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cutlass/examples/57_hopper_grouped_gemm/57_hopper_grouped_gemm.cu

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/***************************************************************************************************
* Copyright (c) 2023 - 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
* SPDX-License-Identifier: BSD-3-Clause
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* contributors may be used to endorse or promote products derived from
* this software without specific prior written permission.
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/*! \file
\brief Hopper Grouped GEMM example using CUTLASS 3 APIs for NVIDIA Hopper architecture.
This example demonstrates an implementation of Grouped GEMM using a TMA + GMMA
warp-specialized cooperative kernel.
For this example all scheduling work is performed on the device.
The new feature showcased in this example is on-the-fly modification of TMA descriptors
to move between groups/problem_count (represented by groups).
To run this example:
$ ./examples/57_hopper_grouped_gemm/57_hopper_grouped_gemm --m=2048 --n=2048 --k=2048 --groups=10
The above example command makes all 10 groups to be sized at the given m, n, k sizes.
Skipping any of the problem dimensions randomizes it across the different groups.
Same applies for alpha and beta values that are randomized across the different groups.
To run this example for a set of problems using the benchmark option:
$ ./examples/57_hopper_grouped_gemm/57_hopper_grouped_gemm --benchmark=./test_benchmark.txt
Where the test_benchmark.txt may look as such:
0 256x512x128
1 256x512x512
2 512x256x128
3 256x256x128
4 256x512x1024
5 1024x512x128 and so on
*/
#include <iostream>
#include <fstream>
#include <sstream>
#include <vector>
#include <float.h>
#include "cutlass/cutlass.h"
#include "cute/tensor.hpp"
#include "cutlass/tensor_ref.h"
#include "cutlass/epilogue/collective/default_epilogue.hpp"
#include "cutlass/epilogue/thread/linear_combination.h"
#include "cutlass/gemm/dispatch_policy.hpp"
#include "cutlass/gemm/group_array_problem_shape.hpp"
#include "cutlass/gemm/collective/collective_builder.hpp"
#include "cutlass/epilogue/collective/collective_builder.hpp"
#include "cutlass/gemm/device/gemm_universal_adapter.h"
#include "cutlass/gemm/kernel/gemm_universal.hpp"
#include "cutlass/util/command_line.h"
#include "cutlass/util/distribution.h"
#include "cutlass/util/host_tensor.h"
#include "cutlass/util/packed_stride.hpp"
#include "cutlass/util/tensor_view_io.h"
#include "cutlass/util/reference/device/gemm.h"
#include "cutlass/util/reference/device/tensor_compare.h"
#include "cutlass/util/reference/device/tensor_fill.h"
#include "helper.h"
using namespace cute;
using ProblemShape = cutlass::gemm::GroupProblemShape<Shape<int,int,int>>; // <M,N,K> per group
using ElementA = cutlass::float_e4m3_t; // Element type for A matrix operand
using ElementB = cutlass::float_e5m2_t; // Element type for B matrix operand
using ElementC = cutlass::half_t; // Element type for C and D matrix operands
#if defined(CUTLASS_ARCH_MMA_MODIFIABLE_TMA_SM90_SUPPORTED)
/////////////////////////////////////////////////////////////////////////////////////////////////
/// GEMM kernel configurations
/////////////////////////////////////////////////////////////////////////////////////////////////
// A matrix configuration
using LayoutA = cutlass::layout::RowMajor; // Layout type for A matrix operand
constexpr int AlignmentA = 128 / cutlass::sizeof_bits<ElementA>::value; // Alignment of A matrix in units of elements (up to 16 bytes)
// B matrix configuration
using LayoutB = cutlass::layout::ColumnMajor; // Layout type for B matrix operand
constexpr int AlignmentB = 128 / cutlass::sizeof_bits<ElementB>::value; // Alignment of B matrix in units of elements (up to 16 bytes)
// C/D matrix configuration
using LayoutC = cutlass::layout::ColumnMajor; // Layout type for C and D matrix operands
constexpr int AlignmentC = 128 / cutlass::sizeof_bits<ElementC>::value; // Alignment of C matrix in units of elements (up to 16 bytes)
// Core kernel configurations
using ElementAccumulator = float; // Element type for internal accumulation
using ArchTag = cutlass::arch::Sm90; // Tag indicating the minimum SM that supports the intended feature
using OperatorClass = cutlass::arch::OpClassTensorOp; // Operator class tag
using TileShape = Shape<_256,_128,_64>; // Threadblock-level tile size
using ClusterShape = Shape<_2,_2,_1>; // Shape of the threadblocks in a cluster
using StageCountType = cutlass::gemm::collective::StageCountAuto; // Stage count maximized based on the tile size
using KernelSchedule = cutlass::gemm::KernelPtrArrayTmaWarpSpecializedCooperativeFP8FastAccum; // Kernel to launch
using EpilogueSchedule = cutlass::epilogue::PtrArrayNoSmemWarpSpecialized; // Epilogue to launch
using CollectiveEpilogue = typename cutlass::epilogue::collective::CollectiveBuilder<
cutlass::arch::Sm90, cutlass::arch::OpClassTensorOp,
TileShape, ClusterShape,
cutlass::epilogue::collective::EpilogueTileAuto,
ElementAccumulator, ElementAccumulator,
ElementC, LayoutC *, AlignmentC,
ElementC, LayoutC *, AlignmentC,
EpilogueSchedule
>::CollectiveOp;
using CollectiveMainloop = typename cutlass::gemm::collective::CollectiveBuilder<
ArchTag, OperatorClass,
ElementA, LayoutA *, AlignmentA,
ElementB, LayoutB *, AlignmentB,
ElementAccumulator,
TileShape, ClusterShape,
cutlass::gemm::collective::StageCountAutoCarveout<
static_cast<int>(sizeof(typename CollectiveEpilogue::SharedStorage))>,
KernelSchedule
>::CollectiveOp;
using GemmKernel = cutlass::gemm::kernel::GemmUniversal<
ProblemShape,
CollectiveMainloop,
CollectiveEpilogue
>;
using Gemm = cutlass::gemm::device::GemmUniversalAdapter<GemmKernel>;
// Reference device GEMM implementation type
using DeviceGemmReference = cutlass::reference::device::Gemm<
ElementA,
LayoutA,
ElementB,
LayoutB,
ElementC,
LayoutC,
ElementAccumulator,
ElementAccumulator>;
using StrideA = typename Gemm::GemmKernel::UnderlyingStrideA;
using StrideB = typename Gemm::GemmKernel::UnderlyingStrideB;
using StrideC = typename Gemm::GemmKernel::UnderlyingStrideC;
using StrideD = typename Gemm::GemmKernel::UnderlyingStrideD;
// Host-side allocations
std::vector<int64_t> offset_A;
std::vector<int64_t> offset_B;
std::vector<int64_t> offset_C;
std::vector<int64_t> offset_D;
std::vector<StrideA> stride_A_host;
std::vector<StrideB> stride_B_host;
std::vector<StrideC> stride_C_host;
std::vector<StrideD> stride_D_host;
std::vector<ElementAccumulator> alpha_host;
std::vector<ElementAccumulator> beta_host;
// Device-side allocations
cutlass::DeviceAllocation<typename ProblemShape::UnderlyingProblemShape> problem_sizes;
cutlass::DeviceAllocation<typename Gemm::ElementA> block_A;
cutlass::DeviceAllocation<typename Gemm::ElementB> block_B;
cutlass::DeviceAllocation<typename Gemm::ElementC> block_C;
cutlass::DeviceAllocation<typename Gemm::EpilogueOutputOp::ElementOutput> block_D;
cutlass::DeviceAllocation<typename Gemm::EpilogueOutputOp::ElementOutput> block_ref_D;
cutlass::DeviceAllocation<const typename Gemm::ElementA *> ptr_A;
cutlass::DeviceAllocation<const typename Gemm::ElementB *> ptr_B;
cutlass::DeviceAllocation<const typename Gemm::ElementC *> ptr_C;
cutlass::DeviceAllocation<typename Gemm::EpilogueOutputOp::ElementOutput *> ptr_D;
cutlass::DeviceAllocation<typename Gemm::EpilogueOutputOp::ElementOutput *> ptr_ref_D;
cutlass::DeviceAllocation<StrideA> stride_A;
cutlass::DeviceAllocation<StrideB> stride_B;
cutlass::DeviceAllocation<StrideC> stride_C;
cutlass::DeviceAllocation<StrideD> stride_D;
// Note, this is an array of pointers to alpha and beta scaling values per group
cutlass::DeviceAllocation<ElementAccumulator*> alpha_device;
cutlass::DeviceAllocation<ElementAccumulator*> beta_device;
cutlass::DeviceAllocation<ElementAccumulator> block_alpha;
cutlass::DeviceAllocation<ElementAccumulator> block_beta;
#endif // defined(CUTLASS_ARCH_MMA_MODIFIABLE_TMA_SM90_SUPPORTED)
/////////////////////////////////////////////////////////////////////////////////////////////////
/// Testbed utility types
/////////////////////////////////////////////////////////////////////////////////////////////////
// Command line options parsing
struct Options {
bool help = false;
float alpha = FLT_MAX;
float beta = FLT_MAX;
int iterations = 10;
int m = 1024, n = 2048, k = 512, groups = 10;
std::string benchmark_path;
std::vector<typename ProblemShape::UnderlyingProblemShape> problem_sizes_host;
int const tma_alignment_bits = 128;
int const alignment = tma_alignment_bits / cutlass::sizeof_bits<ElementA>::value;
// Parses the command line
void parse(int argc, char const **args) {
cutlass::CommandLine cmd(argc, args);
if (cmd.check_cmd_line_flag("help")) {
help = true;
return;
}
cmd.get_cmd_line_argument("m", m);
cmd.get_cmd_line_argument("n", n);
cmd.get_cmd_line_argument("k", k);
cmd.get_cmd_line_argument("groups", groups);
cmd.get_cmd_line_argument("alpha", alpha, FLT_MAX);
cmd.get_cmd_line_argument("beta", beta, FLT_MAX);
cmd.get_cmd_line_argument("iterations", iterations);
cmd.get_cmd_line_argument("benchmark", benchmark_path);
// Decide how to initialize the problems
if (!benchmark_path.empty()) {
if (!benchmark_problems()) {
problem_sizes_host.clear();
return;
}
}
else {
randomize_problems(cmd);
}
}
void randomize_problems(cutlass::CommandLine &cmd) {
int cmd_line_m = -1, cmd_line_n = -1, cmd_line_k = -1;
cmd.get_cmd_line_argument("m", cmd_line_m);
cmd.get_cmd_line_argument("n", cmd_line_n);
cmd.get_cmd_line_argument("k", cmd_line_k);
problem_sizes_host.reserve(groups);
for (int i = groups; i > 0; i--) {
int m = cmd_line_m;
int n = cmd_line_n;
int k = cmd_line_k;
if (m < 1) {
m = ((rand() % 512) + 1);
}
if (n < 1) {
n = ((rand() % 512) + 1);
}
if (k < 1) {
k = alignment * ((rand() % 64) + 1);
}
problem_sizes_host.push_back({m, n, k});
}
}
/// Load a benchmark
bool benchmark_problems() {
std::ifstream file(benchmark_path);
if (!file.good()) {
return false;
}
while (file.good()) {
int idx = -1;
std::string extent_str;
file >> idx >> extent_str;
if (idx < 0 || extent_str.empty()) {
break;
}
cutlass::gemm::GemmCoord extent;
std::vector<std::string> tokens;
cutlass::CommandLine::tokenize(tokens, extent_str, 'x');
for (int i = 0; i < int(tokens.size()); ++i) {
int x = std::atoi(tokens.at(i).c_str());
// round up
if (x % alignment) {
x += (alignment - (x % alignment));
}
extent.at(i) = x;
}
if (extent.product()) {
problem_sizes_host.push_back({extent.m(), extent.n(), extent.k()});
}
}
groups = static_cast<int>(problem_sizes_host.size());
return true;
}
/// Prints the usage statement.
std::ostream & print_usage(std::ostream &out) const {
out << "57_hopper_grouped_gemm\n\n"
<< " Hopper FP8 Grouped GEMM using a Warp Specialized kernel.\n\n"
<< "Options:\n\n"
<< " --help If specified, displays this usage statement\n\n"
<< " --m=<int> Sets the M extent of the GEMM for all groups\n"
<< " --n=<int> Sets the N extent of the GEMM for all groups\n"
<< " --k=<int> Sets the K extent of the GEMM for all groups\n"
<< " --groups=<int> Sets the number of individual GEMM problems for Grouped GEMM\n"
<< " --alpha=<f32> Epilogue scalar alpha\n"
<< " --beta=<f32> Epilogue scalar beta\n\n"
<< " --iterations=<int> Number of profiling iterations to perform\n\n"
<< " --benchmark=<str> Executes a benchmark problem size.\n";
out
<< "\n\nExamples:\n\n"
<< "$ " << "57_hopper_grouped_gemm" << " --m=1024 --n=512 --k=1024 --groups=10 --alpha=2 --beta=0.707 \n\n";
return out;
}
/// Compute performance in GFLOP/s
double gflops(double runtime_s, std::vector<typename ProblemShape::UnderlyingProblemShape> problem_sizes_host) const
{
// Number of real-valued multiply-adds
uint64_t fmas = uint64_t();
for (auto const & problem : problem_sizes_host) {
fmas += static_cast<uint64_t>(get<0>(problem)) *
static_cast<uint64_t>(get<1>(problem)) *
static_cast<uint64_t>(get<2>(problem));
}
// Two flops per multiply-add
uint64_t flop = uint64_t(2) * uint64_t(fmas);
double gflop = double(flop) / double(1.0e9);
return gflop / runtime_s;
}
};
/// Result structure
struct Result
{
double avg_runtime_ms = 0.0;
double gflops = 0.0;
cutlass::Status status = cutlass::Status::kSuccess;
cudaError_t error = cudaSuccess;
bool passed = false;
};
#if defined(CUTLASS_ARCH_MMA_MODIFIABLE_TMA_SM90_SUPPORTED)
/////////////////////////////////////////////////////////////////////////////////////////////////
/// GEMM setup and evaluation
/////////////////////////////////////////////////////////////////////////////////////////////////
/// Helper to initialize a block of device data
template <class Element>
bool initialize_block(
cutlass::DeviceAllocation<Element>& block,
uint64_t seed=2023) {
Element scope_max, scope_min;
int bits_input = cutlass::sizeof_bits<Element>::value;
if (bits_input == 1) {
scope_max = static_cast<Element>(2);
scope_min = static_cast<Element>(0);
} else if (bits_input <= 8) {
scope_max = static_cast<Element>(2);
scope_min = static_cast<Element>(-2);
} else {
scope_max = static_cast<Element>(8);
scope_min = static_cast<Element>(-8);
}
cutlass::reference::device::BlockFillRandomUniform(
block.get(), block.size(), seed, scope_max, scope_min, 0);
return true;
}
/// Allocates device-side data
void allocate(const Options &options) {
int64_t total_elements_A = 0;
int64_t total_elements_B = 0;
int64_t total_elements_C = 0;
int64_t total_elements_D = 0;
for (int32_t i = 0; i < options.groups; ++i) {
auto problem = options.problem_sizes_host.at(i);
auto M = get<0>(problem);
auto N = get<1>(problem);
auto K = get<2>(problem);
offset_A.push_back(total_elements_A);
offset_B.push_back(total_elements_B);
offset_C.push_back(total_elements_C);
offset_D.push_back(total_elements_D);
int64_t elements_A = M * K;
int64_t elements_B = K * N;
int64_t elements_C = M * N;
int64_t elements_D = M * N;
total_elements_A += elements_A;
total_elements_B += elements_B;
total_elements_C += elements_C;
total_elements_D += elements_D;
stride_A_host.push_back(cutlass::make_cute_packed_stride(StrideA{}, cute::make_shape(M, K, Int<1>{})));
stride_B_host.push_back(cutlass::make_cute_packed_stride(StrideB{}, cute::make_shape(N, K, Int<1>{})));
stride_C_host.push_back(cutlass::make_cute_packed_stride(StrideC{}, cute::make_shape(M, N, Int<1>{})));
stride_D_host.push_back(cutlass::make_cute_packed_stride(StrideD{}, cute::make_shape(M, N, Int<1>{})));
}
block_A.reset(total_elements_A);
block_B.reset(total_elements_B);
block_C.reset(total_elements_C);
block_D.reset(total_elements_D);
block_ref_D.reset(total_elements_D);
block_alpha.reset(options.groups);
block_beta.reset(options.groups);
}
/// Initialize operands to be used in the GEMM and reference GEMM
void initialize(const Options &options) {
uint64_t seed = 2020;
problem_sizes.reset(options.groups);
problem_sizes.copy_from_host(options.problem_sizes_host.data());
//
// Assign pointers
//
std::vector<ElementA *> ptr_A_host(options.groups);
std::vector<ElementB *> ptr_B_host(options.groups);
std::vector<ElementC *> ptr_C_host(options.groups);
std::vector<ElementC *> ptr_D_host(options.groups);
std::vector<ElementAccumulator *> ptr_alpha_host(options.groups);
std::vector<ElementAccumulator *> ptr_beta_host(options.groups);
for (int32_t i = 0; i < options.groups; ++i) {
ptr_A_host.at(i) = block_A.get() + offset_A.at(i);
ptr_B_host.at(i) = block_B.get() + offset_B.at(i);
ptr_C_host.at(i) = block_C.get() + offset_C.at(i);
ptr_D_host.at(i) = block_D.get() + offset_D.at(i);
alpha_host.push_back((options.alpha == FLT_MAX) ? static_cast<ElementAccumulator>((rand() % 5) + 1) : options.alpha);
beta_host.push_back((options.beta == FLT_MAX) ? static_cast<ElementAccumulator>(rand() % 5) : options.beta);
ptr_alpha_host.at(i) = block_alpha.get() + i;
ptr_beta_host.at(i) = block_beta.get() + i;
}
ptr_A.reset(options.groups);
ptr_A.copy_from_host(ptr_A_host.data());
ptr_B.reset(options.groups);
ptr_B.copy_from_host(ptr_B_host.data());
ptr_C.reset(options.groups);
ptr_C.copy_from_host(ptr_C_host.data());
ptr_D.reset(options.groups);
ptr_D.copy_from_host(ptr_D_host.data());
stride_A.reset(options.groups);
stride_A.copy_from_host(stride_A_host.data());
stride_B.reset(options.groups);
stride_B.copy_from_host(stride_B_host.data());
stride_C.reset(options.groups);
stride_C.copy_from_host(stride_C_host.data());
stride_D.reset(options.groups);
stride_D.copy_from_host(stride_D_host.data());
alpha_device.reset(options.groups);
alpha_device.copy_from_host(ptr_alpha_host.data());
beta_device.reset(options.groups);
beta_device.copy_from_host(ptr_beta_host.data());
initialize_block(block_A, seed + 2023);
initialize_block(block_B, seed + 2022);
initialize_block(block_C, seed + 2021);
block_alpha.copy_from_host(alpha_host.data());
block_beta.copy_from_host(beta_host.data());
}
/// Populates a Gemm::Arguments structure from the given commandline options
typename Gemm::Arguments args_from_options(const Options &options, bool host_problem_shapes_available = true)
{
cutlass::KernelHardwareInfo hw_info;
// Change device_id to another value if you are running on a machine with multiple GPUs and wish
// to use a GPU other than that with device ID 0.
hw_info.device_id = 0;
hw_info.sm_count = cutlass::KernelHardwareInfo::query_device_multiprocessor_count(hw_info.device_id);
typename Gemm::EpilogueOutputOp::Params params;
if (options.alpha != FLT_MAX && options.beta != FLT_MAX) {
// If both alpha/beta are provided (via cmd line args) and are scalar, i.e., same alpha/beta applies to all batches.
params = typename Gemm::EpilogueOutputOp::Params(
ElementAccumulator(options.alpha), ElementAccumulator(options.beta));
}
else {
// If pointers to alpha/beta are provided, i.e., alpha/beta can differ between batches/groups.
params = typename Gemm::EpilogueOutputOp::Params(alpha_device.get(), beta_device.get());
}
typename Gemm::Arguments arguments;
if (host_problem_shapes_available) {
arguments = typename Gemm::Arguments {
cutlass::gemm::GemmUniversalMode::kGrouped,
{options.groups, problem_sizes.get(), options.problem_sizes_host.data()},
{ptr_A.get(), stride_A.get(), ptr_B.get(), stride_B.get()},
{params, ptr_C.get(), stride_C.get(), ptr_D.get(), stride_D.get()},
hw_info
};
}
else {
arguments = typename Gemm::Arguments {
cutlass::gemm::GemmUniversalMode::kGrouped,
{options.groups, problem_sizes.get(), nullptr},
{ptr_A.get(), stride_A.get(), ptr_B.get(), stride_B.get()},
{params, ptr_C.get(), stride_C.get(), ptr_D.get(), stride_D.get()},
hw_info
};
}
return arguments;
}
bool verify(const Options &options) {
bool passed = true;
for (int32_t i = 0; i < options.groups; ++i) {
auto problem = options.problem_sizes_host.at(i);
auto M = get<0>(problem);
auto N = get<1>(problem);
auto K = get<2>(problem);
cutlass::TensorRef ref_A(block_A.get() + offset_A.at(i), Gemm::LayoutA::packed({M, K}));
cutlass::TensorRef ref_B(block_B.get() + offset_B.at(i), Gemm::LayoutB::packed({K, N}));
cutlass::TensorRef ref_C(block_C.get() + offset_C.at(i), Gemm::LayoutC::packed({M, N}));
cutlass::TensorRef ref_D(block_ref_D.get() + offset_D.at(i), Gemm::LayoutD::packed({M, N}));
//
// Compute reference output
//
// Create instantiation for device reference gemm kernel
DeviceGemmReference gemm_reference;
// Launch device reference gemm kernel
gemm_reference(
{M, N, K},
ElementAccumulator(alpha_host.at(i)),
ref_A,
ref_B,
ElementAccumulator(beta_host.at(i)),
ref_C,
ref_D);
// Wait for kernel to finish
CUDA_CHECK(cudaDeviceSynchronize());
// Check if output from CUTLASS kernel and reference kernel are equal or not
passed &= cutlass::reference::device::BlockCompareEqual(block_ref_D.get() + offset_D.at(i), block_D.get() + offset_D.at(i), M * N);
#if 0
std::cout << "Group: " << i << " Status: " << passed << std::endl;
#endif
}
return passed;
}
/// Execute a given example GEMM computation
template <typename Gemm>
int run(Options &options, bool host_problem_shapes_available = true)
{
allocate(options);
initialize(options);
// Instantiate CUTLASS kernel depending on templates
Gemm gemm;
// Create a structure of gemm kernel arguments suitable for invoking an instance of Gemm
auto arguments = args_from_options(options, host_problem_shapes_available);
// Using the arguments, query for extra workspace required for matrix multiplication computation
size_t workspace_size = Gemm::get_workspace_size(arguments);
// Allocate workspace memory
cutlass::device_memory::allocation<uint8_t> workspace(workspace_size);
// Check if the problem size is supported or not
CUTLASS_CHECK(gemm.can_implement(arguments));
// Initialize CUTLASS kernel with arguments and workspace pointer
CUTLASS_CHECK(gemm.initialize(arguments, workspace.get()));
// Correctness / Warmup iteration
CUTLASS_CHECK(gemm.run());
// Check if output from CUTLASS kernel and reference kernel are equal or not
Result result;
result.passed = verify(options);
std::cout << " Disposition: " << (result.passed ? "Passed" : "Failed") << std::endl;
if (!result.passed) {
exit(-1);
}
// Run profiling loop
if (options.iterations > 0)
{
GpuTimer timer;
timer.start();
for (int iter = 0; iter < options.iterations; ++iter) {
CUTLASS_CHECK(gemm.initialize(arguments, workspace.get()));
CUTLASS_CHECK(gemm.run());
}
timer.stop();
// Compute average setup and runtime and GFLOPs.
float elapsed_ms = timer.elapsed_millis();
result.avg_runtime_ms = double(elapsed_ms) / double(options.iterations);
result.gflops = options.gflops(result.avg_runtime_ms / 1000.0, options.problem_sizes_host);
std::cout << " Problem Sizes, Alpha, Beta " << std::endl;
for (int32_t i = 0; i < options.groups; ++i) {
std::cout << " " << options.problem_sizes_host.at(i);
std::cout << ", " << alpha_host.at(i) << ", " << beta_host.at(i) << std::endl;
}
std::cout << " Groups : " << options.groups << std::endl;
std::cout << " Avg runtime : " << result.avg_runtime_ms << " ms" << std::endl;
std::cout << " GFLOPS : " << result.gflops << std::endl;
}
return 0;
}
#endif // defined(CUTLASS_ARCH_MMA_MODIFIABLE_TMA_SM90_SUPPORTED)
///////////////////////////////////////////////////////////////////////////////////////////////////
int main(int argc, char const **args) {
// CUTLASS must be compiled with CUDA 12.3 Toolkit to run this example
if (__CUDACC_VER_MAJOR__ < 12 || (__CUDACC_VER_MAJOR__ == 12 && __CUDACC_VER_MINOR__ < 3)) {
std::cerr << "This example requires CUDA 12.3 or newer.\n";
// Returning zero so this test passes on older Toolkits. Its actions are no-op.
return 0;
}
cudaDeviceProp props;
int current_device_id;
CUDA_CHECK(cudaGetDevice(&current_device_id));
CUDA_CHECK(cudaGetDeviceProperties(&props, current_device_id));
cudaError_t error = cudaGetDeviceProperties(&props, 0);
if (props.major < 9) {
std::cerr
<< "This example requires a GPU of NVIDIA's Hopper Architecture or "
<< "later (compute capability 90 or greater).\n";
return 0;
}
//
// Parse options
//
Options options;
options.parse(argc, args);
if (options.help) {
options.print_usage(std::cout) << std::endl;
return 0;
}
//
// Evaluate CUTLASS kernels
//
#if defined(CUTLASS_ARCH_MMA_MODIFIABLE_TMA_SM90_SUPPORTED)
run<Gemm>(options);
run<Gemm>(options, false /*host_problem_shapes_available*/);
#endif
return 0;
}
/////////////////////////////////////////////////////////////////////////////////////////////////
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