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CUTLASS 3.5.1 (#1623)

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* CUTLASS 3.5.1

* updates, optimizations, fixes
This commit is contained in:
Vijay Thakkar
2024-07-29 08:46:24 -04:00
committed by GitHub
parent 56b46e2d13
commit be60a0b272
312 changed files with 19793 additions and 6775 deletions
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examples/57_hopper_grouped_gemm/57_hopper_grouped_gemm.cu
+13 -14
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@@ -32,7 +32,7 @@
/*! \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
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
@@ -42,7 +42,7 @@
$ ./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.
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.
@@ -117,7 +117,7 @@ constexpr int AlignmentC = 128 / cutlass::sizeof_bits<ElementC>::value; // A
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 TileShape = Shape<_256,_128,_128>; // 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
@@ -163,10 +163,10 @@ using DeviceGemmReference = cutlass::reference::device::Gemm<
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;
using StrideA = typename Gemm::GemmKernel::InternalStrideA;
using StrideB = typename Gemm::GemmKernel::InternalStrideB;
using StrideC = typename Gemm::GemmKernel::InternalStrideC;
using StrideD = typename Gemm::GemmKernel::InternalStrideD;
// Host-side allocations
std::vector<int64_t> offset_A;
@@ -226,7 +226,7 @@ struct Options {
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;
int const alignment = tma_alignment_bits / cutlass::sizeof_bits<ElementA>::value;
// Parses the command line
void parse(int argc, char const **args) {
@@ -438,10 +438,10 @@ void allocate(const Options &options) {
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>{})));
stride_A_host.push_back(cutlass::make_cute_packed_stride(StrideA{}, {M, K, 1}));
stride_B_host.push_back(cutlass::make_cute_packed_stride(StrideB{}, {N, K, 1}));
stride_C_host.push_back(cutlass::make_cute_packed_stride(StrideC{}, {M, N, 1}));
stride_D_host.push_back(cutlass::make_cute_packed_stride(StrideD{}, {M, N, 1}));
}
@@ -456,7 +456,7 @@ void allocate(const Options &options) {
/// 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);
@@ -695,7 +695,6 @@ int main(int argc, char const **args) {
<< "later (compute capability 90 or greater).\n";
return 0;
}
//
// Parse options
//