {}, {}};
|
||
prewarm_data[0].push_back(at::randn({5, 6, 7}, {at::kCUDA}));
|
||
prewarm_data[1].push_back(at::randn({4, 5, 6}, {at::kCUDA}));
|
||
prewarm_data[2].push_back(at::randn({4, 5, 6}, {at::kCUDA}));
|
||
|
||
std::vector<at::Tensor> input_data;
|
||
input_data.push_back(at::randn({4, 5, 6}, {at::kCUDA}));
|
||
baidu::mirana::poros::ArangeConverter arangeconverter;
|
||
generate_dy_test_helper(graph_IR, &arangeconverter, input_data, true, &prewarm_data);
|
||
}
|
||
|
||
TEST(Converters, ATenArangeStartEndDynamicDtypeConvertsCorrectly) {
|
||
// aten::arange.start(Scalar start, Scalar end, *, int? dtype=None, int? layout=None, Device? device=None, bool? pin_memory=None) -> (Tensor)
|
||
const auto graph_IR = R"IR(
|
||
graph(%0 : Tensor):
|
||
%1 : int = prim::Constant[value=0]()
|
||
%2 : int = prim::Constant[value=1]()
|
||
%s.1 : int = aten::size(%0, %1)
|
||
%s.2 : int = aten::size(%0, %2)
|
||
%3 : None = prim::Constant()
|
||
%4 : Device = prim::Constant[value="cuda"]()
|
||
%5 : int = prim::Constant[value=3]()
|
||
%6 : Tensor = aten::arange(%s.1, %s.2, %5, %3, %4, %3)
|
||
return (%6))IR";
|
||
std::vector<std::vector<at::Tensor>> prewarm_data = {{}, {}, {}};
|
||
prewarm_data[0].push_back(at::randn({1, 8}, {at::kCUDA}));
|
||
prewarm_data[1].push_back(at::randn({1, 2}, {at::kCUDA}));
|
||
prewarm_data[2].push_back(at::randn({1, 5}, {at::kCUDA}));
|
||
|
||
std::vector<at::Tensor> input_data;
|
||
input_data.push_back(at::randn({1, 5}, {at::kCUDA}));
|
||
baidu::mirana::poros::ArangeConverter arangeconverter;
|
||
generate_dy_test_helper(graph_IR, &arangeconverter, input_data, true, &prewarm_data);
|
||
}
|
||
|
||
TEST(Converters, ATenArangeStartConstantEndDynamicDtypeConvertsCorrectly) {
|
||
// aten::arange.start(Scalar start, Scalar end, *, int? dtype=None, int? layout=None, Device? device=None, bool? pin_memory=None) -> (Tensor)
|
||
const auto graph_IR = R"IR(
|
||
graph(%0 : Tensor):
|
||
%s.1 : int = prim::Constant[value=-10]()
|
||
%1 : int = prim::Constant[value=1]()
|
||
%s.2 : int = aten::size(%0, %1)
|
||
%3 : None = prim::Constant()
|
||
%4 : Device = prim::Constant[value="cuda"]()
|
||
%5 : int = prim::Constant[value=6]()
|
||
%6 : Tensor = aten::arange(%s.1, %s.2, %5, %3, %4, %3)
|
||
return (%6))IR";
|
||
std::vector<std::vector<at::Tensor>> prewarm_data = {{}, {}, {}};
|
||
prewarm_data[0].push_back(at::randn({1, 8}, {at::kCUDA}));
|
||
prewarm_data[1].push_back(at::randn({1, 2}, {at::kCUDA}));
|
||
prewarm_data[2].push_back(at::randn({1, 5}, {at::kCUDA}));
|
||
|
||
std::vector<at::Tensor> input_data;
|
||
input_data.push_back(at::randn({1, 5}, {at::kCUDA}));
|
||
baidu::mirana::poros::ArangeConverter arangeconverter;
|
||
generate_dy_test_helper(graph_IR, &arangeconverter, input_data, true, &prewarm_data);
|
||
}
|
||
|
||
TEST(Converters, ATenTensorDynamicDtypeConvertsCorrectly) {
|
||
const auto graph_IR = R"IR(
|
||
graph(%0 : Tensor):
|
||
%1 : bool = prim::Constant[value=0]()
|
||
%2 : Device = prim::Constant[value="cuda:0"]()
|
||
%3 : int = prim::Constant[value=6]()
|
||
%4 : int[] = aten::size(%0)
|
||
%5 : Tensor = aten::tensor(%4, %3, %2, %1)
|
||
return (%5))IR";
|
||
|
||
std::vector<std::vector<at::Tensor>> prewarm_data = {{}, {}, {}};
|
||
prewarm_data[0].push_back(at::randn({11, 2, 1}, {at::kCUDA}));
|
||
prewarm_data[1].push_back(at::randn({10, 2, 1}, {at::kCUDA}));
|
||
prewarm_data[2].push_back(at::randn({10, 2, 1}, {at::kCUDA}));
|
||
|
||
std::vector<at::Tensor> input_data;
|
||
input_data.push_back(at::randn({10, 2, 1}, {at::kCUDA}));
|
||
baidu::mirana::poros::TensorConverter tensorconverter;
|
||
|
||
baidu::mirana::poros::PorosOptions poros_option; // default device GPU
|
||
poros_option.is_dynamic = true;
|
||
// 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, &tensorconverter,
|
||
input_data, graph_output, poros_output, &prewarm_data));
|
||
|
||
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, ATenLinspaceScalarTensorConvertsCorrectly) {
|
||
// aten::linspace(Scalar start, Scalar end, int? steps=None, *, int? dtype=None, int? layout=None, Device? device=None, bool? pin_memory=None) -> (Tensor)
|
||
// aten::linspace目前只能构造dynamic的单测，非dy的单测会被某些pass变为constant
|
||
const auto graph_IR = R"IR(
|
||
graph(%0 : Tensor):
|
||
%2 : int = prim::Constant[value=0]()
|
||
%3 : None = prim::Constant()
|
||
%start : int = prim::Constant[value=-10]()
|
||
%end : int = prim::Constant[value=100]()
|
||
%step : int = aten::size(%0, %2)
|
||
%device : Device = prim::Constant[value="cuda"]()
|
||
%5 : Tensor = aten::linspace(%start, %end, %step, %3, %3, %device, %3)
|
||
%6 : Tensor = aten::mul(%0, %5)
|
||
return (%6))IR";
|
||
|
||
std::vector<at::Tensor> input_data;
|
||
input_data.push_back(at::ones({6}, {at::kCUDA}));
|
||
|
||
std::vector<std::vector<at::Tensor>> prewarm_data = {{}, {}, {}};
|
||
prewarm_data[0].push_back(at::ones({10}, {at::kCUDA}));
|
||
prewarm_data[1].push_back(at::ones({6}, {at::kCUDA}));
|
||
prewarm_data[2].push_back(at::ones({6}, {at::kCUDA}));
|
||
|
||
baidu::mirana::poros::LinspaceConverter linspaceconverter;
|
||
|
||
baidu::mirana::poros::PorosOptions poros_option; // default device GPU
|
||
poros_option.is_dynamic = true;
|
||
// 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, &linspaceconverter,
|
||
input_data, graph_output, poros_output, &prewarm_data));
|
||
|
||
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, ATenLinspaceStartEndDiffTypeConvertsCorrectly) {
|
||
// aten::linspace(Scalar start, Scalar end, int? steps=None, *, int? dtype=None, int? layout=None, Device? device=None, bool? pin_memory=None) -> (Tensor)
|
||
const auto graph_IR = R"IR(
|
||
graph(%0 : Tensor):
|
||
%2 : int = prim::Constant[value=0]()
|
||
%3 : None = prim::Constant()
|
||
%start : int = prim::Constant[value=-10]()
|
||
%end : float = prim::Constant[value=43.3]()
|
||
%step : int = aten::size(%0, %2)
|
||
%device : Device = prim::Constant[value="cuda"]()
|
||
%5 : Tensor = aten::linspace(%start, %end, %step, %3, %3, %device, %3)
|
||
%6 : Tensor = aten::mul(%0, %5)
|
||
return (%6))IR";
|
||
|
||
std::vector<at::Tensor> input_data;
|
||
input_data.push_back(at::ones({6}, {at::kCUDA}));
|
||
|
||
std::vector<std::vector<at::Tensor>> prewarm_data = {{}, {}, {}};
|
||
prewarm_data[0].push_back(at::ones({10}, {at::kCUDA}));
|
||
prewarm_data[1].push_back(at::ones({6}, {at::kCUDA}));
|
||
prewarm_data[2].push_back(at::ones({6}, {at::kCUDA}));
|
||
|
||
baidu::mirana::poros::LinspaceConverter linspaceconverter;
|
||
|
||
baidu::mirana::poros::PorosOptions poros_option; // default device GPU
|
||
poros_option.is_dynamic = true;
|
||
// 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, &linspaceconverter,
|
||
input_data, graph_output, poros_output, &prewarm_data));
|
||
|
||
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, ATenLinspaceStepNoneConvertsCorrectly) {
|
||
std::string graph_IR_str;
|
||
if (TORCH_VERSION_MAJOR < 2 && TORCH_VERSION_MINOR < 11) {
|
||
// aten::linspace(Scalar start, Scalar end, int? steps=None, *, int? dtype=None, int? layout=None, Device? device=None, bool? pin_memory=None) -> (Tensor)
|
||
graph_IR_str = R"IR(
|
||
graph(%0 : Tensor, %1 : Tensor):
|
||
%2 : int = prim::Constant[value=0]()
|
||
%3 : None = prim::Constant()
|
||
%start : int = aten::size(%0, %2)
|
||
%end : float = prim::Constant[value=43.3]()
|
||
%device : Device = prim::Constant[value="cuda"]()
|
||
%5 : Tensor = aten::linspace(%start, %end, %3, %3, %3, %device, %3)
|
||
%6 : Tensor = aten::mul(%1, %5)
|
||
return (%6))IR";
|
||
} else {
|
||
// aten::linspace(Scalar start, Scalar end, int steps, *, int? dtype=None, int? layout=None, Device? device=None, bool? pin_memory=None) -> (Tensor)
|
||
graph_IR_str = R"IR(
|
||
graph(%0 : Tensor, %1 : Tensor):
|
||
%2 : int = prim::Constant[value=0]()
|
||
%3 : None = prim::Constant()
|
||
%start : int = aten::size(%0, %2)
|
||
%end : float = prim::Constant[value=43.3]()
|
||
%step : int = prim::Constant[value=100]()
|
||
%device : Device = prim::Constant[value="cuda"]()
|
||
%5 : Tensor = aten::linspace(%start, %end, %step, %3, %3, %device, %3)
|
||
%6 : Tensor = aten::mul(%1, %5)
|
||
return (%6))IR";
|
||
}
|
||
const std::string graph_IR = graph_IR_str;
|
||
|
||
std::vector<at::Tensor> input_data;
|
||
input_data.push_back(at::ones({1}, {at::kCUDA}));
|
||
input_data.push_back(at::ones({100}, {at::kCUDA}));
|
||
|
||
std::vector<std::vector<at::Tensor>> prewarm_data = {{}, {}, {}};
|
||
prewarm_data[0].push_back(at::ones({6}, {at::kCUDA}));
|
||
prewarm_data[0].push_back(at::ones({100}, {at::kCUDA}));
|
||
prewarm_data[1].push_back(at::ones({1}, {at::kCUDA}));
|
||
prewarm_data[1].push_back(at::ones({100}, {at::kCUDA}));
|
||
prewarm_data[2].push_back(at::ones({1}, {at::kCUDA}));
|
||
prewarm_data[2].push_back(at::ones({100}, {at::kCUDA}));
|
||
|
||
baidu::mirana::poros::LinspaceConverter linspaceconverter;
|
||
|
||
baidu::mirana::poros::PorosOptions poros_option; // default device GPU
|
||
poros_option.is_dynamic = true;
|
||
// 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, &linspaceconverter,
|
||
input_data, graph_output, poros_output, &prewarm_data));
|
||
|
||
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, ATenFulllikeConvertsCorrectly) {
|
||
// aten::full_like(Tensor self, Scalar fill_value, *, int? dtype=None, int? layout=None, Device? device=None, bool? pin_memory=None, int? memory_format=None) -> (Tensor)
|
||
const auto graph_IR = R"IR(
|
||
graph(%0 : Tensor):
|
||
%1 : None = prim::Constant()
|
||
%scalar : float = prim::Constant[value=2.5]()
|
||
%out : Tensor = aten::full_like(%0, %scalar, %1, %1, %1, %1, %1)
|
||
return (%out))IR";
|
||
std::vector<at::Tensor> input_data;
|
||
input_data.push_back(at::randn({2, 3, 4}, {at::kCUDA}));
|
||
baidu::mirana::poros::FulllikeConverter fulllikeconverter;
|
||
generate_dy_test_helper(graph_IR, &fulllikeconverter, input_data);
|
||
}
|
||
|
||
TEST(Converters, ATenFulllikeDefaultTypeConvertsCorrectly) {
|
||
// aten::full_like(Tensor self, Scalar fill_value, *, int? dtype=None, int? layout=None, Device? device=None, bool? pin_memory=None, int? memory_format=None) -> (Tensor)
|
||
const auto graph_IR = R"IR(
|
||
graph(%0 : Tensor):
|
||
%1 : None = prim::Constant()
|
||
%scalar : float = prim::Constant[value=2.5]()
|
||
%out : Tensor = aten::full_like(%0, %scalar, %1, %1, %1, %1, %1)
|
||
return (%out))IR";
|
||
std::vector<at::Tensor> input_data;
|
||
auto options_pyt_int = torch::TensorOptions().device(torch::kCUDA, 0).dtype(torch::kInt);
|
||
input_data.push_back(at::zeros({2, 3, 4}, options_pyt_int));
|
||
baidu::mirana::poros::FulllikeConverter fulllikeconverter;
|
||
generate_dy_test_helper(graph_IR, &fulllikeconverter, input_data);
|
||
}
|
||
|
||
TEST(Converters, ATenFulllikeDtypeConvertsCorrectly) {
|
||
// aten::full_like(Tensor self, Scalar fill_value, *, int? dtype=None, int? layout=None, Device? device=None, bool? pin_memory=None, int? memory_format=None) -> (Tensor)
|
||
// scalar type index in aten and support situation ('o' is support and 'x' is not support):
|
||
// uint8_t -> 0 x
|
||
// int8_t -> 1 x
|
||
// int16_t -> 2 x
|
||
// int -> 3 o
|
||
// int64_t -> 4 x
|
||
// Half -> 5 o
|
||
// float -> 6 o
|
||
// bool -> 11 x
|
||
const auto graph_IR = R"IR(
|
||
graph(%0 : Tensor):
|
||
%1 : None = prim::Constant()
|
||
%2 : int = prim::Constant[value=6]()
|
||
%scalar : int = prim::Constant[value=2]()
|
||
%out : Tensor = aten::full_like(%0, %scalar, %2, %1, %1, %1, %1)
|
||
return (%out))IR";
|
||
std::vector<at::Tensor> input_data;
|
||
input_data.push_back(at::randn({2, 3, 4}, {at::kCUDA}));
|
||
baidu::mirana::poros::FulllikeConverter fulllikeconverter;
|
||
generate_dy_test_helper(graph_IR, &fulllikeconverter, input_data);
|
||
}
|
||
|
||
TEST(Converters, ATenFulllikeDynamicConvertsCorrectly) {
|
||
// aten::full_like(Tensor self, Scalar fill_value, *, int? dtype=None, int? layout=None, Device? device=None, bool? pin_memory=None, int? memory_format=None) -> (Tensor)
|
||
const auto graph_IR = R"IR(
|
||
graph(%0 : Tensor):
|
||
%1 : None = prim::Constant()
|
||
%scalar : int = prim::Constant[value=2]()
|
||
%out : Tensor = aten::full_like(%0, %scalar, %1, %1, %1, %1, %1)
|
||
return (%out))IR";
|
||
std::vector<std::vector<at::Tensor>> prewarm_data = {{}, {}, {}};
|
||
prewarm_data[0].push_back(at::randn({4, 5, 6}, {at::kCUDA}));
|
||
prewarm_data[1].push_back(at::randn({2, 3, 4}, {at::kCUDA}));
|
||
prewarm_data[2].push_back(at::randn({2, 3, 4}, {at::kCUDA}));
|
||
|
||
std::vector<at::Tensor> input_data;
|
||
input_data.push_back(at::randn({2, 3, 4}, {at::kCUDA}));
|
||
baidu::mirana::poros::FulllikeConverter fulllikeconverter;
|
||
generate_dy_test_helper(graph_IR, &fulllikeconverter, input_data, true, &prewarm_data);
|
||
}
|
||
|
||
TEST(Converters, ATenFulllikeDynamicDtypeConvertsCorrectly) {
|
||
// aten::full_like(Tensor self, Scalar fill_value, *, int? dtype=None, int? layout=None, Device? device=None, bool? pin_memory=None, int? memory_format=None) -> (Tensor)
|
||
const auto graph_IR = R"IR(
|
||
graph(%0 : Tensor):
|
||
%1 : None = prim::Constant()
|
||
%2 : int = prim::Constant[value=3]()
|
||
%scalar : float = prim::Constant[value=2.5]()
|
||
%out : Tensor = aten::full_like(%0, %scalar, %2, %1, %1, %1, %1)
|
||
return (%out))IR";
|
||
std::vector<std::vector<at::Tensor>> prewarm_data = {{}, {}, {}};
|
||
prewarm_data[0].push_back(at::randn({4, 5, 6}, {at::kCUDA}));
|
||
prewarm_data[1].push_back(at::randn({2, 3, 4}, {at::kCUDA}));
|
||
prewarm_data[2].push_back(at::randn({2, 3, 4}, {at::kCUDA}));
|
||
|
||
std::vector<at::Tensor> input_data;
|
||
input_data.push_back(at::randn({2, 3, 4}, {at::kCUDA}));
|
||
baidu::mirana::poros::FulllikeConverter fulllikeconverter;
|
||
generate_dy_test_helper(graph_IR, &fulllikeconverter, input_data, true, &prewarm_data);
|
||
} |