CUTLASS 2.7 (#318)
CUTLASS 2.7 Mainloop fusion for GEMM: summation over A or B Strided DGRAD (optimized iterators) Half-precision GELU_taylor activation functions Use these when accumulation and epilogue compute types are all cutlass::half_t Tuning and bug fixes to fused GEMM + GEMM example Support for smaller than 128b aligned Convolutions: see examples Caching of results to accelerate Convolution unit tests Can be enabled or disabled by running cmake .. -DCUTLASS_TEST_ENABLE_CACHED_RESULTS=OFF Corrections and bug fixes reported by the CUTLASS community Thank you for filing these issues! authored-by: Haicheng Wu haichengw@nvidia.com, Manish Gupta manigupta@nvidia.com, Dustyn Blasig dblasig@nvidia.com, Andrew Kerr akerr@nvidia.com
This commit is contained in:
Manish Gupta
GitHub
@ -51,7 +51,6 @@
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#include "cutlass/matrix_coord.h"
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#include "cutlass/tensor_coord.h"
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#include "cutlass/layout/tensor.h"
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#include "cutlass/gemm/gemm.h"
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#include "cutlass/conv/convolution.h"
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#include "cutlass/conv/conv2d_problem_size.h"
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@ -162,10 +162,11 @@ def CreateConv2dOperator(manifest, layout, tile_descriptions, data_type, alignme
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# iterator algorithm (analytic and optimized)
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iterator_algorithms = [IteratorAlgorithm.Analytic, IteratorAlgorithm.Optimized]
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# by default, only generate the largest tile size
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# by default, only generate the largest tile size, largest alignment, and optimized iterator
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if manifest.args.kernels == '':
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tile_descriptions = [tile_descriptions[0],]
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alignment_constraints = [alignment_constraints[0],]
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iterator_algorithms = [IteratorAlgorithm.Optimized]
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operations = []
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@ -212,12 +213,21 @@ def CreateConv2dOperator(manifest, layout, tile_descriptions, data_type, alignme
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# better for problem sizes with large activation channel count
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swizzling_functor_strided_dgrad_ = SwizzlingFunctor.StridedDgradIdentity1
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new_operation = Conv2dOperation(ConvKind.Dgrad, IteratorAlgorithm.Analytic, tile.minimum_compute_capability, tile,\
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A, B, C, element_epilogue, StrideSupport.Strided, epilogue_functor, swizzling_functor_strided_dgrad_)
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if IteratorAlgorithm.Analytic in iterator_algorithms:
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new_operation = Conv2dOperation(ConvKind.Dgrad, IteratorAlgorithm.Analytic, tile.minimum_compute_capability, tile,\
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A, B, C, element_epilogue, StrideSupport.Strided, epilogue_functor, swizzling_functor_strided_dgrad_)
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manifest.append(new_operation)
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operations.append(new_operation)
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manifest.append(new_operation)
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operations.append(new_operation)
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# Strided support for Optimized Dgrad
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if IteratorAlgorithm.Optimized in iterator_algorithms:
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new_operation = Conv2dOperation(ConvKind.Dgrad, IteratorAlgorithm.Optimized, tile.minimum_compute_capability, tile,\
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A, B, C, element_epilogue, StrideSupport.Strided, epilogue_functor, swizzling_functor_strided_dgrad_)
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manifest.append(new_operation)
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operations.append(new_operation)
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#
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# Conv2d Wgrad
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#
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@ -246,34 +256,70 @@ def CreateConv3dOperator(manifest, layout, tile_descriptions, data_type, alignme
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# iterator algorithm (analytic and optimized)
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iterator_algorithms = [IteratorAlgorithm.Analytic, IteratorAlgorithm.Optimized]
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# by default, only generate the largest tile size
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# by default, only generate the largest tile size and optimized iterators
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if manifest.args.kernels == '':
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tile_descriptions = [tile_descriptions[0],]
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iterator_algorithms = [IteratorAlgorithm.Optimized]
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operations = []
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# All tile sizes for Conv3dFprop and Conv3dWgrad
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for tile in tile_descriptions:
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for conv_kind in conv_kinds:
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A = TensorDescription(element_a, layout, alignment)
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B = TensorDescription(element_b, layout, alignment)
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C = TensorDescription(element_c, layout, alignment_c)
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#
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# Conv3d Fprop
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#
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if ConvKind.Fprop in conv_kinds:
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# Strided support for Analytic and Optimized Fprop
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for iterator_algorithm in iterator_algorithms:
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A = TensorDescription(element_a, layout, alignment)
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B = TensorDescription(element_b, layout, alignment)
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C = TensorDescription(element_c, layout, alignment_c)
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# optimized conv3d iterator algorithm is only for Wgrad
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if (iterator_algorithm == IteratorAlgorithm.Optimized) \
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and ((conv_kind == ConvKind.Fprop) or (conv_kind == ConvKind.Dgrad)):
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continue
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# strided support for Fprop (Analytic/Optimized), Dgrad (Analytic), and Wgrad (Analytic)
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new_operation = Conv3dOperation(conv_kind, iterator_algorithm, tile.minimum_compute_capability, tile,\
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A, B, C, element_epilogue, StrideSupport.Strided, epilogue_functor)
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new_operation = Conv3dOperation(ConvKind.Fprop, iterator_algorithm, tile.minimum_compute_capability, tile,\
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A, B, C, element_epilogue, StrideSupport.Strided)
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manifest.append(new_operation)
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operations.append(new_operation)
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#
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# Conv3d Wgrad
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#
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if ConvKind.Wgrad in conv_kinds:
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# Strided support for Analytic and Optimized Wgrad
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for iterator_algorithm in iterator_algorithms:
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new_operation = Conv3dOperation(ConvKind.Wgrad, iterator_algorithm, tile.minimum_compute_capability, tile,\
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A, B, C, element_epilogue, StrideSupport.Strided, epilogue_functor)
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manifest.append(new_operation)
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operations.append(new_operation)
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# All tile sizes for Conv3dDgrad
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for tile in tile_descriptions:
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A = TensorDescription(element_a, layout, alignment)
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B = TensorDescription(element_b, layout, alignment)
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C = TensorDescription(element_c, layout, alignment_c)
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#
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# Conv3d Dgrad
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#
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if ConvKind.Dgrad in conv_kinds:
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# Unity stride for Optimized Dgrad
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new_operation = Conv3dOperation(ConvKind.Dgrad, IteratorAlgorithm.Optimized, tile.minimum_compute_capability, tile,\
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A, B, C, element_epilogue, StrideSupport.Unity, epilogue_functor)
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manifest.append(new_operation)
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operations.append(new_operation)
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# Strided support for Analytic Dgrad
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# Conv3dDgrad has a naive strided support which does not cut down redundant MMAs
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new_operation = Conv3dOperation(ConvKind.Dgrad, IteratorAlgorithm.Analytic, tile.minimum_compute_capability, tile,\
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A, B, C, element_epilogue, StrideSupport.Strided, epilogue_functor)
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manifest.append(new_operation)
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operations.append(new_operation)
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return operations
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###################################################################################################
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###################################################################################################
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@ -1158,7 +1204,7 @@ def GenerateSM75_TensorOp_88128(manifest, args):
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data_type = [DataType.b1, DataType.b1, DataType.s32, DataType.s32]
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CreateGemmOperator(manifest, layouts, tile_descriptions, \
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data_type, alignment_constraints, None, EpilogueFunctor.LinearCombinationClamp)
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data_type, alignment_constraints)
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#
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@ -1934,7 +1980,7 @@ def GenerateSM80_TensorOp_168256(manifest, args):
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data_type = [DataType.b1, DataType.b1, DataType.s32, DataType.s32]
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CreateGemmOperator(manifest, layouts, tile_descriptions, \
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data_type, alignment_constraints, None, EpilogueFunctor.LinearCombinationClamp)
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data_type, alignment_constraints)
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#
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@ -28,7 +28,6 @@
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#include "cutlass/cutlass.h"
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#include "cutlass/numeric_types.h"
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#include "cutlass/complex.h"
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#include "cutlass/layout/matrix.h"
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#include "cutlass/library/library.h"
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