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Move runner to benchmark folder
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@aacostadiaz
aacostadiaz committed May 27, 2024
1 parent 02b48fd commit df71ab7
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Showing 3 changed files with 262 additions and 7 deletions.
2 changes: 1 addition & 1 deletion examples/sycl/common/example_runner.hpp → benchmarks/common/benchmark_runner.hpp
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Expand Up @@ -53,7 +53,7 @@ template <typename T>
static void fill_matrix(std::vector<T> &M)
{
std::generate(std::begin(M), std::end(M), [&]
{ return static_cast<T>( 2*(rand() / double(RAND_MAX)) - 1 ); });
{ return static_cast<T>( (rand() / double(RAND_MAX)) ); });
}

using namespace cute;
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2 changes: 1 addition & 1 deletion examples/sycl/CMakeLists.txt
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Expand Up @@ -27,6 +27,6 @@
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.


if("${DPCPP_SYCL_TARGET}" STREQUAL "intel_gpu_pvc")
if(SYCL_INTEL_TARGET)
add_subdirectory(pvc)
endif()
265 changes: 260 additions & 5 deletions examples/sycl/pvc/pvc_bfloat_dpas_gemm_cute.cpp
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Expand Up @@ -37,17 +37,272 @@
#include "cutlass/util/GPU_Clock.hpp"

#include <cute/tensor.hpp>
#include <random>

#include "cutlass/util/command_line.h"
#include "cutlass/util/device_memory.h"
#include "cutlass/util/packed_stride.hpp"
#include "cutlass/util/reference/device/gemm_complex.h"
#include "cutlass/util/reference/device/tensor_compare.h"

#include "../common/example_runner.hpp"
template <typename T>
static void fill_matrix(std::vector<T> &vector)
{
std::generate(std::begin(vector), std::end(vector), [&] {
return static_cast<T>( (rand() / double(RAND_MAX)) );
});
}

template <typename T>
static void vnni_matrix(
T* dst, const T* src,
int batch, int numRows, int numCols, int factor)
{
for (int b = 0; b < batch; b++) {
for (int r = 0; r < numRows / factor; r++) {
for (int c = 0; c < numCols; c++) {
for (int k = 0; k < factor; k++) {
dst[((b * (numRows / factor) + r) * numCols + c) * factor + k] =
src[((b * (numRows / factor) + r) * factor + k) * numCols + c];
}
}
}
}
}

using namespace cute;

///////////////////////////////////////////////////////////////////////////////////////////////////

// Command line options parsing
struct Options {

bool help;
bool error;

int m, n, k, l, iterations;
float alpha, beta;

Options():
help(false),
error(false),
m(4096), n(4096), k(4096), l(1), iterations(100),
alpha(1.f), beta(0.f)
{ }

// 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, 4096);
cmd.get_cmd_line_argument("n", n, 4096);
cmd.get_cmd_line_argument("k", k, 4096);
cmd.get_cmd_line_argument("l", l, 1);
cmd.get_cmd_line_argument("alpha", alpha, 1.f);
cmd.get_cmd_line_argument("beta", beta, 0.f);
cmd.get_cmd_line_argument("iterations", iterations, 100);
}

/// Prints the usage statement.
std::ostream & print_usage(std::ostream &out) const {

out << "PVC GEMM Example\n\n"
<< "Options:\n\n"
<< " --help If specified, displays this usage statement\n\n"
<< " --m=<int> Sets the M extent of the GEMM\n"
<< " --n=<int> Sets the N extent of the GEMM\n"
<< " --k=<int> Sets the K extent of the GEMM\n"
<< " --l=<int> Sets the L extent (batch count) of the GEMM\n"
<< " --alpha=<s32> Epilogue scalar alpha\n"
<< " --beta=<s32> Epilogue scalar beta\n\n"
<< " --iterations=<int> Iterations\n\n";

return out;
}
};

///////////////////////////////////////////////////////////////////////////////////////////////////

template <
class Gemm
>
struct ExampleRunner {

using StrideA = typename Gemm::GemmKernel::StrideA;
using StrideB = typename Gemm::GemmKernel::StrideB;
using StrideC = typename Gemm::GemmKernel::StrideC;
using StrideD = typename Gemm::GemmKernel::StrideD;

using LayoutA = typename Gemm::LayoutA;
using LayoutB = typename Gemm::LayoutB;
using LayoutC = typename Gemm::LayoutC;
using LayoutD = typename Gemm::LayoutD;

using ElementA = typename Gemm::ElementA;
using ElementB = typename Gemm::ElementB;
using ElementAcc = typename Gemm::ElementAccumulator;

using CollectiveEpilogue = typename Gemm::CollectiveEpilogue;
using ElementC = typename Gemm::ElementC;
using ElementOutput = typename CollectiveEpilogue::ElementOutput;
using ElementCompute = typename CollectiveEpilogue::ElementCompute;
using ElementAccumulator = typename CollectiveEpilogue::ElementAccumulator;

using ProblemShapeType = typename Gemm::GemmKernel::ProblemShape;

//
// Data members
//

/// Initialization
StrideA stride_A;
StrideB stride_B;
StrideC stride_C;
StrideD stride_D;

cutlass::DeviceAllocation<ElementA> block_A;
cutlass::DeviceAllocation<ElementB> block_B;
cutlass::DeviceAllocation<ElementB> block_B_vnni;
cutlass::DeviceAllocation<ElementC> block_C;
cutlass::DeviceAllocation<ElementOutput> block_D;
cutlass::DeviceAllocation<ElementOutput> block_ref_D;

//
// Methods
//

bool verify(const ProblemShapeType& problem_size, ElementCompute alpha, ElementCompute beta) {
auto [M, N, K, L] = problem_size;

cutlass::TensorRef ref_A(block_A.get(), LayoutA::packed({M, K}));
cutlass::TensorRef ref_B(block_B.get(), LayoutB::packed({K, N}));
cutlass::TensorRef ref_C(block_C.get(), LayoutC::packed({M, N}));
cutlass::TensorRef ref_D(block_ref_D.get(), LayoutD::packed({M, N}));

cutlass::reference::device::GemmComplex(
{M, N, K},
alpha,
ref_A,
cutlass::ComplexTransform::kNone,
ref_B,
cutlass::ComplexTransform::kNone,
beta,
ref_C,
ref_D,
ElementAccumulator(0),
L, // batch_count
M * K, // batch_stride_A
K * N, // batch_stride_B
M * N, // batch_stride_C
M * N // batch_stride_D
);

syclcompat::wait();

// Check if output from CUTLASS kernel and reference kernel are relatively equal or not
// need to set a larger error margin for comparison to succeed
auto epsilon = static_cast<ElementOutput>(0.1f);
auto nonzero_floor = static_cast<ElementOutput>(0.1f);

bool passed = cutlass::reference::device::BlockCompareRelativelyEqual(
block_ref_D.get(), block_D.get(), block_D.size(),
epsilon, nonzero_floor);

return passed;
}

/// Initialize operands to be used in the GEMM and reference GEMM
void initialize(const ProblemShapeType& problem_size) {
auto problem_shape_MNKL = cute::append<4>(problem_size, 1);
auto [M, N, K, L] = problem_shape_MNKL;

stride_A = cutlass::make_cute_packed_stride(StrideA{}, cute::make_shape(M, K, L));
stride_B = cutlass::make_cute_packed_stride(StrideB{}, cute::make_shape(N, K, L));
stride_C = cutlass::make_cute_packed_stride(StrideC{}, cute::make_shape(M, N, L));
stride_D = cutlass::make_cute_packed_stride(StrideD{}, cute::make_shape(M, N, L));

block_A.reset(M * K * L);
block_B.reset(K * N * L);
block_B_vnni.reset(K * N * L);
block_C.reset(M * N * L);
block_D.reset(M * N * L);
block_ref_D.reset(M * N * L);

// TODO: Enable initialization on device directly once RNG is
// available through SYCL.
std::vector<ElementA> a(K * M * L);
std::vector<ElementB> b(K * N * L);
std::vector<ElementB> b_vnni(b.size());
std::vector<ElementC> c(M * N * L);
std::vector<ElementC> d(M * N * L, ElementC{0});

fill_matrix(a);
fill_matrix(b);
fill_matrix(c);
vnni_matrix(b_vnni.data(), b.data(), L, K, N, 2);

syclcompat::memcpy(block_A.get(), a.data(), a.size() * sizeof(ElementA));
syclcompat::memcpy(block_B.get(), b.data(), b.size() * sizeof(ElementB));
syclcompat::memcpy(block_B_vnni.get(), b_vnni.data(), b.size() * sizeof(ElementB));
syclcompat::memcpy(block_C.get(), c.data(), c.size() * sizeof(ElementC));
syclcompat::memcpy(block_D.get(), d.data(), d.size() * sizeof(ElementC));
}

void run(const Options& options, const cutlass::KernelHardwareInfo& hw_info) {
ProblemShapeType problem_size = ProblemShapeType{options.m, options.n, options.k, options.l};

initialize(problem_size);

typename Gemm::GemmKernel::Arguments arguments{
cutlass::gemm::GemmUniversalMode::kGemm,
problem_size,
{block_A.get(), stride_A, block_B_vnni.get(), stride_B},
{{options.alpha, options.beta}, block_C.get(), stride_C, block_D.get(), stride_D},
hw_info
};

Gemm gemm_op;

size_t workspace_size = Gemm::get_workspace_size(arguments);
cutlass::device_memory::allocation<uint8_t> workspace(workspace_size);

gemm_op.can_implement(arguments);

gemm_op.initialize(arguments, workspace.get());

// Run the GEMM
gemm_op.run();

syclcompat::wait();

// Verify that the result is correct
bool passed = verify(problem_size, options.alpha, options.beta);
std::cout << "Disposition: " << (passed ? "Passed" : "Failed") << std::endl;

if (passed && options.iterations > 0) {
GPU_Clock timer;
timer.start();
for (int i = 0; i < options.iterations; ++i) {
gemm_op.run();
}
syclcompat::wait();

float cute_time = timer.seconds() / options.iterations;
double tflops = (2.0 * options.m * options.n * options.k * options.l) * 1e-12;
std::cout << "Problem Size: " << options.m << 'x' << options.n << 'x' << options.k << 'x' << options.l << std::endl;
printf("Cutlass GEMM Performance: [%4.3f]TFlop/s (%6.4f)ms\n", tflops / cute_time, cute_time*1000);
}

return;
}

};

int main(int argc, const char** argv)
{
//
Expand Down Expand Up @@ -134,14 +389,14 @@ int main(int argc, const char** argv)
>;

using GemmKernel = cutlass::gemm::kernel::GemmUniversal<
Shape<int, int, int, int>,
CollectiveMainloop,
CollectiveEpilogue
Shape<int, int, int, int>,
CollectiveMainloop,
CollectiveEpilogue
>;

using Gemm = cutlass::gemm::device::GemmUniversalAdapter<GemmKernel>;

PvcExampleRunner<Gemm> runner;
ExampleRunner<Gemm> runner;

runner.run(options, hw_info);

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