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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>
273 lines
14 KiB
C++
273 lines
14 KiB
C++
// Copyright (c) 2022 Baidu, Inc. All Rights Reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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/**
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* @file interpolate_test.cpp
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* @author tianshaoqing@baidu.com
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* @date Wed Sep 27 11:24:21 CST 2021
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* @brief
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**/
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#include <gflags/gflags.h>
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#include <gtest/gtest.h>
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#include "poros/converter/gpu/interpolate.h"
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#include "poros/util/test_util.h"
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static void interpolate_test_helper(const std::string& graph_IR,
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baidu::mirana::poros::IConverter* converter,
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std::vector<int64_t> shape){
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std::vector<at::Tensor> input_data;
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input_data.push_back(at::randn(shape, {at::kCUDA}));
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baidu::mirana::poros::PorosOptions poros_option; // default device GPU
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// 运行原图与engine获取结果
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std::vector<at::Tensor> graph_output;
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std::vector<at::Tensor> poros_output;
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ASSERT_TRUE(baidu::mirana::poros::testutil::run_graph_and_poros(graph_IR, poros_option, converter,
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input_data, graph_output, poros_output));
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ASSERT_EQ(1, graph_output.size());
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ASSERT_EQ(1, poros_output.size());
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ASSERT_TRUE(baidu::mirana::poros::testutil::almost_equal(graph_output[0], poros_output[0], 2e-6));
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}
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static std::string gen_upsample_nearest_nd_graph(bool vec_scales,
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const std::string& op,
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const std::string& output_size,
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const std::string& scales) {
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std::string output_ir("");
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std::string scales_ir("");
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std::string op_ir("");
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if (!vec_scales) {
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output_ir = "int[] = prim::Constant[value=[" + output_size + "]]()";
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if (scales.empty()) {
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scales_ir = "None = prim::Constant()";
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} else {
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scales_ir = "float = prim::Constant[value=" + scales + "]()";
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}
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if (op == "upsample_nearest1d") {
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op_ir = op + "(%0, %1, %2)";
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} else if (op == "upsample_nearest2d") {
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op_ir = op + "(%0, %1, %2, %2)";
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} else if (op == "upsample_nearest3d") {
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op_ir = op + "(%0, %1, %2, %2, %2)";
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} else {
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return "";
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}
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} else {
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if (output_size.empty()) {
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output_ir = "None = prim::Constant()";
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} else {
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output_ir = "int[] = prim::Constant[value=[" + output_size + "]]()";
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}
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if (scales.empty()) {
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scales_ir = "None = prim::Constant()";
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} else {
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scales_ir = "float[] = prim::Constant[value=[" + scales + "]]()";
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}
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op_ir = op + "(%0, %1, %2)";
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}
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return R"IR(
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graph(%0 : Tensor):
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%1 : )IR" + output_ir + R"IR(
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%2 : )IR" + scales_ir + R"IR(
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%3 : Tensor = aten::)IR" + op_ir + R"IR(
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return (%3))IR";
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}
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static std::string gen_upsample_linear_graph(bool vec_scales,
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const std::string& op,
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const std::string& output_size,
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const std::string& align_corners,
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const std::string& scales) {
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std::string output_ir("");
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std::string scales_ir("");
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std::string op_ir("");
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if (!vec_scales) {
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output_ir = "int[] = prim::Constant[value=[" + output_size + "]]()";
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if (scales.empty()) {
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scales_ir = "None = prim::Constant()";
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} else {
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scales_ir = "float = prim::Constant[value=" + scales + "]()";
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}
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if (op == "upsample_linear1d") {
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op_ir = op + "(%0, %1, %2, %3)";
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} else if (op == "upsample_bilinear2d") {
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op_ir = op + "(%0, %1, %2, %3, %3)";
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} else if (op == "upsample_trilinear3d") {
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op_ir = op + "(%0, %1, %2, %3, %3, %3)";
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} else {
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return "";
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}
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} else {
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if (output_size.empty()) {
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output_ir = "None = prim::Constant()";
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} else {
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output_ir = "int[] = prim::Constant[value=[" + output_size + "]]()";
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}
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if (scales.empty()) {
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scales_ir = "None = prim::Constant()";
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} else {
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scales_ir = "float[] = prim::Constant[value=[" + scales + "]]()";
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}
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op_ir = op + "(%0, %1, %2, %3)";
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}
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return R"IR(
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graph(%0 : Tensor):
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%1 : )IR" + output_ir + R"IR(
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%2 : bool = prim::Constant[value=)IR" + align_corners + R"IR(]()
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%3 : )IR" + scales_ir + R"IR(
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%4 : Tensor = aten::)IR" + op_ir + R"IR(
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return (%4))IR";
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}
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TEST(Converters, ATenUpsampleNearest1d) {
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// aten::upsample_nearest1d(Tensor self, int[1] output_size, float? scales=None) -> Tensor
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const auto graph_IR = gen_upsample_nearest_nd_graph(false, "upsample_nearest1d", "10", "");
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baidu::mirana::poros::UnsampleNearest1DConverter unsamplenearest1dconverter;
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interpolate_test_helper(graph_IR, &unsamplenearest1dconverter, {10, 2, 2});
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}
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TEST(Converters, ATenUpsampleNearest1dScalar) {
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// aten::upsample_nearest1d(Tensor self, int[1] output_size, float? scales=None) -> Tensor
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const auto graph_IR = gen_upsample_nearest_nd_graph(false, "upsample_nearest1d", "8", "4.0");
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baidu::mirana::poros::UnsampleNearest1DConverter unsamplenearest1dconverter;
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interpolate_test_helper(graph_IR, &unsamplenearest1dconverter, {10, 2, 2});
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}
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TEST(Converters, ATenUpsampleNearest1dVecScalar) {
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// aten::upsample_nearest1d.vec(Tensor input, int[]? output_size, float[]? scale_factors) -> Tensor
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const auto graph_IR = gen_upsample_nearest_nd_graph(true, "upsample_nearest1d", "", "4.0");
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baidu::mirana::poros::UnsampleNearest1DConverter unsamplenearest1dconverter;
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interpolate_test_helper(graph_IR, &unsamplenearest1dconverter, {10, 2, 2});
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}
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TEST(Converters, ATenUpsampleNearest2d) {
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// aten::upsample_nearest2d(Tensor self, int[2] output_size, float? scales_h=None, float? scales_w=None) -> Tensor
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const auto graph_IR = gen_upsample_nearest_nd_graph(false, "upsample_nearest2d", "10, 8", "");
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baidu::mirana::poros::UnsampleNearest2DConverter unsamplenearest2dconverter;
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interpolate_test_helper(graph_IR, &unsamplenearest2dconverter, {10, 2, 2, 2});
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}
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TEST(Converters, ATenUpsampleNearest2dScalar) {
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// aten::upsample_nearest2d(Tensor self, int[2] output_size, float? scales_h=None, float? scales_w=None) -> Tensor
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const auto graph_IR = gen_upsample_nearest_nd_graph(false, "upsample_nearest2d", "8, 8", "4.0");
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baidu::mirana::poros::UnsampleNearest2DConverter unsamplenearest2dconverter;
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interpolate_test_helper(graph_IR, &unsamplenearest2dconverter, {10, 2, 2, 2});
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}
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TEST(Converters, ATenUpsampleNearest2dVecScalar) {
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// aten::upsample_nearest2d.vec(Tensor input, int[]? output_size, float[]? scale_factors) -> Tensor
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const auto graph_IR = gen_upsample_nearest_nd_graph(true, "upsample_nearest2d", "", "5.0, 4.0");
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baidu::mirana::poros::UnsampleNearest2DConverter unsamplenearest2dconverter;
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interpolate_test_helper(graph_IR, &unsamplenearest2dconverter, {10, 2, 2, 2});
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}
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TEST(Converters, ATenUpsampleNearest3d) {
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// aten::upsample_nearest3d(Tensor self, int[3] output_size, float? scales_d=None, float? scales_h=None, float? scales_w=None) -> Tensor
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const auto graph_IR = gen_upsample_nearest_nd_graph(false, "upsample_nearest3d", "10, 8, 6", "");
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baidu::mirana::poros::UnsampleNearest3DConverter unsamplenearest3dconverter;
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interpolate_test_helper(graph_IR, &unsamplenearest3dconverter, {10, 2, 2, 2, 2});
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}
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TEST(Converters, ATenUpsampleNearest3dScalar) {
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// aten::upsample_nearest3d(Tensor self, int[3] output_size, float? scales_d=None, float? scales_h=None, float? scales_w=None) -> Tensor
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const auto graph_IR = gen_upsample_nearest_nd_graph(false, "upsample_nearest3d", "8, 8, 8", "4.0");
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baidu::mirana::poros::UnsampleNearest3DConverter unsamplenearest3dconverter;
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interpolate_test_helper(graph_IR, &unsamplenearest3dconverter, {10, 2, 2, 2, 2});
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}
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TEST(Converters, ATenUpsampleNearest3dVecScalar) {
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// aten::upsample_nearest3d.vec(Tensor input, int[]? output_size, float[]? scale_factors) -> Tensor
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const auto graph_IR = gen_upsample_nearest_nd_graph(true, "upsample_nearest3d", "", "5.0, 4.0, 3.0");
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baidu::mirana::poros::UnsampleNearest3DConverter unsamplenearest3dconverter;
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interpolate_test_helper(graph_IR, &unsamplenearest3dconverter, {10, 2, 2, 2, 2});
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}
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// start almost equal
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TEST(Converters, ATenUpsampleLinear1dWithAlignCorners) {
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// aten::upsample_linear1d(Tensor self, int[1] output_size, bool align_corners, float? scales=None) -> Tensor
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const auto graph_IR = gen_upsample_linear_graph(false, "upsample_linear1d", "10", "1", "");
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baidu::mirana::poros::UnsampleLinear1DConverter unsamplelinear1dconverter;
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interpolate_test_helper(graph_IR, &unsamplelinear1dconverter, {10, 2, 2});
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}
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TEST(Converters, ATenUpsampleLinear1dWithoutAlignCorners) {
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// aten::upsample_linear1d(Tensor self, int[1] output_size, bool align_corners, float? scales=None) -> Tensor
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const auto graph_IR = gen_upsample_linear_graph(false, "upsample_linear1d", "10", "0", "5.0");
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baidu::mirana::poros::UnsampleLinear1DConverter unsamplelinear1dconverter;
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interpolate_test_helper(graph_IR, &unsamplelinear1dconverter, {10, 2, 2});
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}
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TEST(Converters, ATenUpsampleLinear1dScalesWithoutAlignCorners) {
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// aten::upsample_linear1d(Tensor self, int[1] output_size, bool align_corners, float? scales=None) -> Tensor
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const auto graph_IR = gen_upsample_linear_graph(false, "upsample_linear1d", "8", "0", "4.0");
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baidu::mirana::poros::UnsampleLinear1DConverter unsamplelinear1dconverter;
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interpolate_test_helper(graph_IR, &unsamplelinear1dconverter, {10, 2, 2});
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}
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TEST(Converters, ATenUpsampleLinear1dVecScaleFactorsWithoutAlignCorners) {
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// aten::upsample_linear1d.vec(Tensor input, int[]? output_size, bool align_corners, float[]? scale_factors) -> Tensor
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const auto graph_IR = gen_upsample_linear_graph(true, "upsample_linear1d", "", "0", "4.0");
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baidu::mirana::poros::UnsampleLinear1DConverter unsamplelinear1dconverter;
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interpolate_test_helper(graph_IR, &unsamplelinear1dconverter, {10, 2, 2});
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}
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TEST(Converters, ATenUpsampleBilinear2dWithAlignCorners) {
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// aten::upsample_bilinear2d(Tensor self, int[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None) -> Tensor
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const auto graph_IR = gen_upsample_linear_graph(false, "upsample_bilinear2d", "10, 8", "1", "");
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baidu::mirana::poros::UnsampleBilinear2DConverter unsamplebilinear2dconverter;
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interpolate_test_helper(graph_IR, &unsamplebilinear2dconverter, {10, 2, 2, 2});
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}
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TEST(Converters, ATenUpsampleBilinear2dWithoutAlignCorners) {
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// aten::upsample_bilinear2d(Tensor self, int[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None) -> Tensor
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const auto graph_IR = gen_upsample_linear_graph(false, "upsample_bilinear2d", "10, 8", "0", "");
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baidu::mirana::poros::UnsampleBilinear2DConverter unsamplebilinear2dconverter;
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interpolate_test_helper(graph_IR, &unsamplebilinear2dconverter, {10, 2, 2, 2});
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}
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TEST(Converters, ATenUpsampleBilinear2dScalesWithoutAlignCorners) {
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// aten::upsample_bilinear2d(Tensor self, int[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None) -> Tensor
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const auto graph_IR = gen_upsample_linear_graph(false, "upsample_bilinear2d", "10, 10", "0", "5.0");
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baidu::mirana::poros::UnsampleBilinear2DConverter unsamplebilinear2dconverter;
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interpolate_test_helper(graph_IR, &unsamplebilinear2dconverter, {10, 2, 2, 2});
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}
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TEST(Converters, ATenUpsampleBilinear2dVecScaleFactorsWithoutAlignCorners) {
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// aten::upsample_bilinear2d.vec(Tensor input, int[]? output_size, bool align_corners, float[]? scale_factors) -> Tensor
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const auto graph_IR = gen_upsample_linear_graph(true, "upsample_bilinear2d", "", "0", "5.0, 4.0");
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baidu::mirana::poros::UnsampleBilinear2DConverter unsamplebilinear2dconverter;
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interpolate_test_helper(graph_IR, &unsamplebilinear2dconverter, {10, 2, 2, 2});
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}
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TEST(Converters, ATenUpsampleTrilinear3dWithAlignCorners) {
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// aten::upsample_trilinear3d(Tensor self, int[3] output_size, bool align_corners, float? scales_d=None, float? scales_h=None, float? scales_w=None) -> Tensor
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const auto graph_IR = gen_upsample_linear_graph(false, "upsample_trilinear3d", "10, 8, 6", "1", "");
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baidu::mirana::poros::UnsampleTrilinear3DConverter unsampletrilinear3dconverter;
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interpolate_test_helper(graph_IR, &unsampletrilinear3dconverter, {10, 2, 2, 2, 2});
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}
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TEST(Converters, ATenUpsampleTrilinear3dWithoutAlignCorners) {
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// aten::upsample_trilinear3d(Tensor self, int[3] output_size, bool align_corners, float? scales_d=None, float? scales_h=None, float? scales_w=None) -> Tensor
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const auto graph_IR = gen_upsample_linear_graph(false, "upsample_trilinear3d", "10, 8, 6", "0", "");
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baidu::mirana::poros::UnsampleTrilinear3DConverter unsampletrilinear3dconverter;
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interpolate_test_helper(graph_IR, &unsampletrilinear3dconverter, {10, 2, 2, 2, 2});
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}
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TEST(Converters, ATenUpsampleTrilinear3dVecScaleFactorsWithoutAlignCorners) {
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// aten::upsample_trilinear3d.vec(Tensor input, int[]? output_size, bool align_corners, float[]? scale_factors) -> Tensor
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const auto graph_IR = gen_upsample_linear_graph(true, "upsample_trilinear3d", "", "0", "5.0, 4.0, 3.0");
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baidu::mirana::poros::UnsampleTrilinear3DConverter unsampletrilinear3dconverter;
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interpolate_test_helper(graph_IR, &unsampletrilinear3dconverter, {10, 2, 2, 2, 2});
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} |