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rename and remove useless func

pull/2364/head
oahzxl 2022-10-27 16:40:19 +08:00
parent f5c5d4c4f3
commit 87cddf7e14
2 changed files with 27 additions and 440 deletions
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398
chunk_codegen.py
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@ -12,7 +12,7 @@ except:
CODEGEN_AVAILABLE = False
if CODEGEN_AVAILABLE:
__all__ = ['ActivationCheckpointCodeGen']
__all__ = ['ChunkCodeGen']
else:
__all__ = ['python_code_with_activation_checkpoint']
@ -375,7 +375,7 @@ def emit_ckpt_func(body,
body.append(usage)
def emit_code_with_nested_activation_checkpoint(body, ckpt_func, nodes, emit_node_func, delete_unused_value_func):
def emit_code_with_chunk(body, ckpt_func, nodes, emit_node_func, delete_unused_value_func):
"""Emit code with nested activation checkpoint
When we detect some of the node.activation_checkpoint is a List, we will use
this function to emit the activation checkpoint codes.
@ -392,21 +392,21 @@ def emit_code_with_nested_activation_checkpoint(body, ckpt_func, nodes, emit_nod
end_idx = [item[1] for item in ckpt_regions]
# find the offload regions
offload_regions, offload_labels = _find_offload_regions(nodes)
offload_starts = [item[0] for item in offload_regions]
offload_ends = [item[1] for item in offload_regions]
offload_inputs = []
offload_outputs = []
within_offload_region = False
chunk_regions, chunk_labels = _find_offload_regions(nodes)
chunk_starts = [item[0] for item in chunk_regions]
chunk_ends = [item[1] for item in chunk_regions]
chunk_inputs = []
chunk_outputs = []
within_chunk_region = False
node_list = list(nodes)
# find the input and output var names for each offload region
for idx, (start, end) in enumerate(offload_regions):
for idx, (start, end) in enumerate(chunk_regions):
offload_node_list = node_list[start:end + 1]
inputs, outputs = _find_input_and_output_nodes(offload_node_list)
offload_inputs.append(inputs)
offload_outputs.append(outputs)
chunk_inputs.append(inputs)
chunk_outputs.append(outputs)
# this flag is to prevent repeated insert of save tensors
# hooks definition in ckpt_func
@ -427,10 +427,10 @@ def emit_code_with_nested_activation_checkpoint(body, ckpt_func, nodes, emit_nod
else:
node = node_list[node_idx]
if node_idx in offload_starts:
offload_label = offload_labels[offload_starts.index(node_idx)]
_, offload_input, offload_bar = offload_label
within_offload_region = True
if node_idx in chunk_starts:
chunk_label = chunk_labels[chunk_starts.index(node_idx)]
_, chunk_input, chunk_bar = chunk_label
within_chunk_region = True
# insert hook functions if needed
if not is_hook_inserted:
@ -438,20 +438,20 @@ def emit_code_with_nested_activation_checkpoint(body, ckpt_func, nodes, emit_nod
ckpt_func.insert(0, "\n".join([pack_hook, unpack_hook]) + "\n")
is_hook_inserted = True
if offload_input and offload_bar:
if chunk_input and chunk_bar:
body.append(_gen_save_on_cpu_context())
elif offload_input:
for par in offload_inputs[offload_label[0]]:
elif chunk_input:
for par in chunk_inputs[chunk_label[0]]:
body.append(f"setattr({par}, 'offload', True)\n")
body.append(_gen_save_tensors_hooks_context(offload_input=True))
else:
for par in offload_inputs[offload_label[0]]:
for par in chunk_inputs[chunk_label[0]]:
body.append(f"setattr({par}, 'offload', False)\n")
body.append(_gen_save_tensors_hooks_context(offload_input=False))
if within_offload_region:
if within_chunk_region:
emit_node_func(node, body)
body[-1] = ' ' + body[-1]
delete_unused_value_func(node, body)
@ -460,150 +460,15 @@ def emit_code_with_nested_activation_checkpoint(body, ckpt_func, nodes, emit_nod
emit_node_func(node, body)
delete_unused_value_func(node, body)
if node_idx in offload_ends:
within_offload_region = False
if node_idx in chunk_ends:
within_chunk_region = False
node_idx += 1
def emit_code_with_activation_checkpoint(body, ckpt_func, nodes, emit_node_func, delete_unused_value_func):
# find the activation checkpoint regions
ckpt_regions = _find_ckpt_regions(nodes)
start_idx = [item[0] for item in ckpt_regions]
end_idx = [item[1] for item in ckpt_regions]
input_vars = []
output_vars = []
within_ckpt_region = False
# find the offload regions
offload_regions, offload_labels = _find_offload_regions(nodes)
offload_starts = [item[0] for item in offload_regions]
offload_ends = [item[1] for item in offload_regions]
offload_inputs = []
offload_outputs = []
within_offload_region = False
node_list = list(nodes)
# use this variable to avoid inserting hook functions
# to ckpt_func repeatedly
is_hook_inserted = False
# find the input and output var names for each region
for idx, (start, end) in enumerate(ckpt_regions):
ckpt_node_list = node_list[start:end + 1]
inputs, outputs = _find_input_and_output_nodes(ckpt_node_list)
input_vars.append(inputs)
output_vars.append(outputs)
# find the input and output var names for each offload region
for idx, (start, end) in enumerate(offload_regions):
offload_node_list = node_list[start:end + 1]
inputs, outputs = _find_input_and_output_nodes(offload_node_list)
offload_inputs.append(inputs)
offload_outputs.append(outputs)
# append code text to body
for idx, node in enumerate(node_list):
# if this is the first node of the ckpt region
# append the ckpt function defition
if idx in start_idx:
label = start_idx.index(idx)
ckpt_fn_def = _gen_ckpt_fn_def(label, input_vars[label])
ckpt_func.append(f'{ckpt_fn_def}\n')
within_ckpt_region = True
if idx in offload_starts:
offload_label = offload_labels[offload_starts.index(idx)]
_, offload_input, offload_bar = offload_label
within_offload_region = True
# insert hook functions if needed
if not is_hook_inserted:
pack_hook, unpack_hook = _gen_saved_tensors_hooks()
ckpt_func.insert(0, "\n".join([pack_hook, unpack_hook]) + "\n")
is_hook_inserted = True
if offload_input and offload_bar:
body.append(_gen_save_on_cpu_context())
elif offload_input:
for par in offload_inputs[offload_label[0]]:
body.append(f"setattr({par}, 'offload', True)\n")
body.append(_gen_save_tensors_hooks_context(offload_input=True))
else:
for par in offload_inputs[offload_label[0]]:
body.append(f"setattr({par}, 'offload', False)\n")
body.append(_gen_save_tensors_hooks_context(offload_input=False))
# NOTE: emit_node does not emit a string with newline. It depends
# on delete_unused_values to append one
# NOTE: currently we separate body and ckpt_func definition
if within_ckpt_region:
emit_node_func(node, ckpt_func)
ckpt_func[-1] = ' ' + ckpt_func[-1]
delete_unused_value_func(node, ckpt_func)
elif within_offload_region:
emit_node_func(node, body)
body[-1] = ' ' + body[-1]
delete_unused_value_func(node, body)
else:
emit_node_func(node, body)
delete_unused_value_func(node, body)
if idx in end_idx:
# if this is the last node of the ckpt region
# generate return statement
label = end_idx.index(idx)
return_statement = _gen_ckpt_output(output_vars[label])
return_statement = f' {return_statement}\n\n'
ckpt_func.append(return_statement)
# we need to check if the checkpoint need to offload the input
start_node_idx = start_idx[label]
if hasattr(node_list[start_node_idx], 'activation_offload'):
activation_offload = node_list[start_node_idx].activation_offload
else:
activation_offload = False
# we need to check if the checkpoint need use_reentrant=False
use_reentrant = True
non_leaf_input = 0
for var in input_vars[label]:
input_node = next(item for item in node_list if item.name == var)
if input_node.op != "placeholder":
non_leaf_input = 1
for user in input_node.users:
if hasattr(user, "activation_checkpoint"):
if user.activation_checkpoint == label:
if user.op == "call_module":
if hasattr(user.graph.owning_module.get_submodule(user.target), "inplace"):
use_reentrant = not user.graph.owning_module.get_submodule(user.target).inplace
elif user.op == "call_function":
if "inplace" in user.kwargs:
use_reentrant = not user.kwargs["inplace"]
# if all the inputs are leaf nodes, we need to set use_reentrant = False
if not non_leaf_input:
use_reentrant = False
# generate checkpoint function call in a new line
usage = _gen_ckpt_usage(label, activation_offload, input_vars[label], output_vars[label], use_reentrant)
usage += '\n'
body.append(usage)
within_ckpt_region = False
if idx in offload_ends:
within_offload_region = False
if CODEGEN_AVAILABLE:
class ActivationCheckpointCodeGen(CodeGen):
class ChunkCodeGen(CodeGen):
def _gen_python_code(self, nodes, root_module: str, namespace: _Namespace) -> PythonCode:
free_vars: List[str] = []
@ -796,10 +661,7 @@ if CODEGEN_AVAILABLE:
# if any node has a list of labels for activation_checkpoint, we
# will use nested type of activation checkpoint codegen
if any(isinstance(getattr(node, "activation_checkpoint", None), Iterable) for node in nodes):
emit_code_with_nested_activation_checkpoint(body, ckpt_func, nodes, emit_node, delete_unused_values)
else:
emit_code_with_activation_checkpoint(body, ckpt_func, nodes, emit_node, delete_unused_values)
emit_code_with_chunk(body, ckpt_func, nodes, emit_node, delete_unused_values)
if len(body) == 0:
# If the Graph has no non-placeholder nodes, no lines for the body
@ -833,215 +695,3 @@ if CODEGEN_AVAILABLE:
{prologue}
{code}"""
return PythonCode(fn_code, globals_)
else:
def python_code_with_activation_checkpoint(self, root_module: str, namespace: _Namespace) -> PythonCode:
"""
This method is copied from the _python_code of torch.fx.graph.Graph. Modifications are made so that it can generate
code for activation checkpoint.
"""
free_vars: List[str] = []
body: List[str] = []
globals_: Dict[str, Any] = {}
wrapped_fns: Dict[str, None] = {}
# Wrap string in list to pass by reference
maybe_return_annotation: List[str] = ['']
def add_global(name_hint: str, obj: Any):
"""Add an obj to be tracked as a global.
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
# 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)
def type_repr(o: Any):
if o == ():
# Empty tuple is used for empty tuple type annotation Tuple[()]
return '()'
typename = _type_repr(o)
# This is a generic type, e.g. typing.List[torch.Tensor]
if hasattr(o, '__origin__'):
origin_type = _origin_type_map.get(o.__origin__, o.__origin__)
origin_typename = add_global(_type_repr(origin_type), origin_type)
# Assign global names for each of the inner type variables.
args = [type_repr(arg) for arg in o.__args__]
return f'{origin_typename}[{",".join(args)}]'
# Common case: this is a regular module name like 'foo.bar.baz'
return add_global(typename, o)
# 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(self.nodes):
map_arg(node.args, lambda n: register_last_uses(n, node))
map_arg(node.kwargs, lambda n: register_last_uses(n, node))
# NOTE: we add a variable to distinguish body and ckpt_func
def delete_unused_values(user: Node, body):
"""
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, [])
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')
# 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}')
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)
body.append(f'{repr(node)}{maybe_type_annotation} = '
f'{magic_methods[node.target.__name__].format(*(repr(a) for a in node.args))}')
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'{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)}')
return
elif node.op == 'output':
if node.type is not None:
maybe_return_annotation[0] = f" -> {type_repr(node.type)}"
if self._pytree_info is None:
body.append(f'return {repr(node.args[0])}')
else:
body.append(f'return pytree.tree_unflatten({repr(node.args[0])}, self._out_spec)')
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
if any(isinstance(getattr(node, "activation_checkpoint", None), Iterable) for node in self.nodes):
emit_code_with_nested_activation_checkpoint(body, ckpt_func, self.nodes, emit_node, delete_unused_values)
else:
emit_code_with_activation_checkpoint(body, ckpt_func, self.nodes, emit_node, delete_unused_values)
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 self._pytree_info is not None:
orig_args = self._pytree_info.orig_args
has_orig_self = (orig_args[0] == 'self')
if has_orig_self:
free_vars.insert(0, 'self')
if len(free_vars) > 0: # pytree has placeholders in it
body.insert(
0,
f"{', '.join(free_vars)}, = fx_pytree.tree_flatten_spec([{', '.join(orig_args)}], self._in_spec)\n")
else:
orig_args = free_vars
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 = ''
ckpt_func = ''.join(ckpt_func)
# If the original function didn't have self as its first argument, we
# would have added it.
if len(orig_args) == 0 or orig_args[0] != 'self':
orig_args.insert(0, 'self')
code = ''.join(body)
code = '\n'.join(' ' + line for line in code.split('\n'))
# as we need colossalai.utils.checkpoint, we need to import colossalai
# in forward function
fn_code = f"""
{wrap_stmts}
{ckpt_func}
def forward({', '.join(orig_args)}){maybe_return_annotation[0]}:
{code}"""
return PythonCode(fn_code, globals_)

69
chunk_codegen_run.py
View File

@ -11,7 +11,7 @@ from colossalai.core import global_context as gpc
from colossalai.fx.graph_module import ColoGraphModule
try:
from chunk_codegen import ActivationCheckpointCodeGen
from chunk_codegen import ChunkCodeGen
with_codegen = True
except:
# fall back to older pytorch version
@ -75,7 +75,7 @@ def _run_offload_codegen(rank):
# trace the module and replace codegen
tracer = ColoTracer(trace_act_ckpt=True)
graph = tracer.trace(model)
codegen = ActivationCheckpointCodeGen()
codegen = ChunkCodeGen()
graph.set_codegen(codegen)
# annotate the activation offload part
@ -99,15 +99,7 @@ def _run_offload_codegen(rank):
# assert we have all the components
code = graph.python_code("self").src
assert "def pack_hook_input(self, x):" in code and \
"def unpack_hook(self, packed):" in code and \
"def pack_hook_no_input(self, x):" in code and \
"setattr(x, 'offload', True)" in code and \
"setattr(linear3, 'offload', False)" in code and \
"with torch.autograd.graph.saved_tensors_hooks(self.pack_hook_input, self.unpack_hook):" in code and \
"with torch.autograd.graph.save_on_cpu(pin_memory=True):" in code and \
"with torch.autograd.graph.saved_tensors_hooks(self.pack_hook_no_input, self.unpack_hook):" in code and \
"colossalai.utils.activation_checkpoint.checkpoint(self.checkpoint_0, True, linear4, use_reentrant=False)" in code
print(code)
_test_fwd_and_bwd(model, gm, data)
gpc.destroy()
@ -118,60 +110,5 @@ def test_act_ckpt_codegen():
mp.spawn(_run_offload_codegen, nprocs=1)
def _run_offload_codegen_torch11(rank):
# launch colossalai to make sure we could execute colossalai.utils.checkpoint currectly
colossalai.launch(config={}, rank=rank, world_size=1, host='localhost', port=free_port(), backend='nccl')
# build model and input
model = MyNet().cuda()
data = torch.rand(4, 4).cuda()
# trace the module and replace codegen
tracer = ColoTracer(trace_act_ckpt=True)
graph = tracer.trace(model)
# replace a bound method of an object
graph._python_code = python_code_with_activation_checkpoint.__get__(graph)
# annotate the activation offload part
# also annotate the activation_checkpoint so we could test both types
# of input offload
for node in graph.nodes:
if node.name == "linear0":
setattr(node, "activation_offload", [0, True, False])
if node.name == "linear1":
setattr(node, "activation_offload", [0, True, False])
if node.name == "linear2":
setattr(node, "activation_offload", [1, True, True])
if node.name == "linear4":
setattr(node, "activation_offload", [2, False, True])
if node.name == "linear5":
setattr(node, "activation_checkpoint", [0])
setattr(node, "activation_offload", True)
gm = ColoGraphModule(copy.deepcopy(model), graph)
gm.recompile()
# assert we have all the components
code = graph.python_code("self").src
assert "def pack_hook_input(self, x):" in code and \
"def unpack_hook(self, packed):" in code and \
"def pack_hook_no_input(self, x):" in code and \
"setattr(x, 'offload', True)" in code and \
"setattr(linear3, 'offload', False)" in code and \
"with torch.autograd.graph.saved_tensors_hooks(self.pack_hook_input, self.unpack_hook):" in code and \
"with torch.autograd.graph.save_on_cpu(pin_memory=True):" in code and \
"with torch.autograd.graph.saved_tensors_hooks(self.pack_hook_no_input, self.unpack_hook):" in code and \
"colossalai.utils.activation_checkpoint.checkpoint(self.checkpoint_0, True, linear4, use_reentrant=False)" in code
_test_fwd_and_bwd(model, gm, data)
gpc.destroy()
@pytest.mark.skip(reason="currently torch11 ColoGraphModule is not implemented")
def test_act_ckpt_python_code_torch11():
mp.spawn(_run_offload_codegen_torch11, nprocs=1)
if __name__ == "__main__":
_run_offload_codegen(0)