I am learning cutlass and I am compiling using command below:
nvcc -o sgemm_nt_1 sgemm_nt_1.cu -arch=sm_80 -std=c++17 -I ../cutlass/include -I ../cutlass/tools/util/include --expt-relaxed-constexpr -g -G
The code is : here like below:
So when I use command with -g -G, it will be blocked like:
~/temp_can$ ./sgemm_nt_1
Using device 0: NVIDIA A100 80GB PCIe (SM80, 108 SMs)
M = 5120
N = 5120
K = 4096
Verification by comparison with cuBLAS is disabled, either because the CMake option CUTLASS_ENABLE_CUBLAS was explicitly set to OFF, or because CMake could not find cuBLAS. If you would like to enable verification with cuBLAS, please set the CMake option CUTLASS_ENABLE_CUBLAS to ON, rerun CMake, and recompile this example.
^C
If it is without -g -G, it will be:
~/temp_can$ ./sgemm_nt_1
Using device 0: NVIDIA A100 80GB PCIe (SM80, 108 SMs)
M = 5120
N = 5120
K = 4096
Verification by comparison with cuBLAS is disabled, either because the CMake option CUTLASS_ENABLE_CUBLAS was explicitly set to OFF, or because CMake could not find cuBLAS. If you would like to enable verification with cuBLAS, please set the CMake option CUTLASS_ENABLE_CUBLAS to ON, rerun CMake, and recompile this example.
CUTE_GEMM: [7017.5]GFlop/s (30.6016)ms
Why? Simply because -g -G will result in a very big output file?
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#include <thrust/host_vector.h>
#include <thrust/device_vector.h>
#include <cute/tensor.hpp>
#include "cutlass/util/print_error.hpp"
#include "cutlass/util/GPU_Clock.hpp"
#if defined(CUTLASS_ENABLE_CUBLAS) && CUTLASS_ENABLE_CUBLAS != 0
# include "cutlass/util/cublas_wrappers.hpp"
#endif
#include "cutlass/util/helper_cuda.hpp"
template <class MShape, class NShape, class KShape,
class TA, class AStride, class ABlockLayout, class AThreadLayout,
class TB, class BStride, class BBlockLayout, class BThreadLayout,
class TC, class CStride, class CBlockLayout, class CThreadLayout,
class Alpha, class Beta>
__global__ static
__launch_bounds__(decltype(size(CThreadLayout{}))::value)
void
gemm_device(MShape M, NShape N, KShape K,
TA const* A, AStride dA, ABlockLayout blockA, AThreadLayout tA,
TB const* B, BStride dB, BBlockLayout blockB, BThreadLayout tB,
TC * C, CStride dC, CBlockLayout , CThreadLayout tC,
Alpha alpha, Beta beta)
{
using namespace cute;
using X = Underscore;
// Preconditions
CUTE_STATIC_ASSERT(is_static<ABlockLayout>::value);
CUTE_STATIC_ASSERT(is_static<BBlockLayout>::value);
CUTE_STATIC_ASSERT(is_static<CBlockLayout>::value);
CUTE_STATIC_ASSERT(is_static<AThreadLayout>::value);
CUTE_STATIC_ASSERT(is_static<BThreadLayout>::value);
CUTE_STATIC_ASSERT(is_static<CThreadLayout>::value);
CUTE_STATIC_ASSERT_V(size(tA) == size(tC));
CUTE_STATIC_ASSERT_V(size(tB) == size(tC));
//CUTE_STATIC_ASSERT_V(shape<0>(blockA) == shape<0>(blockC)); // BLK_M
//CUTE_STATIC_ASSERT_V(shape<0>(blockB) == shape<1>(blockC)); // BLK_N
CUTE_STATIC_ASSERT_V(shape<1>(blockA) == shape<1>(blockB)); // BLK_K
// Shared memory buffers
__shared__ TA smemA[cosize_v<ABlockLayout>];
__shared__ TB smemB[cosize_v<BBlockLayout>];
auto sA = make_tensor(make_smem_ptr(smemA), blockA); // (BLK_M,BLK_K)
auto sB = make_tensor(make_smem_ptr(smemB), blockB); // (BLK_N,BLK_K)
// Represent the full tensors
auto mA = make_tensor(make_gmem_ptr(A), make_shape(M,K), dA); // (M,K)
auto mB = make_tensor(make_gmem_ptr(B), make_shape(N,K), dB); // (N,K)
auto mC = make_tensor(make_gmem_ptr(C), make_shape(M,N), dC); // (M,N)
// Get the appropriate blocks for this thread block --
// potential for thread block locality
auto blk_shape = make_shape(size<0>(sA), size<0>(sB), size<1>(sB));// (BLK_M,BLK_N,BLK_K)
auto blk_coord = make_coord(blockIdx.x, blockIdx.y, _); // (m,n,k)
auto gA = local_tile(mA, blk_shape, blk_coord, Step<_1, X,_1>{}); // (BLK_M,BLK_K,k)
auto gB = local_tile(mB, blk_shape, blk_coord, Step< X,_1,_1>{}); // (BLK_N,BLK_K,k)
auto gC = local_tile(mC, blk_shape, blk_coord, Step<_1,_1, X>{}); // (BLK_M,BLK_N)
//
// Partition the copying of A and B tiles across the threads
//
// TUTORIAL: Example of simple partitioning of A|B tiles over tA|tB
// Default is a raked partition, but can be changed with Step<X,Y> parameter
auto tAgA = local_partition(gA, tA, threadIdx.x); // (THR_M,THR_K,k)
auto tAsA = local_partition(sA, tA, threadIdx.x); // (THR_M,THR_K)
auto tBgB = local_partition(gB, tB, threadIdx.x); // (THR_N,THR_K,k)
auto tBsB = local_partition(sB, tB, threadIdx.x); // (THR_N,THR_K)
//
// Define C accumulators and A/B partitioning
//
// TUTORIAL: Example of partitioning via projections of tC
// Partition sA (M,K) by the rows of tC
auto tCsA = local_partition(sA, tC, threadIdx.x, Step<_1, X>{}); // (THR_M,BLK_K)
// Partition sB (N,K) by the cols of tC
auto tCsB = local_partition(sB, tC, threadIdx.x, Step< X,_1>{}); // (THR_N,BLK_K)
// Partition gC (M,N) by the tile of tC
auto tCgC = local_partition(gC, tC, threadIdx.x, Step<_1,_1>{}); // (THR_M,THR_N)
// Allocate the accumulators -- same size as the projected data
auto tCrC = make_fragment_like(tCgC); // (THR_M,THR_N)
// Clear the accumulators
clear(tCrC);
#if 0
if(thread0()) {
print("mA\n");
print(mA.shape()); print("\n"); print(mA.stride());
print("\n\ngA\n");
print(gA.shape()); print("\n"); print(gA.stride());
print("\n\ntAgA\n");
print(tAgA.shape()); print("\n"); print(tAgA.stride());
print("\n\nsA\n");
print(sA.shape()); print("\n"); print(sA.stride());
print("\n\ntAsA\n");
print(tAsA.shape()); print("\n"); print(tAsA.stride());
print("\n\n");
}
#endif
#if 0
if(thread0()) {
print("mB\n");
print(mB.shape()); print("\n"); print(mB.stride());
print("\n\ngB\n");
print(gB.shape()); print("\n"); print(gB.stride());
print("\n\ntBgB\n");
print(tBgB.shape()); print("\n"); print(tBgB.stride());
print("\n\nsB\n");
print(sB.shape()); print("\n"); print(sB.stride());
print("\n\ntBsB\n");
print(tBsB.shape()); print("\n"); print(tBsB.stride());
print("\n\n");
}
#endif
#if 0
if(thread0()) {
print("mC\n");
print(mC.shape()); print("\n"); print(mC.stride());
print("\n\ngC\n");
print(gC.shape()); print("\n"); print(gC.stride());
print("\n\ntCsA\n");
print(tCsA.shape()); print("\n"); print(tCsA.stride());
print("\n\ntCsB\n");
print(tCsB.shape()); print("\n"); print(tCsB.stride());
print("\n\ntCgC\n");
print(tCgC.shape()); print("\n"); print(tCgC.stride());
print("\n\ntCrC\n");
print(tCrC.shape()); print("\n"); print(tCrC.stride());
print("\n\n");
}
#endif
#if 1
// TUTORIAL: Example of a very simple compute loop
// Data is read from global to shared memory via the tA|tB partitioning
// gemm(.) operates on the shared memory directly via the tC partitioning
auto k_max = size<2>(tAgA);
for (int k = 0; k < k_max; ++k)
{
// Copy gmem to smem
copy(tAgA(_,_,k), tAsA);
copy(tBgB(_,_,k), tBsB);
// In case copy uses cp.async, make sure that the cp.async
// instructions are ordered with respect to other cp.async
// instructions (fence), then wait on all the outstanding copy
// operations (wait<0>()). __syncthreads() alone does not do
// this.
//
// NOTE: cp_async_wait<0>() currently issues cp.async.wait_all.
// This is equivalent to cp.async.commit_group followed by
// cp.async_wait_group 0. This should make the first
// cp_async_fence() (which also issues cp.async.commit_group)
// redundant. The tutorial works as-is, so we'll leave the
// redundant fence in for now and study its removal later.
cp_async_fence();
cp_async_wait<0>();
__syncthreads();
// Compute gemm on smem
gemm(tCsA, tCsB, tCrC);
__syncthreads();
}
#endif
//
// Epilogue
//
axpby(alpha, tCrC, beta, tCgC);
}
template <typename TA, typename TB, typename TC,
typename Alpha, typename Beta>
void
gemm(int m, int n, int k,
Alpha alpha,
TA const* A, int ldA,
TB const* B, int ldB,
Beta beta,
TC * C, int ldC,
cudaStream_t stream = 0)
{
using namespace cute;
// Define shapes (dynamic)
auto M = int(m);
auto N = int(n);
auto K = int(k);
// Define strides (mixed)
auto dA = make_stride(Int<1>{}, ldA);
auto dB = make_stride(Int<1>{}, ldB);
auto dC = make_stride(Int<1>{}, ldC);
// Define block sizes (static)
auto bM = Int<128>{};
auto bN = Int<128>{};
auto bK = Int< 8>{};
// Define the block layouts (static)
auto sA = make_layout(make_shape(bM,bK));
auto sB = make_layout(make_shape(bN,bK));
auto sC = make_layout(make_shape(bM,bN));
// Define the thread layouts (static)
auto tA = make_layout(make_shape(Int<32>{}, Int< 8>{}));
auto tB = make_layout(make_shape(Int<32>{}, Int< 8>{}));
auto tC = make_layout(make_shape(Int<16>{}, Int<16>{}));
dim3 dimBlock(size(tC));
dim3 dimGrid(ceil_div(size(M), size(bM)),
ceil_div(size(N), size(bN)));
gemm_device
<<< dimGrid, dimBlock, 0, stream >>>
(M, N, K,
A, dA, sA, tA,
B, dB, sB, tB,
C, dC, sC, tC,
alpha, beta);
}
#include <cstdlib>
#include <cstdio>
#include <cassert>
void test_gemm(int m, int n, int k)
{
cute::device_init(0);
std::cout << "M = " << m << std::endl;
std::cout << "N = " << n << std::endl;
std::cout << "K = " << k << std::endl;
using TA = float;
using TB = float;
using TC = float;
using TI = float;
thrust::host_vector<TA> h_A(m*k);
thrust::host_vector<TB> h_B(n*k);
thrust::host_vector<TC> h_C(m*n);
for (int j = 0; j < m*k; ++j) h_A[j] = static_cast<TA>( 2*(rand() / double(RAND_MAX)) - 1 );
for (int j = 0; j < n*k; ++j) h_B[j] = static_cast<TB>( 2*(rand() / double(RAND_MAX)) - 1 );
for (int j = 0; j < m*n; ++j) h_C[j] = static_cast<TC>(-1);
thrust::device_vector<TA> d_A = h_A;
thrust::device_vector<TB> d_B = h_B;
thrust::device_vector<TC> d_C = h_C;
TI alpha = 1.0;
TI beta = 0.0;
double gflops = (2.0*m*n*k) * 1e-9;
const int timing_iterations = 100;
GPU_Clock timer;
#if defined(CUTLASS_ENABLE_CUBLAS) && CUTLASS_ENABLE_CUBLAS != 0
//
// cuBLas
//
cublasHandle_t handle;
cublasCreate(&handle);
// Run once
d_C = h_C;
blam::cublas::gemm(handle, CUBLAS_OP_N, CUBLAS_OP_T,
m, n, k,
&alpha,
d_A.data().get(), m,
d_B.data().get(), n,
&beta,
d_C.data().get(), m);
CUTE_CHECK_LAST();
thrust::host_vector<TC> cublas_result = d_C;
// Timing iterations
timer.start();
for (int i = 0; i < timing_iterations; ++i) {
blam::cublas::gemm(handle, CUBLAS_OP_N, CUBLAS_OP_T,
m, n, k,
&alpha,
d_A.data().get(), m,
d_B.data().get(), n,
&beta,
d_C.data().get(), m);
}
double cublas_time = timer.seconds() / timing_iterations;
CUTE_CHECK_LAST();
printf("CUBLAS_GEMM: [%6.1f]GFlop/s (%6.4f)ms\n", gflops / cublas_time, cublas_time*1000);
#else
std::cout << "Verification by comparison with cuBLAS is disabled, "
"either because the CMake option CUTLASS_ENABLE_CUBLAS "
"was explicitly set to OFF, or because CMake could not find cuBLAS. "
"If you would like to enable verification with cuBLAS, "
"please set the CMake option CUTLASS_ENABLE_CUBLAS to ON, "
"rerun CMake, and recompile this example.\n";
#endif // CUTLASS_ENABLE_CUBLAS
//
// CuTe
//
// Run once (and check)
d_C = h_C;
gemm(m, n, k,
alpha,
d_A.data().get(), m,
d_B.data().get(), n,
beta,
d_C.data().get(), m);
CUTE_CHECK_LAST();
thrust::host_vector<TC> cute_result = d_C;
// Timing iterations
timer.start();
for (int i = 0; i < timing_iterations; ++i) {
gemm(m, n, k,
alpha,
d_A.data().get(), m,
d_B.data().get(), n,
beta,
d_C.data().get(), m);
}
double cute_time = timer.seconds() / timing_iterations;
CUTE_CHECK_LAST();
printf("CUTE_GEMM: [%6.1f]GFlop/s (%6.4f)ms\n", gflops / cute_time, cute_time*1000);
#if defined(CUTLASS_ENABLE_CUBLAS) && CUTLASS_ENABLE_CUBLAS != 0
printf("Empirical Perf: %.1f%%\n", (cublas_time / cute_time) * 100);
auto host_matrix_to_const_column_major_cute_tensor =
[](const auto& X, int num_rows, int num_cols, int LDX) {
const auto shape = cute::Shape<int, int>{num_rows, num_cols};
const auto strides = cute::Stride<int, int>{1, LDX};
return cute::make_tensor(X.data(), cute::make_layout(shape, strides));
};
const auto A_view = host_matrix_to_const_column_major_cute_tensor(h_A, m, k, m);
// B^T is k x n, so B is n x k.
const auto B_view = host_matrix_to_const_column_major_cute_tensor(h_B, n, k, n);
const auto C_computed_view = host_matrix_to_const_column_major_cute_tensor(cute_result, m, n, m);
const auto C_expected_view = host_matrix_to_const_column_major_cute_tensor(cublas_result, m, n, m);
print_matrix_multiply_mollified_relative_error("float", A_view, B_view, C_computed_view, C_expected_view);
#endif // CUTLASS_ENABLE_CUBLAS
}
int main(int argc, char** argv)
{
int m = 5120;
if (argc >= 2)
sscanf(argv[1], "%d", &m);
int n = 5120;
if (argc >= 3)
sscanf(argv[2], "%d", &n);
int k = 4096;
if (argc >= 4)
sscanf(argv[3], "%d", &k);
test_gemm(m, n, k);
return 0;
}