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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/load_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.
#
#################################################################################################
"""
Load nodes and implementations
"""
import ctypes
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
class LoadImplBase(ImplBase):
"""
Base class for load node implementations
"""
reserved_names = ["accum", "C"]
def __init__(self, node) -> None:
super().__init__(node)
self.element = node.element
self.element_output = node.element_output
self.stride = node.tensor.stride
class AccumulatorImpl(LoadImplBase):
"""
Accumulator node implementation
"""
@staticmethod
def match(node, problem_size: tuple):
return node.name == "accum" and node.tensor.shape == problem_size
class LoadSrcImpl(LoadImplBase):
"""
Load C implementation
"""
@property
def name_camel(self) -> str:
return "TensorC"
@property
def argument_type_c(self):
stride_mnl = self.get_stride_mnl()
tuple_type = tuple_factory(stride_mnl, self.stride_dtype)
class _Argument(ctypes.Structure):
_fields_ = [
("ptr_C", ctypes.c_void_p),
("stride_C", tuple_type)
]
def __init__(self, ptr) -> None:
self.ptr_C = ptr
self.stride_C = tuple_type(stride_mnl)
return _Argument
@staticmethod
def match(node, problem_size: tuple):
return node.name == "C" and node.tensor.shape == problem_size
class AuxLoadImpl(LoadImplBase):
"""
Load arbitrary tensor
"""
@property
def argument_type(self):
stride_mnl = self.get_stride_mnl()
name = self.name
tuple_type = tuple_factory(stride_mnl, self.stride_dtype)
element_type = self.element
class _Argument(ctypes.Structure):
_fields_ = [
("ptr_aux", ctypes.c_void_p),
("null_default", dtype2ctype[element_type]),
("dAux", tuple_type)
]
def __init__(self, kwargs) -> None:
ptr = kwargs[name]
self.ptr_aux = ptr
self.null_default = to_ctype_value(0, element_type)
self.dAux = tuple_type(stride_mnl)
return _Argument
@staticmethod
def match(node, problem_size: tuple):
if node.name in LoadImplBase.reserved_names:
return False
strideMN = node.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 RowBroadcastImpl(LoadImplBase):
"""
Broadcast a row vector
"""
def __init__(self, node) -> None:
super().__init__(node)
self.stride_dtype = "int"
@property
def argument_type(self):
stride_mnl = self.get_stride_mnl()
name = self.name
tuple_type = tuple_factory(stride_mnl, self.stride_dtype)
element_type = self.element
class _Argument(ctypes.Structure):
_fields_ = [
("ptr_row", ctypes.c_void_p),
("null_default", dtype2ctype[element_type]),
("dRow", tuple_type)
]
def __init__(self, kwargs) -> None:
ptr = kwargs[name]
self.ptr_row = ptr
self.null_default = to_ctype_value(0, element_type)
self.dRow = tuple_type(stride_mnl)
return _Argument
@staticmethod
def match(node, problem_size: tuple):
if node.name in LoadImplBase.reserved_names:
return False
strideMN = node.tensor.stride[-2:]
if strideMN == (0, 1):
return True
else:
return False
class ColumnBroadcastImpl(LoadImplBase):
"""
Broadcast a column vector
"""
def __init__(self, node) -> None:
super().__init__(node)
self.stride_dtype = "int"
@property
def argument_type(self):
stride_mnl = self.get_stride_mnl()
name = self.name
tuple_type = tuple_factory(stride_mnl, self.stride_dtype)
element_type = self.element
class _Argument(ctypes.Structure):
_fields_ = [
("ptr_col", ctypes.c_void_p),
("null_default", dtype2ctype[element_type]),
("dCol", tuple_type)
]
def __init__(self, kwargs) -> None:
ptr = kwargs[name]
self.ptr_col = int(ptr)
self.null_default = to_ctype_value(0, element_type)
self.dCol = tuple_type(stride_mnl)
return _Argument
@staticmethod
def match(node, problem_size: tuple):
if node.name in LoadImplBase.reserved_names:
return False
strideMN = node.tensor.stride[-2:]
if strideMN == (1, 0):
return True
else:
return False
class ScalarBroadcastImpl(LoadImplBase):
"""
Broadcast a scalar
"""
def __init__(self, node) -> None:
super().__init__(node)
self.stride_dtype = "int"
@property
def argument_type(self):
stride_mnl = self.get_stride_mnl()
name = self.name
tuple_type = tuple_factory(stride_mnl, self.stride_dtype)
element_type = self.element
if self.tensor.is_constant:
value = self.tensor.value
class _Argument(ctypes.Structure):
_fields_ = [
("scalars", dtype2ctype[element_type]),
("scalar_ptrs", ctypes.c_void_p),
("dScalar", tuple_type)
]
def __init__(self, kwargs) -> None:
self.scalars = to_ctype_value(value, element_type)
self.scalar_ptrs = 0
self.dScalar = tuple_type(stride_mnl)
else:
class _Argument(ctypes.Structure):
_fields_ = [
("scalars", dtype2ctype[element_type]),
("scalar_ptrs", ctypes.c_void_p),
("dScalar", tuple_type)
]
def __init__(self, kwargs) -> None:
scalar_or_ptr = kwargs[name]
if isinstance(scalar_or_ptr, float):
self.scalars = to_ctype_value(scalar_or_ptr, element_type)
self.scalar_ptrs = 0
else:
self.scalar_ptrs = int(scalar_or_ptr)
self.dScalar = tuple_type(stride_mnl)
return _Argument
@staticmethod
def match(node, problem_size: tuple):
if node.name in LoadImplBase.reserved_names:
return False
strideMN = node.tensor.stride[-2:]
if strideMN == (0, 0):
return True
else:
return False
class LoadNode(NodeBase):
"""
Load Node
"""
cnt = 0
possible_impls = [
AccumulatorImpl, LoadSrcImpl, AuxLoadImpl,
RowBroadcastImpl, ColumnBroadcastImpl,
ScalarBroadcastImpl
]
def __init__(self, name: str) -> None:
if name is None:
name = f"load{LoadNode.cnt}"
LoadNode.cnt += 1
super().__init__(name)
self.op = "load"
def type_propagation(self, *args, **kwargs):
"""
Load node loads tensor under type `tensor.element` and returns an array of type `tensor.element`.
"""
if self.tensor is None:
raise RuntimeError(f"The tensor of node {self.name} is unknown.")
self.element = self.tensor.element
self.element_output = self.tensor.element