Source code for torch.ao.nn.quantized.modules.conv

r"""Quantized convolution modules."""

from typing import Optional, List, TypeVar

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.ao.nn.intrinsic as nni
import torch.ao.nn.intrinsic.qat as nniqat

from torch._ops import ops
from torch.nn.common_types import _size_1_t
from torch.nn.modules.utils import _single, _pair, _triple
from torch.nn.utils import fuse_conv_bn_weights

from .utils import _quantize_weight, WeightedQuantizedModule

__all__ = ['Conv1d', 'Conv2d', 'Conv3d', 'ConvTranspose1d', 'ConvTranspose2d', 'ConvTranspose3d']

_SUPPORTED_PADDING = {
    'zeros',
    'reflect'
}


def _reverse_repeat_padding(padding: List[int]) -> List[int]:
    _reversed_padding_repeated_twice: List[int] = []
    N = len(padding)
    for idx in range(N):
        for _ in range(2):
            _reversed_padding_repeated_twice.append(padding[N - idx - 1])
    return _reversed_padding_repeated_twice


class _ConvNd(WeightedQuantizedModule):
    def __init__(self, in_channels, out_channels, kernel_size, stride=1,
                 padding=0, dilation=1, groups=1, bias=True,
                 padding_mode='zeros', device=None, dtype=None):
        # All subclasses have this signature - See PR #49702s
        raise NotImplementedError

    def _init(self, in_channels, out_channels, kernel_size, stride,
              padding, dilation,
              transposed, output_padding,
              groups, bias,
              padding_mode='zeros',
              device=None,
              dtype=None) -> None:
        factory_kwargs = {'device': device, 'dtype': dtype}
        super().__init__()

        if in_channels % groups != 0:
            raise ValueError('in_channels must be divisible by groups')
        if out_channels % groups != 0:
            raise ValueError('out_channels must be divisible by groups')
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.kernel_size = kernel_size
        self.stride = stride
        self.padding = padding
        self.dilation = dilation
        self.transposed = transposed
        self.output_padding = output_padding
        self.groups = groups
        if padding_mode not in _SUPPORTED_PADDING:
            raise ValueError(f"'padding_mode' {padding_mode} is not supported by quantized convolution")
        self.padding_mode = padding_mode
        # Initialize as NCHW. set_weight will internally transpose to NHWC.
        if self.transposed:
            weight_shape = [in_channels, out_channels // self.groups]
        else:
            weight_shape = [out_channels, in_channels // self.groups]
        qweight = torch._empty_affine_quantized(
            weight_shape + list(kernel_size),
            scale=1, zero_point=0, dtype=torch.qint8,
            **{k: v for k, v in factory_kwargs.items() if k != 'dtype'})
        bias_float = (
            torch.zeros(out_channels, dtype=torch.float,
                        **{k: v for k, v in factory_kwargs.items() if k != 'dtype'}) if bias else None)

        self.set_weight_bias(qweight, bias_float)
        self.scale = 1.0
        self.zero_point = 0

    def set_weight_bias(self, qweight, bias_float):
        raise NotImplementedError

    def bias(self):
        raise NotImplementedError

    def _weight_bias(self):
        raise NotImplementedError

    def extra_repr(self):
        s = ('{in_channels}, {out_channels}, kernel_size={kernel_size}'
             ', stride={stride}, scale={scale}, zero_point={zero_point}')
        if self.padding != (0,) * len(self.padding):
            s += ', padding={padding}'
        if self.dilation != (1,) * len(self.dilation):
            s += ', dilation={dilation}'
        if self.output_padding != (0,) * len(self.output_padding):
            s += ', output_padding={output_padding}'
        if self.groups != 1:
            s += ', groups={groups}'
        if self.bias() is None:
            s += ', bias=False'
        return s.format(**self.__dict__)

    # ===== Serialization methods =====
    # The special consideration here is that we have to unpack the weights into
    # their regular QTensor form for serialization. Packed weights should not
    # live outside the process in which they were created, rather they should be
    # derived from the QTensor weight.
    #   self
    #   |--- weight : Tensor
    #   |--- bias : Tensor
    #
    # TODO: maybe change to this when https://github.com/pytorch/pytorch/pull/32958 is landed
    #   self
    #   |--- _packed_params : Conv2dPackedParamsBase or Conv3dPackedParamsBase
    def _save_to_state_dict(self, destination, prefix, keep_vars):
        super()._save_to_state_dict(destination, prefix, keep_vars)
        (w, b) = self._weight_bias()
        destination[prefix + 'weight'] = w
        destination[prefix + 'bias'] = b
        destination[prefix + 'scale'] = torch.tensor(self.scale)
        destination[prefix + 'zero_point'] = torch.tensor(self.zero_point)

    @torch.jit.export
    def __getstate__(self):
        (w, b) = self._weight_bias()
        return (
            self.in_channels,
            self.out_channels,
            self.kernel_size,
            self.stride,
            self.padding,
            self.dilation,
            self.transposed,
            self.output_padding,
            self.groups,
            self.padding_mode,
            w,
            b,
            self.scale,
            self.zero_point,
            self.training
        )

    # ===== Deserialization methods =====
    # Counterpart to the serialization methods, we must pack the serialized
    # QTensor weight into its packed format for use by the FBGEMM ops.
    def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict,
                              missing_keys, unexpected_keys, error_msgs):
        self.set_weight_bias(
            state_dict[prefix + 'weight'], state_dict[prefix + 'bias'])
        state_dict.pop(prefix + 'weight')
        state_dict.pop(prefix + 'bias')
        self.scale = float(state_dict[prefix + 'scale'])
        state_dict.pop(prefix + 'scale')
        self.zero_point = int(state_dict[prefix + 'zero_point'])
        state_dict.pop(prefix + 'zero_point')
        super()._load_from_state_dict(
            state_dict, prefix, local_metadata, False, missing_keys,
            unexpected_keys, error_msgs)

    @torch.jit.export
    def __setstate__(self, state):
        self.in_channels = state[0]
        self.out_channels = state[1]
        self.kernel_size = state[2]
        self.stride = state[3]
        self.padding = state[4]
        self.dilation = state[5]
        self.transposed = state[6]
        self.output_padding = state[7]
        self.groups = state[8]
        self.padding_mode = state[9]
        self.set_weight_bias(state[10], state[11])
        self.scale = state[12]
        self.zero_point = state[13]
        self.training = state[14]

    def __deepcopy__(self, memo):
        new_instance = type(self).__new__(type(self))
        torch.nn.Module.__init__(new_instance)
        state = self.__getstate__()
        new_instance.__setstate__(state)
        return new_instance

    def __copy__(self):
        return self.__deepcopy__({})

    @classmethod
    def get_qconv(cls, mod, activation_post_process, weight_post_process=None):
        r"""Creates a qconv object and returns it.
        """
        if weight_post_process is None:
            weight_post_process = mod.qconfig.weight()
        weight_post_process(mod.weight)
        assert weight_post_process.dtype == torch.qint8, \
            'Weight observer must have a dtype of qint8'
        qweight = _quantize_weight(mod.weight.float(), weight_post_process)
        # the __init__ call used is the one from derived classes and not the one from _ConvNd
        qconv = cls(mod.in_channels, mod.out_channels, mod.kernel_size,
                    mod.stride, mod.padding, mod.dilation, mod.groups,
                    mod.bias is not None, mod.padding_mode)
        qconv.set_weight_bias(qweight, mod.bias)
        if activation_post_process is None or activation_post_process.dtype == torch.float:
            return qconv  # dynamic quantization doesn't need scale/zero_point
        else:
            act_scale, act_zp = activation_post_process.calculate_qparams()
            qconv.scale = float(act_scale)
            qconv.zero_point = int(act_zp)
            return qconv

    @staticmethod
    def from_float(cls, mod):
        if hasattr(mod, "weight_fake_quant"):
            # assert type(mod) == cls.__QAT_MODULE, " nnq." + cls.__name__ + \
            # ".from_float only works for " + cls.__QAT_MODULE.__name__
            if type(mod) == cls._NNIQAT_CONV_BN_MODULE:
                mod.weight, mod.bias = fuse_conv_bn_weights(
                    mod.weight, mod.bias, mod.bn.running_mean, mod.bn.running_var,
                    mod.bn.eps, mod.bn.weight, mod.bn.bias)
            assert hasattr(mod, "activation_post_process"), \
                "Input QAT module must have observer attached"
            weight_post_process = mod.weight_fake_quant
            activation_post_process = mod.activation_post_process
        else:
            assert type(mod) == cls._FLOAT_MODULE, \
                " nnq." + cls.__name__ + ".from_float only works for " + \
                cls._FLOAT_MODULE.__name__ + " but got:" + str(type(mod))
            assert hasattr(mod, "qconfig"), \
                "Input float module must have qconfig defined."
            activation_post_process = None if not hasattr(
                mod, "activation_post_process") else mod.activation_post_process
            if type(mod) in [cls._NNI_CONV_RELU_MODULE, cls._NNI_CONV_ADD_MODULE, cls._NNI_CONV_ADD_RELU_MODULE]:
                mod = mod[0]
            weight_post_process = mod.qconfig.weight()
        return cls.get_qconv(mod, activation_post_process, weight_post_process)

    @classmethod
    def from_reference(cls, ref_qconv, output_scale, output_zero_point):
        r"""Create a (fbgemm/qnnpack) quantized module from a reference quantized module
        Args:
            ref_qconv (Module): a reference quantized  module, either produced by torch.ao.quantization
                                utilities or provided by the user
            output_scale (float): scale for output Tensor
            output_zero_point (int): zero point for output Tensor
        """
        qconv = cls(
            ref_qconv.in_channels,
            ref_qconv.out_channels,
            ref_qconv.kernel_size,  # type: ignore[arg-type]
            ref_qconv.stride,  # type: ignore[arg-type]
            ref_qconv.padding,  # type: ignore[arg-type]
            ref_qconv.dilation,  # type: ignore[arg-type]
            ref_qconv.groups,
            ref_qconv.bias is not None,  # type: ignore[arg-type]
            ref_qconv.padding_mode,
            device=ref_qconv.weight.device,
            dtype=ref_qconv.weight.dtype)
        qweight = ref_qconv.get_quantized_weight()
        qconv.set_weight_bias(qweight, ref_qconv.bias)
        qconv.scale = float(output_scale)
        qconv.zero_point = int(output_zero_point)
        return qconv


class Conv1d(_ConvNd):
    r"""Applies a 1D convolution over a quantized input signal composed of
    several quantized input planes.

    For details on input arguments, parameters, and implementation see
    :class:`~torch.nn.Conv1d`.

    .. note::
        Only `zeros` is supported for the :attr:`padding_mode` argument.

    .. note::
        Only `torch.quint8` is supported for the input data type.


    Attributes:
        weight (Tensor):     packed tensor derived from the learnable weight
                             parameter.
        scale (Tensor):      scalar for the output scale
        zero_point (Tensor): scalar for the output zero point

    See :class:`~torch.nn.Conv1d` for other attributes.

    Examples::

        >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_QENGINE)
        >>> m = nn.quantized.Conv1d(16, 33, 3, stride=2)
        >>> input = torch.randn(20, 16, 100)
        >>> # quantize input to quint8
        >>> # xdoctest: +SKIP
        >>> q_input = torch.quantize_per_tensor(input, scale=1.0, zero_point=0,
        ...                                     dtype=torch.quint8)
        >>> output = m(q_input)

    """

    _FLOAT_MODULE = nn.Conv1d
    _NNIQAT_CONV_BN_MODULE = nniqat.ConvBn1d
    _NNI_CONV_RELU_MODULE = nni.ConvReLU1d
    _NNI_CONV_ADD_MODULE = None
    _NNI_CONV_ADD_RELU_MODULE = None

    def __init__(self,
                 in_channels: int,
                 out_channels: int,
                 kernel_size: _size_1_t,
                 stride: _size_1_t = 1,
                 padding: _size_1_t = 0,
                 dilation: _size_1_t = 1,
                 groups: int = 1,
                 bias: bool = True,
                 padding_mode: str = 'zeros',
                 device=None,
                 dtype=None):
        factory_kwargs = {'device': device, 'dtype': dtype}
        kernel_size = _single(kernel_size)
        stride = _single(stride)
        padding = padding if isinstance(padding, str) else _single(padding)
        dilation = _single(dilation)

        # Subclasses of _ConvNd needs to call _init rather than __init__. See
        # discussion on PR #49702
        super()._init(
            in_channels, out_channels, kernel_size, stride, padding, dilation,
            False, _single(0), groups, bias, padding_mode, **factory_kwargs)

    def _get_name(self):
        return 'QuantizedConv1d'

    def set_weight_bias(self, w: torch.Tensor, b: Optional[torch.Tensor]) -> None:
        if self.padding_mode == 'zeros':
            self._packed_params = torch.ops.quantized.conv1d_prepack(
                w, b, self.stride, self.padding, self.dilation, self.groups)
        else:
            self._packed_params = torch.ops.quantized.conv1d_prepack(
                w, b, self.stride, _pair(0), self.dilation,
                self.groups)

    def _weight_bias(self):
        w, b = torch.ops.quantized.conv1d_unpack(self._packed_params)
        return w, b

    def weight(self):
        return self._weight_bias()[0]

    def bias(self):
        return self._weight_bias()[1]

    def forward(self, input):
        # Temporarily using len(shape) instead of ndim due to JIT issue
        # https://github.com/pytorch/pytorch/issues/23890
        if len(input.shape) != 3:
            raise ValueError("Input shape must be `(N, C, L)`!")
        if self.padding_mode != 'zeros':
            # Padding in Conv1d is stored as (p, p), need to get (p,)
            _reversed_padding_repeated_twice = _reverse_repeat_padding(self.padding[:1])
            input = F.pad(input, _reversed_padding_repeated_twice,
                          mode=self.padding_mode)
        return ops.quantized.conv1d(input, self._packed_params, self.scale, self.zero_point)

    @classmethod
    def from_float(cls, mod):
        r"""Creates a quantized module from a float module or qparams_dict.

        Args:
            mod (Module): a float module, either produced by torch.ao.quantization
              utilities or provided by the user
        """
        return _ConvNd.from_float(cls, mod)


class Conv2d(_ConvNd):
    r"""Applies a 2D convolution over a quantized input signal composed of
    several quantized input planes.

    For details on input arguments, parameters, and implementation see
    :class:`~torch.nn.Conv2d`.

    .. note::
        Only `zeros` is supported for the :attr:`padding_mode` argument.

    .. note::
        Only `torch.quint8` is supported for the input data type.


    Attributes:
        weight (Tensor):     packed tensor derived from the learnable weight
                             parameter.
        scale (Tensor):      scalar for the output scale
        zero_point (Tensor): scalar for the output zero point

    See :class:`~torch.nn.Conv2d` for other attributes.

    Examples::

        >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_QENGINE)
        >>> # With square kernels and equal stride
        >>> m = nn.quantized.Conv2d(16, 33, 3, stride=2)
        >>> # non-square kernels and unequal stride and with padding
        >>> m = nn.quantized.Conv2d(16, 33, (3, 5), stride=(2, 1), padding=(4, 2))
        >>> # non-square kernels and unequal stride and with padding and dilation
        >>> m = nn.quantized.Conv2d(16, 33, (3, 5), stride=(2, 1), padding=(4, 2), dilation=(3, 1))
        >>> input = torch.randn(20, 16, 50, 100)
        >>> # quantize input to quint8
        >>> # xdoctest: +SKIP
        >>> q_input = torch.quantize_per_tensor(input, scale=1.0, zero_point=0, dtype=torch.quint8)
        >>> output = m(q_input)

    """
    _FLOAT_MODULE = nn.Conv2d
    _NNIQAT_CONV_BN_MODULE = nniqat.ConvBn2d
    _NNI_CONV_RELU_MODULE = nni.ConvReLU2d
    _NNI_CONV_ADD_MODULE = nni.ConvAdd2d
    _NNI_CONV_ADD_RELU_MODULE = nni.ConvAddReLU2d

    def __init__(self, in_channels, out_channels, kernel_size, stride=1,
                 padding=0, dilation=1, groups=1, bias=True,
                 padding_mode='zeros', device=None, dtype=None):
        factory_kwargs = {'device': device, 'dtype': dtype}
        kernel_size = _pair(kernel_size)
        stride = _pair(stride)
        padding = _pair(padding)
        dilation = _pair(dilation)
        # Subclasses of _ConvNd need to call _init rather than __init__. See
        # discussion on PR #49702
        super()._init(
            in_channels, out_channels, kernel_size, stride, padding, dilation,
            False, _pair(0), groups, bias, padding_mode, **factory_kwargs)

    def _get_name(self):
        return 'QuantizedConv2d'

    def set_weight_bias(self, w: torch.Tensor, b: Optional[torch.Tensor]) -> None:
        if self.padding_mode == 'zeros':
            self._packed_params = torch.ops.quantized.conv2d_prepack(
                w, b, self.stride, self.padding, self.dilation, self.groups)
        else:
            self._packed_params = torch.ops.quantized.conv2d_prepack(
                w, b, self.stride, _pair(0), self.dilation, self.groups)

    def _weight_bias(self):
        return self._packed_params.unpack()

    def weight(self):
        return self._weight_bias()[0]

    def bias(self):
        return self._weight_bias()[1]

    def forward(self, input):
        # Temporarily using len(shape) instead of ndim due to JIT issue
        # https://github.com/pytorch/pytorch/issues/23890
        if len(input.shape) != 4:
            raise ValueError("Input shape must be `(N, C, H, W)`!")
        if self.padding_mode != 'zeros':
            _reversed_padding_repeated_twice = _reverse_repeat_padding(self.padding)
            input = F.pad(input, _reversed_padding_repeated_twice,
                          mode=self.padding_mode)
        return ops.quantized.conv2d(
            input, self._packed_params, self.scale, self.zero_point)

    @classmethod
    def from_float(cls, mod):
        r"""Creates a quantized module from a float module or qparams_dict.

        Args:
            mod (Module): a float module, either produced by torch.ao.quantization
              utilities or provided by the user
        """
        return _ConvNd.from_float(cls, mod)


class Conv3d(_ConvNd):
    r"""Applies a 3D convolution over a quantized input signal composed of
    several quantized input planes.

    For details on input arguments, parameters, and implementation see
    :class:`~torch.nn.Conv3d`.

    .. note::
        Only `zeros` is supported for the :attr:`padding_mode` argument.

    .. note::
        Only `torch.quint8` is supported for the input data type.


    Attributes:
        weight (Tensor):     packed tensor derived from the learnable weight
                             parameter.
        scale (Tensor):      scalar for the output scale
        zero_point (Tensor): scalar for the output zero point

    See :class:`~torch.nn.Conv3d` for other attributes.

    Examples::

        >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_QENGINE)
        >>> # With square kernels and equal stride
        >>> m = nn.quantized.Conv3d(16, 33, 3, stride=2)
        >>> # non-square kernels and unequal stride and with padding
        >>> m = nn.quantized.Conv3d(16, 33, (3, 5, 5), stride=(1, 2, 2), padding=(1, 2, 2))
        >>> # non-square kernels and unequal stride and with padding and dilation
        >>> m = nn.quantized.Conv3d(16, 33, (3, 5, 5), stride=(1, 2, 2), padding=(1, 2, 2), dilation=(1, 2, 2))
        >>> input = torch.randn(20, 16, 56, 56, 56)
        >>> # quantize input to quint8
        >>> # xdoctest: +SKIP
        >>> q_input = torch.quantize_per_tensor(input, scale=1.0, zero_point=0, dtype=torch.quint8)
        >>> output = m(q_input)

    """
    _FLOAT_MODULE = nn.Conv3d
    _NNIQAT_CONV_BN_MODULE = nniqat.ConvBn3d
    _NNI_CONV_RELU_MODULE = nni.ConvReLU3d
    _NNI_CONV_ADD_MODULE = None
    _NNI_CONV_ADD_RELU_MODULE = None

    def __init__(self, in_channels, out_channels, kernel_size, stride=1,
                 padding=0, dilation=1, groups=1, bias=True,
                 padding_mode='zeros', device=None, dtype=None):
        assert padding_mode != 'reflect', "Conv3d does not support reflection padding"
        factory_kwargs = {'device': device, 'dtype': dtype}
        kernel_size = _triple(kernel_size)
        stride = _triple(stride)
        padding = _triple(padding)
        dilation = _triple(dilation)
        # Subclasses of _ConvNd need to call _init rather than __init__. See
        # discussion on PR #49702
        super()._init(
            in_channels, out_channels, kernel_size, stride, padding, dilation,
            False, _triple(0), groups, bias, padding_mode, **factory_kwargs)

    def _get_name(self):
        return 'QuantizedConv3d'

    def set_weight_bias(self, w: torch.Tensor, b: Optional[torch.Tensor]) -> None:
        if self.padding_mode == 'zeros':
            self._packed_params = torch.ops.quantized.conv3d_prepack(
                w, b, self.stride, self.padding, self.dilation, self.groups)
        else:
            self._packed_params = torch.ops.quantized.conv3d_prepack(
                w, b, self.stride, _triple(0), self.dilation, self.groups)

    def _weight_bias(self):
        return self._packed_params.unpack()

    def weight(self):
        return self._weight_bias()[0]

    def bias(self):
        return self._weight_bias()[1]

    def forward(self, input):
        # Temporarily using len(shape) instead of ndim due to JIT issue
        # https://github.com/pytorch/pytorch/issues/23890
        if len(input.shape) != 5:
            raise ValueError("Input shape must be `(N, C, D, H, W)`!")
        if self.padding_mode != 'zeros':
            _reversed_padding_repeated_twice = _reverse_repeat_padding(self.padding)
            input = F.pad(input, _reversed_padding_repeated_twice,
                          mode=self.padding_mode)
        return ops.quantized.conv3d(
            input, self._packed_params, self.scale, self.zero_point)

    @classmethod
    def from_float(cls, mod):
        r"""Creates a quantized module from a float module or qparams_dict.

        Args:
            mod (Module): a float module, either produced by torch.ao.quantization
              utilities or provided by the user
        """
        return _ConvNd.from_float(cls, mod)

# === Transposed Convolutions ===
MOD = TypeVar('MOD', bound=nn.modules.conv._ConvNd)


class _ConvTransposeNd(_ConvNd):

    _FLOAT_MODULE = MOD

    def __init__(self, in_channels, out_channels, kernel_size, stride,
                 padding, dilation, transposed, output_padding,
                 groups, bias, padding_mode, device=None, dtype=None):
        if padding_mode != 'zeros':
            raise ValueError(f'Only "zeros" padding mode is supported for {self.__class__.__name__}')
        factory_kwargs = {'device': device, 'dtype': dtype}
        # Subclasses of _ConvNd need to call _init rather than __init__. See
        # discussion on PR #49702
        super()._init(
            in_channels, out_channels, kernel_size, stride,
            padding, dilation, transposed, output_padding,
            groups, bias, padding_mode, **factory_kwargs)

    def _input_padding(self, kernel_size: List[int], dilation: List[int], padding: List[int]) -> List[int]:
        res = torch.jit.annotate(List[int], [])
        for kdx in range(len(kernel_size)):
            pad = (dilation[kdx] * (kernel_size[kdx] - 1) - padding[kdx])
            res.append(pad)
        return res

    @classmethod
    def from_float(cls, mod):
        r"""Creates a quantized module from a float module or qparams_dict.
        Args:
            mod (Module): a float module, either produced by torch.ao.quantization
              utilities or provided by the user
        """
        # derived classes override cls._FLOAT_MODULE attribute
        msg = ' nnq.' + cls.__name__ + '.from_float only works for ' + \
              cls._FLOAT_MODULE.__name__  # type: ignore[attr-defined]
        assert type(mod) == cls._FLOAT_MODULE, msg
        assert hasattr(mod, 'qconfig'), \
            'Input float module must have qconfig defined.'
        weight_post_process = mod.qconfig.weight()
        weight_post_process(mod.weight)
        assert weight_post_process.dtype == torch.qint8, \
            'Weight observer must have a dtype of qint8'
        qweight = _quantize_weight(mod.weight.float(), weight_post_process)
        # the __init__ call used is the one from derived classes and not the one from _ConvTransposeNd
        qconv = cls(mod.in_channels, mod.out_channels, mod.kernel_size,  # type: ignore[call-arg]
                    mod.stride, mod.padding, mod.output_padding, mod.groups,
                    mod.bias is not None, mod.dilation, mod.padding_mode)
        qconv.set_weight_bias(qweight, mod.bias)
        if not hasattr(mod, "activation_post_process") or mod.activation_post_process.dtype == torch.float:
            return qconv  # dynamic quantization doesn't need scale/zero_point
        else:
            act_scale, act_zp = mod.activation_post_process.calculate_qparams()
            qconv.scale = float(act_scale)
            qconv.zero_point = int(act_zp)
            return qconv

    @staticmethod
    def from_reference(cls, ref_qconvt, output_scale, output_zero_point):
        r"""Create a (fbgemm/qnnpack) quantized module from a reference quantized module
        Args:
            ref_qconvt (Module): a reference quantized  module, either produced by torch.ao.quantization
                                 utilities or provided by the user
            output_scale (float): scale for output Tensor
            output_zero_point (int): zero point for output Tensor
        """
        qconv = cls(
            ref_qconvt.in_channels,
            ref_qconvt.out_channels,
            ref_qconvt.kernel_size,  # type: ignore[arg-type]
            ref_qconvt.stride,  # type: ignore[arg-type]
            ref_qconvt.padding,  # type: ignore[arg-type]
            ref_qconvt.output_padding,  # type: ignore[arg-type]
            ref_qconvt.groups,
            ref_qconvt.bias is not None,  # type: ignore[arg-type]
            ref_qconvt.dilation,  # type: ignore[arg-type]
            ref_qconvt.padding_mode,
            device=ref_qconvt.weight.device,
            dtype=ref_qconvt.weight.dtype)
        qweight = ref_qconvt.get_quantized_weight()
        qconv.set_weight_bias(qweight, ref_qconvt.bias)
        qconv.scale = float(output_scale)
        qconv.zero_point = int(output_zero_point)
        return qconv


class ConvTranspose1d(_ConvTransposeNd):
    r"""Applies a 1D transposed convolution operator over an input image
    composed of several input planes.
    For details on input arguments, parameters, and implementation see
    :class:`~torch.nn.ConvTranspose1d`.

    .. note:: Currently only the QNNPACK engine is implemented.
        Please, set the `torch.backends.quantized.engine = 'qnnpack'`

    For special notes, please, see :class:`~torch.ao.nn.quantized.Conv1d`

    Attributes:
        weight (Tensor):     packed tensor derived from the learnable weight
                             parameter.
        scale (Tensor):      scalar for the output scale
        zero_point (Tensor): scalar for the output zero point
    See :class:`~torch.nn.ConvTranspose2d` for other attributes.

    Examples::

        >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_QENGINE)
        >>> torch.backends.quantized.engine = 'qnnpack'
        >>> from torch.ao.nn import quantized as nnq
        >>> # With square kernels and equal stride
        >>> m = nnq.ConvTranspose1d(16, 33, 3, stride=2)
        >>> # non-square kernels and unequal stride and with padding
        >>> m = nnq.ConvTranspose1d(16, 33, (3, 5), stride=(2, 1), padding=(4, 2))
        >>> input = torch.randn(20, 16, 50)
        >>> q_input = torch.quantize_per_tensor(input, scale=1.0, zero_point=0, dtype=torch.quint8)
        >>> output = m(q_input)
        >>> # exact output size can be also specified as an argument
        >>> input = torch.randn(1, 16, 12)
        >>> q_input = torch.quantize_per_tensor(input, scale=1.0, zero_point=0, dtype=torch.quint8)
        >>> downsample = nnq.Conv1d(16, 16, 3, stride=2, padding=1)
        >>> upsample = nnq.ConvTranspose1d(16, 16, 3, stride=2, padding=1)
        >>> h = downsample(q_input)
        >>> h.size()
        torch.Size([1, 16, 6])
        >>> # xdoctest: +SKIP("FIXME: output_size is not a parameter)
        >>> output = upsample(h, output_size=input.size())
        >>> output.size()
        torch.Size([1, 16, 12])
    """

    _FLOAT_MODULE = nn.ConvTranspose1d

    def __init__(self, in_channels, out_channels, kernel_size, stride=1,
                 padding=0, output_padding=0, groups=1, bias=True,
                 dilation=1, padding_mode='zeros', device=None, dtype=None):
        factory_kwargs = {'device': device, 'dtype': dtype}
        kernel_size = _single(kernel_size)
        stride = _single(stride)
        padding = _single(padding)
        dilation = _single(dilation)
        output_padding = _single(output_padding)

        super().__init__(
            in_channels, out_channels, kernel_size, stride, padding, dilation,
            True, output_padding, groups, bias, padding_mode, **factory_kwargs)

    def _get_name(self):
        return 'QuantizedConvTranspose1d'

    def set_weight_bias(self, w: torch.Tensor, b: Optional[torch.Tensor]) -> None:
        self._packed_params = torch.ops.quantized.conv_transpose1d_prepack(
            w, b, self.stride, self.padding, self.output_padding, self.dilation,
            self.groups)

    def _weight_bias(self):
        w, b = torch.ops.quantized.conv_transpose1d_unpack(self._packed_params)
        return w, b

    def weight(self):
        (w, _) = self._weight_bias()
        return w

    def bias(self):
        (_, b) = self._weight_bias()
        return b

    def forward(self, input):
        # Temporarily using len(shape) instead of ndim due to JIT issue
        # https://github.com/pytorch/pytorch/issues/23890
        if len(input.shape) != 3:
            raise ValueError("Input shape must be `(N, C, L)`!")
        return torch.ops.quantized.conv_transpose1d(
            input, self._packed_params, self.scale, self.zero_point)

    @classmethod
    def from_reference(cls, ref_qconvt, output_scale, output_zero_point):
        return _ConvTransposeNd.from_reference(cls, ref_qconvt, output_scale, output_zero_point)


class ConvTranspose2d(_ConvTransposeNd):
    r"""Applies a 2D transposed convolution operator over an input image
    composed of several input planes.
    For details on input arguments, parameters, and implementation see
    :class:`~torch.nn.ConvTranspose2d`.

    For special notes, please, see :class:`~torch.ao.nn.quantized.Conv2d`

    Attributes:
        weight (Tensor):     packed tensor derived from the learnable weight
                             parameter.
        scale (Tensor):      scalar for the output scale
        zero_point (Tensor): scalar for the output zero point
    See :class:`~torch.nn.ConvTranspose2d` for other attributes.

    Examples::

        >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_QENGINE)
        >>> # QNNPACK or FBGEMM as backend
        >>> torch.backends.quantized.engine = 'qnnpack'
        >>> # With square kernels and equal stride
        >>> import torch.ao.nn.quantized as nnq
        >>> m = nnq.ConvTranspose2d(16, 33, 3, stride=2)
        >>> # non-square kernels and unequal stride and with padding
        >>> m = nnq.ConvTranspose2d(16, 33, (3, 5), stride=(2, 1), padding=(4, 2))
        >>> input = torch.randn(20, 16, 50, 100)
        >>> q_input = torch.quantize_per_tensor(input, scale=1.0, zero_point=0, dtype=torch.quint8)
        >>> output = m(q_input)
        >>> # exact output size can be also specified as an argument
        >>> input = torch.randn(1, 16, 12, 12)
        >>> q_input = torch.quantize_per_tensor(input, scale=1.0, zero_point=0, dtype=torch.quint8)
        >>> downsample = nnq.Conv2d(16, 16, 3, stride=2, padding=1)
        >>> upsample = nnq.ConvTranspose2d(16, 16, 3, stride=2, padding=1)
        >>> h = downsample(q_input)
        >>> h.size()
        torch.Size([1, 16, 6, 6])
        >>> # xdoctest: +SKIP("FIXME: output_size is not a parameter)
        >>> output = upsample(h, output_size=input.size())
        >>> output.size()
        torch.Size([1, 16, 12, 12])
    """

    _FLOAT_MODULE = nn.ConvTranspose2d

    def __init__(self, in_channels, out_channels, kernel_size, stride=1,
                 padding=0, output_padding=0, groups=1, bias=True,
                 dilation=1, padding_mode='zeros', device=None, dtype=None):
        factory_kwargs = {'device': device, 'dtype': dtype}
        kernel_size = _pair(kernel_size)
        stride = _pair(stride)
        padding = _pair(padding)
        dilation = _pair(dilation)
        output_padding = _pair(output_padding)

        super().__init__(
            in_channels, out_channels, kernel_size, stride, padding, dilation,
            True, output_padding, groups, bias, padding_mode, **factory_kwargs)

    def _get_name(self):
        return 'QuantizedConvTranspose2d'

    def set_weight_bias(self, w: torch.Tensor, b: Optional[torch.Tensor]) -> None:
        self._packed_params = torch.ops.quantized.conv_transpose2d_prepack(
            w, b, self.stride, self.padding, self.output_padding, self.dilation,
            self.groups)

    def _weight_bias(self):
        w, b = torch.ops.quantized.conv2d_unpack(self._packed_params)
        return w, b

    def weight(self):
        (w, _) = self._weight_bias()
        return w

    def bias(self):
        (_, b) = self._weight_bias()
        return b

    def forward(self, input):
        # Temporarily using len(shape) instead of ndim due to JIT issue
        # https://github.com/pytorch/pytorch/issues/23890
        if len(input.shape) != 4:
            raise ValueError("Input shape must be `(N, C, H, W)`!")
        return ops.quantized.conv_transpose2d(
            input, self._packed_params, self.scale, self.zero_point)

    @classmethod
    def from_reference(cls, ref_qconvt, output_scale, output_zero_point):
        return _ConvTransposeNd.from_reference(cls, ref_qconvt, output_scale, output_zero_point)


class ConvTranspose3d(_ConvTransposeNd):
    r"""Applies a 3D transposed convolution operator over an input image
    composed of several input planes.
    For details on input arguments, parameters, and implementation see
    :class:`~torch.nn.ConvTranspose3d`.

    .. note:: Currently only the FBGEMM engine is implemented.
        Please, set the `torch.backends.quantized.engine = 'fbgemm'`

    For special notes, please, see :class:`~torch.ao.nn.quantized.Conv3d`

    Attributes:
        weight (Tensor):     packed tensor derived from the learnable weight
                             parameter.
        scale (Tensor):      scalar for the output scale
        zero_point (Tensor): scalar for the output zero point
    See :class:`~torch.nn.ConvTranspose3d` for other attributes.

    Examples::

        >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_QENGINE)
        >>> torch.backends.quantized.engine = 'fbgemm'
        >>> from torch.ao.nn import quantized as nnq
        >>> # With cubic kernels and equal stride
        >>> m = nnq.ConvTranspose3d(16, 33, 3, stride=2)
        >>> # non-cubic kernels and unequal stride and with padding
        >>> m = nnq.ConvTranspose3d(16, 33, (3, 3, 5), stride=(2, 1, 1), padding=(4, 2, 2))
        >>> input = torch.randn(20, 16, 50, 100, 100)
        >>> q_input = torch.quantize_per_tensor(input, scale=1.0, zero_point=0, dtype=torch.quint8)
        >>> output = m(q_input)
        >>> # exact output size can be also specified as an argument
        >>> input = torch.randn(1, 16, 12, 12, 12)
        >>> q_input = torch.quantize_per_tensor(input, scale=1.0, zero_point=0, dtype=torch.quint8)
        >>> downsample = nnq.Conv3d(16, 16, 3, stride=2, padding=1)
        >>> upsample = nnq.ConvTranspose3d(16, 16, 3, stride=2, padding=1)
        >>> h = downsample(q_input)
        >>> h.size()
        torch.Size([1, 16, 6, 6, 6])
        >>> # xdoctest: +SKIP("FIXME: output_size is not a parameter)
        >>> output = upsample(h, output_size=input.size())
        >>> output.size()
        torch.Size([1, 16, 12, 12, 12])
    """

    _FLOAT_MODULE = nn.ConvTranspose3d

    def __init__(self, in_channels, out_channels, kernel_size, stride=1,
                 padding=0, output_padding=0, groups=1, bias=True,
                 dilation=1, padding_mode='zeros', device=None, dtype=None):
        factory_kwargs = {'device': device, 'dtype': dtype}
        kernel_size = _triple(kernel_size)
        stride = _triple(stride)
        padding = _triple(padding)
        dilation = _triple(dilation)
        output_padding = _triple(output_padding)

        super().__init__(
            in_channels, out_channels, kernel_size, stride, padding, dilation,
            True, output_padding, groups, bias, padding_mode, **factory_kwargs)

    def _get_name(self):
        return 'QuantizedConvTranspose3d'

    def set_weight_bias(self, w: torch.Tensor, b: Optional[torch.Tensor]) -> None:
        self._packed_params = torch.ops.quantized.conv_transpose3d_prepack(
            w, b, self.stride, self.padding, self.output_padding, self.dilation,
            self.groups)

    def _weight_bias(self):
        w, b = torch.ops.quantized.conv3d_unpack(self._packed_params)
        return w, b

    def weight(self):
        (w, _) = self._weight_bias()
        return w

    def bias(self):
        (_, b) = self._weight_bias()
        return b

    def forward(self, input):
        # Temporarily using len(shape) instead of ndim due to JIT issue
        # https://github.com/pytorch/pytorch/issues/23890
        if len(input.shape) != 5:
            raise ValueError("Input shape must be `(N, C, T, H, W)`!")
        return ops.quantized.conv_transpose3d(
            input, self._packed_params, self.scale, self.zero_point)

    @classmethod
    def from_reference(cls, ref_qconvt, output_scale, output_zero_point):
        return _ConvTransposeNd.from_reference(cls, ref_qconvt, output_scale, output_zero_point)