AIStoryBoard/python/venv/Lib/site-packages/torch/ao/quantization/utils.py

# mypy: allow-untyped-defs
"""
Utils shared by different modes of quantization (eager/graph)
"""

import functools
import sys
import warnings
from collections import OrderedDict
from collections.abc import Callable
from inspect import getfullargspec, signature
from typing import Any, Union

import torch
from torch.ao.quantization.quant_type import QuantType
from torch.fx import Node
from torch.nn.utils.parametrize import is_parametrized


if sys.version_info < (3, 12):
    NodePattern = Union[tuple[Node, Node], tuple[Node, tuple[Node, Node]], Any]
    NodePattern.__module__ = "torch.ao.quantization.utils"
else:
    from typing import TypeAliasType

    NodePattern = TypeAliasType(
        "NodePattern", tuple[Node, Node] | tuple[Node, tuple[Node, Node]] | Any
    )


# This is the Quantizer class instance from torch/quantization/fx/quantize.py.
# Define separately to prevent circular imports.
# TODO(future PR): improve this.
# make this public once fixed (can't be public as is because setting the module directly
# doesn't work)
QuantizerCls = Any

# Type for fusion patterns, it can be more complicated than the following actually,
# see pattern.md for docs
# TODO: not sure if typing supports recursive data types

if sys.version_info < (3, 12):
    Pattern = Union[
        Callable,
        tuple[Callable, Callable],
        tuple[Callable, tuple[Callable, Callable]],
        Any,
    ]
    Pattern.__module__ = "torch.ao.quantization.utils"
else:
    from typing import TypeAliasType

    Pattern = TypeAliasType(
        "Pattern",
        Callable
        | tuple[Callable, Callable]
        | tuple[Callable, tuple[Callable, Callable]]
        | Any,
    )


# TODO: maybe rename this to MatchInputNode
class MatchAllNode:
    """A node pattern that matches all nodes, used in defining
    fusion patterns in FX Graph Mode Quantization
    """


module_type_list = {
    torch.nn.ReLU,
    torch.nn.ReLU6,
    torch.nn.AdaptiveAvgPool1d,
    torch.nn.AdaptiveAvgPool2d,
    torch.nn.AdaptiveAvgPool3d,
    torch.nn.AvgPool1d,
    torch.nn.AvgPool2d,
    torch.nn.AvgPool3d,
    torch.nn.MaxPool1d,
    torch.nn.MaxPool2d,
    torch.nn.MaxPool3d,
    torch.nn.Identity,
    torch.nn.Hardsigmoid,
    torch.nn.Sigmoid,
    torch.nn.Tanh,
}
func_list = {
    torch.nn.functional.adaptive_avg_pool1d,
    torch.nn.functional.adaptive_avg_pool2d,
    torch.nn.functional.adaptive_avg_pool3d,
    torch.nn.functional.elu,
    torch.nn.functional.hardswish,
    torch.nn.functional.instance_norm,
    torch.nn.functional.layer_norm,
    torch.nn.functional.leaky_relu,
    torch.nn.functional.silu,
    torch.nn.functional.mish,
    torch.nn.functional.dropout,
    torch.nn.functional.max_pool1d,
    torch.nn.functional.max_pool2d,
    torch.nn.functional.max_pool3d,
    torch.nn.functional.relu,
    torch.nn.functional.hardtanh,
    torch.nn.functional.hardtanh_,
    torch.nn.functional.hardsigmoid,
    torch.nn.functional.sigmoid,
    torch.transpose,
    torch.repeat_interleave,
    torch.sigmoid,
    torch.squeeze,
    torch.stack,
    torch.sum,
    torch.tanh,
    torch.unsqueeze,
    torch.cat,
}
method_list = {
    torch.mean,
    "relu",
    "relu_",
    "contiguous",
    "detach",
    "detach_",
    "hardsigmoid",
    "hardsigmoid_",
    "permute",
    "repeat",
    "repeat_interleave",
    "reshape",
    "resize_",
    "shape",
    "sigmoid",
    "sigmoid_",
    "size",
    "squeeze",
    "squeeze_",
    "tanh",
    "tanh_",
    "transpose",
    "unsqueeze",
    "unsqueeze_",
    "view",
}


# TODO: not used now, remove
def check_node(node, modules):
    # TODO: reuse is_fixed_qparam_node after we move this function to _lower_to_native_backend.py
    is_call_function = node.op == "call_function" and node.target in func_list
    is_call_method = node.op == "call_method" and node.target in method_list
    is_call_module = (
        node.op == "call_module" and type(modules[str(node.target)]) in module_type_list
    )
    return is_call_function, is_call_method, is_call_module


def get_combined_dict(default_dict, additional_dict):
    """
    Combines two dictionaries.

    This function takes two dictionaries as input and returns a new dictionary
    that contains all the key-value pairs from both input dictionaries.
    If there are any duplicate keys in the `additional_dict`, the values
    from the `additional_dict` will overwrite those in the `default_dict`.
    Args:
        default_dict (dict): The main dictionary that will be used as the base
        additional_dict (dict): The dictionary used to update `default_dict`

    Returns:
        dict: The resulting dictionary
    Example:
        >>> x = dict(a=1, b=1)
        >>> y = dict(b=2, c=3)
        >>> get_combined_dict(x, y)
        {'a': 1, 'b': 2, 'c': 3}
    """
    d = default_dict.copy()
    d.update(additional_dict)
    return d


def is_per_tensor(qscheme):
    return qscheme == torch.per_tensor_affine or qscheme == torch.per_tensor_symmetric


def is_per_channel(qscheme):
    return qscheme in [
        torch.per_channel_affine,
        torch.per_channel_affine_float_qparams,
        torch.per_channel_symmetric,
    ]


def getattr_from_fqn(obj: Any, fqn: str) -> Any:
    """
    Given an obj and a fqn such as "foo.bar.baz", returns gm.foo.bar.baz.
    """
    return functools.reduce(getattr, fqn.split("."), obj)


def to_underlying_dtype(qdtype):
    DTYPE_MAPPING = {
        torch.quint8: torch.uint8,
        torch.qint8: torch.int8,
        torch.qint32: torch.int32,
        torch.quint4x2: torch.uint8,
        torch.quint2x4: torch.uint8,
        torch.uint8: torch.uint8,
        torch.int8: torch.int8,
        torch.uint16: torch.uint16,
        torch.int16: torch.int16,
        torch.int32: torch.int32,
        torch.float8_e5m2: torch.float8_e5m2,
        torch.float8_e4m3fn: torch.float8_e4m3fn,
    }
    if qdtype not in DTYPE_MAPPING:
        raise AssertionError("Unsupported dtype: " + str(qdtype))
    return DTYPE_MAPPING[qdtype]


def get_qparam_dict(observer_or_fake_quant):
    from torch.ao.quantization.observer import PlaceholderObserver

    qscheme = getattr(observer_or_fake_quant, "qscheme", None)
    dtype = observer_or_fake_quant.dtype
    qparams = {"qscheme": qscheme, "dtype": dtype}

    if not qscheme or isinstance(observer_or_fake_quant, PlaceholderObserver):
        return {"qscheme": None, "dtype": dtype}

    if is_per_tensor(qscheme):
        qscheme = torch.per_tensor_affine
    elif is_per_channel(qscheme):
        # change symmetric to affine since we do not have symmetric
        # quantized Tensor
        if qscheme == torch.per_channel_symmetric:
            qscheme = torch.per_channel_affine
        qparams["axis"] = observer_or_fake_quant.ch_axis
    else:
        raise RuntimeError(f"Unrecognized qscheme: {qscheme}")
    # update qscheme, since we don't have symmetric quant qscheme
    # in quantized Tensor
    qparams["qscheme"] = qscheme

    scale, zero_point = observer_or_fake_quant.calculate_qparams()
    qparams["scale"] = scale
    qparams["zero_point"] = zero_point

    if hasattr(observer_or_fake_quant, "quant_min"):
        qparams["quant_min"] = observer_or_fake_quant.quant_min
    if hasattr(observer_or_fake_quant, "quant_max"):
        qparams["quant_max"] = observer_or_fake_quant.quant_max

    return qparams


def get_swapped_custom_module_class(
    custom_module, custom_module_class_mapping, qconfig
):
    """Get the observed/quantized custom module class that we need
    to swap `custom_module` to
    Input:
        custom_module: input, can be an instance of either a float or observed custom module
        custom_module_class_mapping: the float to observed or observed to quantized custom module class mapping
        qconfig: qconfig configured for the custom module

    Output:
        corresponding observed/quantized custom module class for input custom module instance
    """
    quant_type = get_quant_type(qconfig)
    class_mapping = custom_module_class_mapping.get(quant_type, {})
    if type(custom_module) not in class_mapping:
        raise AssertionError(
            "did not find corresponding observed "
            f"module class for {type(custom_module)} in mapping: {class_mapping}"
        )
    return class_mapping[type(custom_module)]


def activation_dtype(qconfig):
    if qconfig is None:
        raise AssertionError("qconfig must be provided to determine activation dtype")
    activation = qconfig.activation()
    return activation.dtype


def weight_dtype(qconfig):
    if qconfig is None:
        raise AssertionError("qconfig must be provided to determine weight dtype")
    weight = qconfig.weight()
    return weight.dtype


def activation_is_statically_quantized(qconfig):
    """Given a qconfig, decide if the activation needs to be
    quantized or not, this includes quantizing to quint8, qint8 and qint32 and float16
    """
    return activation_dtype(qconfig) in [
        torch.quint8,
        torch.qint8,
        torch.qint32,
        torch.float16,
        torch.uint8,
        torch.int8,
        torch.int16,
        torch.int32,
        torch.float8_e5m2,
        torch.float8_e4m3fn,
    ] and (not activation_is_dynamically_quantized(qconfig))


def activation_is_dynamically_quantized(qconfig):
    """Given a qconfig, decide if the activation needs to be
    dynamically quantized or not, this includes dynamically quantizing to
    quint8, qint8 and float16
    """
    _activation_dtype, _, activation_is_dynamic = get_qconfig_dtypes(qconfig)
    return activation_is_dynamic


def activation_is_int8_quantized(qconfig):
    """Given a qconfig, decide if the activation needs to be
    quantized to int8 or not, this includes quantizing to quint8, qint8
    """
    return activation_dtype(qconfig) in [
        torch.quint8,
        torch.qint8,
        torch.uint8,
        torch.int8,
    ]


def activation_is_int32_quantized(qconfig):
    """Given a qconfig, decide if the activation needs to be
    quantized to int32 or not
    """
    return activation_dtype(qconfig) in [torch.qint32, torch.int32]


def weight_is_quantized(qconfig):
    """Given a qconfig, decide if the weight needs to be
    quantized or not
    """
    return weight_dtype(qconfig) in [
        torch.quint8,
        torch.qint8,
        torch.float16,
        torch.quint4x2,
        torch.uint8,
        torch.int8,
        torch.int16,
        torch.int32,
        torch.float8_e5m2,
        torch.float8_e4m3fn,
    ]


def weight_is_statically_quantized(qconfig):
    """Given a qconfig, decide if the weight needs to be statically
    quantized or not
    """
    return weight_dtype(qconfig) in [torch.quint8, torch.qint8, torch.uint8, torch.int8]


def op_is_int8_dynamically_quantized(qconfig) -> bool:
    """Given a qconfig, returns True if this op is using int8 dynamic
    quantization
    """
    activation_dtype, weight_dtype, activation_is_dynamic = get_qconfig_dtypes(qconfig)
    return (
        activation_dtype in [torch.quint8, torch.uint8]
        and
        # for now, the lines below assume fbgemm or qnnpack
        weight_dtype in [torch.qint8, torch.int8]
        and activation_is_dynamic
    )


def get_qconfig_dtypes(qconfig):
    r"""returns the qconfig tuple for qconfig:
    (activation_dtype, weight_dtype, activation_is_dynamic)
    """
    if qconfig is None:
        raise AssertionError("qconfig must be provided to extract dtypes")
    activation = qconfig.activation()
    weight = qconfig.weight()
    act_is_dynamic = getattr(activation, "is_dynamic", False)
    return (activation.dtype, weight.dtype, act_is_dynamic)


def get_quant_type(qconfig):
    if qconfig is None:
        raise AssertionError("qconfig must be provided to determine quant type")
    activation = qconfig.activation()
    weight = qconfig.weight()
    static_dtypes = [
        torch.quint8,
        torch.qint8,
        torch.quint4x2,
        torch.qint32,
        torch.uint8,
        torch.int8,
        torch.int16,
        torch.int32,
        torch.float8_e5m2,
        torch.float8_e4m3fn,
    ]
    if weight.dtype in static_dtypes:
        if hasattr(activation, "is_dynamic") and activation.is_dynamic:
            return QuantType.DYNAMIC
        elif activation.dtype in static_dtypes:
            return QuantType.STATIC
        else:
            return QuantType.WEIGHT_ONLY

    if weight.dtype == torch.float16:
        if hasattr(activation, "is_dynamic") and activation.is_dynamic:
            return QuantType.DYNAMIC
        elif activation.dtype == torch.float16:
            return QuantType.STATIC

    raise Exception(  # noqa: TRY002
        f"Unrecognized dtype combination in get_quant_type: activation({activation.dtype}),"
        f"weight({weight.dtype})"
    )


def check_min_max_valid(min_val: torch.Tensor, max_val: torch.Tensor) -> bool:
    """Checks if the given minimum and maximum values are valid, meaning that
    they exist and the min value is less than the max value.
    """
    if min_val.numel() == 0 or max_val.numel() == 0:
        warnings.warn(
            "must run observer before calling calculate_qparams. "
            + "Returning default values.",
            stacklevel=2,
        )
        return False

    if min_val.dim() == 0 or max_val.dim() == 0:
        if min_val == float("inf") and max_val == float("-inf"):
            warnings.warn(
                "must run observer before calling calculate_qparams. "
                + "Returning default values.",
                stacklevel=2,
            )

            return False

        if min_val > max_val:
            raise AssertionError(f"min {min_val} should be less than max {max_val}")
    else:
        if torch.any(min_val > max_val):
            raise AssertionError(f"min {min_val} should be less than max {max_val}")

    return True


def calculate_qmin_qmax(
    quant_min: int,
    quant_max: int,
    has_customized_qrange: bool,
    dtype: torch.dtype,
    reduce_range: bool,
) -> tuple[int, int]:
    r"""Calculates actual qmin and qmax based on the quantization range,
    observer datatype and if range is reduced.
    """
    # TODO(jerryzh): Figure out why custom quant_min/quant_max are still adjusted.
    if has_customized_qrange:
        # This initialization here is to be resolve TorchScript compilation issues and allow
        # using of refinement to decouple initial_qmin and initial_qmax from quantization range.
        # The actual values of initial_qmin and initial_qmax will be reset below.
        if dtype in [torch.qint32, torch.int32]:
            initial_quant_min, initial_quant_max = 0, 2**32 - 1
        else:
            initial_quant_min, initial_quant_max = 0, 255
        # The following assignment of self.qmin and self.qmax to the local variables and the if check refine the
        # attribute from Optional valid integers for use, based on TorchScript's requirements.
        custom_quant_min, custom_quant_max = quant_min, quant_max
        if custom_quant_min is not None and custom_quant_max is not None:
            initial_quant_min, initial_quant_max = (
                custom_quant_min,
                custom_quant_max,
            )

        qrange_len = initial_quant_max - initial_quant_min + 1
        if dtype in [torch.qint8, torch.int8]:
            if not (0 < qrange_len <= 256):
                raise AssertionError(
                    "quantization range should be positive and not exceed the maximum bit range (=256)."
                )
        elif dtype in [torch.qint32, torch.int32]:
            if not (0 < qrange_len <= 2**32):
                raise AssertionError(
                    "quantization range should be positive and not exceed the maximum bit range (=4294967296)."
                )
        if reduce_range:
            quant_min, quant_max = quant_min // 2, quant_max // 2
    else:
        # Fallback onto default 8-bit qmin and qmax calculation if dynamic range is not used.
        if dtype in [torch.qint8, torch.int8]:
            if reduce_range:
                quant_min, quant_max = -64, 63
            else:
                quant_min, quant_max = -128, 127
        elif dtype in [torch.quint8, torch.uint8]:
            if reduce_range:
                quant_min, quant_max = 0, 127
            else:
                quant_min, quant_max = 0, 255
        elif dtype in [torch.qint32, torch.int32]:
            quant_min, quant_max = -1 * (2**31), (2**31) - 1
        elif dtype == torch.uint16:
            quant_min, quant_max = 0, 2**16 - 1
        elif dtype == torch.int16:
            quant_min, quant_max = -(2**15), 2**15 - 1
        else:
            quant_min, quant_max = 0, 15
    return quant_min, quant_max


def _parent_name(target):
    """
    Turn 'foo.bar' into ['foo', 'bar']
    """
    r = target.rsplit(".", 1)
    if len(r) == 1:
        return "", r[0]
    else:
        return r[0], r[1]


def has_no_children_ignoring_parametrizations(module):
    """
    Checks if module._modules is empty or
    if module is a parametrization, checks that module._modules only has
    the 'parametrizations' module
    """
    if len(module._modules) == 0:
        return True
    elif is_parametrized(module):
        return len(module._modules) == 1 and "parametrizations" in module._modules
    else:
        return False


def _get_path_of_module(
    root: torch.nn.Module, submodule: torch.nn.Module
) -> str | None:
    """Get the path (fully qualified name) of a submodule

    Example::

    >> class M(torch.nn.Module):
           def __init__(self) -> None:
               self.linear = torch.nn.Linear(5, 5)
           def forward(self, x):
               return self.linear(x)

    >> m = M()
    >> l = m.linear
    >> _get_path_of_module(m, l)
    "linear"
    """
    for n, p in root.named_modules():
        if submodule is p:
            return n
    return None


def _get_signature_locals(f: Callable, loc: dict[str, Any]) -> dict[str, Any]:
    """Get local keyword arguments

    Example::

    >> def f(self, a, b=9):
           pass
    >> loc = {"a": 6, "c": 7}
    >> _get_signature_locals(f, loc)
    {"a": 6}
    """
    return {k: v for k, v in loc.items() if k in signature(f).parameters}


def _get_default_kwargs(f: Callable) -> "OrderedDict[str, Any]":
    """Get all default keyword arguments from function signature

    Example::

    >> def f(self, a, b=9):
           pass
    >> _get_default_kwargs(f)
    {"b": 9}
    """
    kwargs = {}
    for name, param in signature(f).parameters.items():
        if param.default is not param.empty:
            kwargs[name] = param.default
        elif param.kind is param.VAR_POSITIONAL:
            kwargs[name] = ()
        elif param.kind is param.VAR_KEYWORD:
            kwargs[name] = {}
    return OrderedDict(kwargs)


def _normalize_kwargs(func: Callable, loc: dict[str, Any]) -> "OrderedDict[str, Any]":
    """Given a function and local function arguments, normalize the keyword
    arguments by filling in default arguments from function signature

    Example::

    >> def f(self, key1=3, key2=3):
           pass
    >> loc = {"key2": 6}
    >> _normalize_kwargs(f, loc)
    {"key1": 3, "key2": 6}
    """
    default_kwargs = _get_default_kwargs(func)
    local_kwargs = _get_signature_locals(func, loc)
    normalized_kwargs = default_kwargs.copy()
    for attr, val in local_kwargs.items():
        if attr in normalized_kwargs:
            # override the default keyword arguments
            normalized_kwargs[attr] = val
    return normalized_kwargs


def validate_qmin_qmax(quant_min: int, quant_max: int) -> None:
    r"""Validates that the user-specified quantization range is properly initialized
    and within the given bound supported by the observer dtype.

    To accommodate lower-bit quantization with respect to the existing torch.qint8 and
    torch.quint8 datatypes, the user can choose to use dynamic quantization range by passing
    in a tuple of initial qmin and qmax values. One use case is these customized qmin and qmax
    values are used to calculate static estimates of the scale and zero point for aggressive lower-bit
    fake quantization. These estimates are compared against parameters learned through backpropagation.
    The related literatures for scale and zero point via backpropagation are as follows:

    Learned Step Size Quantization: https://openreview.net/pdf?id=rkgO66VKDS
    Trained Quantization Thresholds: https://arxiv.org/pdf/1903.08066.pdf
    """
    # The variable names are prefixed with "initial" because their values (qmin and qmax) might be adjusted
    # based on whether quantization range is reduced and the datatype (signed/unsigned) used by the observer.
    if not (quant_min <= 0 <= quant_max):
        raise AssertionError("Used-specified quantization range must include 0.")
    if quant_min >= quant_max:
        raise AssertionError(
            "qmin must be strictly less than qmax for user-specified quantization range."
        )


# Functionally equivalent to '_calculate_qparams' in observer.py. Observers must be torchscriptable however and qscheme
# as far as I can tell is not allowed to passed as a parameter in torchscript functions. This makes refactoring observer
# to use this utility a massive pain and very gross. For now Im opting just to duplicate as this code seems unlikely to change
# (last update over 1 year ago) and when torchscript is fully deprecated we can refactor. TODO(jakeszwe, jerryzh168)
def determine_qparams(
    min_val: torch.Tensor,
    max_val: torch.Tensor,
    quant_min: int,
    quant_max: int,
    dtype: torch.dtype,
    eps: torch.Tensor,
    has_customized_qrange: bool,
    qscheme: torch.qscheme = torch.per_tensor_affine,
) -> tuple[torch.Tensor, torch.Tensor]:
    r"""Calculates the quantization parameters, given min and max
    value tensors. Works for both per tensor and per channel cases

    Args:
        min_val: Minimum values per channel
        max_val: Maximum values per channel

    Returns:
        scales: Scales tensor of shape (#channels,)
        zero_points: Zero points tensor of shape (#channels,)
    """
    if not check_min_max_valid(min_val, max_val):
        return torch.tensor([1.0], device=min_val.device.type), torch.tensor(
            [0], device=min_val.device.type
        )

    min_val_neg = torch.min(min_val, torch.zeros_like(min_val))
    max_val_pos = torch.max(max_val, torch.zeros_like(max_val))

    device = min_val_neg.device
    scale = torch.ones(min_val_neg.size(), dtype=torch.double, device=device)
    zero_point = torch.zeros(min_val_neg.size(), dtype=torch.int64, device=device)
    eps = eps.to(device)

    if qscheme == torch.per_tensor_symmetric or qscheme == torch.per_channel_symmetric:
        max_val_pos = torch.max(-min_val_neg, max_val_pos)
        scale = max_val_pos / (float(quant_max - quant_min) / 2)
        scale = torch.max(scale, eps)
        if dtype in [torch.uint8, torch.quint8]:
            if has_customized_qrange:
                # When customized quantization range is used, down-rounded midpoint of the range is chosen.
                zero_point = zero_point.new_full(
                    zero_point.size(), (quant_min + quant_max) // 2
                )
            else:
                zero_point = zero_point.new_full(zero_point.size(), 128)
    elif qscheme == torch.per_channel_affine_float_qparams:
        scale = (max_val - min_val) / float(quant_max - quant_min)
        scale = torch.where(scale > eps, scale, torch.ones_like(scale))
        # We use the quantize function
        # xq = Round(Xf * inv_scale + zero_point),
        # setting zero_point to (-1 * min *inv_scale) we get
        # Xq = Round((Xf - min) * inv_scale)
        zero_point = -1 * min_val / scale
    else:
        scale = (max_val_pos - min_val_neg) / float(quant_max - quant_min)
        scale = torch.max(scale, eps)
        zero_point = quant_min - torch.round(min_val_neg / scale).to(torch.int)
        zero_point = torch.clamp(zero_point, quant_min, quant_max)

    # For scalar values, cast them to Tensors of size 1 to keep the shape
    # consistent with default values in FakeQuantize.
    if len(scale.shape) == 0:
        # TODO: switch to scale.item() after adding JIT support
        scale = torch.tensor([float(scale)], dtype=scale.dtype, device=device)
    if len(zero_point.shape) == 0:
        # TODO: switch to zero_point.item() after adding JIT support
        zero_point = torch.tensor(
            [int(zero_point)], dtype=zero_point.dtype, device=device
        )
        if qscheme == torch.per_channel_affine_float_qparams:
            zero_point = torch.tensor(
                [float(zero_point)], dtype=zero_point.dtype, device=device
            )

    return scale.to(torch.double), zero_point.to(torch.int64)


def _get_num_pos_args(f: Callable) -> int:
    """Get number of positional args for a function

    Example::

    >> def f(self, key1=3, key2=3):
           pass
    >> _get_num_pos_args(f)
    3
    """
    return len(getfullargspec(f).args)


def get_fqn_to_example_inputs(
    model: torch.nn.Module, example_inputs: tuple[Any, ...]
) -> dict[str, tuple[Any, ...]]:
    """Given a model and its example inputs, return a dictionary from
    fully qualified name of submodules to example_inputs for that submodule,
    e.g. {"linear1": (tensor1,), "linear2": (tensor2,), "sub": (tensor3,),
          "sub.linear1": (tensor4,), ...}

    Used to make quantizing submodules easier now that FX Graph Mode Quantization requires
    example inputs.

    Also works for keyword arguments with default values, we would flatten keyword
    arguments as positional arguments and fill in the missing keyword args with default
    values, e.g. if we have a forward function:
    def forward(self, x, key1=3, key2=3):
        ...

    and we call it with self.submodule(x, key2=6)
    we'll get example_inputs: (x, 3, 6)

    user can also override `key1` with positional arguments as well:
    for self.submodule(x, 5, key2=6)
    we'll get: (x, 5, 6)

    variable positional arguments and variable positional keyword arguments in forward
    function are not supported currently, so please make sure no submodules is using
    them.
    """
    root = model
    fqn_to_example_inputs = {}

    def _patched_module_call(self, *args, **kwargs):
        submodule_example_inputs = list(args).copy()
        normalized_kwargs = _normalize_kwargs(self.forward, kwargs)
        # minus 1 to skipping counting `self`
        num_args = _get_num_pos_args(self.forward) - 1
        num_to_pop = num_args - len(submodule_example_inputs)
        while num_to_pop and normalized_kwargs:
            normalized_kwargs.popitem(last=False)
            num_to_pop -= 1
        submodule_example_inputs.extend(normalized_kwargs.values())
        submodule_example_inputs_tuple = tuple(submodule_example_inputs)
        fqn = _get_path_of_module(root, self)
        if fqn is not None:
            fqn_to_example_inputs[fqn] = submodule_example_inputs_tuple
        return orig_module_call(self, *args, **kwargs)

    orig_module_call = torch.nn.Module.__call__
    torch.nn.Module.__call__ = _patched_module_call  # type: ignore[method-assign]
    try:
        model(*example_inputs)
    finally:
        # restore the module call even if there is an exception
        torch.nn.Module.__call__ = orig_module_call  # type: ignore[method-assign]
    return fqn_to_example_inputs


def _assert_and_get_unique_device(module: torch.nn.Module) -> Any:
    """
    Returns the unique device for a module, or None if no device is found.
    Throws an error if multiple devices are detected.
    """
    devices = {p.device for p in module.parameters()} | {
        p.device for p in module.buffers()
    }
    """
    As a temp workaround for AIMP HHC publish we added CPU check.remove it later. T163614564
    """
    if {torch.device("cpu"), torch.device("meta")} == devices:
        warnings.warn(
            "Both 'meta' and 'cpu' are present in the list of devices. Module can have one device. We Select 'cpu'.",
            stacklevel=2,
        )
        devices = {torch.device("cpu")}
    ""
    if len(devices) > 1:
        raise AssertionError(
            "prepare only works with cpu or single-device CUDA modules, "
            f"but got devices {devices}"
        )
    device = next(iter(devices)) if len(devices) > 0 else None
    return device


DEPRECATION_WARNING = (
    "torch.ao.quantization is deprecated and will be removed in 2.10. \n"
    "For migrations of users: \n"
    "1. Eager mode quantization (torch.ao.quantization.quantize, "
    "torch.ao.quantization.quantize_dynamic), please migrate to use torchao eager mode "
    "quantize_ API instead \n"
    "2. FX graph mode quantization (torch.ao.quantization.quantize_fx.prepare_fx,"
    "torch.ao.quantization.quantize_fx.convert_fx, please migrate to use torchao pt2e quantization "
    "API instead (prepare_pt2e, convert_pt2e) \n"
    "3. pt2e quantization has been migrated to torchao (https://github.com/pytorch/ao/tree/main/torchao/quantization/pt2e) \n"
    "see https://github.com/pytorch/ao/issues/2259 for more details"
)


__all__ = [
    "NodePattern",
    "Pattern",
    "MatchAllNode",
    "check_node",
    "get_combined_dict",
    "is_per_tensor",
    "is_per_channel",
    "getattr_from_fqn",
    "get_qparam_dict",
    "get_swapped_custom_module_class",
    "activation_dtype",
    "weight_dtype",
    "activation_is_statically_quantized",
    "activation_is_dynamically_quantized",
    "activation_is_int8_quantized",
    "activation_is_int32_quantized",
    "weight_is_quantized",
    "weight_is_statically_quantized",
    "op_is_int8_dynamically_quantized",
    "get_qconfig_dtypes",
    "get_quant_type",
    "check_min_max_valid",
    "calculate_qmin_qmax",
    "has_no_children_ignoring_parametrizations",
    "get_fqn_to_example_inputs",
    "to_underlying_dtype",
    "determine_qparams",
    "validate_qmin_qmax",
    "DEPRECATION_WARNING",
]