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Rename python/cutlass to python/cutlass_cppgen (#2652)

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Jack Kosaian
2025-09-18 13:26:57 -05:00
committed by Haicheng Wu
parent 4260d4aef9
commit 177a82e251
71 changed files with 1 additions and 1 deletions
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python/cutlass_cppgen/backend/evt/ir/store_nodes.py Normal file
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#################################################################################################
#
# Copyright (c) 2023 - 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.
#
#################################################################################################
"""
Store node and implementations
"""
import ctypes
from cutlass_library import DataType
from cutlass_cppgen.backend.c_types import tuple_factory
from cutlass_cppgen.backend.epilogue import dtype2ctype, to_ctype_value
from cutlass_cppgen.backend.evt.ir.node import NodeBase, ImplBase, NoOpImpl
from cutlass_cppgen.backend.evt.ir.tensor import Tensor
from cutlass_cppgen.backend.library import FloatRoundStyle, FunctionalOp
class StoreImplBase(ImplBase):
"""
Base class for store node implementation
"""
reserved_names = ["D"]
def __init__(self, node) -> None:
super().__init__(node)
self.element = node.element
self.element_output = node.element_output
self.stride = node.store_tensor.stride
class StoreDImpl(StoreImplBase):
"""
Store D implementation
"""
@property
def argument_type_d(self):
stride_mnl = self.get_stride_mnl()
tuple_type = tuple_factory(stride_mnl, self.stride_dtype)
class _Argument(ctypes.Structure):
_fields_ = [
("ptr_D", ctypes.c_void_p),
("stride_D", tuple_type)
]
def __init__(self, ptr: int) -> None:
self.ptr_D = ptr
self.stride_D = tuple_type(stride_mnl)
return _Argument
@staticmethod
def match(node, problem_size: tuple):
if node.name == "D" and node.store_tensor.shape == problem_size:
return True
return False
class AuxStoreImpl(StoreImplBase):
def __init__(self, node) -> None:
super().__init__(node)
self.round_style = FloatRoundStyle.ToNearest
@property
def argument_type(self):
stride_mnl = self.get_stride_mnl()
name = self.name
tuple_type = tuple_factory(stride_mnl, self.stride_dtype)
class _Argument(ctypes.Structure):
_fields_ = [
("ptr_aux", ctypes.c_void_p),
("dAux", tuple_type)
]
def __init__(self, kwargs) -> None:
ptr = kwargs[name]
self.ptr_aux = ptr
self.dAux = tuple_type(stride_mnl)
return _Argument
@staticmethod
def match(node, problem_size: tuple):
if not node.is_output:
return False
if node.name in StoreImplBase.reserved_names:
return False
strideMN = node.store_tensor.stride[-2:]
if (strideMN[0] == 1 and strideMN[1] != 0 or
strideMN[0] != 0 and strideMN[1] == 1 ):
return True
else:
return False
class ReductionImplBase(StoreImplBase):
def __init__(self, node) -> None:
super().__init__(node)
self.element = node.store_tensor.element
self.element_compute = node.element_compute
self.reg_reduce_fn = self.node.reg_reduce_fn
self.gmem_reduce_fn = self.node.gmem_reduce_fn
self.round_style = node.round_style
self.stride_dtype = "int"
def get_reduce_identity(self):
"""
Return the reduction identity of the current reduce_fn
"""
maxes = {
DataType.f32: (2 ** 31) - 1,
DataType.f16: (2 ** 15),
DataType.s32: (2 ** 31) - 1,
DataType.s8: (2 ** 7) - 1
}
mins = {
DataType.f32: -maxes[DataType.f32],
DataType.f16: -maxes[DataType.f16],
DataType.s32: -maxes[DataType.s32],
DataType.s8: -maxes[DataType.s8]
}
if self.reg_reduce_fn == FunctionalOp.Maximum:
if self.element_compute not in mins:
raise Exception(f"No min entry for data type {self.element_compute}")
return to_ctype_value(mins[self.element_compute], self.element_compute)
elif self.reg_reduce_fn == FunctionalOp.Multiplies:
return to_ctype_value(1., self.element_compute)
elif self.reg_reduce_fn == FunctionalOp.Minimum:
if self.element_compute not in maxes:
raise Exception(f"No max entry for data type {self.element_compute}")
return to_ctype_value(maxes[self.element_compute], self.element_compute)
else:
return to_ctype_value(0., self.element_compute)
@property
def argument_type(self):
self.get_reduce_identity()
stride_mnl = self.get_stride_mnl()
name = self.name
tuple_type = tuple_factory(stride_mnl, self.stride_dtype)
element_compute = self.element_compute
reduce_identity = self.get_reduce_identity()
class _Argument(ctypes.Structure):
_fields_ = [
("ptr", ctypes.c_void_p),
("reduce_identity", dtype2ctype[element_compute]),
("dMNL", tuple_type)
]
def __init__(self, kwargs) -> None:
ptr = kwargs[name]
self.ptr = ptr
self.reduce_identity = reduce_identity
self.dMNL = tuple_type(stride_mnl)
return _Argument
class ColumnReductionImpl(ReductionImplBase):
@staticmethod
def match(node, problem_size: tuple):
if not node.is_output:
return False
if node.name in StoreImplBase.reserved_names:
return False
strideMN = node.store_tensor.stride[-2:]
if strideMN == (1, 0):
return True
else:
return False
class RowReductionImpl(ReductionImplBase):
@staticmethod
def match(node, problem_size: tuple):
if not node.is_output:
return False
if node.name in StoreImplBase.reserved_names:
return False
strideMN = node.store_tensor.stride[-2:]
if strideMN == (0, 1):
return True
else:
return False
class ScalarReductionImpl(ReductionImplBase):
@staticmethod
def match(node, problem_size: tuple):
if not node.is_output:
return False
if node.name in StoreImplBase.reserved_names:
return False
strideMN = node.store_tensor.stride[-2:]
if strideMN == (0, 0):
return True
else:
return False
class StoreNode(NodeBase):
"""
Store node
"""
possible_impls = [
AuxStoreImpl, RowReductionImpl,
ColumnReductionImpl, ScalarReductionImpl,
NoOpImpl, StoreDImpl
]
def __init__(self, name: str) -> None:
super().__init__(name)
self.op = "store"
self.is_output = False
self._store_tensor = None
@property
def store_tensor(self) -> Tensor:
"""
Return the output tensor (concept: cutlass_cppgen.backend.evt.ir.tensor)
"""
return self._store_tensor
@store_tensor.setter
def store_tensor(self, kwargs):
"""
Setting the tensor
"""
self._store_tensor = Tensor(**kwargs)
def type_propagation(self, input_node_metas: 'list[NodeBase]'):
"""
The store nodes has element_output = element_input
"""
if self.is_output:
if self.store_tensor is None:
raise RuntimeError(f"The store tensor of node {self.name} is unknown.")
self.element = self.store_tensor.element
assert len(input_node_metas) == 1, "Store node can only have one input node"
self.element_output = input_node_metas[0].element_output
def broadcast_propagation(self, input_node_metas: 'list[NodeBase]'):
super().broadcast_propagation(input_node_metas)
if self.is_output:
self._store_tensor.broadcast(self.tensor.shape)