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Stamp out 1x1x1 clusters, 128x256 CTA shape (#1665)

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Adds 128x256 tile shapes to FP16/BF16 and FP8 generators.
Also adds 1x1x1 clusters to all existing FP16/BF16/FP8 generators.

NOTE: it is important to set kernel filter (--kernels /
CUTLASS_LIBRARY_KERNELS) to a non empty string and skip pruning to get
all of the new configurations.

If profiling exhaustively, they can be set to `*`.

Number of CUTLASS 3.X GEMMs before this commit: 2868
Number of CUTLASS 3.X GEMMs after this commit: 4016

Co-authored-by: Ali Hassani <ahassani@nvidia.com>
This commit is contained in:
Ali Hassani 2024-07-31 20:22:29 -04:00 committed by GitHub
parent 36cbfcf483
commit eee0cab26c
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1 changed files with 20 additions and 0 deletions
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python/cutlass_library/generator.py
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@ -4972,22 +4972,33 @@ def GenerateSM90_TensorOp_16b_WGMMA_gemm(manifest, cuda_version):
0, [4, 1, 1], math_inst, min_cc, max_cc, [2,1,1]),
TileDescription([math_inst.instruction_shape[0], math_inst.instruction_shape[1], math_inst.instruction_shape[2]*4],
0, [4, 1, 1], math_inst, min_cc, max_cc, [1,2,1]),
TileDescription([math_inst.instruction_shape[0], math_inst.instruction_shape[1], math_inst.instruction_shape[2]*4],
0, [4, 1, 1], math_inst, min_cc, max_cc, [1,1,1]),
]
tile_descriptions_medium = [
TileDescription([math_inst.instruction_shape[0]*2, math_inst.instruction_shape[1], math_inst.instruction_shape[2]*4],
0, [4, 1, 1], math_inst, min_cc, max_cc, [2,1,1]),
TileDescription([math_inst.instruction_shape[0]*2, math_inst.instruction_shape[1], math_inst.instruction_shape[2]*4],
0, [4, 1, 1], math_inst, min_cc, max_cc, [1,2,1]),
TileDescription([math_inst.instruction_shape[0]*2, math_inst.instruction_shape[1], math_inst.instruction_shape[2]*4],
0, [4, 1, 1], math_inst, min_cc, max_cc, [1,1,1]),
]
tile_descriptions_large = [
TileDescription([math_inst.instruction_shape[0]*4, math_inst.instruction_shape[1], math_inst.instruction_shape[2]*4],
0, [4, 1, 1], math_inst, min_cc, max_cc, [2,1,1]),
TileDescription([math_inst.instruction_shape[0]*4, math_inst.instruction_shape[1], math_inst.instruction_shape[2]*4],
0, [4, 1, 1], math_inst, min_cc, max_cc, [1,2,1]),
TileDescription([math_inst.instruction_shape[0]*4, math_inst.instruction_shape[1], math_inst.instruction_shape[2]*4],
0, [4, 1, 1], math_inst, min_cc, max_cc, [1,1,1]),
TileDescription([math_inst.instruction_shape[0]*2, math_inst.instruction_shape[1]*2, math_inst.instruction_shape[2]*4],
0, [4, 2, 1], math_inst, min_cc, max_cc, [2,1,1]),
TileDescription([math_inst.instruction_shape[0]*2, math_inst.instruction_shape[1]*2, math_inst.instruction_shape[2]*4],
0, [4, 2, 1], math_inst, min_cc, max_cc, [1,2,1]),
TileDescription([math_inst.instruction_shape[0]*2, math_inst.instruction_shape[1]*2, math_inst.instruction_shape[2]*4],
0, [4, 2, 1], math_inst, min_cc, max_cc, [1,1,1]),
# 128x256x128
TileDescription([math_inst.instruction_shape[0]*2, math_inst.instruction_shape[1]*2, math_inst.instruction_shape[2]*4],
0, [4, 2, 1], math_inst, min_cc, max_cc, [1,1,1]),
]
tile_descriptions = tile_descriptions_medium + tile_descriptions_large
@ -5766,6 +5777,8 @@ def GenerateSM90_TensorOp_fp8_WGMMA_gemm(manifest, cuda_version):
0, [4, 1, 1], math_inst, min_cc, max_cc, [1,2,1]),
TileDescription([math_inst.instruction_shape[0], math_inst.instruction_shape[1], math_inst.instruction_shape[2]*4],
0, [4, 1, 1], math_inst, min_cc, max_cc, [2,1,1]),
TileDescription([math_inst.instruction_shape[0], math_inst.instruction_shape[1], math_inst.instruction_shape[2]*4],
0, [4, 1, 1], math_inst, min_cc, max_cc, [1,1,1]),
]
tile_descriptions_large = [
# 256x128x128
@ -5773,6 +5786,11 @@ def GenerateSM90_TensorOp_fp8_WGMMA_gemm(manifest, cuda_version):
0, [4, 1, 1], math_inst, min_cc, max_cc, [1,2,1]),
TileDescription([math_inst.instruction_shape[0]*4, math_inst.instruction_shape[1], math_inst.instruction_shape[2]*4],
0, [4, 1, 1], math_inst, min_cc, max_cc, [2,1,1]),
TileDescription([math_inst.instruction_shape[0]*4, math_inst.instruction_shape[1], math_inst.instruction_shape[2]*4],
0, [4, 1, 1], math_inst, min_cc, max_cc, [1,1,1]),
# 128x256x128
TileDescription([math_inst.instruction_shape[0]*2, math_inst.instruction_shape[1]*2, math_inst.instruction_shape[2]*4],
0, [4, 1, 1], math_inst, min_cc, max_cc, [1,1,1]),
]
tile_descriptions = [
# 128x128x128
@ -5780,6 +5798,8 @@ def GenerateSM90_TensorOp_fp8_WGMMA_gemm(manifest, cuda_version):
0, [4, 1, 1], math_inst, min_cc, max_cc, [1,2,1]),
TileDescription([math_inst.instruction_shape[0]*2, math_inst.instruction_shape[1], math_inst.instruction_shape[2]*4],
0, [4, 1, 1], math_inst, min_cc, max_cc, [2,1,1]),
TileDescription([math_inst.instruction_shape[0]*2, math_inst.instruction_shape[1], math_inst.instruction_shape[2]*4],
0, [4, 1, 1], math_inst, min_cc, max_cc, [1,1,1]),
]
else: