mirror of https://github.com/hpcaitech/ColossalAI
[fx] added activation checkpoint codegen (#1355)
Frank Lee
GitHub
parent
38fd8844c0
commit
644582eee9
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from .activation_checkpoint_codegen import ActivationCheckpointCodeGen
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__all__ = ['ActivationCheckpointCodeGen']
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import torch
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from typing import List, Callable, Any, Tuple, Dict
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from torch.fx.node import Node, Argument, map_arg, _type_repr, _get_qualified_name
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from torch.fx.graph import _Namespace, PythonCode, _custom_builtins, _is_from_torch, _format_target, magic_methods, CodeGen, _origin_type_map
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__all__ = ['ActivationCheckpointCodeGen']
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class ActivationCheckpointCodeGen(CodeGen):
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def find_input_and_output_nodes(self, nodes: List[Node]):
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"""
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Find the input and output node names which are not found in the given list of nodes.
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"""
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input_nodes = []
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output_nodes = []
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# if a node has an input node which is not in the node list
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# we treat that input node as the input of the checkpoint function
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for node in nodes:
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for input_node in node._input_nodes.keys():
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node_repr = repr(input_node)
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if input_node not in nodes and node_repr not in input_nodes:
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input_nodes.append(node_repr)
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# if a node has a user node which is not in the node list
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# we treat that user node as the node receiving the current node output
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for node in nodes:
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for output_node in node.users.keys():
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node_repr = repr(node)
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if output_node not in nodes and node_repr not in output_nodes:
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output_nodes.append(node_repr)
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return input_nodes, output_nodes
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def find_ckpt_regions(self, nodes: List[Node]):
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"""
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Find the checkpoint regions given a list of consecutive nodes. The outputs will be list
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of tuples, each tuple is in the form of (start_index, end_index).
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"""
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ckpt_nodes = []
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ckpt_regions = []
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start = -1
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end = -1
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current_region = None
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for idx, node in enumerate(nodes):
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if hasattr(node, 'activation_checkpoint'):
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act_ckpt_label = node.activation_checkpoint
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# this activation checkpoint label is not set yet
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# meaning this is the first node of the activation ckpt region
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if current_region is None:
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current_region = act_ckpt_label
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start = idx
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# if activation checkpoint has changed
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# we restart the tracking
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# e.g. node ckpt states = [ckpt1, ckpt2, ckpt2, ckpt2]
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if act_ckpt_label != current_region:
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assert start != -1
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ckpt_regions.append((start, idx - 1))
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current_region = act_ckpt_label
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start = idx
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end = -1
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elif current_region is not None and not hasattr(node, 'activation_checkpoint'):
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# used to check the case below
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# node ckpt states = [ckpt, ckpt, non-ckpt]
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end = idx - 1
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assert start != -1 and end != -1
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ckpt_regions.append((start, end))
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start = end = -1
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current_region = None
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else:
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pass
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return ckpt_regions
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def gen_ckpt_fn_def(self, label, free_vars: List[str]) -> str:
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"""
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Generate the checkpoint function definition
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"""
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return f"def checkpoint_{label}({', '.join(free_vars)}):"
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def gen_ckpt_output(self, output_vars: List[str]) -> str:
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"""
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Generate the return statement for checkpoint region
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"""
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return f"return {', '.join(output_vars)}"
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def gen_ckpt_usage(self, label, input_vars, output_vars):
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"""
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Generate the checkpoint function call code text
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"""
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outputs = ', '.join(output_vars)
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inputs = ', '.join(input_vars)
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return f'{outputs} = torch.utils.checkpoint.checkpoint(checkpoint_{label}, {inputs})'
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def _gen_python_code(self, nodes, root_module: str, namespace: _Namespace) -> PythonCode:
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free_vars: List[str] = []
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body: List[str] = []
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globals_: Dict[str, Any] = {}
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wrapped_fns: Dict[str, None] = {}
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# Wrap string in list to pass by reference
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maybe_return_annotation: List[str] = ['']
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def add_global(name_hint: str, obj: Any):
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"""Add an obj to be tracked as a global.
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We call this for names that reference objects external to the
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Graph, like functions or types.
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Returns: the global name that should be used to reference 'obj' in generated source.
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"""
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if _is_from_torch(obj) and obj != torch.device: # to support registering torch.device
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# HACK: workaround for how torch custom ops are registered. We
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# can't import them like normal modules so they must retain their
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# fully qualified name.
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return _get_qualified_name(obj)
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# normalize the name hint to get a proper identifier
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global_name = namespace.create_name(name_hint, obj)
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if global_name in globals_:
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assert globals_[global_name] is obj
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return global_name
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globals_[global_name] = obj
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return global_name
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# Pre-fill the globals table with registered builtins.
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for name, (_, obj) in _custom_builtins.items():
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add_global(name, obj)
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def type_repr(o: Any):
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if o == ():
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# Empty tuple is used for empty tuple type annotation Tuple[()]
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return '()'
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typename = _type_repr(o)
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if hasattr(o, '__origin__'):
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# This is a generic type, e.g. typing.List[torch.Tensor]
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origin_type = _origin_type_map.get(o.__origin__, o.__origin__)
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origin_typename = add_global(_type_repr(origin_type), origin_type)
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if hasattr(o, '__args__'):
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# Assign global names for each of the inner type variables.
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args = [type_repr(arg) for arg in o.__args__]
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if len(args) == 0:
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# Bare type, such as `typing.Tuple` with no subscript
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# This code-path used in Python < 3.9
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return origin_typename
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return f'{origin_typename}[{",".join(args)}]'
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else:
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# Bare type, such as `typing.Tuple` with no subscript
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# This code-path used in Python 3.9+
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return origin_typename
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# Common case: this is a regular module name like 'foo.bar.baz'
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return add_global(typename, o)
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def _format_args(args: Tuple[Argument, ...], kwargs: Dict[str, Argument]) -> str:
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def _get_repr(arg):
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# Handle NamedTuples (if it has `_fields`) via add_global.
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if isinstance(arg, tuple) and hasattr(arg, '_fields'):
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qualified_name = _get_qualified_name(type(arg))
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global_name = add_global(qualified_name, type(arg))
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return f"{global_name}{repr(tuple(arg))}"
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return repr(arg)
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args_s = ', '.join(_get_repr(a) for a in args)
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kwargs_s = ', '.join(f'{k} = {_get_repr(v)}' for k, v in kwargs.items())
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if args_s and kwargs_s:
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return f'{args_s}, {kwargs_s}'
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return args_s or kwargs_s
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# Run through reverse nodes and record the first instance of a use
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# of a given node. This represents the *last* use of the node in the
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# execution order of the program, which we will use to free unused
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# values
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node_to_last_use: Dict[Node, Node] = {}
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user_to_last_uses: Dict[Node, List[Node]] = {}
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def register_last_uses(n: Node, user: Node):
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if n not in node_to_last_use:
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node_to_last_use[n] = user
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user_to_last_uses.setdefault(user, []).append(n)
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for node in reversed(nodes):
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map_arg(node.args, lambda n: register_last_uses(n, node))
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map_arg(node.kwargs, lambda n: register_last_uses(n, node))
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def delete_unused_values(user: Node):
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"""
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Delete values after their last use. This ensures that values that are
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not used in the remainder of the code are freed and the memory usage
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of the code is optimal.
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"""
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if user.op == 'placeholder':
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return
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if user.op == 'output':
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body.append('\n')
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return
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nodes_to_delete = user_to_last_uses.get(user, [])
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if len(nodes_to_delete):
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to_delete_str = ' = '.join([repr(n) for n in nodes_to_delete] + ['None'])
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body.append(f'; {to_delete_str}\n')
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else:
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body.append('\n')
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def emit_node(node: Node):
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maybe_type_annotation = '' if node.type is None else f' : {type_repr(node.type)}'
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if node.op == 'placeholder':
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assert isinstance(node.target, str)
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maybe_default_arg = '' if not node.args else f' = {repr(node.args[0])}'
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free_vars.append(f'{node.target}{maybe_type_annotation}{maybe_default_arg}')
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raw_name = node.target.replace('*', '')
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if raw_name != repr(node):
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body.append(f'{repr(node)} = {raw_name}\n')
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return
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elif node.op == 'call_method':
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assert isinstance(node.target, str)
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body.append(f'{repr(node)}{maybe_type_annotation} = {_format_target(repr(node.args[0]), node.target)}'
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f'({_format_args(node.args[1:], node.kwargs)})')
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return
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elif node.op == 'call_function':
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assert callable(node.target)
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# pretty print operators
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if node.target.__module__ == '_operator' and node.target.__name__ in magic_methods:
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assert isinstance(node.args, tuple)
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body.append(f'{repr(node)}{maybe_type_annotation} = '
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f'{magic_methods[node.target.__name__].format(*(repr(a) for a in node.args))}')
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return
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# pretty print inplace operators; required for jit.script to work properly
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# not currently supported in normal FX graphs, but generated by torchdynamo
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if node.target.__module__ == '_operator' and node.target.__name__ in inplace_methods:
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body.append(f'{inplace_methods[node.target.__name__].format(*(repr(a) for a in node.args))}; '
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f'{repr(node)}{maybe_type_annotation} = {repr(node.args[0])}')
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return
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qualified_name = _get_qualified_name(node.target)
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global_name = add_global(qualified_name, node.target)
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# special case for getattr: node.args could be 2-argument or 3-argument
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# 2-argument: attribute access; 3-argument: fall through to attrib function call with default value
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if global_name == 'getattr' and \
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isinstance(node.args, tuple) and \
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isinstance(node.args[1], str) and \
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node.args[1].isidentifier() and \
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len(node.args) == 2:
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body.append(
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f'{repr(node)}{maybe_type_annotation} = {_format_target(repr(node.args[0]), node.args[1])}')
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return
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body.append(
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f'{repr(node)}{maybe_type_annotation} = {global_name}({_format_args(node.args, node.kwargs)})')
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if node.meta.get('is_wrapped', False):
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wrapped_fns.setdefault(global_name)
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return
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elif node.op == 'call_module':
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assert isinstance(node.target, str)
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body.append(f'{repr(node)}{maybe_type_annotation} = '
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f'{_format_target(root_module, node.target)}({_format_args(node.args, node.kwargs)})')
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return
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elif node.op == 'get_attr':
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assert isinstance(node.target, str)
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body.append(f'{repr(node)}{maybe_type_annotation} = {_format_target(root_module, node.target)}')
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return
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elif node.op == 'output':
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if node.type is not None:
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maybe_return_annotation[0] = f" -> {type_repr(node.type)}"
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body.append(self.generate_output(node.args[0]))
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return
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raise NotImplementedError(f'node: {node.op} {node.target}')
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#########################################
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# Modified for activation checkpointing #
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#########################################
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# find the activation checkpoint regions
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ckpt_regions = self.find_ckpt_regions(nodes)
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start_idx = [item[0] for item in ckpt_regions]
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end_idx = [item[1] for item in ckpt_regions]
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input_vars = []
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output_vars = []
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within_ckpt_region = False
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node_list = list(nodes)
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# find the input and output var names for each region
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for idx, (start, end) in enumerate(ckpt_regions):
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ckpt_node_list = node_list[start:end + 1]
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inputs, outputs = self.find_input_and_output_nodes(ckpt_node_list)
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input_vars.append(inputs)
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output_vars.append(outputs)
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# append code text to body
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for idx, node in enumerate(node_list):
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# if this is the first node of the ckpt region
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# append the ckpt function defition
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if idx in start_idx:
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label = start_idx.index(idx)
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ckpt_fn_def = self.gen_ckpt_fn_def(label, input_vars[label])
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body.append(f'{ckpt_fn_def}\n')
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within_ckpt_region = True
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# NOTE: emit_node does not emit a string with newline. It depends
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# on delete_unused_values to append one
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emit_node(node)
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# add indentation to the emmited node
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if within_ckpt_region:
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body[-1] = ' ' + body[-1]
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# delete unused values
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delete_unused_values(node)
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if idx in end_idx:
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# if this is the last node of the ckpt region
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# generate return statement
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label = end_idx.index(idx)
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return_statement = self.gen_ckpt_output(output_vars[label])
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return_statement = f' {return_statement}\n'
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body.append(return_statement)
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# generate checkpoint function call in a new line
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usage = self.gen_ckpt_usage(label, input_vars[label], output_vars[label])
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usage += '\n'
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body.append(usage)
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within_ckpt_region = False
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#######################################################
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# Code Change For Activation Checkpointing Stops Here #
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#######################################################
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if len(body) == 0:
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# If the Graph has no non-placeholder nodes, no lines for the body
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# have been emitted. To continue to have valid Python code, emit a
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# single pass statement
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body.append('pass\n')
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if len(wrapped_fns) > 0:
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wrap_name = add_global('wrap', torch.fx.wrap)
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wrap_stmts = '\n'.join([f'{wrap_name}("{name}")' for name in wrapped_fns])
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else:
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wrap_stmts = ''
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if self._body_transformer:
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body = self._body_transformer(body)
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for name, value in self.additional_globals():
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add_global(name, value)
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prologue = self.gen_fn_def(free_vars, maybe_return_annotation[0])
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code = ''.join(body)
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code = '\n'.join(' ' + line for line in code.split('\n'))
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fn_code = f"""
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{wrap_stmts}
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{prologue}
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{code}"""
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return PythonCode(fn_code, globals_)
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@ -0,0 +1,69 @@
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import torch
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import pytest
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from torch.utils.checkpoint import checkpoint
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from torch.fx import GraphModule
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from colossalai.fx import ColoTracer
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try:
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from colossalai.fx.codegen import ActivationCheckpointCodeGen
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except:
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pass
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class MLP(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.linear1 = torch.nn.Linear(4, 4)
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self.linear2 = torch.nn.Linear(4, 4)
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def forward(self, x):
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return self.linear1(x), self.linear1(x)
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class MyModule(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.mlp1 = MLP()
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self.mlp2 = MLP()
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self.linear3 = torch.nn.Linear(4, 4)
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def forward(self, x):
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y1, y2 = checkpoint(self.mlp1, x)
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y3, y4 = checkpoint(self.mlp2, x)
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return y1 + y2 + y3 + y4
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@pytest.mark.skip("torch 1.12 is required")
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def test_act_ckpt_codegen():
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# build model and run forward
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model = MyModule()
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data = torch.rand(4, 4)
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non_fx_out = model(data)
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# trace the module and replace codegen
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tracer = ColoTracer(trace_act_ckpt=True)
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graph = tracer.trace(model)
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codegen = ActivationCheckpointCodeGen()
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graph.set_codegen(codegen)
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# check ops are annotated with ckpt
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ckpt_nodes = ['mlp1_linear1', 'mlp1_linear1_1', 'mlp2_linear1', 'mlp2_linear1_1']
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for node in graph.nodes:
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if node.name in ckpt_nodes:
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assert hasattr(node, 'activation_checkpoint')
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# assert checkpoint function will be generated
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code = graph.python_code('self').src
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assert 'checkpoint_0' in code and 'checkpoint_1' in code
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# recompile and verify the outputs are consistent
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gm = GraphModule(model, graph)
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gm.recompile()
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fx_out = gm(data)
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assert torch.equal(non_fx_out, fx_out)
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if __name__ == '__main__':
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test_act_ckpt_codegen()
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