ColossalAI/colossalai/autochunk/autochunk_codegen.py

from typing import Any, Dict, Iterable, List, Tuple

import torch
from torch.fx.graph import (
    CodeGen,
    PythonCode,
    _custom_builtins,
    _CustomBuiltin,
    _format_target,
    _is_from_torch,
    _Namespace,
    _origin_type_map,
    inplace_methods,
    magic_methods,
)
from torch.fx.node import Argument, Node, _get_qualified_name, _type_repr, map_arg

import colossalai
from .search_chunk import SearchChunk
from .utils import delete_free_var_from_last_use, find_idx_by_name, get_node_shape


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 _gen_loop_start(chunk_input, chunk_output, chunk_ouput_dim, chunk_size=2):
    input_node = chunk_input[0]
    out_shape = get_node_shape(chunk_output)
    out_str = str(list(out_shape))
    context = (
        "chunk_result = torch.empty(%s, dtype=%s.dtype, device=%s.device); chunk_size = %d\nfor chunk_idx in range"
        % (out_str, input_node.name, input_node.name, chunk_size)
    )
    context += "(0, %d, chunk_size):\n" % (out_shape[chunk_ouput_dim])
    return context


def _gen_loop_end(
    chunk_inputs, chunk_non_compute_inputs, chunk_outputs, chunk_outputs_dim, node_list
):
    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_slice = _gen_chunk_slice_dim(
        chunk_outputs_dim, "chunk_idx", chunk_output_shape
    )
    context = "    chunk_result%s = %s;  %s = None\n" % (
        chunk_slice,
        chunk_outputs_name,
        chunk_outputs_name,
    )
    context += (
        chunk_outputs_name + " = chunk_result;  chunk_result = None;  chunk_size = None"
    )

    # determine if its the last use for chunk input
    for chunk_input in chunk_inputs + chunk_non_compute_inputs:
        if all(
            [
                find_idx_by_name(user.name, node_list) <= chunk_outputs_idx
                for user in chunk_input.users.keys()
            ]
        ):
            context += ";  %s = None" % chunk_input.name

    context += "\n"
    return context


def _replace_name(context, name_from, name_to):
    patterns = [(" ", " "), (" ", "."), (" ", ","), ("(", ")"), ("(", ","), (" ", ")")]
    for p in patterns:
        source = p[0] + name_from + p[1]
        target = p[0] + name_to + p[1]
        if source in context:
            context = context.replace(source, target)
    return context


def _replace_reshape_size(context, node_name, reshape_size_dict):
    if node_name not in reshape_size_dict:
        return context
    for size_name, size_value in reshape_size_dict[node_name].items():
        context = context.replace(size_name, size_value)
    return context


def _replace_ones_like(search_chunk: SearchChunk, chunk_infos, region_idx, node_idx, node, body):
    if "ones_like" in node.name:
        meta_node = search_chunk.trace_indice.node_list[node_idx]
        chunk_dim = chunk_infos[region_idx]["node_chunk_dim"][meta_node]["chunk_dim"]
        if get_node_shape(meta_node)[chunk_dim] != 1:
            source_node = meta_node.args[0].args[0]
            if (
                source_node not in chunk_infos[region_idx]["node_chunk_dim"]
                or chunk_infos[region_idx]["node_chunk_dim"][source_node]["chunk_dim"]
                is None
            ):
                chunk_slice = _gen_chunk_slice_dim(
                    chunk_dim, "chunk_idx", get_node_shape(node)
                )
                body[-1] = _replace_name(
                    body[-1], node.args[0].name, node.args[0].name + chunk_slice
                )
    return body


def _replace_input_var(chunk_inputs, region_idx, chunk_inputs_dim, node_idx, body):
    for input_node_idx, input_node in enumerate(chunk_inputs[region_idx]):
        for idx, dim in chunk_inputs_dim[region_idx][input_node_idx].items():
            if idx == node_idx:
                chunk_slice = _gen_chunk_slice_dim(
                    dim[0], "chunk_idx", get_node_shape(input_node)
                )
                body[-1] = _replace_name(
                    body[-1], input_node.name, input_node.name + chunk_slice
                )
    return body


def emit_code_with_chunk(
    body: List[str],
    nodes: Iterable[Node],
    emit_node_func,
    delete_unused_value_func,
    search_chunk: SearchChunk,
    chunk_infos: List,
):
    """
    Emit code with chunk according to chunk_infos.

    It will generate a for loop in chunk regions, and 
    replace inputs and outputs of regions with chunked variables.

    Args:
        body: forward code
        nodes: graph.nodes
        emit_node_func: function to emit node
        delete_unused_value_func: function to remove the unused value
        search_chunk: the class to search all chunks
        chunk_infos: store all information about all chunks.
    """
    node_list = list(nodes)

    # chunk region
    chunk_starts = [i["region"][0] for i in chunk_infos]
    chunk_ends = [i["region"][1] for i in chunk_infos]

    # chunk inputs
    chunk_inputs = [i["inputs"] for i in chunk_infos]  # input with chunk
    chunk_inputs_non_chunk = [
        i["inputs_non_chunk"] for i in chunk_infos
    ]  # input without chunk
    chunk_inputs_dim = [i["inputs_dim"] for i in chunk_infos]  # input chunk dim
    chunk_inputs_names = [j.name for i in chunk_inputs for j in i] + [
        j.name for i in chunk_inputs_non_chunk for j in i
    ]

    # chunk outputs
    chunk_outputs = [i["outputs"][0] for i in chunk_infos]
    chunk_outputs_dim = [i["outputs_dim"] for i in chunk_infos]

    node_list = search_chunk.reorder_graph.reorder_node_list(node_list)
    node_idx = 0
    region_idx = 0
    within_chunk_region = False

    while node_idx < len(node_list):
        node = node_list[node_idx]

        # if is chunk start, generate for loop start
        if node_idx in chunk_starts:
            within_chunk_region = True
            region_idx = chunk_starts.index(node_idx)
            body.append(
                _gen_loop_start(
                    chunk_inputs[region_idx],
                    chunk_outputs[region_idx],
                    chunk_outputs_dim[region_idx],
                    chunk_infos[region_idx]["chunk_size"],
                )
            )

        if within_chunk_region:
            emit_node_func(node, body)
            # replace input var with chunk var
            body = _replace_input_var(
                chunk_inputs, region_idx, chunk_inputs_dim, node_idx, body
            )
            # ones like
            body = _replace_ones_like(
                search_chunk, chunk_infos, region_idx, node_idx, node, body
            )
            # reassgin reshape size
            body[-1] = _replace_reshape_size(
                body[-1], node.name, chunk_infos[region_idx]["reshape_size"]
            )
            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)

        # generate chunk region end
        if node_idx in chunk_ends:
            body.append(
                _gen_loop_end(
                    chunk_inputs[region_idx],
                    chunk_inputs_non_chunk[region_idx],
                    chunk_outputs[region_idx],
                    chunk_outputs_dim[region_idx],
                    node_list,
                )
            )
            within_chunk_region = False

        node_idx += 1


class AutoChunkCodeGen(CodeGen):
    def __init__(self, meta_graph, max_memory=None, print_mem=False):
        super().__init__()
        self.meta_graph = meta_graph
        self.max_memory = max_memory
        self.meta_node = list(meta_graph.graph.nodes)
        # find the chunk regions
        self.search_chunk = SearchChunk(meta_graph, max_memory, print_mem)
        self.chunk_infos = self.search_chunk.search_region()

    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))

        delete_free_var_from_last_use(user_to_last_uses)

        # NOTE: we add a variable to distinguish body and ckpt_func
        def delete_unused_values(user: Node, body, to_keep=[]):
            """
            Delete values after their last use. This ensures that values that are
            not used in the remainder of the code are freed and the memory usage
            of the code is optimal.
            """
            if user.op == "placeholder":
                return
            if user.op == "output":
                body.append("\n")
                return
            nodes_to_delete = user_to_last_uses.get(user, [])
            nodes_to_delete = [i for i in nodes_to_delete if i.name not in to_keep]
            if len(nodes_to_delete):
                to_delete_str = " = ".join(
                    [repr(n) for n in nodes_to_delete] + ["None"]
                )
                body.append(f";  {to_delete_str}\n")
            else:
                body.append("\n")

        # 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,
            nodes,
            emit_node,
            delete_unused_values,
            self.search_chunk,
            self.chunk_infos,
        )

        if len(body) == 0:
            # If the Graph has no non-placeholder nodes, no lines for the body
            # have been emitted. To continue to have valid Python code, emit a
            # single pass statement
            body.append("pass\n")

        if len(wrapped_fns) > 0:
            wrap_name = add_global("wrap", torch.fx.wrap)
            wrap_stmts = "\n".join([f'{wrap_name}("{name}")' for name in wrapped_fns])
        else:
            wrap_stmts = ""

        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_)