activation_checkpoint_codegen.py

import colossalai
import torch
from typing import List, Callable, Any, Tuple, Dict

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__ = ['ActivationCheckpointCodeGen']
else:
    __all__ = ['python_code_with_activation_checkpoint']


def _gen_saved_tensors_hooks():
    """
    Generate saved tensors hooks
    """

    pack_hook = """def pack_hook(self, x):
    if getattr(x, "offload", None):
        return (x.device, x.cpu())
    else:
        return x
"""

    unpack_hook = """def unpack_hook(self, packed):
    if isinstance(packed, tuple):
        device, tensor = packed
        return tensor.to(device)
    else:
        return packed
"""

    return pack_hook, unpack_hook


def _gen_save_tensors_hooks_context():
    """
    Generate save tensors hooks context
    """

    context = "with torch.autograd.graph.saved_tensors_hooks(self.pack_hook, self.unpack_hook):\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(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(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(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_nested_activation_checkpoint(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.

    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
    """
    ckpt_regions = _find_nested_ckpt_regions(nodes, 0)
    start_idx = [item[0] for item in ckpt_regions]
    end_idx = [item[1] for item in ckpt_regions]

    node_list = list(nodes)

    # this flag is to prevent repeated insert of save tensors
    # hooks definition in ckpt_func
    is_hook_inserted = False
    node_idx = 0
    while 1:
        # break if we finish the processing all the nodes
        if node_idx >= len(node_list):
            break

        # process ckpt_regions
        if node_idx in start_idx:
            ckpt_node_list = node_list[node_idx:end_idx[start_idx.index(node_idx)] + 1]
            emit_ckpt_func(body, ckpt_func, ckpt_node_list, emit_node_func, delete_unused_value_func)
            node_idx += len(ckpt_node_list)

        # process node in forward function
        else:
            node = node_list[node_idx]

            # if a node is outside of checkpoint region and want to offload
            # it's input activation, we will use torch.saved_tensors_hooks
            # to complete the offload process.
            if getattr(node, "activation_offload", False):
                if not is_hook_inserted:
                    pack_hook, unpack_hook = _gen_saved_tensors_hooks()
                    ckpt_func.insert(0, "\n".join([pack_hook, unpack_hook]) + "\n")

                for par in node.all_input_nodes:
                    # annotate the input tensor for pack hook
                    body.append(f"setattr({repr(par)}, 'offload', True)\n")

                body.append(_gen_save_tensors_hooks_context())
                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)
            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

    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)

    # 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

        # 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)
        else:
            # if a node is outside of checkpoint region wants to offload
            # it's input activation, we will use torch.saved_tensors_hooks
            # to complete the offload process.
            if getattr(node, "activation_offload", False):
                if not is_hook_inserted:
                    pack_hook, unpack_hook = _gen_saved_tensors_hooks()
                    ckpt_func.insert(0, "\n".join([pack_hook, unpack_hook]) + "\n")

                for par in node.all_input_nodes:
                    # annotate the input tensor for pack hook
                    body.append(f"setattr({repr(par)}, 'offload', True)\n")

                body.append(_gen_save_tensors_hooks_context())
                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 CODEGEN_AVAILABLE:

    class ActivationCheckpointCodeGen(CodeGen):

        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):
                """
                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

                    # 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
            if any(isinstance(getattr(node, "activation_checkpoint", None), list) 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)

            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}"""
            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), list) 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_)