Skip to content

GitLab

Commit e2eb0418 authored by Jun Liu's avatar Jun Liu
Browse files

Merge branch 'develop' into amd-develop

parents f2ba10b8 cbb6f2ab
Hide whitespace changes
Inline Side-by-side
Showing with 336 additions and 426 deletions
test/reduce/reduce_no_index.cpp
View file @ e2eb0418
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
#include <getopt.h>
#include "ck/library/utility/host_common_util.hpp"
#include "profiler/profile_reduce_impl.hpp"
#include <gtest/gtest.h>
using namespace ck;
static struct option long_options[] = {{"inLengths", required_argument, nullptr, 'D'},
{"reduceDimensions", required_argument, nullptr, 'R'},
{"scales", required_argument, nullptr, 'S'},
{"help", no_argument, nullptr, '?'},
{nullptr, 0, nullptr, 0}};
struct ReduceParam
{
bool do_verification{true};
bool propagateNan{false};
bool useIndex{false};
bool time_kernel{false};
bool do_dumpout{false};
int init_method{2};
float alpha{1.0f};
float beta{0.0f};
std::vector<size_t> inLengths{64, 4, 280, 82};
std::vector<int> reduceDims{0, 1, 2, 3};
};
class SimpleAppArgs
std::vector<std::vector<int>> SetGenericReduceDim()
{
private:
int option_index = 0;
return {{0, 1, 2, 3}, {0, 1, 2}, {0, 1, 3}, {0, 2, 3}, {1, 2, 3}, {0}, {1}, {2}, {3}};
}
public:
std::vector<size_t> inLengths;
std::vector<int> reduceDims;
std::vector<float> scales;
template <typename T>
class ReduceWithIndexTest : public ::testing::Test
{
protected:
using InDataType = std::tuple_element_t<0, T>;
using AccDataType = std::tuple_element_t<1, T>;
using OutDataType = std::tuple_element_t<2, T>;
int data_type;
int init_method = 1;
static std::vector<ReduceParam> params;
public:
void show_usage(const char* cmd)
static void SetUpTestSuite()
{
std::cout << "Usage of " << cmd << std::endl;
std::cout << "--inLengths or -D, comma separated list of input tensor dimension lengths "
"(only 4-d tensor supported)"
<< std::endl;
std::cout << "--reduceDimensions or -R comma seperated list of dimension indexes to reduce "
"(only 1 or 3 or 4 dimensions supported)"
<< std::endl;
std::cout << "--scales or -S, comma separated two float values for alpha and beta"
<< std::endl;
std::cout << "Arg1 -- data type (0: fp16, 1: fp32, 3: int8, 5: bp16, 6: fp64)" << std::endl;
std::cout << "Arg2 -- init method(0=no init, 1=single integer value, 2=scope integer "
"value, 3=decimal value)"
<< std::endl;
};
int processArgs(int argc, char* argv[])
{
using ck::host_common::getTypeValuesFromString;
int ch;
// set testcase variables
ReduceParam set;
const auto setReduceDim = SetGenericReduceDim();
while(1)
for(std::size_t i(0); i < setReduceDim.size(); ++i)
{
ch = getopt_long(argc, argv, "D:R:S:", long_options, &option_index);
if(ch == -1)
break;
switch(ch)
{
case 'D':
if(!optarg)
throw std::runtime_error("Invalid option format!");
inLengths = getTypeValuesFromString<size_t>(optarg);
break;
case 'R':
if(!optarg)
throw std::runtime_error("Invalid option format!");
reduceDims = getTypeValuesFromString<int>(optarg);
break;
case 'S':
if(!optarg)
throw std::runtime_error("Invalid option format!");
scales = getTypeValuesFromString<float>(optarg);
break;
case '?':
if(std::string(long_options[option_index].name) == "help")
{
show_usage(argv[0]);
return (-1);
};
break;
default: show_usage(argv[0]); return (-1);
};
};
if(optind + 2 > argc)
throw std::runtime_error("Invalid cmd-line arguments, more argumetns are needed!");
data_type = std::atoi(argv[optind++]);
init_method = std::atoi(argv[optind]);
if(scales.empty())
set.reduceDims = setReduceDim[i];
params.emplace_back(set);
}
}
template <ReduceTensorOp ReduceOpIdType>
void Run()
{
for(auto param : this->params)
{
scales.push_back(1.0f);
scales.push_back(0.0f);
};
bool success = ck::profiler::profile_reduce_impl<InDataType, AccDataType, OutDataType>(
param.do_verification,
param.init_method,
param.do_dumpout,
param.time_kernel,
param.inLengths,
param.reduceDims,
ReduceOpIdType,
param.propagateNan,
param.useIndex,
param.alpha,
param.beta);
EXPECT_TRUE(success);
}
}
};
if(inLengths.size() != 4 ||
(reduceDims.size() != 1 && reduceDims.size() != 3 && reduceDims.size() != 4))
return (-1);
template <typename T>
std::vector<ReduceParam> ReduceWithIndexTest<T>::params = {};
if(data_type != 0 && data_type != 1 && data_type != 3 && data_type != 5 && data_type != 6)
return (-1);
using Reduce_float_types = ::testing::Types<std::tuple<float, float, float>>;
using Reduce_double_types = ::testing::Types<std::tuple<double, double, double>>;
using Reduce_int8t_types = ::testing::Types<std::tuple<int8_t, int8_t, int8_t>>;
using Reduce_half_types = ::testing::Types<std::tuple<ck::half_t, ck::half_t, ck::half_t>>;
using Reduce_bhalf_float_Types = ::testing::Types<std::tuple<ck::bhalf_t, float, ck::bhalf_t>>;
return (0);
};
template <typename TType>
class ReduceWithNoIndexFloat : public ReduceWithIndexTest<TType>
{
};
bool test_reduce_no_index(int data_type,
int init_method,
std::vector<int> reduceDims,
std::vector<size_t> inLengths,
ReduceTensorOp reduceOpId,
bool propagateNan,
float alpha,
float beta)
template <typename TType>
class ReduceWithNoIndexDouble : public ReduceWithIndexTest<TType>
{
using ck::profiler::profile_reduce_impl;
};
bool result = true;
template <typename TType>
class ReduceWithNoIndexInt8 : public ReduceWithIndexTest<TType>
{
};
if(data_type == 0)
{
result = profile_reduce_impl<float, float, float>(true,
init_method,
false,
false,
inLengths,
reduceDims,
reduceOpId,
propagateNan,
false,
alpha,
beta);
}
else if(data_type == 1)
{
result = profile_reduce_impl<ck::half_t, float, ck::half_t>(true,
init_method,
false,
false,
inLengths,
reduceDims,
reduceOpId,
propagateNan,
false,
alpha,
beta);
}
else if(data_type == 3)
{
result = profile_reduce_impl<int8_t, int32_t, int8_t>(true,
init_method,
false,
false,
inLengths,
reduceDims,
reduceOpId,
propagateNan,
false,
alpha,
beta);
}
else if(data_type == 5)
{
result = profile_reduce_impl<ck::bhalf_t, float, ck::bhalf_t>(true,
init_method,
false,
false,
inLengths,
reduceDims,
reduceOpId,
propagateNan,
false,
alpha,
beta);
}
else if(data_type == 6)
{
result = profile_reduce_impl<double, double, double>(true,
init_method,
false,
false,
inLengths,
reduceDims,
reduceOpId,
propagateNan,
false,
alpha,
beta);
}
template <typename TType>
class ReduceWithNoIndexHalf : public ReduceWithIndexTest<TType>
{
};
return (result);
template <typename TType>
class ReduceWithNoIndexBHalfFloat : public ReduceWithIndexTest<TType>
{
};
constexpr ReduceTensorOp reduceOpId = ReduceTensorOp::AVG;
constexpr bool propagateNan = false;
TYPED_TEST_SUITE(ReduceWithNoIndexFloat, Reduce_float_types);
TYPED_TEST_SUITE(ReduceWithNoIndexDouble, Reduce_double_types);
TYPED_TEST_SUITE(ReduceWithNoIndexInt8, Reduce_int8t_types);
TYPED_TEST_SUITE(ReduceWithNoIndexHalf, Reduce_half_types);
TYPED_TEST_SUITE(ReduceWithNoIndexBHalfFloat, Reduce_bhalf_float_Types);
int main(int argc, char* argv[])
TYPED_TEST(ReduceWithNoIndexFloat, ReduceWithNoIndexTestFloat_AMAX)
{
SimpleAppArgs args;
// trigger Run() -> Generic
this->template Run<ReduceTensorOp::AMAX>();
}
bool result = true;
TYPED_TEST(ReduceWithNoIndexFloat, ReduceWithNoIndexTestFloat_MIN)
{
// trigger Run() -> Generic
this->template Run<ReduceTensorOp::MIN>();
}
if(argc == 1)
{
int data_type = 1;
int init_method = 2;
std::vector<size_t> inLengths{64, 4, 280, 80};
std::vector<std::vector<int>> v_reduceDims{
{0, 1, 2, 3}, {0, 1, 2}, {1, 2, 3}, {0, 1, 3}, {0, 2, 3}, {0}, {1}, {2}, {3}};
for(auto& reduceDims : v_reduceDims)
result = result && test_reduce_no_index(data_type,
init_method,
reduceDims,
inLengths,
reduceOpId,
propagateNan,
1.0f,
0.0f);
}
else
{
if(args.processArgs(argc, argv) < 0)
{
throw std::runtime_error(
"Invalid input arguments, test_reduce_no_index could not be executed!");
};
result = test_reduce_no_index(args.data_type,
args.init_method,
args.reduceDims,
args.inLengths,
reduceOpId,
propagateNan,
args.scales[0],
args.scales[1]);
}
TYPED_TEST(ReduceWithNoIndexFloat, ReduceWithNoIndexTestFloat_MAX)
{
// trigger Run() -> Generic
this->template Run<ReduceTensorOp::MAX>();
}
TYPED_TEST(ReduceWithNoIndexDouble, ReduceWithNoIndexTestDouble_AMAX)
{
// trigger Run() -> Generic
this->template Run<ReduceTensorOp::AMAX>();
}
TYPED_TEST(ReduceWithNoIndexDouble, ReduceWithNoIndexTestDouble_MIN)
{
// trigger Run() -> Generic
this->template Run<ReduceTensorOp::MIN>();
}
TYPED_TEST(ReduceWithNoIndexDouble, ReduceWithNoIndexTestDouble_MAX)
{
// trigger Run() -> Generic
this->template Run<ReduceTensorOp::MAX>();
}
TYPED_TEST(ReduceWithNoIndexInt8, ReduceWithNoIndexTestInt8_AMAX)
{
// trigger Run() -> Generic
this->template Run<ReduceTensorOp::AMAX>();
}
TYPED_TEST(ReduceWithNoIndexInt8, ReduceWithNoIndexTestInt8_MIN)
{
// trigger Run() -> Generic
this->template Run<ReduceTensorOp::MIN>();
}
std::cout << "test_reduce_no_index ..... " << (result ? "SUCCESS" : "FAILURE") << std::endl;
TYPED_TEST(ReduceWithNoIndexInt8, ReduceWithNoIndexTestInt8_MAX)
{
// trigger Run() -> Generic
this->template Run<ReduceTensorOp::MAX>();
}
TYPED_TEST(ReduceWithNoIndexHalf, ReduceWithNoIndexTestHalf_AMAX)
{
// trigger Run() -> Generic
this->template Run<ReduceTensorOp::AMAX>();
}
TYPED_TEST(ReduceWithNoIndexHalf, ReduceWithNoIndexTestHalf_MIN)
{
// trigger Run() -> Generic
this->template Run<ReduceTensorOp::MIN>();
}
TYPED_TEST(ReduceWithNoIndexHalf, ReduceWithNoIndexTestHalf_MAX)
{
// trigger Run() -> Generic
this->template Run<ReduceTensorOp::MAX>();
}
TYPED_TEST(ReduceWithNoIndexBHalfFloat, ReduceWithNoIndexTesBtHalfFloat_AMAX)
{
// trigger Run() -> Generic
this->template Run<ReduceTensorOp::AMAX>();
}
return (result ? 0 : -1);
TYPED_TEST(ReduceWithNoIndexBHalfFloat, ReduceWithNoIndexTestBHalfFloat_MIN)
{
// trigger Run() -> Generic
this->template Run<ReduceTensorOp::MIN>();
}
TYPED_TEST(ReduceWithNoIndexBHalfFloat, ReduceWithNoIndexTestBHalfFloat_MAX)
{
// trigger Run() -> Generic
this->template Run<ReduceTensorOp::MAX>();
}
test/reduce/reduce_with_index.cpp
View file @ e2eb0418
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
#include <getopt.h>
#include "ck/library/utility/host_common_util.hpp"
#include "profiler/profile_reduce_impl.hpp"
#include <gtest/gtest.h>
using namespace ck;
static struct option long_options[] = {{"inLengths", required_argument, nullptr, 'D'},
{"reduceDimensions", required_argument, nullptr, 'R'},
{"scales", required_argument, nullptr, 'S'},
{"help", no_argument, nullptr, '?'},
{nullptr, 0, nullptr, 0}};
struct ReduceParam
{
bool do_verification{true};
bool propagateNan{false};
bool useIndex{false};
bool time_kernel{false};
bool do_dumpout{false};
int init_method{2};
float alpha{1.0f};
float beta{0.0f};
std::vector<size_t> inLengths{64, 4, 280, 82};
std::vector<int> reduceDims{0, 1, 2, 3};
};
class SimpleAppArgs
std::vector<std::vector<int>> SetGenericReduceDim()
{
private:
int option_index = 0;
return {{0, 1, 2, 3}, {0, 1, 2}, {0, 1, 3}, {0, 2, 3}, {1, 2, 3}, {0}, {1}, {2}, {3}};
}
public:
std::vector<size_t> inLengths;
std::vector<int> reduceDims;
std::vector<float> scales;
template <typename T>
class ReduceWithIndexTest : public ::testing::Test
{
protected:
using InDataType = std::tuple_element_t<0, T>;
using AccDataType = std::tuple_element_t<1, T>;
using OutDataType = std::tuple_element_t<2, T>;
int data_type;
int init_method = 1;
static std::vector<ReduceParam> params;
public:
void show_usage(const char* cmd)
static void SetUpTestSuite()
{
std::cout << "Usage of " << cmd << std::endl;
std::cout << "--inLengths or -D, comma separated list of input tensor dimension lengths "
"(only 4-d tensor supported)"
<< std::endl;
std::cout << "--reduceDimensions or -R comma seperated list of dimension indexes to reduce "
"(only 1 or 3 or 4 dimensions supported)"
<< std::endl;
std::cout << "--scales or -S, comma separated two float values for alpha and beta"
<< std::endl;
std::cout << "Arg1 -- data type (1: fp32, 3: int8, 5: bp16, 6: fp64)" << std::endl;
std::cout << "Arg2 -- init method(0=no init, 1=single integer value, 2=scope integer "
"value, 3=decimal value)"
<< std::endl;
};
int processArgs(int argc, char* argv[])
{
using ck::host_common::getTypeValuesFromString;
int ch;
// set testcase variables
ReduceParam set;
const auto setReduceDim = SetGenericReduceDim();
while(1)
for(std::size_t i(0); i < setReduceDim.size(); ++i)
{
ch = getopt_long(argc, argv, "D:R:S:", long_options, &option_index);
if(ch == -1)
break;
switch(ch)
{
case 'D':
if(!optarg)
throw std::runtime_error("Invalid option format!");
inLengths = getTypeValuesFromString<size_t>(optarg);
break;
case 'R':
if(!optarg)
throw std::runtime_error("Invalid option format!");
reduceDims = getTypeValuesFromString<int>(optarg);
break;
case 'S':
if(!optarg)
throw std::runtime_error("Invalid option format!");
scales = getTypeValuesFromString<float>(optarg);
break;
case '?':
if(std::string(long_options[option_index].name) == "help")
{
show_usage(argv[0]);
return (-1);
};
break;
default: show_usage(argv[0]); return (-1);
};
};
if(optind + 2 > argc)
throw std::runtime_error("Invalid cmd-line arguments, more argumetns are needed!");
data_type = std::atoi(argv[optind++]);
init_method = std::atoi(argv[optind]);
if(scales.empty())
set.reduceDims = setReduceDim[i];
params.emplace_back(set);
}
}
template <ReduceTensorOp ReduceOpIdType>
void Run()
{
for(auto param : this->params)
{
scales.push_back(1.0f);
scales.push_back(0.0f);
};
bool success = ck::profiler::profile_reduce_impl<InDataType, AccDataType, OutDataType>(
param.do_verification,
param.init_method,
param.do_dumpout,
param.time_kernel,
param.inLengths,
param.reduceDims,
ReduceOpIdType,
param.propagateNan,
param.useIndex,
param.alpha,
param.beta);
EXPECT_TRUE(success);
}
}
};
if(inLengths.size() != 4 ||
(reduceDims.size() != 1 && reduceDims.size() != 3 && reduceDims.size() != 4))
return (-1);
template <typename T>
std::vector<ReduceParam> ReduceWithIndexTest<T>::params = {};
if(data_type != 0 && data_type != 1 && data_type != 3 && data_type != 5 && data_type != 6)
return (-1);
using Reduce_float_types = ::testing::Types<std::tuple<float, float, float>>;
using Reduce_double_types = ::testing::Types<std::tuple<double, double, double>>;
using Reduce_int8t_types = ::testing::Types<std::tuple<int8_t, int8_t, int8_t>>;
using Reduce_half_types = ::testing::Types<std::tuple<ck::half_t, ck::half_t, ck::half_t>>;
using Reduce_bhalf_float_Types = ::testing::Types<std::tuple<ck::bhalf_t, float, ck::bhalf_t>>;
return (0);
};
template <typename TType>
class ReduceWithIndexFloat : public ReduceWithIndexTest<TType>
{
};
bool test_reduce_with_index(int data_type,
int init_method,
std::vector<int> reduceDims,
std::vector<size_t> inLengths,
ReduceTensorOp reduceOpId,
bool propagateNan,
float alpha,
float beta)
template <typename TType>
class ReduceWithIndexDouble : public ReduceWithIndexTest<TType>
{
using ck::profiler::profile_reduce_impl;
};
bool result = true;
template <typename TType>
class ReduceWithIndexInt8 : public ReduceWithIndexTest<TType>
{
};
if(data_type == 0)
{
result = profile_reduce_impl<float, float, float>(true,
init_method,
false,
false,
inLengths,
reduceDims,
reduceOpId,
propagateNan,
true,
alpha,
beta);
}
else if(data_type == 1)
{
result = profile_reduce_impl<ck::half_t, ck::half_t, ck::half_t>(true,
init_method,
false,
false,
inLengths,
reduceDims,
reduceOpId,
propagateNan,
true,
alpha,
beta);
}
else if(data_type == 3)
{
result = profile_reduce_impl<int8_t, int8_t, int8_t>(true,
init_method,
false,
false,
inLengths,
reduceDims,
reduceOpId,
propagateNan,
true,
alpha,
beta);
}
else if(data_type == 5)
{
result = profile_reduce_impl<ck::bhalf_t, float, ck::bhalf_t>(true,
init_method,
false,
false,
inLengths,
reduceDims,
reduceOpId,
propagateNan,
true,
alpha,
beta);
}
else if(data_type == 6)
{
result = profile_reduce_impl<double, double, double>(true,
init_method,
false,
false,
inLengths,
reduceDims,
reduceOpId,
propagateNan,
true,
alpha,
beta);
}
template <typename TType>
class ReduceWithIndexHalf : public ReduceWithIndexTest<TType>
{
};
return (result);
template <typename TType>
class ReduceWithIndexBHalfFloat : public ReduceWithIndexTest<TType>
{
};
constexpr ReduceTensorOp reduceOpId = ReduceTensorOp::AMAX;
constexpr bool propagateNan = false;
TYPED_TEST_SUITE(ReduceWithIndexFloat, Reduce_float_types);
TYPED_TEST_SUITE(ReduceWithIndexDouble, Reduce_double_types);
TYPED_TEST_SUITE(ReduceWithIndexInt8, Reduce_int8t_types);
TYPED_TEST_SUITE(ReduceWithIndexHalf, Reduce_half_types);
TYPED_TEST_SUITE(ReduceWithIndexBHalfFloat, Reduce_bhalf_float_Types);
int main(int argc, char* argv[])
TYPED_TEST(ReduceWithIndexFloat, ReduceWithIndexTestFloat_AMAX)
{
SimpleAppArgs args;
// trigger Run() -> Generic
this->template Run<ReduceTensorOp::AMAX>();
}
bool result = true;
TYPED_TEST(ReduceWithIndexFloat, ReduceWithIndexTestFloat_MIN)
{
// trigger Run() -> Generic
this->template Run<ReduceTensorOp::MIN>();
}
if(argc == 1)
{
int data_type = 1;
int init_method = 2;
std::vector<size_t> inLengths{64, 4, 280, 80};
std::vector<std::vector<int>> v_reduceDims{
{0, 1, 2, 3}, {0, 1, 2}, {1, 2, 3}, {0, 1, 3}, {0, 2, 3}, {0}, {1}, {2}, {3}};
for(auto& reduceDims : v_reduceDims)
result = result && test_reduce_with_index(data_type,
init_method,
reduceDims,
inLengths,
reduceOpId,
propagateNan,
1.0f,
0.0f);
}
else
{
if(args.processArgs(argc, argv) < 0)
{
throw std::runtime_error(
"Invalid input arguments, test_reduce_with_index could not be executed!");
};
result = test_reduce_with_index(args.data_type,
args.init_method,
args.reduceDims,
args.inLengths,
reduceOpId,
propagateNan,
args.scales[0],
args.scales[1]);
}
TYPED_TEST(ReduceWithIndexFloat, ReduceWithIndexTestFloat_MAX)
{
// trigger Run() -> Generic
this->template Run<ReduceTensorOp::MAX>();
}
TYPED_TEST(ReduceWithIndexDouble, ReduceWithIndexTestDouble_AMAX)
{
// trigger Run() -> Generic
this->template Run<ReduceTensorOp::AMAX>();
}
TYPED_TEST(ReduceWithIndexDouble, ReduceWithIndexTestDouble_MIN)
{
// trigger Run() -> Generic
this->template Run<ReduceTensorOp::MIN>();
}
TYPED_TEST(ReduceWithIndexDouble, ReduceWithIndexTestDouble_MAX)
{
// trigger Run() -> Generic
this->template Run<ReduceTensorOp::MAX>();
}
TYPED_TEST(ReduceWithIndexInt8, ReduceWithIndexTestInt8_AMAX)
{
// trigger Run() -> Generic
this->template Run<ReduceTensorOp::AMAX>();
}
TYPED_TEST(ReduceWithIndexInt8, ReduceWithIndexTestInt8_MIN)
{
// trigger Run() -> Generic
this->template Run<ReduceTensorOp::MIN>();
}
std::cout << "test_reduce_with_index ..... " << (result ? "SUCCESS" : "FAILURE") << std::endl;
TYPED_TEST(ReduceWithIndexInt8, ReduceWithIndexTestInt8_MAX)
{
// trigger Run() -> Generic
this->template Run<ReduceTensorOp::MAX>();
}
TYPED_TEST(ReduceWithIndexHalf, ReduceWithIndexTestHalf_AMAX)
{
// trigger Run() -> Generic
this->template Run<ReduceTensorOp::AMAX>();
}
TYPED_TEST(ReduceWithIndexHalf, ReduceWithIndexTestHalf_MIN)
{
// trigger Run() -> Generic
this->template Run<ReduceTensorOp::MIN>();
}
TYPED_TEST(ReduceWithIndexHalf, ReduceWithIndexTestHalf_MAX)
{
// trigger Run() -> Generic
this->template Run<ReduceTensorOp::MAX>();
}
TYPED_TEST(ReduceWithIndexBHalfFloat, ReduceWithIndexTesBtHalfFloat_AMAX)
{
// trigger Run() -> Generic
this->template Run<ReduceTensorOp::AMAX>();
}
return (result ? 0 : -1);
TYPED_TEST(ReduceWithIndexBHalfFloat, ReduceWithIndexTestBHalfFloat_MIN)
{
// trigger Run() -> Generic
this->template Run<ReduceTensorOp::MIN>();
}
TYPED_TEST(ReduceWithIndexBHalfFloat, ReduceWithIndexTestBHalfFloat_MAX)
{
// trigger Run() -> Generic
this->template Run<ReduceTensorOp::MAX>();
}
Markdown is supported
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment