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

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This commit is contained in:
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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56
python/cutlass_cppgen/epilogue/__init__.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.
#
#################################################################################################
from cutlass_cppgen.epilogue.epilogue import (
get_activations,
get_activation_epilogue,
gelu,
hardswish,
identity,
leaky_relu,
relu,
sigmoid,
silu,
tanh,
trace
)
from cutlass_cppgen.epilogue.evt_ops import (
max,
multiply_add,
sum,
permute,
reshape,
maximum,
minimum,
exp
)

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python/cutlass_cppgen/epilogue/epilogue.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.
#
#################################################################################################
"""
Registry of elementwise epilogues
Elementwise epilogues can be added to many CUTLASS kernels in the CUTLAS Python interface via
code like the following for GEMM:
.. highlight:: python
.. code-block:: python
plan = cutlass_cppgen.op.Gemm(element=cutlass_cppgen.DataType.f32, layout=cutlass_cppgen.LayoutType.RowMajor)
plan.activation = cutlass_cppgen.epilogue.relu
"""
from cutlass_cppgen.backend import epilogue, device_cc
gelu = epilogue.gelu
hardswish = epilogue.hardswish
identity = epilogue.identity
leaky_relu = epilogue.leaky_relu
relu = epilogue.relu
sigmoid = epilogue.sigmoid
silu = epilogue.silu
tanh = epilogue.tanh
_activations = [gelu, hardswish, identity, leaky_relu, relu, sigmoid, silu, tanh]
def get_activations() -> list:
"""
Returns a list of available activation functions
:return: list of available activation functions
:rtype: list
"""
return _activations
def get_activation_epilogue(
activation,
element_output,
elements_per_access,
element_accumulator,
element_compute,
):
"""
Return an epilogue corresponding to the activation function, data types, and alignment
used in the kernel
:param activation: elementwise activation function to use
:param element_output: data type of the output
:param elements_per_access: alignment of operand C of the kernel
:type elements_per_access: int
:param element_accumulator: data type of the accumulated output C
:param element_compute: data type in which compute operations should be performed
:return: epilogue functor
"""
if activation not in _activations:
raise Exception(
f"Unsupported activation type {activation}. Available activations are: {_activations}"
)
if activation == identity:
return epilogue.LinearCombination(
element_output, elements_per_access, element_accumulator, element_compute
)
else:
return epilogue.LinearCombinationGeneric(
activation,
element_output,
elements_per_access,
element_accumulator,
element_compute,
)
"""
Frontend for EVT that generates epilogue functor through tracing the input function
"""
from cutlass_cppgen.backend.evt.frontend import PythonASTFrontend
def trace(fn, example_tensors, **kwargs):
"""
Trace `fn(**example_tensors)` and generates epilogue visitor
:param fn or str: Python callable or string of the epilogue function
:param example_tensors: example inputs for fn
:type example_tensors: dict
.. hightlight:: python
.. code-block:: python
import cutlass_cppgen.backend.evt
# Define epilogue function as Python callable
def example_fn(accum, C, alpha, beta, gamma):
D = ((accum + C) * alpha - gamma) / beta
return D
# Define the example tensors
example_inputs = {
"accum": torch.empty(size=(6, 512, 512), dtype=torch.float16, device="cuda"),
"C": torch.empty(size=(6, 512, 512), dtype=torch.float16, device="cuda"),
"alpha": 1.5,
"beta": 0.5,
"gamma": 2.5,
"D": torch.empty(size=(6, 512, 512), dtype=torch.float16, device="cuda")
}
# Generate the epilogue functor
epilogue_visitor = cutlass_cppgen.epilogue.trace(example_fn, example_inputs)
"""
if callable(fn):
class EpilogueFunctor(PythonASTFrontend):
def __init__(self, cc=None, **kwargs):
if not cc:
cc = device_cc()
super().__init__(cc, **kwargs)
pass
setattr(EpilogueFunctor, "__call__", staticmethod(fn))
epilogue_functor = EpilogueFunctor(**kwargs)
epilogue_functor.trace(example_tensors)
return epilogue_functor
elif isinstance(fn, str):
class EpilogueFunctor(PythonASTFrontend):
def __init__(self, cc=None, **kwargs):
self.source = textwrap.dedent(fn)
if not cc:
cc = device_cc()
super().__init__(cc, **kwargs)
def parse(self, example_inputs) -> None:
self.example_inputs = example_inputs
self.ast = ast.parse(self.source)
self.visit(self.ast)
epilogue_functor = EpilogueFunctor(**kwargs)
epilogue_functor.trace(example_tensors)
return epilogue_functor
else:
raise NotImplementedError("Expect a callable Python function")

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python/cutlass_cppgen/epilogue/evt_ops.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.
#
#################################################################################################
"""
Collection of builtin functions used for host reference in EVT
"""
import numpy as np
from cutlass_cppgen.utils.datatypes import is_cupy_tensor, is_numpy_tensor, is_torch_available, is_torch_tensor
if is_torch_available():
import torch
def multiply_add(x, y, z):
return x * y + z
def sum(x, dim):
if is_numpy_tensor(x):
return x.sum(axis=tuple(dim))
elif is_torch_tensor(x):
return torch.sum(x, dim)
def max(x, dim):
if is_numpy_tensor(x):
return x.max(axis=tuple(dim))
elif is_torch_tensor(x):
return torch.amax(x, dim)
def maximum(x, y):
if is_numpy_tensor(x):
return np.maximum(x, y)
elif is_torch_tensor(x):
return torch.maximum(x, torch.tensor(y))
def minimum(x, y):
if is_numpy_tensor(x):
return np.minimum(x, y)
elif is_torch_tensor(x):
return torch.minimum(x, torch.tensor(y))
def exp(x):
if is_numpy_tensor(x):
return np.exp(x)
elif is_torch_tensor(x):
return torch.exp(x)
##############################################################################
# Layout manipulate nodes
##############################################################################
def permute(x, indices: tuple):
if is_numpy_tensor(x):
return np.transpose(x, axes=indices)
elif is_torch_tensor(x):
return x.permute(*indices)
def reshape(x, new_shape: tuple):
if is_numpy_tensor(x):
return np.reshape(x, newshape=new_shape)
elif is_torch_tensor(x):
return x.view(new_shape)