c008b4aea8
* Release 3.3.0 Adds support for mixed precision GEMMs On Hopper and Ampere Adds support for < 16B aligned GEMMs on Hopper Enhancements to EVT Enhancements to Python interface Enhancements to Sub-byte type handling in CuTe Several other bug-fixes and performance improvements. * minor doc update
361 lines
14 KiB
Python
361 lines
14 KiB
Python
#################################################################################################
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#
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# Copyright (c) 2017 - 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# SPDX-License-Identifier: BSD-3-Clause
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#
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# Redistribution and use in source and binary forms, with or without
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# modification, are permitted provided that the following conditions are met:
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#
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# 1. Redistributions of source code must retain the above copyright notice, this
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# list of conditions and the following disclaimer.
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#
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# 2. Redistributions in binary form must reproduce the above copyright notice,
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# this list of conditions and the following disclaimer in the documentation
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# and/or other materials provided with the distribution.
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#
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# 3. Neither the name of the copyright holder nor the names of its
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# contributors may be used to endorse or promote products derived from
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# this software without specific prior written permission.
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#
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# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
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# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
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# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
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# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
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# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
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# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
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# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
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# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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#
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#################################################################################################
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"""
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Utilities for emitting Conv3d kernels
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"""
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import enum
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import os.path
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import shutil
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try:
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import builtins
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if hasattr(builtins, "CUTLASS_IGNORE_PACKAGE") and CUTLASS_IGNORE_PACKAGE == True:
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raise ImportError("Disabling attempt to import cutlass_library")
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from cutlass_library.library import *
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except ImportError:
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from library import *
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###################################################################################################
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#
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class Conv3dOperation:
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#
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def __init__(self, conv_kind, iterator_algorithm, arch, tile_description, A, B, C, element_epilogue, \
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stride_support, epilogue_functor = EpilogueFunctor.LinearCombination, swizzling_functor = SwizzlingFunctor.Identity4):
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self.operation_kind = OperationKind.Conv3d
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self.arch = arch
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self.tile_description = tile_description
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self.conv_kind = conv_kind
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self.A = A
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self.B = B
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self.C = C
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self.element_epilogue = element_epilogue
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self.epilogue_functor = epilogue_functor
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self.iterator_algorithm = iterator_algorithm
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self.stride_support = stride_support
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self.swizzling_functor = swizzling_functor
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#
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def is_mixed_input(self):
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return self.A.element != self.B.element
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#
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def core_name(self):
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''' The basic operation kind is prefixed with a letter indicating the accumulation type. '''
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intermediate_type = ''
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if self.tile_description.math_instruction.opcode_class == OpcodeClass.TensorOp:
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inst_shape = "%d%d%d" % tuple(self.tile_description.math_instruction.instruction_shape)
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if self.tile_description.math_instruction.element_a != self.A.element and \
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self.tile_description.math_instruction.element_a != self.tile_description.math_instruction.element_accumulator:
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intermediate_type = DataTypeNames[self.tile_description.math_instruction.element_a]
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else:
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inst_shape = ''
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return "%s%s%s%s3d_%s" % (ShortDataTypeNames[self.tile_description.math_instruction.element_accumulator], \
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inst_shape, intermediate_type, ConvKindNames[self.conv_kind], IteratorAlgorithmNames[self.iterator_algorithm])
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#
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def extended_name(self):
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''' Append data types if they differ from compute type. '''
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if self.C.element != self.tile_description.math_instruction.element_accumulator and \
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self.A.element != self.tile_description.math_instruction.element_accumulator:
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extended_name = "${element_c}_${core_name}_${element_a}"
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elif self.C.element == self.tile_description.math_instruction.element_accumulator and \
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self.A.element != self.tile_description.math_instruction.element_accumulator:
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extended_name = "${core_name}_${element_a}"
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else:
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extended_name = "${core_name}"
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extended_name = SubstituteTemplate(extended_name, {
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'element_a': DataTypeNames[self.A.element],
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'element_c': DataTypeNames[self.C.element],
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'core_name': self.core_name()
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})
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return extended_name
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#
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def configuration_name(self):
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''' The full procedural name indicates architecture, extended name, tile size, and layout. '''
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opcode_class_name = OpcodeClassNames[self.tile_description.math_instruction.opcode_class]
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threadblock = "%dx%d_%dx%d" % (
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self.tile_description.threadblock_shape[0],
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self.tile_description.threadblock_shape[1],
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self.tile_description.threadblock_shape[2],
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self.tile_description.stages
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)
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if self.stride_support == StrideSupport.Unity:
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configuration_name = "cutlass_${opcode_class}_${extended_name}_${threadblock}_unity_stride"
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else:
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configuration_name = "cutlass_${opcode_class}_${extended_name}_${threadblock}"
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return SubstituteTemplate(
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configuration_name,
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{
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'opcode_class': opcode_class_name,
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'extended_name': self.extended_name(),
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'threadblock': threadblock,
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}
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)
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#
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def procedural_name(self):
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''' The full procedural name indicates architecture, extended name, tile size, and layout. '''
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return self.configuration_name()
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###################################################################################################
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#
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# Emits single instances of a CUTLASS device-wide operator
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#
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###################################################################################################
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class EmitConv3dInstance:
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def __init__(self):
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self.template = """
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// Conv3d${conv_kind_name} ${iterator_algorithm_name} kernel instance "${operation_name}"
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using ${operation_name}_base =
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typename cutlass::conv::kernel::DefaultConv3d${conv_kind_name}<
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${element_a},
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cutlass::layout::TensorNDHWC,
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${element_b},
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cutlass::layout::TensorNDHWC,
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${element_c},
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cutlass::layout::TensorNDHWC,
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${element_accumulator},
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${opcode_class},
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${arch},
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cutlass::gemm::GemmShape<${threadblock_shape_m}, ${threadblock_shape_n}, ${threadblock_shape_k}>,
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cutlass::gemm::GemmShape<${warp_shape_m}, ${warp_shape_n}, ${warp_shape_k} >,
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cutlass::gemm::GemmShape<${instruction_shape_m}, ${instruction_shape_n}, ${instruction_shape_k}>,
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${epilogue_functor}<
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${element_c},
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${epilogue_vector_length},
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${element_accumulator},
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${element_epilogue}
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>,
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${swizzling_functor}, // cutlass::gemm::threadblock::GemmSplitKIdentityThreadblockSwizzle<>,
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${stages},
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cutlass::arch::OpMultiplyAdd,
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${iterator_algorithm},
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${stride_support}
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>::Kernel;
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"""
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def emit(self, operation):
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warp_shape = [int(operation.tile_description.threadblock_shape[idx] / operation.tile_description.warp_count[idx]) for idx in range(3)]
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epilogue_vector_length = int(min(operation.C.alignment * DataTypeSize[operation.C.element], 128) / DataTypeSize[operation.C.element])
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values = {
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'operation_name': operation.procedural_name(),
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'conv_kind': ConvKindTag[operation.conv_kind],
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'conv_kind_name': ConvKindNames[operation.conv_kind].capitalize(),
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'element_a': DataTypeTag[operation.A.element],
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'layout_a': LayoutTag[operation.A.layout],
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'element_b': DataTypeTag[operation.B.element],
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'layout_b': LayoutTag[operation.B.layout],
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'element_c': DataTypeTag[operation.C.element],
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'layout_c': LayoutTag[operation.C.layout],
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'element_accumulator': DataTypeTag[operation.tile_description.math_instruction.element_accumulator],
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'opcode_class': OpcodeClassTag[operation.tile_description.math_instruction.opcode_class],
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'arch': "cutlass::arch::Sm%d" % operation.arch,
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'threadblock_shape_m': str(operation.tile_description.threadblock_shape[0]),
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'threadblock_shape_n': str(operation.tile_description.threadblock_shape[1]),
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'threadblock_shape_k': str(operation.tile_description.threadblock_shape[2]),
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'warp_shape_m': str(warp_shape[0]),
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'warp_shape_n': str(warp_shape[1]),
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'warp_shape_k': str(warp_shape[2]),
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'instruction_shape_m': str(operation.tile_description.math_instruction.instruction_shape[0]),
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'instruction_shape_n': str(operation.tile_description.math_instruction.instruction_shape[1]),
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'instruction_shape_k': str(operation.tile_description.math_instruction.instruction_shape[2]),
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'epilogue_vector_length': str(epilogue_vector_length),
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'epilogue_functor': EpilogueFunctorTag[operation.epilogue_functor],
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'element_epilogue': str(DataTypeTag[operation.element_epilogue]),
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'swizzling_functor': SwizzlingFunctorTag[operation.swizzling_functor],
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'stages': str(operation.tile_description.stages),
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'iterator_algorithm': IteratorAlgorithmTag[operation.iterator_algorithm],
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'iterator_algorithm_name': IteratorAlgorithmNames[operation.iterator_algorithm].capitalize(),
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'stride_support': StrideSupportTag[operation.stride_support]
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}
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return SubstituteTemplate(self.template, values)
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###################################################################################################
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#
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# Generator functions for all layouts
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#
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###################################################################################################
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#
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def GenerateConv3dTensorOp(manifest, tile_descriptions, min_cc, align = 128):
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for tile in tile_descriptions:
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for conv_kind in [ConvKind.Fprop, ConvKind.Dgrad, ConvKind.Wgrad]:
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if conv_kind == ConvKind.Fprop or (tile.math_instruction.element_accumulator in [DataType.f16, DataType.f32]):
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#
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output_types = [tile.math_instruction.element_a, tile.math_instruction.element_accumulator] \
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if DataTypeSize[tile.math_instruction.element_accumulator] == 32 \
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else [tile.math_instruction.element_accumulator,]
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for output_type in output_types:
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A = TensorDescription(tile.math_instruction.element_a, LayoutType.TensorNDHWC, int(align / DataTypeSize[tile.math_instruction.element_a]))
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B = TensorDescription(tile.math_instruction.element_b, LayoutType.TensorNDHWC, int(align / DataTypeSize[tile.math_instruction.element_b]))
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C = TensorDescription(output_type, LayoutType.TensorNDHWC, max(1, int(align / DataTypeSize[output_type])))
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manifest.append(Conv3dOperation(conv_kind, min_cc, tile, A, B, C, tile.math_instruction.element_accumulator))
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###################################################################################################
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#
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# Emitters functions for all targets
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#
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###################################################################################################
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class EmitConv3dConfigurationLibrary:
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def __init__(self, operation_path, configuration_name):
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self.configuration_name = configuration_name
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self.configuration_path = os.path.join(operation_path, "%s.cu" % configuration_name)
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self.instance_emitter = EmitConv3dInstance()
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self.instance_template = """
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${operation_instance}
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// Derived class
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struct ${operation_name} :
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public ${operation_name}_base { };
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///////////////////////////////////////////////////////////////////////////////////////////////////
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"""
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self.header_template = """
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/*
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Generated by conv3d_operation.py - Do not edit.
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*/
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///////////////////////////////////////////////////////////////////////////////////////////////////
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#include "cutlass/cutlass.h"
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#include "cutlass/library/library.h"
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#include "cutlass/library/manifest.h"
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#include "library_internal.h"
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#include "conv3d_operation.h"
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///////////////////////////////////////////////////////////////////////////////////////////////////
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"""
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self.configuration_header = """
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namespace cutlass {
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namespace library {
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// Initialize all instances
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void initialize_${configuration_name}(Manifest &manifest) {
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"""
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self.configuration_instance = """
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using Operation_${operation_name} = cutlass::conv::device::ImplicitGemmConvolution<
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${operation_name}>;
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manifest.append(new cutlass::library::Conv3dOperation<
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Operation_${operation_name}>(
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"${operation_name}"));
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"""
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self.configuration_epilogue = """
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}
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"""
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self.epilogue_template = """
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///////////////////////////////////////////////////////////////////////////////////////////////////
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} // namespace library
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} // namespace cutlass
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///////////////////////////////////////////////////////////////////////////////////////////////////
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"""
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#
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def __enter__(self):
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self.configuration_file = open(self.configuration_path, "w")
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self.configuration_file.write(SubstituteTemplate(self.header_template, {
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'configuration_name': self.configuration_name
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}))
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self.operations = []
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return self
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#
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def emit(self, operation):
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self.operations.append(operation)
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self.configuration_file.write(SubstituteTemplate(self.instance_template, {
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'configuration_name': self.configuration_name,
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'operation_name': operation.procedural_name(),
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'operation_instance': self.instance_emitter.emit(operation)
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}))
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#
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def __exit__(self, exception_type, exception_value, traceback):
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self.configuration_file.write(SubstituteTemplate(self.configuration_header, {
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'configuration_name': self.configuration_name
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}))
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for operation in self.operations:
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self.configuration_file.write(SubstituteTemplate(self.configuration_instance, {
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'configuration_name': self.configuration_name,
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'operation_name': operation.procedural_name()
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}))
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self.configuration_file.write(self.configuration_epilogue)
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self.configuration_file.write(self.epilogue_template)
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self.configuration_file.close()
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###################################################################################################
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###################################################################################################
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