Source code for cutlass.library_defaults

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"""
Classes containing valid operations for a given compute capability and data types.
"""

import logging
from cuda import __version__

# Strip any additional information from the CUDA version
_cuda_version = __version__.split("rc")[0]

# Imports from CUTLASS profiler generator and manifest scripts
import generator as prof_generator
import manifest as prof_manifest

import cutlass
from cutlass.utils.check import valid_stage_count
from cutlass.utils.datatypes import td_from_profiler_td, td_from_profiler_op, has_binding_type


_generator_ccs = [50, 60, 61, 70, 75, 80, 90]


[docs]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 # Dictionary mapping from alignment (int) to a list of kernels that fit the alignment # constraint for the data type combination self.kernels_by_alignment = {}
[docs] def add(self, operation): """ Add an operation to the list of supported kernels """ alignment = operation.A.alignment if alignment not in self.kernels_by_alignment: self.kernels_by_alignment[alignment] = [] self.kernels_by_alignment[alignment].append(operation)
@property def alignments(self): """ Returns an unsorted list of alignments supported by this data type combination :return: unsorted list of alignments supported by this data type combination :rtype: list """ return list(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
[docs] def operations(self, alignment: int): """ Returns operations satisfying the alignment constraint indicated by `alignment` :param alignment: alignment constraint of operations to return :type alignment: int :return: list of operations :rtype: list """ if alignment not in self.kernels_by_alignment: raise Exception( f"No operations of alignment {alignment} found for data type and layout " f"combination {self.datatype_comb} {self.layout_comb}" ) return self.kernels_by_alignment[alignment]
[docs] def find_alignment(self, shape: tuple, layout: cutlass.LayoutType) -> 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 :return: maximum alignment supported by the data type combination and tensor size :rtype: int """ # Determine the leading dimension of the shape if layout == cutlass.LayoutType.RowMajor: ld = shape[0] elif layout == cutlass.LayoutType.RowMajor: ld = shape[1] else: raise Exception(f"Unexpected or unsupported layout {layout}") for alignment in sorted(list(self.kernels_by_alignment.keys()), reverse=True): if ld % alignment == 0: return alignment # Default to alignment of 1 if no others match return 1
[docs] 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)
[docs]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.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.OperationKind, gemm_kinds: list, allowed_math_operations: list = [ cutlass.MathOperation.multiply_add, cutlass.MathOperation.multiply_add_saturate, ] ): 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(prof_generator, generate_function_name): cutlass.logger.warning(f"No generator found for architecture {kernel_cc}") return generate_function = getattr(prof_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 = prof_generator.define_parser().parse_args(args) manifest = prof_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 # 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].items(): for op in op_list: 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 datatype_comb = (mi.element_a, mi.element_b, mi.element_accumulator) # Skip any data types that do not currently have conversions via cutlass_bindings if False in [has_binding_type(elt) for elt in datatype_comb]: continue # Prune operations that don't fit in shared memory td = td_from_profiler_op(op) if not valid_stage_count(target_cc, td)[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.DataType.tf32, cutlass.DataType.tf32, cutlass.DataType.f32): # TF32 kernels only supported on SM80 and beyond if self.cc < 80: continue elif self.cc == 90: if (op.A.element != cutlass.DataType.f32 or op.B.element != cutlass.DataType.f32 or op.C.element != cutlass.DataType.f32): continue datatype_comb = (cutlass.DataType.f32, cutlass.DataType.f32, cutlass.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.OpcodeClass.TensorOp in self.operations_by_opclass: self.op_class = cutlass.OpcodeClass.TensorOp else: self.op_class = cutlass.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.OpcodeClass.Simt not in self.operations_by_opclass: self.operations_by_opclass[cutlass.OpcodeClass.Simt] = {} types = [ (cutlass.DataType.s8, cutlass.DataType.s8, cutlass.DataType.s8), (cutlass.DataType.s8, cutlass.DataType.s8, cutlass.DataType.s32), (cutlass.DataType.f16, cutlass.DataType.f16, cutlass.DataType.f16), (cutlass.DataType.f16, cutlass.DataType.f16, cutlass.DataType.f32), (cutlass.DataType.f32, cutlass.DataType.f32, cutlass.DataType.f32), (cutlass.DataType.f64, cutlass.DataType.f64, cutlass.DataType.f64), ] layouts = [ (cutlass.LayoutType.RowMajor, cutlass.LayoutType.RowMajor), (cutlass.LayoutType.RowMajor, cutlass.LayoutType.ColumnMajor), (cutlass.LayoutType.ColumnMajor, cutlass.LayoutType.RowMajor), (cutlass.LayoutType.ColumnMajor, cutlass.LayoutType.ColumnMajor), ] alignment = 1 epilogue_functor = cutlass.EpilogueFunctor.LinearCombination swizzling_functor = cutlass.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.OpcodeClass.Simt]: continue A = cutlass.TensorDescription(type_comb[0], layout_comb[0], alignment) B = cutlass.TensorDescription(type_comb[1], layout_comb[1], alignment) C = cutlass.TensorDescription(type_comb[2], cutlass.LayoutType.ColumnMajor, alignment) math_inst = cutlass.MathInstruction( [1, 1, 1], type_comb[0], type_comb[1], type_comb[2], cutlass.OpcodeClass.Simt, cutlass.MathOperation.multiply_add ) td = cutlass.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, td_from_profiler_td(td))[0]: continue new_operation = prof_manifest.GemmOperation( cutlass.GemmKind.Universal, td.minimum_compute_capability, td, A, B, C, type_comb[2], epilogue_functor, swizzling_functor) new_kernels = KernelsForDataType(type_comb, layout_comb) new_kernels.add(new_operation) self.operations_by_opclass[cutlass.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()
[docs] def opclass_supports_combination( self, op_class: cutlass.OpcodeClass, datatype_comb: tuple, layout_comb: tuple ) -> 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.OpcodeClass :param datatype_comb: tuple of data types for (element_A, element_B, element_accumulator) :type datatype_comb: tuple[cutlass.DataType] :param layout_comb: tuple of data types for (layout_A, layout_B) :type layout_comb: tuple[cutlass.LayoutType] :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}") return (datatype_comb, layout_comb) in self.operations_by_opclass[op_class]
[docs] def supporting_opclasses( self, element_a: cutlass.DataType, element_b: cutlass.DataType, element_accumulator: cutlass.DataType, layout_a: cutlass.LayoutType, layout_b: cutlass.LayoutType, ) -> 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.DataType :param element_b: data type of operand B :type element_b: cutlass.DataType :param element_accumulator: data type of accumulator :type element_accumulator: cutlass.DataType :param layout_a: layout of operand A :type layout_a: cutlass.LayoutType :param layout_b: layout of operand B :type layout_b: cutlass.LayoutType :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): supporting_op_classes.add(op_class) return supporting_op_classes
[docs] def operations( self, op_class: cutlass.OpcodeClass, element_a: cutlass.DataType, element_b: cutlass.DataType, element_accumulator: cutlass.DataType, layout_a: cutlass.LayoutType, layout_b: cutlass.LayoutType, ) -> KernelsForDataType: """ Returns whether the provided operation class supports the provided data type combination :param op_class: operation class to consider :type op_class: cutlass.OpcodeClass :param element_a: data type of operand A :type element_a: cutlass.DataType :param element_b: data type of operand B :type element_b: cutlass.DataType :param element_accumulator: data type of accumulator :type element_accumulator: cutlass.DataType :param layout_a: layout of operand A :type layout_a: cutlass.LayoutType :param layout_b: layout of operand B :type layout_b: cutlass.LayoutType :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): 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)]
[docs]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.GemmKind.Universal, cutlass.GemmKind.Universal3x] # Construct options for each CC for kernel_cc in _generator_ccs: self.registry[kernel_cc] = ArchOptions(target_cc, kernel_cc, cutlass.OperationKind.Gemm, gemm_kinds)
[docs] def options_for_cc(self, cc: int) -> ArchOptions: return self.registry.get(cc, None)