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CUTLASS 3.1 (#915)

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Co-authored-by: Aniket Shivam <ashivam@nvidia.com>
This commit is contained in:
ANIKET SHIVAM
2023-04-14 23:19:34 -04:00
committed by GitHub
co-authored by Aniket Shivam
parent 9b8166e3f0
commit d572cc1aab
482 changed files with 37175 additions and 16410 deletions
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examples/24_gemm_grouped/gemm_grouped.cu
+34 -34
View File
@@ -37,7 +37,7 @@
leading dimensions and problem sizes are stored in arrays in GMEM.
This differs from "Batched Array" GEMM because the size of each GEMM problem in the Grouped GEMM
concept may be distinct.
concept may be distinct.
This benchmark program initializes a workspace with random problem sizes for a given number of
groups. Command line options enable overriding M, N, and/or K dimensions with uniform values to
@@ -186,7 +186,7 @@ struct Options {
//
// Methods
//
//
Options():
help(false),
@@ -216,7 +216,7 @@ struct Options {
cmd.get_cmd_line_argument("alignment", alignment, 8);
cmd.get_cmd_line_argument("groups", problem_count, 15);
cmd.get_cmd_line_argument("alpha", alpha, 1.0f);
cmd.get_cmd_line_argument("beta", beta, 0.0f);
cmd.get_cmd_line_argument("beta", beta, 0.0f);
cmd.get_cmd_line_argument("iterations", iterations, 20);
cmd.get_cmd_line_argument("streams", cuda_streams, 0);
cmd.get_cmd_line_argument("verbose", verbose, false);
@@ -455,13 +455,13 @@ struct Options {
/// Compute performance in GFLOP/s
double gflops(double runtime_s) const {
// Number of real-valued multiply-adds
// Number of real-valued multiply-adds
int64_t fmas = int64_t();
for (auto const & problem : problem_sizes) {
fmas += problem.product();
}
// Two flops per multiply-add
return 2.0 * double(fmas) / double(1.0e9) / runtime_s;
}
@@ -546,7 +546,7 @@ public:
template <typename Element>
void initialize_tensor(
Element *ptr,
size_t capacity,
size_t capacity,
cutlass::Distribution::Kind dist_kind,
uint32_t seed) {
@@ -578,7 +578,7 @@ public:
cutlass::reference::device::BlockFillRandomUniform(
ptr, capacity, seed, scope_max, scope_min, 0);
}
}
else if (dist_kind == cutlass::Distribution::Gaussian) {
cutlass::reference::device::BlockFillRandomGaussian(
@@ -589,7 +589,7 @@ public:
// Fill with increasing elements
cutlass::reference::device::BlockFillSequential(
ptr, capacity, Element(1), Element());
}
}
else {
// Fill with all 1s
@@ -674,13 +674,13 @@ public:
ptr_A.reset(problem_count());
ptr_A.copy_from_host(ptr_A_host.data());
ptr_B.reset(problem_count());
ptr_B.copy_from_host(ptr_B_host.data());
ptr_C.reset(problem_count());
ptr_C.copy_from_host(ptr_C_host.data());
ptr_D.reset(problem_count());
ptr_D.copy_from_host(ptr_D_host.data());
@@ -712,7 +712,7 @@ public:
MatrixCoord extent_A{problem.m(), problem.k()};
MatrixCoord extent_B{problem.k(), problem.n()};
MatrixCoord extent_C{problem.m(), problem.n()};
cutlass::TensorView<ElementA, LayoutA> view_A(block_A.get() + offset_A.at(i), layout_A, extent_A);
cutlass::TensorView<ElementB, LayoutB> view_B(block_B.get() + offset_B.at(i), layout_B, extent_B);
cutlass::TensorView<ElementC, LayoutC> view_C(block_C.get() + offset_C.at(i), layout_C, extent_C);
@@ -724,18 +724,18 @@ public:
cutlass::reference::device::GemmComplex<
ElementA, LayoutA,
ElementB, LayoutB,
ElementC, LayoutC,
ElementC, LayoutC,
ElementCompute, ElementAccumulator
>(
problem,
options.alpha,
options.alpha,
view_A,
Gemm::kTransformA,
view_B,
Gemm::kTransformB,
options.beta,
view_C,
view_Ref_device,
options.beta,
view_C,
view_Ref_device,
ElementAccumulator(0)
);
@@ -781,8 +781,8 @@ public:
std::cout << "Conventionally executed as " << this->options.problem_bins.size() << " batched GEMMs:\n";
for (auto const & bin : this->options.problem_bins) {
std::cout << " [" << bin_idx << "]: "
<< bin.first.m() << "-by-" << bin.first.n() << "-by-" << bin.first.k()
std::cout << " [" << bin_idx << "]: "
<< bin.first.m() << "-by-" << bin.first.n() << "-by-" << bin.first.k()
<< ", batch count: " << bin.second.size() << "\n";
++bin_idx;
@@ -832,7 +832,7 @@ public:
for (auto const & bin : this->options.problem_bins) {
int first_idx = bin.second.front();
bin_problem_sizes.push_back(this->options.problem_sizes.at(first_idx));
bin_count.push_back(int32_t(bin.second.size()));
@@ -974,7 +974,7 @@ public:
std::cerr << "CUTLASS error on line " << __LINE__ << std::endl;
return result;
}
}
//
@@ -1027,7 +1027,7 @@ public:
int last_stream_idx = 0;
for (int iter = 0; iter < this->options.iterations; ++iter) {
for (int bin_idx = 0; bin_idx < int32_t(bin_problem_sizes.size()); ++bin_idx) {
cutlass::gemm::GemmCoord const & problem = bin_problem_sizes[bin_idx];
@@ -1098,7 +1098,7 @@ public:
std::cerr << "cudaEventRecord() failed: " << cudaGetErrorString(result.error) << std::endl;
return result;
}
//
// Wait for work to be completed
//
@@ -1129,10 +1129,10 @@ public:
for (auto event : events) {
(void)cudaEventDestroy(event);
}
for (auto stream : cuda_streams) {
if (stream) {
(void)cudaStreamDestroy(stream);
(void)cudaStreamDestroy(stream);
}
}
@@ -1203,8 +1203,8 @@ public:
int tiles = Gemm::problem_tile_count(problem);
total_tiles += tiles;
std::cout << " [" << idx << "]: "
<< problem.m() << "-by-" << problem.n() << "-by-" << problem.k()
std::cout << " [" << idx << "]: "
<< problem.m() << "-by-" << problem.n() << "-by-" << problem.k()
<< " (" << tiles << " threadblock tiles)" << "\n";
++idx;
@@ -1442,12 +1442,12 @@ int main(int argc, char const **args) {
}
if (__CUDACC_VER_MAJOR__ < 11 || props.major < 8) {
//
// This example requires an NVIDIA Ampere-architecture GPU.
//
std::cout
std::cout
<< "CUTLASS's Grouped GEMM example requires a GPU of NVIDIA's Ampere Architecture or "
<< "later (compute capability 80 or greater).\n";
@@ -1497,9 +1497,9 @@ int main(int argc, char const **args) {
cutlass::gemm::GemmShape<64, 64, 32>,
cutlass::gemm::GemmShape<16, 8, 16>,
cutlass::epilogue::thread::LinearCombination<
ElementOutput,
ElementOutput,
128 / cutlass::sizeof_bits<ElementOutput>::value,
ElementAccumulator,
ElementAccumulator,
ElementAccumulator
>,
cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<8>,
@@ -1519,8 +1519,8 @@ int main(int argc, char const **args) {
cutlass::ComplexTransform::kNone,
8,
ElementOutput, LayoutC,
ElementAccumulator,
cutlass::arch::OpClassTensorOp,
ElementAccumulator,
cutlass::arch::OpClassTensorOp,
cutlass::arch::Sm80,
cutlass::gemm::GemmShape<128, 128, 32>,
cutlass::gemm::GemmShape<64, 64, 32>,
@@ -1531,7 +1531,7 @@ int main(int argc, char const **args) {
// NOTE: Threadblock swizzling is currently not supported by CUTLASS's grouped kernels.
// This parameter is passed in at present to match the APIs of other kernels. The parameter
// is unused within the kernel.
cutlass::gemm::threadblock::GemmBatchedIdentityThreadblockSwizzle,
cutlass::gemm::threadblock::GemmBatchedIdentityThreadblockSwizzle,
4>::GemmKernel;
using GemmGrouped = cutlass::gemm::device::GemmGrouped<GemmKernel>;