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

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Jack Kosaian
2025-09-23 14:10:50 -07:00
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/dag_ir.py
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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.
#
#################################################################################################
"""
DAG IR used by Python EVT
"""
import networkx as nx
from cutlass_library import DataType
from cutlass_cppgen.backend.evt.ir.compute_nodes import ComputeNode
from cutlass_cppgen.backend.evt.ir.node import NodeBase
from cutlass_cppgen.backend.library import ActivationOp
from cutlass_cppgen.backend.utils import device_cc
class DAGIR:
"""
``DAGIR`` is the main data structure used in the EVT Intermediate Representation.
It consists of a series of ``Node`` s, each representing epilogue visitor nodes.
In the DAGIR, ``node`` is an string of its name. ``node_meta`` is the underlying class of the node
"""
def __init__(self, cc, element_compute=DataType.f32) -> None:
# The EVT DAGIR is managed through the nextworkX Digraph class
self._graph = nx.DiGraph()
self.element_compute = element_compute
self.reduction_names = []
self.cc = cc
self.identity_counter = 0
#
# IR manipulator
#
def add_node(self, meta: NodeBase):
"""
Add a node to dag ir
"""
if self.has_node(meta.name):
raise SyntaxError(f"Variable '{meta.name}' cannot be defined twice.")
self._graph.add_node(meta.name, meta=meta)
def add_edge(self, src: str, dst: str, weight: int=0):
"""
Add an edge src -> dst to dag ir with weight
"""
if not self.has_node(src):
raise SyntaxError(f"Variable '{src}' is undefined.")
if not self.has_node(dst):
raise SyntaxError(f"Variable '{dst}' is undefined.")
if self._graph.has_edge(src, dst):
# The DiGraph doesn't support multiple edges between two nodes
# We insert an identity node in such case as a workaround
identity_name = f"autogen_identity_{self.identity_counter}"
self.identity_counter += 1
compute_node = ComputeNode(
name=identity_name, fn=ActivationOp.Identity,
element_output=self.element_compute,
element_compute=self.element_compute)
self.add_node(compute_node)
self.add_edge(src, identity_name, 0)
self.add_edge(identity_name, dst, weight)
else:
self._graph.add_edge(src, dst, weight=weight)
def remove_node(self, node: str):
"""
Remove node from dag ir
"""
self._graph.remove_node(node)
def remove_edge(self, src: str, dst: str):
"""
Remove edge src -> dst
"""
self._graph.remove_edge(src, dst)
#
# Helper functions for getting attrs
#
def has_node(self, node: str) -> bool:
"""
Check if the node is in the graph
"""
return self._graph.has_node(node)
def in_degree(self, node: str):
"""
Get the input degree of node
"""
return self._graph.in_degree(node)
def in_edges(self, node: str):
"""
Get the input edges of node
"""
return [edge for edge in self._graph.in_edges(node)]
def out_degree(self, node: str):
"""
Get the output degree of node
"""
return self._graph.out_degree(node)
def out_edges(self, node: str):
"""
Get the output edges of node
"""
return [edge for edge in self._graph.out_edges(node)]
def get_node_meta(self, node: str):
"""
Get the meta data of the node
"""
return self._graph.nodes[node]["meta"]
def get_edge_weight(self, src, dst):
"""
Get the edge weight of edge src->dst
"""
return self._graph.get_edge_data(src, dst)["weight"]
#
# High-level helper functions
#
def all_reachable_nodes(self, node: str):
"""
Get all the nodes reachable from the current node (exclude)
"""
return list(nx.dfs_preorder_nodes(self._graph, source=node))
def get_users(self, node: str):
"""
Get all users of the current node
"""
return [edge[1] for edge in self.out_edges(node)]
def get_all_inputs(self, node: str):
"""
Get all the input nodes sorted by edge weight
"""
in_edges = self.in_edges(node)
edge_weights = [self.get_edge_weight(*edge) for edge in in_edges]
return [edge[0] for _, edge in sorted(zip(edge_weights, in_edges))]
def get_all_inputs_meta(self, node: str):
"""
Get all the input node metas sorted by edge weight
"""
return [self.get_node_meta(input_node) for input_node in self.get_all_inputs(node)]
def replace_all_uses_with(self, node1, node2):
"""
Replace all uses of node1 with node2
"""
for edge in self.out_edges(node1):
weight = self.get_edge_weight(*edge)
user = edge[1]
self.add_edge(node2, user, weight)
self.remove_edge(node1, user)
self.remove_node(node1)
#
# Node accessor
#
def nodes_topological_order(self):
"""
Get the nodes in the unique lexicographical topological order
It generates a unique ordering of nodes by first sorting topologically
and then additionally by sorting lexicographically.
Although topological_sort alone also works, this generates a unique key
for each epilogue visitor pattern and ensures the compilation cache can be reused.
:return: list[str]
"""
return list(nx.lexicographical_topological_sort(self._graph))
def node_metas_topological_order(self):
"""
Get the node metas in topological order
:return: list[NodeBase]
"""
return [self.get_node_meta(node) for node in self.nodes_topological_order()]
@property
def nodes(self):
"""
Get all nodes
:return: list[str]
"""
return list(self._graph.nodes)
@property
def nodes_meta(self):
"""
Get all node metas
:return: list[NodeBase]
"""
return [data[1]['meta'] for data in self._graph.nodes.data()]
@property
def edges(self):
"""
Get all edges
:return: list[(str, str)]
"""
return list(self._graph.edges)
#
# Path
#
def has_path(self, src: str, target: str) -> bool:
"""
Return True is a path exists from src to target
"""
return nx.has_path(self._graph, src, target)