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451 lines
21 KiB
C++
451 lines
21 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 reduce_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/reduce.h"
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#include "poros/util/test_util.h"
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static void reduce_test_helper(const std::string& graph_IR,
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baidu::mirana::poros::IConverter* converter,
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std::vector<int64_t> shape1,
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bool single_input = true,
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std::vector<int64_t> shape2 = {4, 4},
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bool single_output = true,
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bool int_flag = false){
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std::vector<at::Tensor> input_data;
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if(int_flag) {
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auto options_pyt_long = torch::TensorOptions().device(torch::kCUDA, 0).dtype(torch::kLong);
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input_data.push_back(at::randint(1000, shape1, options_pyt_long));
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} else {
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input_data.push_back(at::randn(shape1, {at::kCUDA}));
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}
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if (!single_input){
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input_data.push_back(at::randn(shape2, {at::kCUDA}));
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}
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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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if (single_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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} else {
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ASSERT_EQ(2, graph_output.size());
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ASSERT_EQ(2, poros_output.size());
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}
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for (size_t i = 0; i < graph_output.size(); i++) {
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ASSERT_TRUE(baidu::mirana::poros::testutil::almost_equal(graph_output[i], poros_output[i], 2e-6));
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}
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}
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static std::string gen_basic_graph(const std::string& op) {
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return R"IR(
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graph(%0 : Tensor):
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%1 : None = prim::Constant()
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%2 : Tensor = aten::)IR" +
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op + R"IR((%0, %1)
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return (%2))IR";
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}
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static std::string gen_min_max_graph(const std::string& op) {
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return R"IR(
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graph(%0 : Tensor):
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%1 : Tensor = aten::)IR" +
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op + R"IR((%0)
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return (%1))IR";
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}
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static std::string gen_min_max_other_graph(const std::string& op) {
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return R"IR(
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graph(%0 : Tensor, %1 : Tensor):
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%1 : Tensor = aten::)IR" +
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op + R"IR((%0, %1)
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return (%1))IR";
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}
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static std::string gen_min_max_dim_graph(const std::string& op, const std::string& dim) {
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return R"IR(
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graph(%0 : Tensor):
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%1 : int = prim::Constant[value=)IR" + dim + R"IR(]()
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%2 : bool = prim::Constant[value=0]()
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%3 : Tensor, %4 : Tensor = aten::)IR" + op + R"IR((%0, %1, %2)
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return (%3, %4))IR";
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}
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static std::string gen_argmin_argmax_graph(const std::string& op, const std::string& dim, const std::string& keepdim) {
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return R"IR(
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graph(%0 : Tensor):
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%1 : int = prim::Constant[value=)IR" + dim + R"IR(]()
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%2 : bool = prim::Constant[value=)IR" + keepdim + R"IR(]()
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%3 : Tensor = aten::)IR" + op + R"IR((%0, %1, %2)
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return (%3))IR";
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}
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static std::string gen_argmin_argmax_dim_none_graph(const std::string& op, const std::string& keepdim) {
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return R"IR(
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graph(%0 : Tensor):
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%1 : None = prim::Constant()
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%2 : bool = prim::Constant[value=)IR" + keepdim + R"IR(]()
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%3 : Tensor = aten::)IR" + op + R"IR((%0, %1, %2)
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return (%3))IR";
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}
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static std::string gen_mean_sum_dim_graph(const std::string& op, const std::string& dim, const std::string& keepdim) {
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return R"IR(
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graph(%0 : Tensor):
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%1 : int[] = prim::Constant[value=[)IR" + dim + R"IR(]]()
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%2 : bool = prim::Constant[value=)IR" + keepdim + R"IR(]()
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%3 : None = prim::Constant()
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%4 : Tensor = aten::)IR" + op + R"IR((%0, %1, %2, %3)
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return (%4))IR";
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}
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static std::string gen_prod_dim_graph(const std::string& op, const std::string& dim, const std::string& keepdim) {
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return R"IR(
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graph(%0 : Tensor):
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%1 : int = prim::Constant[value=)IR" + dim + R"IR(]()
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%2 : bool = prim::Constant[value=)IR" + keepdim + R"IR(]()
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%3 : None = prim::Constant()
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%4 : Tensor = aten::)IR" + op + R"IR((%0, %1, %2, %3)
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return (%4))IR";
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}
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TEST(Converters, ATenMeanConvertsCorrectly) {
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// aten::mean(Tensor self, *, ScalarType? dtype=None) -> Tensor
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const auto graph_IR = gen_basic_graph("mean");
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baidu::mirana::poros::MeanConverter meanconverter;
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reduce_test_helper(graph_IR, &meanconverter, {4, 4});
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}
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TEST(Converters, ATenMeanDimConvertsCorrectly) {
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// aten::mean.dim(Tensor self, int[1] dim, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor
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const auto graph_IR = gen_mean_sum_dim_graph("mean", "1", "0");
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baidu::mirana::poros::MeanConverter meanconverter;
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reduce_test_helper(graph_IR, &meanconverter, {4, 4, 4});
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}
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TEST(Converters, ATenMeanMltiDimsConvertsCorrectly) {
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// aten::mean.dim(Tensor self, int[1] dim, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor
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const auto graph_IR = gen_mean_sum_dim_graph("mean", "0, 1", "0");
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baidu::mirana::poros::MeanConverter meanconverter;
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reduce_test_helper(graph_IR, &meanconverter, {4, 4, 4});
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}
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TEST(Converters, ATenMeanKeepDimsConvertsCorrectly) {
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// aten::mean.dim(Tensor self, int[1] dim, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor
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const auto graph_IR = gen_mean_sum_dim_graph("mean", "1", "1");
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baidu::mirana::poros::MeanConverter meanconverter;
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reduce_test_helper(graph_IR, &meanconverter, {4, 4});
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}
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TEST(Converters, ATenMeanDimNegOneIndexConvertsCorrectly) {
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// aten::mean.dim(Tensor self, int[1] dim, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor
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const auto graph_IR = gen_mean_sum_dim_graph("mean", "-1", "0");
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baidu::mirana::poros::MeanConverter meanconverter;
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reduce_test_helper(graph_IR, &meanconverter, {4, 4, 4});
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}
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TEST(Converters, ATenMeanDimNegOneIndexKeepDimsConvertsCorrectly) {
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// aten::mean.dim(Tensor self, int[1] dim, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor
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const auto graph_IR = gen_mean_sum_dim_graph("mean", "-1", "1");
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baidu::mirana::poros::MeanConverter meanconverter;
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reduce_test_helper(graph_IR, &meanconverter, {4, 4, 4});
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}
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TEST(Converters, ATenMeanDimNegIndexConvertsCorrectly) {
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// aten::mean.dim(Tensor self, int[1] dim, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor
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const auto graph_IR = gen_mean_sum_dim_graph("mean", "-2", "0");
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baidu::mirana::poros::MeanConverter meanconverter;
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reduce_test_helper(graph_IR, &meanconverter, {4, 4, 4});
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}
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TEST(Converters, ATenMeanDimNegIndexKeepDimsConvertsCorrectly) {
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// aten::mean.dim(Tensor self, int[1] dim, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor
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const auto graph_IR = gen_mean_sum_dim_graph("mean", "-2", "1");
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baidu::mirana::poros::MeanConverter meanconverter;
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reduce_test_helper(graph_IR, &meanconverter, {4, 4, 4});
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}
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TEST(Converters, ATenSumConvertsCorrectly) {
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// aten::sum(Tensor self, *, ScalarType? dtype=None) -> Tensor
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const auto graph_IR = gen_basic_graph("sum");
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baidu::mirana::poros::SumConverter sumconverter;
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reduce_test_helper(graph_IR, &sumconverter, {4, 4});
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}
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TEST(Converters, ATenSumDimConvertsCorrectly) {
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// aten::sum.dim_IntList(Tensor self, int[1] dim, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor
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const auto graph_IR = gen_mean_sum_dim_graph("sum", "1", "0");
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baidu::mirana::poros::SumConverter sumconverter;
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reduce_test_helper(graph_IR, &sumconverter, {4, 4, 4});
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}
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TEST(Converters, ATenSumMltiDimsConvertsCorrectly) {
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// aten::sum.dim_IntList(Tensor self, int[1] dim, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor
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const auto graph_IR = gen_mean_sum_dim_graph("sum", "0, 1", "0");
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baidu::mirana::poros::SumConverter sumconverter;
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reduce_test_helper(graph_IR, &sumconverter, {4, 4, 4});
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}
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TEST(Converters, ATenSumKeepDimsConvertsCorrectly) {
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// aten::sum.dim_IntList(Tensor self, int[1] dim, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor
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const auto graph_IR = gen_mean_sum_dim_graph("sum", "1", "1");
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baidu::mirana::poros::SumConverter sumconverter;
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reduce_test_helper(graph_IR, &sumconverter, {4, 4});
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}
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TEST(Converters, ATenSumDimNegOneIndexConvertsCorrectly) {
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// aten::sum.dim_IntList(Tensor self, int[1] dim, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor
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const auto graph_IR = gen_mean_sum_dim_graph("sum", "-1", "0");
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baidu::mirana::poros::SumConverter sumconverter;
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reduce_test_helper(graph_IR, &sumconverter, {4, 4, 4});
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}
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TEST(Converters, ATenSumDimNegOneIndexKeepDimsConvertsCorrectly) {
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// aten::sum.dim_IntList(Tensor self, int[1] dim, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor
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const auto graph_IR = gen_mean_sum_dim_graph("sum", "-1", "1");
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baidu::mirana::poros::SumConverter sumconverter;
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reduce_test_helper(graph_IR, &sumconverter, {4, 4, 4});
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}
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TEST(Converters, ATenSumDimNegIndexConvertsCorrectly) {
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// aten::sum.dim_IntList(Tensor self, int[1] dim, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor
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const auto graph_IR = gen_mean_sum_dim_graph("sum", "-2", "0");
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baidu::mirana::poros::SumConverter sumconverter;
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reduce_test_helper(graph_IR, &sumconverter, {4, 4, 4});
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}
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TEST(Converters, ATenSumDimNegIndexKeepDimsConvertsCorrectly) {
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// aten::sum.dim_IntList(Tensor self, int[1] dim, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor
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const auto graph_IR = gen_mean_sum_dim_graph("sum", "-2", "1");
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baidu::mirana::poros::SumConverter sumconverter;
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reduce_test_helper(graph_IR, &sumconverter, {4, 4, 4});
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}
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TEST(Converters, ATenProdConvertsCorrectly) {
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// aten::prod(Tensor self, *, ScalarType? dtype=None) -> Tensor
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const auto graph_IR = gen_basic_graph("prod");
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baidu::mirana::poros::ProdConverter prodconverter;
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reduce_test_helper(graph_IR, &prodconverter, {4, 4});
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}
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TEST(Converters, ATenProdDimConvertsCorrectly) {
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// aten::prod.dim_int(Tensor self, int dim, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor
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const auto graph_IR = gen_prod_dim_graph("prod", "1", "0");
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baidu::mirana::poros::ProdConverter prodconverter;
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reduce_test_helper(graph_IR, &prodconverter, {4, 4, 4});
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}
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TEST(Converters, ATenProdKeepDimsConvertsCorrectly) {
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// aten::prod.dim_int(Tensor self, int dim, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor
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const auto graph_IR = gen_prod_dim_graph("prod", "1", "1");
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baidu::mirana::poros::ProdConverter prodconverter;
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reduce_test_helper(graph_IR, &prodconverter, {4, 4});
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}
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TEST(Converters, ATenMaxConvertsCorrectly) {
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// aten::max(Tensor self) -> Tensor
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const auto graph_IR = gen_min_max_graph("max");
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baidu::mirana::poros::MaxMinConverter maxminconverter;
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reduce_test_helper(graph_IR, &maxminconverter, {4, 4});
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}
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TEST(Converters, ATenMinConvertsCorrectly) {
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// aten::min(Tensor self) -> Tensor
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const auto graph_IR = gen_min_max_graph("min");
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baidu::mirana::poros::MaxMinConverter maxminconverter;
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reduce_test_helper(graph_IR, &maxminconverter, {4, 4});
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}
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TEST(Converters, ATenMaxOtherConvertsCorrectly) {
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// aten::max.other(Tensor self, Tensor other) -> Tensor
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const auto graph_IR = gen_min_max_other_graph("max");
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baidu::mirana::poros::MaxMinConverter maxminconverter;
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reduce_test_helper(graph_IR, &maxminconverter, {4, 4}, false, {4, 4});
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reduce_test_helper(graph_IR, &maxminconverter, {3, 4}, false, {4});
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reduce_test_helper(graph_IR, &maxminconverter, {4}, false, {3, 4});
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reduce_test_helper(graph_IR, &maxminconverter, {4, 1}, false, {1, 4});
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reduce_test_helper(graph_IR, &maxminconverter, {3, 4, 3}, false, {4, 3});
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reduce_test_helper(graph_IR, &maxminconverter, {4, 3}, false, {3, 4, 3});
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}
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TEST(Converters, ATenMinOtherConvertsCorrectly) {
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// aten::min.other(Tensor self, Tensor other) -> Tensor
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const auto graph_IR = gen_min_max_other_graph("min");
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baidu::mirana::poros::MaxMinConverter maxminconverter;
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reduce_test_helper(graph_IR, &maxminconverter, {4, 4}, false, {4, 4});
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reduce_test_helper(graph_IR, &maxminconverter, {3, 4}, false, {4});
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reduce_test_helper(graph_IR, &maxminconverter, {4}, false, {3, 4});
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reduce_test_helper(graph_IR, &maxminconverter, {4, 1}, false, {1, 4});
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reduce_test_helper(graph_IR, &maxminconverter, {3, 4, 3}, false, {4, 3});
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reduce_test_helper(graph_IR, &maxminconverter, {4, 3}, false, {3, 4, 3});
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}
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TEST(Converters, ATenMaxDimConvertsCorrectly) {
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// aten::max.dim(Tensor self, int dim, bool keepdim=False) -> (Tensor values, Tensor indices)
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const auto graph_IR = gen_min_max_dim_graph("max", "0");
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baidu::mirana::poros::MaxMinConverter maxminconverter;
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reduce_test_helper(graph_IR, &maxminconverter, {4, 5, 3}, true, {}, false);
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const auto graph_IR2 = gen_min_max_dim_graph("max", "1");
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reduce_test_helper(graph_IR2, &maxminconverter, {4, 5, 3}, true, {}, false);
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const auto graph_IR3 = gen_min_max_dim_graph("max", "-1");
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reduce_test_helper(graph_IR3, &maxminconverter, {4, 5, 3}, true, {}, false);
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const auto graph_IR4 = gen_min_max_dim_graph("max", "-1");
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reduce_test_helper(graph_IR4, &maxminconverter, {4, 3}, true, {}, false);
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}
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TEST(Converters, ATenMinDimConvertsCorrectly) {
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// aten::min.dim(Tensor self, int dim, bool keepdim=False) -> (Tensor values, Tensor indices)
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const auto graph_IR = gen_min_max_dim_graph("min", "0");
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baidu::mirana::poros::MaxMinConverter maxminconverter;
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reduce_test_helper(graph_IR, &maxminconverter, {4, 5, 3}, true, {}, false);
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const auto graph_IR2 = gen_min_max_dim_graph("min", "1");
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reduce_test_helper(graph_IR2, &maxminconverter, {4, 5, 3}, true, {}, false);
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const auto graph_IR3 = gen_min_max_dim_graph("min", "-1");
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reduce_test_helper(graph_IR3, &maxminconverter, {4, 5, 3}, true, {}, false);
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const auto graph_IR4 = gen_min_max_dim_graph("min", "-1");
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reduce_test_helper(graph_IR4, &maxminconverter, {4, 3}, true, {}, false);
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}
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TEST(Converters, ATenMaxDimDynamicConvertsCorrectly) {
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// aten::max.dim(Tensor self, int dim, bool keepdim=False) -> (Tensor values, Tensor indices)
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const auto graph_IR = gen_min_max_dim_graph("max", "0");
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baidu::mirana::poros::MaxMinConverter maxminconverter;
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std::vector<std::vector<at::Tensor>> prewarm_data = {{}, {}, {}};
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prewarm_data[0].push_back(at::randn({4, 5, 6}, {at::kCUDA}));
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prewarm_data[1].push_back(at::randn({3, 4, 5}, {at::kCUDA}));
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prewarm_data[2].push_back(at::randn({3, 4, 5}, {at::kCUDA}));
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std::vector<at::Tensor> input_data;
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input_data.push_back(at::randn({3, 4, 5}, {at::kCUDA}));
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baidu::mirana::poros::PorosOptions poros_option; // default device GPU
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poros_option.is_dynamic = true;
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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, &maxminconverter,
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input_data, graph_output, poros_output, &prewarm_data));
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ASSERT_EQ(2, graph_output.size());
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ASSERT_EQ(2, 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(baidu::mirana::poros::testutil::almost_equal(graph_output[1], poros_output[1], 2e-6));
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}
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TEST(Converters, ATenMinDimDynamicConvertsCorrectly) {
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// aten::max.dim(Tensor self, int dim, bool keepdim=False) -> (Tensor values, Tensor indices)
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const auto graph_IR = gen_min_max_dim_graph("min", "1");
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baidu::mirana::poros::MaxMinConverter maxminconverter;
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std::vector<std::vector<at::Tensor>> prewarm_data = {{}, {}, {}};
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prewarm_data[0].push_back(at::randn({4, 5, 6}, {at::kCUDA}));
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prewarm_data[1].push_back(at::randn({3, 4, 5}, {at::kCUDA}));
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prewarm_data[2].push_back(at::randn({3, 4, 5}, {at::kCUDA}));
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std::vector<at::Tensor> input_data;
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input_data.push_back(at::randn({3, 4, 5}, {at::kCUDA}));
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baidu::mirana::poros::PorosOptions poros_option; // default device GPU
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poros_option.is_dynamic = true;
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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, &maxminconverter,
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input_data, graph_output, poros_output, &prewarm_data));
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ASSERT_EQ(2, graph_output.size());
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ASSERT_EQ(2, 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(baidu::mirana::poros::testutil::almost_equal(graph_output[1], poros_output[1], 2e-6));
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}
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TEST(Converters, ArgmaxConvertersCorrectly) {
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// aten::argmax(Tensor self, int? dim=None, bool keepdim=False) -> (Tensor)
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baidu::mirana::poros::ArgmaxArgminConverter argmaxargminconverter;
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const auto graph_IR1 = gen_argmin_argmax_graph("argmax", "0", "0");
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reduce_test_helper(graph_IR1, &argmaxargminconverter, {4, 4}, true, {}, true);
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const auto graph_IR2 = gen_argmin_argmax_graph("argmax", "1", "0");
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reduce_test_helper(graph_IR2, &argmaxargminconverter, {4, 4}, true, {}, true);
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const auto graph_IR3 = gen_argmin_argmax_graph("argmax", "2", "0");
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reduce_test_helper(graph_IR3, &argmaxargminconverter, {4, 4, 6}, true, {}, true);
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const auto graph_IR4 = gen_argmin_argmax_graph("argmax", "3", "0");
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reduce_test_helper(graph_IR4, &argmaxargminconverter, {4, 4, 6, 8}, true, {}, true);
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const auto graph_IR5 = gen_argmin_argmax_graph("argmax", "0", "1");
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reduce_test_helper(graph_IR5, &argmaxargminconverter, {4, 4}, true, {}, true);
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const auto graph_IR6 = gen_argmin_argmax_graph("argmax", "1", "1");
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reduce_test_helper(graph_IR6, &argmaxargminconverter, {4, 4}, true, {}, true);
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const auto graph_IR7 = gen_argmin_argmax_graph("argmax", "-1", "1");
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reduce_test_helper(graph_IR7, &argmaxargminconverter, {4, 4}, true, {}, true);
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const auto graph_IR8 = gen_argmin_argmax_graph("argmax", "-1", "0");
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reduce_test_helper(graph_IR8, &argmaxargminconverter, {4, 4}, true, {}, true);
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// test input tensor of int type
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const auto graph_IR9 = gen_argmin_argmax_graph("argmax", "1", "0");
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reduce_test_helper(graph_IR9, &argmaxargminconverter, {4, 4}, true, {}, true, true);
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const auto graph_IR10 = gen_argmin_argmax_graph("argmax", "-1", "0");
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reduce_test_helper(graph_IR10, &argmaxargminconverter, {4, 4}, true, {}, true, true);
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}
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TEST(Converters, ArgminConvertersCorrectly) {
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// aten::argmin(Tensor self, int? dim=None, bool keepdim=False) -> (Tensor)
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baidu::mirana::poros::ArgmaxArgminConverter argmaxargminconverter;
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const auto graph_IR1 = gen_argmin_argmax_graph("argmin", "0", "0");
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reduce_test_helper(graph_IR1, &argmaxargminconverter, {4, 4}, true, {}, true);
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const auto graph_IR2 = gen_argmin_argmax_graph("argmin", "1", "0");
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reduce_test_helper(graph_IR2, &argmaxargminconverter, {4, 4}, true, {}, true);
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const auto graph_IR3 = gen_argmin_argmax_graph("argmin", "2", "0");
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reduce_test_helper(graph_IR3, &argmaxargminconverter, {4, 4, 6}, true, {}, true);
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const auto graph_IR4 = gen_argmin_argmax_graph("argmin", "3", "0");
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reduce_test_helper(graph_IR4, &argmaxargminconverter, {4, 4, 6, 8}, true, {}, true);
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const auto graph_IR5 = gen_argmin_argmax_graph("argmin", "0", "1");
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reduce_test_helper(graph_IR5, &argmaxargminconverter, {4, 4}, true, {}, true);
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const auto graph_IR6 = gen_argmin_argmax_graph("argmin", "1", "1");
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reduce_test_helper(graph_IR6, &argmaxargminconverter, {4, 4}, true, {}, true);
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const auto graph_IR7 = gen_argmin_argmax_graph("argmin", "-1", "1");
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reduce_test_helper(graph_IR7, &argmaxargminconverter, {4, 4}, true, {}, true);
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// test input tensor of int type
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const auto graph_IR9 = gen_argmin_argmax_graph("argmin", "1", "0");
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reduce_test_helper(graph_IR9, &argmaxargminconverter, {4, 4}, true, {}, true, true);
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const auto graph_IR10 = gen_argmin_argmax_graph("argmin", "-1", "0");
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reduce_test_helper(graph_IR10, &argmaxargminconverter, {4, 4}, true, {}, true, true);
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}
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// TODO: to imp dim=None
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// TEST(Converters, ArgmaxNoneDimConvertersCorrectly) {
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// // aten::argmax(Tensor self, int? dim=None, bool keepdim=False) -> (Tensor)
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// baidu::mirana::poros::ArgmaxArgminConverter argmaxargminconverter;
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// const auto graph_IR1 = gen_argmin_argmax_dim_none_graph("argmax", "0");
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// reduce_test_helper(graph_IR1, &argmaxargminconverter, {4, 4}, true, {}, true);
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// }
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