CUTLASS 3.3.0 (#1167)
* Release 3.3.0 Adds support for mixed precision GEMMs On Hopper and Ampere Adds support for < 16B aligned GEMMs on Hopper Enhancements to EVT Enhancements to Python interface Enhancements to Sub-byte type handling in CuTe Several other bug-fixes and performance improvements. * minor doc update
This commit is contained in:
Pradeep Ramani
@@ -34,16 +34,52 @@
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Utilities for enumerating CUTLASS library kernels
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"""
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import argparse
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import enum
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from itertools import product
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import logging
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import os.path
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import shutil
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import argparse
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import logging
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from cutlass_library.library import *
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from cutlass_library.manifest import *
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from itertools import product
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import sys
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# Certain usecases of cutlass_library nearly always prefer to run as scripts with
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# relative imports, rather than via an installed Python package. An example of this
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# is using CUTLASS's CMake system to generate a library of kernels to be profiled.
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# To make it easy to use these use cases when an existing installation of cutlass_library
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# exists, this global flag can be set to true (via command-line arguments) to ensure
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# that package-based installations are not used.
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# Create a temporary argument parser to check only for the availability of the
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# --disable-cutlass-package-imports argument, which controls whether package-based
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# imports are disabled.
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def _add_package_disablement_flag(argparser):
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argparser.add_argument("--disable-cutlass-package-imports", action='store_true', required=False,
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help="Disable use of cutlass_library from Python package")
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_parser = argparse.ArgumentParser()
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_add_package_disablement_flag(_parser)
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_args, _ = _parser.parse_known_args()
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# Add `CUTLASS_IGNORE_PACKAGE` to `builtins` so that it is visible for gating future
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# imports without requiring importing another module. Ideally, we would just place this
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# as a global variable in a module to that could be imported and checked (e.g.,
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# utils.CUTLASS_IGNORE_PACKAGE). However, this raises the issue of determining
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# where this module should be sourced (from the cutlass_library package or from
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# a relative import), which is the problem this variable is being used to solve in the
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# first place.
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import builtins
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builtins.CUTLASS_IGNORE_PACKAGE = _args.disable_cutlass_package_imports
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try:
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if CUTLASS_IGNORE_PACKAGE:
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raise ImportError("Disabling attempt to import cutlass_library")
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from cutlass_library.library import *
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from cutlass_library.manifest import *
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except ImportError:
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from library import *
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from manifest import *
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###################################################################################################
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#
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@@ -79,7 +115,7 @@ def EpilogueAlignment(max_alignment, tile, epilogue_steps = 8):
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return min(max_alignment, elements_per_thread)
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def DefaultSwizzlingFunctor():
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return SwizzlingFunctor.Identity8;
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return SwizzlingFunctor.Identity8
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# To use StreamK decomposition for basic GEMMs, set `swizzling_functor = SwizzlingFunctor.StreamK`
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#
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@@ -103,7 +139,7 @@ def CreateGemmOperator(manifest, layouts, tile_descriptions, data_type, \
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for tile_description in tile_descriptions:
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for alignment in alignment_constraints:
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for complex_transform in complex_transforms:
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# If alignment is a tuple or a list, then we have different alignments for A and B
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alignment_a = alignment if isinstance(alignment, int) else alignment[0]
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alignment_b = alignment if isinstance(alignment, int) else alignment[1]
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@@ -121,7 +157,6 @@ def CreateGemmOperator(manifest, layouts, tile_descriptions, data_type, \
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return operations
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# Generates 3.0 API based GemmUniversal API kernels. Alignment constraints are folded in with layouts
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def CreateGemmUniversal3xOperator(
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manifest, layouts, tile_descriptions, data_types,
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@@ -157,11 +192,14 @@ def CreateGemmUniversal3xOperator(
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C = TensorDescription(data_type["c_type"], layout[2][0], layout[2][1])
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D = TensorDescription(data_type["d_type"], layout[2][0], layout[2][1])
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extra_args = {}
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gemm_kind = GemmKind.Universal3x
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element_compute = data_type.get("epi_type", data_type["acc_type"])
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operation = GemmOperation(
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GemmKind.Universal3x, tile_description.minimum_compute_capability,
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gemm_kind, tile_description.minimum_compute_capability,
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tile_description, A, B, C, element_compute, epilogue_functor, swizzling_functor, D,
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kernel_schedule, epilogue_schedule, tile_scheduler)
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kernel_schedule, epilogue_schedule, tile_scheduler, extra_args)
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manifest.append(operation)
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operations.append(operation)
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@@ -2153,7 +2191,6 @@ def GenerateSM80_PlanarComplexTensorOp_16816(manifest, cuda_version):
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CreateGemmPlanarComplexOperator(manifest, layouts, tile_descriptions, \
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data_type_mixed, alignment_constraints, complex_transforms)
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#
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def GenerateSM80_TensorOp_16816_mixed_input_upcast_a(manifest, cuda_version):
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@@ -2225,8 +2262,9 @@ def GenerateSM80_TensorOp_16816_mixed_input_upcast_a(manifest, cuda_version):
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math_inst.element_accumulator,
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]
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# streamk uses more regs which can cause spill for the biggest warp tile size when the accumulators are 32bit.
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operations = CreateGemmOperator(manifest, layouts, tile_descriptions, \
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data_type, alignment_constraints)
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data_type, alignment_constraints, None, EpilogueFunctor.LinearCombination, SwizzlingFunctor.Identity8)
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# Avoid emitting two kernels if the accumulator type does not differ from the input type (e.g. F16 accumulation)
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if math_inst.element_a != math_inst.element_accumulator:
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@@ -2239,14 +2277,13 @@ def GenerateSM80_TensorOp_16816_mixed_input_upcast_a(manifest, cuda_version):
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]
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operations += CreateGemmOperator(manifest, layouts, tile_descriptions, \
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data_type_mixed, alignment_constraints)
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data_type_mixed, alignment_constraints, None, EpilogueFunctor.LinearCombination, SwizzlingFunctor.Identity8)
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for op in operations:
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if (DataTypeSize[op.C.element] == 16) and \
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(op.tile_description.threadblock_shape[1] <= 32):
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op.C.alignment = 4
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#
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def GenerateSM80_TensorOp_16816_mixed_input_upcast_b(manifest, cuda_version):
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@@ -2287,8 +2324,7 @@ def GenerateSM80_TensorOp_16816_mixed_input_upcast_b(manifest, cuda_version):
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# inner list contains the alignment constraints for operands/matrices
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# [[alignA, alignB, alignC],..]
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alignment_constraints = [[8, 16, 8],]
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for math_inst in math_instructions:
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tile_descriptions = [
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# 128x128
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@@ -2321,8 +2357,9 @@ def GenerateSM80_TensorOp_16816_mixed_input_upcast_b(manifest, cuda_version):
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math_inst.element_accumulator,
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]
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# streamk uses more regs which can cause spill for the biggest warp tile size when the accumulators are 32bit.
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CreateGemmOperator(manifest, layouts, tile_descriptions, \
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data_type, alignment_constraints)
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data_type, alignment_constraints, None, EpilogueFunctor.LinearCombination, SwizzlingFunctor.Identity8)
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# Avoid emitting two kernels if the accumulator type does not differ from the input type (e.g. F16 accumulation)
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if math_inst.element_a != math_inst.element_accumulator:
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@@ -2335,12 +2372,12 @@ def GenerateSM80_TensorOp_16816_mixed_input_upcast_b(manifest, cuda_version):
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]
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operations = CreateGemmOperator(manifest, layouts, tile_descriptions, \
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data_type_mixed, alignment_constraints)
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data_type_mixed, alignment_constraints, None, EpilogueFunctor.LinearCombination, SwizzlingFunctor.Identity8)
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for op in operations:
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if op.tile_description.threadblock_shape[1] <= 32:
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op.C.alignment = 4
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#
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def GenerateSM80_TensorOp_16832_TN(manifest, cuda_version):
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@@ -2723,6 +2760,7 @@ def GenerateSM80_TensorOp_16864_Interleaved(manifest, cuda_version):
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for op in operations:
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op.C.alignment = 16
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#
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#
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def GenerateSM80_TensorOp_168256(manifest, cuda_version):
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@@ -4458,6 +4496,154 @@ def GenerateSM90_TensorOp_16b_WGMMA_gemm(manifest, cuda_version):
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[[KernelScheduleType.TmaWarpSpecializedCooperative, EpilogueScheduleType.TmaWarpSpecializedCooperative]],
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tile_schedulers=[TileSchedulerType.StreamK])
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#
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def GenerateSM90_TensorOp_16b_WGMMA_alignx_gemm(manifest, cuda_version):
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if not CudaToolkitVersionSatisfies(cuda_version, 12, 0):
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return
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# layouts for ABC and their alignments.
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layouts = [
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[[LayoutType.RowMajor, 4], [LayoutType.ColumnMajor, 4], [LayoutType.ColumnMajor, 1]],
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[[LayoutType.RowMajor, 4], [LayoutType.RowMajor, 4], [LayoutType.ColumnMajor, 1]],
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[[LayoutType.ColumnMajor, 4], [LayoutType.ColumnMajor, 4], [LayoutType.ColumnMajor, 1]],
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[[LayoutType.ColumnMajor, 4], [LayoutType.RowMajor, 4], [LayoutType.ColumnMajor, 1]],
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[[LayoutType.RowMajor, 2], [LayoutType.ColumnMajor, 2], [LayoutType.ColumnMajor, 1]],
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[[LayoutType.RowMajor, 2], [LayoutType.RowMajor, 2], [LayoutType.ColumnMajor, 1]],
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[[LayoutType.ColumnMajor, 2], [LayoutType.ColumnMajor, 2], [LayoutType.ColumnMajor, 1]],
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[[LayoutType.ColumnMajor, 2], [LayoutType.RowMajor, 2], [LayoutType.ColumnMajor, 1]],
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]
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math_instructions = [
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MathInstruction(
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[64, 128, 16],
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DataType.f16, DataType.f16, DataType.f16,
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OpcodeClass.TensorOp,
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MathOperation.multiply_add),
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MathInstruction(
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[64, 128, 16],
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DataType.f16, DataType.f16, DataType.f32,
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OpcodeClass.TensorOp,
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MathOperation.multiply_add),
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MathInstruction(
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[64, 128, 16],
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DataType.bf16, DataType.bf16, DataType.f32,
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OpcodeClass.TensorOp,
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MathOperation.multiply_add),
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]
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min_cc = 90
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max_cc = 90
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for math_inst in math_instructions:
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tile_descriptions_small = [
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# TileDescription([math_inst.instruction_shape[0], math_inst.instruction_shape[1], math_inst.instruction_shape[2]*4],
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# 0, [4, 1, 1], math_inst, min_cc, max_cc, [1,1,1]),
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]
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tile_descriptions_medium = [
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TileDescription([math_inst.instruction_shape[0]*2, math_inst.instruction_shape[1], math_inst.instruction_shape[2]*4],
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0, [4, 1, 1], math_inst, min_cc, max_cc, [1,1,1]),
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# TileDescription([math_inst.instruction_shape[0], math_inst.instruction_shape[1]*2, math_inst.instruction_shape[2]*4],
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# 0, [4, 1, 1], math_inst, min_cc, max_cc, [1,1,1]),
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]
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tile_descriptions = tile_descriptions_small + tile_descriptions_medium
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data_type = {
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"a_type" : math_inst.element_a,
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"b_type" : math_inst.element_b,
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"c_type" : math_inst.element_accumulator,
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"d_type" : math_inst.element_accumulator,
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"acc_type" : math_inst.element_accumulator,
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"epi_type" : math_inst.element_accumulator
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}
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# Set alignment c based on Destination format.
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for layout in layouts:
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if data_type["c_type"] in [DataType.s32, DataType.f32]:
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layout[2][1] = 4
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elif data_type["c_type"] in [DataType.f16, DataType.bf16]:
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layout[2][1] = 8
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schedules = [
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# [KernelScheduleType.ScheduleAuto, EpilogueScheduleType.ScheduleAuto],
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[KernelScheduleType.CpAsyncWarpSpecialized, EpilogueScheduleType.NoSmemWarpSpecialized]
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]
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stream_k_schedules = []
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if CudaToolkitVersionSatisfies(cuda_version, 12, 1):
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schedules += [
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[KernelScheduleType.CpAsyncWarpSpecializedCooperative, EpilogueScheduleType.NoSmemWarpSpecialized],
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# [KernelScheduleType.CpAsyncWarpSpecializedPingpong, EpilogueScheduleType.NoSmemWarpSpecialized]
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]
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stream_k_schedules += [[KernelScheduleType.CpAsyncWarpSpecializedCooperative, EpilogueScheduleType.NoSmemWarpSpecialized]]
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CreateGemmUniversal3xOperator(manifest, layouts, tile_descriptions, data_type, schedules)
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if CudaToolkitVersionSatisfies(cuda_version, 12, 1):
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# Add stream-K variants
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CreateGemmUniversal3xOperator(manifest, layouts, tile_descriptions, data_type, stream_k_schedules, tile_schedulers=[TileSchedulerType.StreamK])
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# persistent kernels with TMA epilogues
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# if CudaToolkitVersionSatisfies(cuda_version, 12, 1):
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# CreateGemmUniversal3xOperator(manifest, layouts, tile_descriptions, data_type,
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# [[KernelScheduleType.CpAsyncWarpSpecializedPingpong, EpilogueScheduleType.TmaWarpSpecialized],
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# [KernelScheduleType.CpAsyncWarpSpecializedCooperative, EpilogueScheduleType.TmaWarpSpecializedCooperative]])
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# CreateGemmUniversal3xOperator(manifest, layouts, tile_descriptions, data_type,
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# [[KernelScheduleType.CpAsyncWarpSpecializedCooperative, EpilogueScheduleType.TmaWarpSpecializedCooperative]],
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# tile_schedulers=[TileSchedulerType.StreamK])
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# # Emit instance without C allocation + load
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# data_type["c_type"] = DataType.void
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# CreateGemmUniversal3xOperator(manifest, layouts, tile_descriptions, data_type,
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# [[KernelScheduleType.CpAsyncWarpSpecializedPingpong, EpilogueScheduleType.TmaWarpSpecialized],
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# [KernelScheduleType.CpAsyncWarpSpecializedCooperative, EpilogueScheduleType.TmaWarpSpecializedCooperative]])
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# CreateGemmUniversal3xOperator(manifest, layouts, tile_descriptions, data_type,
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# [[KernelScheduleType.CpAsyncWarpSpecializedCooperative, EpilogueScheduleType.TmaWarpSpecializedCooperative]],
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# tile_schedulers=[TileSchedulerType.StreamK])
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# for mixed precision kernels, also generate kernels that write output matrix in the A/B format
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# Avoid emitting two kernels if the accumulator type does not differ from the input type (e.g. F16 accumulation)
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if math_inst.element_a != math_inst.element_accumulator:
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data_type_mixed = {
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"a_type" : math_inst.element_a,
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"b_type" : math_inst.element_b,
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"c_type" : math_inst.element_a,
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"d_type" : math_inst.element_a,
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"acc_type" : math_inst.element_accumulator,
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"epi_type" : math_inst.element_accumulator
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}
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# Set alignment c based on Destination format.
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for layout in layouts:
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if data_type_mixed["c_type"] in [DataType.s32, DataType.f32]:
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layout[2][1] = 4
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elif data_type_mixed["c_type"] in [DataType.f16, DataType.bf16]:
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layout[2][1] = 8
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CreateGemmUniversal3xOperator(manifest, layouts, tile_descriptions, data_type_mixed, schedules)
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CreateGemmUniversal3xOperator(manifest, layouts, tile_descriptions, data_type_mixed, stream_k_schedules, tile_schedulers=[TileSchedulerType.StreamK])
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# persistent kernels with TMA epilogues
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# if CudaToolkitVersionSatisfies(cuda_version, 12, 1):
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# CreateGemmUniversal3xOperator(manifest, layouts, tile_descriptions, data_type_mixed,
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# [[KernelScheduleType.CpAsyncWarpSpecializedPingpong, EpilogueScheduleType.TmaWarpSpecialized],
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# [KernelScheduleType.CpAsyncWarpSpecializedCooperative, EpilogueScheduleType.TmaWarpSpecializedCooperative]])
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# CreateGemmUniversal3xOperator(manifest, layouts, tile_descriptions, data_type_mixed,
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# [[KernelScheduleType.CpAsyncWarpSpecializedCooperative, EpilogueScheduleType.TmaWarpSpecializedCooperative]],
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# tile_schedulers=[TileSchedulerType.StreamK])
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# # Emit instance without C allocation+load
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# data_type_mixed["c_type"] = DataType.void
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# CreateGemmUniversal3xOperator(manifest, layouts, tile_descriptions, data_type_mixed,
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# [[KernelScheduleType.CpAsyncWarpSpecializedPingpong, EpilogueScheduleType.TmaWarpSpecialized],
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# [KernelScheduleType.CpAsyncWarpSpecializedCooperative, EpilogueScheduleType.TmaWarpSpecializedCooperative]])
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# CreateGemmUniversal3xOperator(manifest, layouts, tile_descriptions, data_type_mixed,
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# [[KernelScheduleType.CpAsyncWarpSpecializedCooperative, EpilogueScheduleType.TmaWarpSpecializedCooperative]],
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# tile_schedulers=[TileSchedulerType.StreamK])
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#
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def GenerateSM90_TensorOp_tf32_WGMMA_gemm(manifest, cuda_version):
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if not CudaToolkitVersionSatisfies(cuda_version, 12, 0):
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@@ -4582,6 +4768,91 @@ def GenerateSM90_TensorOp_tf32_WGMMA_gemm(manifest, cuda_version):
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CreateGemmUniversal3xOperator(manifest, layouts_tf32_tn_nn_nt, tile_descriptions, data_types, schedules_default)
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CreateGemmUniversal3xOperator(manifest, layouts_tf32_tt, tile_descriptions, data_types, schedules_transposed_epilogue)
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#
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def GenerateSM90_TensorOp_tf32_WGMMA_alignx_gemm(manifest, cuda_version):
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if not CudaToolkitVersionSatisfies(cuda_version, 12, 0):
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return
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# layouts for ABC and their alignments.
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layouts = [
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[[LayoutType.RowMajor, 2], [LayoutType.ColumnMajor, 2], [LayoutType.ColumnMajor, 1]],
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[[LayoutType.RowMajor, 2], [LayoutType.RowMajor, 2], [LayoutType.ColumnMajor, 1]],
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[[LayoutType.ColumnMajor, 2], [LayoutType.ColumnMajor, 2], [LayoutType.ColumnMajor, 1]],
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[[LayoutType.ColumnMajor, 2], [LayoutType.RowMajor, 2], [LayoutType.ColumnMajor, 1]],
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[[LayoutType.RowMajor, 1], [LayoutType.ColumnMajor, 1], [LayoutType.ColumnMajor, 1]],
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[[LayoutType.RowMajor, 1], [LayoutType.RowMajor, 1], [LayoutType.ColumnMajor, 1]],
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[[LayoutType.ColumnMajor, 1], [LayoutType.ColumnMajor, 1], [LayoutType.ColumnMajor, 1]],
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[[LayoutType.ColumnMajor, 1], [LayoutType.RowMajor, 1], [LayoutType.ColumnMajor, 1]],
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]
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math_inst = MathInstruction(
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[64, 128, 8],
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DataType.tf32, DataType.tf32, DataType.f32,
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OpcodeClass.TensorOp,
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MathOperation.multiply_add)
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min_cc = 90
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max_cc = 90
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tile_descriptions_medium = [
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TileDescription([math_inst.instruction_shape[0]*2, math_inst.instruction_shape[1], math_inst.instruction_shape[2]*4],
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0, [4, 1, 1], math_inst, min_cc, max_cc, [1,1,1])
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]
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tile_descriptions_small = [
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# TileDescription([math_inst.instruction_shape[0], math_inst.instruction_shape[1], math_inst.instruction_shape[2]*4],
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# 0, [4, 1, 1], math_inst, min_cc, max_cc, [1,1,1])
|
||||
]
|
||||
|
||||
tile_descriptions = tile_descriptions_medium + tile_descriptions_small
|
||||
|
||||
data_types = [
|
||||
{
|
||||
"a_type" : math_inst.element_a,
|
||||
"b_type" : math_inst.element_b,
|
||||
"c_type" : math_inst.element_accumulator,
|
||||
"d_type" : math_inst.element_accumulator,
|
||||
"acc_type" : math_inst.element_accumulator,
|
||||
"epi_type" : math_inst.element_accumulator
|
||||
},
|
||||
{
|
||||
"a_type" : DataType.f32,
|
||||
"b_type" : DataType.f32,
|
||||
"c_type" : math_inst.element_accumulator,
|
||||
"d_type" : math_inst.element_accumulator,
|
||||
"acc_type" : math_inst.element_accumulator,
|
||||
"epi_type" : DataType.f32
|
||||
}
|
||||
]
|
||||
|
||||
is_tt_layout = lambda v: v[0][0] == LayoutType.RowMajor and v[1][0] == LayoutType.RowMajor
|
||||
# Split kernels into TN/NT, NN or TT layouts
|
||||
layouts_tn_nn_nt = filter(lambda v: not is_tt_layout(v), layouts)
|
||||
layouts_tt = filter(is_tt_layout, layouts)
|
||||
|
||||
CreateGemmUniversal3xOperator(manifest, layouts_tn_nn_nt, tile_descriptions, data_types, [
|
||||
# [KernelScheduleType.ScheduleAuto, EpilogueScheduleType.ScheduleAuto],
|
||||
[KernelScheduleType.CpAsyncWarpSpecialized, EpilogueScheduleType.NoSmemWarpSpecialized],
|
||||
])
|
||||
|
||||
# Kernels with TT layout use EpilogueTransposed (NoSmemWarpSpecialized with swapped strides),
|
||||
# because they use NN kernels underneath and transposing its epilogue will get the correct output
|
||||
CreateGemmUniversal3xOperator(manifest, layouts_tt, tile_descriptions, data_types, [
|
||||
# [KernelScheduleType.ScheduleAuto, EpilogueScheduleType.EpilogueTransposed],
|
||||
[KernelScheduleType.CpAsyncWarpSpecialized, EpilogueScheduleType.EpilogueTransposed]
|
||||
])
|
||||
|
||||
if CudaToolkitVersionSatisfies(cuda_version, 12, 1):
|
||||
CreateGemmUniversal3xOperator(manifest, layouts_tn_nn_nt, tile_descriptions, data_types, [
|
||||
# [KernelScheduleType.CpAsyncWarpSpecializedPingpong, EpilogueScheduleType.NoSmemWarpSpecialized],
|
||||
[KernelScheduleType.CpAsyncWarpSpecializedCooperative, EpilogueScheduleType.NoSmemWarpSpecialized]
|
||||
])
|
||||
|
||||
# Stream-K schedules
|
||||
CreateGemmUniversal3xOperator(manifest, layouts_tn_nn_nt, tile_descriptions, data_types, [
|
||||
[KernelScheduleType.CpAsyncWarpSpecializedCooperative, EpilogueScheduleType.NoSmemWarpSpecialized]
|
||||
], tile_schedulers=[TileSchedulerType.StreamK])
|
||||
|
||||
#
|
||||
def GenerateSM90_TensorOp_int8_WGMMA_gemm(manifest, cuda_version):
|
||||
if not CudaToolkitVersionSatisfies(cuda_version, 12, 0):
|
||||
@@ -4677,6 +4948,81 @@ def GenerateSM90_TensorOp_int8_WGMMA_gemm(manifest, cuda_version):
|
||||
tile_schedulers=[TileSchedulerType.Persistent, TileSchedulerType.StreamK]
|
||||
)
|
||||
|
||||
#
|
||||
def GenerateSM90_TensorOp_int8_WGMMA_alignx_gemm(manifest, cuda_version):
|
||||
if not CudaToolkitVersionSatisfies(cuda_version, 12, 0):
|
||||
return
|
||||
|
||||
# layouts for ABC and their alignments
|
||||
layouts = [
|
||||
[[LayoutType.RowMajor, 8], [LayoutType.ColumnMajor, 8], [LayoutType.ColumnMajor, 1]],
|
||||
[[LayoutType.RowMajor, 4], [LayoutType.ColumnMajor, 4], [LayoutType.ColumnMajor, 1]],
|
||||
]
|
||||
|
||||
math_instructions = [
|
||||
MathInstruction(
|
||||
[64, 128, 32],
|
||||
DataType.s8, DataType.s8, DataType.s32,
|
||||
OpcodeClass.TensorOp,
|
||||
MathOperation.multiply_add),
|
||||
MathInstruction(
|
||||
[64, 128, 32],
|
||||
DataType.u8, DataType.u8, DataType.s32,
|
||||
OpcodeClass.TensorOp,
|
||||
MathOperation.multiply_add),
|
||||
]
|
||||
|
||||
min_cc = 90
|
||||
max_cc = 90
|
||||
|
||||
for math_inst in math_instructions:
|
||||
tile_descriptions_small = [
|
||||
# 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, [1,1,1]),
|
||||
]
|
||||
tile_descriptions = tile_descriptions_medium + tile_descriptions_small
|
||||
|
||||
data_types = [
|
||||
{
|
||||
"a_type" : math_inst.element_a,
|
||||
"b_type" : math_inst.element_b,
|
||||
"c_type" : math_inst.element_accumulator,
|
||||
"d_type" : math_inst.element_accumulator,
|
||||
"acc_type" : math_inst.element_accumulator,
|
||||
"epi_type" : math_inst.element_accumulator
|
||||
},
|
||||
{
|
||||
"a_type" : math_inst.element_a,
|
||||
"b_type" : math_inst.element_b,
|
||||
"c_type" : DataType.s8,
|
||||
"d_type" : math_inst.element_a,
|
||||
"acc_type" : math_inst.element_accumulator,
|
||||
"epi_type" : DataType.f32
|
||||
}
|
||||
]
|
||||
|
||||
for data_type in data_types:
|
||||
for layout in layouts:
|
||||
layout[2][1] = 128 // DataTypeSize[data_type["d_type"]]
|
||||
CreateGemmUniversal3xOperator(manifest, layouts, tile_descriptions, data_type, [
|
||||
# [KernelScheduleType.ScheduleAuto, EpilogueScheduleType.NoSmemWarpSpecialized],
|
||||
[KernelScheduleType.CpAsyncWarpSpecialized, EpilogueScheduleType.NoSmemWarpSpecialized]
|
||||
])
|
||||
|
||||
if CudaToolkitVersionSatisfies(cuda_version, 12, 1):
|
||||
CreateGemmUniversal3xOperator(manifest, layouts, tile_descriptions, data_type, [
|
||||
# [KernelScheduleType.CpAsyncWarpSpecializedPingpong, EpilogueScheduleType.NoSmemWarpSpecialized],
|
||||
[KernelScheduleType.CpAsyncWarpSpecializedCooperative, EpilogueScheduleType.NoSmemWarpSpecialized]
|
||||
])
|
||||
CreateGemmUniversal3xOperator(manifest, layouts, tile_descriptions, data_type,
|
||||
[[KernelScheduleType.CpAsyncWarpSpecializedCooperative, EpilogueScheduleType.NoSmemWarpSpecialized]],
|
||||
tile_schedulers=[TileSchedulerType.StreamK])
|
||||
|
||||
#
|
||||
def GenerateSM90_TensorOp_fp8_WGMMA_gemm(manifest, cuda_version):
|
||||
if not CudaToolkitVersionSatisfies(cuda_version, 12, 0):
|
||||
return
|
||||
@@ -4882,6 +5228,188 @@ def GenerateSM90_TensorOp_fp8_WGMMA_gemm(manifest, cuda_version):
|
||||
[KernelScheduleType.TmaWarpSpecializedCooperativeFP8FastAccum, EpilogueScheduleType.TmaWarpSpecializedCooperative]],
|
||||
tile_schedulers=[TileSchedulerType.StreamK])
|
||||
|
||||
#
|
||||
def GenerateSM90_TensorOp_fp8_WGMMA_alignx_gemm(manifest, cuda_version):
|
||||
if not CudaToolkitVersionSatisfies(cuda_version, 12, 0):
|
||||
return
|
||||
|
||||
# layouts for ABC and their alignments
|
||||
layouts = [
|
||||
[[LayoutType.RowMajor, 8], [LayoutType.ColumnMajor, 8], [LayoutType.ColumnMajor, 1]], # TN Layout
|
||||
[[LayoutType.RowMajor, 4], [LayoutType.ColumnMajor, 4], [LayoutType.ColumnMajor, 1]], # TN Layout
|
||||
]
|
||||
|
||||
math_instructions = [
|
||||
# inst 64x128x32
|
||||
MathInstruction(
|
||||
[64, 128, 32],
|
||||
DataType.e4m3, DataType.e4m3, DataType.f32,
|
||||
OpcodeClass.TensorOp,
|
||||
MathOperation.multiply_add),
|
||||
MathInstruction(
|
||||
[64, 128, 32],
|
||||
DataType.e4m3, DataType.e5m2, DataType.f32,
|
||||
OpcodeClass.TensorOp,
|
||||
MathOperation.multiply_add),
|
||||
MathInstruction(
|
||||
[64, 128, 32],
|
||||
DataType.e5m2, DataType.e4m3, DataType.f32,
|
||||
OpcodeClass.TensorOp,
|
||||
MathOperation.multiply_add),
|
||||
MathInstruction(
|
||||
[64, 128, 32],
|
||||
DataType.e5m2, DataType.e5m2, DataType.f32,
|
||||
OpcodeClass.TensorOp,
|
||||
MathOperation.multiply_add),
|
||||
# inst 64x64x32
|
||||
# MathInstruction(
|
||||
# [64, 64, 32],
|
||||
# DataType.e4m3, DataType.e4m3, DataType.f32,
|
||||
# OpcodeClass.TensorOp,
|
||||
# MathOperation.multiply_add),
|
||||
# MathInstruction(
|
||||
# [64, 64, 32],
|
||||
# DataType.e4m3, DataType.e5m2, DataType.f32,
|
||||
# OpcodeClass.TensorOp,
|
||||
# MathOperation.multiply_add),
|
||||
# MathInstruction(
|
||||
# [64, 64, 32],
|
||||
# DataType.e5m2, DataType.e4m3, DataType.f32,
|
||||
# OpcodeClass.TensorOp,
|
||||
# MathOperation.multiply_add),
|
||||
# MathInstruction(
|
||||
# [64, 64, 32],
|
||||
# DataType.e5m2, DataType.e5m2, DataType.f32,
|
||||
# OpcodeClass.TensorOp,
|
||||
# MathOperation.multiply_add),
|
||||
]
|
||||
|
||||
min_cc = 90
|
||||
max_cc = 90
|
||||
|
||||
for math_inst in math_instructions:
|
||||
data_types = [
|
||||
{
|
||||
"a_type" : math_inst.element_a,
|
||||
"b_type" : math_inst.element_b,
|
||||
"c_type" : DataType.f32,
|
||||
"d_type" : DataType.f32,
|
||||
"acc_type" : math_inst.element_accumulator,
|
||||
"epi_type" : math_inst.element_accumulator
|
||||
},
|
||||
{
|
||||
"a_type" : math_inst.element_a,
|
||||
"b_type" : math_inst.element_b,
|
||||
"c_type" : DataType.f32,
|
||||
"d_type" : DataType.e4m3,
|
||||
"acc_type" : math_inst.element_accumulator,
|
||||
"epi_type" : math_inst.element_accumulator
|
||||
},
|
||||
{
|
||||
"a_type" : math_inst.element_a,
|
||||
"b_type" : math_inst.element_b,
|
||||
"c_type" : DataType.f32,
|
||||
"d_type" : DataType.e5m2,
|
||||
"acc_type" : math_inst.element_accumulator,
|
||||
"epi_type" : math_inst.element_accumulator
|
||||
},
|
||||
{
|
||||
"a_type" : math_inst.element_a,
|
||||
"b_type" : math_inst.element_b,
|
||||
"c_type" : DataType.bf16,
|
||||
"d_type" : DataType.bf16,
|
||||
"acc_type" : math_inst.element_accumulator,
|
||||
"epi_type" : math_inst.element_accumulator
|
||||
},
|
||||
{
|
||||
"a_type" : math_inst.element_a,
|
||||
"b_type" : math_inst.element_b,
|
||||
"c_type" : DataType.bf16,
|
||||
"d_type" : DataType.e4m3,
|
||||
"acc_type" : math_inst.element_accumulator,
|
||||
"epi_type" : math_inst.element_accumulator
|
||||
},
|
||||
{
|
||||
"a_type" : math_inst.element_a,
|
||||
"b_type" : math_inst.element_b,
|
||||
"c_type" : DataType.bf16,
|
||||
"d_type" : DataType.e5m2,
|
||||
"acc_type" : math_inst.element_accumulator,
|
||||
"epi_type" : math_inst.element_accumulator
|
||||
},
|
||||
{
|
||||
"a_type" : math_inst.element_a,
|
||||
"b_type" : math_inst.element_b,
|
||||
"c_type" : DataType.f16,
|
||||
"d_type" : DataType.f16,
|
||||
"acc_type" : math_inst.element_accumulator,
|
||||
"epi_type" : math_inst.element_accumulator
|
||||
},
|
||||
{
|
||||
"a_type" : math_inst.element_a,
|
||||
"b_type" : math_inst.element_b,
|
||||
"c_type" : DataType.f16,
|
||||
"d_type" : DataType.e4m3,
|
||||
"acc_type" : math_inst.element_accumulator,
|
||||
"epi_type" : math_inst.element_accumulator
|
||||
},
|
||||
{
|
||||
"a_type" : math_inst.element_a,
|
||||
"b_type" : math_inst.element_b,
|
||||
"c_type" : DataType.f16,
|
||||
"d_type" : DataType.e5m2,
|
||||
"acc_type" : math_inst.element_accumulator,
|
||||
"epi_type" : math_inst.element_accumulator
|
||||
},
|
||||
]
|
||||
|
||||
if math_inst.instruction_shape[1] == 128:
|
||||
tile_descriptions = [
|
||||
# 128x128x128
|
||||
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]),
|
||||
]
|
||||
|
||||
# elif math_inst.instruction_shape[1] == 64:
|
||||
# tile_descriptions = [
|
||||
# # 256x64x128
|
||||
# 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]),
|
||||
# ]
|
||||
|
||||
else:
|
||||
assert False, "math inst is not supported"
|
||||
|
||||
if CudaToolkitVersionSatisfies(cuda_version, 12, 1):
|
||||
schedules = [
|
||||
# [KernelScheduleType.ScheduleAuto, EpilogueScheduleType.ScheduleAuto],
|
||||
[KernelScheduleType.CpAsyncWarpSpecializedCooperative, EpilogueScheduleType.NoSmemWarpSpecialized],
|
||||
# [KernelScheduleType.CpAsyncWarpSpecializedPingpong, EpilogueScheduleType.NoSmemWarpSpecialized],
|
||||
[KernelScheduleType.CpAsyncWarpSpecialized, EpilogueScheduleType.NoSmemWarpSpecialized],
|
||||
]
|
||||
stream_k_schedules = [[KernelScheduleType.CpAsyncWarpSpecializedCooperative, EpilogueScheduleType.NoSmemWarpSpecialized]]
|
||||
else:
|
||||
schedules = [
|
||||
# [KernelScheduleType.ScheduleAuto, EpilogueScheduleType.ScheduleAuto],
|
||||
[KernelScheduleType.CpAsyncWarpSpecialized, EpilogueScheduleType.NoSmemWarpSpecialized]
|
||||
]
|
||||
stream_k_schedules = []
|
||||
|
||||
|
||||
for data_type in data_types:
|
||||
# With No-SMEM epilogues
|
||||
CreateGemmUniversal3xOperator(manifest, layouts, tile_descriptions, data_type, schedules)
|
||||
|
||||
if CudaToolkitVersionSatisfies(cuda_version, 12, 1):
|
||||
# Persistent kernels with TMA epilogues
|
||||
# CreateGemmUniversal3xOperator(manifest, layouts, tile_descriptions, data_type,
|
||||
# [[KernelScheduleType.CpAsyncWarpSpecializedCooperative, EpilogueScheduleType.TmaWarpSpecializedCooperative]])
|
||||
|
||||
# Add stream-K variants (with and without TMA epilogues)
|
||||
CreateGemmUniversal3xOperator(manifest, layouts, tile_descriptions, data_type, stream_k_schedules, tile_schedulers=[TileSchedulerType.StreamK])
|
||||
# CreateGemmUniversal3xOperator(manifest, layouts, tile_descriptions, data_type,
|
||||
# [[KernelScheduleType.CpAsyncWarpSpecializedCooperative, EpilogueScheduleType.TmaWarpSpecializedCooperative]],
|
||||
# tile_schedulers=[TileSchedulerType.StreamK])
|
||||
|
||||
#
|
||||
def GenerateSM90_TensorOp_1684(manifest, cuda_version):
|
||||
@@ -5488,9 +6016,13 @@ def GenerateSM90_TensorOp_1684_symm_complex_gaussian(manifest, cuda_version):
|
||||
#
|
||||
def GenerateSM90(manifest, cuda_version):
|
||||
GenerateSM90_TensorOp_16b_WGMMA_gemm(manifest, cuda_version)
|
||||
GenerateSM90_TensorOp_16b_WGMMA_alignx_gemm(manifest, cuda_version)
|
||||
GenerateSM90_TensorOp_tf32_WGMMA_gemm(manifest, cuda_version)
|
||||
GenerateSM90_TensorOp_tf32_WGMMA_alignx_gemm(manifest, cuda_version)
|
||||
GenerateSM90_TensorOp_int8_WGMMA_gemm(manifest, cuda_version)
|
||||
GenerateSM90_TensorOp_int8_WGMMA_alignx_gemm(manifest, cuda_version)
|
||||
GenerateSM90_TensorOp_fp8_WGMMA_gemm(manifest, cuda_version)
|
||||
GenerateSM90_TensorOp_fp8_WGMMA_alignx_gemm(manifest, cuda_version)
|
||||
GenerateSM90_TensorOp_1684(manifest, cuda_version)
|
||||
GenerateSM90_TensorOp_1684_complex(manifest, cuda_version)
|
||||
GenerateSM90_TensorOp_1684_complex_gaussian(manifest, cuda_version)
|
||||
@@ -5543,6 +6075,7 @@ def define_parser():
|
||||
parser.add_argument("--disable-full-archs-compilation", action="store_true", required=False, help="Disable compilation for every archs in --architectures")
|
||||
parser.add_argument("--log-level", default='info', type=numeric_log_level, required=False,
|
||||
help='Logging level to be used by the generator script')
|
||||
_add_package_disablement_flag(parser)
|
||||
return parser
|
||||
|
||||
|
||||
|
||||
Reference in New Issue
Block a user