mirror of
https://github.com/ROCm/composable_kernel.git
synced 2026-03-23 16:47:40 +00:00
211d64e18a
--------- Co-authored-by: Ding, Yi <yi.ding@amd.com> Co-authored-by: felix <felix.li@amd.com>
531 lines
22 KiB
C++
531 lines
22 KiB
C++
// SPDX-License-Identifier: MIT
|
|
// Copyright (c) 2024-2025, Advanced Micro Devices, Inc. All rights reserved.
|
|
|
|
#include <hip/hip_runtime.h>
|
|
|
|
#include <cstring>
|
|
#include <iostream>
|
|
#include <ostream>
|
|
#include <string>
|
|
#include <tuple>
|
|
|
|
#include "ck_tile/host.hpp"
|
|
#include "flatmm_basic.hpp"
|
|
#include <type_traits>
|
|
|
|
template <typename T>
|
|
constexpr const char* DataTypeToString()
|
|
{
|
|
if constexpr(std::is_same_v<T, ck_tile::half_t>)
|
|
{
|
|
return "fp16";
|
|
}
|
|
else if constexpr(std::is_same_v<T, ck_tile::fp8_t>)
|
|
{
|
|
return "fp8";
|
|
}
|
|
else if constexpr(std::is_same_v<T, ck_tile::bf8_t>)
|
|
{
|
|
return "bf8";
|
|
}
|
|
else if constexpr(std::is_same_v<T, ck_tile::bf16_t>)
|
|
{
|
|
return "bf16";
|
|
}
|
|
else
|
|
{
|
|
return "unknown";
|
|
}
|
|
}
|
|
|
|
template <typename Layout>
|
|
static constexpr inline auto is_row_major(Layout layout_)
|
|
{
|
|
return ck_tile::bool_constant<std::is_same_v<ck_tile::remove_cvref_t<decltype(layout_)>,
|
|
ck_tile::tensor_layout::gemm::RowMajor>>{};
|
|
}
|
|
|
|
// mfma_type, 0:32x32, 1:16x16
|
|
template <typename FlatmmConfig, typename T>
|
|
auto shuffle_b(const ck_tile::HostTensor<T>& t)
|
|
{
|
|
assert(t.get_lengths().size() == 2);
|
|
int n_ = t.get_lengths()[1];
|
|
int k_ = t.get_lengths()[0];
|
|
|
|
constexpr int MaxVecSize = 16 / sizeof(T);
|
|
constexpr int KLane = ck_tile::get_warp_size() / FlatmmConfig::N_Warp_Tile;
|
|
constexpr int ItemsPerAccess = std::min(MaxVecSize, FlatmmConfig::K_Warp_Tile / KLane);
|
|
|
|
ck_tile::HostTensor<T> t_view({n_ / FlatmmConfig::N_Warp_Tile,
|
|
FlatmmConfig::N_Warp_Tile,
|
|
k_ / ItemsPerAccess,
|
|
ItemsPerAccess});
|
|
std::copy(t.begin(), t.end(), t_view.begin());
|
|
return ck_tile::reference_permute(t_view, {0, 2, 1, 3});
|
|
}
|
|
|
|
template <typename FlatmmConfig, typename T>
|
|
auto shuffle_b_v1(const ck_tile::HostTensor<T>& t)
|
|
{
|
|
assert(t.get_lengths().size() == 2);
|
|
int n_ = t.get_lengths()[1];
|
|
int k_ = t.get_lengths()[0];
|
|
|
|
constexpr int MaxVecSize = 16 / sizeof(T);
|
|
constexpr int KLane = ck_tile::get_warp_size() / FlatmmConfig::N_Warp_Tile;
|
|
constexpr int ItemsPerAccess = std::min(MaxVecSize, FlatmmConfig::K_Warp_Tile / KLane);
|
|
constexpr int NRepeat = FlatmmConfig::N_Tile / FlatmmConfig::N_Warp_Tile / FlatmmConfig::N_Warp;
|
|
|
|
ck_tile::HostTensor<T> t_view({n_ / FlatmmConfig::N_Tile,
|
|
FlatmmConfig::N_Warp,
|
|
FlatmmConfig::N_Warp_Tile,
|
|
NRepeat,
|
|
k_ / ItemsPerAccess,
|
|
ItemsPerAccess});
|
|
std::copy(t.begin(), t.end(), t_view.begin());
|
|
return ck_tile::reference_permute(t_view, {0, 3, 1, 4, 2, 5});
|
|
}
|
|
|
|
template <typename ADataType, typename BDataType, typename AccDataType, typename CDataType>
|
|
auto calculate_rtol_atol(const ck_tile::index_t K,
|
|
const ck_tile::index_t kbatch,
|
|
const float max_accumulated_value)
|
|
{
|
|
using ComputeType =
|
|
std::conditional_t<sizeof(ADataType) < sizeof(BDataType), ADataType, BDataType>;
|
|
// Calculate thresholds
|
|
const auto rtol = ck_tile::get_relative_threshold<ComputeType, CDataType, AccDataType>(
|
|
ck_tile::integer_divide_ceil(K, kbatch));
|
|
const auto atol = ck_tile::get_absolute_threshold<ComputeType, CDataType, AccDataType>(
|
|
max_accumulated_value / kbatch, ck_tile::integer_divide_ceil(K, kbatch));
|
|
// Calculate error due to split_k accumulation
|
|
const auto rtol_split_k =
|
|
ck_tile::get_relative_threshold<CDataType, CDataType, CDataType>(kbatch);
|
|
const auto atol_split_k = ck_tile::get_absolute_threshold<CDataType, CDataType, CDataType>(
|
|
max_accumulated_value, kbatch);
|
|
// Use higher threshold
|
|
return ck_tile::make_tuple(std::max(rtol, rtol_split_k), std::max(atol, atol_split_k));
|
|
}
|
|
|
|
template <typename FlatmmConfig,
|
|
typename ADataType,
|
|
typename BDataType,
|
|
typename DsDatatype,
|
|
typename AccDataType,
|
|
typename CDataType,
|
|
typename ALayout,
|
|
typename BLayout,
|
|
typename DsLayout,
|
|
typename ELayout,
|
|
typename ScaleM,
|
|
typename ScaleN,
|
|
bool persistent,
|
|
typename CDEElementWise>
|
|
float flatmm_calc(const ck_tile::ScaleFlatmmHostArgs<ScaleM, ScaleN>& args,
|
|
const ck_tile::stream_config& s)
|
|
{
|
|
using CodegenFlatmmShape = ck_tile::TileGemmShape<
|
|
ck_tile::sequence<FlatmmConfig::M_Tile, FlatmmConfig::N_Tile, FlatmmConfig::K_Tile>,
|
|
ck_tile::sequence<FlatmmConfig::M_Warp, FlatmmConfig::N_Warp, FlatmmConfig::K_Warp>,
|
|
ck_tile::sequence<FlatmmConfig::M_Warp_Tile,
|
|
FlatmmConfig::N_Warp_Tile,
|
|
FlatmmConfig::K_Warp_Tile>>;
|
|
|
|
using TilePartitioner =
|
|
ck_tile::GemmSpatiallyLocalTilePartitioner<CodegenFlatmmShape,
|
|
FlatmmConfig::TileParitionerGroupNum,
|
|
FlatmmConfig::TileParitionerM01>;
|
|
|
|
using Traits = ck_tile::TileGemmTraits<FlatmmConfig::kPadM,
|
|
FlatmmConfig::kPadN,
|
|
FlatmmConfig::kPadK,
|
|
ALayout,
|
|
BLayout,
|
|
ELayout,
|
|
FlatmmConfig::NumWaveGroups>;
|
|
|
|
using CodegenGemmTraits = ck_tile::TileGemmUniversalTraits<FlatmmConfig::kPadM,
|
|
FlatmmConfig::kPadN,
|
|
FlatmmConfig::kPadK,
|
|
FlatmmConfig::DoubleSmemBuffer,
|
|
ALayout,
|
|
BLayout,
|
|
ELayout,
|
|
FlatmmConfig::TransposeC,
|
|
FlatmmConfig::UseStructuredSparsity,
|
|
persistent,
|
|
FlatmmConfig::NumWaveGroups,
|
|
true>;
|
|
|
|
using GemmPipelineProblem =
|
|
ck_tile::GemmPipelineProblem<ADataType, BDataType, AccDataType, CodegenFlatmmShape, Traits>;
|
|
|
|
using BaseGemmPipeline = ck_tile::BaseFlatmmPipelineAGmemBGmemCRegV1<GemmPipelineProblem>;
|
|
|
|
const ck_tile::index_t k_grain = args.k_batch * FlatmmConfig::K_Tile;
|
|
const ck_tile::index_t K_split = (args.K + k_grain - 1) / k_grain * FlatmmConfig::K_Tile;
|
|
const ck_tile::index_t num_loop = TilePartitioner::GetLoopNum(K_split);
|
|
const bool has_hot_loop = BaseGemmPipeline::BlockHasHotloop(num_loop);
|
|
const ck_tile::TailNumber tail_num = BaseGemmPipeline::GetBlockLoopTailNum(num_loop);
|
|
float ave_time{0};
|
|
|
|
const auto Run = [&](const auto has_hot_loop_,
|
|
const auto tail_number_,
|
|
const auto memory_operation_) {
|
|
constexpr bool has_hot_loop_v = has_hot_loop_.value;
|
|
constexpr auto tail_number_v = tail_number_.value;
|
|
constexpr auto scheduler = FlatmmConfig::Scheduler;
|
|
constexpr auto memory_operation = memory_operation_.value;
|
|
|
|
using CodegenPipelineProblem = ck_tile::FlatmmPipelineProblem<ADataType,
|
|
BDataType,
|
|
AccDataType,
|
|
CodegenFlatmmShape,
|
|
CodegenGemmTraits,
|
|
scheduler,
|
|
has_hot_loop_v,
|
|
tail_number_v>;
|
|
|
|
using CodegenFlatmmPipeline =
|
|
ck_tile::FlatmmPipelineAGmemBGmemCRegV1<CodegenPipelineProblem>;
|
|
|
|
using GemmEpilogue = ck_tile::CShuffleEpilogue<
|
|
ck_tile::CShuffleEpilogueProblem<ADataType,
|
|
BDataType,
|
|
DsDatatype,
|
|
AccDataType,
|
|
CDataType,
|
|
DsLayout,
|
|
ELayout,
|
|
CDEElementWise,
|
|
TilePartitioner::MPerBlock,
|
|
TilePartitioner::NPerBlock,
|
|
FlatmmConfig::M_Warp,
|
|
FlatmmConfig::N_Warp,
|
|
FlatmmConfig::M_Warp_Tile,
|
|
FlatmmConfig::N_Warp_Tile,
|
|
FlatmmConfig::K_Warp_Tile,
|
|
CodegenPipelineProblem::TransposeC,
|
|
memory_operation,
|
|
FlatmmConfig::NumWaveGroups,
|
|
false,
|
|
1,
|
|
FlatmmConfig::TiledMMAPermuteN>>;
|
|
|
|
// ToDo: Will add the codegen part to test different pipeline policies in GEMM.
|
|
// Now we only use the BlockGemmASmemBSmemCRegV1DefaultPolicy.
|
|
using Kernel = ck_tile::FlatmmKernel<TilePartitioner, CodegenFlatmmPipeline, GemmEpilogue>;
|
|
|
|
auto kargs = Kernel::MakeKernelArgs(args);
|
|
|
|
const dim3 grids = Kernel::GridSize(kargs);
|
|
constexpr dim3 blocks = Kernel::BlockSize();
|
|
|
|
if(!Kernel::IsSupportedArgument(kargs))
|
|
{
|
|
throw std::runtime_error("Wrong! Arguments not supported! Skipping gemm!\n");
|
|
}
|
|
|
|
if(s.log_level_ > 0)
|
|
{
|
|
std::cout << "Launching kernel with args:" << CodegenFlatmmShape::GetName() << "\n"
|
|
<< "Shape: " << CodegenFlatmmShape::GetName() << "\n"
|
|
<< "problem: " << CodegenPipelineProblem::GetName() << "\n"
|
|
<< "pipeline: " << CodegenFlatmmPipeline::GetName() << "\n"
|
|
<< "grid: {" << grids.x << ", " << grids.y << ", " << grids.z << "}"
|
|
<< ", blocks: {" << blocks.x << ", " << blocks.y << ", " << blocks.z << "}"
|
|
<< std::endl;
|
|
}
|
|
|
|
if(s.flush_cache_)
|
|
{
|
|
std::cout << "Flushing cache..." << std::endl;
|
|
static constexpr ck_tile::index_t APackedSize =
|
|
std::is_same_v<BDataType, ck_tile::pk_int4_t> ? 2 : 1;
|
|
static constexpr ck_tile::index_t BPackedSize =
|
|
std::is_same_v<BDataType, ck_tile::pk_int4_t> ? 2 : 1;
|
|
|
|
ck_tile::HostTensor<ADataType> a_m(ck_tile::host_tensor_descriptor(
|
|
args.M, args.K, args.stride_A, is_row_major(ALayout{})));
|
|
ck_tile::HostTensor<BDataType> b_n(ck_tile::host_tensor_descriptor(
|
|
args.K, args.N, args.stride_B, is_row_major(BLayout{})));
|
|
|
|
auto size_a_buffer = a_m.get_element_space_size_in_bytes() / APackedSize;
|
|
auto size_b_buffer = b_n.get_element_space_size_in_bytes() / BPackedSize;
|
|
|
|
ck_tile::RotatingMemWrapper<ADataType, BDataType> rotating_mem(
|
|
kargs.a_ptr, kargs.b_ptr, s.rotating_count_, size_a_buffer, size_b_buffer);
|
|
rotating_mem.Print();
|
|
|
|
auto run_flush_cache = [&]() {
|
|
// flush icache
|
|
ck_tile::flush_icache();
|
|
// rotating mem
|
|
rotating_mem.Next();
|
|
// clear c mem
|
|
if(args.k_batch > 1)
|
|
hipGetErrorString(hipMemsetAsync(
|
|
args.e_ptr, 0, args.M * args.N * sizeof(CDataType), s.stream_id_));
|
|
};
|
|
ave_time = ck_tile::launch_kernel_time_mask(
|
|
s,
|
|
run_flush_cache,
|
|
ck_tile::make_kernel<FlatmmConfig::kBlockPerCu>(Kernel{}, grids, blocks, 0, kargs));
|
|
}
|
|
else
|
|
{
|
|
ave_time = ck_tile::launch_kernel(
|
|
s,
|
|
ck_tile::make_kernel<FlatmmConfig::kBlockPerCu>(Kernel{}, grids, blocks, 0, kargs));
|
|
}
|
|
return ave_time;
|
|
};
|
|
|
|
const auto RunSplitk = [&](const auto has_hot_loop_, const auto tail_number_) {
|
|
if(args.k_batch == 1)
|
|
{
|
|
Run(has_hot_loop_,
|
|
tail_number_,
|
|
ck_tile::integral_constant<ck_tile::memory_operation_enum,
|
|
ck_tile::memory_operation_enum::set>{});
|
|
}
|
|
else
|
|
{
|
|
Run(has_hot_loop_,
|
|
tail_number_,
|
|
ck_tile::integral_constant<ck_tile::memory_operation_enum,
|
|
ck_tile::memory_operation_enum::atomic_add>{});
|
|
}
|
|
};
|
|
BaseGemmPipeline::TailHandler(RunSplitk, has_hot_loop, tail_num);
|
|
return ave_time;
|
|
}
|
|
|
|
template <typename FlatmmConfig,
|
|
typename ADataType,
|
|
typename BDataType,
|
|
typename DsDatatype,
|
|
typename AccDataType,
|
|
typename CDataType,
|
|
typename ALayout,
|
|
typename BLayout,
|
|
typename DsLayout,
|
|
typename CLayout,
|
|
typename ScaleM,
|
|
typename ScaleN,
|
|
bool UsePersistentKernel = false,
|
|
typename CDEElementWise = ck_tile::element_wise::PassThrough>
|
|
float invoke_flatmm(ck_tile::DeviceMem& a_dev_buf,
|
|
ck_tile::DeviceMem& b_shuffle_dev_buf,
|
|
ck_tile::DeviceMem& c_dev_buf,
|
|
ck_tile::index_t M,
|
|
ck_tile::index_t N,
|
|
ck_tile::index_t K,
|
|
ck_tile::index_t stride_A,
|
|
ck_tile::index_t stride_B,
|
|
ck_tile::index_t stride_C,
|
|
ck_tile::index_t kbatch,
|
|
ScaleM scale_m,
|
|
ScaleN scale_n,
|
|
int n_warmup,
|
|
int n_repeat)
|
|
{
|
|
ck_tile::ScaleFlatmmHostArgs<ScaleM, ScaleN> args = {a_dev_buf.GetDeviceBuffer(),
|
|
b_shuffle_dev_buf.GetDeviceBuffer(),
|
|
{},
|
|
c_dev_buf.GetDeviceBuffer(),
|
|
kbatch,
|
|
M,
|
|
N,
|
|
K,
|
|
stride_A,
|
|
stride_B,
|
|
{},
|
|
stride_C,
|
|
scale_m,
|
|
scale_n};
|
|
|
|
float ave_time = flatmm_calc<FlatmmConfig,
|
|
ADataType,
|
|
BDataType,
|
|
DsDatatype,
|
|
AccDataType,
|
|
CDataType,
|
|
ALayout,
|
|
BLayout,
|
|
DsLayout,
|
|
CLayout,
|
|
ScaleM,
|
|
ScaleN,
|
|
UsePersistentKernel,
|
|
CDEElementWise>(
|
|
args, ck_tile::stream_config{nullptr, true, 1, n_warmup, n_repeat, true, true, 50});
|
|
|
|
std::size_t flop = std::size_t(2) * M * N * K;
|
|
std::size_t num_byte =
|
|
sizeof(ADataType) * M * K + sizeof(BDataType) * N * K + sizeof(CDataType) * M * N;
|
|
float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
|
|
float gb_per_sec = num_byte / 1.E6 / ave_time;
|
|
|
|
std::cout << "Run Flatmm kernel with DataType = " << DataTypeToString<ADataType>()
|
|
<< " M =" << M << " N =" << N << " K =" << K << " StrideA =" << stride_A
|
|
<< " StrideB =" << stride_B << " StrideC =" << stride_C << " : " << ave_time
|
|
<< " ms, " << tflops << " TFlops, " << gb_per_sec << " GB/s, " << std::endl;
|
|
|
|
return ave_time;
|
|
}
|
|
|
|
auto create_args(int argc, char* argv[])
|
|
{
|
|
ck_tile::ArgParser arg_parser;
|
|
arg_parser.insert("m", "256", "m dimension")
|
|
.insert("n", "256", "n dimension")
|
|
.insert("k", "128", "k dimension")
|
|
.insert("a_layout", "R", "A tensor data layout - Row by default")
|
|
.insert("b_layout", "C", "B tensor data layout - Row by default")
|
|
.insert("c_layout", "R", "C tensor data layout - Row by default")
|
|
.insert("stride_a", "0", "Tensor A stride")
|
|
.insert("stride_b", "0", "Tensor B stride")
|
|
.insert("stride_c", "0", "Tensor C stride")
|
|
.insert("v", "1", "0. No validation, 1. Validation on CPU, 2. Validation on GPU")
|
|
.insert("prec", "fp8", "data type. fp16/bf16/fp8/bf8")
|
|
.insert("wave_tile", "16", "only support 16(16x16) or 32(32x32)")
|
|
.insert("warmup", "50", "number of iterations before benchmark the kernel")
|
|
.insert("repeat", "100", "number of iterations to benchmark the kernel")
|
|
.insert("timer", "gpu", "gpu:gpu timer, cpu:cpu timer")
|
|
.insert("split_k", "1", "splitK value")
|
|
.insert("init", "0", "0:random, 1:linear, 2:constant(1)")
|
|
.insert("scale", "0", "0:without scale, 1:per-token/channel scale, only for fp8/bf8")
|
|
.insert("persistent", "0", "0: no persistent, 1: persistent kernel")
|
|
.insert("warp_tile",
|
|
"0",
|
|
"0: 16x16, 1: 32x32, 2: 16x16x128 (950 only), 3: 32x32x64 (950 only)");
|
|
bool result = arg_parser.parse(argc, argv);
|
|
return std::make_tuple(result, arg_parser);
|
|
}
|
|
|
|
#include "run_flatmm_example.inc"
|
|
|
|
template <template <typename PreType> typename FlatmmConfig>
|
|
int run_flatmm_example(int argc, char* argv[])
|
|
{
|
|
auto [result, arg_parser] = create_args(argc, argv);
|
|
if(!result)
|
|
return -1;
|
|
|
|
using Row = ck_tile::tensor_layout::gemm::RowMajor;
|
|
using Col = ck_tile::tensor_layout::gemm::ColumnMajor;
|
|
|
|
std::string data_type = arg_parser.get_str("prec");
|
|
std::string a_layout = arg_parser.get_str("a_layout");
|
|
std::string b_layout = arg_parser.get_str("b_layout");
|
|
int scale_opt = arg_parser.get_int("scale");
|
|
int persistent_opt = arg_parser.get_int("persistent");
|
|
if(a_layout == "R" && b_layout == "C")
|
|
{
|
|
if(data_type == "fp16")
|
|
{
|
|
run_flatmm_example_with_layouts<ck_tile::half_t, FlatmmConfig<ck_tile::half_t>>(
|
|
argc, argv, Row{}, Col{}, Row{});
|
|
}
|
|
else if(data_type == "bf16")
|
|
{
|
|
run_flatmm_example_with_layouts<ck_tile::bf16_t, FlatmmConfig<ck_tile::bf16_t>>(
|
|
argc, argv, Row{}, Col{}, Row{});
|
|
}
|
|
else if(data_type == "fp8")
|
|
{
|
|
if(scale_opt == 0)
|
|
{
|
|
if(persistent_opt == 0)
|
|
{
|
|
run_flatmm_example_with_layouts<ck_tile::fp8_t, FlatmmConfig<ck_tile::fp8_t>>(
|
|
argc, argv, Row{}, Col{}, Row{});
|
|
}
|
|
else
|
|
{
|
|
run_flatmm_example_with_layouts<ck_tile::fp8_t,
|
|
FlatmmConfig<ck_tile::fp8_t>,
|
|
-1,
|
|
-1,
|
|
true>(argc, argv, Row{}, Col{}, Row{});
|
|
}
|
|
}
|
|
else
|
|
{
|
|
if(persistent_opt == 0)
|
|
{
|
|
run_flatmm_example_with_layouts<ck_tile::fp8_t,
|
|
FlatmmConfig<ck_tile::fp8_t>,
|
|
1,
|
|
1>(argc, argv, Row{}, Col{}, Row{});
|
|
}
|
|
else
|
|
{
|
|
run_flatmm_example_with_layouts<ck_tile::fp8_t,
|
|
FlatmmConfig<ck_tile::fp8_t>,
|
|
1,
|
|
1,
|
|
true>(argc, argv, Row{}, Col{}, Row{});
|
|
}
|
|
}
|
|
}
|
|
else if(data_type == "bf8")
|
|
{
|
|
if(scale_opt == 0)
|
|
{
|
|
run_flatmm_example_with_layouts<ck_tile::bf8_t, FlatmmConfig<ck_tile::bf8_t>>(
|
|
argc, argv, Row{}, Col{}, Row{});
|
|
}
|
|
else
|
|
{
|
|
run_flatmm_example_with_layouts<ck_tile::bf8_t, FlatmmConfig<ck_tile::bf8_t>, 1, 1>(
|
|
argc, argv, Row{}, Col{}, Row{});
|
|
}
|
|
}
|
|
else
|
|
{
|
|
throw std::runtime_error("Unsupported data_type!");
|
|
}
|
|
}
|
|
else
|
|
{
|
|
throw std::runtime_error("Unsupported data layout configuration for A,B and C tensors!");
|
|
}
|
|
return -1;
|
|
}
|
|
|
|
int main(int argc, char* argv[])
|
|
{
|
|
auto [result, arg_parser] = create_args(argc, argv);
|
|
if(!result)
|
|
return EXIT_FAILURE;
|
|
|
|
try
|
|
{
|
|
int warp_tile = arg_parser.get_int("warp_tile");
|
|
if(warp_tile == 0)
|
|
{
|
|
return !run_flatmm_example<FlatmmConfig16>(argc, argv);
|
|
}
|
|
else if(warp_tile == 1)
|
|
{
|
|
return !run_flatmm_example<FlatmmConfig32>(argc, argv);
|
|
}
|
|
else if(warp_tile == 2)
|
|
{
|
|
return !run_flatmm_example<FlatmmConfig16_950>(argc, argv);
|
|
}
|
|
else
|
|
{
|
|
return !run_flatmm_example<FlatmmConfig32_950>(argc, argv);
|
|
}
|
|
}
|
|
catch(const std::runtime_error& e)
|
|
{
|
|
std::cerr << "Runtime error: " << e.what() << '\n';
|
|
return EXIT_FAILURE;
|
|
}
|
|
}
|