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generator.py
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#################################################################################################
#
# Copyright (c) 2017 - 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: BSD-3-Clause
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
#
#################################################################################################
"""
Utilities for enumerating CUTLASS library kernels
"""
import argparse
import enum
from itertools import chain, product
import logging
import os.path
import shutil
import sys
import copy
from typing import Any, Dict, Optional, Sequence, Tuple
_LOGGER = logging.getLogger(__name__)
def logging_prefix(indent_level: int = 0) -> str:
"""String prefix for start of each debug log entry"""
prefix = '*** '
indent = ' '
return f"{prefix}{indent_level * indent}"
def log_debug_line(line: str, indent_level: int = 0) -> None:
"""Log one line of debug output"""
prefix = logging_prefix(indent_level)
_LOGGER.debug(prefix + line)
# Certain usecases of cutlass_library nearly always prefer to run as scripts with
# relative imports, rather than via an installed Python package. An example of this
# is using CUTLASS's CMake system to generate a library of kernels to be profiled.
# To make it easy to use these use cases when an existing installation of cutlass_library
# exists, this global flag can be set to true (via command-line arguments) to ensure
# that package-based installations are not used.
# Create a temporary argument parser to check only for the availability of the
# --disable-cutlass-package-imports argument, which controls whether package-based
# imports are disabled.
def _add_package_disablement_flag(argparser):
argparser.add_argument("--disable-cutlass-package-imports", action='store_true', required=False,
help="Disable use of cutlass_library from Python package")
_parser = argparse.ArgumentParser()
_add_package_disablement_flag(_parser)
_args, _ = _parser.parse_known_args()
# Add `CUTLASS_IGNORE_PACKAGE` to `builtins` so that it is visible for gating future
# imports without requiring importing another module. Ideally, we would just place this
# as a global variable in a module to that could be imported and checked (e.g.,
# utils.CUTLASS_IGNORE_PACKAGE). However, this raises the issue of determining
# where this module should be sourced (from the cutlass_library package or from
# a relative import), which is the problem this variable is being used to solve in the
# first place.
import builtins
builtins.CUTLASS_IGNORE_PACKAGE = _args.disable_cutlass_package_imports
try:
if CUTLASS_IGNORE_PACKAGE:
raise ImportError("Disabling attempt to import cutlass_library")
from cutlass_library.library import *
from cutlass_library.manifest import *
from cutlass_library.emit_kernel_listing import emit_gemm_kernel_testlist
except ImportError:
from library import *
from manifest import *
from emit_kernel_listing import emit_gemm_kernel_testlist
###################################################################################################
#
def CudaToolkitVersionSatisfies(semantic_ver_string, major, minor, patch = 0):
# by default, use the latest CUDA Toolkit version
cuda_version = [11, 0, 132]
# Update cuda_version based on parsed string
if semantic_ver_string != '':
for i, x in enumerate([int(x) for x in semantic_ver_string.split('.')[:3]]):
if i < len(cuda_version):
cuda_version[i] = x
else:
cuda_version.append(x)
return cuda_version >= [major, minor, patch]
###################################################################################################
###################################################################################################
#
def EpilogueAlignment(max_alignment, tile, epilogue_steps = 8):
''' Helper to compute the maximum alignment of the epilogue '''
def product(X, identity = 1):
result = identity
for item in X:
result *= item
return result
elements_per_thread = product(tile.threadblock_shape[:-1]) // product(tile.warp_count) // 32 // epilogue_steps
return min(max_alignment, elements_per_thread)
def DefaultSwizzlingFunctor():
return SwizzlingFunctor.Identity8
# To use StreamK decomposition for basic GEMMs, set `swizzling_functor = SwizzlingFunctor.StreamK`
#
def CreateGemmOperator(manifest, layouts, tile_descriptions, data_type, \
alignment_constraints, complex_transforms = None, epilogue_functor = EpilogueFunctor.LinearCombination, \
swizzling_functor = DefaultSwizzlingFunctor()):
if complex_transforms is None:
complex_transforms = [(ComplexTransform.none, ComplexTransform.none),]
element_a, element_b, element_c, element_epilogue = data_type
operations = []
# by default, only generate the largest tile and largest alignment
if manifest.kernel_filter == '':
tile_descriptions = [tile_descriptions[0],]
alignment_constraints = [alignment_constraints[0],]
for layout in layouts:
for tile_description in tile_descriptions:
for alignment in alignment_constraints:
for complex_transform in complex_transforms:
# If alignment is a tuple or a list, then we have different alignments for A and B
alignment_a = alignment if isinstance(alignment, int) else alignment[0]
alignment_b = alignment if isinstance(alignment, int) else alignment[1]
alignment_c = min(8, alignment_a) if isinstance(alignment, int) else alignment[2]
A = TensorDescription(element_a, layout[0], alignment_a, complex_transform[0])
B = TensorDescription(element_b, layout[1], alignment_b, complex_transform[1])
C = TensorDescription(element_c, layout[2], alignment_c)
new_operation = GemmOperation(GemmKind.Universal, tile_description.minimum_compute_capability, \
tile_description, A, B, C, element_epilogue, epilogue_functor, swizzling_functor)
manifest.append(new_operation)
operations.append(new_operation)
return operations
# Generates 3.0 API based GemmUniversal API kernels. Alignment constraints are folded in with layouts
def CreateGemmUniversal3xOperator(
manifest, layouts, tile_descriptions, data_types,
schedules = [[KernelScheduleType.ScheduleAuto, EpilogueScheduleType.ScheduleAuto]],
complex_transforms=None,
epilogue_functor=EpilogueFunctor.LinearCombination,
swizzling_functor=SwizzlingFunctor.Identity1,
tile_schedulers=[TileSchedulerType.Default],
gemm_kind=GemmKind.Universal3x):
if type(data_types) is dict:
data_types = [data_types]
for s in schedules:
assert(len(s) == 2)
if complex_transforms is None:
complex_transforms = [(ComplexTransform.none, ComplexTransform.none), ]
operations = []
# by default, only generate the largest tile and largest alignment
if manifest.kernel_filter == '':
tile_descriptions = [tile_descriptions[0]]
combinations = product(layouts, tile_descriptions, data_types, complex_transforms, schedules, tile_schedulers)
for layout, tile_description, data_type, complex_transform, schedules, tile_scheduler in combinations:
kernel_schedule, epilogue_schedule = schedules
A = TensorDescription(
data_type["a_type"], layout[0][0], layout[0][1], complex_transform[0])
B = TensorDescription(
data_type["b_type"], layout[1][0], layout[1][1], complex_transform[1])
C = TensorDescription(data_type["c_type"], layout[2][0], layout[2][1])
D = TensorDescription(data_type["d_type"], layout[2][0], layout[2][1])
gemm_op_extra_args = {}
element_compute = data_type.get("epi_type", data_type["acc_type"])
if "sf_type" in data_type:
gemm_op_extra_args["ScaleFactorA"] = data_type["sf_type"]
gemm_op_extra_args["ScaleFactorB"] = data_type["sf_type"]
gemm_op_extra_args["ScaleFactorD"] = { "tensor": TensorDescription(data_type["sfd_type"]["type"], data_type["sfd_type"]["layout"]),
"vector_size" : data_type["sfd_type"]["vector_size"]}
assert is_block_scaled(gemm_kind)
A_dtype = data_type["a_type"]
B_dtype = data_type["b_type"]
A_dtype_bits = DataTypeSize[A_dtype]
B_dtype_bits = DataTypeSize[B_dtype]
is_A_dtype_narrow = A_dtype_bits < B_dtype_bits
if is_A_dtype_narrow:
narrow_dtype, wide_dtype = (A_dtype, B_dtype)
narrow_dtype_bits, wide_dtype_bits = (A_dtype_bits, B_dtype_bits)
else:
narrow_dtype, wide_dtype = (B_dtype, A_dtype)
narrow_dtype_bits, wide_dtype_bits = (B_dtype_bits, A_dtype_bits)
mixed_input_modes = [None]
if narrow_dtype_bits != wide_dtype_bits:
if narrow_dtype == DataType.s4 and (wide_dtype == DataType.e4m3 or wide_dtype == DataType.e5m2):
mixed_input_modes = [MixedInputMode.ScaleOnly]
else:
mixed_input_modes = [MixedInputMode.ConvertOnly, MixedInputMode.ScaleOnly, MixedInputMode.ScaleWithZeroPoint]
mixed_input_shuffle_options = [False]
if (mixed_input_modes[0] is not None) and (wide_dtype_bits == 16) and (narrow_dtype_bits == 4 or narrow_dtype_bits == 8):
mixed_input_shuffle_options = [False, True]
for mixed_input_mode, mixed_input_shuffle in product(mixed_input_modes, mixed_input_shuffle_options):
operation = GemmOperation(
gemm_kind, tile_description.minimum_compute_capability,
tile_description, A, B, C, element_compute, epilogue_functor, swizzling_functor, D,
kernel_schedule, epilogue_schedule, tile_scheduler,
mixed_input_mode=mixed_input_mode, mixed_input_shuffle=mixed_input_shuffle, **gemm_op_extra_args)
manifest.append(operation)
operations.append(operation)
return operations
# Generates 3.0 API based GemmUniversal API kernels. Alignment constraints are folded in with layouts
def CreateSparseGemmUniversal3xOperator(
manifest, layouts, tile_descriptions, data_types,
schedules = [[KernelScheduleType.ScheduleAuto, EpilogueScheduleType.ScheduleAuto]],
complex_transforms=None,
epilogue_functor=EpilogueFunctor.LinearCombination,
swizzling_functor=SwizzlingFunctor.Identity1,
tile_schedulers=[TileSchedulerType.Default]):
if type(data_types) is dict:
data_types = [data_types]
for s in schedules:
assert(len(s) == 2)
if complex_transforms is None:
complex_transforms = [(ComplexTransform.none, ComplexTransform.none), ]
operations = []
# by default, only generate the largest tile and largest alignment
if manifest.kernel_filter == '':
tile_descriptions = [tile_descriptions[0]]
combinations = product(layouts, tile_descriptions, data_types, complex_transforms, schedules, tile_schedulers)
for layout, tile_description, data_type, complex_transform, schedules, tile_scheduler in combinations:
kernel_schedule, epilogue_schedule = schedules
A = TensorDescription(
data_type["a_type"], layout[0][0], layout[0][1], complex_transform[0])
B = TensorDescription(
data_type["b_type"], layout[1][0], layout[1][1], complex_transform[1])
# Currently assume tensor C/D have same layout requirement.
C = TensorDescription(data_type["c_type"], layout[2][0], layout[2][1])
D = TensorDescription(data_type["d_type"], layout[2][0], layout[2][1])
element_compute = data_type.get("epi_type", data_type["acc_type"])
operation = GemmOperation(
GemmKind.SparseUniversal3x, tile_description.minimum_compute_capability,
tile_description, A, B, C, element_compute, epilogue_functor, swizzling_functor, D,
kernel_schedule, epilogue_schedule, tile_scheduler)
manifest.append(operation)
operations.append(operation)
return operations
#
def CreateSparseGemmOperator(manifest, layouts, tile_descriptions, data_type, \
alignment_constraints, complex_transforms = None, epilogue_functor = EpilogueFunctor.LinearCombination, \
swizzling_functor = SwizzlingFunctor.Identity8):
if complex_transforms is None:
complex_transforms = [(ComplexTransform.none, ComplexTransform.none),]
element_a, element_b, element_c, element_epilogue = data_type
gemm_kinds = [GemmKind.Sparse]
operations = []
# by default, only generate the largest tile and largest alignment
if manifest.kernel_filter == '':
tile_descriptions = [tile_descriptions[0],]
alignment_constraints = [alignment_constraints[0],]
for layout in layouts:
for tile_description in tile_descriptions:
for alignment in alignment_constraints:
for complex_transform in complex_transforms:
alignment_c = min(8, alignment)
A = TensorDescription(element_a, layout[0], alignment, complex_transform[0])
B = TensorDescription(element_b, layout[1], alignment, complex_transform[1])
C = TensorDescription(element_c, layout[2], alignment_c)
new_operation = GemmOperation(GemmKind.Sparse, tile_description.minimum_compute_capability, \
tile_description, A, B, C, element_epilogue, epilogue_functor, swizzling_functor)
manifest.append(new_operation)
operations.append(new_operation)
return operations
#
def CreateGemmPlanarComplexOperator(manifest, layouts, tile_descriptions, data_type, \
alignment_constraints, complex_transforms):
if complex_transforms is None:
complex_transforms = [(ComplexTransform.none, ComplexTransform.none),]
element_a, element_b, element_c, element_epilogue = data_type
gemm_kinds = [GemmKind.PlanarComplex, GemmKind.PlanarComplexArray]
# by default, only generate the largest tile and largest alignment
if manifest.kernel_filter == '':
tile_descriptions = [tile_descriptions[0],]
alignment_constraints = [alignment_constraints[0],]
for gemm_kind in gemm_kinds:
for layout in layouts:
for tile_description in tile_descriptions:
for alignment in alignment_constraints:
for complex_transform in complex_transforms:
alignment_c = min(8, alignment)
A = TensorDescription(element_a, layout[0], alignment, complex_transform[0])
B = TensorDescription(element_b, layout[1], alignment, complex_transform[1])
C = TensorDescription(element_c, layout[2], alignment_c)
manifest.append(GemmOperation(gemm_kind, \
tile_description.minimum_compute_capability, \
tile_description, A, B, C, element_epilogue))
return
#
def CreateGemmGroupedOperator(manifest, layouts, tile_descriptions, data_type, \
alignment_constraints, complex_transforms = None, epilogue_functor = EpilogueFunctor.LinearCombination, \
swizzling_functor = SwizzlingFunctor.Identity8):
if complex_transforms is None:
complex_transforms = [(ComplexTransform.none, ComplexTransform.none),]
element_a, element_b, element_c, element_epilogue = data_type
operations = []
# by default, only generate the largest tile and largest alignment
if manifest.kernel_filter == '':
tile_descriptions = [tile_descriptions[0],]
alignment_constraints = [alignment_constraints[0],]
for layout in layouts:
for tile_description in tile_descriptions:
for alignment in alignment_constraints:
for complex_transform in complex_transforms:
alignment_c = min(8, alignment)
A = TensorDescription(element_a, layout[0], alignment, complex_transform[0])
B = TensorDescription(element_b, layout[1], alignment, complex_transform[1])
C = TensorDescription(element_c, layout[2], alignment_c)
new_operation = GroupedGemmOperation(GemmKind.Grouped, tile_description.minimum_compute_capability, \
tile_description, A, B, C, element_epilogue, epilogue_functor, swizzling_functor)
manifest.append(new_operation)
operations.append(new_operation)
return operations
#
def CreateRankKOperator(manifest, layouts, fill_modes, tile_descriptions, data_type, \
alignment_constraints, blas_mode, epilogue_functor = EpilogueFunctor.LinearCombination, \
swizzling_functor = SwizzlingFunctor.Identity8):
element_a, element_c, element_epilogue = data_type
operations = []
# by default, only generate the largest tile and largest alignment
if manifest.kernel_filter == '':
tile_descriptions = [tile_descriptions[0],]
alignment_constraints = [alignment_constraints[0],]
for layout in layouts:
for fill_mode in fill_modes:
for tile_description in tile_descriptions:
for alignment in alignment_constraints:
# SERK supported layouts (RowMajor, ColumnMajor) with no conjugation
complex_transform = ComplexTransform.none
# HERK supported layouts (RowMajor + conj, ColumnMajor)
if blas_mode == BlasMode.hermitian and layout[0] == LayoutType.RowMajor:
complex_transform = ComplexTransform.conj
alignment_c = 1 # Alignment only applies to A in SYRK
A = TensorDescription(element_a, layout[0], alignment, complex_transform)
C = SymmetricTensorDescription(element_c, layout[1], fill_mode, alignment_c)
# Rank-K update
new_operation = RankKOperation(RankKKind.Universal, tile_description.minimum_compute_capability, \
tile_description, A, C, element_epilogue, epilogue_functor, swizzling_functor, blas_mode)
manifest.append(new_operation)
operations.append(new_operation)
# Rank-2K update
new_operation = Rank2KOperation(RankKKind.Universal, tile_description.minimum_compute_capability, \
tile_description, A, C, element_epilogue, epilogue_functor, swizzling_functor, blas_mode)
manifest.append(new_operation)
operations.append(new_operation)
return operations
#
def CreateTrmmOperator(manifest, layouts, side_modes, fill_modes, diag_types, tile_descriptions, data_type, \
alignment_constraints, complex_transforms = None, epilogue_functor = EpilogueFunctor.LinearCombination, \
swizzling_functor = SwizzlingFunctor.Identity8):
if complex_transforms is None:
complex_transforms = [(ComplexTransform.none),]
element_a, element_b, element_c, element_epilogue = data_type
operations = []
# by default, only generate the largest tile and largest alignment
if manifest.kernel_filter == '':
tile_descriptions = [tile_descriptions[0],]
alignment_constraints = [alignment_constraints[0],]
for layout in layouts:
for side_mode in side_modes:
for fill_mode in fill_modes:
for diag_type in diag_types:
for tile_description in tile_descriptions:
for alignment in alignment_constraints:
for complex_transform in complex_transforms:
alignment_c = min(8, alignment)
A = TriangularTensorDescription(element_a, layout[0], side_mode, fill_mode, diag_type,
alignment, complex_transform)
B = TensorDescription(element_b, layout[1], alignment)
C = TensorDescription(element_c, layout[2], alignment_c)
new_operation = TrmmOperation(TrmmKind.Universal, tile_description.minimum_compute_capability, \
tile_description, A, B, C, element_epilogue, epilogue_functor, swizzling_functor)
manifest.append(new_operation)
operations.append(new_operation)
return operations
#
def CreateSymmOperator(manifest, layouts, side_modes, fill_modes, tile_descriptions, data_type, \
alignment_constraints, blas_mode, epilogue_functor = EpilogueFunctor.LinearCombination, \
swizzling_functor = SwizzlingFunctor.Identity8):
element_a, element_b, element_c, element_epilogue = data_type
operations = []
# by default, only generate the largest tile and largest alignment
if manifest.kernel_filter == '':
tile_descriptions = [tile_descriptions[0],]
alignment_constraints = [alignment_constraints[0],]
for layout in layouts:
for side_mode in side_modes:
for fill_mode in fill_modes:
for tile_description in tile_descriptions:
for alignment in alignment_constraints:
# SYMM supported layouts (RowMajor, ColumnMajor) with no conjugation
complex_transform = ComplexTransform.none
alignment_a = 1 # No vectorized access for the triangular matrix
alignment_c = min(8, alignment)
A = SymmetricTensorDescription(element_a, layout[0], fill_mode, alignment_a, complex_transform, side_mode)
# tensor A and B have same data type and layout
B = TensorDescription(element_b, layout[0], alignment)
C = TensorDescription(element_c, layout[1], alignment_c)
# SYMM/HEMM update
new_operation = SymmOperation(SymmKind.Universal, tile_description.minimum_compute_capability, \
tile_description, A, B, C, element_epilogue, epilogue_functor, swizzling_functor, blas_mode)
manifest.append(new_operation)
operations.append(new_operation)
# SYMM/HEMM update
new_operation = SymmOperation(SymmKind.Universal, tile_description.minimum_compute_capability, \
tile_description, A, B, C, element_epilogue, epilogue_functor, swizzling_functor, blas_mode)
manifest.append(new_operation)
operations.append(new_operation)
return operations
###########################################################################################################
# ConvolutionOperator support variations
# ____________________________________________________________________
# ConvolutionalOperator | Analytic | Optimized
# ____________________________________________________________________
# | Fprop | (strided) | (strided)
# | Dgrad | (strided, unity*) | (strided, unity)
# | Wgrad | (strided) | (strided)
# ____________________________________________________________________
#
# Note : Operator marked (*) are supported but not generated to keep the instantiated kernel count low
###########################################################################################################
# Convolution for 2D operations
def CreateConv2dOperator(manifest, layout, tile_descriptions, data_type, alignment_constraints, \
conv_kinds = [ConvKind.Fprop, ConvKind.Dgrad, ConvKind.Wgrad], \
epilogue_functor = EpilogueFunctor.LinearCombination, swizzling_functor = SwizzlingFunctor.Identity4):
element_a, element_b, element_c, element_epilogue = data_type
# one exceptional case
# iterator algorithm (analytic and optimized)
iterator_algorithms = [IteratorAlgorithm.Analytic, IteratorAlgorithm.Optimized]
# by default, only generate the largest tile size, largest alignment, and optimized iterator
if manifest.kernel_filter == '':
tile_descriptions = [tile_descriptions[0],]
alignment_constraints = [alignment_constraints[0],]
iterator_algorithms = [IteratorAlgorithm.Optimized]
operations = []
for tile in tile_descriptions:
for alignment in alignment_constraints:
alignment_c = min(8, alignment)
A = TensorDescription(element_a, layout[0], alignment)
B = TensorDescription(element_b, layout[1], alignment)
C = TensorDescription(element_c, layout[2], alignment_c)
swizzling_functor_ = swizzling_functor
#
# Conv2d Fprop
#
if ConvKind.Fprop in conv_kinds:
# Strided support for Analytic and Optimized Fprop
for iterator_algorithm in iterator_algorithms:
new_operations = [
# None grouped kernel
Conv2dOperation(ConvKind.Fprop, iterator_algorithm, tile.minimum_compute_capability, tile,\
A, B, C, element_epilogue, StrideSupport.Unity, epilogue_functor, swizzling_functor_),
]
# Instance group conv kernel
if tile.math_instruction.opcode_class == OpcodeClass.TensorOp and A.layout == LayoutType.TensorNHWC and \
tile.minimum_compute_capability >= 80:
# SingleGroup kernel
new_operations.append(Conv2dOperation(ConvKind.Fprop, iterator_algorithm, tile.minimum_compute_capability, tile,\
A, B, C, element_epilogue, StrideSupport.Unity, epilogue_functor, swizzling_functor_, group_mode=GroupMode.SingleGroup))
# Analytic iterator supports MultipleGroup mode
if iterator_algorithm == IteratorAlgorithm.Analytic:
new_operations.append(Conv2dOperation(ConvKind.Fprop, iterator_algorithm, tile.minimum_compute_capability, tile,\
A, B, C, element_epilogue, StrideSupport.Unity, epilogue_functor, swizzling_functor_, group_mode=GroupMode.MultipleGroup))
for new_operation in new_operations:
manifest.append(new_operation)
operations.append(new_operation)
#
# Conv2d Dgrad
#
if ConvKind.Dgrad in conv_kinds:
# Unity stride for Analytic and Optimized Dgrad
for iterator_algorithm in iterator_algorithms:
new_operation = Conv2dOperation(ConvKind.Dgrad, iterator_algorithm, tile.minimum_compute_capability, tile,\
A, B, C, element_epilogue, StrideSupport.Unity, epilogue_functor, swizzling_functor_)
manifest.append(new_operation)
operations.append(new_operation)
# Strided support for Analytic Dgrad
# strided dgrad uses a special threadblock swizzle
# note that SwizzlingFunctor.StridedDgradHorizontal might be
# better for problem sizes with large activation channel count
swizzling_functor_strided_dgrad_ = SwizzlingFunctor.StridedDgradIdentity1
if IteratorAlgorithm.Analytic in iterator_algorithms:
new_operation = Conv2dOperation(ConvKind.Dgrad, IteratorAlgorithm.Analytic, tile.minimum_compute_capability, tile,\
A, B, C, element_epilogue, StrideSupport.Strided, epilogue_functor, swizzling_functor_strided_dgrad_)
manifest.append(new_operation)
operations.append(new_operation)
# Strided support for Optimized Dgrad
if IteratorAlgorithm.Optimized in iterator_algorithms:
new_operation = Conv2dOperation(ConvKind.Dgrad, IteratorAlgorithm.Optimized, tile.minimum_compute_capability, tile,\
A, B, C, element_epilogue, StrideSupport.Strided, epilogue_functor, swizzling_functor_strided_dgrad_)
manifest.append(new_operation)
operations.append(new_operation)
#
# Conv2d Wgrad
#
if ConvKind.Wgrad in conv_kinds:
# Strided support for Analytic and Optimized Wgrad
for iterator_algorithm in iterator_algorithms:
new_operation = Conv2dOperation(ConvKind.Wgrad, iterator_algorithm, tile.minimum_compute_capability, tile,\
A, B, C, element_epilogue, StrideSupport.Strided, epilogue_functor, swizzling_functor_)
manifest.append(new_operation)
operations.append(new_operation)
return operations
# Convolution for 2D operations specialized for few channels
def CreateConv2dFixedChannelsOperator(manifest, layout, tile_descriptions, data_type, channel_counts, \
conv_kinds = [ConvKind.Fprop, ConvKind.Dgrad, ConvKind.Wgrad], \
epilogue_functor = EpilogueFunctor.LinearCombination, swizzling_functor = SwizzlingFunctor.Identity4):
element_a, element_b, element_c, element_epilogue = data_type
# one exceptional case
# iterator algorithm (analytic and optimized)
iterator_algorithms = [IteratorAlgorithm.FixedChannels,]
# by default, only generate the largest tile size, largest alignment, and optimized iterator
if manifest.kernel_filter == '':
tile_descriptions = [tile_descriptions[0],]
channel_counts = [channel_counts[0],]
operations = []
for tile in tile_descriptions:
for channel_count in channel_counts:
alignment_c = EpilogueAlignment(channel_count, tile)
A = TensorDescription(element_a, layout[0], channel_count)
B = TensorDescription(element_b, layout[1], channel_count)
C = TensorDescription(element_c, layout[2], alignment_c)
swizzling_functor_ = swizzling_functor
#
# Conv2d Fprop
#
if ConvKind.Fprop in conv_kinds:
# Strided support for Analytic and Optimized Fprop
for iterator_algorithm in iterator_algorithms:
new_operation = Conv2dOperation(ConvKind.Fprop, iterator_algorithm, tile.minimum_compute_capability, tile,\
A, B, C, element_epilogue, StrideSupport.Strided, epilogue_functor, swizzling_functor_)
manifest.append(new_operation)
operations.append(new_operation)
return operations
# Convolution for 2D operations specialized for few channels
def CreateConv2dFewChannelsOperator(manifest, layout, tile_descriptions, data_type, channel_counts, \
conv_kinds = [ConvKind.Fprop, ConvKind.Dgrad, ConvKind.Wgrad], \
epilogue_functor = EpilogueFunctor.LinearCombination, swizzling_functor = SwizzlingFunctor.Identity4):
element_a, element_b, element_c, element_epilogue = data_type
# one exceptional case
# iterator algorithm (analytic and optimized)
iterator_algorithms = [IteratorAlgorithm.FewChannels,]
# by default, only generate the largest tile size, largest alignment, and optimized iterator
if manifest.kernel_filter == '':
tile_descriptions = [tile_descriptions[0],]
channel_counts = [channel_counts[0],]
operations = []
for tile in tile_descriptions:
for channel_count in channel_counts:
alignment_c = EpilogueAlignment(channel_count, tile)
A = TensorDescription(element_a, layout[0], channel_count)
B = TensorDescription(element_b, layout[1], channel_count)
C = TensorDescription(element_c, layout[2], alignment_c)
swizzling_functor_ = swizzling_functor
#
# Conv2d Fprop
#
if ConvKind.Fprop in conv_kinds:
# Strided support for Analytic and Optimized Fprop
for iterator_algorithm in iterator_algorithms:
new_operation = Conv2dOperation(ConvKind.Fprop, iterator_algorithm, tile.minimum_compute_capability, tile,\
A, B, C, element_epilogue, StrideSupport.Strided, epilogue_functor, swizzling_functor_)
manifest.append(new_operation)
operations.append(new_operation)
return operations
# Convolution for 3D operations
def CreateConv3dOperator(manifest, layout, tile_descriptions, data_type, alignment, \
conv_kinds = [ConvKind.Fprop, ConvKind.Dgrad, ConvKind.Wgrad], epilogue_functor = EpilogueFunctor.LinearCombination):
element_a, element_b, element_c, element_epilogue = data_type
# one exceptional case
alignment_c = min(8, alignment)
# iterator algorithm (analytic and optimized)
iterator_algorithms = [IteratorAlgorithm.Analytic, IteratorAlgorithm.Optimized]
# by default, only generate the largest tile size and optimized iterators
if manifest.kernel_filter == '':
tile_descriptions = [tile_descriptions[0],]
iterator_algorithms = [IteratorAlgorithm.Optimized]
operations = []
# All tile sizes for Conv3dFprop and Conv3dWgrad
for tile in tile_descriptions:
A = TensorDescription(element_a, layout, alignment)
B = TensorDescription(element_b, layout, alignment)
C = TensorDescription(element_c, layout, alignment_c)
#
# Conv3d Fprop
#
if ConvKind.Fprop in conv_kinds:
# Strided support for Analytic and Optimized Fprop
for iterator_algorithm in iterator_algorithms:
new_operation = Conv3dOperation(ConvKind.Fprop, iterator_algorithm, tile.minimum_compute_capability, tile,\
A, B, C, element_epilogue, StrideSupport.Strided)
manifest.append(new_operation)
operations.append(new_operation)
#
# Conv3d Wgrad
#
if ConvKind.Wgrad in conv_kinds:
# Strided support for Analytic and Optimized Wgrad
for iterator_algorithm in iterator_algorithms:
new_operation = Conv3dOperation(ConvKind.Wgrad, iterator_algorithm, tile.minimum_compute_capability, tile,\
A, B, C, element_epilogue, StrideSupport.Strided, epilogue_functor)
manifest.append(new_operation)
operations.append(new_operation)
# All tile sizes for Conv3dDgrad
for tile in tile_descriptions:
A = TensorDescription(element_a, layout, alignment)
B = TensorDescription(element_b, layout, alignment)
C = TensorDescription(element_c, layout, alignment_c)
#
# Conv3d Dgrad
#
if ConvKind.Dgrad in conv_kinds:
# Unity stride for Optimized Dgrad
new_operation = Conv3dOperation(ConvKind.Dgrad, IteratorAlgorithm.Optimized, tile.minimum_compute_capability, tile,\
A, B, C, element_epilogue, StrideSupport.Unity, epilogue_functor)
manifest.append(new_operation)
operations.append(new_operation)
# Strided support for Analytic Dgrad
# Conv3dDgrad has a naive strided support which does not cut down redundant MMAs
new_operation = Conv3dOperation(ConvKind.Dgrad, IteratorAlgorithm.Analytic, tile.minimum_compute_capability, tile,\
A, B, C, element_epilogue, StrideSupport.Strided, epilogue_functor)
manifest.append(new_operation)
operations.append(new_operation)
return operations
# Convolution for Depthwise 2d conv
def CreateDepthwiseConv2dOperator(manifest, layout, tile_descriptions, data_type, alignment_constraints, \
conv_kinds = [ConvKind.Fprop, ConvKind.Dgrad, ConvKind.Wgrad], \
epilogue_functor = EpilogueFunctor.LinearCombination, swizzling_functor = SwizzlingFunctor.Identity4):
element_a, element_b, element_c, element_epilogue = data_type
# iterator algorithm (FixedStrideDilation, Optimized)
iterator_algorithms = [IteratorAlgorithm.FixedStrideDilation, IteratorAlgorithm.Optimized]
# by default, only generate the largest tile size, largest alignment, and optimized iterator
if manifest.kernel_filter == '':
tile_descriptions = [tile_descriptions[0],]
alignment_constraints = [alignment_constraints[0],]
operations = []
for tile in tile_descriptions:
for alignment in alignment_constraints:
alignment_c = min(8, alignment)
A = TensorDescription(element_a, layout[0], alignment)
B = TensorDescription(element_b, layout[1], alignment)
C = TensorDescription(element_c, layout[2], alignment_c)
swizzling_functor_ = swizzling_functor
if ConvKind.Fprop in conv_kinds:
# Strided support for Optimized and FixedStridedDilation Depthwise Conv
for iterator_algorithm in iterator_algorithms:
stride_support = StrideSupport.Strided
if iterator_algorithm == IteratorAlgorithm.FixedStrideDilation:
if tile.stride == [-1, -1] or tile.dilation == [-1,-1]:
continue
stride_support = StrideSupport.Fixed
if iterator_algorithm == IteratorAlgorithm.Optimized:
if tile.stride != [-1, -1] or tile.dilation != [-1,-1]:
continue
new_operation = Conv2dOperation(ConvKind.Fprop,
iterator_algorithm,
tile.minimum_compute_capability,
tile,
A, B, C,
element_epilogue,
stride_support,
epilogue_functor,
swizzling_functor_,
group_mode=GroupMode.Depthwise)
manifest.append(new_operation)
operations.append(new_operation)
return operations
class ConvOperation3x:
"""All parameters of a CUTLASS 3 convolution operation.
Unlike CUTLASS 2 convolutions, CUTLASS 3 convolutions do not
distinguish between 2-D and 3-D convolutions by kernel class name.
Instead, for CUTLASS 3 convolutions, the tensor layouts encode
whether the convolution is 2-D or 3-D. Thus, this class deduces
the OperationKind (either Conv2d or Conv3d) from the layouts,
rather than taking it as a constructor parameter.
"""
def __init__(self,
conv_kind: ConvKind,
tile_description: TileDescription,
A: TensorDescription,
B: TensorDescription,
C: TensorDescription,
element_compute: Optional[DataType] = None,
D: Optional[TensorDescription] = None,
kernel_schedule: KernelScheduleType = KernelScheduleType.ScheduleAuto,
epilogue_schedule: EpilogueScheduleType = EpilogueScheduleType.ScheduleAuto,
tile_scheduler: TileSchedulerType = TileSchedulerType.Default,
log_indent_level: int = 1):
log_debug_line(f'ConvOperation3x::init: conv_kind: {conv_kind}', log_indent_level)
log_indent_level = log_indent_level + 1
self.conv_kind = conv_kind
self.tile_description = tile_description
self.A = A
self.B = B
self.C = C
self.element_compute = C.element if element_compute is None else element_compute
self.kernel_schedule = kernel_schedule
self.epilogue_schedule = epilogue_schedule
self.arch = tile_description.minimum_compute_capability
self.tile_scheduler = tile_scheduler
if D == None:
self.D = C
else:
self.D = D
self.is_3x = True
self.group_mode = GroupMode.NoneGroup # CUTLASS 3 convolutions currently aren't grouped
operation_kind = None
for layout in (A.layout, B.layout, C.layout):
assert(isinstance(layout, LayoutType))
new_operation_kind = convolution_tensor_layout_type_to_operation_kind(layout)
if operation_kind is None:
operation_kind = new_operation_kind
else: # CUTLASS 3 convolutions don't permit mixing 2-D and 3-D layouts.
assert(operation_kind == new_operation_kind)
assert(operation_kind is not None)
self.operation_kind = operation_kind
def __str__(self):
return f"ConvOperation3x: operation_kind={self.operation_kind}, conv_kind={self.conv_kind}, tile_description={self.tile_description}"
def is_complex(self):
complex_operators = [
MathOperation.multiply_add_complex,
MathOperation.multiply_add_complex_gaussian,
MathOperation.multiply_add_complex_fast_f32
]
return self.tile_description.math_instruction.math_operation in complex_operators
def is_mixed_input(self):
return self.A.element != self.B.element
def accumulator_type(self):
accum = self.tile_description.math_instruction.element_accumulator
if self.is_complex():
return get_complex_from_real(accum)
return accum
def short_math_name(self):
prefix = ''
if self.tile_description.math_instruction.math_operation == MathOperation.multiply_add_complex_gaussian:
prefix = 'g'
return prefix + DataTypeNames[self.accumulator_type()]
def is_tensor_op(self):
tensor_ops = [
OpcodeClass.TensorOp,
OpcodeClass.WmmaTensorOp
]
return self.tile_description.math_instruction.opcode_class in tensor_ops
def instruction_shape_string(self):
math_operations_map = {
MathOperation.xor_popc: 'xor',
MathOperation.and_popc: 'and'
}
if self.is_tensor_op():
is0, is1, is2 = self.tile_description.math_instruction.instruction_shape
math_op = self.tile_description.math_instruction.math_operation
math_op_string = math_operations_map[math_op] if math_op in math_operations_map.keys() else ''
return f"{is0}x{is1}x{is2}{math_op_string}"
else:
return ''
def intermediate_type_string(self):
'''
Name of the distinct intermediate type used by the tensor operation,
or the empty string if none.
Tensor ops (opcode_clas *TensorOp) may use an intermediate data type
that differs from the element type of A or the accumulator type.
'''
if not self.is_tensor_op():
return ''