xref: /aosp_15_r20/external/executorch/backends/transforms/utils.py (revision 523fa7a60841cd1ecfb9cc4201f1ca8b03ed023a)

# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.

from typing import Optional

import torch
from executorch.exir import ExportedProgram

from torch._export.utils import (
    get_buffer,
    get_lifted_tensor_constant,
    get_param,
    is_buffer,
    is_lifted_tensor_constant,
    is_param,
)


def is_get_attr_node(node: torch.fx.Node) -> bool:
    """
    Returns true if the given node is a get attr node for a tensor of the model
    """
    return isinstance(node, torch.fx.Node) and node.op == "get_attr"


def is_param_node(exp_prog: ExportedProgram, node: torch.fx.Node) -> bool:
    return (
        is_get_attr_node(node)
        or is_param(exp_prog, node)
        or is_buffer(exp_prog, node)
        or is_lifted_tensor_constant(exp_prog, node)
    )


def get_param_tensor(
    exp_prog: ExportedProgram, node: torch.fx.Node
) -> Optional[torch.Tensor]:
    if node is None:
        return None
    elif is_param(exp_prog, node):
        return get_param(exp_prog, node)
    elif is_buffer(exp_prog, node):
        return get_buffer(exp_prog, node)
    elif is_lifted_tensor_constant(exp_prog, node):
        return get_lifted_tensor_constant(exp_prog, node)
    elif is_get_attr_node(node):
        # This is a hack to support both lifted and unlifted graph
        try:
            return getattr(node.graph.owning_module, node.target)
        except AttributeError:
            return getattr(exp_prog.graph_module, node.target)
    raise RuntimeError(f"unsupported param type, {node.op}.")