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CUTLASS 3.3.0 (#1167)

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* 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
This commit is contained in:
Pradeep Ramani
2023-11-02 08:09:05 -07:00
committed by GitHub
parent 922fb5108b
commit c008b4aea8
263 changed files with 16214 additions and 5008 deletions
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25
python/cutlass_library/library.py
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@@ -400,6 +400,9 @@ ShortComplexLayoutNames = {
class KernelScheduleType(enum.Enum):
ScheduleAuto = enum_auto()
Multistage = enum_auto()
CpAsyncWarpSpecialized = enum_auto()
CpAsyncWarpSpecializedPingpong = enum_auto()
CpAsyncWarpSpecializedCooperative = enum_auto()
Tma = enum_auto()
TmaWarpSpecialized = enum_auto()
TmaWarpSpecializedPingpong = enum_auto()
@@ -411,6 +414,9 @@ class KernelScheduleType(enum.Enum):
KernelScheduleTag = {
KernelScheduleType.ScheduleAuto: 'cutlass::gemm::collective::KernelScheduleAuto',
KernelScheduleType.Multistage: 'cutlass::gemm::KernelMultistage',
KernelScheduleType.CpAsyncWarpSpecialized: 'cutlass::gemm::KernelCpAsyncWarpSpecialized',
KernelScheduleType.CpAsyncWarpSpecializedPingpong: 'cutlass::gemm::KernelCpAsyncWarpSpecializedPingpong',
KernelScheduleType.CpAsyncWarpSpecializedCooperative: 'cutlass::gemm::KernelCpAsyncWarpSpecializedCooperative',
KernelScheduleType.Tma: 'cutlass::gemm::KernelTma',
KernelScheduleType.TmaWarpSpecialized: 'cutlass::gemm::KernelTmaWarpSpecialized',
KernelScheduleType.TmaWarpSpecializedPingpong: 'cutlass::gemm::KernelTmaWarpSpecializedPingpong',
@@ -424,6 +430,9 @@ KernelScheduleTag = {
KernelScheduleSuffixes = {
KernelScheduleType.ScheduleAuto: '',
KernelScheduleType.Multistage: '_cpasync',
KernelScheduleType.CpAsyncWarpSpecialized: '_cpasync_warpspecialized',
KernelScheduleType.CpAsyncWarpSpecializedPingpong: '_cpasync_warpspecialized_pingpong',
KernelScheduleType.CpAsyncWarpSpecializedCooperative: '_cpasync_warpspecialized_cooperative',
KernelScheduleType.Tma: '_unspecialized',
KernelScheduleType.TmaWarpSpecialized: '_warpspecialized',
KernelScheduleType.TmaWarpSpecializedPingpong: '_warpspecialized_pingpong',
@@ -541,7 +550,6 @@ class OpcodeClass(enum.Enum):
WmmaTensorOp = enum_auto()
SparseTensorOp = enum_auto()
OpcodeClassNames = {
OpcodeClass.Simt: 'simt',
OpcodeClass.TensorOp: 'tensorop',
@@ -628,19 +636,20 @@ class GemmKind(enum.Enum):
Sparse = enum_auto()
Universal = enum_auto()
Universal3x = enum_auto()
SparseUniversal3x = enum_auto()
PlanarComplex = enum_auto()
PlanarComplexArray = enum_auto()
Grouped = enum_auto()
#
GemmKindNames = {
GemmKind.Gemm: "gemm",
GemmKind.Sparse: "spgemm",
GemmKind.Universal: "gemm",
GemmKind.Universal3x: "gemm",
GemmKind.SparseUniversal3x: "spgemm",
GemmKind.PlanarComplex: "gemm_planar_complex",
GemmKind.PlanarComplexArray: "gemm_planar_complex_array",
GemmKind.Grouped: "gemm_grouped"
GemmKind.Grouped: "gemm_grouped",
}
#
@@ -797,7 +806,7 @@ class GroupMode(enum.Enum):
NoneGroup = enum_auto() # dense conv (G=1)
SingleGroup = enum_auto() # grouped convolution (single group per CTA)
MultipleGroup = enum_auto() # grouped convolution ( multiple groups per CTA)
Depthwise = enum_auto() # Depthwise convolution ( C=K=G )
Depthwise = enum_auto() # Depthwise convolution ( C=K=G )
#
GroupModeTag = {
@@ -818,14 +827,18 @@ GroupModeNames = {
#
class MathInstruction:
def __init__(self, instruction_shape, element_a, element_b, element_accumulator, opcode_class, math_operation = MathOperation.multiply_add):
def __init__(self,
instruction_shape, \
element_a, element_b, element_accumulator, \
opcode_class, math_operation = MathOperation.multiply_add \
):
self.instruction_shape = instruction_shape
self.element_a = element_a
self.element_b = element_b
self.element_accumulator = element_accumulator
self.opcode_class = opcode_class
self.math_operation = math_operation
#
class TileDescription: