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  1. 524
      colossalai/autochunk/autochunk_codegen.py

524
colossalai/autochunk/autochunk_codegen.py
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@ -1,21 +1,25 @@
from typing import Any, Dict, Iterable, List, Tuple
import torch
from torch.fx.graph import (
CodeGen,
PythonCode,
_custom_builtins,
_CustomBuiltin,
_format_target,
_is_from_torch,
_Namespace,
_origin_type_map,
inplace_methods,
magic_methods,
)
from torch.fx.node import Argument, Node, _get_qualified_name, _type_repr, map_arg
import colossalai
from colossalai.fx.codegen.activation_checkpoint_codegen import CODEGEN_AVAILABLE
if CODEGEN_AVAILABLE:
from torch.fx.graph import (
CodeGen,
PythonCode,
_custom_builtins,
_CustomBuiltin,
_format_target,
_is_from_torch,
_Namespace,
_origin_type_map,
inplace_methods,
magic_methods,
)
from torch.fx.node import Argument, Node, _get_qualified_name, _type_repr, map_arg
from .search_chunk import SearchChunk
from .utils import delete_free_var_from_last_use, find_idx_by_name, get_node_shape
@ -96,7 +100,7 @@ def _gen_loop_end(
Returns:
context (str): generated str
"""
"""
chunk_outputs_name = chunk_outputs.name
chunk_outputs_idx = find_idx_by_name(chunk_outputs_name, node_list)
chunk_output_shape = chunk_outputs.meta["tensor_meta"].shape
@ -302,279 +306,287 @@ def emit_code_with_chunk(
node_idx += 1
class AutoChunkCodeGen(CodeGen):
def __init__(self, meta_graph, max_memory=None, print_mem=False):
super().__init__()
self.meta_graph = meta_graph
self.max_memory = max_memory
self.meta_node = list(meta_graph.graph.nodes)
# find the chunk regions
self.search_chunk = SearchChunk(meta_graph, max_memory, print_mem)
self.chunk_infos = self.search_chunk.search_region()
if CODEGEN_AVAILABLE:
def _gen_python_code(
self, nodes, root_module: str, namespace: _Namespace
) -> PythonCode:
free_vars: List[str] = []
body: List[str] = []
globals_: Dict[str, Any] = {}
wrapped_fns: Dict[str, None] = {}
class AutoChunkCodeGen(CodeGen):
def __init__(self, meta_graph, max_memory=None, print_mem=False):
super().__init__()
self.meta_graph = meta_graph
self.max_memory = max_memory
self.meta_node = list(meta_graph.graph.nodes)
# find the chunk regions
self.search_chunk = SearchChunk(meta_graph, max_memory, print_mem)
self.chunk_infos = self.search_chunk.search_region()
# Wrap string in list to pass by reference
maybe_return_annotation: List[str] = [""]
def _gen_python_code(
self, nodes, root_module: str, namespace: _Namespace
) -> PythonCode:
free_vars: List[str] = []
body: List[str] = []
globals_: Dict[str, Any] = {}
wrapped_fns: Dict[str, None] = {}
def add_global(name_hint: str, obj: Any):
"""Add an obj to be tracked as a global.
# Wrap string in list to pass by reference
maybe_return_annotation: List[str] = [""]
We call this for names that reference objects external to the
Graph, like functions or types.
def add_global(name_hint: str, obj: Any):
"""Add an obj to be tracked as a global.
Returns: the global name that should be used to reference 'obj' in generated source.
"""
if (
_is_from_torch(obj) and obj != torch.device
): # to support registering torch.device
# HACK: workaround for how torch custom ops are registered. We
# can't import them like normal modules so they must retain their
# fully qualified name.
return _get_qualified_name(obj)
# normalize the name hint to get a proper identifier
global_name = namespace.create_name(name_hint, obj)
if global_name in globals_:
assert globals_[global_name] is obj
We call this for names that reference objects external to the
Graph, like functions or types.
Returns: the global name that should be used to reference 'obj' in generated source.
"""
if (
_is_from_torch(obj) and obj != torch.device
): # to support registering torch.device
# HACK: workaround for how torch custom ops are registered. We
# can't import them like normal modules so they must retain their
# fully qualified name.
return _get_qualified_name(obj)
# normalize the name hint to get a proper identifier
global_name = namespace.create_name(name_hint, obj)
if global_name in globals_:
assert globals_[global_name] is obj
return global_name
globals_[global_name] = obj
return global_name
globals_[global_name] = obj
return global_name
# set _custom_builtins here so that we needn't import colossalai in forward
_custom_builtins["colossalai"] = _CustomBuiltin("import colossalai", colossalai)
# set _custom_builtins here so that we needn't import colossalai in forward
_custom_builtins["colossalai"] = _CustomBuiltin(
"import colossalai", colossalai
)
# Pre-fill the globals table with registered builtins.
for name, (_, obj) in _custom_builtins.items():
add_global(name, obj)
# Pre-fill the globals table with registered builtins.
for name, (_, obj) in _custom_builtins.items():
add_global(name, obj)
def type_repr(o: Any):
if o == ():
# Empty tuple is used for empty tuple type annotation Tuple[()]
return "()"
def type_repr(o: Any):
if o == ():
# Empty tuple is used for empty tuple type annotation Tuple[()]
return "()"
typename = _type_repr(o)
typename = _type_repr(o)
if hasattr(o, "__origin__"):
# This is a generic type, e.g. typing.List[torch.Tensor]
origin_type = _origin_type_map.get(o.__origin__, o.__origin__)
origin_typename = add_global(_type_repr(origin_type), origin_type)
if hasattr(o, "__origin__"):
# This is a generic type, e.g. typing.List[torch.Tensor]
origin_type = _origin_type_map.get(o.__origin__, o.__origin__)
origin_typename = add_global(_type_repr(origin_type), origin_type)
if hasattr(o, "__args__"):
# Assign global names for each of the inner type variables.
args = [type_repr(arg) for arg in o.__args__]
if hasattr(o, "__args__"):
# Assign global names for each of the inner type variables.
args = [type_repr(arg) for arg in o.__args__]
if len(args) == 0:
# Bare type, such as `typing.Tuple` with no subscript
# This code-path used in Python < 3.9
return origin_typename
if len(args) == 0:
return f'{origin_typename}[{",".join(args)}]'
else:
# Bare type, such as `typing.Tuple` with no subscript
# This code-path used in Python < 3.9
# This code-path used in Python 3.9+
return origin_typename
return f'{origin_typename}[{",".join(args)}]'
# Common case: this is a regular module name like 'foo.bar.baz'
return add_global(typename, o)
def _format_args(
args: Tuple[Argument, ...], kwargs: Dict[str, Argument]
) -> str:
def _get_repr(arg):
# Handle NamedTuples (if it has `_fields`) via add_global.
if isinstance(arg, tuple) and hasattr(arg, "_fields"):
qualified_name = _get_qualified_name(type(arg))
global_name = add_global(qualified_name, type(arg))
return f"{global_name}{repr(tuple(arg))}"
return repr(arg)
args_s = ", ".join(_get_repr(a) for a in args)
kwargs_s = ", ".join(f"{k} = {_get_repr(v)}" for k, v in kwargs.items())
if args_s and kwargs_s:
return f"{args_s}, {kwargs_s}"
return args_s or kwargs_s
# Run through reverse nodes and record the first instance of a use
# of a given node. This represents the *last* use of the node in the
# execution order of the program, which we will use to free unused
# values
node_to_last_use: Dict[Node, Node] = {}
user_to_last_uses: Dict[Node, List[Node]] = {}
def register_last_uses(n: Node, user: Node):
if n not in node_to_last_use:
node_to_last_use[n] = user
user_to_last_uses.setdefault(user, []).append(n)
for node in reversed(nodes):
map_arg(node.args, lambda n: register_last_uses(n, node))
map_arg(node.kwargs, lambda n: register_last_uses(n, node))
delete_free_var_from_last_use(user_to_last_uses)
# NOTE: we add a variable to distinguish body and ckpt_func
def delete_unused_values(user: Node, body, to_keep=[]):
"""
Delete values after their last use. This ensures that values that are
not used in the remainder of the code are freed and the memory usage
of the code is optimal.
"""
if user.op == "placeholder":
return
if user.op == "output":
body.append("\n")
return
nodes_to_delete = user_to_last_uses.get(user, [])
nodes_to_delete = [i for i in nodes_to_delete if i.name not in to_keep]
if len(nodes_to_delete):
to_delete_str = " = ".join(
[repr(n) for n in nodes_to_delete] + ["None"]
)
body.append(f"; {to_delete_str}\n")
else:
# Bare type, such as `typing.Tuple` with no subscript
# This code-path used in Python 3.9+
return origin_typename
# Common case: this is a regular module name like 'foo.bar.baz'
return add_global(typename, o)
def _format_args(
args: Tuple[Argument, ...], kwargs: Dict[str, Argument]
) -> str:
def _get_repr(arg):
# Handle NamedTuples (if it has `_fields`) via add_global.
if isinstance(arg, tuple) and hasattr(arg, "_fields"):
qualified_name = _get_qualified_name(type(arg))
global_name = add_global(qualified_name, type(arg))
return f"{global_name}{repr(tuple(arg))}"
return repr(arg)
args_s = ", ".join(_get_repr(a) for a in args)
kwargs_s = ", ".join(f"{k} = {_get_repr(v)}" for k, v in kwargs.items())
if args_s and kwargs_s:
return f"{args_s}, {kwargs_s}"
return args_s or kwargs_s
# Run through reverse nodes and record the first instance of a use
# of a given node. This represents the *last* use of the node in the
# execution order of the program, which we will use to free unused
# values
node_to_last_use: Dict[Node, Node] = {}
user_to_last_uses: Dict[Node, List[Node]] = {}
def register_last_uses(n: Node, user: Node):
if n not in node_to_last_use:
node_to_last_use[n] = user
user_to_last_uses.setdefault(user, []).append(n)
for node in reversed(nodes):
map_arg(node.args, lambda n: register_last_uses(n, node))
map_arg(node.kwargs, lambda n: register_last_uses(n, node))
delete_free_var_from_last_use(user_to_last_uses)
# NOTE: we add a variable to distinguish body and ckpt_func
def delete_unused_values(user: Node, body, to_keep=[]):
"""
Delete values after their last use. This ensures that values that are
not used in the remainder of the code are freed and the memory usage
of the code is optimal.
"""
if user.op == "placeholder":
return
if user.op == "output":
body.append("\n")
return
nodes_to_delete = user_to_last_uses.get(user, [])
nodes_to_delete = [i for i in nodes_to_delete if i.name not in to_keep]
if len(nodes_to_delete):
to_delete_str = " = ".join(
[repr(n) for n in nodes_to_delete] + ["None"]
)
body.append(f"; {to_delete_str}\n")
else:
body.append("\n")
body.append("\n")
# NOTE: we add a variable to distinguish body and ckpt_func
def emit_node(node: Node, body):
maybe_type_annotation = (
"" if node.type is None else f" : {type_repr(node.type)}"
)
if node.op == "placeholder":
assert isinstance(node.target, str)
maybe_default_arg = "" if not node.args else f" = {repr(node.args[0])}"
free_vars.append(
f"{node.target}{maybe_type_annotation}{maybe_default_arg}"
# NOTE: we add a variable to distinguish body and ckpt_func
def emit_node(node: Node, body):
maybe_type_annotation = (
"" if node.type is None else f" : {type_repr(node.type)}"
)
raw_name = node.target.replace("*", "")
if raw_name != repr(node):
body.append(f"{repr(node)} = {raw_name}\n")
return
elif node.op == "call_method":
assert isinstance(node.target, str)
body.append(
f"{repr(node)}{maybe_type_annotation} = {_format_target(repr(node.args[0]), node.target)}"
f"({_format_args(node.args[1:], node.kwargs)})"
)
return
elif node.op == "call_function":
assert callable(node.target)
# pretty print operators
if (
node.target.__module__ == "_operator"
and node.target.__name__ in magic_methods
):
assert isinstance(node.args, tuple)
if node.op == "placeholder":
assert isinstance(node.target, str)
maybe_default_arg = (
"" if not node.args else f" = {repr(node.args[0])}"
)
free_vars.append(
f"{node.target}{maybe_type_annotation}{maybe_default_arg}"
)
raw_name = node.target.replace("*", "")
if raw_name != repr(node):
body.append(f"{repr(node)} = {raw_name}\n")
return
elif node.op == "call_method":
assert isinstance(node.target, str)
body.append(
f"{repr(node)}{maybe_type_annotation} = "
f"{magic_methods[node.target.__name__].format(*(repr(a) for a in node.args))}"
f"{repr(node)}{maybe_type_annotation} = {_format_target(repr(node.args[0]), node.target)}"
f"({_format_args(node.args[1:], node.kwargs)})"
)
return
# pretty print inplace operators; required for jit.script to work properly
# not currently supported in normal FX graphs, but generated by torchdynamo
if (
node.target.__module__ == "_operator"
and node.target.__name__ in inplace_methods
):
elif node.op == "call_function":
assert callable(node.target)
# pretty print operators
if (
node.target.__module__ == "_operator"
and node.target.__name__ in magic_methods
):
assert isinstance(node.args, tuple)
body.append(
f"{repr(node)}{maybe_type_annotation} = "
f"{magic_methods[node.target.__name__].format(*(repr(a) for a in node.args))}"
)
return
# pretty print inplace operators; required for jit.script to work properly
# not currently supported in normal FX graphs, but generated by torchdynamo
if (
node.target.__module__ == "_operator"
and node.target.__name__ in inplace_methods
):
body.append(
f"{inplace_methods[node.target.__name__].format(*(repr(a) for a in node.args))}; "
f"{repr(node)}{maybe_type_annotation} = {repr(node.args[0])}"
)
return
qualified_name = _get_qualified_name(node.target)
global_name = add_global(qualified_name, node.target)
# special case for getattr: node.args could be 2-argument or 3-argument
# 2-argument: attribute access; 3-argument: fall through to attrib function call with default value
if (
global_name == "getattr"
and isinstance(node.args, tuple)
and isinstance(node.args[1], str)
and node.args[1].isidentifier()
and len(node.args) == 2
):
body.append(
f"{repr(node)}{maybe_type_annotation} = {_format_target(repr(node.args[0]), node.args[1])}"
)
return
body.append(
f"{inplace_methods[node.target.__name__].format(*(repr(a) for a in node.args))}; "
f"{repr(node)}{maybe_type_annotation} = {repr(node.args[0])}"
f"{repr(node)}{maybe_type_annotation} = {global_name}({_format_args(node.args, node.kwargs)})"
)
if node.meta.get("is_wrapped", False):
wrapped_fns.setdefault(global_name)
return
qualified_name = _get_qualified_name(node.target)
global_name = add_global(qualified_name, node.target)
# special case for getattr: node.args could be 2-argument or 3-argument
# 2-argument: attribute access; 3-argument: fall through to attrib function call with default value
if (
global_name == "getattr"
and isinstance(node.args, tuple)
and isinstance(node.args[1], str)
and node.args[1].isidentifier()
and len(node.args) == 2
):
elif node.op == "call_module":
assert isinstance(node.target, str)
body.append(
f"{repr(node)}{maybe_type_annotation} = {_format_target(repr(node.args[0]), node.args[1])}"
f"{repr(node)}{maybe_type_annotation} = "
f"{_format_target(root_module, node.target)}({_format_args(node.args, node.kwargs)})"
)
return
body.append(
f"{repr(node)}{maybe_type_annotation} = {global_name}({_format_args(node.args, node.kwargs)})"
)
if node.meta.get("is_wrapped", False):
wrapped_fns.setdefault(global_name)
return
elif node.op == "call_module":
assert isinstance(node.target, str)
body.append(
f"{repr(node)}{maybe_type_annotation} = "
f"{_format_target(root_module, node.target)}({_format_args(node.args, node.kwargs)})"
)
return
elif node.op == "get_attr":
assert isinstance(node.target, str)
body.append(
f"{repr(node)}{maybe_type_annotation} = {_format_target(root_module, node.target)}"
elif node.op == "get_attr":
assert isinstance(node.target, str)
body.append(
f"{repr(node)}{maybe_type_annotation} = {_format_target(root_module, node.target)}"
)
return
elif node.op == "output":
if node.type is not None:
maybe_return_annotation[0] = f" -> {type_repr(node.type)}"
body.append(self.generate_output(node.args[0]))
return
raise NotImplementedError(f"node: {node.op} {node.target}")
# Modified for activation checkpointing
ckpt_func = []
# if any node has a list of labels for activation_checkpoint, we
# will use nested type of activation checkpoint codegen
emit_code_with_chunk(
body,
nodes,
emit_node,
delete_unused_values,
self.search_chunk,
self.chunk_infos,
)
if len(body) == 0:
# If the Graph has no non-placeholder nodes, no lines for the body
# have been emitted. To continue to have valid Python code, emit a
# single pass statement
body.append("pass\n")
if len(wrapped_fns) > 0:
wrap_name = add_global("wrap", torch.fx.wrap)
wrap_stmts = "\n".join(
[f'{wrap_name}("{name}")' for name in wrapped_fns]
)
return
elif node.op == "output":
if node.type is not None:
maybe_return_annotation[0] = f" -> {type_repr(node.type)}"
body.append(self.generate_output(node.args[0]))
return
raise NotImplementedError(f"node: {node.op} {node.target}")
# Modified for activation checkpointing
ckpt_func = []
# if any node has a list of labels for activation_checkpoint, we
# will use nested type of activation checkpoint codegen
emit_code_with_chunk(
body,
nodes,
emit_node,
delete_unused_values,
self.search_chunk,
self.chunk_infos,
)
if len(body) == 0:
# If the Graph has no non-placeholder nodes, no lines for the body
# have been emitted. To continue to have valid Python code, emit a
# single pass statement
body.append("pass\n")
if len(wrapped_fns) > 0:
wrap_name = add_global("wrap", torch.fx.wrap)
wrap_stmts = "\n".join([f'{wrap_name}("{name}")' for name in wrapped_fns])
else:
wrap_stmts = ""
else:
wrap_stmts = ""
if self._body_transformer:
body = self._body_transformer(body)
if self._body_transformer:
body = self._body_transformer(body)
for name, value in self.additional_globals():
add_global(name, value)
for name, value in self.additional_globals():
add_global(name, value)
# as we need colossalai.utils.checkpoint, we need to import colossalai
# in forward function
prologue = self.gen_fn_def(free_vars, maybe_return_annotation[0])
prologue = "".join(ckpt_func) + prologue
prologue = prologue
# as we need colossalai.utils.checkpoint, we need to import colossalai
# in forward function
prologue = self.gen_fn_def(free_vars, maybe_return_annotation[0])
prologue = "".join(ckpt_func) + prologue
prologue = prologue
code = "".join(body)
code = "\n".join(" " + line for line in code.split("\n"))
fn_code = f"""
{wrap_stmts}
code = "".join(body)
code = "\n".join(" " + line for line in code.split("\n"))
fn_code = f"""
{wrap_stmts}
{prologue}
{code}"""
# print(fn_code)
return PythonCode(fn_code, globals_)
{prologue}
{code}"""
# print(fn_code)
return PythonCode(fn_code, globals_)

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