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https://github.com/ROCm/composable_kernel.git
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Rewrite *sh reduce unit tests to gtest: part 1 (#1407)
* Rewrite .sh test to Gtest * review chnages * Removew unused comments * Review v2 * Typo * Separete UT: AMAX, MAX, MIN; added template params to trigger them * Update test/reduce/reduce_no_index.cpp --------- Co-authored-by: Bartłomiej Kocot <barkocot@amd.com>
This commit is contained in:
Mateusz Ozga
@@ -1,248 +1,203 @@
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// SPDX-License-Identifier: MIT
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// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
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// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
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#include <getopt.h>
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#include "ck/library/utility/host_common_util.hpp"
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#include "profiler/profile_reduce_impl.hpp"
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#include <gtest/gtest.h>
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using namespace ck;
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static struct option long_options[] = {{"inLengths", required_argument, nullptr, 'D'},
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{"reduceDimensions", required_argument, nullptr, 'R'},
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{"scales", required_argument, nullptr, 'S'},
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{"help", no_argument, nullptr, '?'},
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{nullptr, 0, nullptr, 0}};
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class SimpleAppArgs
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struct ReduceParam
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{
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private:
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int option_index = 0;
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public:
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std::vector<size_t> inLengths;
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std::vector<int> reduceDims;
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std::vector<float> scales;
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int data_type;
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int init_method = 1;
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public:
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void show_usage(const char* cmd)
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{
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std::cout << "Usage of " << cmd << std::endl;
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std::cout << "--inLengths or -D, comma separated list of input tensor dimension lengths "
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"(only 4-d tensor supported)"
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<< std::endl;
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std::cout << "--reduceDimensions or -R comma seperated list of dimension indexes to reduce "
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"(only 1 or 3 or 4 dimensions supported)"
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<< std::endl;
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std::cout << "--scales or -S, comma separated two float values for alpha and beta"
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<< std::endl;
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std::cout << "Arg1 -- data type (1: fp32, 3: int8, 5: bp16, 6: fp64)" << std::endl;
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std::cout << "Arg2 -- init method(0=no init, 1=single integer value, 2=scope integer "
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"value, 3=decimal value)"
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<< std::endl;
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};
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int processArgs(int argc, char* argv[])
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{
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using ck::host_common::getTypeValuesFromString;
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int ch;
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while(1)
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{
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ch = getopt_long(argc, argv, "D:R:S:", long_options, &option_index);
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if(ch == -1)
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break;
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switch(ch)
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{
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case 'D':
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if(!optarg)
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throw std::runtime_error("Invalid option format!");
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inLengths = getTypeValuesFromString<size_t>(optarg);
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break;
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case 'R':
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if(!optarg)
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throw std::runtime_error("Invalid option format!");
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reduceDims = getTypeValuesFromString<int>(optarg);
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break;
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case 'S':
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if(!optarg)
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throw std::runtime_error("Invalid option format!");
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scales = getTypeValuesFromString<float>(optarg);
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break;
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case '?':
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if(std::string(long_options[option_index].name) == "help")
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{
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show_usage(argv[0]);
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return (-1);
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};
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break;
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default: show_usage(argv[0]); return (-1);
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};
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};
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if(optind + 2 > argc)
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throw std::runtime_error("Invalid cmd-line arguments, more argumetns are needed!");
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data_type = std::atoi(argv[optind++]);
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init_method = std::atoi(argv[optind]);
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if(scales.empty())
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{
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scales.push_back(1.0f);
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scales.push_back(0.0f);
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};
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if(inLengths.size() != 4 ||
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(reduceDims.size() != 1 && reduceDims.size() != 3 && reduceDims.size() != 4))
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return (-1);
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if(data_type != 0 && data_type != 1 && data_type != 3 && data_type != 5 && data_type != 6)
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return (-1);
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return (0);
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};
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bool do_verification{true};
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bool propagateNan{false};
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bool useIndex{false};
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bool time_kernel{false};
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bool do_dumpout{false};
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int init_method{2};
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float alpha{1.0f};
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float beta{0.0f};
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std::vector<size_t> inLengths{64, 4, 280, 82};
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std::vector<int> reduceDims{0, 1, 2, 3};
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};
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bool test_reduce_with_index(int data_type,
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int init_method,
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std::vector<int> reduceDims,
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std::vector<size_t> inLengths,
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ReduceTensorOp reduceOpId,
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bool propagateNan,
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float alpha,
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float beta)
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std::vector<std::vector<int>> SetGenericReduceDim()
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{
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using ck::profiler::profile_reduce_impl;
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bool result = true;
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if(data_type == 0)
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{
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result = profile_reduce_impl<float, float, float>(true,
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init_method,
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false,
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false,
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inLengths,
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reduceDims,
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reduceOpId,
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propagateNan,
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true,
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alpha,
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beta);
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}
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else if(data_type == 1)
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{
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result = profile_reduce_impl<ck::half_t, ck::half_t, ck::half_t>(true,
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init_method,
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false,
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false,
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inLengths,
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reduceDims,
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reduceOpId,
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propagateNan,
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true,
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alpha,
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beta);
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}
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else if(data_type == 3)
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{
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result = profile_reduce_impl<int8_t, int8_t, int8_t>(true,
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init_method,
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false,
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false,
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inLengths,
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reduceDims,
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reduceOpId,
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propagateNan,
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true,
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alpha,
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beta);
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}
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else if(data_type == 5)
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{
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result = profile_reduce_impl<ck::bhalf_t, float, ck::bhalf_t>(true,
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init_method,
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false,
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false,
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inLengths,
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reduceDims,
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reduceOpId,
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propagateNan,
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true,
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alpha,
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beta);
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}
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else if(data_type == 6)
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{
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result = profile_reduce_impl<double, double, double>(true,
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init_method,
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false,
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false,
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inLengths,
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reduceDims,
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reduceOpId,
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propagateNan,
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true,
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alpha,
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beta);
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}
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return (result);
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};
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constexpr ReduceTensorOp reduceOpId = ReduceTensorOp::AMAX;
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constexpr bool propagateNan = false;
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int main(int argc, char* argv[])
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{
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SimpleAppArgs args;
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bool result = true;
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if(argc == 1)
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{
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int data_type = 1;
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int init_method = 2;
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std::vector<size_t> inLengths{64, 4, 280, 80};
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std::vector<std::vector<int>> v_reduceDims{
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{0, 1, 2, 3}, {0, 1, 2}, {1, 2, 3}, {0, 1, 3}, {0, 2, 3}, {0}, {1}, {2}, {3}};
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for(auto& reduceDims : v_reduceDims)
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result = result && test_reduce_with_index(data_type,
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init_method,
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reduceDims,
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inLengths,
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reduceOpId,
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propagateNan,
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1.0f,
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0.0f);
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}
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else
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{
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if(args.processArgs(argc, argv) < 0)
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{
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throw std::runtime_error(
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"Invalid input arguments, test_reduce_with_index could not be executed!");
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};
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result = test_reduce_with_index(args.data_type,
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args.init_method,
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args.reduceDims,
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args.inLengths,
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reduceOpId,
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propagateNan,
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args.scales[0],
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args.scales[1]);
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}
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std::cout << "test_reduce_with_index ..... " << (result ? "SUCCESS" : "FAILURE") << std::endl;
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return (result ? 0 : -1);
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return {{0, 1, 2, 3}, {0, 1, 2}, {0, 1, 3}, {0, 2, 3}, {1, 2, 3}, {0}, {1}, {2}, {3}};
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}
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template <typename T>
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class ReduceWithIndexTest : public ::testing::Test
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{
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protected:
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using InDataType = std::tuple_element_t<0, T>;
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using AccDataType = std::tuple_element_t<1, T>;
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using OutDataType = std::tuple_element_t<2, T>;
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static std::vector<ReduceParam> params;
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static void SetUpTestSuite()
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{
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// set testcase variables
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ReduceParam set;
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const auto setReduceDim = SetGenericReduceDim();
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for(std::size_t i(0); i < setReduceDim.size(); ++i)
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{
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set.reduceDims = setReduceDim[i];
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params.emplace_back(set);
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}
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}
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template <ReduceTensorOp ReduceOpIdType>
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void Run()
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{
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for(auto param : this->params)
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{
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bool success = ck::profiler::profile_reduce_impl<InDataType, AccDataType, OutDataType>(
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param.do_verification,
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param.init_method,
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param.do_dumpout,
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param.time_kernel,
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param.inLengths,
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param.reduceDims,
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ReduceOpIdType,
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param.propagateNan,
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param.useIndex,
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param.alpha,
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param.beta);
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EXPECT_TRUE(success);
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}
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}
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};
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template <typename T>
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std::vector<ReduceParam> ReduceWithIndexTest<T>::params = {};
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using Reduce_float_types = ::testing::Types<std::tuple<float, float, float>>;
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using Reduce_double_types = ::testing::Types<std::tuple<double, double, double>>;
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using Reduce_int8t_types = ::testing::Types<std::tuple<int8_t, int8_t, int8_t>>;
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using Reduce_half_types = ::testing::Types<std::tuple<ck::half_t, ck::half_t, ck::half_t>>;
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using Reduce_bhalf_float_Types = ::testing::Types<std::tuple<ck::bhalf_t, float, ck::bhalf_t>>;
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template <typename TType>
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class ReduceWithIndexFloat : public ReduceWithIndexTest<TType>
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{
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};
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template <typename TType>
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class ReduceWithIndexDouble : public ReduceWithIndexTest<TType>
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{
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};
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template <typename TType>
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class ReduceWithIndexInt8 : public ReduceWithIndexTest<TType>
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{
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};
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template <typename TType>
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class ReduceWithIndexHalf : public ReduceWithIndexTest<TType>
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{
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};
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template <typename TType>
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class ReduceWithIndexBHalfFloat : public ReduceWithIndexTest<TType>
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{
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};
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TYPED_TEST_SUITE(ReduceWithIndexFloat, Reduce_float_types);
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TYPED_TEST_SUITE(ReduceWithIndexDouble, Reduce_double_types);
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TYPED_TEST_SUITE(ReduceWithIndexInt8, Reduce_int8t_types);
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TYPED_TEST_SUITE(ReduceWithIndexHalf, Reduce_half_types);
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TYPED_TEST_SUITE(ReduceWithIndexBHalfFloat, Reduce_bhalf_float_Types);
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TYPED_TEST(ReduceWithIndexFloat, ReduceWithIndexTestFloat_AMAX)
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{
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// trigger Run() -> Generic
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this->template Run<ReduceTensorOp::AMAX>();
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}
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TYPED_TEST(ReduceWithIndexFloat, ReduceWithIndexTestFloat_MIN)
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{
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// trigger Run() -> Generic
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this->template Run<ReduceTensorOp::MIN>();
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}
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TYPED_TEST(ReduceWithIndexFloat, ReduceWithIndexTestFloat_MAX)
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{
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// trigger Run() -> Generic
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this->template Run<ReduceTensorOp::MAX>();
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}
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TYPED_TEST(ReduceWithIndexDouble, ReduceWithIndexTestDouble_AMAX)
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{
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// trigger Run() -> Generic
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this->template Run<ReduceTensorOp::AMAX>();
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}
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TYPED_TEST(ReduceWithIndexDouble, ReduceWithIndexTestDouble_MIN)
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{
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// trigger Run() -> Generic
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this->template Run<ReduceTensorOp::MIN>();
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}
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TYPED_TEST(ReduceWithIndexDouble, ReduceWithIndexTestDouble_MAX)
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{
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// trigger Run() -> Generic
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this->template Run<ReduceTensorOp::MAX>();
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}
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TYPED_TEST(ReduceWithIndexInt8, ReduceWithIndexTestInt8_AMAX)
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{
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// trigger Run() -> Generic
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this->template Run<ReduceTensorOp::AMAX>();
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}
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TYPED_TEST(ReduceWithIndexInt8, ReduceWithIndexTestInt8_MIN)
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{
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// trigger Run() -> Generic
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this->template Run<ReduceTensorOp::MIN>();
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}
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TYPED_TEST(ReduceWithIndexInt8, ReduceWithIndexTestInt8_MAX)
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{
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// trigger Run() -> Generic
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this->template Run<ReduceTensorOp::MAX>();
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}
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TYPED_TEST(ReduceWithIndexHalf, ReduceWithIndexTestHalf_AMAX)
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{
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// trigger Run() -> Generic
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this->template Run<ReduceTensorOp::AMAX>();
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}
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TYPED_TEST(ReduceWithIndexHalf, ReduceWithIndexTestHalf_MIN)
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{
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// trigger Run() -> Generic
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this->template Run<ReduceTensorOp::MIN>();
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}
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TYPED_TEST(ReduceWithIndexHalf, ReduceWithIndexTestHalf_MAX)
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{
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// trigger Run() -> Generic
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this->template Run<ReduceTensorOp::MAX>();
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}
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TYPED_TEST(ReduceWithIndexBHalfFloat, ReduceWithIndexTesBtHalfFloat_AMAX)
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{
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// trigger Run() -> Generic
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this->template Run<ReduceTensorOp::AMAX>();
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}
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TYPED_TEST(ReduceWithIndexBHalfFloat, ReduceWithIndexTestBHalfFloat_MIN)
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{
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// trigger Run() -> Generic
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this->template Run<ReduceTensorOp::MIN>();
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}
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TYPED_TEST(ReduceWithIndexBHalfFloat, ReduceWithIndexTestBHalfFloat_MAX)
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{
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// trigger Run() -> Generic
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this->template Run<ReduceTensorOp::MAX>();
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}
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