ColossalAI/colossalai/autochunk/utils.py

96 lines
2.9 KiB
Python

from typing import Any, Callable, Dict, Iterable, List, Tuple
from torch.fx.node import Node
def is_non_compute_node(node):
if any(i in node.op for i in ["placeholder", "get_attr", "output"]) or any(
i in node.name for i in ["getitem", "getattr"]
):
return True
return False
def get_node_shape(node):
if hasattr(node.meta["tensor_meta"], "shape"):
return node.meta["tensor_meta"].shape
return None
def is_non_compute_node_except_placeholder(node):
if any(i in node.op for i in ["get_attr", "output"]) or any(
i in node.name for i in ["getitem", "getattr"]
):
return True
return False
def is_non_compute_node_except_placeholder_output(node):
if any(i in node.op for i in ["get_attr"]) or any(
i in node.name for i in ["getitem", "getattr"]
):
return True
return False
def find_idx_by_name(name, nodes_list):
for idx, node in enumerate(nodes_list):
if node.name == name:
return idx
raise RuntimeError("name %s not found in node list" % name)
def delete_free_var_from_last_use(user_to_last_uses):
for key, value in user_to_last_uses.items():
for n in value:
if n.op == "placeholder":
user_to_last_uses[key].remove(n)
def find_chunk_all_input_nodes(nodes: List[Node]):
"""
Find non-compute input and output node names.
input nodes are nodes used in the list
output nodes are nodes will use nodes in the list
"""
input_nodes = []
for node in nodes:
for input_node in node._input_nodes.keys():
if input_node not in nodes and input_node not in input_nodes:
input_nodes.append(input_node)
return input_nodes
def find_chunk_compute_input_and_output_nodes(nodes: List[Node]):
"""
Find non-compute input and output node names.
input nodes are nodes used in the list
output nodes are nodes will use nodes in the list
"""
input_nodes = []
output_nodes = []
# if a node has an input node which is not in the node list
# we treat that input node as the input of the checkpoint function
for node in nodes:
for input_node in node._input_nodes.keys():
if (
input_node not in nodes
and input_node not in input_nodes
and not is_non_compute_node_except_placeholder(input_node)
):
input_nodes.append(input_node)
# if a node has a user node which is not in the node list
# we treat that user node as the node receiving the current node output
for node in nodes:
for output_node in node.users.keys():
if (
output_node not in nodes
and node not in output_nodes
and not is_non_compute_node_except_placeholder_output(output_node)
):
output_nodes.append(node)
return input_nodes, output_nodes