VidChain-exercise
/
VTimeLLM
/flash-attention
/csrc
/cutlass
/python
/cutlass_library
/gemm_operation.py
| # | |
| # 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 emitting GEMM kernels | |
| """ | |
| import collections | |
| import enum | |
| import functools | |
| import logging | |
| import operator | |
| import os.path | |
| import shutil | |
| try: | |
| import builtins | |
| if hasattr(builtins, "CUTLASS_IGNORE_PACKAGE") and CUTLASS_IGNORE_PACKAGE == True: | |
| raise ImportError("Disabling attempt to import cutlass_library") | |
| from cutlass_library.library import * | |
| except ImportError: | |
| from library import * | |
| _LOGGER = logging.getLogger(__name__) | |
| ################################################################################################### | |
| # | |
| # Data structure modeling a GEMM operation | |
| # | |
| ################################################################################################### | |
| # | |
| class GemmOperation: | |
| # | |
| def __init__(self, gemm_kind, arch, tile_description, A, B, C, element_epilogue, \ | |
| epilogue_functor = EpilogueFunctor.LinearCombination, swizzling_functor = SwizzlingFunctor.Identity8, D = None, | |
| kernel_schedule = KernelScheduleType.ScheduleAuto, epilogue_schedule = EpilogueScheduleType.ScheduleAuto, | |
| tile_scheduler = TileSchedulerType.Default, mixed_input_mode = None, mixed_input_shuffle = False, | |
| ScaleFactorA = None, ScaleFactorB = None, ScaleFactorD = None, | |
| ScaleFactorMVecSize = None, ScaleFactorNVecSize = None, ScaleFactorKVecSize = None): | |
| kinds_3x = { | |
| GemmKind.Universal3x, | |
| GemmKind.SparseUniversal3x, | |
| GemmKind.BlockScaledUniversal3x, | |
| GemmKind.GroupedUniversal3x, | |
| GemmKind.GroupedBlockScaledUniversal3x, | |
| GemmKind.BlockwiseUniversal3x, | |
| GemmKind.GroupedBlockwiseUniversal3x, | |
| } | |
| self.is_3x = gemm_kind in kinds_3x | |
| self.prefix = "3x" if self.is_3x else "" | |
| self.operation_kind = OperationKind.Gemm | |
| self.arch = arch | |
| self.tile_description = tile_description | |
| self.gemm_kind = gemm_kind | |
| self.A = A | |
| self.B = B | |
| self.C = C | |
| self.D = D | |
| if is_block_scaled(gemm_kind): | |
| self.ScaleFactorA = ScaleFactorA | |
| self.ScaleFactorB = ScaleFactorB | |
| self.ScaleFactorD = ScaleFactorD["tensor"] | |
| self.ScaleFactorVectorSize = ScaleFactorD["vector_size"] | |
| if is_blockwise(gemm_kind): | |
| self.ScaleFactorMVecSize = ScaleFactorMVecSize | |
| self.ScaleFactorNVecSize = ScaleFactorNVecSize | |
| self.ScaleFactorKVecSize = ScaleFactorKVecSize | |
| if self.D == None: | |
| self.D = self.C | |
| if not self.is_3x: | |
| assert(kernel_schedule == KernelScheduleType.ScheduleAuto) | |
| assert(epilogue_schedule == EpilogueScheduleType.ScheduleAuto) | |
| self.kernel_schedule = kernel_schedule | |
| self.epilogue_schedule = epilogue_schedule | |
| self.element_epilogue = element_epilogue | |
| self.epilogue_functor = epilogue_functor | |
| if self.is_3x and epilogue_functor == EpilogueFunctor.LinearCombination: | |
| self.epilogue_functor = EpilogueFunctor3x.LinearCombination | |
| self.swizzling_functor = swizzling_functor | |
| self.tile_scheduler = tile_scheduler | |
| # Only enable mixed input mode and mixed input shuffle for Hopper | |
| self.mixed_input_mode = None | |
| if self.is_mixed_input() and self.arch >= 90 and self.arch < 100: | |
| self.mixed_input_mode = mixed_input_mode | |
| self.mixed_input_shuffle = (self.mixed_input_mode is not None) and mixed_input_shuffle | |
| # | |
| 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 is_planar_complex(self): | |
| return self.gemm_kind in (GemmKind.PlanarComplex, GemmKind.PlanarComplexArray) | |
| # | |
| 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): | |
| if self.tile_description.math_instruction.math_operation == MathOperation.multiply_add_complex_gaussian: | |
| return "g%s" % ShortDataTypeNames[self.accumulator_type()] | |
| return ShortDataTypeNames[self.accumulator_type()] | |
| # | |
| def core_name(self): | |
| ''' The basic operation kind is prefixed with a letter indicating the accumulation type. ''' | |
| inst_shape = '' | |
| inst_operation = '' | |
| intermediate_type = '' | |
| math_operations_map = { | |
| MathOperation.xor_popc: 'xor', | |
| MathOperation.and_popc: 'and', | |
| MathOperation.multiply_add_fast_accum: 'fastaccum', | |
| } | |
| tensor_ops = [ | |
| OpcodeClass.TensorOp, | |
| OpcodeClass.WmmaTensorOp, | |
| OpcodeClass.SparseTensorOp, | |
| OpcodeClass.BlockScaledTensorOp, | |
| ] | |
| is_tensor_op = self.tile_description.math_instruction.opcode_class in tensor_ops | |
| if is_tensor_op: | |
| 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 '' | |
| inst_shape = "{0}{1}{2}".format(*tuple(self.tile_description.math_instruction.instruction_shape)) if not self.is_3x else "" | |
| inst_shape += math_op_string | |
| if self.tile_description.math_instruction.element_a != self.A.element and \ | |
| self.tile_description.math_instruction.element_a != self.tile_description.math_instruction.element_accumulator: | |
| intermediate_type = DataTypeNames[self.tile_description.math_instruction.element_a] | |
| short_math_name = self.short_math_name() if not self.is_3x else "" | |
| return "%s%s%s%s" % (short_math_name, inst_shape, intermediate_type, GemmKindNames[self.gemm_kind]) | |
| # Generates a string representing the MMA instruction. | |
| def extended_name(self): | |
| ''' Append data types if they differ from compute type. ''' | |
| element_sfa = "" | |
| element_sfb = "" | |
| if self.is_complex(): | |
| extended_name = "${core_name}" | |
| else: | |
| if self.is_mixed_input(): | |
| extended_name = "${core_name}_${element_a}_${element_b}" | |
| if self.C.element != self.tile_description.math_instruction.element_accumulator: | |
| extended_name = "${element_c}_" + extended_name | |
| elif is_blockwise(self.gemm_kind): | |
| extended_name = "${core_name}_${element_sfa}x${element_a}_${element_sfb}x${element_b}" | |
| element_sfa = DataTypeNames[self.accumulator_type()] | |
| element_sfb = DataTypeNames[self.accumulator_type()] | |
| else: | |
| extended_name = "${core_name}" | |
| if self.C.element != self.tile_description.math_instruction.element_accumulator: | |
| extended_name = "${element_c}_" + extended_name | |
| if self.A.element != self.tile_description.math_instruction.element_accumulator: | |
| extended_name += "_${element_a}" | |
| extended_name = SubstituteTemplate(extended_name, { | |
| 'element_a': DataTypeNames[self.A.element], | |
| 'element_sfa' : element_sfa, | |
| 'element_b': DataTypeNames[self.B.element], | |
| 'element_sfb' : element_sfb, | |
| 'element_c': DataTypeNames[self.C.element], | |
| 'core_name': self.core_name() | |
| }) | |
| return extended_name | |
| # | |
| def mixed_input_mode_name(self): | |
| mode_name_mapping = { | |
| MixedInputMode.ConvertOnly: "_cvt", | |
| MixedInputMode.ScaleOnly: "_scl", | |
| MixedInputMode.ScaleWithZeroPoint: "_sclzr" | |
| } | |
| mode_name = mode_name_mapping.get(self.mixed_input_mode, "") | |
| if self.mixed_input_shuffle: | |
| mode_name = mode_name + "_shfl" | |
| return mode_name | |
| def extended_name_3x(self): | |
| '''Generates a string representing the MMA atom. Assumes accumulator type is C type.''' | |
| extended_name = "{core_name}_{element_a}_{element_b}_{element_acc}_{element_c}_{element_d}".format( | |
| element_a = DataTypeNames[self.A.element], | |
| element_b = DataTypeNames[self.B.element], | |
| element_acc = DataTypeNames[self.accumulator_type()], | |
| element_c = DataTypeNames[self.C.element], | |
| element_d = DataTypeNames[self.D.element], | |
| core_name = self.core_name()) | |
| if is_block_scaled(self.gemm_kind): | |
| d_type_names = DataTypeNames[self.D.element] | |
| if self.ScaleFactorD.element != DataType.void: | |
| d_type_names = DataTypeNames[self.ScaleFactorD.element] + "x" + d_type_names | |
| extended_name = "{core_name}_{element_sfa}x{element_a}_{element_sfb}x{element_b}_{element_acc}_{element_c}_{element_d}".format( | |
| element_sfa = DataTypeNames[self.ScaleFactorA], | |
| element_a = DataTypeNames[self.A.element], | |
| element_sfb = DataTypeNames[self.ScaleFactorB], | |
| element_b = DataTypeNames[self.B.element], | |
| element_acc = DataTypeNames[self.accumulator_type()], | |
| element_c = DataTypeNames[self.C.element], | |
| element_d = d_type_names, | |
| core_name = self.core_name()) | |
| if is_blockwise(self.gemm_kind): | |
| d_type_names = DataTypeNames[self.D.element] | |
| extended_name = "{core_name}_{sfvec_m_size}x{sfvec_k_size}{element_sfa}x{element_a}_{sfvec_n_size}x{sfvec_k_size}{element_sfb}x{element_b}_{element_acc}_{element_c}_{element_d}".format( | |
| element_sfa = DataTypeNames[self.accumulator_type()], | |
| element_a = DataTypeNames[self.A.element], | |
| element_sfb = DataTypeNames[self.accumulator_type()], | |
| element_b = DataTypeNames[self.B.element], | |
| element_acc = DataTypeNames[self.accumulator_type()], | |
| element_c = DataTypeNames[self.C.element], | |
| element_d = d_type_names, | |
| sfvec_m_size = self.ScaleFactorMVecSize, | |
| sfvec_n_size = self.ScaleFactorNVecSize, | |
| sfvec_k_size = self.ScaleFactorKVecSize, | |
| core_name = self.core_name()) | |
| if self.mixed_input_mode != None: | |
| extended_name = extended_name + self.mixed_input_mode_name() | |
| return extended_name | |
| def datatype_name_3x(self): | |
| '''Generates a string representing the MMA atom. Assumes accumulator type is C type.''' | |
| datatype_name = "{element_a}_{element_b}_{element_acc}_{element_c}_{element_d}".format( | |
| element_a = DataTypeNames[self.A.element], | |
| element_b = DataTypeNames[self.B.element], | |
| element_acc = DataTypeNames[self.accumulator_type()], | |
| element_c = DataTypeNames[self.C.element], | |
| element_d = DataTypeNames[self.D.element]) | |
| return datatype_name | |
| # Generates a short string representing the AB layout tags (e.g. nt or tn) | |
| def layout_name(self): | |
| if self.is_complex() or self.is_planar_complex(): | |
| return "%s%s" % ( | |
| ShortComplexLayoutNames[(self.A.layout, self.A.complex_transform)], | |
| ShortComplexLayoutNames[(self.B.layout, self.B.complex_transform)] | |
| ) | |
| return "%s%s" % (ShortLayoutTypeNames[self.A.layout], ShortLayoutTypeNames[self.B.layout]) | |
| # Generates a short string representing the ABC layout tags (e.g. ntn or tnn) | |
| def layout_name_3x(self): | |
| if self.is_complex() or self.is_planar_complex(): | |
| return "{}{}{}".format( | |
| ShortComplexLayoutNames[(self.A.layout, self.A.complex_transform)], | |
| ShortComplexLayoutNames[(self.B.layout, self.B.complex_transform)], | |
| ShortComplexLayoutNames[(self.C.layout, self.C.complex_transform)]) | |
| else: | |
| return "{}{}{}".format( | |
| ShortLayoutTypeNames[self.A.layout], | |
| ShortLayoutTypeNames[self.B.layout], | |
| ShortLayoutTypeNames[self.C.layout]) | |
| # Generates a short string representing underlying kernel schedule type | |
| def kernel_schedule_name_3x(self): | |
| return KernelScheduleSuffixes[self.kernel_schedule] | |
| # Generates a short string representing underlying epilogue schedule type | |
| def epilogue_schedule_name_3x(self): | |
| if is_block_scaled(self.gemm_kind): | |
| if self.ScaleFactorD.element != DataType.void: | |
| return EpilogueScheduleSuffixes[self.epilogue_schedule] + "_epiVs" + str(self.ScaleFactorVectorSize)+ShortLayoutTypeNames[self.ScaleFactorD.layout] | |
| return EpilogueScheduleSuffixes[self.epilogue_schedule] | |
| # Generate a short string representing the operation class | |
| def opcode_class_name(self): | |
| return OpcodeClassNames[self.tile_description.math_instruction.opcode_class] | |
| def get_collective_tile_shape(self): | |
| """ | |
| Get the tile shape passed to the collective builder. | |
| On Blackwell, this is different than the operation.tile_description.tile_shape. | |
| """ | |
| is_sm100_kernel = (self.arch == 100) | |
| if not is_sm100_kernel: | |
| return self.tile_description.tile_shape | |
| opcode_class_main = self.tile_description.math_instruction.opcode_class | |
| instruction_shape = self.tile_description.math_instruction.instruction_shape | |
| tile_shape_m, tile_shape_n, tile_shape_k = self.tile_description.tile_shape | |
| if opcode_class_main in [OpcodeClass.TensorOp, OpcodeClass.BlockScaledTensorOp, OpcodeClass.SparseTensorOp]: | |
| tile_shape_m = instruction_shape[0] | |
| tile_shape_n = instruction_shape[1] | |
| return (tile_shape_m, tile_shape_n, tile_shape_k) | |
| # Generates the full kernel function name | |
| def procedural_name(self): | |
| ''' The full procedural name indicates architecture, extended name, tile size, and layout. ''' | |
| opcode_class_name = OpcodeClassNames[self.tile_description.math_instruction.opcode_class] | |
| if self.arch >= 90: | |
| kernel_name_template = "cutlass{p}_sm{ar}_{op}_{ex}{ct}{cs}_{l}_{s}_align{al}{t}{k}{e}" | |
| tile_shape = self.get_collective_tile_shape() | |
| return kernel_name_template.format( | |
| p = self.prefix, | |
| ar = self.arch, | |
| op = opcode_class_name, | |
| ex = self.extended_name_3x(), | |
| ct = '_' + 'x'.join([str(i) for i in tile_shape]) if tile_shape[0] > 0 else "", | |
| cs = '_' + 'x'.join([str(i) for i in self.tile_description.cluster_shape]), | |
| l = self.tile_description.stages, | |
| s = self.layout_name_3x(), | |
| al = str(max(self.A.alignment, self.B.alignment)), | |
| t = TileSchedulerSuffixes[self.tile_scheduler], | |
| k = self.kernel_schedule_name_3x(), | |
| e = self.epilogue_schedule_name_3x()) | |
| else: | |
| threadblock = self.tile_description.procedural_name() | |
| return "cutlass{p}_{op}_{ex}_{tb}_{l}_align{a}".format( | |
| p = self.prefix, | |
| op = opcode_class_name, | |
| ex = self.extended_name(), | |
| tb = threadblock, | |
| l = self.layout_name(), | |
| a = str(max(self.A.alignment, self.B.alignment))) | |
| # | |
| def configuration_name(self): | |
| ''' The full procedural name indicates architecture, extended name, tile size, and layout. ''' | |
| return self.procedural_name() | |
| def __hash__(self): | |
| return hash(self.configuration_name()) | |
| def __eq__(self, other): | |
| return self.configuration_name() == other.configuration_name() | |
| ################################################################################################### | |
| # | |
| # Data structure modeling a grouped GEMM operation | |
| # | |
| ################################################################################################### | |
| # | |
| class GroupedGemmOperation(GemmOperation): | |
| # | |
| def __init__(self, gemm_kind, arch, tile_description, A, B, C, element_epilogue, \ | |
| epilogue_functor = EpilogueFunctor.LinearCombination, swizzling_functor = SwizzlingFunctor.Identity8, \ | |
| scheduler_mode = GroupScheduleMode.Device): | |
| super().__init__(gemm_kind, arch, tile_description, A, B, C, element_epilogue, \ | |
| epilogue_functor, swizzling_functor) | |
| self.scheduler_mode = scheduler_mode | |
| # | |
| def procedural_name(self): | |
| ''' The full procedural name indicates architecture, extended name, tile size, and layout. ''' | |
| base = super().procedural_name() | |
| return SubstituteTemplate( | |
| base + "_schedule${schedule}", | |
| { | |
| 'schedule': ShortGroupScheduleModeNames[self.scheduler_mode] | |
| }) | |
| ################################################################################################### | |
| # | |
| # Emits single instances of a CUTLASS device-wide operator | |
| # | |
| ################################################################################################### | |
| # | |
| class EmitGemmInstance: | |
| ''' Responsible for emitting a CUTLASS template definition''' | |
| def __init__(self, operation_suffix = ''): | |
| self.operation_suffix = operation_suffix | |
| self.includes = [] | |
| self.gemm_template = """ | |
| // Gemm operator ${operation_name} | |
| using Operation_${operation_name} = cutlass::gemm::device::Gemm< | |
| ${element_a}, ${layout_a}, | |
| ${element_b}, ${layout_b}, | |
| ${element_c}, ${layout_c}, | |
| ${element_accumulator}, | |
| ${opcode_class}, | |
| ${arch}, | |
| cutlass::gemm::GemmShape<${threadblock_shape_m}, ${threadblock_shape_n}, ${threadblock_shape_k}>, | |
| cutlass::gemm::GemmShape<${warp_shape_m}, ${warp_shape_n}, ${warp_shape_k}>, | |
| cutlass::gemm::GemmShape<${instruction_shape_m}, ${instruction_shape_n}, ${instruction_shape_k}>, | |
| ${epilogue_functor}< | |
| ${element_c}, | |
| ${epilogue_vector_length}, | |
| ${element_accumulator}, | |
| ${element_epilogue} | |
| >, | |
| ${swizzling_functor}, | |
| ${stages}, | |
| ${align_a}, | |
| ${align_b}, | |
| false, | |
| ${math_operation} | |
| ${residual} | |
| >; | |
| """ | |
| self.gemm_complex_template = """ | |
| // Gemm operator ${operation_name} | |
| using Operation_${operation_name} = cutlass::gemm::device::GemmComplex< | |
| ${element_a}, ${layout_a}, | |
| ${element_b}, ${layout_b}, | |
| ${element_c}, ${layout_c}, | |
| ${element_accumulator}, | |
| ${opcode_class}, | |
| ${arch}, | |
| cutlass::gemm::GemmShape<${threadblock_shape_m}, ${threadblock_shape_n}, ${threadblock_shape_k}>, | |
| cutlass::gemm::GemmShape<${warp_shape_m}, ${warp_shape_n}, ${warp_shape_k}>, | |
| cutlass::gemm::GemmShape<${instruction_shape_m}, ${instruction_shape_n}, ${instruction_shape_k}>, | |
| ${epilogue_functor}< | |
| ${element_c}, | |
| ${epilogue_vector_length}, | |
| ${element_accumulator}, | |
| ${element_epilogue} | |
| >, | |
| ${swizzling_functor}, | |
| ${stages}, | |
| ${transform_a}, | |
| ${transform_b}, | |
| ${math_operation} | |
| ${residual} | |
| >; | |
| """ | |
| # | |
| def instance_template(self): | |
| return """ | |
| ${compile_guard_start} | |
| manifest.append(new ${gemm_kind}<Operation_${operation_name}>("${operation_name}")); | |
| ${compile_guard_end} | |
| """ | |
| # | |
| def emit(self, operation): | |
| warp_shape = [operation.tile_description.threadblock_shape[idx] // operation.tile_description.warp_count[idx] for idx in range(3)] | |
| epilogue_vector_length = int(min(operation.C.alignment * DataTypeSize[operation.C.element], 128) / DataTypeSize[operation.C.element]) | |
| residual = '' | |
| values = { | |
| 'operation_name': operation.procedural_name(), | |
| 'element_a': DataTypeTag[operation.A.element], | |
| 'layout_a': LayoutTag[operation.A.layout], | |
| 'element_b': DataTypeTag[operation.B.element], | |
| 'layout_b': LayoutTag[operation.B.layout], | |
| 'element_c': DataTypeTag[operation.C.element], | |
| 'layout_c': LayoutTag[operation.C.layout], | |
| 'element_accumulator': DataTypeTag[operation.accumulator_type()], | |
| 'opcode_class': OpcodeClassTag[operation.tile_description.math_instruction.opcode_class], | |
| 'arch': "cutlass::arch::Sm%d" % operation.arch, | |
| 'threadblock_shape_m': str(operation.tile_description.threadblock_shape[0]), | |
| 'threadblock_shape_n': str(operation.tile_description.threadblock_shape[1]), | |
| 'threadblock_shape_k': str(operation.tile_description.threadblock_shape[2]), | |
| 'warp_shape_m': str(warp_shape[0]), | |
| 'warp_shape_n': str(warp_shape[1]), | |
| 'warp_shape_k': str(warp_shape[2]), | |
| 'instruction_shape_m': str(operation.tile_description.math_instruction.instruction_shape[0]), | |
| 'instruction_shape_n': str(operation.tile_description.math_instruction.instruction_shape[1]), | |
| 'instruction_shape_k': str(operation.tile_description.math_instruction.instruction_shape[2]), | |
| 'epilogue_vector_length': str(epilogue_vector_length), | |
| 'element_epilogue': str(DataTypeTag[operation.element_epilogue]), | |
| 'epilogue_functor': EpilogueFunctorTag[operation.epilogue_functor], | |
| 'swizzling_functor': SwizzlingFunctorTag[operation.swizzling_functor], | |
| 'stages': str(operation.tile_description.stages), | |
| 'align_a': str(operation.A.alignment), | |
| 'align_b': str(operation.B.alignment), | |
| 'transform_a': ComplexTransformTag[operation.A.complex_transform], | |
| 'transform_b': ComplexTransformTag[operation.B.complex_transform], | |
| 'math_operation': MathOperationTag[operation.tile_description.math_instruction.math_operation], | |
| 'residual': residual | |
| } | |
| template = self.gemm_complex_template if operation.is_complex() else self.gemm_template | |
| return SubstituteTemplate(template, values) | |
| ################################################################################################### | |
| class EmitSparseGemmInstance: | |
| ''' Responsible for emitting a CUTLASS template definition''' | |
| def __init__(self, operation_suffix = ''): | |
| self.operation_suffix = operation_suffix | |
| self.includes = [] | |
| self.gemm_template = """ | |
| // Gemm operator ${operation_name} | |
| using Operation_${operation_name} = cutlass::gemm::device::SparseGemm< | |
| ${element_a}, ${layout_a}, | |
| ${element_b}, ${layout_b}, | |
| ${element_c}, ${layout_c}, | |
| ${element_accumulator}, | |
| ${opcode_class}, | |
| ${arch}, | |
| cutlass::gemm::GemmShape<${threadblock_shape_m}, ${threadblock_shape_n}, ${threadblock_shape_k}>, | |
| cutlass::gemm::GemmShape<${warp_shape_m}, ${warp_shape_n}, ${warp_shape_k}>, | |
| cutlass::gemm::GemmShape<${instruction_shape_m}, ${instruction_shape_n}, ${instruction_shape_k}>, | |
| ${epilogue_functor}< | |
| ${element_c}, | |
| ${epilogue_vector_length}, | |
| ${element_accumulator}, | |
| ${element_epilogue} | |
| >, | |
| ${swizzling_functor}, | |
| ${stages}, | |
| ${align_a}, | |
| ${align_b}, | |
| false, | |
| ${math_operation} | |
| ${residual} | |
| >; | |
| """ | |
| # | |
| def instance_template(self): | |
| return """ | |
| ${compile_guard_start} | |
| manifest.append(new ${gemm_kind}<Operation_${operation_name}>("${operation_name}")); | |
| ${compile_guard_end} | |
| """ | |
| # | |
| def emit(self, operation): | |
| warp_shape = [operation.tile_description.threadblock_shape[idx] // operation.tile_description.warp_count[idx] for idx in range(3)] | |
| epilogue_vector_length = int(min(operation.C.alignment * DataTypeSize[operation.C.element], 128) / DataTypeSize[operation.C.element]) | |
| residual = '' | |
| values = { | |
| 'operation_name': operation.procedural_name(), | |
| 'element_a': DataTypeTag[operation.A.element], | |
| 'layout_a': LayoutTag[operation.A.layout], | |
| 'element_b': DataTypeTag[operation.B.element], | |
| 'layout_b': LayoutTag[operation.B.layout], | |
| 'element_c': DataTypeTag[operation.C.element], | |
| 'layout_c': LayoutTag[operation.C.layout], | |
| 'element_accumulator': DataTypeTag[operation.accumulator_type()], | |
| 'opcode_class': OpcodeClassTag[operation.tile_description.math_instruction.opcode_class], | |
| 'arch': "cutlass::arch::Sm%d" % operation.arch, | |
| 'threadblock_shape_m': str(operation.tile_description.threadblock_shape[0]), | |
| 'threadblock_shape_n': str(operation.tile_description.threadblock_shape[1]), | |
| 'threadblock_shape_k': str(operation.tile_description.threadblock_shape[2]), | |
| 'warp_shape_m': str(warp_shape[0]), | |
| 'warp_shape_n': str(warp_shape[1]), | |
| 'warp_shape_k': str(warp_shape[2]), | |
| 'instruction_shape_m': str(operation.tile_description.math_instruction.instruction_shape[0]), | |
| 'instruction_shape_n': str(operation.tile_description.math_instruction.instruction_shape[1]), | |
| 'instruction_shape_k': str(operation.tile_description.math_instruction.instruction_shape[2]), | |
| 'epilogue_vector_length': str(epilogue_vector_length), | |
| 'element_epilogue': str(DataTypeTag[operation.element_epilogue]), | |
| 'epilogue_functor': EpilogueFunctorTag[operation.epilogue_functor], | |
| 'swizzling_functor': SwizzlingFunctorTag[operation.swizzling_functor], | |
| 'stages': str(operation.tile_description.stages), | |
| 'align_a': str(operation.A.alignment), | |
| 'align_b': str(operation.B.alignment), | |
| 'transform_a': ComplexTransformTag[operation.A.complex_transform], | |
| 'transform_b': ComplexTransformTag[operation.B.complex_transform], | |
| 'math_operation': MathOperationTag[operation.tile_description.math_instruction.math_operation], | |
| 'residual': residual | |
| } | |
| template = self.gemm_template | |
| return SubstituteTemplate(template, values) | |
| ################################################################################################### | |
| # | |
| class EmitGemmUniversalInstance: | |
| ''' Responsible for emitting a CUTLASS template definition''' | |
| def __init__(self, operation_suffix = ''): | |
| self.operation_suffix = operation_suffix | |
| self.includes = [ | |
| "cutlass/cutlass.h", | |
| "cutlass/numeric_types.h", | |
| "cutlass/arch/arch.h", | |
| "cutlass/arch/mma.h", | |
| "cutlass/layout/matrix.h", | |
| "cutlass/gemm/device/gemm.h", | |
| "cutlass/gemm/device/gemm_universal_adapter.h", | |
| "cutlass/gemm/kernel/default_gemm_universal.h", | |
| ] | |
| self.builtin_epilogue_functor_template = """ | |
| ${epilogue_functor}< | |
| ${element_c}, | |
| ${epilogue_vector_length}, | |
| ${element_accumulator}, | |
| ${element_epilogue} | |
| > | |
| """ | |
| self.gemm_template = """ | |
| // Gemm operator ${operation_name} | |
| using ${operation_name}_base = | |
| typename cutlass::gemm::kernel::DefaultGemmUniversal< | |
| ${element_b}, ${layout_b}, ${transform_b}, ${align_b}, // transposed B operand | |
| ${element_a}, ${layout_a}, ${transform_a}, ${align_a}, // transposed A operand | |
| ${element_c}, ${layout_c}, | |
| ${element_accumulator}, | |
| ${opcode_class}, | |
| ${arch}, | |
| cutlass::gemm::GemmShape<${threadblock_shape_m}, ${threadblock_shape_n}, ${threadblock_shape_k}>, | |
| cutlass::gemm::GemmShape<${warp_shape_m}, ${warp_shape_n}, ${warp_shape_k}>, | |
| cutlass::gemm::GemmShape<${instruction_shape_m}, ${instruction_shape_n}, ${instruction_shape_k}>, | |
| ${epilogue_functor}, | |
| ${swizzling_functor}, | |
| ${stages}, | |
| ${math_operation} | |
| >::GemmKernel; | |
| // Define named type | |
| struct ${operation_name}${operation_suffix} : | |
| public ${operation_name}_base { }; | |
| """ | |
| self.gemm_template_interleaved = """ | |
| // Gemm operator ${operation_name} | |
| using ${operation_name}_base = | |
| typename cutlass::gemm::kernel::DefaultGemmUniversal< | |
| ${element_a}, ${layout_a}, ${transform_a}, ${align_a}, | |
| ${element_b}, ${layout_b}, ${transform_b}, ${align_b}, | |
| ${element_c}, ${layout_c}, | |
| ${element_accumulator}, | |
| ${opcode_class}, | |
| ${arch}, | |
| cutlass::gemm::GemmShape<${threadblock_shape_m}, ${threadblock_shape_n}, ${threadblock_shape_k}>, | |
| cutlass::gemm::GemmShape<${warp_shape_m}, ${warp_shape_n}, ${warp_shape_k}>, | |
| cutlass::gemm::GemmShape<${instruction_shape_m}, ${instruction_shape_n}, ${instruction_shape_k}>, | |
| ${epilogue_functor}, | |
| ${swizzling_functor}, | |
| ${stages}, | |
| ${math_operation} | |
| >::GemmKernel; | |
| // Define named type | |
| struct ${operation_name}${operation_suffix} : | |
| public ${operation_name}_base { }; | |
| """ | |
| # | |
| def instance_template(self): | |
| return """ | |
| ${compile_guard_start} | |
| manifest.append(new ${gemm_kind}< | |
| cutlass::gemm::device::GemmUniversalAdapter<${operation_name}> | |
| >("${operation_name}")); | |
| ${compile_guard_end} | |
| """ | |
| # | |
| def emit(self, operation): | |
| threadblock_shape = operation.tile_description.threadblock_shape | |
| warp_count = operation.tile_description.warp_count | |
| warp_shape = [threadblock_shape[idx] // warp_count[idx] for idx in range(3)] | |
| transpose_layouts = { | |
| LayoutType.ColumnMajor: LayoutType.RowMajor, | |
| LayoutType.RowMajor: LayoutType.ColumnMajor | |
| } | |
| if operation.A.layout in transpose_layouts.keys() and \ | |
| operation.B.layout in transpose_layouts.keys() and \ | |
| operation.C.layout in transpose_layouts.keys(): | |
| instance_layout_A = transpose_layouts[operation.A.layout] | |
| instance_layout_B = transpose_layouts[operation.B.layout] | |
| instance_layout_C = transpose_layouts[operation.C.layout] | |
| gemm_template = self.gemm_template | |
| else: | |
| instance_layout_A, instance_layout_B, instance_layout_C = \ | |
| (operation.A.layout, operation.B.layout, operation.C.layout) | |
| gemm_template = self.gemm_template_interleaved | |
| # | |
| # Support built-in epilogue functors or user-defined functions | |
| if isinstance(operation.epilogue_functor, enum.Enum): | |
| epilogue_vector_length = \ | |
| min(operation.C.alignment * DataTypeSize[operation.C.element], 128) // DataTypeSize[operation.C.element] | |
| values = { | |
| 'epilogue_vector_length': str(epilogue_vector_length), | |
| 'element_epilogue': str(DataTypeTag[operation.element_epilogue]), | |
| 'epilogue_functor': EpilogueFunctorTag[operation.epilogue_functor], | |
| } | |
| epilogue_functor = SubstituteTemplate(self.builtin_epilogue_functor_template, values) | |
| else: | |
| epilogue_functor = self.epilogue_functor.emit_declaration() | |
| # | |
| values = { | |
| 'operation_name': operation.procedural_name(), | |
| 'operation_suffix': self.operation_suffix, | |
| 'element_a': DataTypeTag[operation.A.element], | |
| 'layout_a': LayoutTag[instance_layout_A], | |
| 'element_b': DataTypeTag[operation.B.element], | |
| 'layout_b': LayoutTag[instance_layout_B], | |
| 'element_c': DataTypeTag[operation.C.element], | |
| 'layout_c': LayoutTag[instance_layout_C], | |
| 'element_accumulator': DataTypeTag[operation.accumulator_type()], | |
| 'opcode_class': OpcodeClassTag[operation.tile_description.math_instruction.opcode_class], | |
| 'arch': "cutlass::arch::Sm%d" % operation.arch, | |
| 'threadblock_shape_m': str(operation.tile_description.threadblock_shape[0]), | |
| 'threadblock_shape_n': str(operation.tile_description.threadblock_shape[1]), | |
| 'threadblock_shape_k': str(operation.tile_description.threadblock_shape[2]), | |
| 'warp_shape_m': str(warp_shape[0]), | |
| 'warp_shape_n': str(warp_shape[1]), | |
| 'warp_shape_k': str(warp_shape[2]), | |
| 'instruction_shape_m': str(operation.tile_description.math_instruction.instruction_shape[0]), | |
| 'instruction_shape_n': str(operation.tile_description.math_instruction.instruction_shape[1]), | |
| 'instruction_shape_k': str(operation.tile_description.math_instruction.instruction_shape[2]), | |
| 'epilogue_functor': epilogue_functor, | |
| 'swizzling_functor': SwizzlingFunctorTag[operation.swizzling_functor], | |
| 'stages': str(operation.tile_description.stages), | |
| 'align_a': str(operation.A.alignment), | |
| 'align_b': str(operation.B.alignment), | |
| 'transform_a': ComplexTransformTag[operation.A.complex_transform], | |
| 'transform_b': ComplexTransformTag[operation.B.complex_transform], | |
| 'math_operation': MathOperationTag[operation.tile_description.math_instruction.math_operation] | |
| } | |
| return SubstituteTemplate(gemm_template, values) | |
| ################################################################################################### | |
| class EmitGemmUniversal3xInstance: | |
| ''' Responsible for emitting a CUTLASS 3.x template definition''' | |
| def __init__(self, operation_suffix = ''): | |
| self.operation_suffix = operation_suffix | |
| self.includes = [ | |
| "cutlass/cutlass.h", | |
| "cutlass/gemm/gemm.h", | |
| "cutlass/numeric_types.h", | |
| "cutlass/gemm/kernel/gemm_universal.hpp", | |
| "cutlass/gemm/collective/collective_builder.hpp", | |
| "cutlass/epilogue/collective/collective_builder.hpp", | |
| "cutlass/detail/blockwise_scale_layout.hpp", | |
| ] | |
| self.builtin_epilogue_functor_template = \ | |
| """${epilogue_functor}< | |
| ${element_d}, | |
| ${element_epilogue}, | |
| ${element_c}, | |
| ${element_epilogue} | |
| >""" | |
| self.gemm_template = """ | |
| using ${operation_name}_epilogue = | |
| typename cutlass::epilogue::collective::CollectiveBuilder< | |
| ${arch}, ${opcode_class_epi}, | |
| cute::Shape<cute::_${tile_shape_m}, cute::_${tile_shape_n}, cute::_${tile_shape_k}>, | |
| cute::Shape<${cluster_shape_m}, ${cluster_shape_n}, ${cluster_shape_k}>, | |
| ${epi_tile_mn}, | |
| ${element_accumulator}, ${element_epilogue}, | |
| ${element_c}, ${layout_c}, ${align_c}, | |
| ${element_d}, ${layout_d}, ${align_d}, | |
| ${epilogue_schedule}, | |
| ${epilogue_functor} | |
| >::CollectiveOp; | |
| ${mixed_dtype_prepare_code} | |
| ${blockwise_prepare_code} | |
| using ${operation_name}_mainloop = | |
| typename cutlass::gemm::collective::CollectiveBuilder< | |
| ${arch}, ${opcode_class_main}, | |
| ${element_a}, ${layout_a}, ${align_a}, | |
| ${element_b}, ${layout_b}, ${align_b}, | |
| ${element_accumulator}, | |
| cute::Shape<cute::_${tile_shape_m}, cute::_${tile_shape_n}, cute::_${tile_shape_k}>, | |
| cute::Shape<${cluster_shape_m}, ${cluster_shape_n}, ${cluster_shape_k}>, | |
| ${stages}, | |
| ${kernel_schedule} | |
| >::CollectiveOp; | |
| // Gemm operator ${operation_name} | |
| using ${operation_name}_base = cutlass::gemm::kernel::GemmUniversal< | |
| ${problem_shape}, | |
| ${operation_name}_mainloop, | |
| ${operation_name}_epilogue, | |
| ${tile_scheduler}>; | |
| // Define named type | |
| struct ${operation_name} : | |
| public ${operation_name}_base { }; | |
| """ | |
| # | |
| def instance_template(self): | |
| return """ | |
| ${compile_guard_start} | |
| { | |
| using GemmKernel = cutlass::gemm::device::GemmUniversalAdapter<${operation_name}>; | |
| manifest.append( | |
| new ${gemm_kind}<GemmKernel>("${operation_name}")); | |
| } | |
| ${compile_guard_end} | |
| """ | |
| def emit_block_scale_epilogue_functor(self, operation): | |
| block_scaled_template = """ | |
| ${epilogue_functor}< | |
| ${epi_vs}, | |
| ${element_d}, | |
| ${element_accumulator}, | |
| ${element_sfd}, | |
| ${layout_sfd}, | |
| ${element_c}, | |
| ${element_scalar} | |
| > | |
| """ | |
| block_scaled_values = { | |
| 'epi_vs' : str(operation.ScaleFactorVectorSize), | |
| 'element_d': str(DataTypeTag[operation.D.element]), | |
| 'element_sfd': str(DataTypeTag[operation.ScaleFactorD.element]), | |
| 'layout_sfd': LayoutTag[operation.ScaleFactorD.layout], | |
| 'epilogue_functor': EpilogueFunctor3xTag[EpilogueFunctor3x.LinearCombinationBlockScaleFactor], | |
| 'element_accumulator': str(DataTypeTag[operation.accumulator_type()]), | |
| 'element_scalar': str(DataTypeTag[operation.accumulator_type()]), | |
| 'element_c': str(DataTypeTag[operation.C.element]), | |
| } | |
| return SubstituteTemplate(block_scaled_template, block_scaled_values) | |
| def pointerize_if_grouped(operation, layout): | |
| return layout if not is_grouped(operation.gemm_kind) else layout + "* " | |
| def transform_layout_A_if_blockwise(operation, layout): | |
| layout_sfa = f"{operation.procedural_name()}_LayoutSFA" | |
| layout_sfa = layout_sfa if not is_grouped(operation.gemm_kind) else layout_sfa + "* " | |
| return layout if not is_blockwise(operation.gemm_kind) else f"cute::tuple<{layout}, {layout_sfa}>" | |
| def transform_layout_B_if_blockwise(operation, layout): | |
| layout_sfb = f"{operation.procedural_name()}_LayoutSFB" | |
| layout_sfb = layout_sfb if not is_grouped(operation.gemm_kind) else layout_sfb + "* " | |
| return layout if not is_blockwise(operation.gemm_kind) else f"cute::tuple<{layout}, {layout_sfb}>" | |
| def problem_shape(operation): | |
| gemm_shape_type = "cute::Shape<int,int,int,int>" | |
| grouped_gemm_shape_type = "cute::Shape<int,int,int>" | |
| grouped_gemm_shape_type = "cutlass::gemm::GroupProblemShape<" + grouped_gemm_shape_type + ">" | |
| return gemm_shape_type if not is_grouped(operation.gemm_kind) else grouped_gemm_shape_type | |
| def emit(self, operation): | |
| _LOGGER.debug("*** EmitGemmConfigurationLibrary::emit(operation)") | |
| _LOGGER.debug("*** operation.procedural_name(): " + operation.procedural_name()) | |
| _LOGGER.debug("*** tile_shape: " + str(operation.tile_description.tile_shape)) | |
| _LOGGER.debug("*** warp_count: " + str(operation.tile_description.warp_count)) | |
| opcode_class_main = operation.tile_description.math_instruction.opcode_class | |
| opcode_class_epi = opcode_class_main | |
| tile_shape = operation.tile_description.tile_shape | |
| instruction_shape = operation.tile_description.math_instruction.instruction_shape | |
| cluster_m = operation.tile_description.cluster_shape[0] | |
| cluster_n = operation.tile_description.cluster_shape[1] | |
| cta_n = tile_shape[1] // cluster_n if cluster_n > 0 else tile_shape[1] | |
| tile_shape_m, tile_shape_n, tile_shape_k = operation.get_collective_tile_shape() | |
| # stage count set to zero indicates builder automatic stage selection | |
| if operation.tile_description.stages > 0: | |
| stage_count_string = f"cutlass::gemm::collective::StageCount<{str(operation.tile_description.stages)}>" | |
| elif opcode_class_main == OpcodeClass.SparseTensorOp and operation.arch == 100: | |
| stage_count_string = f"cutlass::gemm::collective::StageCountAutoCarveoutEpi<{str(operation.procedural_name())}_epilogue>" | |
| else: | |
| stage_count_string = f"cutlass::gemm::collective::StageCountAutoCarveout<static_cast<int>(sizeof(typename {str(operation.procedural_name())}_epilogue::SharedStorage))>" | |
| epi_tile_mn = "cutlass::epilogue::collective::EpilogueTileAuto" | |
| instance_layout_A, instance_layout_B, instance_layout_C , instance_layout_D = \ | |
| (operation.A.layout, operation.B.layout, operation.C.layout, operation.D.layout) | |
| # 3.0 profiler integration only supports trivial epilogues for now | |
| epilogue_vector_length = 1 | |
| # Support built-in epilogue functors or user-defined functions | |
| if isinstance(operation.epilogue_functor, enum.Enum): | |
| values = { | |
| 'element_epilogue': str(DataTypeTag[operation.element_epilogue]), | |
| 'epilogue_functor': EpilogueFunctor3xTag[operation.epilogue_functor], | |
| } | |
| epilogue_functor = SubstituteTemplate(self.builtin_epilogue_functor_template, values) | |
| if is_block_scaled(operation.gemm_kind) and operation.ScaleFactorD.element != DataType.void: | |
| epilogue_functor = self.emit_block_scale_epilogue_functor(operation) | |
| else: | |
| epilogue_functor = self.epilogue_functor.emit_declaration() | |
| if is_block_scaled(operation.gemm_kind) and operation.ScaleFactorD.element != DataType.void: | |
| epilogue_functor = self.emit_block_scale_epilogue_functor(operation) | |
| # | |
| # Cutlass3x complex kernels' ElementA(B) is a tuple in collective mainloop builder, e.g. cute::tuple<Element, Transform>, Transform : cute::identity / cute::conjugate. | |
| element_a = DataTypeTag[operation.A.element] if not operation.is_complex() else f"cute::tuple<{str(DataTypeTag[operation.A.element])},{str(ComplexTransformTag3x[operation.A.complex_transform])}>" | |
| element_b = DataTypeTag[operation.B.element] if not operation.is_complex() else f"cute::tuple<{str(DataTypeTag[operation.B.element])},{str(ComplexTransformTag3x[operation.B.complex_transform])}>" | |
| epilogue_schedule_type = EpilogueScheduleTag[operation.epilogue_schedule] | |
| if opcode_class_main == OpcodeClass.BlockScaledTensorOp: | |
| is_no_smem_epilogue = operation.epilogue_schedule in [EpilogueScheduleType.NoSmemWarpSpecialized1Sm, EpilogueScheduleType.NoSmemWarpSpecialized2Sm] | |
| grouped = is_grouped(operation.gemm_kind) | |
| if cta_n == 256 and operation.kernel_schedule == to_grouped_schedule(KernelScheduleType.Nvf4TmaWarpSpecialized1SmSm100, grouped): | |
| epi_tile_mn = "cute::Shape<cute::_128,cute::_64>" | |
| if not is_no_smem_epilogue: | |
| epilogue_schedule_type = EpilogueScheduleTag[to_grouped_schedule(EpilogueScheduleType.TmaWarpSpecialized1Sm, grouped)] | |
| if cta_n == 256 and operation.kernel_schedule == to_grouped_schedule(KernelScheduleType.Nvf4TmaWarpSpecialized2SmSm100, grouped): | |
| epi_tile_mn = "cute::Shape<cute::_128,cute::_64>" | |
| if not is_no_smem_epilogue: | |
| epilogue_schedule_type = EpilogueScheduleTag[to_grouped_schedule(EpilogueScheduleType.TmaWarpSpecialized2Sm, grouped)] | |
| element_a = f'cute::tuple<{str(element_a)},{str(DataTypeTag[operation.ScaleFactorA])}>' | |
| element_b = f'cute::tuple<{str(element_b)},{str(DataTypeTag[operation.ScaleFactorB])}>' | |
| alignment_c = get_tma_alignment(operation.C.element) \ | |
| if is_tma_epilogue(operation.epilogue_schedule) and opcode_class_epi != OpcodeClass.Simt \ | |
| else operation.C.alignment | |
| alignment_d = get_tma_alignment(operation.D.element) \ | |
| if is_tma_epilogue(operation.epilogue_schedule) and opcode_class_epi != OpcodeClass.Simt \ | |
| else operation.D.alignment | |
| operation_name_str = operation.procedural_name() | |
| layout_a_str = LayoutTag[instance_layout_A] | |
| layout_b_str = LayoutTag[instance_layout_B] | |
| mixed_dtype_prepare_code = "" | |
| if operation.mixed_input_mode != None: | |
| A_dtype = operation.A.element | |
| B_dtype = operation.B.element | |
| 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) | |
| narrow_tag = DataTypeTag[narrow_dtype] | |
| wide_tag = DataTypeTag[wide_dtype] | |
| scale_tag = DataTypeTag[wide_dtype] | |
| zero_tag = DataTypeTag[wide_dtype] | |
| do_shuffle = False | |
| value_shuffle_str = "" | |
| if narrow_dtype_bits == 4 and wide_dtype_bits == 16: | |
| value_shuffle_str = "cute::Layout<cute::Shape<cute::_2,cute::_4>, cute::Stride<cute::_4,cute::_1>>" | |
| do_shuffle = True | |
| if narrow_dtype_bits == 8 and wide_dtype_bits == 16: | |
| value_shuffle_str = "cute::Layout<cute::Shape<cute::_2,cute::_2>, cute::Stride<cute::_2,cute::_1>>" | |
| do_shuffle = True | |
| do_shuffle = operation.mixed_input_shuffle and do_shuffle | |
| if do_shuffle: | |
| if is_A_dtype_narrow: | |
| stride_narrow_str = f"cutlass::detail::TagToStrideA_t<{layout_a_str}>" | |
| layout_a_str = f"{operation_name_str}_LayoutNarrowReordered" | |
| else: | |
| stride_narrow_str = f"cutlass::detail::TagToStrideB_t<{layout_b_str}>" | |
| layout_b_str = f"{operation_name_str}_LayoutNarrowReordered" | |
| # The {operation_name_str}_ prefixs in mixed_dtype_prepare_code and | |
| # layout_{a, b}_str are to prevent errors in Windows platform unity build | |
| mixed_dtype_prepare_code = f""" | |
| using {operation_name_str}_StrideNarrow = {stride_narrow_str}; | |
| using {operation_name_str}_ValueShuffle = {value_shuffle_str}; | |
| static constexpr int {operation_name_str}_NumShuffleAtoms = 1; | |
| using {operation_name_str}_MmaAtomShape = cute::Layout<cute::Shape<cute::_1, cute::Int<{operation_name_str}_NumShuffleAtoms>>>; | |
| using {operation_name_str}_LayoutAtomQuant = decltype(cutlass::compute_memory_reordering_atom<{wide_tag}, {operation_name_str}_MmaAtomShape, {operation_name_str}_ValueShuffle>()); | |
| using {operation_name_str}_LayoutNarrowReordered = decltype(cute::tile_to_shape({operation_name_str}_LayoutAtomQuant{{}}, cute::Layout<cute::Shape<int,int,int>, {operation_name_str}_StrideNarrow>{{}})); | |
| """ | |
| mixed_input_modes_to_element = { | |
| MixedInputMode.ConvertOnly: narrow_tag, | |
| MixedInputMode.ScaleOnly: f"cute::tuple<{narrow_tag}, {scale_tag}>", | |
| MixedInputMode.ScaleWithZeroPoint: f"cute::tuple<{narrow_tag}, {scale_tag}, {zero_tag}>" | |
| } | |
| narrow_element = mixed_input_modes_to_element.get(operation.mixed_input_mode, narrow_tag) | |
| if narrow_dtype == DataType.s4 and (wide_dtype == DataType.e4m3 or wide_dtype == DataType.e5m2): | |
| narrow_element = f"cute::tuple<{narrow_tag}, cutlass::Array<{scale_tag}, 8>>" | |
| if is_A_dtype_narrow: | |
| element_a = narrow_element | |
| else: | |
| element_b = narrow_element | |
| blockwise_prepare_code = "" | |
| if is_blockwise(operation.gemm_kind): | |
| sfm_vec_size = operation.ScaleFactorMVecSize | |
| sfn_vec_size = operation.ScaleFactorNVecSize | |
| sfk_vec_size = operation.ScaleFactorKVecSize | |
| blockwise_prepare_code = f""" | |
| using {operation_name_str}_ScaleConfig = cutlass::detail::Sm{operation.arch}BlockwiseScaleConfig<{sfm_vec_size}, {sfn_vec_size}, {sfk_vec_size}>; | |
| using {operation_name_str}_LayoutSFA = decltype({operation_name_str}_ScaleConfig::deduce_layoutSFA()); | |
| using {operation_name_str}_LayoutSFB = decltype({operation_name_str}_ScaleConfig::deduce_layoutSFB()); | |
| """ | |
| values = { | |
| 'operation_name': operation_name_str, | |
| 'operation_suffix': self.operation_suffix, | |
| 'problem_shape': self.problem_shape(operation), | |
| 'element_a': element_a, | |
| 'layout_a': self.transform_layout_A_if_blockwise(operation, self.pointerize_if_grouped(operation, layout_a_str)), | |
| 'element_b': element_b, | |
| 'layout_b': self.transform_layout_B_if_blockwise(operation, self.pointerize_if_grouped(operation, layout_b_str)), | |
| 'element_c': DataTypeTag[operation.C.element], | |
| 'layout_c': self.pointerize_if_grouped(operation, LayoutTag[instance_layout_C]), | |
| 'element_d': DataTypeTag[operation.D.element], | |
| 'layout_d': self.pointerize_if_grouped(operation, LayoutTag[instance_layout_D]), | |
| 'element_accumulator': DataTypeTag[operation.accumulator_type()], | |
| 'opcode_class_main': OpcodeClassTag[opcode_class_main], | |
| 'opcode_class_epi': OpcodeClassTag[opcode_class_epi], | |
| 'arch': "cutlass::arch::Sm%d" % operation.arch, | |
| 'tile_shape_m': str(tile_shape_m), | |
| 'tile_shape_n': str(tile_shape_n), | |
| 'tile_shape_k': str(tile_shape_k), | |
| 'cluster_shape_m': 'cute::_' + str(operation.tile_description.cluster_shape[0]) if operation.tile_description.cluster_shape[0] > 0 else "int", | |
| 'cluster_shape_n': 'cute::_' + str(operation.tile_description.cluster_shape[1]) if operation.tile_description.cluster_shape[1] > 0 else "int", | |
| 'cluster_shape_k': 'cute::_' + str(operation.tile_description.cluster_shape[2]) if operation.tile_description.cluster_shape[2] > 0 else "int", | |
| 'instruction_shape_m': str(instruction_shape[0]), | |
| 'instruction_shape_n': str(instruction_shape[1]), | |
| 'instruction_shape_k': str(instruction_shape[2]), | |
| 'kernel_schedule' : str(KernelScheduleTag[operation.kernel_schedule]), | |
| 'epilogue_schedule' : str(epilogue_schedule_type), | |
| 'epi_tile_mn' : epi_tile_mn, | |
| 'epilogue_functor': epilogue_functor, | |
| 'stages': stage_count_string, | |
| 'align_a': str(operation.A.alignment), | |
| 'align_b': str(operation.B.alignment), | |
| 'align_c': str(alignment_c), | |
| 'align_d': str(alignment_d), | |
| 'transform_a': ComplexTransformTag[operation.A.complex_transform], | |
| 'transform_b': ComplexTransformTag[operation.B.complex_transform], | |
| 'math_operation': MathOperationTag[operation.tile_description.math_instruction.math_operation], | |
| 'epilogue_vector_length': str(epilogue_vector_length), | |
| 'element_epilogue': str(DataTypeTag[operation.element_epilogue]), | |
| 'tile_scheduler': str(TileSchedulerTag[operation.tile_scheduler]), | |
| 'mixed_dtype_prepare_code': mixed_dtype_prepare_code, | |
| 'blockwise_prepare_code' : blockwise_prepare_code | |
| } | |
| return SubstituteTemplate(self.gemm_template, values) | |
| ################################################################################################### | |
| # | |
| class EmitGemmPlanarComplexInstance: | |
| ''' Responsible for emitting a CUTLASS template definition''' | |
| def __init__(self, operation_suffix = ''): | |
| self.operation_suffix = operation_suffix | |
| self.includes = [] | |
| self.template = """ | |
| // Gemm operator ${operation_name} | |
| using Operation_${operation_name} = typename cutlass::gemm::kernel::DefaultGemmPlanarComplexUniversal< | |
| ${element_a}, ${layout_a}, ${transform_a}, ${alignment_a}, | |
| ${element_b}, ${layout_b}, ${transform_b}, ${alignment_b}, | |
| ${element_c}, cutlass::layout::RowMajor, | |
| ${element_accumulator}, | |
| ${opcode_class}, | |
| ${arch}, | |
| cutlass::gemm::GemmShape<${threadblock_shape_m}, ${threadblock_shape_n}, ${threadblock_shape_k}>, | |
| cutlass::gemm::GemmShape<${warp_shape_m}, ${warp_shape_n}, ${warp_shape_k}>, | |
| cutlass::gemm::GemmShape<${instruction_shape_m}, ${instruction_shape_n}, ${instruction_shape_k}>, | |
| cutlass::epilogue::thread::LinearCombinationPlanarComplex< | |
| ${element_c}, | |
| ${alignment_c}, | |
| ${element_accumulator}, | |
| ${element_epilogue} | |
| >, | |
| cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<>, | |
| ${stages}, | |
| ${math_operator} | |
| >::GemmKernel; | |
| struct ${operation_name} : | |
| public Operation_${operation_name} { }; | |
| """ | |
| # | |
| def instance_template(self): | |
| return """ | |
| ${compile_guard_start} | |
| manifest.append(new ${gemm_kind}< | |
| cutlass::gemm::device::GemmUniversalAdapter<${operation_name}> | |
| >("${operation_name}")); | |
| ${compile_guard_end} | |
| """ | |
| # | |
| def emit(self, operation): | |
| warp_shape = [operation.tile_description.threadblock_shape[idx] // operation.tile_description.warp_count[idx] for idx in range(3)] | |
| # exchange and transpose A and B types, layouts, and complex transforms since the C layout is row-major | |
| transposed_layout_A = TransposedLayout[operation.A.layout] | |
| transposed_layout_B = TransposedLayout[operation.B.layout] | |
| values = { | |
| 'operation_name': operation.procedural_name(), | |
| 'element_a': DataTypeTag[operation.B.element], | |
| 'layout_a': LayoutTag[transposed_layout_B], | |
| 'transform_a': ComplexTransformTag[operation.B.complex_transform], | |
| 'alignment_a': str(operation.B.alignment), | |
| 'element_b': DataTypeTag[operation.A.element], | |
| 'layout_b': LayoutTag[transposed_layout_A], | |
| 'transform_b': ComplexTransformTag[operation.A.complex_transform], | |
| 'alignment_b': str(operation.A.alignment), | |
| 'element_c': DataTypeTag[operation.C.element], | |
| 'layout_c': LayoutTag[operation.C.layout], | |
| 'element_accumulator': DataTypeTag[operation.tile_description.math_instruction.element_accumulator], | |
| 'opcode_class': OpcodeClassTag[operation.tile_description.math_instruction.opcode_class], | |
| 'arch': "cutlass::arch::Sm%d" % operation.arch, | |
| 'threadblock_shape_m': str(operation.tile_description.threadblock_shape[0]), | |
| 'threadblock_shape_n': str(operation.tile_description.threadblock_shape[1]), | |
| 'threadblock_shape_k': str(operation.tile_description.threadblock_shape[2]), | |
| 'warp_shape_m': str(warp_shape[0]), | |
| 'warp_shape_n': str(warp_shape[1]), | |
| 'warp_shape_k': str(warp_shape[2]), | |
| 'instruction_shape_m': str(operation.tile_description.math_instruction.instruction_shape[0]), | |
| 'instruction_shape_n': str(operation.tile_description.math_instruction.instruction_shape[1]), | |
| 'instruction_shape_k': str(operation.tile_description.math_instruction.instruction_shape[2]), | |
| 'alignment_c': str(operation.C.alignment), | |
| 'element_epilogue': str(DataTypeTag[operation.element_epilogue]), | |
| 'stages': str(operation.tile_description.stages), | |
| 'math_operator': 'cutlass::arch::OpMultiplyAdd' | |
| } | |
| return SubstituteTemplate(self.template, values) | |
| ################################################################################################### | |
| # | |
| class EmitGemmPlanarComplexArrayInstance: | |
| ''' Responsible for emitting a CUTLASS template definition''' | |
| def __init__(self, operation_suffix = ''): | |
| self.operation_suffix = operation_suffix | |
| self.includes = [] | |
| self.template = """ | |
| // Gemm operator ${operation_name} | |
| using Operation_${operation_name} = typename cutlass::gemm::kernel::DefaultGemmPlanarComplexUniversal< | |
| ${element_a}, ${layout_a}, ${transform_a}, ${alignment_a}, | |
| ${element_b}, ${layout_b}, ${transform_b}, ${alignment_b}, | |
| ${element_c}, cutlass::layout::RowMajor, | |
| ${element_accumulator}, | |
| ${opcode_class}, | |
| ${arch}, | |
| cutlass::gemm::GemmShape<${threadblock_shape_m}, ${threadblock_shape_n}, ${threadblock_shape_k}>, | |
| cutlass::gemm::GemmShape<${warp_shape_m}, ${warp_shape_n}, ${warp_shape_k}>, | |
| cutlass::gemm::GemmShape<${instruction_shape_m}, ${instruction_shape_n}, ${instruction_shape_k}>, | |
| cutlass::epilogue::thread::LinearCombinationPlanarComplex< | |
| ${element_c}, | |
| ${alignment_c}, | |
| ${element_accumulator}, | |
| ${element_epilogue} | |
| >, | |
| cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<>, | |
| ${stages}, | |
| ${math_operator} | |
| >::GemmArrayKernel; | |
| struct ${operation_name} : public Operation_${operation_name} { }; | |
| """ | |
| # | |
| def instance_template(self): | |
| return """ | |
| ${compile_guard_start} | |
| manifest.append(new ${gemm_kind}< | |
| cutlass::gemm::device::GemmUniversalAdapter<${operation_name}> | |
| >("${operation_name}")); | |
| ${compile_guard_end} | |
| """ | |
| # | |
| def emit(self, operation): | |
| warp_shape = [operation.tile_description.threadblock_shape[idx] // operation.tile_description.warp_count[idx] for idx in range(3)] | |
| # exchange and transpose A and B types, layouts, and complex transforms since the C layout is row-major | |
| transposed_layout_A = TransposedLayout[operation.A.layout] | |
| transposed_layout_B = TransposedLayout[operation.B.layout] | |
| values = { | |
| 'operation_name': operation.procedural_name(), | |
| 'element_a': DataTypeTag[operation.B.element], | |
| 'layout_a': LayoutTag[transposed_layout_B], | |
| 'transform_a': ComplexTransformTag[operation.B.complex_transform], | |
| 'alignment_a': str(operation.B.alignment), | |
| 'element_b': DataTypeTag[operation.A.element], | |
| 'layout_b': LayoutTag[transposed_layout_A], | |
| 'transform_b': ComplexTransformTag[operation.A.complex_transform], | |
| 'alignment_b': str(operation.A.alignment), | |
| 'element_c': DataTypeTag[operation.C.element], | |
| 'layout_c': LayoutTag[operation.C.layout], | |
| 'element_accumulator': DataTypeTag[operation.tile_description.math_instruction.element_accumulator], | |
| 'opcode_class': OpcodeClassTag[operation.tile_description.math_instruction.opcode_class], | |
| 'arch': "cutlass::arch::Sm%d" % operation.arch, | |
| 'threadblock_shape_m': str(operation.tile_description.threadblock_shape[0]), | |
| 'threadblock_shape_n': str(operation.tile_description.threadblock_shape[1]), | |
| 'threadblock_shape_k': str(operation.tile_description.threadblock_shape[2]), | |
| 'warp_shape_m': str(warp_shape[0]), | |
| 'warp_shape_n': str(warp_shape[1]), | |
| 'warp_shape_k': str(warp_shape[2]), | |
| 'instruction_shape_m': str(operation.tile_description.math_instruction.instruction_shape[0]), | |
| 'instruction_shape_n': str(operation.tile_description.math_instruction.instruction_shape[1]), | |
| 'instruction_shape_k': str(operation.tile_description.math_instruction.instruction_shape[2]), | |
| 'alignment_c': str(operation.C.alignment), | |
| 'element_epilogue': str(DataTypeTag[operation.element_epilogue]), | |
| 'stages': str(operation.tile_description.stages), | |
| 'math_operator': 'cutlass::arch::OpMultiplyAdd' | |
| } | |
| return SubstituteTemplate(self.template, values) | |
| ################################################################################################### | |
| # | |
| class EmitGemmGroupedInstance: | |
| ''' Responsible for emitting a CUTLASS template definition''' | |
| def __init__(self, operation_suffix = ''): | |
| self.operation_suffix = operation_suffix | |
| self.includes = [ | |
| "cutlass/cutlass.h", | |
| "cutlass/numeric_types.h", | |
| "cutlass/arch/arch.h", | |
| "cutlass/arch/mma.h", | |
| "cutlass/layout/matrix.h", | |
| "cutlass/gemm/device/gemm.h", | |
| "cutlass/gemm/kernel/gemm_grouped.h", | |
| "cutlass/gemm/kernel/default_gemm_grouped.h", | |
| "cutlass/gemm/device/gemm_grouped.h" | |
| ] | |
| self.builtin_epilogue_functor_template = \ | |
| """${epilogue_functor}< | |
| ${element_c}, | |
| ${epilogue_vector_length}, | |
| ${element_accumulator}, | |
| ${element_epilogue} | |
| >""" | |
| self.gemm_template = """ | |
| // Gemm operator ${operation_name} | |
| using ${operation_name}_base = | |
| typename cutlass::gemm::kernel::DefaultGemmGrouped< | |
| ${element_a}, ${layout_a}, ${transform_a}, ${align_a}, | |
| ${element_b}, ${layout_b}, ${transform_b}, ${align_b}, | |
| ${element_c}, ${layout_c}, | |
| ${element_accumulator}, | |
| ${opcode_class}, | |
| ${arch}, | |
| cutlass::gemm::GemmShape<${threadblock_shape_m}, ${threadblock_shape_n}, ${threadblock_shape_k}>, | |
| cutlass::gemm::GemmShape<${warp_shape_m}, ${warp_shape_n}, ${warp_shape_k}>, | |
| cutlass::gemm::GemmShape<${instruction_shape_m}, ${instruction_shape_n}, ${instruction_shape_k}>, | |
| ${epilogue_functor}, | |
| ${swizzling_functor}, | |
| ${stages}, | |
| ${scheduler_mode}, | |
| ${math_operation} | |
| >::GemmKernel; | |
| // Define named type | |
| struct ${operation_name}${operation_suffix} : | |
| public ${operation_name}_base { }; | |
| """ | |
| # | |
| def instance_template(self): | |
| return """ | |
| ${compile_guard_start} | |
| manifest.append(new ${gemm_kind}< | |
| cutlass::gemm::device::GemmGrouped<${operation_name}> | |
| >("${operation_name}")); | |
| ${compile_guard_end} | |
| """ | |
| # | |
| def emit(self, operation): | |
| threadblock_shape = operation.tile_description.threadblock_shape | |
| warp_count = operation.tile_description.warp_count | |
| warp_shape = [threadblock_shape[idx] // warp_count[idx] for idx in range(3)] | |
| transpose_layouts = { | |
| LayoutType.ColumnMajor: LayoutType.RowMajor, | |
| LayoutType.RowMajor: LayoutType.ColumnMajor | |
| } | |
| instance_layout_A, instance_layout_B, instance_layout_C = \ | |
| (operation.A.layout, operation.B.layout, operation.C.layout) | |
| # | |
| # Support built-in epilogue functors or user-defined functions | |
| if isinstance(operation.epilogue_functor, enum.Enum): | |
| epilogue_vector_length = \ | |
| min(operation.C.alignment * DataTypeSize[operation.C.element], 128) // DataTypeSize[operation.C.element] | |
| values = { | |
| 'epilogue_vector_length': str(epilogue_vector_length), | |
| 'element_epilogue': str(DataTypeTag[operation.element_epilogue]), | |
| 'epilogue_functor': EpilogueFunctorTag[operation.epilogue_functor], | |
| } | |
| epilogue_functor = SubstituteTemplate(self.builtin_epilogue_functor_template, values) | |
| else: | |
| epilogue_functor = self.epilogue_functor.emit_declaration() | |
| # | |
| values = { | |
| 'operation_name': operation.procedural_name(), | |
| 'operation_suffix': self.operation_suffix, | |
| 'element_a': DataTypeTag[operation.A.element], | |
| 'layout_a': LayoutTag[instance_layout_A], | |
| 'element_b': DataTypeTag[operation.B.element], | |
| 'layout_b': LayoutTag[instance_layout_B], | |
| 'element_c': DataTypeTag[operation.C.element], | |
| 'layout_c': LayoutTag[instance_layout_C], | |
| 'element_accumulator': DataTypeTag[operation.accumulator_type()], | |
| 'opcode_class': OpcodeClassTag[operation.tile_description.math_instruction.opcode_class], | |
| 'arch': "cutlass::arch::Sm%d" % operation.arch, | |
| 'threadblock_shape_m': str(operation.tile_description.threadblock_shape[0]), | |
| 'threadblock_shape_n': str(operation.tile_description.threadblock_shape[1]), | |
| 'threadblock_shape_k': str(operation.tile_description.threadblock_shape[2]), | |
| 'warp_shape_m': str(warp_shape[0]), | |
| 'warp_shape_n': str(warp_shape[1]), | |
| 'warp_shape_k': str(warp_shape[2]), | |
| 'instruction_shape_m': str(operation.tile_description.math_instruction.instruction_shape[0]), | |
| 'instruction_shape_n': str(operation.tile_description.math_instruction.instruction_shape[1]), | |
| 'instruction_shape_k': str(operation.tile_description.math_instruction.instruction_shape[2]), | |
| 'epilogue_functor': epilogue_functor, | |
| 'swizzling_functor': SwizzlingFunctorTag[operation.swizzling_functor], | |
| 'stages': str(operation.tile_description.stages), | |
| 'align_a': str(operation.A.alignment), | |
| 'align_b': str(operation.B.alignment), | |
| 'transform_a': ComplexTransformTag[operation.A.complex_transform], | |
| 'transform_b': ComplexTransformTag[operation.B.complex_transform], | |
| 'scheduler_mode': GroupScheduleModeTag[operation.scheduler_mode], | |
| 'math_operation': MathOperationTag[operation.tile_description.math_instruction.math_operation] | |
| } | |
| return SubstituteTemplate(self.gemm_template, values) | |
| ################################################################################################### | |
| # | |
| # Emitters functions for all targets | |
| # | |
| ################################################################################################### | |
| class EmitGemmConfigurationLibrary: | |
| def __init__(self, operation_path, configuration_name): | |
| self.configuration_name = configuration_name | |
| self.configuration_path = os.path.join(operation_path, "%s.cu" % configuration_name).replace('\\', '/') | |
| self.instance_emitter = { | |
| GemmKind.Gemm: EmitGemmInstance, | |
| GemmKind.Sparse: EmitSparseGemmInstance, | |
| GemmKind.Universal: EmitGemmUniversalInstance, | |
| GemmKind.Universal3x: EmitGemmUniversal3xInstance, | |
| GemmKind.SparseUniversal3x: EmitGemmUniversal3xInstance, | |
| GemmKind.BlockScaledUniversal3x: EmitGemmUniversal3xInstance, | |
| GemmKind.PlanarComplex: EmitGemmPlanarComplexInstance, | |
| GemmKind.PlanarComplexArray: EmitGemmPlanarComplexArrayInstance, | |
| GemmKind.Grouped: EmitGemmGroupedInstance, | |
| GemmKind.GroupedUniversal3x: EmitGemmUniversal3xInstance, | |
| GemmKind.GroupedBlockScaledUniversal3x: EmitGemmUniversal3xInstance, | |
| GemmKind.BlockwiseUniversal3x: EmitGemmUniversal3xInstance, | |
| GemmKind.GroupedBlockwiseUniversal3x: EmitGemmUniversal3xInstance, | |
| } | |
| self.gemm_kind_wrappers = { | |
| GemmKind.Gemm: 'GemmOperation', | |
| GemmKind.Sparse: 'GemmSparseOperation', | |
| GemmKind.Universal: 'GemmUniversalOperation', | |
| GemmKind.Universal3x: 'GemmUniversal3xOperation', | |
| GemmKind.SparseUniversal3x: 'SparseGemmUniversal3xOperation', | |
| GemmKind.BlockScaledUniversal3x: 'BlockScaledGemmUniversal3xOperation', | |
| GemmKind.PlanarComplex: 'GemmPlanarComplexOperation', | |
| GemmKind.PlanarComplexArray: 'GemmPlanarComplexArrayOperation', | |
| GemmKind.Grouped: 'GemmGroupedOperation', | |
| GemmKind.GroupedUniversal3x: 'GroupedGemmUniversal3xOperation', | |
| GemmKind.GroupedBlockScaledUniversal3x: 'GroupedBlockScaledGemmUniversal3xOperation', | |
| GemmKind.BlockwiseUniversal3x: 'BlockwiseGemmUniversal3xOperation', | |
| GemmKind.GroupedBlockwiseUniversal3x: 'GroupedBlockwiseGemmUniversal3xOperation', | |
| } | |
| self.wmma_guard_start = "#if defined(CUTLASS_ARCH_WMMA_SM${sm_number}_ENABLED)" | |
| self.separator = """ | |
| /////////////////////////////////////////////////////////////////////////////////////////////////// | |
| """ | |
| self.header_template = """ | |
| /* | |
| Generated by gemm_operation.py - Do not edit. | |
| */ | |
| """ | |
| self.initialize_function_template = """ | |
| /////////////////////////////////////////////////////////////////////////////////////////////////// | |
| namespace cutlass { | |
| namespace library { | |
| /////////////////////////////////////////////////////////////////////////////////////////////////// | |
| void initialize_${configuration_name}(Manifest &manifest) { | |
| """ | |
| self.epilogue_template = """ | |
| } | |
| /////////////////////////////////////////////////////////////////////////////////////////////////// | |
| } // namespace library | |
| } // namespace cutlass | |
| /////////////////////////////////////////////////////////////////////////////////////////////////// | |
| """ | |
| def __enter__(self): | |
| _LOGGER.debug("*** EmitGemmConfigurationLibrary::__enter__") | |
| _LOGGER.debug("*** configuration_path (file to write): " + | |
| str(self.configuration_path)) | |
| self.configuration_file = open(self.configuration_path, "w") | |
| self.configuration_file.write(self.header_template) | |
| self.configuration_file.write(self.separator) | |
| self.includes = collections.OrderedDict([ | |
| ("cutlass/cutlass.h", None), | |
| ("cutlass/library/library.h", None), | |
| ("cutlass/library/manifest.h", None), | |
| ("library_internal.h", None), | |
| ("gemm_operation.h", None), | |
| ("gemm_operation_3x.hpp", None), | |
| ("grouped_gemm_operation_3x.hpp", None), | |
| ("sparse_gemm_operation_3x.hpp", None), | |
| ("block_scaled_gemm_operation_3x.hpp", None), | |
| ("blockwise_gemm_operation_3x.hpp", None), | |
| ("cutlass/arch/wmma.h", None), | |
| ("cutlass/numeric_types.h", None) | |
| ]) | |
| self.instance_definitions = [] | |
| self.instance_wrappers = [] | |
| self.operations = [] | |
| return self | |
| def emit(self, operation): | |
| _LOGGER.debug("*** EmitGemmConfigurationLibrary::emit(operation)") | |
| _LOGGER.debug("*** operation.gemm_kind: " + str(operation.gemm_kind)) | |
| emitter = self.instance_emitter[operation.gemm_kind]() | |
| for incl in emitter.includes: | |
| self.includes[incl] = None | |
| self.operations.append(operation) | |
| self.instance_definitions.append(emitter.emit(operation)) | |
| self.instance_wrappers.append(SubstituteTemplate(emitter.instance_template(), { | |
| 'configuration_name': self.configuration_name, | |
| 'operation_name': operation.procedural_name(), | |
| 'gemm_kind': self.gemm_kind_wrappers[operation.gemm_kind], | |
| 'compile_guard_start': SubstituteTemplate(self.wmma_guard_start, {'sm_number': str(operation.arch)}) \ | |
| if operation.tile_description.math_instruction.opcode_class == OpcodeClass.WmmaTensorOp else "", | |
| 'compile_guard_end': "#endif" \ | |
| if operation.tile_description.math_instruction.opcode_class == OpcodeClass.WmmaTensorOp else "" | |
| })) | |
| def __exit__(self, exception_type, exception_value, traceback): | |
| # Write includes | |
| for incl, _ in self.includes.items(): | |
| include_statement = "#include \"%s\"\n" % incl | |
| self.configuration_file.write(include_statement) | |
| self.configuration_file.write(self.separator) | |
| # Write instance definitions in top-level namespace | |
| for instance_definition in self.instance_definitions: | |
| self.configuration_file.write(instance_definition) | |
| # Add wrapper objects within initialize() function | |
| self.configuration_file.write(SubstituteTemplate(self.initialize_function_template, { | |
| 'configuration_name': self.configuration_name | |
| })) | |
| for instance_wrapper in self.instance_wrappers: | |
| self.configuration_file.write(instance_wrapper) | |
| self.configuration_file.write(self.epilogue_template) | |
| self.configuration_file.close() | |
| ################################################################################################### | |
| ################################################################################################### | |