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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>
79 lines
3.0 KiB
C++
79 lines
3.0 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 roll_test.cpp
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* @author tianshaoqing@baidu.com
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* @date Wed Jul 20 19:34:51 CST 2022
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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/roll.h"
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#include "poros/util/test_util.h"
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static void roll_test_helper(const std::string& graph_IR,
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std::vector<int64_t> shape,
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bool is_dynamic = false,
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std::vector<std::vector<at::Tensor>>* prewarm_data = nullptr) {
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std::vector<at::Tensor> input_data;
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int64_t shape_mul = 1;
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for (int64_t& s : shape) {
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shape_mul *= s;
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}
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input_data.push_back(at::randint(0, shape_mul, shape, {at::kCUDA}));
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baidu::mirana::poros::RollConverter rollconverter;
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baidu::mirana::poros::PorosOptions poros_option; // default device GPU
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poros_option.is_dynamic = is_dynamic;
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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, &rollconverter,
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input_data, graph_output, poros_output, prewarm_data));
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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(graph_output[0].equal(poros_output[0]));
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}
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static std::string gen_roll_graph(const std::string& shifts, const std::string& dims) {
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return R"IR(
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graph(%0 : Tensor):
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%1 : int[] = prim::Constant[value=)IR" + shifts + R"IR(]()
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%2 : int[] = prim::Constant[value=)IR" + dims + R"IR(]()
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%3 : Tensor = aten::roll(%0, %1, %2)
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return (%3))IR";
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}
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TEST(Converters, ATenRollConvertsCorrectly) {
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// aten::roll(Tensor self, int[1] shifts, int[1] dims=[]) -> (Tensor)
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const std::string graph_IR = gen_roll_graph("[-1, 0, -2, 3]", "[0, 1, 2, 3]");
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roll_test_helper(graph_IR, {4, 4, 4, 4});
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}
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TEST(Converters, ATenRollConvertsCorrectlyShiftsGreaterThanDims) {
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// aten::roll(Tensor self, int[1] shifts, int[1] dims=[]) -> (Tensor)
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const std::string graph_IR = gen_roll_graph("[-99, 100, 51, -21]", "[0, 1, 2, 3]");
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roll_test_helper(graph_IR, {4, 4, 4, 4});
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}
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TEST(Converters, ATenRollConvertsCorrectlyShiftSomeDims) {
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// aten::roll(Tensor self, int[1] shifts, int[1] dims=[]) -> (Tensor)
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const std::string graph_IR = gen_roll_graph("[0, -2, 3]", "[0, 1, 3]");
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roll_test_helper(graph_IR, {4, 4, 4, 4});
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} |