pytorch/torch/_export/utils.py

# mypy: allow-untyped-defs
import ast
import dataclasses
import inspect
import math
import operator
import re
from inspect import Parameter
from typing import Any, Dict, Iterable, List, Optional, Tuple, Type

import torch
from torch._subclasses.fake_tensor import FakeTensor
from torch.export import ExportedProgram
from torch.export.exported_program import (
    _name_hoo_subgraph_placeholders,
    _rename_without_collisions,
)
from torch.export.graph_signature import InputKind, OutputKind
from torch.utils._pytree import (
    _register_pytree_node,
    Context,
    FlattenFunc,
    FromDumpableContextFn,
    GetAttrKey,
    KeyPath,
    keystr,
    MappingKey,
    SequenceKey,
    ToDumpableContextFn,
    tree_flatten_with_path,
    UnflattenFunc,
)


placeholder_prefixes = {
    InputKind.USER_INPUT: "",
    InputKind.PARAMETER: "p_",
    InputKind.BUFFER: "b_",
    InputKind.CONSTANT_TENSOR: "c_",
    InputKind.CUSTOM_OBJ: "obj_",
    InputKind.TOKEN: "token",
}


def _check_input_constraints_for_graph(
    input_placeholders: List[torch.fx.Node], flat_args_with_path, range_constraints
):
    def get_keystr(key_path: KeyPath) -> str:
        """For a given index into the flat_args, return a human readable string
        describing how to access it, e.g. "*args["foo"][0].bar"
        """
        # Prefix the keypath with "*args" or "**kwargs" to make it clearer where
        # the arguments come from. Ultimately we ought to serialize the
        # original arg names for the best error message here.
        args_kwargs_key_path = key_path[0]
        assert isinstance(args_kwargs_key_path, SequenceKey)
        if args_kwargs_key_path.idx == 0:
            return f"*args{keystr(key_path[1:])}"
        else:
            kwarg_key = key_path[1]
            assert isinstance(kwarg_key, MappingKey)
            name = str(kwarg_key)[1:-1]  # get rid of the enclosed []
            return f"{name}{keystr(key_path[2:])}"

    import sympy

    from torch._export.passes.add_runtime_assertions_for_constraints_pass import (
        _convert_range_to_int,
    )
    from torch.utils._sympy.solve import try_solve

    if len(flat_args_with_path) != len(input_placeholders):
        raise RuntimeError(
            "Unexpected number of inputs "
            f"(expected {len(input_placeholders)}, got {len(flat_args_with_path)})"
        )
    # NOTE: export already guarantees that the same symbol is used in metadata
    # for all InputDims related by equality constraints, so we can just unify
    # symbols with given input dimension values to check equality constraints.
    unification_map: Dict[sympy.Symbol, Any] = {}
    for (key_path, arg), node in zip(flat_args_with_path, input_placeholders):
        node_val = node.meta.get("val")
        if isinstance(node_val, FakeTensor):
            if not isinstance(arg, torch.Tensor):
                raise RuntimeError(
                    f"Expected input at {get_keystr(key_path)} to be a tensor, but got {type(arg)}",
                )

            if len(node_val.shape) != len(arg.shape):
                raise RuntimeError(
                    f"Unexpected number of dimensions in input at {get_keystr(key_path)}.shape "
                    f"(expected {node_val.shape}, got {arg.shape})"
                )

            for j, (arg_dim, node_dim) in enumerate(zip(arg.shape, node_val.shape)):
                # TODO(avik): Assert the following property in the IR verifier:
                # node_dim is either an int or a SymInt containing an int or a unary sympy.Expr
                if (
                    isinstance(node_dim, torch.SymInt)
                    and len(node_dim.node.expr.free_symbols) == 1
                ):
                    symbol = next(iter(node_dim.node.expr.free_symbols))
                    if symbol in unification_map:
                        existing_dim = node_dim.node.expr.subs(unification_map)
                        if arg_dim != existing_dim:
                            raise RuntimeError(
                                f"Expected input at {get_keystr(key_path)}.shape[{j}] to be equal to "
                                f"{existing_dim}, but got {arg_dim}",
                            )
                    else:
                        if (
                            isinstance(arg_dim, torch.SymInt)
                            and not arg_dim.node.expr.is_number
                        ):
                            # This can happen when, say, arg is a fake tensor.
                            # We do not run checks on symbolic shapes of fake inputs as
                            # such checks can affect the shape env.
                            pass
                        else:
                            if isinstance(node_dim.node.expr, sympy.Symbol):
                                # Short cut for try_solve below. Also useful in cases where
                                # sympy.Eq(node_dim.node.expr, arg_dim) would evaluate to False
                                # purely because symbol is constrained to be size-like,
                                # e.g., when node_dim.node.expr = symbol and arg_dim = 0.
                                unification_map[symbol] = int(arg_dim)
                            else:
                                solution = try_solve(
                                    sympy.Eq(node_dim.node.expr, arg_dim), symbol
                                )
                                if solution is None:
                                    raise RuntimeError(  # noqa: B904
                                        f"Expected input {node.name}.shape[{j}] = {arg_dim} to be "
                                        f"of the form {node_dim.node.expr}, where {symbol} is an integer"
                                    )
                                else:
                                    unification_map[symbol] = int(solution[1])

                    if node_dim.node.expr in range_constraints:
                        min_val, max_val = _convert_range_to_int(
                            range_constraints[node_dim.node.expr]
                        )
                        # NOTE: we allow dimensions to be 0/1 at runtime
                        if min_val > 2:
                            if arg_dim < min_val:
                                raise RuntimeError(
                                    f"Expected input at {get_keystr(key_path)}.shape[{j}] to be >= "
                                    f"{min_val}, but got {arg_dim}",
                                )
                        if max_val < math.inf:
                            if arg_dim > max_val:
                                raise RuntimeError(
                                    f"Expected input at {get_keystr(key_path)}.shape[{j}] to be <= "
                                    f"{max_val}, but got {arg_dim}",
                                )
                else:
                    if arg_dim != node_dim:
                        if (
                            isinstance(node_dim, torch.SymInt)
                            and not node_dim.node.expr.is_number
                        ):
                            # this means we deferred a guard from export analysis to runtime, let this pass
                            # we'll add a runtime assert checking equality to this replacement expression
                            continue
                        raise RuntimeError(
                            f"Expected input at {get_keystr(key_path)}.shape[{j}] to be equal to "
                            f"{node_dim}, but got {arg_dim}",
                        )
        elif isinstance(node_val, (int, float, str)):
            if type(arg) != type(node_val) or arg != node_val:
                raise RuntimeError(
                    f"Expected input at {get_keystr(key_path)} to be equal to {node_val}, but got {arg}",
                )


def register_dataclass_as_pytree_node(
    cls: Type[Any],
    flatten_fn: Optional[FlattenFunc] = None,
    unflatten_fn: Optional[UnflattenFunc] = None,
    *,
    serialized_type_name: Optional[str] = None,
    to_dumpable_context: Optional[ToDumpableContextFn] = None,
    from_dumpable_context: Optional[FromDumpableContextFn] = None,
    return_none_fields: bool = False,
) -> None:
    assert dataclasses.is_dataclass(
        cls
    ), f"Only dataclasses can be registered with this function: {cls}"

    def default_flatten_fn(obj: Any) -> Tuple[List[Any], Context]:
        flattened = []
        flat_names = []
        none_names = []
        for f in dataclasses.fields(obj):
            name, val = f.name, getattr(obj, f.name)
            if val is not None or return_none_fields:
                flattened.append(val)
                flat_names.append(name)
            else:
                none_names.append(name)
        return flattened, [flat_names, none_names]

    def default_unflatten_fn(values: Iterable[Any], context: Context) -> Any:
        flat_names, none_names = context
        return cls(**dict(zip(flat_names, values)), **dict.fromkeys(none_names))

    def default_flatten_fn_with_keys(obj: Any) -> Tuple[List[Any], Context]:
        flattened, (flat_names, none_names) = flatten_fn(obj)  # type: ignore[misc]
        return [(MappingKey(k), v) for k, v in zip(flat_names, flattened)], flat_names

    flatten_fn = flatten_fn if flatten_fn is not None else default_flatten_fn
    unflatten_fn = unflatten_fn if unflatten_fn is not None else default_unflatten_fn

    if (to_dumpable_context is None) ^ (from_dumpable_context is None):
        raise ValueError(
            f"Both to_dumpable_context and from_dumpable_context for {cls} must "
            "be None or registered."
        )

    _register_pytree_node(
        cls,
        flatten_fn,
        unflatten_fn,
        serialized_type_name=serialized_type_name,
        flatten_with_keys_fn=default_flatten_fn_with_keys,
        to_dumpable_context=to_dumpable_context,
        from_dumpable_context=from_dumpable_context,
    )


def is_param(program: ExportedProgram, node: torch.fx.Node) -> bool:
    """
    Checks if the given node is a parameter within the exported program
    """

    return node.name in program.graph_signature.inputs_to_parameters


def get_param(
    program: ExportedProgram,
    node: torch.fx.Node,
) -> Optional[torch.nn.Parameter]:
    """
    Returns the parameter associated with the given node in the exported program.
    Returns None if the node is not a parameter within the exported program
    """

    if is_param(program, node):
        parameter_name = program.graph_signature.inputs_to_parameters[node.name]
        return program.state_dict[parameter_name]

    return None


def is_buffer(program: ExportedProgram, node: torch.fx.Node) -> bool:
    """
    Checks if the given node is a buffer within the exported program
    """

    return node.name in program.graph_signature.inputs_to_buffers


def get_buffer(
    program: ExportedProgram,
    node: torch.fx.Node,
) -> Optional[torch.Tensor]:
    """
    Returns the buffer associated with the given node in the exported program.
    Returns None if the node is not a buffer within the exported program
    """

    if is_buffer(program, node):
        buffer_name = program.graph_signature.inputs_to_buffers[node.name]
        if buffer_name in program.graph_signature.non_persistent_buffers:
            return program.constants[buffer_name]
        else:
            return program.state_dict[buffer_name]

    return None


def is_lifted_tensor_constant(
    program: ExportedProgram,
    node: torch.fx.Node,
) -> bool:
    """
    Checks if the given node is a lifted tensor constant within the exported program
    """

    return node.name in program.graph_signature.inputs_to_lifted_tensor_constants


def get_lifted_tensor_constant(
    program: ExportedProgram,
    node: torch.fx.Node,
) -> Optional[torch.Tensor]:
    """
    Returns the lifted tensor constant associated with the given node in the exported program.
    Returns None if the node is not a lifted tensor constant within the exported program
    """

    if is_lifted_tensor_constant(program, node):
        lifted_tensor_name = program.graph_signature.inputs_to_lifted_tensor_constants[
            node.name
        ]
        return program.constants[lifted_tensor_name]

    return None


def sequential_split(gm: torch.fx.GraphModule, node_call_back) -> torch.fx.GraphModule:
    """
    sequential_split creates a new graph module that splits the input graph module into multiple submodules
    based on the node_call_back. It doesn't mutate the input graph module. The node_call_back should return
    True if the node is a delimiter.  Delimiter will be the first node in the next submodule.
    """
    from torch.fx.passes.split_module import split_module

    split_map = {}
    split_id = 0
    for node in gm.graph.nodes:
        if node_call_back(node):
            split_id += 1
        split_map[node] = split_id

    new_gm = split_module(
        gm,
        gm,
        lambda node: split_map[node],
        keep_original_order=True,
        keep_original_node_name=True,
    )
    # Keep the codegen from original graph module to preserve e.g. pytree info.
    new_gm.graph._codegen = gm.graph._codegen
    new_gm.recompile()
    return new_gm


def nodes_filter(nodes: List[torch.fx.Node], node_call_back) -> List[torch.fx.Node]:
    """Returns the nodes that match the node_call_back as a list."""
    return [node for node in nodes if node_call_back(node)]


def nodes_first(
    nodes: List[torch.fx.Node], node_call_back=None
) -> Optional[torch.fx.Node]:
    """
    Returns the first node that matches the node_call_back. If no node matches, returns None.
    When node_call_back is None, returns the first node in the node list.
    """
    ret = nodes_filter(nodes, node_call_back if node_call_back else lambda node: True)
    if len(ret) > 0:
        return ret[0]
    return None


def nodes_count(nodes: List[torch.fx.Node], node_call_back) -> int:
    """Returns the number of nodes that match the node_call_back."""
    return len(nodes_filter(nodes, node_call_back))


def nodes_map(nodes: List[torch.fx.Node], node_call_back) -> List[torch.fx.Node]:
    """
    Sequentially visit the nodes list and invoke node_call_back on each element.
    Returns the nodes list after the node_call_back is invoked on each element.
    """
    for node in nodes:
        node_call_back(node)
    return nodes


def node_replace_(old_node: torch.fx.Node, new_node: torch.fx.Node) -> None:
    """
    Replace all uses of old_node with new_node.
    """
    old_node.replace_all_uses_with(new_node)
    old_node.users.clear()
    old_node.graph.erase_node(old_node)


def node_inline_(call_mod_node: torch.fx.Node) -> None:
    """
    Inline the submodule of the given node into the parent module.
    Note: we only support the case where submodule takes tensors inputs.
    """
    assert call_mod_node.op == "call_module"
    gm = call_mod_node.graph.owning_module

    assert isinstance(call_mod_node.target, str)
    sub_gm = getattr(gm, call_mod_node.target)

    phs = (node for node in sub_gm.graph.nodes if node.op == "placeholder")
    body = (
        node for node in sub_gm.graph.nodes if node.op not in ("placeholder", "output")
    )
    output = [node for node in sub_gm.graph.nodes if node.op == "output"]

    for ph, arg in zip(phs, call_mod_node.args):
        assert isinstance(arg, torch.fx.Node)
        node_replace_(ph, arg)

    with gm.graph.inserting_before(call_mod_node):
        for node in body:
            new_node = gm.graph.node_copy(node)
            node_replace_(node, new_node)

        if len(output) > 0:
            assert len(output) == 1 and len(output[0].args) == 1
            new_output = output[0].args[0]

            if isinstance(new_output, torch.fx.Node):
                # Clear the users of the output node and set
                # the users to be the users of original call_module node.
                new_output.users.clear()
                node_replace_(call_mod_node, new_output)
            elif isinstance(new_output, (list, tuple)):
                # Pop subgraph output node from users.
                for node in new_output:
                    node.users.pop(output[0])

                # Inline the get_item calls for the output node.
                get_item_users = nodes_filter(
                    list(call_mod_node.users.keys()),
                    lambda node: node.op == "call_function"
                    and node.target == operator.getitem,
                )
                # get_item_node.args[1] is the idx referring to new_output[idx]
                nodes_map(
                    get_item_users,
                    lambda get_item_node: node_replace_(
                        get_item_node,
                        new_output[get_item_node.args[1]],
                    ),
                )
                call_mod_node.graph.erase_node(call_mod_node)
            else:
                raise NotImplementedError(
                    f"Unsupported output type {type(new_output)}. Expect it to be a Node or a list/tuple of Nodes."
                )
        else:
            call_mod_node.graph.erase_node(call_mod_node)

    gm.delete_all_unused_submodules()
    gm.recompile()
    return gm


def _get_torch_jit_trace_forward_signature(mod: torch.nn.Module):
    """
    Get source code and parse argument names using AST. The function returns
    a signature of the forward() function.

    # TODO: Directly provide inspect.signature compatible TS-d module.
    """
    ast_mod = ast.parse(mod.code)
    ast_func_def: ast.FunctionDef = ast_mod.body[0]  # type: ignore[assignment]

    # FIXME(jiashenc): TorchScript should only allow positional or keywords arguments.
    arg_type_map = {"args": Parameter.POSITIONAL_OR_KEYWORD}

    # Traverse all argument types in AST tree and create associated parameters.
    param_list = []
    for arg_type, param_type in arg_type_map.items():
        arg_name_list = [a.arg for a in getattr(ast_func_def.args, arg_type)]
        for arg_name in arg_name_list:
            if arg_name == "self":
                continue  # Skip self argument.
            param_list.append(inspect.Parameter(arg_name, param_type))

    return inspect.Signature(parameters=param_list)


def _bind_signature_to_inputs(mod, fake_args, fake_kwargs):
    if isinstance(mod, (torch.jit.ScriptModule, torch.jit.TracedModule)):
        sig = _get_torch_jit_trace_forward_signature(mod)

        # Sanity check for placeholder names coming from TorchScript.
        assert len(sig.parameters) == len(fake_args) + len(fake_kwargs), (
            "Arguments other than POSITIONAL_OR_KEYWORD kinds in forward() "
            "are not supported in _get_torch_jit_trace_forward_signature"
        )
    else:
        sig = inspect.signature(mod.forward)

    return sig.bind(*fake_args, **fake_kwargs).arguments


def placeholder_naming_pass(
    gm: torch.fx.GraphModule,
    export_graph_signature: torch.export.ExportGraphSignature,
    mod: torch.nn.Module,
    fake_args,
    fake_kwargs,
    fake_params_buffers,
    constants: Dict[str, Any],
) -> None:
    """
    This pass is run at the end of _export_non_strict() to assign better placeholder node names:
        - User inputs:
            These follow the signature of mod.forward(), e.g. forward(x, y) produces nodes x, y.
            For nested inputs from dictionaries, lists, tuples, or dataclasses,
            the names are a concatenation of the path to the tensor.
                e.g. x = {
                    'a': torch.randn(),
                    'b': [torch.randn(), torch.randn()]
                }
            produces nodes x_a, x_b_0, x_b_1.
        - Parameters/buffers/constants/custom objects:
            These follow the FQN of the object, prefixed by "p", "b", "c", "obj" respectively.
                e.g. self.bar.l0.weight produces "p_bar_l0_weight".
        - Effect tokens:
            These are named token, token_1, ...
    """

    def _strip_name(x):
        if x.startswith("L__self___"):
            x = x[len("L__self___") :]
        x = re.sub(r"[^a-zA-Z0-9]", "_", x)
        return x

    def _extract_pytree_key(x):
        if isinstance(x, MappingKey):
            x = re.sub(r"[^a-zA-Z0-9]", "_", str(x.key))
            return x
        elif isinstance(x, SequenceKey):
            return str(x.idx)
        elif isinstance(x, GetAttrKey):
            return x.name
        else:
            raise RuntimeError(f"Pytree key of type {type(x)} not handled for {x}")

    name_map: Dict[str, str] = {}

    # map user input names with mod.forward() signature
    combined_args = _bind_signature_to_inputs(mod, fake_args, fake_kwargs)

    flat_args_with_path, _ = tree_flatten_with_path(combined_args)
    user_input_names = [
        spec.arg.name
        for spec in export_graph_signature.input_specs
        if spec.kind == InputKind.USER_INPUT
    ]

    # use pytree path to name nested user inputs
    for (arg_path, arg), user_input_name in zip(flat_args_with_path, user_input_names):
        if user_input_name:
            _rename_without_collisions(
                name_map,
                user_input_name,
                placeholder_prefixes[InputKind.USER_INPUT]
                + "_".join(_extract_pytree_key(x).lower() for x in arg_path),
                is_placeholder=True,
            )

    # use graph signature input specs to map param/buffer/constant names
    # name effect tokens as token, token_1, ... (these aren't visible to user)
    for spec in export_graph_signature.input_specs:
        if spec.kind == InputKind.USER_INPUT:
            continue
        if spec.kind == InputKind.TOKEN:
            base_name = ""
        else:
            base_name = _strip_name(spec.target).lower()
        base_name = re.sub(r"[^a-zA-Z0-9]", "_", base_name)

        _rename_without_collisions(
            name_map,
            spec.arg.name,
            placeholder_prefixes[spec.kind] + base_name,
            is_placeholder=True,
        )

    # handle naming collisions with call_function/get_attr inputs.
    # here, we want to prioritize user input names over call_function names
    # e.g. not have forward(self, mul): lead to a placeholder node called mul_13,
    # so we increment the suffix of call_function nodes as needed
    for node in gm.graph.nodes:
        if node.op == "placeholder":
            continue
        _rename_without_collisions(name_map, node.name, node.name)

    # assign new node names
    for node in gm.graph.nodes:
        if node.op == "placeholder":
            assert node.name in name_map
            node.name = node.target = name_map[node.name]
        elif node.name in name_map:
            node.name = name_map[node.name]

    # propagate names to higher order op subgraphs
    _name_hoo_subgraph_placeholders(gm)

    # re-generate graph module code
    gm.recompile()

    # modify graph signature (input specs, output specs, user input mutations)
    for spec in export_graph_signature.input_specs:
        assert spec.arg.name in name_map
        spec.arg.name = name_map[spec.arg.name]
        if (  # handle targets for custom objects
            spec.kind == InputKind.CUSTOM_OBJ and spec.target in name_map
        ):
            spec.target = name_map[spec.target][4:]  # strip obj_ prefix

    for spec in export_graph_signature.output_specs:
        if spec.arg.name in name_map:
            spec.arg.name = name_map[spec.arg.name]
        if spec.kind == OutputKind.USER_INPUT_MUTATION and spec.target in name_map:
            spec.target = name_map[spec.target]

    # rename keys in constants dict for custom objects
    for name in list(constants.keys()):
        constant = constants[name]
        if name in name_map and not isinstance(
            constant, torch.Tensor
        ):  # rename custom objects with generic names
            new_name = name_map[name]
            if (
                new_name != name
                and re.match(r"arg(\d+)_1", name)
                and new_name != placeholder_prefixes[InputKind.CUSTOM_OBJ] + name
            ):
                constants[new_name] = constant
                del constants[name]


def remove_proxy_from_state_dict(state_dict: Dict, in_place: bool) -> Dict:
    """
    If `in_place` is false, return a new copy of `state_dict` with "proxy" removed from `v.__dict__`.
    `v` is the values in the dictionary.
    If `in_place` is true, modify `state_dict` in place.
    """
    if in_place:
        for k, v in state_dict.items():
            if hasattr(v, "proxy"):
                delattr(state_dict[k], "proxy")
        return state_dict
    else:
        new_state_dict = {}
        for k, v in state_dict.items():
            if hasattr(v, "proxy"):
                new_state_dict[k] = v.clone().detach()
            else:
                new_state_dict[k] = v
        return new_state_dict


def _detect_fake_mode_from_gm(
    gm: torch.fx.GraphModule,
) -> torch._subclasses.fake_tensor.FakeTensorMode:
    """
    For a given graph module, we look at the "val" of placeholder nodes to find the fake inputs.
    Additionally, if gm doesn't have placeholders, we further look at the "example_value" or "val" of other nodes.
    If no fake mode is found, we return None for fake_mode.
    """
    from torch._guards import detect_fake_mode

    fake_inps: List[torch.Tensor] = []
    fake_vals: List[torch.Tensor] = []
    for node in gm.graph.nodes:
        if node.op == "placeholder" and "val" in node.meta:
            fake_val = node.meta["val"]
            if fake_val is not None and isinstance(fake_val, torch.Tensor):
                fake_inps.append(fake_val)
        elif len(fake_inps) == 0 and (
            "example_value" in node.meta or "val" in node.meta
        ):
            fake_val = None
            if "example_value" in node.meta:
                fake_val = node.meta["example_value"]
            elif "val" in node.meta:
                fake_val = node.meta["val"]
            if fake_val is not None and isinstance(fake_val, torch.Tensor):
                fake_vals.append(fake_val)

    return detect_fake_mode(fake_inps + fake_vals)