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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>
91 lines
3.5 KiB
C++
91 lines
3.5 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 concat_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/concat.h"
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#include "poros/util/test_util.h"
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static void cat_test_helper(const std::string& graph_IR,
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std::vector<int64_t> shape1 = {5},
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std::vector<int64_t> shape2 = {5},
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bool Triple_inputs = false,
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std::vector<int64_t> shape3 = {5}){
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std::vector<at::Tensor> input_data;
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input_data.push_back(at::randn(shape1, {at::kCUDA}));
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input_data.push_back(at::randn(shape2, {at::kCUDA}));
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if (Triple_inputs){
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input_data.push_back(at::randn(shape3, {at::kCUDA}));
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}
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baidu::mirana::poros::PorosOptions poros_option; // default device GPU
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baidu::mirana::poros::ConcatConverter concatconverter;
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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, &concatconverter,
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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(graph_output[0].equal(poros_output[0]));
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}
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static std::string gen_double_inputs_cat_graph(const std::string& dim) {
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return R"IR(
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graph(%0 : Tensor, %1 : Tensor):
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%2 : Tensor[] = prim::ListConstruct(%0, %1)
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%3 : int = prim::Constant[value=)IR" + dim + R"IR(]()
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%4 : Tensor = aten::cat(%2, %3)
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return (%4))IR";
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}
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static std::string gen_triple_inputs_cat_graph(const std::string& dim) {
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return R"IR(
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graph(%0 : Tensor, %1 : Tensor, %2 : Tensor):
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%3 : Tensor[] = prim::ListConstruct(%0, %1, %2)
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%4 : int = prim::Constant[value=)IR" + dim + R"IR(]()
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%5 : Tensor = aten::cat(%3, %4)
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return (%5))IR";
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}
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TEST(Converters, ATenCatPureTensorConvertsCorrectly) {
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// aten::cat(Tensor[] tensors, int dim=0) -> Tensor
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const auto graph_IR = gen_double_inputs_cat_graph("0");
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cat_test_helper(graph_IR);
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}
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TEST(Converters, ATenCatPureTensorNegDimConvertsCorrectly) {
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// aten::cat(Tensor[] tensors, int dim=0) -> Tensor
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const auto graph_IR = gen_double_inputs_cat_graph("-1");
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cat_test_helper(graph_IR, {5, 3}, {5, 4});
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}
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TEST(Converters, ATenCatTripleTensorConvertsCorrectly) {
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// aten::cat(Tensor[] tensors, int dim=0) -> Tensor
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const auto graph_IR = gen_triple_inputs_cat_graph("1");
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cat_test_helper(graph_IR, {5, 2, 2}, {5, 7, 2}, true, {5, 3, 2});
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}
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TEST(Converters, ATenCatTripleTensorNegdimConvertsCorrectly) {
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// aten::cat(Tensor[] tensors, int dim=0) -> Tensor
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const auto graph_IR = gen_triple_inputs_cat_graph("-1");
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cat_test_helper(graph_IR, {5, 6, 7}, {5, 6, 3}, true, {5, 6, 5});
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} |