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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 GitHub
parent 74825181f2
commit b234a8c024
71 changed files with 1 additions and 1 deletions
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33
python/cutlass_cppgen/backend/evt/frontend/__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.backend.evt.frontend.python_ast import PythonASTFrontend

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python/cutlass_cppgen/backend/evt/frontend/frontend_base.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.
#
#################################################################################################
"""
Base class for Python EVT Frontend
"""
from typing import Union
from cutlass_library import DataType
from cutlass_cppgen.backend.evt.ir import (
ComputeNode,
DAGIR,
LayoutNode,
LoadNode,
StoreNode,
)
from cutlass_cppgen.backend.evt.passes import (
EVTGraphDrawer,
EVTPassManager,
GetSmemSize,
PassDAG2Tree,
PassGetArgumentType,
PassGetImpl,
PassFixElementD,
PassLayoutManipulateElimination,
PassPreprocessRed,
PassShapeTypePropagation,
)
from cutlass_cppgen.backend.evt.passes.util import cc_map
from cutlass_cppgen.backend.utils import device_cc
from cutlass_cppgen.epilogue.evt_ops import permute, reshape
from cutlass_cppgen.utils.datatypes import library_type
class EVTFrontendBase:
layout_fns = {
"permute": permute,
"reshape": reshape
}
def __init__(self, cc, element_compute=DataType.f32, additional_passes=[], **kwargs) -> None:
self.cc = cc
self.element_compute = library_type(element_compute)
self.dag_ir = DAGIR(self.cc, self.element_compute)
self.compute_cnt = 0
self.layout_cnt = 0
self.imm_cnt = 0
self.pass_manager = EVTPassManager(
self.dag_ir,
[
PassPreprocessRed,
PassGetArgumentType,
PassShapeTypePropagation,
PassLayoutManipulateElimination,
PassGetImpl,
PassDAG2Tree,
PassFixElementD
] + additional_passes)
if self.cc == 80:
self._epilogue_stages = 1
else:
self._epilogue_stages = None
@property
def epilogue_stages(self):
return self._epilogue_stages
@epilogue_stages.setter
def epilogue_stages(self, stages):
self._epilogue_stages = stages
def parse(self, *args, **kwargs):
raise NotImplementedError(f"The 'parse' function must be overloaded in frontend class")
def trace(self, *args, **kwargs):
# Parse the input
self.parse(*args, **kwargs)
# Verify the DAG IR to ensure that "D" is the output node with out_degree = 0
if (self.cc >= 90):
if (self.dag_ir.out_degree("D") != 0):
raise RuntimeError(
f"On SM90 or higher, D is expected to be a output node with 0 users to "
f"enable smem reuse between C and D, but got {self.dag_ir.out_degree('D')}")
# Run the passes
self.pass_manager()
# Set the epilogue type
self.epilogue_thread_type = self.dag_ir.epilogue_thread_type
if cc_map[self.cc] in [90, 100]:
self.arg_c_type = self.dag_ir.arg_c_type
self.arg_d_type = self.dag_ir.arg_d_type
self.reduction_names = self.dag_ir.reduction_names
#
# Helper functions for DAG IR manipulation
#
def add_node(self, node):
self.dag_ir.add_node(node)
def add_edge(self, src, tgt, weight=0):
self.dag_ir.add_edge(src, tgt, weight=weight)
def set_tensor(self, node_name, example):
"""
Add an example tensor to node {node_name} in the DAG IR
"""
meta = self.dag_ir.get_node_meta(node_name)
meta.tensor = {"tensor": example}
def set_store_tensor(self, node_name, example):
"""
Add an example tensor to node {node_name} in the DAG IR
"""
meta = self.dag_ir.get_node_meta(node_name)
meta.store_tensor = {"tensor": example}
def mark_output(self, node_name):
"""
Mark a store node as output
"""
meta = self.dag_ir.get_node_meta(node_name)
if not isinstance(meta, StoreNode):
raise ValueError(
f"Only StoreNodes can be marked as output. "
f"Got {type(meta).__name__}: {node_name}")
meta.is_output = True
# Add node with specific type
def add_load_node(self, name, example):
"""
Add a Load node to DAG IR
:param name: name of the loaded variable
:type name: str
:param example: example input
:type example: np.ndarray|torch.Tensor|cupy.ndarray|float
"""
if name is None:
raise ValueError(f"Name is not provided.")
if example is None:
raise ValueError(f"Example input for {name} is not provided.")
load_node = LoadNode(name)
load_node.tensor = {"tensor": example}
# Special logics for accumulator
if name == "accum":
if load_node.tensor.rank == 2:
new_shape = tuple([1, ] + list(load_node.tensor.shape))
load_node.tensor.broadcast(new_shape)
elif load_node.tensor.rank < 2 or load_node.tensor.rank > 3:
raise ValueError(f"Expect example inputs for 'accum' be a rank-2 or rank-3 tensor. Got {load_node.tensor.shape}.")
self.add_node(load_node)
def add_imm(self, value: Union[float,int]):
"""
Add an immediate scalar value to DAG IR
:param value: the value of the immediate scalar
:type value: float
"""
try:
value = float(value)
except:
raise ValueError(f"{type(value).__name__} cannot be converted to float.")
name = f"imm_{value}_k{self.imm_cnt}".replace('.', '_')
self.imm_cnt += 1
load_node = LoadNode(name)
load_node.tensor = {"tensor": value, "is_constant": True}
self.add_node(load_node)
return name
def add_compute_node(self, op, name=None):
"""
Add a compute node.
:param op: the computation op
:param name: the node name (optional)
:type name: str
:return: the name of the compute node
"""
if name is None:
name = f"compute_{self.compute_cnt}"
self.compute_cnt += 1
compute_node = ComputeNode(
name=name, fn=op,
element_output=self.element_compute,
element_compute=self.element_compute)
self.add_node(compute_node)
return compute_node.name
def add_layout_node(self, op, kwargs, name=None):
"""
Add a layout node.
:param op: the layout op
:type op: evt_ops
:param name: the node name (optional)
:type name: str
:return: the name of the layout node
"""
if name is None:
name = f"layout_{self.layout_cnt}"
self.layout_cnt += 1
layout_node = LayoutNode(name=name, fn=op, kwargs=kwargs)
self.add_node(layout_node)
return layout_node.name
def add_store_node(self, name):
store_node = StoreNode(name)
self.add_node(store_node)
#
# Visualization The DAG IR
#
def visualize(self, name="dag_ir"):
"""
Visualize the dag ir with svg file
:param name: the name of the graph
"""
drawer = EVTGraphDrawer(self.dag_ir, name)
try:
for name, graph in drawer.get_dot_graph():
graph.write_svg(f"./{name}.svg")
except:
raise RuntimeError(
"'dot' is not found in path. GraphDrawer is disabled. "
"Please install it with 'sudo apt-get install graphviz'."
)
#
# Get shared memory size
#
def get_smem_size(self, tile_description):
"""
Get the shared memory size of the epilogue
"""
smem_size = GetSmemSize(self.dag_ir)(tile_description)
return smem_size

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python/cutlass_cppgen/backend/evt/frontend/python_ast.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.
#
#################################################################################################
"""
Python AST frontend that parses input into DAG IR
"""
import ast
import inspect
import textwrap
from cutlass_library import DataType
import cutlass_cppgen
from cutlass_cppgen.backend.evt.frontend.frontend_base import EVTFrontendBase
from cutlass_cppgen.backend.epilogue import identity, relu, tanh, sigmoid, silu, hardswish, gelu
from cutlass_cppgen.backend.library import FunctionalOp
class PythonASTFrontend(EVTFrontendBase, ast.NodeVisitor):
def __init__(self, cc, element_compute=DataType.f32, **kwargs):
super().__init__(cc, element_compute, **kwargs)
# Flags
# If this state is True, visit_Constant returns values without creating imm node
self.no_imm = False
self.visiting_return = False
def parse(self, example_inputs):
self.example_inputs = example_inputs
self.source = textwrap.dedent(inspect.getsource(self.__call__))
self.ast = ast.parse(self.source)
self.visit(self.ast)
#
# Helper functions
#
@staticmethod
def ast_op_to_bindings(op):
mapping = {
ast.Add: FunctionalOp.Plus,
ast.Sub: FunctionalOp.Minus,
ast.Mult: FunctionalOp.Multiplies,
ast.Div: FunctionalOp.Divides,
"maximum": FunctionalOp.Maximum,
"minimum": FunctionalOp.Minimum,
"identity": identity.binding_type,
"relu": relu.binding_type,
"tanh": tanh.binding_type,
"sigmoid": sigmoid.binding_type,
"silu": silu.binding_type,
"hardswish": hardswish.binding_type,
"gelu": gelu.binding_type,
"multiply_add": FunctionalOp.MultiplyAdd,
"sum": (FunctionalOp.Plus, FunctionalOp.AtomicAdd),
"max": (FunctionalOp.Maximum, FunctionalOp.AtomicMaximum),
"exp": FunctionalOp.Exp
}
return mapping[op]
#
# Visiting different node types
#
def visit_FunctionDef(self, node: ast.FunctionDef):
# Visit args and register load nodes
for arg in node.args.args:
self.visit(arg)
for expr in node.body:
self.visit(expr)
def visit_arg(self, node: ast.arg):
# Name of the argument
name = node.arg
try:
example_tensor = self.example_inputs[name]
except:
raise RuntimeError(f"Example input for {name} is not provided.")
self.add_load_node(name, example_tensor)
def visit_Name(self, node: ast.Name):
return node.id
def visit_Constant(self, node: ast.Constant):
if self.no_imm:
return node.value
else:
name = self.add_imm(node.value)
return name
def visit_Tuple(self, node: ast.Tuple):
results = []
for elt in node.elts:
results.append(self.visit(elt))
return tuple(results)
def visit_keyword(self, node: ast.keyword):
return {node.arg: self.visit(node.value)}
def visit_BinOp(self, node: ast.BinOp):
if self.visiting_return:
raise SyntaxError("Return value cannot be an expression")
lhs = self.visit(node.left)
rhs = self.visit(node.right)
op = self.ast_op_to_bindings(type(node.op))
name = self.add_compute_node(op)
# Add edges
# The edge weights are used to sort the input args
self.add_edge(lhs, name, weight=0)
self.add_edge(rhs, name, weight=1)
return name
def visit_Assign(self, node: ast.BinOp):
target = self.visit(node.targets[0])
value = self.visit(node.value)
# Create the assign node
self.add_store_node(target)
# Add edges
self.add_edge(value, target)
return target
def visit_Call(self, node: ast.Call):
if self.visiting_return:
raise SyntaxError("Return value cannot be an expression")
func = self.visit(node.func)
args = [self.visit(arg) for arg in node.args]
if func in self.layout_fns.keys():
# Parse kwargs
# By default, visiting imm automatically creates a load node
# However, in function call, keyword args are used to set
# specific function attributes such as indices for permute
# So no_imm is set to True temporarily
self.no_imm = True
kwargs = {}
for kw in node.keywords:
kwargs.update(self.visit(kw))
self.no_imm = False
op = self.layout_fns[func]
name = self.add_layout_node(op, kwargs)
else:
op = self.ast_op_to_bindings(func)
name = self.add_compute_node(op)
# Add edges
for idx, arg in enumerate(args):
self.add_edge(arg, name, weight=idx)
return name
def visit_Return(self, node: ast.Return):
self.visiting_return = True
results = self.visit(node.value)
self.visiting_return = False
self.return_names = results
if not isinstance(results, tuple):
results = (results,)
for rst in results:
try:
example_tensor = self.example_inputs[rst]
except:
raise RuntimeError(f"Example input for {rst} is not provided.")
self.set_store_tensor(rst, example_tensor)
self.mark_output(rst)