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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>
105 lines
4.6 KiB
C++
105 lines
4.6 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 replication_pad_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/replication_pad.h"
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#include "poros/util/test_util.h"
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static void replicationpad_test_helper(const std::string& graph_IR,
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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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baidu::mirana::poros::ReplicationPadConverter replicationpadconverter;
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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, &replicationpadconverter,
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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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ASSERT_TRUE(graph_output[0].equal(poros_output[0]));
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}
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static std::string gen_replicationpad_graph(const std::string& op,
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const std::string& padding) {
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return R"IR(
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graph(%0 : Tensor):
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%1 : int[] = prim::Constant[value=[)IR" + padding + R"IR(]]()
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%2 : Tensor = aten::)IR" + op + R"IR((%0, %1)
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return (%2))IR";
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}
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TEST(Converters, ATenReplicationPad1DConvertsCorrectly) {
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// aten::replication_pad1d(Tensor self, int[2] padding) -> Tensor
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const auto graph_IR = gen_replicationpad_graph("replication_pad1d", "2, 3");
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replicationpad_test_helper(graph_IR, {1, 3, 4});
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}
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TEST(Converters, ATenReplicationPad1DRightZeroConvertsCorrectly) {
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// aten::replication_pad1d(Tensor self, int[2] padding) -> Tensor
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const auto graph_IR = gen_replicationpad_graph("replication_pad1d", "2, 0");
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replicationpad_test_helper(graph_IR, {1, 3, 4});
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}
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TEST(Converters, ATenReplicationPad1DLeftZeroConvertsCorrectly) {
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// aten::replication_pad1d(Tensor self, int[2] padding) -> Tensor
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const auto graph_IR = gen_replicationpad_graph("replication_pad1d", "0, 3");
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replicationpad_test_helper(graph_IR, {1, 3, 4});
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}
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TEST(Converters, ATenReplicationPad2DConvertsCorrectly) {
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// aten::replication_pad2d(Tensor self, int[4] padding) -> Tensor
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const auto graph_IR = gen_replicationpad_graph("replication_pad2d", "2, 3, 2, 3");
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replicationpad_test_helper(graph_IR, {1, 3, 4, 5});
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}
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TEST(Converters, ATenReplicationPad2DBottomZeroConvertsCorrectly) {
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// aten::replication_pad2d(Tensor self, int[4] padding) -> Tensor
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const auto graph_IR = gen_replicationpad_graph("replication_pad2d", "2, 0, 2, 0");
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replicationpad_test_helper(graph_IR, {1, 3, 4, 5});
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}
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TEST(Converters, ATenReplicationPad2DTopZeroConvertsCorrectly) {
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// aten::replication_pad2d(Tensor self, int[4] padding) -> Tensor
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const auto graph_IR = gen_replicationpad_graph("replication_pad2d", "0, 3, 0, 3");
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replicationpad_test_helper(graph_IR, {1, 3, 4, 5});
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}
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TEST(Converters, ATenReplicationPad3DConvertsCorrectly) {
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// aten::replication_pad3d(Tensor self, int[6] padding) -> Tensor
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const auto graph_IR = gen_replicationpad_graph("replication_pad3d", "2, 3, 2, 3, 1, 4");
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replicationpad_test_helper(graph_IR, {1, 3, 4, 5, 3});
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}
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TEST(Converters, ATenReplicationPad3DRightBottomZeroConvertsCorrectly) {
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// aten::replication_pad3d(Tensor self, int[6] padding) -> Tensor
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const auto graph_IR = gen_replicationpad_graph("replication_pad3d", "2, 0, 2, 0, 1, 0");
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replicationpad_test_helper(graph_IR, {1, 3, 4, 5, 3});
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}
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TEST(Converters, ATenReplicationPad3DLeftTopZeroConvertsCorrectly) {
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// aten::replication_pad3d(Tensor self, int[6] padding) -> Tensor
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const auto graph_IR = gen_replicationpad_graph("replication_pad3d", "2, 0, 2, 0, 1, 0");
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replicationpad_test_helper(graph_IR, {1, 3, 4, 5, 3});
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} |