import torch from typing import List, Callable, Any, Tuple, Dict 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 __all__ = ['ActivationCheckpointCodeGen'] class ActivationCheckpointCodeGen(CodeGen): def find_input_and_output_nodes(self, 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(node_repr) # 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(node_repr) return input_nodes, output_nodes def find_ckpt_regions(self, nodes: List[Node]): """ Find the 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_nodes = [] ckpt_regions = [] start = -1 end = -1 current_region = None for idx, node in enumerate(nodes): if hasattr(node, 'activation_checkpoint'): act_ckpt_label = node.activation_checkpoint # 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 not hasattr(node, 'activation_checkpoint'): # 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 return ckpt_regions def gen_ckpt_fn_def(self, label, free_vars: List[str]) -> str: """ Generate the checkpoint function definition """ return f"def checkpoint_{label}({', '.join(free_vars)}):" def gen_ckpt_output(self, output_vars: List[str]) -> str: """ Generate the return statement for checkpoint region """ return f"return {', '.join(output_vars)}" def gen_ckpt_usage(self, label, input_vars, output_vars): """ Generate the checkpoint function call code text """ outputs = ', '.join(output_vars) inputs = ', '.join(input_vars) return f'{outputs} = torch.utils.checkpoint.checkpoint(checkpoint_{label}, {inputs})' 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 # 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)) def delete_unused_values(user: Node): """ 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') def emit_node(node: Node): 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 # ######################################### # find the activation checkpoint regions ckpt_regions = self.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 node_list = list(nodes) # 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 = self.find_input_and_output_nodes(ckpt_node_list) input_vars.append(inputs) output_vars.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 = self.gen_ckpt_fn_def(label, input_vars[label]) body.append(f'{ckpt_fn_def}\n') within_ckpt_region = True # NOTE: emit_node does not emit a string with newline. It depends # on delete_unused_values to append one emit_node(node) # add indentation to the emmited node if within_ckpt_region: body[-1] = ' ' + body[-1] # delete unused values delete_unused_values(node) 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 = self.gen_ckpt_output(output_vars[label]) return_statement = f' {return_statement}\n' body.append(return_statement) # generate checkpoint function call in a new line usage = self.gen_ckpt_usage(label, input_vars[label], output_vars[label]) usage += '\n' body.append(usage) within_ckpt_region = False ####################################################### # Code Change For Activation Checkpointing Stops Here # ####################################################### 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) prologue = self.gen_fn_def(free_vars, maybe_return_annotation[0]) code = ''.join(body) code = '\n'.join(' ' + line for line in code.split('\n')) fn_code = f""" {wrap_stmts} {prologue} {code}""" return PythonCode(fn_code, globals_)