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generator.py
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#
# \file generator.py
#
# \brief Generates the CUTLASS Library's instances
#
import enum
import os.path
import shutil
import argparse
from library import *
from manifest import *
from itertools import product
###################################################################################################
#
def CudaToolkitVersionSatisfies(semantic_ver_string, major, minor, patch = 0):
# by default, use the latest CUDA Toolkit version
cuda_version = [11, 0, 132]
# Update cuda_version based on parsed string
if semantic_ver_string != '':
for i, x in enumerate([int(x) for x in semantic_ver_string.split('.')]):
if i < len(cuda_version):
cuda_version[i] = x
else:
cuda_version.append(x)
return cuda_version >= [major, minor, patch]
###################################################################################################
###################################################################################################
#
def EpilogueAlignment(max_alignment, tile, epilogue_steps = 8):
''' Helper to compute the maximum alignment of the epilogue '''
def product(X, identity = 1):
result = identity
for item in X:
result *= item
return result
elements_per_thread = product(tile.threadblock_shape[:-1]) // product(tile.warp_count) // 32 // epilogue_steps
return min(max_alignment, elements_per_thread)
def DefaultSwizzlingFunctor():
return SwizzlingFunctor.Identity8;
# To use StreamK decomposition for basic GEMMs, set `swizzling_functor = SwizzlingFunctor.StreamK`
#
def CreateGemmOperator(manifest, layouts, tile_descriptions, data_type, \
alignment_constraints, complex_transforms = None, epilogue_functor = EpilogueFunctor.LinearCombination, \
swizzling_functor = DefaultSwizzlingFunctor()):
if complex_transforms is None:
complex_transforms = [(ComplexTransform.none, ComplexTransform.none),]
element_a, element_b, element_c, element_epilogue = data_type
operations = []
# by default, only generate the largest tile and largest alignment
if manifest.kernel_filter == '':
tile_descriptions = [tile_descriptions[0],]
alignment_constraints = [alignment_constraints[0],]
for layout in layouts:
for tile_description in tile_descriptions:
for alignment in alignment_constraints:
for complex_transform in complex_transforms:
alignment_c = min(8, alignment)
A = TensorDescription(element_a, layout[0], alignment, complex_transform[0])
B = TensorDescription(element_b, layout[1], alignment, complex_transform[1])
C = TensorDescription(element_c, layout[2], alignment_c)
new_operation = GemmOperation(GemmKind.Universal, tile_description.minimum_compute_capability, \
tile_description, A, B, C, element_epilogue, epilogue_functor, swizzling_functor)
manifest.append(new_operation)
operations.append(new_operation)
return operations
# Generates 3.0 API based GemmUniversal API kernels. Alignment constraits are folded in with layouts
def CreateGemmUniversal3xOperator(
manifest, layouts, tile_descriptions, data_type,
complex_transforms=None,
epilogue_functor=EpilogueFunctor.LinearCombination,
swizzling_functor=SwizzlingFunctor.Identity1):
if complex_transforms is None:
complex_transforms = [(ComplexTransform.none, ComplexTransform.none), ]
element_a, element_b, element_c, element_epilogue = data_type
operations = []
# by default, only generate the largest tile and largest alignment
if manifest.kernel_filter == '':
tile_descriptions = [tile_descriptions[0]]
for layout in layouts:
for tile_description in tile_descriptions:
for complex_transform in complex_transforms:
A = TensorDescription(
element_a, layout[0][0], layout[0][1], complex_transform[0])
B = TensorDescription(
element_b, layout[1][0], layout[1][1], complex_transform[1])
C = TensorDescription(element_c, layout[2][0], layout[2][1])
operation = GemmOperation(
GemmKind.Universal3x, tile_description.minimum_compute_capability,
tile_description, A, B, C, element_epilogue, epilogue_functor, swizzling_functor)
manifest.append(operation)
operations.append(operation)
return operations
#
def CreateSparseGemmOperator(manifest, layouts, tile_descriptions, data_type, \
alignment_constraints, complex_transforms = None, epilogue_functor = EpilogueFunctor.LinearCombination, \
swizzling_functor = SwizzlingFunctor.Identity8):
if complex_transforms is None:
complex_transforms = [(ComplexTransform.none, ComplexTransform.none),]
element_a, element_b, element_c, element_epilogue = data_type
gemm_kinds = [GemmKind.Sparse]
operations = []
# by default, only generate the largest tile and largest alignment
if manifest.kernel_filter == '':
tile_descriptions = [tile_descriptions[0],]
alignment_constraints = [alignment_constraints[0],]
for layout in layouts:
for tile_description in tile_descriptions:
for alignment in alignment_constraints:
for complex_transform in complex_transforms:
alignment_c = min(8, alignment)
A = TensorDescription(element_a, layout[0], alignment, complex_transform[0])
B = TensorDescription(element_b, layout[1], alignment, complex_transform[1])
C = TensorDescription(element_c, layout[2], alignment_c)
new_operation = GemmOperation(GemmKind.Sparse, tile_description.minimum_compute_capability, \
tile_description, A, B, C, element_epilogue, epilogue_functor, swizzling_functor)
manifest.append(new_operation)
operations.append(new_operation)
return operations
#
def CreateGemmPlanarComplexOperator(manifest, layouts, tile_descriptions, data_type, \
alignment_constraints, complex_transforms):
if complex_transforms is None:
complex_transforms = [(ComplexTransform.none, ComplexTransform.none),]
element_a, element_b, element_c, element_epilogue = data_type
gemm_kinds = [GemmKind.PlanarComplex, GemmKind.PlanarComplexArray]
# by default, only generate the largest tile and largest alignment
if manifest.kernel_filter == '':
tile_descriptions = [tile_descriptions[0],]
alignment_constraints = [alignment_constraints[0],]
for gemm_kind in gemm_kinds:
for layout in layouts:
for tile_description in tile_descriptions:
for alignment in alignment_constraints:
for complex_transform in complex_transforms:
alignment_c = min(8, alignment)
A = TensorDescription(element_a, layout[0], alignment, complex_transform[0])
B = TensorDescription(element_b, layout[1], alignment, complex_transform[1])
C = TensorDescription(element_c, layout[2], alignment_c)
manifest.append(GemmOperation(gemm_kind, \
tile_description.minimum_compute_capability, \
tile_description, A, B, C, element_epilogue))
return
#
def CreateGemmGroupedOperator(manifest, layouts, tile_descriptions, data_type, \
alignment_constraints, complex_transforms = None, epilogue_functor = EpilogueFunctor.LinearCombination, \
swizzling_functor = SwizzlingFunctor.Identity8):
if complex_transforms is None:
complex_transforms = [(ComplexTransform.none, ComplexTransform.none),]
element_a, element_b, element_c, element_epilogue = data_type
operations = []
# by default, only generate the largest tile and largest alignment
if manifest.kernel_filter == '':
tile_descriptions = [tile_descriptions[0],]
alignment_constraints = [alignment_constraints[0],]
for layout in layouts:
for tile_description in tile_descriptions:
for alignment in alignment_constraints:
for complex_transform in complex_transforms:
alignment_c = min(8, alignment)
A = TensorDescription(element_a, layout[0], alignment, complex_transform[0])
B = TensorDescription(element_b, layout[1], alignment, complex_transform[1])
C = TensorDescription(element_c, layout[2], alignment_c)
new_operation = GroupedGemmOperation(GemmKind.Grouped, tile_description.minimum_compute_capability, \
tile_description, A, B, C, element_epilogue, epilogue_functor, swizzling_functor)
manifest.append(new_operation)
operations.append(new_operation)
return operations
#
def CreateRankKOperator(manifest, layouts, fill_modes, tile_descriptions, data_type, \
alignment_constraints, blas_mode, epilogue_functor = EpilogueFunctor.LinearCombination, \
swizzling_functor = SwizzlingFunctor.Identity8):
element_a, element_c, element_epilogue = data_type
operations = []
# by default, only generate the largest tile and largest alignment
if manifest.kernel_filter == '':
tile_descriptions = [tile_descriptions[0],]
alignment_constraints = [alignment_constraints[0],]
for layout in layouts:
for fill_mode in fill_modes:
for tile_description in tile_descriptions:
for alignment in alignment_constraints:
# SERK supported layouts (RowMajor, ColumnMajor) with no conjugation
complex_transform = ComplexTransform.none
# HERK supported layouts (RowMajor + conj, ColumnMajor)
if blas_mode == BlasMode.hermitian and layout[0] == LayoutType.RowMajor:
complex_transform = ComplexTransform.conj
alignment_c = 1 # Alignment only applies to A in SYRK
A = TensorDescription(element_a, layout[0], alignment, complex_transform)
C = SymmetricTensorDescription(element_c, layout[1], fill_mode, alignment_c)
# Rank-K update
new_operation = RankKOperation(RankKKind.Universal, tile_description.minimum_compute_capability, \
tile_description, A, C, element_epilogue, epilogue_functor, swizzling_functor, blas_mode)
manifest.append(new_operation)
operations.append(new_operation)
# Rank-2K update
new_operation = Rank2KOperation(RankKKind.Universal, tile_description.minimum_compute_capability, \
tile_description, A, C, element_epilogue, epilogue_functor, swizzling_functor, blas_mode)
manifest.append(new_operation)
operations.append(new_operation)
return operations
#
def CreateTrmmOperator(manifest, layouts, side_modes, fill_modes, diag_types, tile_descriptions, data_type, \
alignment_constraints, complex_transforms = None, epilogue_functor = EpilogueFunctor.LinearCombination, \
swizzling_functor = SwizzlingFunctor.Identity8):
if complex_transforms is None:
complex_transforms = [(ComplexTransform.none),]
element_a, element_b, element_c, element_epilogue = data_type
operations = []
# by default, only generate the largest tile and largest alignment
if manifest.kernel_filter == '':
tile_descriptions = [tile_descriptions[0],]
alignment_constraints = [alignment_constraints[0],]
for layout in layouts:
for side_mode in side_modes:
for fill_mode in fill_modes:
for diag_type in diag_types:
for tile_description in tile_descriptions:
for alignment in alignment_constraints:
for complex_transform in complex_transforms:
alignment_c = min(8, alignment)
A = TriangularTensorDescription(element_a, layout[0], side_mode, fill_mode, diag_type,
alignment, complex_transform)
B = TensorDescription(element_b, layout[1], alignment)
C = TensorDescription(element_c, layout[2], alignment_c)
new_operation = TrmmOperation(TrmmKind.Universal, tile_description.minimum_compute_capability, \
tile_description, A, B, C, element_epilogue, epilogue_functor, swizzling_functor)
manifest.append(new_operation)
operations.append(new_operation)
return operations
#
def CreateSymmOperator(manifest, layouts, side_modes, fill_modes, tile_descriptions, data_type, \
alignment_constraints, blas_mode, epilogue_functor = EpilogueFunctor.LinearCombination, \
swizzling_functor = SwizzlingFunctor.Identity8):
element_a, element_b, element_c, element_epilogue = data_type
operations = []
# by default, only generate the largest tile and largest alignment
if manifest.kernel_filter == '':
tile_descriptions = [tile_descriptions[0],]
alignment_constraints = [alignment_constraints[0],]
for layout in layouts:
for side_mode in side_modes:
for fill_mode in fill_modes:
for tile_description in tile_descriptions:
for alignment in alignment_constraints:
# SYMM supported layouts (RowMajor, ColumnMajor) with no conjugation
complex_transform = ComplexTransform.none
alignment_a = 1 # No vectorized access for the triangular matrix
alignment_c = min(8, alignment)
A = SymmetricTensorDescription(element_a, layout[0], fill_mode, alignment_a, complex_transform, side_mode)
# tensor A and B have same data type and layout
B = TensorDescription(element_b, layout[0], alignment)
C = TensorDescription(element_c, layout[1], alignment_c)
# SYMM/HEMM update
new_operation = SymmOperation(SymmKind.Universal, tile_description.minimum_compute_capability, \
tile_description, A, B, C, element_epilogue, epilogue_functor, swizzling_functor, blas_mode)
manifest.append(new_operation)
operations.append(new_operation)
# SYMM/HEMM update
new_operation = SymmOperation(SymmKind.Universal, tile_description.minimum_compute_capability, \
tile_description, A, B, C, element_epilogue, epilogue_functor, swizzling_functor, blas_mode)
manifest.append(new_operation)
operations.append(new_operation)
return operations
###########################################################################################################
# ConvolutionOperator support variations
# ____________________________________________________________________
# ConvolutionalOperator | Analytic | Optimized
# ____________________________________________________________________
# | Fprop | (strided) | (strided)
# | Dgrad | (strided, unity*) | (strided, unity)
# | Wgrad | (strided) | (strided)
# ____________________________________________________________________
#
# Note : Operator marked (*) are supported but not generated to keep the instantiated kernel count low
###########################################################################################################
# Convolution for 2D operations
def CreateConv2dOperator(manifest, layout, tile_descriptions, data_type, alignment_constraints, \
conv_kinds = [ConvKind.Fprop, ConvKind.Dgrad, ConvKind.Wgrad], \
epilogue_functor = EpilogueFunctor.LinearCombination, swizzling_functor = SwizzlingFunctor.Identity4):
element_a, element_b, element_c, element_epilogue = data_type
# one exceptional case
# iterator algorithm (analytic and optimized)
iterator_algorithms = [IteratorAlgorithm.Analytic, IteratorAlgorithm.Optimized]
# by default, only generate the largest tile size, largest alignment, and optimized iterator
if manifest.kernel_filter == '':
tile_descriptions = [tile_descriptions[0],]
alignment_constraints = [alignment_constraints[0],]
iterator_algorithms = [IteratorAlgorithm.Optimized]
operations = []
for tile in tile_descriptions:
for alignment in alignment_constraints:
alignment_c = min(8, alignment)
A = TensorDescription(element_a, layout[0], alignment)
B = TensorDescription(element_b, layout[1], alignment)
C = TensorDescription(element_c, layout[2], alignment_c)
swizzling_functor_ = swizzling_functor
#
# Conv2d Fprop
#
if ConvKind.Fprop in conv_kinds:
# Strided support for Analytic and Optimized Fprop
for iterator_algorithm in iterator_algorithms:
new_operations = [
# None grouped kernel
Conv2dOperation(ConvKind.Fprop, iterator_algorithm, tile.minimum_compute_capability, tile,\
A, B, C, element_epilogue, StrideSupport.Strided, epilogue_functor, swizzling_functor_),
]
# Instance group conv kernel
if tile.math_instruction.opcode_class == OpcodeClass.TensorOp and A.layout == LayoutType.TensorNHWC and \
tile.minimum_compute_capability >= 80:
# SingleGroup kernel
new_operations.append(Conv2dOperation(ConvKind.Fprop, iterator_algorithm, tile.minimum_compute_capability, tile,\
A, B, C, element_epilogue, StrideSupport.Strided, epilogue_functor, swizzling_functor_, group_mode=GroupMode.SingleGroup))
# Analytic iterator supports MultipleGroup mode
if iterator_algorithm == IteratorAlgorithm.Analytic:
new_operations.append(Conv2dOperation(ConvKind.Fprop, iterator_algorithm, tile.minimum_compute_capability, tile,\
A, B, C, element_epilogue, StrideSupport.Strided, epilogue_functor, swizzling_functor_, group_mode=GroupMode.MultipleGroup))
for new_operation in new_operations:
manifest.append(new_operation)
operations.append(new_operation)
#
# Conv2d Dgrad
#
if ConvKind.Dgrad in conv_kinds:
# Unity stride for Analytic and Optimized Dgrad
for iterator_algorithm in iterator_algorithms:
new_operation = Conv2dOperation(ConvKind.Dgrad, iterator_algorithm, tile.minimum_compute_capability, tile,\
A, B, C, element_epilogue, StrideSupport.Unity, epilogue_functor, swizzling_functor_)
manifest.append(new_operation)
operations.append(new_operation)
# Strided support for Analytic Dgrad
# strided dgrad uses a special threadblock swizzle
# note that SwizzlingFunctor.StridedDgradHorizontal might be
# better for problem sizes with large activation channel count
swizzling_functor_strided_dgrad_ = SwizzlingFunctor.StridedDgradIdentity1
if IteratorAlgorithm.Analytic in iterator_algorithms:
new_operation = Conv2dOperation(ConvKind.Dgrad, IteratorAlgorithm.Analytic, tile.minimum_compute_capability, tile,\
A, B, C, element_epilogue, StrideSupport.Strided, epilogue_functor, swizzling_functor_strided_dgrad_)
manifest.append(new_operation)
operations.append(new_operation)
# Strided support for Optimized Dgrad
if IteratorAlgorithm.Optimized in iterator_algorithms:
new_operation = Conv2dOperation(ConvKind.Dgrad, IteratorAlgorithm.Optimized, tile.minimum_compute_capability, tile,\
A, B, C, element_epilogue, StrideSupport.Strided, epilogue_functor, swizzling_functor_strided_dgrad_)
manifest.append(new_operation)
operations.append(new_operation)
#
# Conv2d Wgrad
#
if ConvKind.Wgrad in conv_kinds:
# Strided support for Analytic and Optimized Wgrad
for iterator_algorithm in iterator_algorithms:
new_operation = Conv2dOperation(ConvKind.Wgrad, iterator_algorithm, tile.minimum_compute_capability, tile,\
A, B, C, element_epilogue, StrideSupport.Strided, epilogue_functor, swizzling_functor_)
manifest.append(new_operation)
operations.append(new_operation)
return operations
# Convolution for 2D operations specialized for few channels
def CreateConv2dFixedChannelsOperator(manifest, layout, tile_descriptions, data_type, channel_counts, \
conv_kinds = [ConvKind.Fprop, ConvKind.Dgrad, ConvKind.Wgrad], \
epilogue_functor = EpilogueFunctor.LinearCombination, swizzling_functor = SwizzlingFunctor.Identity4):
element_a, element_b, element_c, element_epilogue = data_type
# one exceptional case
# iterator algorithm (analytic and optimized)
iterator_algorithms = [IteratorAlgorithm.FixedChannels,]
# by default, only generate the largest tile size, largest alignment, and optimized iterator
if manifest.kernel_filter == '':
tile_descriptions = [tile_descriptions[0],]
channel_counts = [channel_counts[0],]
operations = []
for tile in tile_descriptions:
for channel_count in channel_counts:
alignment_c = EpilogueAlignment(channel_count, tile)
A = TensorDescription(element_a, layout[0], channel_count)
B = TensorDescription(element_b, layout[1], channel_count)
C = TensorDescription(element_c, layout[2], alignment_c)
swizzling_functor_ = swizzling_functor
#
# Conv2d Fprop
#
if ConvKind.Fprop in conv_kinds:
# Strided support for Analytic and Optimized Fprop
for iterator_algorithm in iterator_algorithms:
new_operation = Conv2dOperation(ConvKind.Fprop, iterator_algorithm, tile.minimum_compute_capability, tile,\
A, B, C, element_epilogue, StrideSupport.Strided, epilogue_functor, swizzling_functor_)
manifest.append(new_operation)
operations.append(new_operation)
return operations
# Convolution for 2D operations specialized for few channels
def CreateConv2dFewChannelsOperator(manifest, layout, tile_descriptions, data_type, channel_counts, \
conv_kinds = [ConvKind.Fprop, ConvKind.Dgrad, ConvKind.Wgrad], \
epilogue_functor = EpilogueFunctor.LinearCombination, swizzling_functor = SwizzlingFunctor.Identity4):
element_a, element_b, element_c, element_epilogue = data_type
# one exceptional case
# iterator algorithm (analytic and optimized)
iterator_algorithms = [IteratorAlgorithm.FewChannels,]
# by default, only generate the largest tile size, largest alignment, and optimized iterator
if manifest.kernel_filter == '':
tile_descriptions = [tile_descriptions[0],]
channel_counts = [channel_counts[0],]
operations = []
for tile in tile_descriptions:
for channel_count in channel_counts:
alignment_c = EpilogueAlignment(channel_count, tile)
A = TensorDescription(element_a, layout[0], channel_count)
B = TensorDescription(element_b, layout[1], channel_count)
C = TensorDescription(element_c, layout[2], alignment_c)
swizzling_functor_ = swizzling_functor
#
# Conv2d Fprop
#
if ConvKind.Fprop in conv_kinds:
# Strided support for Analytic and Optimized Fprop
for iterator_algorithm in iterator_algorithms:
new_operation = Conv2dOperation(ConvKind.Fprop, iterator_algorithm, tile.minimum_compute_capability, tile,\
A, B, C, element_epilogue, StrideSupport.Strided, epilogue_functor, swizzling_functor_)
manifest.append(new_operation)
operations.append(new_operation)
return operations
# Convolution for 3D operations
def CreateConv3dOperator(manifest, layout, tile_descriptions, data_type, alignment, \
conv_kinds = [ConvKind.Fprop, ConvKind.Dgrad, ConvKind.Wgrad], epilogue_functor = EpilogueFunctor.LinearCombination):
element_a, element_b, element_c, element_epilogue = data_type
# one exceptional case
alignment_c = min(8, alignment)
# iterator algorithm (analytic and optimized)
iterator_algorithms = [IteratorAlgorithm.Analytic, IteratorAlgorithm.Optimized]
# by default, only generate the largest tile size and optimized iterators
if manifest.kernel_filter == '':
tile_descriptions = [tile_descriptions[0],]
iterator_algorithms = [IteratorAlgorithm.Optimized]
operations = []
# All tile sizes for Conv3dFprop and Conv3dWgrad
for tile in tile_descriptions:
A = TensorDescription(element_a, layout, alignment)
B = TensorDescription(element_b, layout, alignment)
C = TensorDescription(element_c, layout, alignment_c)
#
# Conv3d Fprop
#
if ConvKind.Fprop in conv_kinds:
# Strided support for Analytic and Optimized Fprop
for iterator_algorithm in iterator_algorithms:
new_operation = Conv3dOperation(ConvKind.Fprop, iterator_algorithm, tile.minimum_compute_capability, tile,\
A, B, C, element_epilogue, StrideSupport.Strided)
manifest.append(new_operation)
operations.append(new_operation)
#
# Conv3d Wgrad
#
if ConvKind.Wgrad in conv_kinds:
# Strided support for Analytic and Optimized Wgrad
for iterator_algorithm in iterator_algorithms:
new_operation = Conv3dOperation(ConvKind.Wgrad, iterator_algorithm, tile.minimum_compute_capability, tile,\
A, B, C, element_epilogue, StrideSupport.Strided, epilogue_functor)
manifest.append(new_operation)
operations.append(new_operation)
# All tile sizes for Conv3dDgrad
for tile in tile_descriptions:
A = TensorDescription(element_a, layout, alignment)
B = TensorDescription(element_b, layout, alignment)
C = TensorDescription(element_c, layout, alignment_c)
#
# Conv3d Dgrad
#
if ConvKind.Dgrad in conv_kinds:
# Unity stride for Optimized Dgrad
new_operation = Conv3dOperation(ConvKind.Dgrad, IteratorAlgorithm.Optimized, tile.minimum_compute_capability, tile,\
A, B, C, element_epilogue, StrideSupport.Unity, epilogue_functor)
manifest.append(new_operation)
operations.append(new_operation)
# Strided support for Analytic Dgrad
# Conv3dDgrad has a naive strided support which does not cut down redundant MMAs
new_operation = Conv3dOperation(ConvKind.Dgrad, IteratorAlgorithm.Analytic, tile.minimum_compute_capability, tile,\
A, B, C, element_epilogue, StrideSupport.Strided, epilogue_functor)
manifest.append(new_operation)
operations.append(new_operation)
return operations
# Convolution for Depthwise 2d conv
def CreateDepthwiseConv2dOperator(manifest, layout, tile_descriptions, data_type, alignment_constraints, \
conv_kinds = [ConvKind.Fprop, ConvKind.Dgrad, ConvKind.Wgrad], \
epilogue_functor = EpilogueFunctor.LinearCombination, swizzling_functor = SwizzlingFunctor.Identity4):
element_a, element_b, element_c, element_epilogue = data_type
# iterator algorithm (FixedStrideDilation, Optimized)
iterator_algorithms = [IteratorAlgorithm.FixedStrideDilation, IteratorAlgorithm.Optimized]
# by default, only generate the largest tile size, largest alignment, and optimized iterator
if manifest.kernel_filter == '':
tile_descriptions = [tile_descriptions[0],]
alignment_constraints = [alignment_constraints[0],]
operations = []
for tile in tile_descriptions:
for alignment in alignment_constraints:
alignment_c = min(8, alignment)
A = TensorDescription(element_a, layout[0], alignment)
B = TensorDescription(element_b, layout[1], alignment)
C = TensorDescription(element_c, layout[2], alignment_c)
swizzling_functor_ = swizzling_functor
if ConvKind.Fprop in conv_kinds:
# Strided support for Optimized and FixedStridedDilation Depthwise Conv
for iterator_algorithm in iterator_algorithms:
stride_support = StrideSupport.Strided
if iterator_algorithm == IteratorAlgorithm.FixedStrideDilation:
if tile.stride == [-1, -1] or tile.dilation == [-1,-1]:
continue
stride_support = StrideSupport.Fixed
if iterator_algorithm == IteratorAlgorithm.Optimized:
if tile.stride != [-1, -1] or tile.dilation != [-1,-1]:
continue
new_operation = Conv2dOperation(ConvKind.Fprop,
iterator_algorithm,
tile.minimum_compute_capability,
tile,
A, B, C,
element_epilogue,
stride_support,
epilogue_functor,
swizzling_functor_,
group_mode=GroupMode.Depthwise)
manifest.append(new_operation)
operations.append(new_operation)
return operations
###################################################################################################
###################################################################################################
#
def GenerateSM50_Simt(manifest, cuda_version):
layouts = [
(LayoutType.ColumnMajor, LayoutType.ColumnMajor, LayoutType.ColumnMajor),
(LayoutType.ColumnMajor, LayoutType.RowMajor, LayoutType.ColumnMajor),
(LayoutType.RowMajor, LayoutType.ColumnMajor, LayoutType.ColumnMajor),
(LayoutType.RowMajor, LayoutType.RowMajor, LayoutType.ColumnMajor),
]
math_instructions = [
MathInstruction( \
[1, 1, 1], \
DataType.f32, DataType.f32, DataType.f32, \
OpcodeClass.Simt, \
MathOperation.multiply_add),
MathInstruction( \
[1, 1, 1], \
DataType.f64, DataType.f64, DataType.f64, \
OpcodeClass.Simt, \
MathOperation.multiply_add),
]
min_cc = 50
max_cc = 1024
alignment_constraints = [1,]
for math_inst in math_instructions:
tile_descriptions = [
TileDescription([128, 128, 8], 2, [4, 2, 1], math_inst, min_cc, max_cc),
TileDescription([128, 64, 8], 2, [2, 2, 1], math_inst, min_cc, max_cc),
TileDescription([ 64, 128, 8], 2, [2, 2, 1], math_inst, min_cc, max_cc),
TileDescription([ 64, 64, 8], 2, [2, 1, 1], math_inst, min_cc, max_cc),
TileDescription([128, 32, 8], 2, [2, 1, 1], math_inst, min_cc, max_cc),
TileDescription([ 32, 128, 8], 2, [1, 2, 1], math_inst, min_cc, max_cc),
]
data_type = [
math_inst.element_a,
math_inst.element_b,
math_inst.element_accumulator,
math_inst.element_accumulator,
]
CreateGemmOperator(manifest, layouts, tile_descriptions, \
data_type, alignment_constraints)
if math_inst.element_a == DataType.f32:
conv_layout = (LayoutType.TensorNHWC, LayoutType.TensorNHWC, LayoutType.TensorNHWC)
CreateConv2dOperator(manifest, conv_layout, tile_descriptions, data_type, alignment_constraints)
#
#
def GenerateSM50_Simt_complex(manifest, cuda_version):
layouts = [
(LayoutType.ColumnMajor, LayoutType.ColumnMajor, LayoutType.ColumnMajor),
(LayoutType.ColumnMajor, LayoutType.RowMajor, LayoutType.ColumnMajor),
(LayoutType.RowMajor, LayoutType.ColumnMajor, LayoutType.ColumnMajor),
(LayoutType.RowMajor, LayoutType.RowMajor, LayoutType.ColumnMajor),
]
math_instructions = [
MathInstruction( \
[1, 1, 1], \
DataType.f32, DataType.f32, DataType.f32, \
OpcodeClass.Simt, \
MathOperation.multiply_add_complex),
]
min_cc = 50
max_cc = 1024
alignment_constraints = [1,]
for math_inst in math_instructions:
tile_descriptions = [
TileDescription([128, 64, 8], 2, [2, 2, 1], math_inst, min_cc, max_cc),
TileDescription([ 64, 128, 8], 2, [2, 2, 1], math_inst, min_cc, max_cc),
TileDescription([ 64, 64, 8], 2, [2, 1, 1], math_inst, min_cc, max_cc),
TileDescription([128, 32, 8], 2, [2, 1, 1], math_inst, min_cc, max_cc),
TileDescription([ 32, 128, 8], 2, [1, 2, 1], math_inst, min_cc, max_cc),
TileDescription([128, 128, 8], 2, [4, 2, 1], math_inst, min_cc, max_cc),
]
data_type = [
DataType.cf32,
DataType.cf32,
DataType.cf32,
DataType.cf32,
]
CreateGemmOperator(manifest, layouts, tile_descriptions, \
data_type, alignment_constraints)
conv_layout = (LayoutType.TensorNHWC, LayoutType.TensorNHWC, LayoutType.TensorNHWC)
CreateConv2dOperator(manifest, conv_layout, tile_descriptions, data_type, alignment_constraints)
#
#
def GenerateSM50(manifest, cuda_version):
GenerateSM50_Simt(manifest, cuda_version)
GenerateSM50_Simt_complex(manifest, cuda_version)
###################################################################################################
###################################################################################################
#
def GenerateSM60_Simt(manifest, cuda_version):
layouts = [
(LayoutType.ColumnMajor, LayoutType.ColumnMajor, LayoutType.ColumnMajor),
(LayoutType.ColumnMajor, LayoutType.RowMajor, LayoutType.ColumnMajor),
(LayoutType.RowMajor, LayoutType.ColumnMajor, LayoutType.ColumnMajor),
(LayoutType.RowMajor, LayoutType.RowMajor, LayoutType.ColumnMajor),
]
math_instructions = [
MathInstruction( \
[1, 1, 1], \
DataType.f16, DataType.f16, DataType.f16, \
OpcodeClass.Simt, \
MathOperation.multiply_add),
]
min_cc = 60
max_cc = 1024
alignment_constraints = [1,]
for math_inst in math_instructions:
tile_descriptions = [
TileDescription([256, 128, 8], 2, [4, 2, 1], math_inst, min_cc, max_cc),
TileDescription([128, 256, 8], 2, [4, 2, 1], math_inst, min_cc, max_cc),
TileDescription([128, 128, 8], 2, [4, 2, 1], math_inst, min_cc, max_cc),
TileDescription([128, 64, 8], 2, [2, 2, 1], math_inst, min_cc, max_cc),
TileDescription([ 64, 128, 8], 2, [2, 2, 1], math_inst, min_cc, max_cc),
TileDescription([ 64, 64, 8], 2, [2, 1, 1], math_inst, min_cc, max_cc),
TileDescription([128, 32, 8], 2, [2, 1, 1], math_inst, min_cc, max_cc),
TileDescription([ 32, 128, 8], 2, [1, 2, 1], math_inst, min_cc, max_cc),
]
data_type = [
math_inst.element_a,
math_inst.element_b,
math_inst.element_accumulator,
math_inst.element_accumulator,
]
CreateGemmOperator(manifest, layouts, tile_descriptions, \
data_type, alignment_constraints)
#
def GenerateSM60_Simt_DepthwiseConv2d(manifest, cuda_version):
math_instructions = [
MathInstruction( \
[1, 1, 1], \
DataType.f16, DataType.f16, DataType.f16, \
OpcodeClass.Simt, \
MathOperation.multiply_add),
]
min_cc = 60
max_cc = 1024
alignment_constraints = [8,]
filter_3x3 = [3, 3]
filter_5x5 = [5, 5]
# [stride_h, stride_w]
# [-1, -1] means all stride size.
strides = [[-1,-1], [1, 1], [2, 2]]
# [dilation_h, dilation_w]
# [-1, -1] means all dilation size.
dilations = [[-1,-1], [1, 1], [2, 2]]
#groups per thread block
g16 = 16
g32 = 32
g64 = 64
#output shape per thread block
npq_1x4x4 = [1, 4, 4]
npq_1x8x8 = [1, 8, 8]
npq_1x10x10 = [1, 10, 10]
tile_descriptions = []
for math_inst in math_instructions:
for stride, dilation in product(strides, dilations):
tile_descriptions.extend([
# filter3x3 ThreadBlock_output, filter, stage, warp
Direct2dConvFixedStrideDilationTileDescription(npq_1x8x8+[g32], filter_3x3, 3, stride, dilation,[4, 1, 1],math_inst, min_cc, max_cc),
Direct2dConvFixedStrideDilationTileDescription(npq_1x8x8+[g64], filter_3x3, 3, stride, dilation,[4, 1, 1],math_inst, min_cc, max_cc),
Direct2dConvFixedStrideDilationTileDescription(npq_1x8x8+[g16], filter_3x3, 3, stride, dilation,[4, 1, 1],math_inst, min_cc, max_cc),
Direct2dConvFixedStrideDilationTileDescription(npq_1x10x10+[g64], filter_3x3, 2, stride, dilation,[4, 1, 1],math_inst, min_cc, max_cc),
Direct2dConvFixedStrideDilationTileDescription(npq_1x4x4+[g32], filter_3x3, 4, stride, dilation, [4, 1, 1], math_inst, min_cc, max_cc),
Direct2dConvFixedStrideDilationTileDescription(npq_1x4x4+[g64], filter_3x3, 4, stride, dilation,[4, 1, 1], math_inst, min_cc, max_cc),
Direct2dConvFixedStrideDilationTileDescription(npq_1x4x4+[g16], filter_3x3, 4, stride, dilation, [4, 1, 1], math_inst, min_cc, max_cc),
# filter5x5 ThreadBlock_output, filter, stage, warp
Direct2dConvFixedStrideDilationTileDescription(npq_1x8x8+[g32], filter_5x5, 3, stride, dilation,[4, 1, 1],math_inst, min_cc, max_cc),
Direct2dConvFixedStrideDilationTileDescription(npq_1x8x8+[g64], filter_5x5, 3, stride, dilation,[4, 1, 1],math_inst, min_cc, max_cc),
Direct2dConvFixedStrideDilationTileDescription(npq_1x8x8+[g16], filter_5x5, 3, stride, dilation,[4, 1, 1],math_inst, min_cc, max_cc),
Direct2dConvFixedStrideDilationTileDescription(npq_1x10x10+[g64], filter_5x5, 2, stride, dilation,[4, 1, 1],math_inst, min_cc, max_cc),
Direct2dConvFixedStrideDilationTileDescription(npq_1x4x4+[g32], filter_5x5, 4, stride, dilation,[4, 1, 1],math_inst, min_cc, max_cc),
Direct2dConvFixedStrideDilationTileDescription(npq_1x4x4+[g64], filter_5x5, 4, stride, dilation,[4, 1, 1],math_inst, min_cc, max_cc),
Direct2dConvFixedStrideDilationTileDescription(npq_1x4x4+[g16], filter_5x5, 4, stride, dilation,[4, 1, 1],math_inst, min_cc, max_cc)
])
data_type = [
math_inst.element_a,
math_inst.element_b,
math_inst.element_accumulator,
math_inst.element_accumulator,
]
conv_layout = (LayoutType.TensorNHWC, LayoutType.TensorNHWC, LayoutType.TensorNHWC)
CreateDepthwiseConv2dOperator(manifest, conv_layout, tile_descriptions, data_type, alignment_constraints)
#
#
def GenerateSM60(manifest, cuda_version):
GenerateSM60_Simt(manifest, cuda_version)
GenerateSM60_Simt_DepthwiseConv2d(manifest, cuda_version)
###################################################################################################
###################################################################################################
#
def GenerateSM61_Simt(manifest, cuda_version):
layouts = [
(LayoutType.ColumnMajor, LayoutType.ColumnMajor, LayoutType.ColumnMajor),
(LayoutType.ColumnMajor, LayoutType.RowMajor, LayoutType.ColumnMajor),
(LayoutType.RowMajor, LayoutType.ColumnMajor, LayoutType.ColumnMajor),
(LayoutType.RowMajor, LayoutType.RowMajor, LayoutType.ColumnMajor),
]
math_instructions = [
MathInstruction( \
[1, 1, 4], \
DataType.s8, DataType.s8, DataType.s32, \
OpcodeClass.Simt, \
MathOperation.multiply_add),
]
min_cc = 61
max_cc = 1024
alignment_constraints = [1,]
for math_inst in math_instructions:
tile_descriptions = [
TileDescription([128, 128, 32], 2, [4, 2, 1], math_inst, min_cc, max_cc),
TileDescription([128, 64, 32], 2, [2, 2, 1], math_inst, min_cc, max_cc),
TileDescription([ 64, 128, 32], 2, [2, 2, 1], math_inst, min_cc, max_cc),
TileDescription([ 64, 64, 32], 2, [2, 1, 1], math_inst, min_cc, max_cc),
TileDescription([128, 32, 32], 2, [2, 1, 1], math_inst, min_cc, max_cc),
TileDescription([ 32, 128, 32], 2, [1, 2, 1], math_inst, min_cc, max_cc),
]
data_type = [
math_inst.element_a,
math_inst.element_b,
math_inst.element_accumulator,
math_inst.element_accumulator,
]
data_type_mixed = [
math_inst.element_a,
math_inst.element_b,
math_inst.element_a,
math_inst.element_accumulator,
]
CreateGemmOperator(manifest, layouts, tile_descriptions, \
data_type, alignment_constraints)
CreateGemmOperator(manifest, layouts, tile_descriptions, \
data_type_mixed, alignment_constraints)
#
#