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#################################################################################################
#
# Copyright (c) 2023 - 2024 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.
#
#################################################################################################
"""
Classes containing valid operations for a given compute capability and data types.
"""
from itertools import combinations_with_replacement
import logging
from cuda import __version__
import cutlass_library
from cutlass_library.library import ConvKind, IteratorAlgorithm, StrideSupport, GroupMode
import cutlass
from cutlass.utils.check import valid_stage_count
from cutlass.utils.datatypes import td_from_profiler_td, td_from_profiler_op
_generator_ccs = [50, 60, 61, 70, 75, 80, 90]
# Strip any additional information from the CUDA version
_cuda_version = __version__.split("rc")[0]
class KernelsForDataType:
"""
Container class for keeping track of kernels that correspond to a particular combination
of data types for operands A, B, and accumulator
"""
def __init__(self, datatype_comb: tuple, layout_comb: tuple):
self.datatype_comb = datatype_comb
self.layout_comb = layout_comb
self.math_operations = set()
# Dictionary mapping from alignment (int) to a list of kernels that fit the alignment
# constraint for the data type combination
self.kernels_by_alignment = {}
def add(self, operation):
"""
Add an operation to the list of supported kernels
"""
alignment_key = f"{operation.A.alignment} {operation.B.alignment} {operation.C.alignment}"
if alignment_key not in self.kernels_by_alignment:
self.kernels_by_alignment[alignment_key] = []
self.kernels_by_alignment[alignment_key].append(operation)
self.math_operations.add(operation.tile_description.math_instruction.math_operation)
def alignments(self, operand: str):
"""
Returns an unsorted list of alignments supported by this data type combination
:param operand: identifier of operand in question (e.g., A, B, C)
:type operand: str
:return: unsorted list of alignments supported by this data type combination
:rtype: list
"""
operand_idx = self._operand_idx(operand)
return [int(key.split(" ")[operand_idx]) for key in self.kernels_by_alignment.keys()]
@property
def all_operations(self):
"""
Returns a list of all operations supported by this data type combination
:return: list of all operations supported by this data type combination
:rtype: list
"""
ops = []
for _, alignment_ops in self.kernels_by_alignment.items():
ops.extend(alignment_ops)
return ops
def default_operation(self, math_operation: cutlass.MathOperation):
key = sorted(list(self.kernels_by_alignment.keys()))[0]
kernels = self.kernels_by_alignment[key]
if math_operation is not None:
kernels = [x for x in kernels if x.tile_description.math_instruction.math_operation == math_operation]
return kernels[0]
def operations(self, alignment_A: int, alignment_B: int, alignment_C: int, math_operation: cutlass.MathOperation):
"""
Returns operations satisfying the alignment constraints
:param alignment_A: alignment constraint of operations to return
:type alignment_A: int
:param alignment_B: alignment constraint of operations to return
:type alignment_B: int
:param alignment_C: alignment constraint of operations to return
:type alignment_C: int
:param math_operation: math operation to consider
:type math_operation: cutlass.MathOperation
:return: list of operations
:rtype: list
"""
key = f"{alignment_A} {alignment_B} {alignment_C}"
if key not in self.kernels_by_alignment:
og_key = key
# Reconcile A, B, and C alignments by trying to align to the minimum
min_alignment = min(alignment_A, alignment_B, alignment_C)
key = f"{min_alignment} {min_alignment} {min_alignment}"
if key not in self.kernels_by_alignment:
# Finally, go through all available alignment combinations and find
# one for which all values are less than those passed in.
key = None
alignments = sorted([(int(x) for x in k.split(" ")) for k in self.kernels_by_alignment.keys()], reverse=True)
for align_A, align_B, align_C in alignments:
if align_A <= alignment_A and align_B <= alignment_B and align_C <= alignment_C:
key = f"{align_A} {align_B} {align_C}"
break
if key is None:
raise Exception(
f"No operations of alignment {og_key} found for data type and layout "
f"combination {self.datatype_comb} {self.layout_comb}. Compatible alignments "
f"are {self.kernels_by_alignment.keys()}"
)
ops = self.kernels_by_alignment[key]
if math_operation is not None:
ops = [op for op in ops if op.tile_description.math_instruction.math_operation == math_operation]
return ops
def _operand_idx(self, key: str) -> int:
operand_list = ["A", "B", "C"]
if key not in operand_list:
raise Exception(f"Unexpected operand {operand}")
return operand_list.index(key)
def find_alignment(self, shape: tuple, layout: cutlass.LayoutType, operand=str) -> int:
"""
Returns the most preferable alignment for a given shape and layout
:param shape: extent of each dimension of the tensor
:type shape: tuple
:param layout: layout of the tensor
:type layout: cutlass.LayoutType
:param operand: descriptor of the operand in question
:type operand: str
:return: maximum alignment supported by the data type combination and tensor size
:rtype: int
"""
operand_idx = self._operand_idx(operand)
# Determine the leading dimension of the shape
if layout == cutlass.LayoutType.ColumnMajor:
ld = shape[-2]
elif layout == cutlass.LayoutType.RowMajor:
ld = shape[-1]
elif layout == cutlass.LayoutType.TensorNHWC:
ld = shape[-1]
else:
raise Exception(f"Unexpected or unsupported layout {layout}")
for alignments in sorted(list(self.kernels_by_alignment.keys()), reverse=True):
alignment = int(alignments.split(" ")[operand_idx])
if ld % alignment == 0:
return alignment
# Default to alignment of 1 if no others match
return 1
def sort(self):
"""
Sorts each list of kernels in `kernels_by_alignment` in descending order of threadblock shape
"""
key = lambda op: (
op.tile_description.threadblock_shape[0]
* op.tile_description.threadblock_shape[1]
* op.tile_description.threadblock_shape[2]
)
for alignment in self.kernels_by_alignment.keys():
self.kernels_by_alignment[alignment].sort(key=key, reverse=True)
def supports_math_operation(self, math_operation: cutlass.MathOperation) -> bool:
"""
Returns whether `math_operation` is supported by at least one operation.
:param math_operation: math operation to consider
:type math_operation: cutlass.MathOperation
:return: whether math_operation is supported by at least one operation
:rtype: bool
"""
return math_operation is None or math_operation in self.math_operations
class ArchOptions:
"""
Structure for keeping track of kernels available on a given compute capability
:param target_cc: compute capability of the device on which kernels will be run
:type target_cc: int
:param kernel_cc: compute capability of the kernels to generate
:type kernel_cc: int
:param operation_kind: type of operation to register
:type operation_kind: cutlass_library.OperationKind
:param gemm_kinds: types of GEMM operations that can be included
:type gemm_kinds: list
:param allowed_math_operations: types of primitive math operations allowed
:type allowed_math_operations: list
"""
def __init__(
self,
target_cc: int,
kernel_cc: int,
operation_kind: cutlass_library.OperationKind,
gemm_kinds: list,
allowed_math_operations: list = [
cutlass_library.MathOperation.multiply_add,
cutlass_library.MathOperation.multiply_add_saturate,
cutlass_library.MathOperation.multiply_add_mixed_input_upcast,
cutlass_library.MathOperation.multiply_add_fast_f32
]
):
self.cc = kernel_cc
# Dictionary with following structure:
# Key: OpcodeClass
# Value: Dictionary with the following structure:
# Key: tuple of ((DataType, DataType, DataType), (LayoutType, LayoutType, LayoutType),
# representing ((element_a, element_b, element_accumulator), (layout_a, layout_b))
# Value: KernelsForDataType
self.operations_by_opclass = {}
self.op_class = None
self.allowed_math_operations = allowed_math_operations
# Identify the method within CUTLASS generator script that generates kernel
# descriptions for the target CC
generate_function_name = "GenerateSM" + str(kernel_cc)
if not hasattr(cutlass_library.generator, generate_function_name):
cutlass.logger.warning(f"No generator found for architecture {kernel_cc}")
return
generate_function = getattr(cutlass_library.generator, generate_function_name)
# Initialize a default manifest and populate it with valid kernel descriptions
# for the target CC
args = [
"--kernels=all",
f"--log-level={logging.getLevelName(cutlass.logger.level)}"
]
manifest_args = cutlass_library.generator.define_parser().parse_args(args)
manifest = cutlass_library.manifest.Manifest(manifest_args)
generate_function(manifest, _cuda_version)
if operation_kind not in manifest.operations:
# No kernels generated for this architecture, this could be because the CUDA
# toolkit is insufficient to support operations in this CC
cutlass.logger.warning(f"No operations of type {operation_kind} found for CC {kernel_cc}")
return
# Only one CC should be returned, given the setup above of calling only the generation scripts
# for a given CC
if len(manifest.operations[operation_kind].keys()) != 1 or kernel_cc not in manifest.operations[operation_kind]:
raise Exception(f"Error finding kernels for SM{kernel_cc}. Check that your CUDA toolkit version "
"is sufficient for the architecture in question.")
# Iterate through the available operations for this operation kind and
# find available opclasses and data types
for name, op_list in manifest.operations[operation_kind][kernel_cc].items():
for op in op_list:
if operation_kind == cutlass_library.OperationKind.Gemm:
if op.gemm_kind not in gemm_kinds:
continue
mi = op.tile_description.math_instruction
if mi.math_operation not in self.allowed_math_operations:
continue
# Prune operations that don't fit in shared memory
td = td_from_profiler_op(op)
if not valid_stage_count(target_cc, kernel_cc, td, verbose=False)[0]:
continue
if mi.opcode_class not in self.operations_by_opclass:
self.operations_by_opclass[mi.opcode_class] = {}
datatype_comb = (mi.element_a, mi.element_b, mi.element_accumulator)
layout_comb = (op.A.layout, op.B.layout)
# Register TF32 kernels as F32 to enable F32 -> TF32 conversion + TF32 Tensor Core operations
if datatype_comb == (cutlass_library.DataType.tf32, cutlass_library.DataType.tf32, cutlass_library.DataType.f32):
# TF32 kernels only supported on SM80 and beyond
if self.cc < 80:
continue
elif self.cc == 90:
if (op.A.element != cutlass_library.DataType.f32
or op.B.element != cutlass_library.DataType.f32
or op.C.element != cutlass_library.DataType.f32):
continue
datatype_comb = (cutlass_library.DataType.f32, cutlass_library.DataType.f32, cutlass_library.DataType.f32)
opclass_dict = self.operations_by_opclass[mi.opcode_class]
key = (datatype_comb, layout_comb)
if key not in opclass_dict:
opclass_dict[key] = KernelsForDataType(datatype_comb, layout_comb)
opclass_dict[key].add(op)
# Set the default opclass to TensorOp, if available. Otherwise default to SIMT
if cutlass_library.OpcodeClass.TensorOp in self.operations_by_opclass:
self.op_class = cutlass_library.OpcodeClass.TensorOp
else:
self.op_class = cutlass_library.OpcodeClass.Simt
# The profiler's generator may generate only a limited set of combinations of operands for SIMT kernels.
# Here, we generate additional versions via a generic TileDescription.
if cutlass_library.OpcodeClass.Simt not in self.operations_by_opclass:
self.operations_by_opclass[cutlass_library.OpcodeClass.Simt] = {}
if operation_kind == cutlass_library.OperationKind.Gemm:
types = [
(cutlass_library.DataType.s8, cutlass_library.DataType.s8, cutlass_library.DataType.s8),
(cutlass_library.DataType.s8, cutlass_library.DataType.s8, cutlass_library.DataType.s32),
(cutlass_library.DataType.f16, cutlass_library.DataType.f16, cutlass_library.DataType.f16),
(cutlass_library.DataType.f16, cutlass_library.DataType.f16, cutlass_library.DataType.f32),
(cutlass_library.DataType.f32, cutlass_library.DataType.f32, cutlass_library.DataType.f32),
(cutlass_library.DataType.f64, cutlass_library.DataType.f64, cutlass_library.DataType.f64),
]
# Add FP8 A/B/C
fp8_types = [cutlass_library.DataType.e4m3, cutlass_library.DataType.e5m2]
for type_comb in combinations_with_replacement(fp8_types, 3):
types.append(type_comb)
# Add FP8 A/B with FP32 C
for type_comb in combinations_with_replacement(fp8_types, 2):
types.append(type_comb + (cutlass.DataType.f32,))
layouts = [
(cutlass_library.LayoutType.RowMajor, cutlass_library.LayoutType.RowMajor),
(cutlass_library.LayoutType.RowMajor, cutlass_library.LayoutType.ColumnMajor),
(cutlass_library.LayoutType.ColumnMajor, cutlass_library.LayoutType.RowMajor),
(cutlass_library.LayoutType.ColumnMajor, cutlass_library.LayoutType.ColumnMajor),
]
elif operation_kind == cutlass_library.OperationKind.Conv2d:
types = [
(cutlass_library.DataType.f16, cutlass_library.DataType.f16, cutlass_library.DataType.f16),
(cutlass_library.DataType.f16, cutlass_library.DataType.f16, cutlass_library.DataType.f32),
(cutlass_library.DataType.f32, cutlass_library.DataType.f32, cutlass_library.DataType.f32),
(cutlass_library.DataType.f64, cutlass_library.DataType.f64, cutlass_library.DataType.f64),
]
layouts = [
(cutlass_library.LayoutType.TensorNHWC, cutlass_library.LayoutType.TensorNHWC),
]
else:
raise NotImplementedError(f"Operation kind {operation_kind} is currently unsupported.")
alignment = 1
epilogue_functor = cutlass_library.EpilogueFunctor.LinearCombination
swizzling_functor = cutlass_library.SwizzlingFunctor.Identity8
for type_comb in types:
for layout_comb in layouts:
comb = (type_comb, layout_comb)
if comb in self.operations_by_opclass[cutlass_library.OpcodeClass.Simt]:
continue
A = cutlass_library.TensorDescription(type_comb[0], layout_comb[0], alignment)
B = cutlass_library.TensorDescription(type_comb[1], layout_comb[1], alignment)
C = cutlass_library.TensorDescription(type_comb[2], cutlass_library.LayoutType.ColumnMajor, alignment)
math_inst = cutlass_library.MathInstruction(
[1, 1, 1],
type_comb[0],
type_comb[1],
type_comb[2],
cutlass_library.OpcodeClass.Simt,
cutlass_library.MathOperation.multiply_add
)
td = cutlass_library.TileDescription(
[128, 128, 8], 2, [4, 2, 1], math_inst, 50, 1024)
# Prune operations that don't fit in shared memory
if not valid_stage_count(target_cc, kernel_cc, td_from_profiler_td(td), verbose=False)[0]:
continue
new_kernels = KernelsForDataType(type_comb, layout_comb)
if operation_kind == cutlass_library.OperationKind.Gemm:
new_operation = cutlass_library.manifest.GemmOperation(
cutlass_library.GemmKind.Universal, td.minimum_compute_capability,
td, A, B, C, type_comb[2], epilogue_functor, swizzling_functor)
new_kernels.add(new_operation)
elif operation_kind == cutlass_library.OperationKind.Conv2d:
for conv_kind in [ConvKind.Fprop, ConvKind.Dgrad, ConvKind.Wgrad]:
new_operation = cutlass_library.manifest.Conv2dOperation(
conv_kind, IteratorAlgorithm.Analytic, td.minimum_compute_capability, td,
A, B, C, type_comb[2], StrideSupport.Strided, epilogue_functor, swizzling_functor,
group_mode=GroupMode.SingleGroup
)
new_kernels.add(new_operation)
self.operations_by_opclass[cutlass_library.OpcodeClass.Simt][comb] = new_kernels
# Sort all operations
for oc in self.operations_by_opclass.keys():
for comb in self.operations_by_opclass[oc].keys():
self.operations_by_opclass[oc][comb].sort()
def opclass_supports_combination(
self, op_class: cutlass_library.OpcodeClass, datatype_comb: tuple, layout_comb: tuple, math_operation: cutlass_library.MathOperation
) -> bool:
"""
Returns whether the provided operation class supports the provided data type and layout combination
:param op_class: operation class to consider
:type op_class: cutlass_library.OpcodeClass
:param datatype_comb: tuple of data types for (element_A, element_B, element_accumulator)
:type datatype_comb: tuple[cutlass_library.DataType]
:param layout_comb: tuple of data types for (layout_A, layout_B)
:type layout_comb: tuple[cutlass_library.LayoutType]
:param math_operation: math operation to consider or None if any can be considered
:type math_operation: cutlass.MathOperation
:return: set of operation classes that support the provided data type and layout combination
:rtype: set
"""
if op_class not in self.operations_by_opclass:
raise Exception(f"Unexpected or unsupported operation class {op_class}")
if operations := self.operations_by_opclass[op_class].get((datatype_comb, layout_comb)):
if math_operation is not None:
return operations.supports_math_operation(math_operation)
else:
return True
return False
def supporting_opclasses(
self,
element_a: cutlass_library.DataType,
element_b: cutlass_library.DataType,
element_accumulator: cutlass_library.DataType,
layout_a: cutlass_library.LayoutType,
layout_b: cutlass_library.LayoutType,
math_operation: cutlass_library.MathOperation,
) -> set:
"""
Returns a set of operation classes that support the provided data type combination
:param element_a: data type of operand A
:type element_a: cutlass_library.DataType
:param element_b: data type of operand B
:type element_b: cutlass_library.DataType
:param element_accumulator: data type of accumulator
:type element_accumulator: cutlass_library.DataType
:param layout_a: layout of operand A
:type layout_a: cutlass_library.LayoutType
:param layout_b: layout of operand B
:type layout_b: cutlass_library.LayoutType
:param math_operation: math operation to consider
:type math_operation: cutlass.MathOperation
:return: set of operation classes that support the provided data type combination
:rtype: set
"""
supporting_op_classes = set()
datatype_comb = (element_a, element_b, element_accumulator)
layout_comb = (layout_a, layout_b)
for op_class in self.operations_by_opclass.keys():
if self.opclass_supports_combination(op_class, datatype_comb, layout_comb, math_operation):
supporting_op_classes.add(op_class)
return supporting_op_classes
def operations(
self,
op_class: cutlass_library.OpcodeClass,
element_a: cutlass_library.DataType,
element_b: cutlass_library.DataType,
element_accumulator: cutlass_library.DataType,
layout_a: cutlass_library.LayoutType,
layout_b: cutlass_library.LayoutType,
math_operation: cutlass_library.MathOperation,
) -> KernelsForDataType:
"""
Returns whether the provided operation class supports the provided data type combination
:param op_class: operation class to consider
:type op_class: cutlass_library.OpcodeClass
:param element_a: data type of operand A
:type element_a: cutlass_library.DataType
:param element_b: data type of operand B
:type element_b: cutlass_library.DataType
:param element_accumulator: data type of accumulator
:type element_accumulator: cutlass_library.DataType
:param layout_a: layout of operand A
:type layout_a: cutlass_library.LayoutType
:param layout_b: layout of operand B
:type layout_b: cutlass_library.LayoutType
:param math_operation: math operation to consider
:type math_operation: cutlass.MathOperation
:return: container of kernels by alignment supported by the provided combination of parameters
:rtype: KernelsForDataType
"""
datatype_comb = (element_a, element_b, element_accumulator)
layout_comb = (layout_a, layout_b)
if not self.opclass_supports_combination(op_class, datatype_comb, layout_comb, math_operation):
raise Exception(
f"Data type layout combination {datatype_comb}, {layout_comb} "
f"is not supported by opcode class {op_class} on CC {self.cc}."
)
return self.operations_by_opclass[op_class][(datatype_comb, layout_comb)]
class OptionRegistry:
"""
Container of all architecture-specific options
:param target_cc: compute capability of the device on which operations will be run
:type target_cc: int
"""
def __init__(self, target_cc: int):
self.registry = {}
gemm_kinds = [cutlass_library.GemmKind.Universal, cutlass_library.GemmKind.Universal3x]
operation_kinds = [cutlass_library.OperationKind.Gemm, cutlass_library.OperationKind.Conv2d]
# Construct options for each CC
for kernel_cc in _generator_ccs:
self.registry[kernel_cc] = {}
for opkind in operation_kinds:
self.registry[kernel_cc][opkind] = ArchOptions(target_cc, kernel_cc, opkind, gemm_kinds)
def options_for_cc(self, cc: int, op_kind=cutlass_library.OperationKind.Gemm) -> ArchOptions:
return self.registry.get(cc, None)[op_kind]
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