mirror of https://github.com/hpcaitech/ColossalAI
650 lines
27 KiB
Python
650 lines
27 KiB
Python
import colossalai
|
|
import torch
|
|
import copy
|
|
from typing import List, Callable, Any, Tuple, Dict, Iterable
|
|
|
|
try:
|
|
from torch.fx.node import Node, Argument, map_arg, _type_repr, _get_qualified_name
|
|
from torch.fx.graph import _Namespace, PythonCode, _custom_builtins, _is_from_torch, _format_target, magic_methods, CodeGen, _origin_type_map, inplace_methods, _CustomBuiltin
|
|
CODEGEN_AVAILABLE = True
|
|
except:
|
|
from torch.fx.graph import _Namespace, PythonCode, _custom_builtins, _is_from_torch, _format_target, magic_methods, _origin_type_map, _format_args, _CustomBuiltin
|
|
from torch.fx.node import Node, Argument, map_arg, _type_repr, _get_qualified_name
|
|
CODEGEN_AVAILABLE = False
|
|
|
|
if CODEGEN_AVAILABLE:
|
|
__all__ = ['ChunkCodeGen']
|
|
else:
|
|
__all__ = ['python_code_with_activation_checkpoint']
|
|
|
|
|
|
def _gen_chunk_slice_dim(chunk_dim, chunk_idx_name, shape):
|
|
new_shape = "["
|
|
for idx, i in enumerate(shape):
|
|
if idx == chunk_dim:
|
|
new_shape += "%s:%s + chunk_size" % (chunk_idx_name, chunk_idx_name)
|
|
else:
|
|
new_shape += ":"
|
|
new_shape += ", "
|
|
new_shape = new_shape[:-2] + "]"
|
|
return new_shape
|
|
|
|
|
|
def _get_first_non_single_dim(shape):
|
|
for idx, i in enumerate(shape):
|
|
if i == 1:
|
|
continue
|
|
else:
|
|
return idx
|
|
raise RuntimeError("can not get first non single dim for shape", shape)
|
|
|
|
|
|
def _gen_loop_start(chunk_input_meta, chunk_output, chunk_size=2):
|
|
if len(chunk_input_meta) == 1:
|
|
node = chunk_input_meta[0]
|
|
node_shape = node.meta['tensor_meta'].shape
|
|
chunk_dim = _get_first_non_single_dim(node_shape)
|
|
chunk_slice = _gen_chunk_slice_dim(chunk_dim, "gen_chunk_idx", node_shape)
|
|
out_shape = str(list(chunk_output.meta['tensor_meta'].shape))
|
|
|
|
context = "chunk_result = torch.empty(%s, dtype=%s.dtype, device=%s.device); chunk_size = %d\nfor gen_chunk_idx in range" % (
|
|
out_shape, node.name, node.name, chunk_size)
|
|
context += "(0, %s.shape[%d], chunk_size):\n" % (node.name, chunk_dim)
|
|
context += " chunk_tensor = %s%s\n" % (node.name, chunk_slice)
|
|
else:
|
|
raise NotImplementedError("input with size %d not implemented" % len(chunk_input_meta))
|
|
return context
|
|
|
|
|
|
def _gen_loop_end(chunk_outputs, chunk_inputs, node_list):
|
|
chunk_inputs_name = chunk_inputs[0].name
|
|
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
|
|
chunk_dim = _get_first_non_single_dim(chunk_output_shape)
|
|
chunk_slice = _gen_chunk_slice_dim(chunk_dim, "gen_chunk_idx", chunk_output_shape)
|
|
context = " chunk_result%s = %s\n" % (chunk_slice, chunk_outputs_name)
|
|
|
|
context += chunk_outputs_name + " = chunk_result; chunk_result = None; chunk_size = None"
|
|
|
|
# determine if its the last use for chunk input
|
|
users_name = list(chunk_inputs[0].users.keys())
|
|
if all([_find_idx_by_name(user.name, node_list) <= chunk_outputs_idx for user in users_name]):
|
|
context += "; %s = None" % chunk_inputs_name
|
|
|
|
context += "\n"
|
|
return context
|
|
|
|
|
|
def _find_input_and_output_nodes(nodes: List[Node]):
|
|
"""
|
|
Find the input and output node names which are not found in the given list of nodes.
|
|
"""
|
|
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():
|
|
node_repr = repr(input_node)
|
|
if input_node not in nodes and node_repr not in input_nodes:
|
|
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():
|
|
node_repr = repr(node)
|
|
if output_node not in nodes and node_repr not in output_nodes:
|
|
output_nodes.append(output_node)
|
|
|
|
return input_nodes, output_nodes
|
|
|
|
|
|
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 _find_offload_regions(nodes: List[Node]):
|
|
"""This function is to find the offload regions
|
|
In pofo algorithm, during annotation, we will annotate the offload region with the
|
|
list in the form of [idx, offload_input, offload_bar]. idx indicates the offload
|
|
region's index, offload_input is a bool type indicates whether we need to offload
|
|
the input, offload_bar is a bool type indicates whether we need to offload all the
|
|
intermediate x_bars of this region.
|
|
"""
|
|
offload_regions = []
|
|
offload_labels = []
|
|
start = -1
|
|
end = -1
|
|
current_region = None
|
|
|
|
for idx, node in enumerate(nodes):
|
|
if hasattr(node, 'activation_offload') and isinstance(getattr(node, 'activation_offload', None), Iterable):
|
|
act_offload_label = node.activation_offload
|
|
|
|
if current_region == None:
|
|
current_region = act_offload_label
|
|
start = idx
|
|
offload_labels.append(act_offload_label)
|
|
|
|
if act_offload_label != current_region:
|
|
assert start != -1
|
|
offload_regions.append((start, idx - 1))
|
|
offload_labels.append(act_offload_label)
|
|
current_region = act_offload_label
|
|
start = idx
|
|
end = -1
|
|
|
|
else:
|
|
if current_region is not None:
|
|
end = idx - 1
|
|
assert start != -1 and end != -1
|
|
offload_regions.append((start, end))
|
|
start = end = -1
|
|
current_region = None
|
|
|
|
else:
|
|
pass
|
|
|
|
return offload_regions, offload_labels
|
|
|
|
|
|
def _gen_ckpt_fn_def(label, free_vars: List[str]) -> str:
|
|
"""
|
|
Generate the checkpoint function definition
|
|
"""
|
|
return f"def checkpoint_{label}({', '.join(['self'] + free_vars)}):"
|
|
|
|
|
|
def _gen_ckpt_output(output_vars: List[str]) -> str:
|
|
"""
|
|
Generate the return statement for checkpoint region
|
|
"""
|
|
return f"return {', '.join(output_vars)}"
|
|
|
|
|
|
def _gen_ckpt_usage(label, activation_offload, input_vars, output_vars, use_reentrant=True):
|
|
"""
|
|
Generate the checkpoint function call code text
|
|
"""
|
|
outputs = ', '.join(output_vars)
|
|
inputs = ', '.join(input_vars)
|
|
return f'{outputs} = colossalai.utils.activation_checkpoint.checkpoint(self.checkpoint_{label}, {activation_offload}, {inputs}, use_reentrant={use_reentrant})'
|
|
|
|
|
|
def _end_of_ckpt(node: Node, check_idx: int) -> bool:
|
|
"""Check if the node could end the ckpt region
|
|
|
|
Args:
|
|
node (Node): torch.fx.Node
|
|
check_idx (int): the index of checkpoint level for
|
|
nested checkpoint
|
|
|
|
Returns:
|
|
bool
|
|
"""
|
|
if hasattr(node, "activation_checkpoint"):
|
|
if isinstance(node.activation_checkpoint, list):
|
|
return node.activation_checkpoint[check_idx] == None
|
|
else:
|
|
return False
|
|
else:
|
|
return True
|
|
|
|
|
|
def _find_nested_ckpt_regions(nodes, check_idx=0):
|
|
"""
|
|
Find the nested checkpoint regions given a list of consecutive nodes. The outputs
|
|
will be list of tuples, each tuple is in the form of (start_index, end_index).
|
|
"""
|
|
ckpt_regions = []
|
|
start = -1
|
|
end = -1
|
|
current_region = None
|
|
|
|
for idx, node in enumerate(nodes):
|
|
if hasattr(node, 'activation_checkpoint'):
|
|
if isinstance(getattr(node, 'activation_checkpoint'), int):
|
|
act_ckpt_label = node.activation_checkpoint
|
|
else:
|
|
act_ckpt_label = node.activation_checkpoint[check_idx]
|
|
|
|
# this activation checkpoint label is not set yet
|
|
# meaning this is the first node of the activation ckpt region
|
|
if current_region is None:
|
|
current_region = act_ckpt_label
|
|
start = idx
|
|
|
|
# if activation checkpoint has changed
|
|
# we restart the tracking
|
|
# e.g. node ckpt states = [ckpt1, ckpt2, ckpt2, ckpt2]
|
|
if act_ckpt_label != current_region:
|
|
assert start != -1
|
|
ckpt_regions.append((start, idx - 1))
|
|
current_region = act_ckpt_label
|
|
start = idx
|
|
end = -1
|
|
elif current_region is not None and _end_of_ckpt(node, check_idx):
|
|
# used to check the case below
|
|
# node ckpt states = [ckpt, ckpt, non-ckpt]
|
|
end = idx - 1
|
|
assert start != -1 and end != -1
|
|
ckpt_regions.append((start, end))
|
|
start = end = -1
|
|
current_region = None
|
|
else:
|
|
pass
|
|
|
|
if current_region is not None:
|
|
end = len(nodes) - 1
|
|
ckpt_regions.append((start, end))
|
|
return ckpt_regions
|
|
|
|
|
|
def emit_ckpt_func(body,
|
|
ckpt_func,
|
|
node_list: List[Node],
|
|
emit_node_func,
|
|
delete_unused_value_func,
|
|
level=0,
|
|
in_ckpt=False):
|
|
"""Emit ckpt fuction in nested way
|
|
|
|
Args:
|
|
body: forward code, in recursive calls, this part will be checkpoint
|
|
functions code
|
|
ckpt_func: checkpoint functions code, in recursive calls, this part
|
|
will be a buffer
|
|
node_list (List[Node]): list of torch.fx.Node
|
|
emit_node_func: function to emit a node
|
|
delete_unused_value_func: function to delete unused value
|
|
level (int, optional): checkpoint level. Defaults to 0.
|
|
in_ckpt (bool, optional): indicates wether the func is in recursive
|
|
call. Defaults to False.
|
|
"""
|
|
inputs, outputs = _find_input_and_output_nodes(node_list)
|
|
|
|
# if the current checkpoint function use int as label, using old generation method
|
|
if isinstance(node_list[0].activation_checkpoint, int):
|
|
label = node_list[0].activation_checkpoint
|
|
ckpt_fn_def = _gen_ckpt_fn_def(label, inputs)
|
|
ckpt_func.append(f'{ckpt_fn_def}\n')
|
|
for node in node_list:
|
|
emit_node_func(node, ckpt_func)
|
|
ckpt_func[-1] = ' ' + ckpt_func[-1]
|
|
delete_unused_value_func(node, ckpt_func)
|
|
|
|
ckpt_func.append(' ' + _gen_ckpt_output(outputs) + '\n\n')
|
|
activation_offload = getattr(node_list[0], "activation_offload", False)
|
|
usage = _gen_ckpt_usage(label, activation_offload, inputs, outputs, False)
|
|
usage += "\n"
|
|
body.append(usage)
|
|
|
|
# use nested ckpt function codegen
|
|
else:
|
|
# label given by each layer, e.g. if you are currently at level [0, 1, 1]
|
|
# the label will be '0_1_1'
|
|
label = "_".join([str(idx) for idx in node_list[0].activation_checkpoint[:level + 1]])
|
|
ckpt_fn_def = _gen_ckpt_fn_def(label, inputs)
|
|
ckpt_func.append(f'{ckpt_fn_def}\n')
|
|
|
|
# if there is more level to fetch
|
|
if level + 1 < len(node_list[0].activation_checkpoint):
|
|
ckpt_regions = _find_nested_ckpt_regions(node_list, level + 1)
|
|
start_idx = [item[0] for item in ckpt_regions]
|
|
end_idx = [item[1] for item in ckpt_regions]
|
|
|
|
# use ckpt_func_buffer to store nested checkpoint functions
|
|
ckpt_func_buffer = []
|
|
node_idx = 0
|
|
while 1:
|
|
if node_idx >= len(node_list):
|
|
break
|
|
|
|
if node_idx in start_idx:
|
|
ckpt_node_list = node_list[node_idx:end_idx[start_idx.index(node_idx)] + 1]
|
|
emit_ckpt_func(ckpt_func, ckpt_func_buffer, ckpt_node_list, emit_node_func,
|
|
delete_unused_value_func, level + 1, True)
|
|
node_idx += len(ckpt_node_list)
|
|
|
|
else:
|
|
node = node_list[node_idx]
|
|
emit_node_func(node, ckpt_func)
|
|
ckpt_func[-1] = ' ' + ckpt_func[-1]
|
|
delete_unused_value_func(node, ckpt_func)
|
|
node_idx += 1
|
|
|
|
ckpt_func.append(' ' + _gen_ckpt_output(outputs) + '\n\n')
|
|
ckpt_func += ckpt_func_buffer
|
|
activation_offload = getattr(node_list[0], "activation_offload", False)
|
|
usage = _gen_ckpt_usage(label, activation_offload, inputs, outputs, False) + '\n'
|
|
if in_ckpt:
|
|
usage = ' ' + usage
|
|
body.append(usage)
|
|
|
|
# last level
|
|
else:
|
|
for node in node_list:
|
|
emit_node_func(node, ckpt_func)
|
|
ckpt_func[-1] = ' ' + ckpt_func[-1]
|
|
delete_unused_value_func(node, ckpt_func)
|
|
|
|
ckpt_func.append(' ' + _gen_ckpt_output(outputs) + '\n\n')
|
|
activation_offload = getattr(node_list[0], "activation_offload", False)
|
|
usage = _gen_ckpt_usage(label, activation_offload, inputs, outputs, False) + '\n'
|
|
if in_ckpt:
|
|
usage = ' ' + usage
|
|
body.append(usage)
|
|
|
|
|
|
def emit_code_with_chunk(body, ckpt_func, nodes, emit_node_func, delete_unused_value_func, meta_nodes):
|
|
"""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.
|
|
|
|
Args:
|
|
body: forward code
|
|
ckpt_func: checkpoint functions code
|
|
nodes: graph.nodes
|
|
emit_node_func: function to emit node
|
|
delete_unused_value_func: function to remove the unused value
|
|
"""
|
|
|
|
# find the offload regions
|
|
chunk_regions = [(2, 5)]
|
|
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(chunk_regions):
|
|
offload_node_list = node_list[start:end + 1]
|
|
inputs, outputs = _find_input_and_output_nodes(offload_node_list)
|
|
chunk_inputs.append(inputs)
|
|
chunk_outputs.append(outputs)
|
|
chunk_inputs_idx = [[_find_idx_by_name(j.name, node_list) for j in i] for i in chunk_inputs]
|
|
chunk_outputs_idx = [[_find_idx_by_name(j.name, node_list) for j in i] for i in chunk_outputs]
|
|
chunk_inputs_names = []
|
|
for i in chunk_inputs:
|
|
for j in i:
|
|
chunk_inputs_names.append(j.name)
|
|
|
|
# this flag is to prevent repeated insert of save tensors
|
|
# hooks definition in ckpt_func
|
|
node_idx = 0
|
|
region_idx = 0
|
|
while node_idx < len(node_list):
|
|
node = node_list[node_idx]
|
|
|
|
if node_idx in chunk_starts:
|
|
within_chunk_region = True
|
|
|
|
# add for loop
|
|
chunk_input_meta = [meta_nodes[i] for i in chunk_inputs_idx[region_idx]]
|
|
body.append(_gen_loop_start(chunk_input_meta, node_list[chunk_ends[region_idx]]))
|
|
|
|
if within_chunk_region:
|
|
emit_node_func(node, body)
|
|
# replace input var with chunk var
|
|
if node_idx in chunk_starts:
|
|
body[-1] = body[-1].replace("("+ chunk_inputs[region_idx][0].name +")", '(chunk_tensor)')
|
|
body[-1] = ' ' + body[-1]
|
|
delete_unused_value_func(node, body, chunk_inputs_names)
|
|
|
|
else:
|
|
emit_node_func(node, body)
|
|
if node_idx not in chunk_inputs:
|
|
delete_unused_value_func(node, body, chunk_inputs_names)
|
|
|
|
if node_idx in chunk_ends:
|
|
body.append(_gen_loop_end(node, chunk_inputs[region_idx], node_list))
|
|
within_chunk_region = False
|
|
region_idx += 1
|
|
|
|
node_idx += 1
|
|
|
|
|
|
if CODEGEN_AVAILABLE:
|
|
|
|
class ChunkCodeGen(CodeGen):
|
|
def __init__(self, meta_graph):
|
|
super().__init__()
|
|
self.meta_node = list(meta_graph.graph.nodes)
|
|
|
|
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] = {}
|
|
|
|
# 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)
|
|
|
|
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 len(args) == 0:
|
|
# Bare type, such as `typing.Tuple` with no subscript
|
|
# This code-path used in Python < 3.9
|
|
return origin_typename
|
|
|
|
return f'{origin_typename}[{",".join(args)}]'
|
|
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))
|
|
|
|
# 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')
|
|
|
|
# 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
|
|
|
|
# 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'{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)}"
|
|
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, ckpt_func, nodes, emit_node, delete_unused_values, self.meta_node)
|
|
|
|
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 = ''
|
|
|
|
if self._body_transformer:
|
|
body = self._body_transformer(body)
|
|
|
|
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
|
|
|
|
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_)
|