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Source code for torch.ao.nn.qat.dynamic.modules.linear

-import torch
-
-__all__ = ["Linear"]
-
-
[docs]class Linear(torch.ao.nn.qat.Linear):
-    r"""
-    A linear module attached with FakeQuantize modules for weight,
-    used for dynamic quantization aware training.
-
-    We adopt the same interface as `torch.nn.Linear`, please see
-    https://pytorch.org/docs/stable/nn.html#torch.nn.Linear
-    for documentation.
-
-    Similar to `torch.nn.Linear`, with FakeQuantize modules initialized to
-    default.
-    """
-
-    def __init__(self, in_features, out_features, bias=True,
-                 qconfig=None, device=None, dtype=None) -> None:
-        super().__init__(in_features, out_features, bias, qconfig, device, dtype)
-        if not torch.ao.quantization.qconfig._activation_is_memoryless(qconfig):
-            raise ValueError(
-                "Dynamic QAT requires a memoryless observer." +
-                "This means a MovingAverage observer with averaging constant equal to 1"
-            )

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

-import torch
-import torch.nn as nn
-from torch.nn.modules.utils import _single, _pair, _triple
-from torch.ao.nn.intrinsic import _FusedModule
-from typing import Tuple, TypeVar, Union
-from torch.nn.common_types import _size_1_t, _size_2_t, _size_3_t
-
-__all__ = [
-    "Conv1d",
-    "Conv2d",
-    "Conv3d"
-]
-
-MOD = TypeVar('MOD', bound=nn.modules.conv._ConvNd)
-
-class _ConvNd(nn.modules.conv._ConvNd):
-
-    _FLOAT_MODULE = MOD
-
-    def __init__(self,
-                 in_channels: int,
-                 out_channels: int,
-                 kernel_size: Tuple[int, ...],
-                 stride: Tuple[int, ...],
-                 padding: Tuple[int, ...],
-                 dilation: Tuple[int, ...],
-                 transposed: bool,
-                 output_padding: Tuple[int, ...],
-                 groups: int,
-                 bias: bool,
-                 padding_mode: str,
-                 qconfig=None,
-                 device=None,
-                 dtype=None) -> None:
-        factory_kwargs = {"device": device, "dtype": dtype}
-        nn.modules.conv._ConvNd.__init__(self, in_channels, out_channels, kernel_size,
-                                         stride, padding, dilation, transposed,
-                                         output_padding, groups, bias, padding_mode, **factory_kwargs)
-        assert qconfig, 'qconfig must be provided for QAT module'
-        self.qconfig = qconfig
-        self.weight_fake_quant = qconfig.weight(factory_kwargs=factory_kwargs)
-
-    def forward(self, input):
-        return self._conv_forward(input, self.weight_fake_quant(self.weight), self.bias)
-
-    @staticmethod
-    def from_float(cls, mod):
-        r"""Create a qat module from a float module
-
-            Args:
-               `mod`: a float module, either produced by torch.ao.quantization utilities
-               or directly from user
-        """
-        assert type(mod) == cls._FLOAT_MODULE, (
-            "qat."
-            + cls.__name__
-            + ".from_float only works for "
-            + cls._FLOAT_MODULE.__name__  # type: ignore[attr-defined]
-        )
-        assert hasattr(mod, 'qconfig'), 'Input float module must have qconfig defined'
-        assert mod.qconfig, 'Input float module must have a valid qconfig'
-        if issubclass(type(mod), _FusedModule):
-            mod = mod[0]  # type: ignore[index]
-        qconfig = mod.qconfig
-        qat_conv = cls(mod.in_channels, mod.out_channels, mod.kernel_size,
-                       stride=mod.stride, padding=mod.padding, dilation=mod.dilation,
-                       groups=mod.groups, bias=mod.bias is not None,
-                       padding_mode=mod.padding_mode, qconfig=qconfig)
-        qat_conv.weight = mod.weight
-        qat_conv.bias = mod.bias
-        return qat_conv
-
-    def to_float(self):
-        """ This works for both single qat conv, and the qat conv - relu modules
-        to convert the qat module to a floating point module
-        """
-        cls = type(self)
-        conv = cls._FLOAT_CONV_MODULE(  # type: ignore[attr-defined, operator]
-            self.in_channels,
-            self.out_channels,
-            self.kernel_size,  # type: ignore[arg-type]
-            self.stride,  # type: ignore[arg-type]
-            self.padding,  # type: ignore[arg-type]
-            self.dilation,  # type: ignore[arg-type]
-            self.groups,
-            self.bias is not None,
-            self.padding_mode)
-        conv.weight = torch.nn.Parameter(self.weight.detach())
-        if self.bias is not None:
-            conv.bias = torch.nn.Parameter(self.bias.detach())
-        # conv relu
-        if issubclass(cls, _FusedModule):
-            modules = [conv]
-            assert hasattr(cls, "_FLOAT_RELU_MODULE")
-            relu = cls._FLOAT_RELU_MODULE()  # type: ignore[attr-defined]
-            modules.append(relu)
-            fused = cls._FLOAT_MODULE(*modules)  # type: ignore[arg-type, attr-defined, operator]
-            fused.train(self.training)
-            return fused
-        else:
-            return conv
-
-class Conv1d(_ConvNd, nn.Conv1d):
-    r"""
-    A Conv1d module attached with FakeQuantize modules for weight,
-    used for quantization aware training.
-
-    We adopt the same interface as :class:`~torch.nn.Conv1d`
-
-    Similar to :class:`~torch.nn.Conv2d`, with FakeQuantize modules initialized to
-    default.
-
-    Attributes:
-        weight_fake_quant: fake quant module for weight
-    """
-    _FLOAT_MODULE = nn.Conv1d
-    _FLOAT_CONV_MODULE = nn.Conv1d
-
-    def __init__(self,
-                 in_channels: int,
-                 out_channels: int,
-                 kernel_size: _size_1_t,
-                 stride: _size_1_t = 1,
-                 padding: Union[str, _size_1_t] = 0,
-                 dilation: _size_1_t = 1,
-                 groups: int = 1,
-                 bias: bool = True,
-                 padding_mode: str = 'zeros',
-                 qconfig=None,
-                 device=None,
-                 dtype=None) -> None:
-        kernel_size_ = _single(kernel_size)
-        stride_ = _single(stride)
-        padding_ = padding if isinstance(padding, str) else _single(padding)
-        dilation_ = _single(dilation)
-        super().__init__(
-            in_channels,
-            out_channels,
-            kernel_size_,
-            stride=stride_,
-            padding=padding_,
-            dilation=dilation_,
-            transposed=False,
-            output_padding=_single(0),
-            groups=groups,
-            bias=bias,
-            padding_mode=padding_mode,
-            qconfig=qconfig,
-            device=device,
-            dtype=dtype)
-
-    @classmethod
-    def from_float(cls, mod):
-        return super().from_float(cls, mod)
-
-
[docs]class Conv2d(_ConvNd, nn.Conv2d):
-    r"""
-    A Conv2d module attached with FakeQuantize modules for weight,
-    used for quantization aware training.
-
-    We adopt the same interface as `torch.nn.Conv2d`, please see
-    https://pytorch.org/docs/stable/nn.html?highlight=conv2d#torch.nn.Conv2d
-    for documentation.
-
-    Similar to `torch.nn.Conv2d`, with FakeQuantize modules initialized to
-    default.
-
-    Attributes:
-        weight_fake_quant: fake quant module for weight
-    """
-    _FLOAT_MODULE = nn.Conv2d
-    _FLOAT_CONV_MODULE = nn.Conv2d
-
-    def __init__(self,
-                 in_channels: int,
-                 out_channels: int,
-                 kernel_size: _size_2_t,
-                 stride: _size_2_t = 1,
-                 padding: Union[str, _size_2_t] = 0,
-                 dilation: _size_2_t = 1,
-                 groups: int = 1,
-                 bias: bool = True,
-                 padding_mode: str = 'zeros',
-                 qconfig=None,
-                 device=None,
-                 dtype=None) -> None:
-        kernel_size_ = _pair(kernel_size)
-        stride_ = _pair(stride)
-        padding_ = padding if isinstance(padding, str) else _pair(padding)
-        dilation_ = _pair(dilation)
-        super().__init__(
-            in_channels,
-            out_channels,
-            kernel_size_,
-            stride=stride_,
-            padding=padding_,
-            dilation=dilation_,
-            transposed=False,
-            output_padding=_pair(0),
-            groups=groups,
-            bias=bias,
-            padding_mode=padding_mode,
-            qconfig=qconfig,
-            device=device,
-            dtype=dtype)
-
-    def forward(self, input):
-        return self._conv_forward(input, self.weight_fake_quant(self.weight), self.bias)
-
-    @classmethod
-    def from_float(cls, mod):
-        return super().from_float(cls, mod)

-
-
[docs]class Conv3d(_ConvNd, nn.Conv3d):
-    r"""
-    A Conv3d module attached with FakeQuantize modules for weight,
-    used for quantization aware training.
-
-    We adopt the same interface as `torch.nn.Conv3d`, please see
-    https://pytorch.org/docs/stable/nn.html?highlight=conv3d#torch.nn.Conv3d
-    for documentation.
-
-    Similar to `torch.nn.Conv3d`, with FakeQuantize modules initialized to
-    default.
-
-    Attributes:
-        weight_fake_quant: fake quant module for weight
-    """
-    _FLOAT_MODULE = nn.Conv3d
-    _FLOAT_CONV_MODULE = nn.Conv3d
-
-    def __init__(self,
-                 in_channels: int,
-                 out_channels: int,
-                 kernel_size: _size_3_t,
-                 stride: _size_3_t = 1,
-                 padding: Union[str, _size_3_t] = 0,
-                 dilation: _size_3_t = 1,
-                 groups: int = 1,
-                 bias: bool = True,
-                 padding_mode: str = 'zeros',
-                 qconfig=None,
-                 device=None,
-                 dtype=None) -> None:
-        kernel_size_ = _triple(kernel_size)
-        stride_ = _triple(stride)
-        padding_ = padding if isinstance(padding, str) else _triple(padding)
-        dilation_ = _triple(dilation)
-        super().__init__(
-            in_channels,
-            out_channels,
-            kernel_size_,
-            stride=stride_,
-            padding=padding_,
-            dilation=dilation_,
-            transposed=False,
-            output_padding=_triple(0),
-            groups=groups,
-            bias=bias,
-            padding_mode=padding_mode,
-            qconfig=qconfig,
-            device=device,
-            dtype=dtype)
-
-    def forward(self, input):
-        return self._conv_forward(input, self.weight_fake_quant(self.weight), self.bias)
-
-    @classmethod
-    def from_float(cls, mod):
-        return super().from_float(cls, mod)

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Source code for torch.ao.nn.qat.modules.linear

-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from torch.ao.nn.intrinsic import LinearReLU
-from torch.nn.utils.parametrize import (
-    is_parametrized,
-    type_before_parametrizations,
-    transfer_parametrizations_and_params,
-)
-
-__all__ = [
-    "Linear"
-]
-
-
[docs]class Linear(nn.Linear):
-    r"""
-    A linear module attached with FakeQuantize modules for weight,
-    used for quantization aware training.
-
-    We adopt the same interface as `torch.nn.Linear`, please see
-    https://pytorch.org/docs/stable/nn.html#torch.nn.Linear
-    for documentation.
-
-    Similar to `torch.nn.Linear`, with FakeQuantize modules initialized to
-    default.
-
-    Attributes:
-        weight: fake quant module for weight
-    """
-    _FLOAT_MODULE = nn.Linear
-
-    def __init__(self, in_features, out_features, bias=True,
-                 qconfig=None, device=None, dtype=None) -> None:
-        factory_kwargs = {'device': device, 'dtype': dtype}
-        super().__init__(in_features, out_features, bias, **factory_kwargs)
-        assert qconfig, 'qconfig must be provided for QAT module'
-        self.qconfig = qconfig
-        self.weight_fake_quant = qconfig.weight(factory_kwargs=factory_kwargs)
-
-    def forward(self, input):
-        return F.linear(input, self.weight_fake_quant(self.weight), self.bias)
-
-
[docs]    @classmethod
-    def from_float(cls, mod):
-        r"""Create a qat module from a float module or qparams_dict
-            Args: `mod` a float module, either produced by torch.ao.quantization utilities
-            or directly from user
-        """
-        assert type_before_parametrizations(mod) == cls._FLOAT_MODULE, (
-            " qat."
-            + cls.__name__
-            + ".from_float only works for "
-            + cls._FLOAT_MODULE.__name__
-        )
-        assert hasattr(mod, "qconfig"), "Input float module must have qconfig defined"
-        assert mod.qconfig, "Input float module must have a valid qconfig"
-        if type_before_parametrizations(mod) == LinearReLU:
-            mod = mod[0]
-
-        qconfig = mod.qconfig
-        qat_linear = cls(mod.in_features, mod.out_features, bias=mod.bias is not None, qconfig=qconfig)
-
-        if is_parametrized(mod, "weight"):
-            transfer_parametrizations_and_params(mod, qat_linear, "weight")
-        else:
-            qat_linear.weight = mod.weight
-
-        if is_parametrized(mod, "bias"):
-            transfer_parametrizations_and_params(mod, qat_linear, "bias")
-        else:
-            qat_linear.bias = mod.bias
-
-        return qat_linear

-
-    def to_float(self):
-        linear = torch.nn.Linear(self.in_features, self.out_features, self.bias is not None)
-        linear.weight = torch.nn.Parameter(self.weight.detach())
-        if self.bias is not None:
-            linear.bias = torch.nn.Parameter(self.bias.detach())
-        linear.train(self.training)
-        return linear

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Source code for torch.ao.nn.quantizable.modules.activation

-import torch
-import torch.jit  # this is needed to avoid a circular import
-from torch import nn
-import torch.nn.functional as nnF
-
-from torch import Tensor
-from typing import Optional, Tuple
-
-import warnings
-
-__all__ = [
-    "MultiheadAttention"
-]
-
-
[docs]class MultiheadAttention(nn.MultiheadAttention):
-    _FLOAT_MODULE = nn.MultiheadAttention
-
-    r"""Quantizable implementation of the MultiheadAttention.
-
-    Note::
-        Please, refer to :class:`~torch.nn.MultiheadAttention` for more
-        information
-
-    Allows the model to jointly attend to information from different
-    representation subspaces.
-    See reference: Attention Is All You Need
-
-    The original MHA module is not quantizable.
-    This reimplements it by explicitly instantiating the linear layers.
-
-    .. math::
-        \text{MultiHead}(Q, K, V) = \text{Concat}(head_1,\dots,head_h)W^O
-        \text{where} head_i = \text{Attention}(QW_i^Q, KW_i^K, VW_i^V)
-
-    Args:
-        embed_dim: total dimension of the model.
-        num_heads: parallel attention heads.
-        dropout: a Dropout layer on attn_output_weights. Default: 0.0.
-        bias: add bias as module parameter. Default: True.
-        add_bias_kv: add bias to the key and value sequences at dim=0.
-        add_zero_attn: add a new batch of zeros to the key and
-                       value sequences at dim=1.
-        kdim: total number of features in key. Default: None.
-        vdim: total number of features in value. Default: None.
-        batch_first: If ``True``, then the input and output tensors are provided
-            as (batch, seq, feature). Default: ``False`` (seq, batch, feature).
-
-    Note that if :attr:`kdim` and :attr:`vdim` are None, they will be set
-    to :attr:`embed_dim` such that query, key, and value have the same
-    number of features.
-
-    Examples::
-
-        >>> import torch.ao.nn.quantizable as nnqa
-        >>> multihead_attn = nnqa.MultiheadAttention(embed_dim, num_heads)
-        >>> attn_output, attn_output_weights = multihead_attn(query, key, value)
-
-    Note::
-        Please, follow the quantization flow to convert the quantizable MHA.
-    """
-    __constants__ = ['batch_first']
-
-    def __init__(self, embed_dim: int, num_heads: int,
-                 dropout: float = 0., bias: bool = True,
-                 add_bias_kv: bool = False, add_zero_attn: bool = False,
-                 kdim: int = None, vdim: int = None, batch_first: bool = False,
-                 device=None, dtype=None) -> None:
-        factory_kwargs = {'device': device, 'dtype': dtype}
-        super().__init__(embed_dim, num_heads, dropout,
-                         bias, add_bias_kv,
-                         add_zero_attn, kdim, vdim, batch_first,
-                         **factory_kwargs)
-        self.linear_Q = nn.Linear(self.embed_dim, self.embed_dim, bias=bias, **factory_kwargs)
-        self.linear_K = nn.Linear(self.kdim, self.embed_dim, bias=bias, **factory_kwargs)
-        self.linear_V = nn.Linear(self.vdim, self.embed_dim, bias=bias, **factory_kwargs)
-        # for the type: ignore, see https://github.com/pytorch/pytorch/issues/58969
-        self.out_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=bias, **factory_kwargs)  # type: ignore[assignment]
-
-        # Functionals
-        self.q_scaling_product = torch.ao.nn.quantized.FloatFunctional()
-        # note: importing torch.ao.nn.quantized at top creates a circular import
-
-        # Quant/Dequant
-        self.quant_attn_output = torch.ao.quantization.QuantStub()
-        self.quant_attn_output_weights = torch.ao.quantization.QuantStub()
-        self.dequant_q = torch.ao.quantization.DeQuantStub()
-        self.dequant_k = torch.ao.quantization.DeQuantStub()
-        self.dequant_v = torch.ao.quantization.DeQuantStub()
-
-    def _get_name(self):
-        return 'QuantizableMultiheadAttention'
-
-    @classmethod
-    def from_float(cls, other):
-        assert type(other) == cls._FLOAT_MODULE
-        assert hasattr(other, 'qconfig'), "The float module must have 'qconfig'"
-        # Setting the dropout to 0.0!
-        observed = cls(other.embed_dim, other.num_heads, other.dropout,
-                       (other.in_proj_bias is not None),
-                       (other.bias_k is not None),
-                       other.add_zero_attn, other.kdim, other.vdim,
-                       other.batch_first)
-        observed.bias_k = other.bias_k
-        observed.bias_v = other.bias_v
-        observed.qconfig = other.qconfig
-
-        # Set the linear weights
-        # for the type: ignores, see https://github.com/pytorch/pytorch/issues/58969
-        observed.out_proj.weight = other.out_proj.weight  # type: ignore[has-type]
-        observed.out_proj.bias = other.out_proj.bias  # type: ignore[has-type]
-        if other._qkv_same_embed_dim:
-            # Use separate params
-            bias = other.in_proj_bias
-            _start = 0
-            _end = _start + other.embed_dim
-            weight = other.in_proj_weight[_start:_end, :]
-            if bias is not None:
-                bias = torch.nn.Parameter(bias[_start:_end], bias.requires_grad)
-            observed.linear_Q.weight = torch.nn.Parameter(weight,
-                                                          weight.requires_grad)
-            observed.linear_Q.bias = bias
-
-            bias = other.in_proj_bias
-            _start = _end
-            _end = _start + other.embed_dim
-            weight = other.in_proj_weight[_start:_end, :]
-            if bias is not None:
-                bias = torch.nn.Parameter(bias[_start:_end], bias.requires_grad)
-            observed.linear_K.weight = torch.nn.Parameter(weight,
-                                                          weight.requires_grad)
-            observed.linear_K.bias = bias
-
-            bias = other.in_proj_bias
-            _start = _end
-            weight = other.in_proj_weight[_start:, :]
-            if bias is not None:
-                bias = torch.nn.Parameter(bias[_start:], bias.requires_grad)
-            observed.linear_V.weight = torch.nn.Parameter(weight,
-                                                          weight.requires_grad)
-            observed.linear_V.bias = bias
-        else:
-            observed.linear_Q.weight = nn.Parameter(other.q_proj_weight)
-            observed.linear_K.weight = nn.Parameter(other.k_proj_weight)
-            observed.linear_V.weight = nn.Parameter(other.v_proj_weight)
-            if other.in_proj_bias is None:
-                observed.linear_Q.bias = None  # type: ignore[assignment]
-                observed.linear_K.bias = None  # type: ignore[assignment]
-                observed.linear_V.bias = None  # type: ignore[assignment]
-            else:
-                observed.linear_Q.bias = nn.Parameter(other.in_proj_bias[0:other.embed_dim])
-                observed.linear_K.bias = nn.Parameter(other.in_proj_bias[other.embed_dim:(other.embed_dim * 2)])
-                observed.linear_V.bias = nn.Parameter(other.in_proj_bias[(other.embed_dim * 2):])
-        observed.eval()
-        # Explicit prepare
-        observed = torch.ao.quantization.prepare(observed, inplace=True)
-        return observed
-
-
[docs]    @torch.jit.unused
-    def dequantize(self):
-        r"""Utility to convert the quantized MHA back to float.
-
-        The motivation for this is that it is not trivial to conver the weights
-        from the format that is used in the quantized version back to the
-        float.
-        """
-        fp = self._FLOAT_MODULE(self.embed_dim, self.num_heads, self.dropout,
-                                (self.in_proj_bias is not None),
-                                (self.bias_k is not None),
-                                self.add_zero_attn, self.kdim, self.vdim, self.batch_first)
-        assert fp._qkv_same_embed_dim == self._qkv_same_embed_dim
-        if self.bias_k is not None:
-            fp.bias_k = nn.Parameter(self.bias_k.dequantize())
-        if self.bias_v is not None:
-            fp.bias_v = nn.Parameter(self.bias_v.dequantize())
-
-        # Set the linear weights
-        # Note: Because the linear layers are quantized, mypy does not nkow how
-        # to deal with them -- might need to ignore the typing checks.
-        # for the type: ignore[has-type], see https://github.com/pytorch/pytorch/issues/58969
-        w, b = self.out_proj._weight_bias()  # type: ignore[operator, has-type]
-        fp.out_proj.weight = nn.Parameter(w.dequantize())
-        if b is not None:
-            fp.out_proj.bias = nn.Parameter(b)
-
-        wQ, bQ = self.linear_Q._weight_bias()  # type: ignore[operator]
-        wQ = wQ.dequantize()
-        wK, bK = self.linear_K._weight_bias()  # type: ignore[operator]
-        wK = wK.dequantize()
-        wV, bV = self.linear_V._weight_bias()  # type: ignore[operator]
-        wV = wV.dequantize()
-        if fp._qkv_same_embed_dim:
-            # Use separate params
-            _start = 0
-            _end = _start + fp.embed_dim
-            fp.in_proj_weight[_start:_end, :] = wQ
-            if fp.in_proj_bias is not None:
-                assert all(bQ == 0)
-                fp.in_proj_bias[_start:_end] = bQ
-
-            _start = _end
-            _end = _start + fp.embed_dim
-            fp.in_proj_weight[_start:_end, :] = wK
-            if fp.in_proj_bias is not None:
-                assert all(bK == 0)
-                fp.in_proj_bias[_start:_end] = bK
-
-            _start = _end
-            fp.in_proj_weight[_start:, :] = wV
-            if fp.in_proj_bias is not None:
-                assert all(bV == 0)
-                fp.in_proj_bias[_start:] = bV
-        else:
-            fp.q_proj_weight = nn.Parameter(wQ)
-            fp.k_proj_weight = nn.Parameter(wK)
-            fp.v_proj_weight = nn.Parameter(wV)
-            if fp.in_proj_bias is None:
-                self.linear_Q.bias = None
-                self.linear_K.bias = None
-                self.linear_V.bias = None
-            else:
-                fp.in_proj_bias[0:fp.embed_dim] = bQ
-                fp.in_proj_bias[fp.embed_dim:(fp.embed_dim * 2)] = bK
-                fp.in_proj_bias[(fp.embed_dim * 2):] = bV
-
-        return fp

-
-
-    @classmethod
-    def from_observed(cls, other):
-        # The whole flow is float -> observed -> quantized
-        # This class does float -> observed only
-        # See nn.quantized.MultiheadAttention
-        raise NotImplementedError("It looks like you are trying to prepare an "
-                                  "MHA module. Please, see "
-                                  "the examples on quantizable MHAs.")
-
-
[docs]    def forward(self,
-                query: Tensor,
-                key: Tensor,
-                value: Tensor,
-                key_padding_mask: Optional[Tensor] = None,
-                need_weights: bool = True,
-                attn_mask: Optional[Tensor] = None,
-                average_attn_weights: bool = True,
-                is_causal: bool = False) -> Tuple[Tensor, Optional[Tensor]]:
-        r"""
-    Note::
-        Please, refer to :func:`~torch.nn.MultiheadAttention.forward` for more
-        information
-
-    Args:
-        query, key, value: map a query and a set of key-value pairs to an output.
-            See "Attention Is All You Need" for more details.
-        key_padding_mask: if provided, specified padding elements in the key will
-            be ignored by the attention. When given a binary mask and a value is True,
-            the corresponding value on the attention layer will be ignored.
-        need_weights: output attn_output_weights.
-        attn_mask: 2D or 3D mask that prevents attention to certain positions. A 2D mask will be broadcasted for all
-            the batches while a 3D mask allows to specify a different mask for the entries of each batch.
-
-    Shape:
-        - Inputs:
-        - query: :math:`(L, N, E)` where L is the target sequence length, N is the batch size, E is
-          the embedding dimension. :math:`(N, L, E)` if ``batch_first`` is ``True``.
-        - key: :math:`(S, N, E)`, where S is the source sequence length, N is the batch size, E is
-          the embedding dimension. :math:`(N, S, E)` if ``batch_first`` is ``True``.
-        - value: :math:`(S, N, E)` where S is the source sequence length, N is the batch size, E is
-          the embedding dimension. :math:`(N, S, E)` if ``batch_first`` is ``True``.
-        - key_padding_mask: :math:`(N, S)` where N is the batch size, S is the source sequence length.
-          If a BoolTensor is provided, the positions with the
-          value of ``True`` will be ignored while the position with the value of ``False`` will be unchanged.
-        - attn_mask: 2D mask :math:`(L, S)` where L is the target sequence length, S is the source sequence length.
-          3D mask :math:`(N*num_heads, L, S)` where N is the batch size, L is the target sequence length,
-          S is the source sequence length. attn_mask ensure that position i is allowed to attend the unmasked
-          positions. If a BoolTensor is provided, positions with ``True``
-          is not allowed to attend while ``False`` values will be unchanged. If a FloatTensor
-          is provided, it will be added to the attention weight.
-        - is_causal: If specified, applies a causal mask as attention mask. Mutually exclusive with providing attn_mask.
-          Default: ``False``.
-        - average_attn_weights: If true, indicates that the returned ``attn_weights`` should be averaged across
-          heads. Otherwise, ``attn_weights`` are provided separately per head. Note that this flag only has an
-          effect when ``need_weights=True.``. Default: True (i.e. average weights across heads)
-
-        - Outputs:
-        - attn_output: :math:`(L, N, E)` where L is the target sequence length, N is the batch size,
-          E is the embedding dimension. :math:`(N, L, E)` if ``batch_first`` is ``True``.
-        - attn_output_weights: If ``average_attn_weights=True``, returns attention weights averaged
-          across heads of shape :math:`(N, L, S)`, where N is the batch size, L is the target sequence length,
-          S is the source sequence length. If ``average_attn_weights=False``, returns attention weights per
-          head of shape :math:`(N, num_heads, L, S)`.
-        """
-        return self._forward_impl(query, key, value, key_padding_mask,
-                                  need_weights, attn_mask, average_attn_weights,
-                                  is_causal)

-
-    def _forward_impl(self,
-                      query: Tensor,
-                      key: Tensor,
-                      value: Tensor,
-                      key_padding_mask: Optional[Tensor] = None,
-                      need_weights: bool = True,
-                      attn_mask: Optional[Tensor] = None,
-                      average_attn_weights: bool = True,
-                      is_causal: bool = False) -> Tuple[Tensor, Optional[Tensor]]:
-        # This version will not deal with the static key/value pairs.
-        # Keeping it here for future changes.
-        #
-        # TODO: This method has some duplicate lines with the
-        # `torch.nn.functional.multi_head_attention`. Will need to refactor.
-        static_k = None
-        static_v = None
-
-        if attn_mask is not None and is_causal:
-            raise AssertionError("Only allow causal mask or attn_mask")
-
-        if is_causal:
-            raise AssertionError("causal mask not supported by AO MHA module")
-
-        if self.batch_first:
-            query, key, value = [x.transpose(0, 1) for x in (query, key, value)]
-
-        tgt_len, bsz, embed_dim_to_check = query.size()
-        assert self.embed_dim == embed_dim_to_check
-        # allow MHA to have different sizes for the feature dimension
-        assert key.size(0) == value.size(0) and key.size(1) == value.size(1)
-
-        head_dim = self.embed_dim // self.num_heads
-        assert head_dim * self.num_heads == self.embed_dim, "embed_dim must be divisible by num_heads"
-        scaling = float(head_dim) ** -0.5
-
-        q = self.linear_Q(query)
-        k = self.linear_K(key)
-        v = self.linear_V(value)
-
-        q = self.q_scaling_product.mul_scalar(q, scaling)
-
-        if attn_mask is not None:
-            if attn_mask.dtype == torch.uint8:
-                warnings.warn("Byte tensor for attn_mask in nn.MultiheadAttention is deprecated. Use bool tensor instead.")
-                attn_mask = attn_mask.to(torch.bool)
-            assert attn_mask.is_floating_point() or attn_mask.dtype == torch.bool, \
-                'Only float and bool types are supported for attn_mask, not {}'.format(attn_mask.dtype)
-
-            if attn_mask.dim() == 2:
-                attn_mask = attn_mask.unsqueeze(0)
-                if list(attn_mask.size()) != [1, query.size(0), key.size(0)]:
-                    raise RuntimeError('The size of the 2D attn_mask is not correct.')
-            elif attn_mask.dim() == 3:
-                if list(attn_mask.size()) != [bsz * self.num_heads, query.size(0), key.size(0)]:
-                    raise RuntimeError('The size of the 3D attn_mask is not correct.')
-            else:
-                raise RuntimeError("attn_mask's dimension {} is not supported".format(attn_mask.dim()))
-            # attn_mask's dim is 3 now.
-
-        # convert ByteTensor key_padding_mask to bool
-        if key_padding_mask is not None and key_padding_mask.dtype == torch.uint8:
-            warnings.warn("Byte tensor for key_padding_mask in nn.MultiheadAttention is deprecated. Use bool tensor instead.")
-            key_padding_mask = key_padding_mask.to(torch.bool)
-        if self.bias_k is not None and self.bias_v is not None:
-            if static_k is None and static_v is None:
-
-                # Explicitly assert that bias_k and bias_v are not None
-                # in a way that TorchScript can understand.
-                bias_k = self.bias_k
-                assert bias_k is not None
-                bias_v = self.bias_v
-                assert bias_v is not None
-
-                k = torch.cat([k, bias_k.repeat(1, bsz, 1)])
-                v = torch.cat([v, bias_v.repeat(1, bsz, 1)])
-                if attn_mask is not None:
-                    attn_mask = nnF.pad(attn_mask, (0, 1))
-                if key_padding_mask is not None:
-                    key_padding_mask = nnF.pad(key_padding_mask, (0, 1))
-            else:
-                assert static_k is None, "bias cannot be added to static key."
-                assert static_v is None, "bias cannot be added to static value."
-        else:
-            assert self.bias_k is None
-            assert self.bias_v is None
-
-        q = q.contiguous().view(tgt_len, bsz * self.num_heads, head_dim).transpose(0, 1)
-        if k is not None:
-            k = k.contiguous().view(-1, bsz * self.num_heads, head_dim).transpose(0, 1)
-        if v is not None:
-            v = v.contiguous().view(-1, bsz * self.num_heads, head_dim).transpose(0, 1)
-
-        if static_k is not None:
-            assert static_k.size(0) == bsz * self.num_heads
-            assert static_k.size(2) == head_dim
-            k = static_k
-
-        if static_v is not None:
-            assert static_v.size(0) == bsz * self.num_heads
-            assert static_v.size(2) == head_dim
-            v = static_v
-
-        src_len = k.size(1)
-
-        if key_padding_mask is not None:
-            assert key_padding_mask.size(0) == bsz
-            assert key_padding_mask.size(1) == src_len
-
-        if self.add_zero_attn:
-            src_len += 1
-            k_zeros = torch.zeros((k.size(0), 1) + k.size()[2:])
-            if k.is_quantized:
-                k_zeros = torch.quantize_per_tensor(k_zeros, k.q_scale(), k.q_zero_point(), k.dtype)
-            k = torch.cat([k, k_zeros], dim=1)
-            v_zeros = torch.zeros((v.size(0), 1) + k.size()[2:])
-            if v.is_quantized:
-                v_zeros = torch.quantize_per_tensor(v_zeros, v.q_scale(), v.q_zero_point(), v.dtype)
-            v = torch.cat([v, v_zeros], dim=1)
-
-            if attn_mask is not None:
-                attn_mask = nnF.pad(attn_mask, (0, 1))
-            if key_padding_mask is not None:
-                key_padding_mask = nnF.pad(key_padding_mask, (0, 1))
-
-        # Leaving the quantized zone here
-        q = self.dequant_q(q)
-        k = self.dequant_k(k)
-        v = self.dequant_v(v)
-        attn_output_weights = torch.bmm(q, k.transpose(1, 2))
-        assert list(attn_output_weights.size()) == [bsz * self.num_heads, tgt_len, src_len]
-
-        if attn_mask is not None:
-            if attn_mask.dtype == torch.bool:
-                attn_output_weights.masked_fill_(attn_mask, float('-inf'))
-            else:
-                attn_output_weights += attn_mask
-
-        if key_padding_mask is not None:
-            attn_output_weights = attn_output_weights.view(bsz, self.num_heads, tgt_len, src_len)
-            attn_output_weights = attn_output_weights.masked_fill(
-                key_padding_mask.unsqueeze(1).unsqueeze(2),
-                float('-inf'),
-            )
-            attn_output_weights = attn_output_weights.view(bsz * self.num_heads, tgt_len, src_len)
-
-        attn_output_weights = nnF.softmax(
-            attn_output_weights, dim=-1)
-        attn_output_weights = nnF.dropout(attn_output_weights, p=self.dropout, training=self.training)
-
-        attn_output = torch.bmm(attn_output_weights, v)
-        assert list(attn_output.size()) == [bsz * self.num_heads, tgt_len, head_dim]
-        if self.batch_first:
-            attn_output = attn_output.view(bsz, tgt_len, self.embed_dim)
-        else:
-            attn_output = attn_output.transpose(0, 1).contiguous().view(tgt_len, bsz, self.embed_dim)
-
-        # Reentering the quantized zone
-        attn_output = self.quant_attn_output(attn_output)
-        # for the type: ignore[has-type], see https://github.com/pytorch/pytorch/issues/58969
-        attn_output = self.out_proj(attn_output)  # type: ignore[has-type]
-        attn_output_weights = self.quant_attn_output_weights(attn_output_weights)
-
-        if need_weights:
-            # average attention weights over heads
-            attn_output_weights = attn_output_weights.view(bsz, self.num_heads, tgt_len, src_len)
-            if average_attn_weights:
-                attn_output_weights = attn_output_weights.mean(dim=1)
-            return attn_output, attn_output_weights
-        else:
-            return attn_output, None

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- - - - - - - - \ No newline at end of file diff --git a/master/_modules/torch/ao/nn/quantizable/modules/rnn.html b/master/_modules/torch/ao/nn/quantizable/modules/rnn.html deleted file mode 100644 index 4c7ab4048e74..000000000000 --- a/master/_modules/torch/ao/nn/quantizable/modules/rnn.html +++ /dev/null @@ -1,1215 +0,0 @@ - - - - - - - - - - - - - torch.ao.nn.quantizable.modules.rnn — PyTorch master documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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Source code for torch.ao.nn.quantizable.modules.rnn

-import numbers
-from typing import Optional, Tuple
-import warnings
-
-import torch
-from torch import Tensor
-
-"""
-We will recreate all the RNN modules as we require the modules to be decomposed
-into its building blocks to be able to observe.
-"""
-
-__all__ = [
-    "LSTMCell",
-    "LSTM"
-]
-
-class LSTMCell(torch.nn.Module):
-    r"""A quantizable long short-term memory (LSTM) cell.
-
-    For the description and the argument types, please, refer to :class:`~torch.nn.LSTMCell`
-
-    Examples::
-
-        >>> import torch.ao.nn.quantizable as nnqa
-        >>> rnn = nnqa.LSTMCell(10, 20)
-        >>> input = torch.randn(6, 10)
-        >>> hx = torch.randn(3, 20)
-        >>> cx = torch.randn(3, 20)
-        >>> output = []
-        >>> for i in range(6):
-        ...     hx, cx = rnn(input[i], (hx, cx))
-        ...     output.append(hx)
-    """
-    _FLOAT_MODULE = torch.nn.LSTMCell
-
-    def __init__(self, input_dim: int, hidden_dim: int, bias: bool = True,
-                 device=None, dtype=None) -> None:
-        factory_kwargs = {'device': device, 'dtype': dtype}
-        super().__init__()
-        self.input_size = input_dim
-        self.hidden_size = hidden_dim
-        self.bias = bias
-
-        self.igates = torch.nn.Linear(input_dim, 4 * hidden_dim, bias=bias, **factory_kwargs)
-        self.hgates = torch.nn.Linear(hidden_dim, 4 * hidden_dim, bias=bias, **factory_kwargs)
-        self.gates = torch.ao.nn.quantized.FloatFunctional()
-
-        self.input_gate = torch.nn.Sigmoid()
-        self.forget_gate = torch.nn.Sigmoid()
-        self.cell_gate = torch.nn.Tanh()
-        self.output_gate = torch.nn.Sigmoid()
-
-        self.fgate_cx = torch.ao.nn.quantized.FloatFunctional()
-        self.igate_cgate = torch.ao.nn.quantized.FloatFunctional()
-        self.fgate_cx_igate_cgate = torch.ao.nn.quantized.FloatFunctional()
-
-        self.ogate_cy = torch.ao.nn.quantized.FloatFunctional()
-
-        self.initial_hidden_state_qparams: Tuple[float, int] = (1.0, 0)
-        self.initial_cell_state_qparams: Tuple[float, int] = (1.0, 0)
-        self.hidden_state_dtype: torch.dtype = torch.quint8
-        self.cell_state_dtype: torch.dtype = torch.quint8
-
-    def forward(self, x: Tensor, hidden: Optional[Tuple[Tensor, Tensor]] = None) -> Tuple[Tensor, Tensor]:
-        if hidden is None or hidden[0] is None or hidden[1] is None:
-            hidden = self.initialize_hidden(x.shape[0], x.is_quantized)
-        hx, cx = hidden
-
-        igates = self.igates(x)
-        hgates = self.hgates(hx)
-        gates = self.gates.add(igates, hgates)
-
-        input_gate, forget_gate, cell_gate, out_gate = gates.chunk(4, 1)
-
-        input_gate = self.input_gate(input_gate)
-        forget_gate = self.forget_gate(forget_gate)
-        cell_gate = self.cell_gate(cell_gate)
-        out_gate = self.output_gate(out_gate)
-
-        fgate_cx = self.fgate_cx.mul(forget_gate, cx)
-        igate_cgate = self.igate_cgate.mul(input_gate, cell_gate)
-        fgate_cx_igate_cgate = self.fgate_cx_igate_cgate.add(fgate_cx, igate_cgate)
-        cy = fgate_cx_igate_cgate
-
-        # TODO: make this tanh a member of the module so its qparams can be configured
-        tanh_cy = torch.tanh(cy)
-        hy = self.ogate_cy.mul(out_gate, tanh_cy)
-        return hy, cy
-
-    def initialize_hidden(self, batch_size: int, is_quantized: bool = False) -> Tuple[Tensor, Tensor]:
-        h, c = torch.zeros((batch_size, self.hidden_size)), torch.zeros((batch_size, self.hidden_size))
-        if is_quantized:
-            (h_scale, h_zp) = self.initial_hidden_state_qparams
-            (c_scale, c_zp) = self.initial_cell_state_qparams
-            h = torch.quantize_per_tensor(h, scale=h_scale, zero_point=h_zp, dtype=self.hidden_state_dtype)
-            c = torch.quantize_per_tensor(c, scale=c_scale, zero_point=c_zp, dtype=self.cell_state_dtype)
-        return h, c
-
-    def _get_name(self):
-        return 'QuantizableLSTMCell'
-
-    @classmethod
-    def from_params(cls, wi, wh, bi=None, bh=None):
-        """Uses the weights and biases to create a new LSTM cell.
-
-        Args:
-            wi, wh: Weights for the input and hidden layers
-            bi, bh: Biases for the input and hidden layers
-        """
-        assert (bi is None) == (bh is None)  # Either both None or both have values
-        input_size = wi.shape[1]
-        hidden_size = wh.shape[1]
-        cell = cls(input_dim=input_size, hidden_dim=hidden_size,
-                   bias=(bi is not None))
-        cell.igates.weight = torch.nn.Parameter(wi)
-        if bi is not None:
-            cell.igates.bias = torch.nn.Parameter(bi)
-        cell.hgates.weight = torch.nn.Parameter(wh)
-        if bh is not None:
-            cell.hgates.bias = torch.nn.Parameter(bh)
-        return cell
-
-    @classmethod
-    def from_float(cls, other):
-        assert type(other) == cls._FLOAT_MODULE
-        assert hasattr(other, 'qconfig'), "The float module must have 'qconfig'"
-        observed = cls.from_params(other.weight_ih, other.weight_hh,
-                                   other.bias_ih, other.bias_hh)
-        observed.qconfig = other.qconfig
-        observed.igates.qconfig = other.qconfig
-        observed.hgates.qconfig = other.qconfig
-        return observed
-
-
-class _LSTMSingleLayer(torch.nn.Module):
-    r"""A single one-directional LSTM layer.
-
-    The difference between a layer and a cell is that the layer can process a
-    sequence, while the cell only expects an instantaneous value.
-    """
-    def __init__(self, input_dim: int, hidden_dim: int, bias: bool = True,
-                 device=None, dtype=None) -> None:
-        factory_kwargs = {'device': device, 'dtype': dtype}
-        super().__init__()
-        self.cell = LSTMCell(input_dim, hidden_dim, bias=bias, **factory_kwargs)
-
-    def forward(self, x: Tensor, hidden: Optional[Tuple[Tensor, Tensor]] = None):
-        result = []
-        seq_len = x.shape[0]
-        for i in range(seq_len):
-            hidden = self.cell(x[i], hidden)
-            result.append(hidden[0])  # type: ignore[index]
-        result_tensor = torch.stack(result, 0)
-        return result_tensor, hidden
-
-    @classmethod
-    def from_params(cls, *args, **kwargs):
-        cell = LSTMCell.from_params(*args, **kwargs)
-        layer = cls(cell.input_size, cell.hidden_size, cell.bias)
-        layer.cell = cell
-        return layer
-
-
-class _LSTMLayer(torch.nn.Module):
-    r"""A single bi-directional LSTM layer."""
-    def __init__(self, input_dim: int, hidden_dim: int, bias: bool = True,
-                 batch_first: bool = False, bidirectional: bool = False,
-                 device=None, dtype=None) -> None:
-        factory_kwargs = {'device': device, 'dtype': dtype}
-        super().__init__()
-        self.batch_first = batch_first
-        self.bidirectional = bidirectional
-        self.layer_fw = _LSTMSingleLayer(input_dim, hidden_dim, bias=bias, **factory_kwargs)
-        if self.bidirectional:
-            self.layer_bw = _LSTMSingleLayer(input_dim, hidden_dim, bias=bias, **factory_kwargs)
-
-    def forward(self, x: Tensor, hidden: Optional[Tuple[Tensor, Tensor]] = None):
-        if self.batch_first:
-            x = x.transpose(0, 1)
-        if hidden is None:
-            hx_fw, cx_fw = (None, None)
-        else:
-            hx_fw, cx_fw = hidden
-        hidden_bw: Optional[Tuple[Tensor, Tensor]] = None
-        if self.bidirectional:
-            if hx_fw is None:
-                hx_bw = None
-            else:
-                hx_bw = hx_fw[1]
-                hx_fw = hx_fw[0]
-            if cx_fw is None:
-                cx_bw = None
-            else:
-                cx_bw = cx_fw[1]
-                cx_fw = cx_fw[0]
-            if hx_bw is not None and cx_bw is not None:
-                hidden_bw = hx_bw, cx_bw
-        if hx_fw is None and cx_fw is None:
-            hidden_fw = None
-        else:
-            hidden_fw = torch.jit._unwrap_optional(hx_fw), torch.jit._unwrap_optional(cx_fw)
-        result_fw, hidden_fw = self.layer_fw(x, hidden_fw)
-
-        if hasattr(self, 'layer_bw') and self.bidirectional:
-            x_reversed = x.flip(0)
-            result_bw, hidden_bw = self.layer_bw(x_reversed, hidden_bw)
-            result_bw = result_bw.flip(0)
-
-            result = torch.cat([result_fw, result_bw], result_fw.dim() - 1)
-            if hidden_fw is None and hidden_bw is None:
-                h = None
-                c = None
-            elif hidden_fw is None:
-                (h, c) = torch.jit._unwrap_optional(hidden_bw)
-            elif hidden_bw is None:
-                (h, c) = torch.jit._unwrap_optional(hidden_fw)
-            else:
-                h = torch.stack([hidden_fw[0], hidden_bw[0]], 0)  # type: ignore[list-item]
-                c = torch.stack([hidden_fw[1], hidden_bw[1]], 0)  # type: ignore[list-item]
-        else:
-            result = result_fw
-            h, c = torch.jit._unwrap_optional(hidden_fw)  # type: ignore[assignment]
-
-        if self.batch_first:
-            result.transpose_(0, 1)
-
-        return result, (h, c)
-
-    @classmethod
-    def from_float(cls, other, layer_idx=0, qconfig=None, **kwargs):
-        r"""
-        There is no FP equivalent of this class. This function is here just to
-        mimic the behavior of the `prepare` within the `torch.ao.quantization`
-        flow.
-        """
-        assert hasattr(other, 'qconfig') or (qconfig is not None)
-
-        input_size = kwargs.get('input_size', other.input_size)
-        hidden_size = kwargs.get('hidden_size', other.hidden_size)
-        bias = kwargs.get('bias', other.bias)
-        batch_first = kwargs.get('batch_first', other.batch_first)
-        bidirectional = kwargs.get('bidirectional', other.bidirectional)
-
-        layer = cls(input_size, hidden_size, bias, batch_first, bidirectional)
-        layer.qconfig = getattr(other, 'qconfig', qconfig)
-        wi = getattr(other, f'weight_ih_l{layer_idx}')
-        wh = getattr(other, f'weight_hh_l{layer_idx}')
-        bi = getattr(other, f'bias_ih_l{layer_idx}', None)
-        bh = getattr(other, f'bias_hh_l{layer_idx}', None)
-
-        layer.layer_fw = _LSTMSingleLayer.from_params(wi, wh, bi, bh)
-
-        if other.bidirectional:
-            wi = getattr(other, f'weight_ih_l{layer_idx}_reverse')
-            wh = getattr(other, f'weight_hh_l{layer_idx}_reverse')
-            bi = getattr(other, f'bias_ih_l{layer_idx}_reverse', None)
-            bh = getattr(other, f'bias_hh_l{layer_idx}_reverse', None)
-            layer.layer_bw = _LSTMSingleLayer.from_params(wi, wh, bi, bh)
-        return layer
-
-
-
[docs]class LSTM(torch.nn.Module):
-    r"""A quantizable long short-term memory (LSTM).
-
-    For the description and the argument types, please, refer to :class:`~torch.nn.LSTM`
-
-    Attributes:
-        layers : instances of the `_LSTMLayer`
-
-    .. note::
-        To access the weights and biases, you need to access them per layer.
-        See examples below.
-
-    Examples::
-
-        >>> import torch.ao.nn.quantizable as nnqa
-        >>> rnn = nnqa.LSTM(10, 20, 2)
-        >>> input = torch.randn(5, 3, 10)
-        >>> h0 = torch.randn(2, 3, 20)
-        >>> c0 = torch.randn(2, 3, 20)
-        >>> output, (hn, cn) = rnn(input, (h0, c0))
-        >>> # To get the weights:
-        >>> # xdoctest: +SKIP
-        >>> print(rnn.layers[0].weight_ih)
-        tensor([[...]])
-        >>> print(rnn.layers[0].weight_hh)
-        AssertionError: There is no reverse path in the non-bidirectional layer
-    """
-    _FLOAT_MODULE = torch.nn.LSTM
-
-    def __init__(self, input_size: int, hidden_size: int,
-                 num_layers: int = 1, bias: bool = True,
-                 batch_first: bool = False, dropout: float = 0.,
-                 bidirectional: bool = False,
-                 device=None, dtype=None) -> None:
-        factory_kwargs = {'device': device, 'dtype': dtype}
-        super().__init__()
-        self.input_size = input_size
-        self.hidden_size = hidden_size
-        self.num_layers = num_layers
-        self.bias = bias
-        self.batch_first = batch_first
-        self.dropout = float(dropout)
-        self.bidirectional = bidirectional
-        self.training = False  # We don't want to train using this module
-        num_directions = 2 if bidirectional else 1
-
-        if not isinstance(dropout, numbers.Number) or not 0 <= dropout <= 1 or \
-                isinstance(dropout, bool):
-            raise ValueError("dropout should be a number in range [0, 1] "
-                             "representing the probability of an element being "
-                             "zeroed")
-        if dropout > 0:
-            warnings.warn("dropout option for quantizable LSTM is ignored. "
-                          "If you are training, please, use nn.LSTM version "
-                          "followed by `prepare` step.")
-            if num_layers == 1:
-                warnings.warn("dropout option adds dropout after all but last "
-                              "recurrent layer, so non-zero dropout expects "
-                              "num_layers greater than 1, but got dropout={} "
-                              "and num_layers={}".format(dropout, num_layers))
-
-        layers = [_LSTMLayer(self.input_size, self.hidden_size,
-                             self.bias, batch_first=False,
-                             bidirectional=self.bidirectional, **factory_kwargs)]
-        for layer in range(1, num_layers):
-            layers.append(_LSTMLayer(self.hidden_size, self.hidden_size,
-                                     self.bias, batch_first=False,
-                                     bidirectional=self.bidirectional,
-                                     **factory_kwargs))
-        self.layers = torch.nn.ModuleList(layers)
-
-    def forward(self, x: Tensor, hidden: Optional[Tuple[Tensor, Tensor]] = None):
-        if self.batch_first:
-            x = x.transpose(0, 1)
-
-        max_batch_size = x.size(1)
-        num_directions = 2 if self.bidirectional else 1
-        if hidden is None:
-            zeros = torch.zeros(num_directions, max_batch_size,
-                                self.hidden_size, dtype=torch.float,
-                                device=x.device)
-            zeros.squeeze_(0)
-            if x.is_quantized:
-                zeros = torch.quantize_per_tensor(zeros, scale=1.0,
-                                                  zero_point=0, dtype=x.dtype)
-            hxcx = [(zeros, zeros) for _ in range(self.num_layers)]
-        else:
-            hidden_non_opt = torch.jit._unwrap_optional(hidden)
-            if isinstance(hidden_non_opt[0], Tensor):
-                hx = hidden_non_opt[0].reshape(self.num_layers, num_directions,
-                                               max_batch_size,
-                                               self.hidden_size)
-                cx = hidden_non_opt[1].reshape(self.num_layers, num_directions,
-                                               max_batch_size,
-                                               self.hidden_size)
-                hxcx = [(hx[idx].squeeze(0), cx[idx].squeeze(0)) for idx in range(self.num_layers)]
-            else:
-                hxcx = hidden_non_opt
-
-        hx_list = []
-        cx_list = []
-        for idx, layer in enumerate(self.layers):
-            x, (h, c) = layer(x, hxcx[idx])
-            hx_list.append(torch.jit._unwrap_optional(h))
-            cx_list.append(torch.jit._unwrap_optional(c))
-        hx_tensor = torch.stack(hx_list)
-        cx_tensor = torch.stack(cx_list)
-
-        # We are creating another dimension for bidirectional case
-        # need to collapse it
-        hx_tensor = hx_tensor.reshape(-1, hx_tensor.shape[-2], hx_tensor.shape[-1])
-        cx_tensor = cx_tensor.reshape(-1, cx_tensor.shape[-2], cx_tensor.shape[-1])
-
-        if self.batch_first:
-            x = x.transpose(0, 1)
-
-        return x, (hx_tensor, cx_tensor)
-
-    def _get_name(self):
-        return 'QuantizableLSTM'
-
-    @classmethod
-    def from_float(cls, other, qconfig=None):
-        assert isinstance(other, cls._FLOAT_MODULE)
-        assert (hasattr(other, 'qconfig') or qconfig)
-        observed = cls(other.input_size, other.hidden_size, other.num_layers,
-                       other.bias, other.batch_first, other.dropout,
-                       other.bidirectional)
-        observed.qconfig = getattr(other, 'qconfig', qconfig)
-        for idx in range(other.num_layers):
-            observed.layers[idx] = _LSTMLayer.from_float(other, idx, qconfig,
-                                                         batch_first=False)
-        observed.eval()
-        observed = torch.ao.quantization.prepare(observed, inplace=True)
-        return observed
-
-    @classmethod
-    def from_observed(cls, other):
-        # The whole flow is float -> observed -> quantized
-        # This class does float -> observed only
-        raise NotImplementedError("It looks like you are trying to convert a "
-                                  "non-quantizable LSTM module. Please, see "
-                                  "the examples on quantizable LSTMs.")

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Source code for torch.ao.nn.quantized.dynamic.modules.linear

-import torch
-import torch.ao.nn.quantized as nnq
-from torch.ao.nn.quantized.modules.utils import _quantize_weight
-import torch.ao.nn.intrinsic as nni
-
-__all__ = [
-    "Linear",
-]
-
-
-
[docs]class Linear(nnq.Linear):
-    r"""
-    A dynamic quantized linear module with floating point tensor as inputs and outputs.
-    We adopt the same interface as `torch.nn.Linear`, please see
-    https://pytorch.org/docs/stable/nn.html#torch.nn.Linear for documentation.
-
-    Similar to :class:`torch.nn.Linear`, attributes will be randomly
-    initialized at module creation time and will be overwritten later
-
-    Attributes:
-        weight (Tensor): the non-learnable quantized weights of the module which are of
-                         shape :math:`(\text{out\_features}, \text{in\_features})`.
-        bias (Tensor): the non-learnable floating point bias of the module of shape
-                       :math:`(\text{out\_features})`. If :attr:`bias` is ``True``,
-                       the values are initialized to zero.
-
-    Examples::
-
-        >>> # xdoctest: +SKIP
-        >>> m = nn.quantized.dynamic.Linear(20, 30)
-        >>> input = torch.randn(128, 20)
-        >>> output = m(input)
-        >>> print(output.size())
-        torch.Size([128, 30])
-    """
-    # version used in this class is different from the parent class nnq.Linear
-    _version = 4
-
-    def __init__(self, in_features, out_features, bias_=True, dtype=torch.qint8):
-        super().__init__(in_features, out_features, bias_, dtype=dtype)
-        # We don't muck around with buffers or attributes or anything here
-        # to keep the module simple. *everything* is simply a Python attribute.
-        # Serialization logic is explicitly handled in the below serialization and
-        # deserialization modules
-        self.version = 4
-
-    def forward(self, x):
-        # Note that we can handle self.bias == None case.
-        if self._packed_params.dtype == torch.qint8:
-            if self.version is None or self.version < 4:
-                Y = torch.ops.quantized.linear_dynamic(
-                    x, self._packed_params._packed_params)
-            else:
-                Y = torch.ops.quantized.linear_dynamic(
-                    x, self._packed_params._packed_params, reduce_range=True)
-        elif self._packed_params.dtype == torch.float16:
-            Y = torch.ops.quantized.linear_dynamic_fp16(
-                x, self._packed_params._packed_params)
-        else:
-            raise RuntimeError('Unsupported dtype on dynamic quantized linear!')
-        return Y.to(x.dtype)
-
-    def _get_name(self):
-        return 'DynamicQuantizedLinear'
-
-    def extra_repr(self):
-        extra_repr_str = 'in_features={}, out_features={}, dtype={}'.format(
-            self.in_features, self.out_features, self._packed_params.dtype
-        )
-        if self._packed_params.dtype == torch.qint8:
-            extra_repr_str += ', qscheme={}'.format(self.weight().qscheme())
-        return extra_repr_str
-
-    def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict,
-                              missing_keys, unexpected_keys, error_msgs):
-        version = local_metadata.get('version', None)
-        self.version = version
-        super()._load_from_state_dict(state_dict, prefix, local_metadata, False,
-                                      missing_keys, unexpected_keys, error_msgs)
-
-
[docs]    @classmethod
-    def from_float(cls, mod):
-        r"""Create a dynamic 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
-        """
-        float_modules = [torch.nn.Linear, torch.nn.modules.linear.NonDynamicallyQuantizableLinear,
-                         torch.ao.nn.intrinsic.modules.fused.LinearReLU, torch.ao.nn.qat.dynamic.Linear]
-
-        assert type(mod) in float_modules, \
-            'nn.quantized.dynamic.Linear.from_float only works for one of' + \
-            str([float_mod.__name__ for float_mod in float_modules])
-        assert hasattr(mod, 'qconfig'), 'Input float module must have qconfig defined'
-        if type(mod) == nni.LinearReLU:
-            mod = mod[0]
-        if mod.qconfig is not None and mod.qconfig.weight is not None:
-            weight_observer = mod.qconfig.weight()
-        else:
-            # We have the circular import issues if we import the qconfig in the beginning of this file:
-            # https://github.com/pytorch/pytorch/pull/24231. The current workaround is to postpone the
-            # import until we need it.
-            from torch.ao.quantization.qconfig import default_dynamic_qconfig
-            weight_observer = default_dynamic_qconfig.weight()
-        dtype = weight_observer.dtype
-        assert dtype in [torch.qint8, torch.float16], "The only supported dtypes for " \
-            "dynamic quantized linear are qint8 and float16 got: {}".format(dtype)
-        weight_observer(mod.weight)
-        if dtype == torch.qint8:
-            qweight = _quantize_weight(mod.weight.float(), weight_observer)
-        elif dtype == torch.float16:
-            qweight = mod.weight.float()
-        else:
-            raise RuntimeError('Unsupported dtype specified for dynamic quantized Linear!')
-        qlinear = cls(mod.in_features, mod.out_features, dtype=dtype)
-        qlinear.set_weight_bias(qweight, mod.bias)
-        return qlinear

-
-
[docs]    @classmethod
-    def from_reference(cls, ref_qlinear):
-        """ Create a (fbgemm/qnnpack) dynamic quantized module from a reference quantized
-        module
-        Args:
-            ref_qlinear (Module): a reference quantized  module, either produced by
-            torch.ao.quantization functions or provided by the user
-        """
-        qlinear = cls(ref_qlinear.in_features, ref_qlinear.out_features, dtype=ref_qlinear.weight_dtype)
-        qweight = ref_qlinear.get_quantized_weight()
-        bias = ref_qlinear.bias
-        qlinear.set_weight_bias(qweight, bias)
-        return qlinear

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Source code for torch.ao.nn.quantized.dynamic.modules.rnn

-import numbers
-import warnings
-
-import torch
-import torch.nn as nn
-from torch import Tensor  # noqa: F401
-from torch._jit_internal import Tuple, Optional, List, Union, Dict  # noqa: F401
-from torch.nn.utils.rnn import PackedSequence
-from torch.ao.nn.quantized.modules.utils import _quantize_weight
-
-__all__ = ['pack_weight_bias', 'PackedParameter', 'RNNBase', 'LSTM', 'GRU', 'RNNCellBase', 'RNNCell', 'LSTMCell',
-           'GRUCell', "apply_permutation"]
-
-
-def _apply_permutation(tensor: Tensor, permutation: Tensor, dim: int = 1) -> Tensor:
-    return tensor.index_select(dim, permutation)
-
-
-def apply_permutation(tensor: Tensor, permutation: Tensor, dim: int = 1) -> Tensor:
-    warnings.warn("apply_permutation is deprecated, please use tensor.index_select(dim, permutation) instead")
-    return _apply_permutation(tensor, permutation, dim)
-
-
-def pack_weight_bias(qweight, bias, dtype):
-
-    if dtype == torch.qint8:
-        # for each layer, for each direction we need to quantize and pack
-        # weights and pack parameters in this order:
-        #
-        #   w_ih, w_hh
-        packed_weight = \
-            torch.ops.quantized.linear_prepack(qweight, bias)
-
-        return packed_weight
-    else:
-        # for each layer, for each direction we need to quantize and pack
-        # weights and pack parameters in this order:
-        #
-        #   packed_ih, packed_hh, b_ih, b_hh
-        packed_weight = torch.ops.quantized.linear_prepack_fp16(
-            qweight, bias)
-
-        return packed_weight
-
-
-class PackedParameter(torch.nn.Module):
-    def __init__(self, param):
-        super().__init__()
-        self.param = param
-
-    def _save_to_state_dict(self, destination, prefix, keep_vars):
-        super()._save_to_state_dict(destination, prefix, keep_vars)
-        destination[prefix + 'param'] = self.param
-
-    def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict,
-                              missing_keys, unexpected_keys, error_msgs):
-        self.param = state_dict[prefix + 'param']
-        super()._load_from_state_dict(state_dict, prefix, local_metadata, False,
-                                      missing_keys, unexpected_keys, error_msgs)
-
-
-class RNNBase(torch.nn.Module):
-
-    _FLOAT_MODULE = nn.RNNBase
-
-    _version = 2
-
-    def __init__(self, mode, input_size, hidden_size,
-                 num_layers=1, bias=True, batch_first=False,
-                 dropout=0., bidirectional=False, dtype=torch.qint8):
-        super().__init__()
-
-        self.mode = mode
-        self.input_size = input_size
-        self.hidden_size = hidden_size
-        self.num_layers = num_layers
-        self.bias = bias
-        self.batch_first = batch_first
-        self.dropout = float(dropout)
-        self.bidirectional = bidirectional
-        self.dtype = dtype
-        self.version = 2
-        self.training = False
-        num_directions = 2 if bidirectional else 1
-
-        # "type: ignore" is required since ints and Numbers are not fully comparable
-        # https://github.com/python/mypy/issues/8566
-        if not isinstance(dropout, numbers.Number) \
-                or not 0 <= dropout <= 1 or isinstance(dropout, bool):  # type: ignore[operator]
-            raise ValueError("dropout should be a number in range [0, 1] "
-                             "representing the probability of an element being "
-                             "zeroed")
-        if dropout > 0 and num_layers == 1:  # type: ignore[operator]
-            warnings.warn("dropout option adds dropout after all but last "
-                          "recurrent layer, so non-zero dropout expects "
-                          "num_layers greater than 1, but got dropout={} and "
-                          "num_layers={}".format(dropout, num_layers))
-
-        if mode == 'LSTM':
-            gate_size = 4 * hidden_size
-        elif mode == 'GRU':
-            gate_size = 3 * hidden_size
-        else:
-            raise ValueError("Unrecognized RNN mode: " + mode)
-
-        _all_weight_values = []
-        for layer in range(num_layers):
-            for direction in range(num_directions):
-                layer_input_size = input_size if layer == 0 else hidden_size * num_directions
-
-                w_ih = torch.randn(gate_size, layer_input_size).to(torch.float)
-                w_hh = torch.randn(gate_size, hidden_size).to(torch.float)
-                b_ih = torch.randn(gate_size).to(torch.float)
-                b_hh = torch.randn(gate_size).to(torch.float)
-                if dtype == torch.qint8:
-                    w_ih = torch.quantize_per_tensor(w_ih, scale=0.1, zero_point=0, dtype=torch.qint8)
-                    w_hh = torch.quantize_per_tensor(w_hh, scale=0.1, zero_point=0, dtype=torch.qint8)
-                    packed_ih = \
-                        torch.ops.quantized.linear_prepack(w_ih, b_ih)
-                    packed_hh = \
-                        torch.ops.quantized.linear_prepack(w_hh, b_hh)
-                    if self.version is None or self.version < 2:
-                        cell_params = torch.ops.quantized.make_quantized_cell_params_dynamic(
-                            packed_ih, packed_hh, b_ih, b_hh)
-                    else:
-                        cell_params = torch.ops.quantized.make_quantized_cell_params_dynamic(
-                            packed_ih, packed_hh, b_ih, b_hh, True)
-                else:
-                    packed_ih = torch.ops.quantized.linear_prepack_fp16(w_ih, b_ih)
-                    packed_hh = torch.ops.quantized.linear_prepack_fp16(w_hh, b_hh)
-                    cell_params = torch.ops.quantized.make_quantized_cell_params_fp16(
-                        packed_ih, packed_hh)
-
-                _all_weight_values.append(PackedParameter(cell_params))
-        self._all_weight_values = torch.nn.ModuleList(_all_weight_values)
-
-    def _get_name(self):
-        return 'DynamicQuantizedRNN'
-
-    def extra_repr(self):
-        s = '{input_size}, {hidden_size}'
-        if self.num_layers != 1:
-            s += ', num_layers={num_layers}'
-        if self.bias is not True:
-            s += ', bias={bias}'
-        if self.batch_first is not False:
-            s += ', batch_first={batch_first}'
-        if self.dropout != 0:
-            s += ', dropout={dropout}'
-        if self.bidirectional is not False:
-            s += ', bidirectional={bidirectional}'
-        return s.format(**self.__dict__)
-
-    def __repr__(self):
-        # We don't want to show `ModuleList` children, hence custom
-        # `__repr__`. This is the same as nn.Module.__repr__, except the check
-        # for the `PackedParameter` and `nn.ModuleList`.
-        # You should still override `extra_repr` to add more info.
-        extra_lines = []
-        extra_repr = self.extra_repr()
-        # empty string will be split into list ['']
-        if extra_repr:
-            extra_lines = extra_repr.split('\n')
-        child_lines = []
-        for key, module in self._modules.items():
-            if isinstance(module, (PackedParameter, nn.ModuleList)):
-                continue
-            mod_str = repr(module)
-            mod_str = nn.modules.module._addindent(mod_str, 2)
-            child_lines.append('(' + key + '): ' + mod_str)
-        lines = extra_lines + child_lines
-
-        main_str = self._get_name() + '('
-        if lines:
-            # simple one-liner info, which most builtin Modules will use
-            if len(extra_lines) == 1 and not child_lines:
-                main_str += extra_lines[0]
-            else:
-                main_str += '\n  ' + '\n  '.join(lines) + '\n'
-
-        main_str += ')'
-        return main_str
-
-    def check_input(self, input: Tensor, batch_sizes: Optional[Tensor]) -> None:
-        expected_input_dim = 2 if batch_sizes is not None else 3
-        if input.dim() != expected_input_dim:
-            raise RuntimeError(
-                'input must have {} dimensions, got {}'.format(
-                    expected_input_dim, input.dim()))
-        if self.input_size != input.size(-1):
-            raise RuntimeError(
-                'input.size(-1) must be equal to input_size. Expected {}, got {}'.format(
-                    self.input_size, input.size(-1)))
-
-    def get_expected_hidden_size(self, input: Tensor, batch_sizes: Optional[Tensor]) -> Tuple[int, int, int]:
-        if batch_sizes is not None:
-            mini_batch = int(batch_sizes[0])
-        else:
-            mini_batch = input.size(0) if self.batch_first else input.size(1)
-        num_directions = 2 if self.bidirectional else 1
-        expected_hidden_size = (self.num_layers * num_directions,
-                                mini_batch, self.hidden_size)
-        return expected_hidden_size
-
-    def check_hidden_size(
-        self, hx: Tensor, expected_hidden_size: Tuple[int, int, int],
-        msg: str = 'Expected hidden size {}, got {}'
-    ) -> None:
-        if hx.size() != expected_hidden_size:
-            raise RuntimeError(msg.format(
-                expected_hidden_size, list(hx.size())))
-
-    def check_forward_args(self, input: Tensor, hidden: Tensor, batch_sizes: Optional[Tensor]) -> None:
-        self.check_input(input, batch_sizes)
-        expected_hidden_size = self.get_expected_hidden_size(input, batch_sizes)
-        self.check_hidden_size(hidden, expected_hidden_size,
-                               msg='Expected hidden size {}, got {}')
-
-    def permute_hidden(self, hx: Tensor, permutation: Optional[Tensor]) -> Tensor:
-        if permutation is None:
-            return hx
-        return _apply_permutation(hx, permutation)
-
-    def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict,
-                              missing_keys, unexpected_keys, error_msgs):
-        version = local_metadata.get('version', None)
-        self.version = version
-        super()._load_from_state_dict(state_dict, prefix, local_metadata, False,
-                                      missing_keys, unexpected_keys, error_msgs)
-
-    def set_weight_bias(self, weight_bias_dict):
-
-        def weight_bias_name(ihhh, layer, suffix):
-            weight_name = "weight_{}_l{}{}".format(ihhh, layer, suffix)
-            bias_name = "bias_{}_l{}{}".format(ihhh, layer, suffix)
-            return weight_name, bias_name
-
-        num_directions = 2 if self.bidirectional else 1
-        # TODO: dedup with __init__ of RNNBase
-        _all_weight_values = []
-        for layer in range(self.num_layers):
-            for direction in range(num_directions):
-                suffix = "_reverse" if direction == 1 else ""
-                w_ih_name, b_ih_name = weight_bias_name("ih", layer, suffix)
-                w_hh_name, b_hh_name = weight_bias_name("hh", layer, suffix)
-                w_ih = weight_bias_dict[w_ih_name]
-                b_ih = weight_bias_dict[b_ih_name]
-                w_hh = weight_bias_dict[w_hh_name]
-                b_hh = weight_bias_dict[b_hh_name]
-                if w_ih.dtype == torch.qint8:
-                    packed_ih = torch.ops.quantized.linear_prepack(w_ih, b_ih)
-                    packed_hh = torch.ops.quantized.linear_prepack(w_hh, b_hh)
-                    if self.version is None or self.version < 2:
-                        cell_params = torch.ops.quantized.make_quantized_cell_params_dynamic(
-                            packed_ih, packed_hh, b_ih, b_hh)
-                    else:
-                        cell_params = torch.ops.quantized.make_quantized_cell_params_dynamic(
-                            packed_ih, packed_hh, b_ih, b_hh, True)
-                else:
-                    packed_ih = torch.ops.quantized.linear_prepack_fp16(w_ih, b_ih)
-                    packed_hh = torch.ops.quantized.linear_prepack_fp16(w_hh, b_hh)
-                    cell_params = torch.ops.quantized.make_quantized_cell_params_fp16(
-                        packed_ih, packed_hh)
-
-                _all_weight_values.append(PackedParameter(cell_params))
-        self._all_weight_values = torch.nn.ModuleList(_all_weight_values)
-
-    @classmethod
-    def from_float(cls, mod):
-        assert type(mod) in {torch.nn.LSTM,
-                             torch.nn.GRU}, 'nn.quantized.dynamic.RNNBase.from_float only works for nn.LSTM and nn.GRU'
-        assert hasattr(
-            mod,
-            'qconfig'
-        ), 'Input float module must have qconfig defined'
-
-        if mod.qconfig is not None and mod.qconfig.weight is not None:
-            weight_observer_method = mod.qconfig.weight
-        else:
-            # We have the circular import issues if we import the qconfig in the beginning of this file:
-            # https://github.com/pytorch/pytorch/pull/24231. The current workaround is to postpone the
-            # import until we need it.
-            from torch.ao.quantization.qconfig import default_dynamic_qconfig
-            weight_observer_method = default_dynamic_qconfig.weight
-
-        dtype = weight_observer_method().dtype
-        supported_scalar_types = [torch.qint8, torch.float16]
-        if dtype not in supported_scalar_types:
-            raise RuntimeError('Unsupported dtype for dynamic RNN quantization: {}'.format(dtype))
-        # RNNBase can be either LSTM or GRU
-        qRNNBase: Union[LSTM, GRU]
-        if mod.mode == 'LSTM':
-            qRNNBase = LSTM(mod.input_size, mod.hidden_size, mod.num_layers,
-                            mod.bias, mod.batch_first, mod.dropout, mod.bidirectional, dtype)
-        elif mod.mode == 'GRU':
-            qRNNBase = GRU(mod.input_size, mod.hidden_size, mod.num_layers,
-                           mod.bias, mod.batch_first, mod.dropout, mod.bidirectional, dtype)
-        else:
-            raise NotImplementedError('Only LSTM/GRU is supported for QuantizedRNN for now')
-
-        num_directions = 2 if mod.bidirectional else 1
-
-        assert mod.bias
-
-        _all_weight_values = []
-        for layer in range(qRNNBase.num_layers):
-            for direction in range(num_directions):
-                suffix = '_reverse' if direction == 1 else ''
-
-                def retrieve_weight_bias(ihhh):
-                    weight_name = 'weight_{}_l{}{}'.format(ihhh, layer, suffix)
-                    bias_name = 'bias_{}_l{}{}'.format(ihhh, layer, suffix)
-                    weight = getattr(mod, weight_name)
-                    bias = getattr(mod, bias_name)
-                    return weight, bias
-
-                weight_ih, bias_ih = retrieve_weight_bias('ih')
-                weight_hh, bias_hh = retrieve_weight_bias('hh')
-
-                if dtype == torch.qint8:
-                    def quantize_and_pack(w, b):
-                        weight_observer = weight_observer_method()
-                        weight_observer(w)
-                        qweight = _quantize_weight(w.float(), weight_observer)
-                        packed_weight = \
-                            torch.ops.quantized.linear_prepack(qweight, b)
-                        return packed_weight
-                    packed_ih = quantize_and_pack(weight_ih, bias_ih)
-                    packed_hh = quantize_and_pack(weight_hh, bias_hh)
-                    if qRNNBase.version is None or qRNNBase.version < 2:
-                        cell_params = torch.ops.quantized.make_quantized_cell_params_dynamic(
-                            packed_ih, packed_hh, bias_ih, bias_hh)
-                    else:
-                        cell_params = torch.ops.quantized.make_quantized_cell_params_dynamic(
-                            packed_ih, packed_hh, bias_ih, bias_hh, True)
-
-                elif dtype == torch.float16:
-                    packed_ih = torch.ops.quantized.linear_prepack_fp16(
-                        weight_ih.float(), bias_ih)
-                    packed_hh = torch.ops.quantized.linear_prepack_fp16(
-                        weight_hh.float(), bias_hh)
-
-                    cell_params = torch.ops.quantized.make_quantized_cell_params_fp16(
-                        packed_ih, packed_hh)
-                else:
-                    raise RuntimeError('Unsupported dtype specified for dynamic quantized LSTM!')
-
-                _all_weight_values.append(PackedParameter(cell_params))
-        qRNNBase._all_weight_values = torch.nn.ModuleList(_all_weight_values)
-
-        return qRNNBase
-
-    def _weight_bias(self):
-        # Returns a dict of weights and biases
-        weight_bias_dict: Dict[str, Dict] = {'weight' : {}, 'bias' : {}}
-        count = 0
-        num_directions = 2 if self.bidirectional else 1
-        for layer in range(self.num_layers):
-            for direction in range(num_directions):
-                suffix = '_reverse' if direction == 1 else ''
-                key_name1 = 'weight_ih_l{layer_idx}{suffix}'.format(layer_idx=layer, suffix=suffix)
-                key_name2 = 'weight_hh_l{layer_idx}{suffix}'.format(layer_idx=layer, suffix=suffix)
-                # packed weights are part of torchbind class, CellParamsSerializationType
-                # Within the packed weight class, the weight and bias are accessible as Tensors
-                packed_weight_bias = self._all_weight_values[count].param.__getstate__()[0][4]
-                weight_bias_dict['weight'][key_name1] = packed_weight_bias[0].__getstate__()[0][0]
-                weight_bias_dict['weight'][key_name2] = packed_weight_bias[1].__getstate__()[0][0]
-                key_name1 = 'bias_ih_l{layer_idx}{suffix}'.format(layer_idx=layer, suffix=suffix)
-                key_name2 = 'bias_hh_l{layer_idx}{suffix}'.format(layer_idx=layer, suffix=suffix)
-                weight_bias_dict['bias'][key_name1] = packed_weight_bias[0].__getstate__()[0][1]
-                weight_bias_dict['bias'][key_name2] = packed_weight_bias[1].__getstate__()[0][1]
-                count = count + 1
-        return weight_bias_dict
-
-    def get_weight(self):
-        return self._weight_bias()['weight']
-
-    def get_bias(self):
-        return self._weight_bias()['bias']
-
-
-
[docs]class LSTM(RNNBase):
-    r"""
-    A dynamic quantized LSTM module with floating point tensor as inputs and outputs.
-    We adopt the same interface as `torch.nn.LSTM`, please see
-    https://pytorch.org/docs/stable/nn.html#torch.nn.LSTM for documentation.
-
-    Examples::
-
-        >>> # xdoctest: +SKIP
-        >>> rnn = nn.LSTM(10, 20, 2)
-        >>> input = torch.randn(5, 3, 10)
-        >>> h0 = torch.randn(2, 3, 20)
-        >>> c0 = torch.randn(2, 3, 20)
-        >>> output, (hn, cn) = rnn(input, (h0, c0))
-    """
-    _FLOAT_MODULE = nn.LSTM
-
-    __overloads__ = {'forward': ['forward_packed', 'forward_tensor']}
-
-    def __init__(self, *args, **kwargs):
-        super().__init__('LSTM', *args, **kwargs)
-
-    def _get_name(self):
-        return 'DynamicQuantizedLSTM'
-
-    def forward_impl(
-        self, input: Tensor, hx: Optional[Tuple[Tensor, Tensor]],
-        batch_sizes: Optional[Tensor], max_batch_size: int,
-        sorted_indices: Optional[Tensor]
-    ) -> Tuple[Tensor, Tuple[Tensor, Tensor]]:
-        if hx is None:
-            num_directions = 2 if self.bidirectional else 1
-            zeros = torch.zeros(self.num_layers * num_directions,
-                                max_batch_size, self.hidden_size,
-                                dtype=input.dtype, device=input.device)
-            hx = (zeros, zeros)
-        else:
-            # Each batch of the hidden state should match the input sequence that
-            # the user believes he/she is passing in.
-            hx = self.permute_hidden(hx, sorted_indices)
-
-        self.check_forward_args(input, hx, batch_sizes)
-
-        _all_params = ([m.param for m in self._all_weight_values])
-        if batch_sizes is None:
-            result = torch.quantized_lstm(input, hx, _all_params, self.bias, self.num_layers,
-                                          float(self.dropout), self.training, self.bidirectional,
-                                          self.batch_first, dtype=self.dtype, use_dynamic=True)
-        else:
-            result = torch.quantized_lstm(input, batch_sizes, hx, _all_params, self.bias,
-                                          self.num_layers, float(self.dropout), self.training,
-                                          self.bidirectional, dtype=self.dtype, use_dynamic=True)
-        output = result[0]
-        hidden = result[1:]
-
-        return output, hidden
-
-    @torch.jit.export
-    def forward_tensor(
-        self, input: Tensor, hx: Optional[Tuple[Tensor, Tensor]] = None
-    ) -> Tuple[Tensor, Tuple[Tensor, Tensor]]:
-        batch_sizes = None
-        max_batch_size = input.size(0) if self.batch_first else input.size(1)
-        sorted_indices = None
-        unsorted_indices = None
-
-        output, hidden = self.forward_impl(
-            input, hx, batch_sizes, max_batch_size, sorted_indices)
-
-        return output, self.permute_hidden(hidden, unsorted_indices)
-
-    @torch.jit.export
-    def forward_packed(
-        self, input: PackedSequence, hx: Optional[Tuple[Tensor, Tensor]] = None
-    ) -> Tuple[PackedSequence, Tuple[Tensor, Tensor]]:
-        input_, batch_sizes, sorted_indices, unsorted_indices = input
-        max_batch_size = int(batch_sizes[0])
-
-        output_, hidden = self.forward_impl(
-            input_, hx, batch_sizes, max_batch_size, sorted_indices
-        )
-
-        output = PackedSequence(output_, batch_sizes,
-                                sorted_indices, unsorted_indices)
-        return output, self.permute_hidden(hidden, unsorted_indices)
-
-    # "type: ignore" is required due to issue #43072
-    def permute_hidden(  # type: ignore[override]
-        self, hx: Tuple[Tensor, Tensor], permutation: Optional[Tensor]
-    ) -> Tuple[Tensor, Tensor]:
-        if permutation is None:
-            return hx
-        return _apply_permutation(hx[0], permutation), _apply_permutation(hx[1], permutation)
-
-    # "type: ignore" is required due to issue #43072
-    def check_forward_args(  # type: ignore[override]
-        self, input: Tensor, hidden: Tuple[Tensor, Tensor], batch_sizes: Optional[Tensor]
-    ) -> None:
-        self.check_input(input, batch_sizes)
-        expected_hidden_size = self.get_expected_hidden_size(input, batch_sizes)
-
-        self.check_hidden_size(hidden[0], expected_hidden_size,
-                               'Expected hidden[0] size {}, got {}')
-        self.check_hidden_size(hidden[1], expected_hidden_size,
-                               'Expected hidden[1] size {}, got {}')
-
-    @torch.jit.ignore
-    def forward(self, input, hx=None):
-        if isinstance(input, PackedSequence):
-            return self.forward_packed(input, hx)
-        else:
-            return self.forward_tensor(input, hx)
-
-    @classmethod
-    def from_float(cls, mod):
-        return super(LSTM, cls).from_float(mod)
-
-    @classmethod
-    def from_reference(cls, ref_mod):
-        assert hasattr(ref_mod, "weight_ih_l0_dtype"), "We are assuming weight_ih_l0 "
-        "exists in LSTM, may need to relax the assumption to support the use case"
-        qmod = cls(
-            ref_mod.input_size,
-            ref_mod.hidden_size,
-            ref_mod.num_layers,
-            ref_mod.bias,
-            ref_mod.batch_first,
-            ref_mod.dropout,
-            ref_mod.bidirectional,
-            # assuming there is layer 0, which should be OK
-            ref_mod.weight_ih_l0_dtype,
-        )
-        qmod.set_weight_bias(ref_mod.get_quantized_weight_bias_dict())
-        return qmod

-
-
-
[docs]class GRU(RNNBase):
-    r"""Applies a multi-layer gated recurrent unit (GRU) RNN to an input sequence.
-
-
-    For each element in the input sequence, each layer computes the following
-    function:
-
-    .. math::
-        \begin{array}{ll}
-            r_t = \sigma(W_{ir} x_t + b_{ir} + W_{hr} h_{(t-1)} + b_{hr}) \\
-            z_t = \sigma(W_{iz} x_t + b_{iz} + W_{hz} h_{(t-1)} + b_{hz}) \\
-            n_t = \tanh(W_{in} x_t + b_{in} + r_t * (W_{hn} h_{(t-1)}+ b_{hn})) \\
-            h_t = (1 - z_t) * n_t + z_t * h_{(t-1)}
-        \end{array}
-
-    where :math:`h_t` is the hidden state at time `t`, :math:`x_t` is the input
-    at time `t`, :math:`h_{(t-1)}` is the hidden state of the layer
-    at time `t-1` or the initial hidden state at time `0`, and :math:`r_t`,
-    :math:`z_t`, :math:`n_t` are the reset, update, and new gates, respectively.
-    :math:`\sigma` is the sigmoid function, and :math:`*` is the Hadamard product.
-
-    In a multilayer GRU, the input :math:`x^{(l)}_t` of the :math:`l` -th layer
-    (:math:`l >= 2`) is the hidden state :math:`h^{(l-1)}_t` of the previous layer multiplied by
-    dropout :math:`\delta^{(l-1)}_t` where each :math:`\delta^{(l-1)}_t` is a Bernoulli random
-    variable which is :math:`0` with probability :attr:`dropout`.
-
-    Args:
-        input_size: The number of expected features in the input `x`
-        hidden_size: The number of features in the hidden state `h`
-        num_layers: Number of recurrent layers. E.g., setting ``num_layers=2``
-            would mean stacking two GRUs together to form a `stacked GRU`,
-            with the second GRU taking in outputs of the first GRU and
-            computing the final results. Default: 1
-        bias: If ``False``, then the layer does not use bias weights `b_ih` and `b_hh`.
-            Default: ``True``
-        batch_first: If ``True``, then the input and output tensors are provided
-            as (batch, seq, feature). Default: ``False``
-        dropout: If non-zero, introduces a `Dropout` layer on the outputs of each
-            GRU layer except the last layer, with dropout probability equal to
-            :attr:`dropout`. Default: 0
-        bidirectional: If ``True``, becomes a bidirectional GRU. Default: ``False``
-
-    Inputs: input, h_0
-        - **input** of shape `(seq_len, batch, input_size)`: tensor containing the features
-          of the input sequence. The input can also be a packed variable length
-          sequence. See :func:`torch.nn.utils.rnn.pack_padded_sequence`
-          for details.
-        - **h_0** of shape `(num_layers * num_directions, batch, hidden_size)`: tensor
-          containing the initial hidden state for each element in the batch.
-          Defaults to zero if not provided. If the RNN is bidirectional,
-          num_directions should be 2, else it should be 1.
-
-    Outputs: output, h_n
-        - **output** of shape `(seq_len, batch, num_directions * hidden_size)`: tensor
-          containing the output features h_t from the last layer of the GRU,
-          for each `t`. If a :class:`torch.nn.utils.rnn.PackedSequence` has been
-          given as the input, the output will also be a packed sequence.
-          For the unpacked case, the directions can be separated
-          using ``output.view(seq_len, batch, num_directions, hidden_size)``,
-          with forward and backward being direction `0` and `1` respectively.
-
-          Similarly, the directions can be separated in the packed case.
-        - **h_n** of shape `(num_layers * num_directions, batch, hidden_size)`: tensor
-          containing the hidden state for `t = seq_len`
-
-          Like *output*, the layers can be separated using
-          ``h_n.view(num_layers, num_directions, batch, hidden_size)``.
-
-    Shape:
-        - Input1: :math:`(L, N, H_{in})` tensor containing input features where
-          :math:`H_{in}=\text{input\_size}` and `L` represents a sequence length.
-        - Input2: :math:`(S, N, H_{out})` tensor
-          containing the initial hidden state for each element in the batch.
-          :math:`H_{out}=\text{hidden\_size}`
-          Defaults to zero if not provided. where :math:`S=\text{num\_layers} * \text{num\_directions}`
-          If the RNN is bidirectional, num_directions should be 2, else it should be 1.
-        - Output1: :math:`(L, N, H_{all})` where :math:`H_{all}=\text{num\_directions} * \text{hidden\_size}`
-        - Output2: :math:`(S, N, H_{out})` tensor containing the next hidden state
-          for each element in the batch
-
-    Attributes:
-        weight_ih_l[k] : the learnable input-hidden weights of the :math:`\text{k}^{th}` layer
-            (W_ir|W_iz|W_in), of shape `(3*hidden_size, input_size)` for `k = 0`.
-            Otherwise, the shape is `(3*hidden_size, num_directions * hidden_size)`
-        weight_hh_l[k] : the learnable hidden-hidden weights of the :math:`\text{k}^{th}` layer
-            (W_hr|W_hz|W_hn), of shape `(3*hidden_size, hidden_size)`
-        bias_ih_l[k] : the learnable input-hidden bias of the :math:`\text{k}^{th}` layer
-            (b_ir|b_iz|b_in), of shape `(3*hidden_size)`
-        bias_hh_l[k] : the learnable hidden-hidden bias of the :math:`\text{k}^{th}` layer
-            (b_hr|b_hz|b_hn), of shape `(3*hidden_size)`
-
-    .. note::
-        All the weights and biases are initialized from :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})`
-        where :math:`k = \frac{1}{\text{hidden\_size}}`
-
-    .. include:: ../cudnn_persistent_rnn.rst
-
-    Examples::
-
-        >>> # xdoctest: +SKIP
-        >>> rnn = nn.GRU(10, 20, 2)
-        >>> input = torch.randn(5, 3, 10)
-        >>> h0 = torch.randn(2, 3, 20)
-        >>> output, hn = rnn(input, h0)
-    """
-    _FLOAT_MODULE = nn.GRU
-
-    __overloads__ = {'forward': ['forward_packed', 'forward_tensor']}
-
-    def __init__(self, *args, **kwargs):
-        super().__init__('GRU', *args, **kwargs)
-
-    def _get_name(self):
-        return 'DynamicQuantizedGRU'
-
-    def check_forward_args(self, input: Tensor, hidden: Tensor, batch_sizes: Optional[Tensor]) -> None:
-        self.check_input(input, batch_sizes)
-        expected_hidden_size = self.get_expected_hidden_size(input, batch_sizes)
-
-        self.check_hidden_size(hidden, expected_hidden_size,
-                               'Expected hidden size {}, got {}')
-
-    def forward_impl(
-        self, input: Tensor, hx: Optional[Tensor],
-        batch_sizes: Optional[Tensor], max_batch_size: int,
-        sorted_indices: Optional[Tensor]
-    ) -> Tuple[Tensor, Tensor]:
-        if hx is None:
-            num_directions = 2 if self.bidirectional else 1
-            zeros = torch.zeros(self.num_layers * num_directions,
-                                max_batch_size, self.hidden_size,
-                                dtype=input.dtype, device=input.device)
-            hx = zeros
-        else:
-            # Each batch of the hidden state should match the input sequence that
-            # the user believes he/she is passing in.
-            hx = self.permute_hidden(hx, sorted_indices)
-
-        self.check_forward_args(input, hx, batch_sizes)
-
-        _all_params = ([m.param for m in self._all_weight_values])
-        if batch_sizes is None:
-            result = torch.quantized_gru(input,
-                                         hx,
-                                         _all_params,
-                                         self.bias,
-                                         self.num_layers,
-                                         self.dropout,
-                                         self.training,
-                                         self.bidirectional,
-                                         self.batch_first)
-        else:
-            result = torch.quantized_gru(input,
-                                         batch_sizes,
-                                         hx,
-                                         _all_params,
-                                         self.bias,
-                                         self.num_layers,
-                                         self.dropout,
-                                         self.training,
-                                         self.bidirectional)
-        output = result[0]
-        hidden = result[1]
-
-        return output, hidden
-
-
-    @torch.jit.export
-    def forward_tensor(
-        self, input: Tensor, hx: Optional[Tensor] = None
-    ) -> Tuple[Tensor, Tensor]:
-        batch_sizes = None
-        max_batch_size = input.size(0) if self.batch_first else input.size(1)
-        sorted_indices = None
-        unsorted_indices = None
-
-        output, hidden = self.forward_impl(
-            input, hx, batch_sizes, max_batch_size, sorted_indices)
-
-        return output, self.permute_hidden(hidden, unsorted_indices)
-
-    @torch.jit.export
-    def forward_packed(
-        self, input: PackedSequence, hx: Optional[Tensor] = None
-    ) -> Tuple[PackedSequence, Tensor]:
-        input_, batch_sizes, sorted_indices, unsorted_indices = input
-        max_batch_size = int(batch_sizes[0])
-        output_, hidden = self.forward_impl(
-            input_, hx, batch_sizes, max_batch_size, sorted_indices
-        )
-
-        output = PackedSequence(output_, batch_sizes,
-                                sorted_indices, unsorted_indices)
-        return output, self.permute_hidden(hidden, unsorted_indices)
-
-    def permute_hidden(
-        self, hx: Tensor, permutation: Optional[Tensor]
-    ) -> Tensor:
-        if permutation is None:
-            return hx
-        return _apply_permutation(hx, permutation)
-
-    @torch.jit.ignore
-    def forward(self, input, hx=None):
-        if isinstance(input, PackedSequence):
-            return self.forward_packed(input, hx)
-        else:
-            return self.forward_tensor(input, hx)
-
-    @classmethod
-    def from_float(cls, mod):
-        return super(GRU, cls).from_float(mod)
-
-    @classmethod
-    def from_reference(cls, ref_mod):
-        assert hasattr(ref_mod, "weight_ih_l0_dtype"), "We are assuming weight_ih_l0 "
-        "exists in LSTM, may need to relax the assumption to support the use case"
-        qmod = cls(
-            ref_mod.input_size,
-            ref_mod.hidden_size,
-            ref_mod.num_layers,
-            ref_mod.bias,
-            ref_mod.batch_first,
-            ref_mod.dropout,
-            ref_mod.bidirectional,
-            # assuming there is layer 0, which should be OK
-            ref_mod.weight_ih_l0_dtype,
-        )
-        qmod.set_weight_bias(ref_mod.get_quantized_weight_bias_dict())
-        return qmod

-
-class RNNCellBase(torch.nn.Module):
-    # _FLOAT_MODULE = nn.CellRNNBase
-    __constants__ = ['input_size', 'hidden_size', 'bias']
-
-    def __init__(self, input_size, hidden_size, bias=True, num_chunks=4, dtype=torch.qint8):
-        super().__init__()
-        self.input_size = input_size
-        self.hidden_size = hidden_size
-        self.bias = bias
-        self.weight_dtype = dtype
-        if bias:
-            self.bias_ih = torch.randn(num_chunks * hidden_size).to(dtype=torch.float)
-            self.bias_hh = torch.randn(num_chunks * hidden_size).to(dtype=torch.float)
-        else:
-            self.register_parameter('bias_ih', None)
-            self.register_parameter('bias_hh', None)
-
-        weight_ih = torch.randn(num_chunks * hidden_size, input_size).to(torch.float)
-        weight_hh = torch.randn(num_chunks * hidden_size, hidden_size).to(torch.float)
-        if dtype == torch.qint8:
-            weight_ih = torch.quantize_per_tensor(weight_ih, scale=1, zero_point=0, dtype=torch.qint8)
-            weight_hh = torch.quantize_per_tensor(weight_hh, scale=1, zero_point=0, dtype=torch.qint8)
-
-        if dtype == torch.qint8:
-            # for each layer, for each direction we need to quantize and pack
-            # weights and pack parameters in this order:
-            #
-            #   w_ih, w_hh
-            packed_weight_ih = \
-                torch.ops.quantized.linear_prepack(weight_ih, self.bias_ih)
-            packed_weight_hh = \
-                torch.ops.quantized.linear_prepack(weight_hh, self.bias_hh)
-        else:
-            # for each layer, for each direction we need to quantize and pack
-            # weights and pack parameters in this order:
-            #
-            #   packed_ih, packed_hh, b_ih, b_hh
-            packed_weight_ih = torch.ops.quantized.linear_prepack_fp16(
-                weight_ih, self.bias_ih)
-            packed_weight_hh = torch.ops.quantized.linear_prepack_fp16(
-                weight_hh, self.bias_hh)
-
-        self._packed_weight_ih = packed_weight_ih
-        self._packed_weight_hh = packed_weight_hh
-
-    def _get_name(self):
-        return 'DynamicQuantizedRNNBase'
-
-    def extra_repr(self):
-        s = '{input_size}, {hidden_size}'
-        if 'bias' in self.__dict__ and self.bias is not True:
-            s += ', bias={bias}'
-        if 'nonlinearity' in self.__dict__ and self.nonlinearity != "tanh":
-            s += ', nonlinearity={nonlinearity}'
-        return s.format(**self.__dict__)
-
-    def check_forward_input(self, input):
-        if input.size(1) != self.input_size:
-            raise RuntimeError(
-                "input has inconsistent input_size: got {}, expected {}".format(
-                    input.size(1), self.input_size))
-
-    def check_forward_hidden(self, input: Tensor, hx: Tensor, hidden_label: str = '') -> None:
-        if input.size(0) != hx.size(0):
-            raise RuntimeError(
-                "Input batch size {} doesn't match hidden{} batch size {}".format(
-                    input.size(0), hidden_label, hx.size(0)))
-
-        if hx.size(1) != self.hidden_size:
-            raise RuntimeError(
-                "hidden{} has inconsistent hidden_size: got {}, expected {}".format(
-                    hidden_label, hx.size(1), self.hidden_size))
-
-    @classmethod
-    def from_float(cls, mod):
-        assert type(mod) in {torch.nn.LSTMCell,
-                             torch.nn.GRUCell,
-                             torch.nn.RNNCell}, 'nn.quantized.dynamic.RNNCellBase.from_float \
-                                 only works for nn.LSTMCell, nn.GRUCell and nn.RNNCell'
-        assert hasattr(
-            mod, 'qconfig'), 'Input float module must have qconfig defined'
-
-        if mod.qconfig is not None and mod.qconfig.weight is not None:
-            weight_observer_method = mod.qconfig.weight
-        else:
-            # We have the circular import issues if we import the qconfig in the beginning of this file:
-            # https://github.com/pytorch/pytorch/pull/24231. The current workaround is to postpone the
-            # import until we need it.
-            from torch.ao.quantization.qconfig import default_dynamic_qconfig
-            weight_observer_method = default_dynamic_qconfig.weight
-
-        dtype = weight_observer_method().dtype
-        supported_scalar_types = [torch.qint8, torch.float16]
-        if dtype not in supported_scalar_types:
-            raise RuntimeError('Unsupported dtype for dynamic RNN quantization: {}'.format(dtype))
-
-        qRNNCellBase: Union[LSTMCell, GRUCell, RNNCell]
-
-        if type(mod) == torch.nn.LSTMCell:
-            qRNNCellBase = LSTMCell(mod.input_size, mod.hidden_size, bias=mod.bias, dtype=dtype)
-        elif type(mod) == torch.nn.GRUCell:
-            qRNNCellBase = GRUCell(mod.input_size, mod.hidden_size, bias=mod.bias, dtype=dtype)
-        elif type(mod) == torch.nn.RNNCell:
-            qRNNCellBase = RNNCell(mod.input_size, mod.hidden_size, bias=mod.bias, nonlinearity=mod.nonlinearity, dtype=dtype)
-        else:
-            raise NotImplementedError('Only LSTMCell, GRUCell and RNNCell \
-            are supported for QuantizedRNN for now')
-
-        assert mod.bias
-
-        def _observe_and_quantize_weight(weight):
-            if dtype == torch.qint8:
-                weight_observer = weight_observer_method()
-                weight_observer(weight)
-                qweight = _quantize_weight(weight.float(), weight_observer)
-                return qweight
-            else:
-                return weight.float()
-
-        qRNNCellBase._packed_weight_ih = pack_weight_bias(_observe_and_quantize_weight(mod.weight_ih), mod.bias_ih, dtype)
-        qRNNCellBase._packed_weight_hh = pack_weight_bias(_observe_and_quantize_weight(mod.weight_hh), mod.bias_hh, dtype)
-        return qRNNCellBase
-
-    @classmethod
-    def from_reference(cls, ref_mod):
-        assert hasattr(ref_mod, "weight_ih_dtype"), "We are assuming weight_ih "
-        "exists in reference module, may need to relax the assumption to support the use case"
-        if hasattr(ref_mod, "nonlinearity"):
-            qmod = cls(
-                ref_mod.input_size,
-                ref_mod.hidden_size,
-                ref_mod.bias,
-                ref_mod.nonlinearity,
-                dtype=ref_mod.weight_ih_dtype
-            )
-        else:
-            qmod = cls(
-                ref_mod.input_size,
-                ref_mod.hidden_size,
-                ref_mod.bias,
-                dtype=ref_mod.weight_ih_dtype
-            )
-        weight_bias_dict = {
-            "weight": {
-                "weight_ih": ref_mod.get_quantized_weight_ih(),
-                "weight_hh": ref_mod.get_quantized_weight_hh(),
-            },
-            "bias": {
-                "bias_ih": ref_mod.bias_ih,
-                "bias_hh": ref_mod.bias_hh,
-            }
-        }
-        qmod.set_weight_bias(weight_bias_dict)
-        return qmod
-
-    def _weight_bias(self):
-        # Returns a dict of weights and biases
-        weight_bias_dict: Dict[str, Dict] = {'weight' : {}, 'bias' : {}}
-        w1, b1 = self._packed_weight_ih.__getstate__()[0]
-        w2, b2 = self._packed_weight_hh.__getstate__()[0]
-        # TODO: these can be simplified to one level? e.g. using weight_ih as key
-        # directly
-        weight_bias_dict['weight']['weight_ih'] = w1
-        weight_bias_dict['weight']['weight_hh'] = w2
-        weight_bias_dict['bias']['bias_ih'] = b1
-        weight_bias_dict['bias']['bias_hh'] = b2
-        return weight_bias_dict
-
-    def get_weight(self):
-        return self._weight_bias()['weight']
-
-    def get_bias(self):
-        return self._weight_bias()['bias']
-
-    def set_weight_bias(self, weight_bias_dict):
-        # TODO: these can be simplified to one level? e.g. using weight_ih as key
-        # directly
-        self._packed_weight_ih = pack_weight_bias(
-            weight_bias_dict["weight"]["weight_ih"],
-            weight_bias_dict["bias"]["bias_ih"],
-            self.weight_dtype)
-        self._packed_weight_hh = pack_weight_bias(
-            weight_bias_dict["weight"]["weight_hh"],
-            weight_bias_dict["bias"]["bias_hh"],
-            self.weight_dtype)
-
-    def _save_to_state_dict(self, destination, prefix, keep_vars):
-        super()._save_to_state_dict(destination, prefix, keep_vars)
-        destination[prefix + '_packed_weight_ih'] = self._packed_weight_ih
-        destination[prefix + '_packed_weight_hh'] = self._packed_weight_hh
-
-    def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict,
-                              missing_keys, unexpected_keys, error_msgs):
-        self._packed_weight_ih = state_dict.pop(prefix + '_packed_weight_ih')
-        self._packed_weight_hh = state_dict.pop(prefix + '_packed_weight_hh')
-        super()._load_from_state_dict(state_dict, prefix, local_metadata, False,
-                                      missing_keys, unexpected_keys, error_msgs)
-
-
-
[docs]class RNNCell(RNNCellBase):
-    r"""An Elman RNN cell with tanh or ReLU non-linearity.
-    A dynamic quantized RNNCell module with floating point tensor as inputs and outputs.
-    Weights are quantized to 8 bits. We adopt the same interface as `torch.nn.RNNCell`,
-    please see https://pytorch.org/docs/stable/nn.html#torch.nn.RNNCell for documentation.
-
-    Examples::
-
-        >>> # xdoctest: +SKIP
-        >>> rnn = nn.RNNCell(10, 20)
-        >>> input = torch.randn(6, 3, 10)
-        >>> hx = torch.randn(3, 20)
-        >>> output = []
-        >>> for i in range(6):
-        ...     hx = rnn(input[i], hx)
-        ...     output.append(hx)
-    """
-    __constants__ = ['input_size', 'hidden_size', 'bias', 'nonlinearity']
-
-    def __init__(self, input_size, hidden_size, bias=True, nonlinearity="tanh", dtype=torch.qint8):
-        super().__init__(input_size, hidden_size, bias, num_chunks=1, dtype=dtype)
-        self.nonlinearity = nonlinearity
-
-    def _get_name(self):
-        return 'DynamicQuantizedRNNCell'
-
-    def forward(self, input: Tensor, hx: Optional[Tensor] = None) -> Tensor:
-        self.check_forward_input(input)
-        if hx is None:
-            hx = torch.zeros(input.size(0), self.hidden_size, dtype=input.dtype, device=input.device)
-        self.check_forward_hidden(input, hx, '')
-        if self.nonlinearity == "tanh":
-            ret = torch.ops.quantized.quantized_rnn_tanh_cell_dynamic(
-                input, hx,
-                self._packed_weight_ih, self._packed_weight_hh,
-                self.bias_ih, self.bias_hh)
-        elif self.nonlinearity == "relu":
-            ret = torch.ops.quantized.quantized_rnn_relu_cell_dynamic(
-                input, hx,
-                self._packed_weight_ih, self._packed_weight_hh,
-                self.bias_ih, self.bias_hh)
-        else:
-            ret = input  # TODO: remove when jit supports exception flow
-            raise RuntimeError(
-                "Unknown nonlinearity: {}".format(self.nonlinearity))
-        return ret
-
-    @classmethod
-    def from_float(cls, mod):
-        return super(RNNCell, cls).from_float(mod)

-
-
-
[docs]class LSTMCell(RNNCellBase):
-    r"""A long short-term memory (LSTM) cell.
-
-    A dynamic quantized LSTMCell module with floating point tensor as inputs and outputs.
-    Weights are quantized to 8 bits. We adopt the same interface as `torch.nn.LSTMCell`,
-    please see https://pytorch.org/docs/stable/nn.html#torch.nn.LSTMCell for documentation.
-
-    Examples::
-
-        >>> # xdoctest: +SKIP
-        >>> rnn = nn.LSTMCell(10, 20)
-        >>> input = torch.randn(6, 3, 10)
-        >>> hx = torch.randn(3, 20)
-        >>> cx = torch.randn(3, 20)
-        >>> output = []
-        >>> for i in range(6):
-        ...     hx, cx = rnn(input[i], (hx, cx))
-        ...     output.append(hx)
-    """
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, num_chunks=4, **kwargs)  # type: ignore[misc]
-
-    def _get_name(self):
-        return 'DynamicQuantizedLSTMCell'
-
-    def forward(self, input: Tensor, hx: Optional[Tuple[Tensor, Tensor]] = None) -> Tuple[Tensor, Tensor]:
-        self.check_forward_input(input)
-        if hx is None:
-            zeros = torch.zeros(input.size(0), self.hidden_size, dtype=input.dtype, device=input.device)
-            hx = (zeros, zeros)
-        self.check_forward_hidden(input, hx[0], '[0]')
-        self.check_forward_hidden(input, hx[1], '[1]')
-        return torch.ops.quantized.quantized_lstm_cell_dynamic(
-            input, hx,
-            self._packed_weight_ih, self._packed_weight_hh,
-            self.bias_ih, self.bias_hh)
-
-    @classmethod
-    def from_float(cls, mod):
-        return super(LSTMCell, cls).from_float(mod)

-
-
-
[docs]class GRUCell(RNNCellBase):
-    r"""A gated recurrent unit (GRU) cell
-
-    A dynamic quantized GRUCell module with floating point tensor as inputs and outputs.
-    Weights are quantized to 8 bits. We adopt the same interface as `torch.nn.GRUCell`,
-    please see https://pytorch.org/docs/stable/nn.html#torch.nn.GRUCell for documentation.
-
-    Examples::
-
-        >>> # xdoctest: +SKIP
-        >>> rnn = nn.GRUCell(10, 20)
-        >>> input = torch.randn(6, 3, 10)
-        >>> hx = torch.randn(3, 20)
-        >>> output = []
-        >>> for i in range(6):
-        ...     hx = rnn(input[i], hx)
-        ...     output.append(hx)
-    """
-
-    def __init__(self, input_size, hidden_size, bias=True, dtype=torch.qint8):
-        super().__init__(input_size, hidden_size, bias, num_chunks=3, dtype=dtype)
-
-    def _get_name(self):
-        return 'DynamicQuantizedGRUCell'
-
-    def forward(self, input: Tensor, hx: Optional[Tensor] = None) -> Tensor:
-        self.check_forward_input(input)
-        if hx is None:
-            hx = torch.zeros(input.size(0), self.hidden_size, dtype=input.dtype, device=input.device)
-        self.check_forward_hidden(input, hx, '')
-        return torch.ops.quantized.quantized_gru_cell_dynamic(
-            input, hx,
-            self._packed_weight_ih, self._packed_weight_hh,
-            self.bias_ih, self.bias_hh,
-        )
-
-    @classmethod
-    def from_float(cls, mod):
-        return super(GRUCell, cls).from_float(mod)

-
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-
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-

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

-import torch
-
-__all__ = [
-    "ReLU6",
-    "Hardswish",
-    "ELU",
-    "LeakyReLU",
-    "Sigmoid",
-    "Softmax",
-    "MultiheadAttention",
-    "PReLU"
-]
-
-
[docs]class ReLU6(torch.nn.ReLU):
-    r"""Applies the element-wise function:
-
-    :math:`\text{ReLU6}(x) = \min(\max(x_0, x), q(6))`, where :math:`x_0` is the
-    zero_point, and :math:`q(6)` is the quantized representation of number 6.
-
-    Args:
-        inplace: can optionally do the operation in-place. Default: ``False``
-
-    Shape:
-        - Input: :math:`(N, *)` where `*` means, any number of additional
-          dimensions
-        - Output: :math:`(N, *)`, same shape as the input
-
-    .. image:: ../scripts/activation_images/ReLU6.png
-
-    Examples::
-
-        >>> m = nn.quantized.ReLU6()
-        >>> input = torch.randn(2)
-        >>> # xdoctest: +SKIP
-        >>> input = torch.quantize_per_tensor(input, 1.0, 0, dtype=torch.qint32)
-        >>> output = m(input)
-    """
-    def __init__(self, inplace=False):
-        super().__init__(inplace)
-        self.inplace = inplace
-
-    def forward(self, input):
-        return torch.ops.quantized.relu6(input, self.inplace)
-
-    def _get_name(self):
-        return 'QuantizedReLU6'
-
-    @staticmethod
-    def from_float(mod):
-        return ReLU6(mod.inplace)

-
-
[docs]class Hardswish(torch.nn.Hardswish):
-    r"""This is the quantized version of :class:`~torch.nn.Hardswish`.
-
-    Args:
-        scale: quantization scale of the output tensor
-        zero_point: quantization zero point of the output tensor
-    """
-    def __init__(self, scale, zero_point, device=None, dtype=None):
-        factory_kwargs = {'device': device, 'dtype': dtype}
-        super().__init__()
-        self.register_buffer('scale', torch.tensor(scale, **factory_kwargs))
-        self.register_buffer('zero_point', torch.tensor(zero_point, **factory_kwargs))
-
-    def forward(self, input):
-        return torch.ops.quantized.hardswish(input, self.scale, self.zero_point)
-
-    def _get_name(self):
-        return 'QuantizedHardswish'
-
-    @staticmethod
-    def from_float(mod):
-        scale, zero_point = mod.activation_post_process.calculate_qparams()
-        return Hardswish(float(scale), int(zero_point))
-
-    @classmethod
-    def from_reference(cls, mod, scale, zero_point):
-        return cls(float(scale), int(zero_point))

-
-
[docs]class ELU(torch.nn.ELU):
-    r"""This is the quantized equivalent of :class:`~torch.nn.ELU`.
-
-    Args:
-        scale: quantization scale of the output tensor
-        zero_point: quantization zero point of the output tensor
-        alpha: the alpha constant
-    """
-    def __init__(self, scale, zero_point, alpha=1.):
-        super().__init__(alpha)
-        self.scale = scale
-        self.zero_point = zero_point
-
-    def forward(self, input):
-        return torch.ao.nn.quantized.functional.elu(
-            input, self.scale, self.zero_point, self.alpha)
-
-    def _get_name(self):
-        return 'QuantizedELU'
-
-    @staticmethod
-    def from_float(mod):
-        scale, zero_point = mod.activation_post_process.calculate_qparams()
-        return ELU(float(scale), int(zero_point), mod.alpha)
-
-    @classmethod
-    def from_reference(cls, mod, scale, zero_point):
-        return cls(float(scale), int(zero_point), mod.alpha)

-
-
[docs]class LeakyReLU(torch.nn.LeakyReLU):
-    r"""This is the quantized equivalent of :class:`~torch.nn.LeakyReLU`.
-
-    Args:
-        scale: quantization scale of the output tensor
-        zero_point: quantization zero point of the output tensor
-        negative_slope: Controls the angle of the negative slope. Default: 1e-2
-    """
-    def __init__(self, scale: float, zero_point: int, negative_slope: float = 1e-2,
-                 inplace: bool = False, device=None, dtype=None) -> None:
-        factory_kwargs = {'device': device, 'dtype': dtype}
-        super().__init__(negative_slope, inplace)
-        self.register_buffer('scale', torch.tensor(scale, **factory_kwargs))
-        self.register_buffer('zero_point', torch.tensor(zero_point, **factory_kwargs))
-
-    def forward(self, input):
-        return torch.ops.quantized.leaky_relu(
-            input, self.negative_slope, self.inplace, self.scale, self.zero_point)
-
-    def _get_name(self):
-        return 'QuantizedLeakyReLU'
-
-    @classmethod
-    def from_float(cls, mod):
-        scale, zero_point = mod.activation_post_process.calculate_qparams()
-        return cls(float(scale), int(zero_point), mod.negative_slope, mod.inplace)
-
-    @classmethod
-    def from_reference(cls, mod, scale, zero_point):
-        return cls(float(scale), int(zero_point), mod.negative_slope, mod.inplace)

-
-
[docs]class Sigmoid(torch.nn.Sigmoid):
-    r"""This is the quantized equivalent of :class:`~torch.nn.Sigmoid`.
-
-    Args:
-        scale: quantization scale of the output tensor
-        zero_point: quantization zero point of the output tensor
-    """
-
-    def __init__(self, output_scale: float, output_zero_point: int):
-        super().__init__()
-        self.output_scale = output_scale
-        self.output_zero_point = output_zero_point
-
-    def forward(self, input):
-        return torch.ops.quantized.sigmoid(input, self.output_scale, self.output_zero_point)
-
-    @classmethod
-    def from_float(cls, mod):
-        output_scale, output_zero_point = mod.activation_post_process.calculate_qparams()
-        return cls(float(output_scale), int(output_zero_point))

-
-class Softmax(torch.nn.Softmax):
-    r"""This is the quantized version of :class:`~torch.nn.Softmax`.
-
-    Args:
-        dim: A dimension along which Softmax will be computed (so every slice along dim will sum to 1).
-        scale: quantization scale of the output tensor
-        zero_point: quantization zero point of the output tensor
-    """
-    def __init__(self, dim=None, scale=1.0, zero_point=0):
-        super().__init__()
-        self.dim = dim
-        self.scale = scale
-        self.zero_point = zero_point
-
-    def forward(self, input):
-        dim = self.dim
-        if dim is None:
-            stacklevel = 3
-            # Note: adding the mypy ignore on _get_softmax_dim seems less bad
-            # than making `_get_softmax_dim` an official API.
-            dim = torch.nn.functional._get_softmax_dim(  # type: ignore[attr-defined]
-                "softmax", input.dim(), stacklevel)
-        return torch.ops.quantized.softmax(
-            input, dim, self.scale, self.zero_point)
-
-    def _get_name(self):
-        return 'QuantizedSoftmax'
-
-    @staticmethod
-    def from_float(mod):
-        scale, zero_point = mod.activation_post_process.calculate_qparams()
-        return Softmax(mod.dim, float(scale), int(zero_point))
-
-    @classmethod
-    def from_reference(cls, mod, scale, zero_point):
-        return cls(mod.dim, float(scale), int(zero_point))
-
-
-class MultiheadAttention(torch.ao.nn.quantizable.MultiheadAttention):
-    _FLOAT_MODULE = torch.ao.nn.quantizable.MultiheadAttention
-
-    def _get_name(self):
-        return "QuantizedMultiheadAttention"
-
-    @classmethod
-    def from_float(cls, other):
-        # The whole flow is float -> observed -> quantized
-        # This class does observed -> quantized only
-        raise NotImplementedError("It looks like you are trying to convert a "
-                                  "non-observed MHA module. Please, see "
-                                  "the examples on quantizable MHAs.")
-
-    @classmethod
-    def from_observed(cls, other):
-        converted = torch.ao.quantization.convert(other, mapping=None,
-                                                  inplace=False,
-                                                  remove_qconfig=True,
-                                                  convert_custom_config_dict=None)
-        converted.__class__ = cls
-        # Remove the parameters for the bias_k and bias_v to quantize them
-        # TODO: This is a potential source of accuracy drop.
-        #       quantized cat takes the scale and zp of the first
-        #       element, which might lose the precision in the bias_k
-        #       and the bias_v (which are cat'ed with k/v being first).
-        if converted.bias_k is not None:
-            bias_k = converted._parameters.pop('bias_k')
-            sc, zp = torch._choose_qparams_per_tensor(bias_k,
-                                                      reduce_range=False)
-            bias_k = torch.quantize_per_tensor(bias_k, sc, zp, torch.quint8)
-            setattr(converted, 'bias_k', bias_k)  # noqa: B010
-
-        if converted.bias_v is not None:
-            bias_v = converted._parameters.pop('bias_v')
-            sc, zp = torch._choose_qparams_per_tensor(bias_k,
-                                                      reduce_range=False)
-            bias_v = torch.quantize_per_tensor(bias_v, sc, zp, torch.quint8)
-            setattr(converted, 'bias_v', bias_v)  # noqa: B010
-
-        return converted
-
-class PReLU(torch.nn.Module):
-    r"""This is the quantized equivalent of :class:`~torch.nn.PReLU`.
-
-    Args:
-        scale: quantization scale of the output tensor
-        zero_point: quantization zero point of the output tensor
-        num_parameters: number of parameters: 1, or the number of channels at input. Default: 1
-    """
-    def __init__(self, output_scale: float, output_zero_point: int,
-                 num_parameters: int = 1) -> None:
-        super().__init__()
-        self.num_parameters = num_parameters
-        self.scale = output_scale
-        self.zero_point = output_zero_point
-        w = torch.randn(num_parameters, dtype=torch.float)
-        qw = torch.quantize_per_tensor(w, scale=1.0, zero_point=0, dtype=torch.quint8)
-        self.set_weight(qw)
-
-    def set_weight(self, w: torch.Tensor) -> None:
-        self.weight = w
-
-    def forward(self, input: torch.Tensor) -> torch.Tensor:
-        return torch.ops.quantized.prelu(input, self.weight, self.scale, self.zero_point)
-
-    def _get_name(self):
-        return 'QuantizedPReLU'
-
-    @classmethod
-    def from_float(cls, mod):
-        scale, zero_point = mod.activation_post_process.calculate_qparams()
-        qprelu = cls(float(scale), int(zero_point), mod.num_parameters)
-        float_wt = mod.weight.float()
-        observer = mod.qconfig.weight()
-        wt_scale, wt_zp = observer.calculate_qparams()
-        qweight = torch.quantize_per_tensor(
-            float_wt, float(wt_scale), int(wt_zp), torch.quint8)
-        qprelu.set_weight(qweight)
-        return qprelu
-
-    @classmethod
-    def from_reference(cls, mod, scale, zero_point):
-        qprelu = cls(float(scale), int(zero_point), mod.num_parameters)
-        float_wt = mod.weight.float()
-        observer = mod.qconfig.weight()
-        wt_scale, wt_zp = observer.calculate_qparams()
-        qweight = torch.quantize_per_tensor(
-            float_wt, float(wt_scale), int(wt_zp), torch.quint8)
-        qprelu.set_weight(qweight)
-        return qprelu
-
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Source code for torch.ao.nn.quantized.modules.batchnorm

-import torch
-import torch.ao.nn.intrinsic as nni
-
-__all__ = [
-    "BatchNorm2d",
-    "BatchNorm3d"
-]
-
-class _BatchNorm(torch.nn.modules.batchnorm._BatchNorm):
-    def __init__(self, num_features, eps=1e-5, momentum=0.1, device=None, dtype=None) -> None:
-        factory_kwargs = {'device': device, 'dtype': dtype}
-        super().__init__(num_features, eps, momentum, True, True, **factory_kwargs)
-        self.register_buffer('scale', torch.tensor(1.0, **factory_kwargs))
-        self.register_buffer('zero_point', torch.tensor(0, **factory_kwargs))
-
-    @staticmethod
-    def from_float(cls, mod):
-        activation_post_process = mod.activation_post_process
-        if type(mod) == cls._NNI_BN_RELU_MODULE:
-            mod = mod[0]
-        scale, zero_point = activation_post_process.calculate_qparams()
-        new_mod = cls(mod.num_features, mod.eps)
-        new_mod.weight = mod.weight
-        new_mod.bias = mod.bias
-        new_mod.running_mean = mod.running_mean
-        new_mod.running_var = mod.running_var
-        new_mod.scale = scale
-        new_mod.zero_point = zero_point
-        return new_mod
-
-    @classmethod
-    def from_reference(cls, bn, output_scale, output_zero_point):
-        qbn = cls(
-            bn.num_features,
-            bn.eps,
-            bn.momentum,
-            device=bn.weight.device,
-            dtype=bn.weight.dtype
-        )
-        qbn.weight = bn.weight
-        qbn.bias = bn.bias
-        qbn.running_mean = bn.running_mean
-        qbn.running_var = bn.running_var
-        qbn.scale = output_scale
-        qbn.zero_point = output_zero_point
-        return qbn
-
-
[docs]class BatchNorm2d(_BatchNorm):
-    r"""This is the quantized version of :class:`~torch.nn.BatchNorm2d`.
-    """
-
-    _NNI_BN_RELU_MODULE = nni.BNReLU2d
-
-    def __init__(self, num_features, eps=1e-5, momentum=0.1, device=None, dtype=None) -> None:
-        factory_kwargs = {'device': device, 'dtype': dtype}
-        super().__init__(num_features, eps, momentum, **factory_kwargs)
-
-    def _get_name(self):
-        return 'QuantizedBatchNorm2d'
-
-    def _check_input_dim(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)`!")
-
-    def forward(self, input: torch.Tensor) -> torch.Tensor:
-        # disabling this since this is not symbolically traceable
-        # self._check_input_dim(input)
-        return torch.ops.quantized.batch_norm2d(
-            input, self.weight, self.bias, self.running_mean,
-            self.running_var, self.eps, self.scale, self.zero_point)
-
-    @classmethod
-    def from_float(cls, mod):
-        return _BatchNorm.from_float(cls, mod)

-
-
[docs]class BatchNorm3d(_BatchNorm):
-    r"""This is the quantized version of :class:`~torch.nn.BatchNorm3d`.
-    """
-
-    _NNI_BN_RELU_MODULE = nni.BNReLU3d
-
-    def __init__(self, num_features, eps=1e-5, momentum=0.1, device=None, dtype=None):
-        factory_kwargs = {'device': device, 'dtype': dtype}
-        super().__init__(num_features, eps, momentum, **factory_kwargs)
-
-    def _get_name(self):
-        return 'QuantizedBatchNorm3d'
-
-    def _check_input_dim(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, H, W)`!")
-
-    def forward(self, input: torch.Tensor) -> torch.Tensor:
-        # disabling this since this is not symbolically traceable
-        # self._check_input_dim(input)
-        return torch.ops.quantized.batch_norm3d(
-            input, self.weight, self.bias, self.running_mean,
-            self.running_var, self.eps, self.scale, self.zero_point)
-
-    @classmethod
-    def from_float(cls, mod):
-        return _BatchNorm.from_float(cls, mod)

-
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- © Copyright 2023, PyTorch Contributors. - -

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

-# coding=utf-8
-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("'padding_mode' {} is not supported by quantized convolution".format(padding_mode))
-        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
-
-
-
[docs]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)
-
-
[docs]    @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)

-
-
-
[docs]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)
-
-
[docs]    @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)

-
-
-
[docs]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)
-
-
[docs]    @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('Only "zeros" padding mode is supported for {}'.format(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
-
-
-
[docs]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)

-
-
-
[docs]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)

-
-
-
[docs]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)

-
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- - - - - - - - \ No newline at end of file diff --git a/master/_modules/torch/ao/nn/quantized/modules/embedding_ops.html b/master/_modules/torch/ao/nn/quantized/modules/embedding_ops.html deleted file mode 100644 index b582202950f6..000000000000 --- a/master/_modules/torch/ao/nn/quantized/modules/embedding_ops.html +++ /dev/null @@ -1,1105 +0,0 @@ - - - - - - - - - - - - - torch.ao.nn.quantized.modules.embedding_ops — PyTorch master documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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Source code for torch.ao.nn.quantized.modules.embedding_ops

-import torch
-import torch.nn as nn
-from torch import Tensor  # noqa: F401
-from torch._jit_internal import Optional, List  # noqa: F401
-
-from .utils import _hide_packed_params_repr
-from .utils import _quantize_weight
-
-__all__ = ['EmbeddingPackedParams', 'Embedding', 'EmbeddingBag']
-
-class EmbeddingPackedParams(torch.nn.Module):
-    _version = 1
-
-    def __init__(self, num_embeddings, embedding_dim, dtype=torch.quint8):
-        super().__init__()
-        self.dtype = dtype
-        if self.dtype in [torch.quint8, torch.quint4x2]:
-            scales = torch.ones(num_embeddings, dtype=torch.float)
-            zero_points = torch.zeros(num_embeddings, dtype=torch.float)
-            wq = torch._empty_per_channel_affine_quantized([num_embeddings, embedding_dim], scales=scales,
-                                                           zero_points=zero_points,
-                                                           axis=0, dtype=self.dtype)
-            self.set_weight(wq)
-        else:
-            raise NotImplementedError(f'Unsupported dtype on quantized embedding! Supports quint8 and quint4x2. Got dtype: {dtype}')
-
-    @torch.jit.export
-    def set_weight(self, weight: torch.Tensor) -> None:
-        if self.dtype in [torch.quint8, torch.quint4x2]:
-            self._packed_weight = torch.ops.quantized.embedding_bag_prepack(weight)
-        else:
-            raise NotImplementedError('Unsupported dtype for quantized embedding prepack! Supports quint8 and quint4x2.')
-
-
-    @torch.jit.export
-    def _weight(self):
-        if self.dtype in [torch.quint8, torch.quint4x2]:
-            return torch.ops.quantized.embedding_bag_unpack(self._packed_weight)
-        else:
-            raise NotImplementedError('Unsupported dtype for quantized embedding unpack! Supports quint8 and quint4x2.')
-
-    def forward(self, x):
-        return x
-
-    # Version 1
-    #   self
-    #   |--- _packed_weight : Tensor representing weight of EmbeddingPackedParamsBase
-    #   |--- dtype : torch.dtype
-
-    def _save_to_state_dict(self, destination, prefix, keep_vars):
-        super()._save_to_state_dict(destination, prefix, keep_vars)
-        destination[prefix + 'dtype'] = self.dtype
-        destination[prefix + '_packed_weight'] = self._weight()
-
-    def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict,
-                              missing_keys, unexpected_keys, error_msgs):
-        self.dtype = state_dict[prefix + 'dtype']
-        state_dict.pop(prefix + 'dtype')
-
-        weight = state_dict[prefix + '_packed_weight']
-        state_dict.pop(prefix + '_packed_weight')
-        self.set_weight(weight)
-
-        super()._load_from_state_dict(state_dict, prefix, local_metadata, False,
-                                      missing_keys, unexpected_keys, error_msgs)
-
-    def __repr__(self):
-        return self._weight().__repr__()
-
-
[docs]class Embedding(torch.nn.Module):
-    r"""
-    A quantized Embedding module with quantized packed weights as inputs.
-    We adopt the same interface as `torch.nn.Embedding`, please see
-    https://pytorch.org/docs/stable/nn.html#torch.nn.Embedding for documentation.
-
-    Similar to :class:`~torch.nn.Embedding`, attributes will be randomly
-    initialized at module creation time and will be overwritten later
-
-    Attributes:
-        weight (Tensor): the non-learnable quantized weights of the module of
-                         shape :math:`(\text{num\_embeddings}, \text{embedding\_dim})`.
-
-    Examples::
-        >>> m = nn.quantized.Embedding(num_embeddings=10, embedding_dim=12)
-        >>> indices = torch.tensor([9, 6, 5, 7, 8, 8, 9, 2, 8])
-        >>> output = m(indices)
-        >>> print(output.size())
-        torch.Size([9, 12])
-
-    """
-    _version = 1
-
-    def __init__(self, num_embeddings: int, embedding_dim: int, padding_idx: Optional[int] = None,
-                 max_norm: Optional[float] = None, norm_type: float = 2., scale_grad_by_freq: bool = False,
-                 sparse: bool = False, _weight: Optional[Tensor] = None, dtype=torch.quint8) -> None:
-        super().__init__()
-        self.num_embeddings = num_embeddings
-        self.embedding_dim = embedding_dim
-        self.dtype = dtype
-
-        if _weight is None:
-            scales = torch.ones(num_embeddings, dtype=torch.float)
-            zero_points = torch.zeros(num_embeddings, dtype=torch.float)
-            qweight = torch._empty_per_channel_affine_quantized([num_embeddings, embedding_dim],
-                                                                scales=scales, zero_points=zero_points,
-                                                                axis=0, dtype=torch.quint8)
-        else:
-            assert list(_weight.shape) == [num_embeddings, embedding_dim], \
-                'Shape of weight does not match num_embeddings and embedding_dim'
-            qweight = _weight
-
-        self._packed_params = EmbeddingPackedParams(num_embeddings, embedding_dim, dtype)
-        self._packed_params.set_weight(qweight)
-
-    def forward(self, indices: Tensor) -> Tensor:
-        if self.dtype == torch.quint4x2:
-            return torch.ops.quantized.embedding_4bit(self._packed_params._packed_weight, indices)
-        else:
-            return torch.ops.quantized.embedding_byte(self._packed_params._packed_weight, indices)
-
-    def _get_name(self):
-        return 'QuantizedEmbedding'
-
-    def __repr__(self):
-        return _hide_packed_params_repr(self, EmbeddingPackedParams)
-
-    def extra_repr(self):
-        extra_repr_str = 'num_embeddings={}, embedding_dim={}, dtype={}, qscheme={}'.format(
-            self.num_embeddings, self.embedding_dim, self._packed_params.dtype, self.weight().qscheme()
-        )
-
-        return extra_repr_str
-
-    def set_weight(self, w: torch.Tensor) -> None:
-        self._packed_params.set_weight(w)
-
-    def weight(self):
-        return self._packed_params._weight()
-
-
[docs]    @classmethod
-    def from_float(cls, mod):
-        r"""Create a quantized embedding module from a float module
-
-        Args:
-            mod (Module): a float module, either produced by torch.ao.quantization
-                          utilities or provided by user
-        """
-        if hasattr(mod, 'weight_fake_quant'):
-            assert type(mod) == torch.ao.nn.qat.Embedding, 'nnq.' + cls.__name__ + '.from_float ' + \
-                'with fake quant only works for ' + torch.ao.nn.qat.Embedding.__name__
-            weight_observer = mod.weight_fake_quant
-            activation_post_process = mod.activation_post_process
-        else:
-            assert type(mod) == nn.Embedding, 'nnq.' + cls.__name__ + '.from_float only works for ' + \
-                nn.Embedding.__name__
-            assert hasattr(mod, 'qconfig'), 'Embedding input float module must have qconfig defined'
-            from torch.ao.quantization import float_qparams_weight_only_qconfig
-            if mod.qconfig is not None and mod.qconfig.weight is not None:  # type: ignore[union-attr]
-                weight_observer = mod.qconfig.weight()  # type: ignore[union-attr, operator]
-            else:
-                weight_observer = float_qparams_weight_only_qconfig.weight()
-
-        dtype = weight_observer.dtype
-        is_float_qparams_qconfig = weight_observer.qscheme == torch.per_channel_affine_float_qparams
-        assert is_float_qparams_qconfig, \
-            'Embedding quantization is only supported with float_qparams_weight_only_qconfig.'
-
-        assert dtype == torch.quint8 or dtype == torch.quint4x2, \
-            f'The only supported dtype for nnq.Embedding is torch.quint8 and torch.quint4x2, got {dtype}'
-
-        # Run the observer to calculate qparams.
-        weight_observer(mod.weight)
-        qweight = _quantize_weight(mod.weight.float(), weight_observer)
-
-        # Create quantized Embedding module and pass in the quantized weight
-        qembedding = Embedding(mod.num_embeddings, mod.embedding_dim)
-        qembedding.set_weight(qweight)
-        return qembedding

-
-    @classmethod
-    def from_reference(cls, ref_embedding):
-        qembedding = cls(
-            ref_embedding.num_embeddings,
-            ref_embedding.embedding_dim,
-            ref_embedding.padding_idx,
-            ref_embedding.max_norm,
-            ref_embedding.norm_type,
-            ref_embedding.scale_grad_by_freq,
-            ref_embedding.sparse,
-            ref_embedding.get_quantized_weight(),
-            ref_embedding.weight_dtype,
-        )
-        return qembedding

-
-
[docs]class EmbeddingBag(Embedding):
-    r"""
-    A quantized EmbeddingBag module with quantized packed weights as inputs.
-    We adopt the same interface as `torch.nn.EmbeddingBag`, please see
-    https://pytorch.org/docs/stable/nn.html#torch.nn.EmbeddingBag for documentation.
-
-    Similar to :class:`~torch.nn.EmbeddingBag`, attributes will be randomly
-    initialized at module creation time and will be overwritten later
-
-    Attributes:
-        weight (Tensor): the non-learnable quantized weights of the module of
-                         shape :math:`(\text{num\_embeddings}, \text{embedding\_dim})`.
-
-    Examples::
-        >>> m = nn.quantized.EmbeddingBag(num_embeddings=10, embedding_dim=12, include_last_offset=True, mode='sum')
-        >>> indices = torch.tensor([9, 6, 5, 7, 8, 8, 9, 2, 8, 6, 6, 9, 1, 6, 8, 8, 3, 2, 3, 6, 3, 6, 5, 7, 0, 8, 4, 6, 5, 8, 2, 3])
-        >>> offsets = torch.tensor([0, 19, 20, 28, 28, 32])
-        >>> output = m(indices, offsets)
-        >>> print(output.size())
-        torch.Size([5, 12])
-
-    """
-    _version = 1
-
-    def __init__(self, num_embeddings: int, embedding_dim: int,
-                 max_norm: Optional[float] = None, norm_type: float = 2., scale_grad_by_freq: bool = False,
-                 mode: str = 'sum', sparse: bool = False, _weight: Optional[Tensor] = None,
-                 include_last_offset: bool = False, dtype=torch.quint8) -> None:
-        super().__init__(num_embeddings, embedding_dim, _weight=_weight, dtype=dtype)
-
-        self.mode = mode
-        self.pruned_weights = False
-        self.include_last_offset = include_last_offset
-        self.dtype = dtype
-
-    def forward(self, indices: Tensor, offsets: Optional[Tensor] = None, per_sample_weights: Optional[Tensor] = None,
-                compressed_indices_mapping: Optional[Tensor] = None) -> Tensor:
-        if self.dtype == torch.quint4x2:
-            return torch.ops.quantized.embedding_bag_4bit(self._packed_params._packed_weight, indices, offsets, False, 0,
-                                                          self.pruned_weights, per_sample_weights, compressed_indices_mapping,
-                                                          self.include_last_offset)
-        else:
-            return torch.ops.quantized.embedding_bag_byte(self._packed_params._packed_weight, indices, offsets, False, 0,
-                                                          self.pruned_weights, per_sample_weights, compressed_indices_mapping,
-                                                          self.include_last_offset)
-
-    def _get_name(self):
-        return 'QuantizedEmbeddingBag'
-
-
[docs]    @classmethod
-    def from_float(cls, mod):
-        r"""Create a quantized embedding_bag module from a float module
-
-        Args:
-            mod (Module): a float module, either produced by torch.ao.quantization
-                          utilities or provided by user
-        """
-        if hasattr(mod, 'weight_fake_quant'):
-            weight_observer = mod.weight_fake_quant
-        else:
-            assert type(mod) == nn.EmbeddingBag, 'nnq.' + cls.__name__ + '.from_float only works for ' + \
-                nn.EmbeddingBag.__name__
-            assert hasattr(mod, 'qconfig'), 'EmbeddingBag input float module must have qconfig defined'
-            from torch.ao.quantization.qconfig import float_qparams_weight_only_qconfig
-            if mod.qconfig is not None and mod.qconfig.weight is not None:  # type: ignore[union-attr]
-                weight_observer = mod.qconfig.weight()  # type: ignore[union-attr, operator]
-            else:
-                weight_observer = float_qparams_weight_only_qconfig.weight()
-
-        dtype = weight_observer.dtype
-        is_float_qparams_qconfig = weight_observer.qscheme == torch.per_channel_affine_float_qparams
-        assert is_float_qparams_qconfig, \
-            'EmbeddingBag quantization is only supported with float_qparams_weight_only_qconfig.'
-
-        assert dtype == torch.quint8 or dtype == torch.quint4x2, \
-            f'The only supported dtype for nnq.EmbeddingBag is torch.quint8 and torch.quint4x2, got {dtype}'
-
-        # Run the observer to calculate qparams.
-        weight_observer(mod.weight)
-        qweight = _quantize_weight(mod.weight.float(), weight_observer)
-
-        # Create quantized EmbeddingBag module and pass in the quantized weight
-        qembedding_bag = EmbeddingBag(mod.num_embeddings, mod.embedding_dim, dtype=dtype)
-        qembedding_bag.set_weight(qweight)
-        return qembedding_bag

-
-    @classmethod
-    def from_reference(cls, ref_embedding_bag):
-        qembedding_bag = cls(
-            ref_embedding_bag.num_embeddings,
-            ref_embedding_bag.embedding_dim,
-            ref_embedding_bag.max_norm,
-            ref_embedding_bag.norm_type,
-            ref_embedding_bag.scale_grad_by_freq,
-            ref_embedding_bag.mode,
-            ref_embedding_bag.sparse,
-            ref_embedding_bag.get_quantized_weight(),
-            ref_embedding_bag.include_last_offset,
-            ref_embedding_bag.weight_dtype,
-        )
-        return qembedding_bag

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

-from typing import List
-
-import torch
-from torch import Tensor
-from torch._ops import ops
-
-__all__ = ['FloatFunctional', 'FXFloatFunctional', 'QFunctional']
-
-
[docs]class FloatFunctional(torch.nn.Module):
-    r"""State collector class for float operations.
-
-    The instance of this class can be used instead of the ``torch.`` prefix for
-    some operations. See example usage below.
-
-    .. note::
-
-        This class does not provide a ``forward`` hook. Instead, you must use
-        one of the underlying functions (e.g. ``add``).
-
-    Examples::
-
-        >>> f_add = FloatFunctional()
-        >>> a = torch.tensor(3.0)
-        >>> b = torch.tensor(4.0)
-        >>> f_add.add(a, b)  # Equivalent to ``torch.add(a, b)``
-
-    Valid operation names:
-        - add
-        - cat
-        - mul
-        - add_relu
-        - add_scalar
-        - mul_scalar
-    """
-    def __init__(self):
-        super().__init__()
-        self.activation_post_process = torch.nn.Identity()
-
-    def forward(self, x):
-        raise RuntimeError("FloatFunctional is not intended to use the " +
-                           "'forward'. Please use the underlying operation")
-
-    r"""Operation equivalent to ``torch.add(Tensor, Tensor)``"""
-    def add(self, x: Tensor, y: Tensor) -> Tensor:
-        r = torch.add(x, y)
-        r = self.activation_post_process(r)
-        return r
-
-    r"""Operation equivalent to ``torch.add(Tensor, float)``"""
-    def add_scalar(self, x: Tensor, y: float) -> Tensor:
-        r = torch.add(x, y)
-        # Note: this operation is not observed because the observation is not
-        # needed for the quantized op.
-        return r
-
-    r"""Operation equivalent to ``torch.mul(Tensor, Tensor)``"""
-    def mul(self, x: Tensor, y: Tensor) -> Tensor:
-        r = torch.mul(x, y)
-        r = self.activation_post_process(r)
-        return r
-
-    r"""Operation equivalent to ``torch.mul(Tensor, float)``"""
-    def mul_scalar(self, x: Tensor, y: float) -> Tensor:
-        r = torch.mul(x, y)
-        # Note: this operation is not observed because the observation is not
-        # needed for the quantized op.
-        return r
-
-    r"""Operation equivalent to ``torch.cat``"""
-    def cat(self, x: List[Tensor], dim: int = 0) -> Tensor:
-        r = torch.cat(x, dim=dim)
-        r = self.activation_post_process(r)
-        return r
-
-    r"""Operation equivalent to ``relu(torch.add(x,y))``"""
-    def add_relu(self, x: Tensor, y: Tensor) -> Tensor:
-        r = torch.add(x, y)
-        r = torch.nn.functional.relu(r)
-        r = self.activation_post_process(r)
-        return r

-
-
[docs]class FXFloatFunctional(torch.nn.Module):
-    r""" module to replace FloatFunctional module before FX graph mode quantization,
-    since activation_post_process will be inserted in top level module directly
-
-    Valid operation names:
-        - add
-        - cat
-        - mul
-        - add_relu
-        - add_scalar
-        - mul_scalar
-    """
-    def forward(self, x):
-        raise RuntimeError("FloatFunctional is not intended to use the " +
-                           "'forward'. Please use the underlying operation")
-
-    r"""Operation equivalent to ``torch.add(Tensor, Tensor)``"""
-    def add(self, x: Tensor, y: Tensor) -> Tensor:
-        r = torch.add(x, y)
-        return r
-
-    r"""Operation equivalent to ``torch.add(Tensor, float)``"""
-    def add_scalar(self, x: Tensor, y: float) -> Tensor:
-        r = torch.add(x, y)
-        return r
-
-    r"""Operation equivalent to ``torch.mul(Tensor, Tensor)``"""
-    def mul(self, x: Tensor, y: Tensor) -> Tensor:
-        r = torch.mul(x, y)
-        return r
-
-    r"""Operation equivalent to ``torch.mul(Tensor, float)``"""
-    def mul_scalar(self, x: Tensor, y: float) -> Tensor:
-        r = torch.mul(x, y)
-        return r
-
-    r"""Operation equivalent to ``torch.cat``"""
-    def cat(self, x: List[Tensor], dim: int = 0) -> Tensor:
-        r = torch.cat(x, dim=dim)
-        return r
-
-    r"""Operation equivalent to ``relu(torch.add(x,y))``"""
-    def add_relu(self, x: Tensor, y: Tensor) -> Tensor:
-        r = torch.add(x, y)
-        r = torch.nn.functional.relu(r)
-        return r

-
-
[docs]class QFunctional(torch.nn.Module):
-    r"""Wrapper class for quantized operations.
-
-    The instance of this class can be used instead of the
-    ``torch.ops.quantized`` prefix. See example usage below.
-
-    .. note::
-
-        This class does not provide a ``forward`` hook. Instead, you must use
-        one of the underlying functions (e.g. ``add``).
-
-    Examples::
-
-        >>> q_add = QFunctional()
-        >>> # xdoctest: +SKIP
-        >>> a = torch.quantize_per_tensor(torch.tensor(3.0), 1.0, 0, torch.qint32)
-        >>> b = torch.quantize_per_tensor(torch.tensor(4.0), 1.0, 0, torch.qint32)
-        >>> q_add.add(a, b)  # Equivalent to ``torch.ops.quantized.add(a, b, 1.0, 0)``
-
-    Valid operation names:
-        - add
-        - cat
-        - mul
-        - add_relu
-        - add_scalar
-        - mul_scalar
-    """
-    def __init__(self):
-        super().__init__()
-        self.scale = 1.0
-        self.zero_point = 0
-        self.activation_post_process = torch.nn.Identity()
-
-    def _save_to_state_dict(self, destination, prefix, keep_vars):
-        super()._save_to_state_dict(destination, prefix, keep_vars)
-        destination[prefix + 'scale'] = torch.tensor(self.scale)
-        destination[prefix + 'zero_point'] = torch.tensor(self.zero_point)
-
-    def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict,
-                              missing_keys, unexpected_keys, error_msgs):
-
-        self.scale = float(state_dict.pop(prefix + 'scale'))
-        self.zero_point = int(state_dict.pop(prefix + 'zero_point'))
-        super()._load_from_state_dict(state_dict, prefix, local_metadata, False,
-                                      missing_keys, unexpected_keys, error_msgs)
-
-    def _get_name(self):
-        return 'QFunctional'
-
-    def extra_repr(self):
-        return 'scale={}, zero_point={}'.format(
-            self.scale, self.zero_point
-        )
-
-    def forward(self, x):
-        raise RuntimeError("Functional is not intended to use the " +
-                           "'forward'. Please use the underlying operation")
-
-    r"""Operation equivalent to ``torch.ops.quantized.add``"""
-    def add(self, x: Tensor, y: Tensor) -> Tensor:
-        r = ops.quantized.add(x, y, scale=self.scale, zero_point=self.zero_point)
-        r = self.activation_post_process(r)
-        return r
-
-    r"""Operation equivalent to ``torch.ops.quantized.add(Tensor, float)``"""
-    def add_scalar(self, x: Tensor, y: float) -> Tensor:
-        r = ops.quantized.add_scalar(x, y)
-        # Note: this operation is not observed because the observation is not
-        # needed for the quantized op.
-        return r
-
-    r"""Operation equivalent to ``torch.ops.quantized.mul(Tensor, Tensor)``"""
-    def mul(self, x: Tensor, y: Tensor) -> Tensor:
-        r = ops.quantized.mul(x, y, scale=self.scale, zero_point=self.zero_point)
-        r = self.activation_post_process(r)
-        return r
-
-    r"""Operation equivalent to ``torch.ops.quantized.mul(Tensor, float)``"""
-    def mul_scalar(self, x: Tensor, y: float) -> Tensor:
-        r = ops.quantized.mul_scalar(x, y)
-        # Note: this operation is not observed because the observation is not
-        # needed for the quantized op.
-        return r
-
-    r"""Operation equivalent to ``torch.ops.quantized.cat``"""
-    def cat(self, x: List[Tensor], dim: int = 0) -> Tensor:
-        r = ops.quantized.cat(x, scale=self.scale, zero_point=self.zero_point, dim=dim)
-        r = self.activation_post_process(r)
-        return r
-
-    r"""Operation equivalent to ``torch.ops.quantized.add_relu``"""
-    def add_relu(self, x: Tensor, y: Tensor) -> Tensor:
-        r = ops.quantized.add_relu(x, y, scale=self.scale, zero_point=self.zero_point)
-        r = self.activation_post_process(r)
-        return r
-
-    @classmethod
-    def from_float(cls, mod):
-        assert type(mod) == FloatFunctional,\
-            "QFunctional.from_float expects an instance of FloatFunctional"
-        scale, zero_point = mod.activation_post_process.calculate_qparams()  # type: ignore[operator]
-        new_mod = QFunctional()
-        new_mod.scale = float(scale)
-        new_mod.zero_point = int(zero_point)
-        return new_mod

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- - - - - - - - \ No newline at end of file diff --git a/master/_modules/torch/ao/nn/quantized/modules/linear.html b/master/_modules/torch/ao/nn/quantized/modules/linear.html deleted file mode 100644 index 6c66c61f563d..000000000000 --- a/master/_modules/torch/ao/nn/quantized/modules/linear.html +++ /dev/null @@ -1,1113 +0,0 @@ - - - - - - - - - - - - - torch.ao.nn.quantized.modules.linear — PyTorch master documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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Source code for torch.ao.nn.quantized.modules.linear

-from collections.abc import Iterable
-import torch
-
-import torch.nn as nn
-import torch.ao.nn.intrinsic as nni
-import torch.ao.nn.intrinsic.qat as nniqat
-from torch.nn.utils.fusion import fuse_linear_bn_weights
-from torch.nn.utils.parametrize import type_before_parametrizations
-
-from typing import Optional
-
-from .utils import _quantize_weight, _hide_packed_params_repr, WeightedQuantizedModule
-
-__all__ = ['LinearPackedParams', 'Linear']
-
-
-class LinearPackedParams(torch.nn.Module):
-    _version = 3
-
-    def __init__(self, dtype=torch.qint8):
-        super().__init__()
-        self.dtype = dtype
-        if self.dtype == torch.qint8:
-            wq = torch._empty_affine_quantized([1, 1], scale=1.0, zero_point=0, dtype=torch.qint8)
-        elif self.dtype == torch.float16:
-            wq = torch.zeros([1, 1], dtype=torch.float)
-        self.set_weight_bias(wq, None)
-
-    @torch.jit.export
-    def set_weight_bias(self, weight: torch.Tensor, bias: Optional[torch.Tensor]) -> None:
-        if self.dtype == torch.qint8:
-            self._packed_params = torch.ops.quantized.linear_prepack(weight, bias)
-        elif self.dtype == torch.float16:
-            self._packed_params = torch.ops.quantized.linear_prepack_fp16(weight, bias)
-        else:
-            raise RuntimeError('Unsupported dtype on dynamic quantized linear!')
-
-
-    @torch.jit.export
-    def _weight_bias(self):
-        if self.dtype == torch.qint8:
-            return torch.ops.quantized.linear_unpack(self._packed_params)
-        elif self.dtype == torch.float16:
-            return torch.ops.quantized.linear_unpack_fp16(self._packed_params)
-        else:
-            raise RuntimeError('Unsupported dtype on dynamic quantized linear!')
-
-    def forward(self, x):
-        return x
-
-    # Version 1
-    #   self
-    #   |--- weight : Tensor
-    #   |--- bias : Tensor
-    #
-    # Version 2
-    #   self
-    #   |--- weight : Tensor
-    #   |--- bias : Tensor
-    #   |--- dtype : torch.dtype
-    #
-    # Version 3
-    #   self
-    #   |--- _packed_params : (Tensor, Tensor) representing (weight, bias)
-    #                         of LinearPackedParams
-    #   |--- dtype : torch.dtype
-    def _save_to_state_dict(self, destination, prefix, keep_vars):
-        super()._save_to_state_dict(destination, prefix, keep_vars)
-        destination[prefix + 'dtype'] = self.dtype
-        destination[prefix + '_packed_params'] = self._weight_bias()
-
-    def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict,
-                              missing_keys, unexpected_keys, error_msgs):
-        version = local_metadata.get('version', None)
-        if version is None or version < 2:
-            self.dtype = torch.qint8
-        else:
-            self.dtype = state_dict[prefix + 'dtype']
-            state_dict.pop(prefix + 'dtype')
-
-        if version is None or version < 3:
-            self.set_weight_bias(state_dict[prefix + 'weight'], state_dict[prefix + 'bias'])
-            state_dict.pop(prefix + 'weight')
-            state_dict.pop(prefix + 'bias')
-
-        if version == 3:
-            weight, bias = state_dict[prefix + '_packed_params']
-            state_dict.pop(prefix + '_packed_params')
-            self.set_weight_bias(weight, bias)
-
-        super()._load_from_state_dict(state_dict, prefix, local_metadata, False,
-                                      missing_keys, unexpected_keys, error_msgs)
-
-
-    def __repr__(self):
-        return self._weight_bias().__repr__()
-
-
-
[docs]class Linear(WeightedQuantizedModule):
-    r"""
-    A quantized linear module with quantized tensor as inputs and outputs.
-    We adopt the same interface as `torch.nn.Linear`, please see
-    https://pytorch.org/docs/stable/nn.html#torch.nn.Linear for documentation.
-
-    Similar to :class:`~torch.nn.Linear`, attributes will be randomly
-    initialized at module creation time and will be overwritten later
-
-    Attributes:
-        weight (Tensor): the non-learnable quantized weights of the module of
-                         shape :math:`(\text{out\_features}, \text{in\_features})`.
-        bias (Tensor): the non-learnable bias of the module of shape :math:`(\text{out\_features})`.
-                If :attr:`bias` is ``True``, the values are initialized to zero.
-        scale: `scale` parameter of output Quantized Tensor, type: double
-        zero_point: `zero_point` parameter for output Quantized Tensor, type: long
-
-    Examples::
-
-        >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_QENGINE)
-        >>> m = nn.quantized.Linear(20, 30)
-        >>> input = torch.randn(128, 20)
-        >>> # xdoctest: +SKIP
-        >>> input = torch.quantize_per_tensor(input, 1.0, 0, torch.quint8)
-        >>> output = m(input)
-        >>> print(output.size())
-        torch.Size([128, 30])
-    """
-    _version = 3
-    _FLOAT_MODULE = (nn.Linear, nn.modules.linear.NonDynamicallyQuantizableLinear)
-
-    def __init__(self, in_features, out_features, bias_=True,
-                 dtype=torch.qint8):
-        super().__init__()
-        # We don't muck around with buffers or attributes or anything here
-        # to keep the module simple. *everything* is simply a Python attribute.
-        # Serialization logic is explicitly handled in the below serialization and
-        # deserialization modules
-        self.in_features = in_features
-        self.out_features = out_features
-        bias = None
-        if bias_:
-            bias = torch.zeros(out_features, dtype=torch.float)
-
-        if dtype == torch.qint8:
-            qweight = torch._empty_affine_quantized(
-                [out_features, in_features], scale=1, zero_point=0, dtype=torch.qint8)
-        elif dtype == torch.float16:
-            qweight = torch.zeros([out_features, in_features], dtype=torch.float)
-        else:
-            raise RuntimeError('Unsupported dtype specified for quantized Linear!')
-
-        self._packed_params = LinearPackedParams(dtype)
-        self._packed_params.set_weight_bias(qweight, bias)
-        self.scale = 1.0
-        self.zero_point = 0
-
-    def _get_name(self):
-        return 'QuantizedLinear'
-
-    def extra_repr(self):
-        return 'in_features={}, out_features={}, scale={}, zero_point={}, qscheme={}'.format(
-            self.in_features, self.out_features, self.scale, self.zero_point, self.weight().qscheme()
-        )
-
-    def __repr__(self):
-        return _hide_packed_params_repr(self, LinearPackedParams)
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        return torch.ops.quantized.linear(
-            x, self._packed_params._packed_params, self.scale, self.zero_point)
-
-    # ===== 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.
-    #
-    # Version 1
-    #   self
-    #   |--- scale : float
-    #   |--- zero_point : int
-    #   |--- weight : Tensor
-    #   |--- bias : Tensor
-    #
-    # Version 2
-    #   self
-    #   |--- scale : float
-    #   |--- zero_point : int
-    #   |--- _packed_params : Module
-    #        |--- weight : Tensor
-    #        |--- bias : Tensor
-    #
-    # Version 3
-    #   self
-    #   |--- scale : float
-    #   |--- zero_point : int
-    #   |--- _packed_params : Module
-    #        |--- _packed_params : (Tensor, Tensor) representing weight, bias
-    #                              of LinearPackedParams C++ struct
-    #
-    def _save_to_state_dict(self, destination, prefix, keep_vars):
-        super()._save_to_state_dict(destination, prefix, keep_vars)
-        destination[prefix + 'scale'] = torch.tensor(self.scale)
-        destination[prefix + 'zero_point'] = torch.tensor(self.zero_point)
-
-    # ===== 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.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')
-
-        version = local_metadata.get('version', None)
-
-        if version is None or version == 1:
-            # We moved the parameters into a LinearPackedParameters submodule
-            weight = state_dict.pop(prefix + 'weight')
-            bias = state_dict.pop(prefix + 'bias')
-            state_dict.update({prefix + '_packed_params.weight': weight,
-                               prefix + '_packed_params.bias': bias})
-
-        super()._load_from_state_dict(
-            state_dict, prefix, local_metadata, False,
-            missing_keys, unexpected_keys, error_msgs)
-
-    # Function rather than property to make sure that JIT serialization doesn't
-    # register this as an attribute
-    def _weight_bias(self):
-        return self._packed_params._weight_bias()
-
-    def weight(self):
-        return self._weight_bias()[0]
-
-    def bias(self):
-        return self._weight_bias()[1]
-
-    def set_weight_bias(self, w: torch.Tensor, b: Optional[torch.Tensor]) -> None:
-        self._packed_params.set_weight_bias(w, b)
-
-
[docs]    @classmethod
-    def from_float(cls, mod):
-        r"""Create a quantized module from an observed float module
-
-        Args:
-            mod (Module): a float module, either produced by torch.ao.quantization
-                          utilities or provided by the user
-        """
-        if hasattr(mod, 'weight_fake_quant'):
-            if type_before_parametrizations(mod) == nniqat.LinearBn1d:
-                mod.weight, mod.bias = fuse_linear_bn_weights(
-                    mod.weight, mod.bias, mod.bn.running_mean, mod.bn.running_var,
-                    mod.bn.eps, mod.bn.weight, mod.bn.bias)
-            weight_post_process = mod.weight_fake_quant
-            activation_post_process = mod.activation_post_process
-        else:
-            # This function does not participate in JIT, so it is OK to ignore
-            # the type mismatch in assignment. Also, mypy has an issue with
-            # iterables not being implemented, so we are ignoring those too.
-            if not isinstance(cls._FLOAT_MODULE, Iterable):
-                cls._FLOAT_MODULE = [cls._FLOAT_MODULE]  # type: ignore[assignment]
-            supported_modules = ', '.join([float_mod.__name__ for float_mod in cls._FLOAT_MODULE])  # type: ignore[attr-defined]
-            error_msg = 'nnq.{}.from_float only works for {}, but got: {}'.format(cls.__name__, supported_modules, type(mod))
-            assert type_before_parametrizations(mod) in cls._FLOAT_MODULE, error_msg.format()  # type: ignore[attr-defined]
-            assert hasattr(mod, 'qconfig'), 'Input float module must have qconfig defined'
-            activation_post_process = mod.activation_post_process
-            if type_before_parametrizations(mod) == nni.LinearReLU:
-                mod = mod[0]
-            weight_post_process = mod.qconfig.weight()
-        weight_post_process(mod.weight)
-        dtype = weight_post_process.dtype
-        act_scale, act_zp = activation_post_process.calculate_qparams()
-        assert dtype == torch.qint8, 'Weight observer must have dtype torch.qint8'
-        qweight = _quantize_weight(mod.weight.float(), weight_post_process)
-        qlinear = cls(mod.in_features,
-                      mod.out_features,
-                      dtype=dtype)
-        qlinear.set_weight_bias(qweight, mod.bias)
-        qlinear.scale = float(act_scale)
-        qlinear.zero_point = int(act_zp)
-        return qlinear

-
-
[docs]    @classmethod
-    def from_reference(cls, ref_qlinear, output_scale, output_zero_point):
-        r"""Create a (fbgemm/qnnpack) quantized module from a reference quantized module
-
-        Args:
-            ref_qlinear (Module): a reference quantized linear 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
-        """
-        qlinear = cls(
-            ref_qlinear.in_features,
-            ref_qlinear.out_features)
-        qweight = ref_qlinear.get_quantized_weight()
-        qlinear.set_weight_bias(qweight, ref_qlinear.bias)
-
-        qlinear.scale = float(output_scale)
-        qlinear.zero_point = int(output_zero_point)
-        return qlinear

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

-import torch
-
-__all__ = ['LayerNorm', 'GroupNorm', 'InstanceNorm1d', 'InstanceNorm2d', 'InstanceNorm3d']
-
-
[docs]class LayerNorm(torch.nn.LayerNorm):
-    r"""This is the quantized version of :class:`~torch.nn.LayerNorm`.
-
-    Additional args:
-        * **scale** - quantization scale of the output, type: double.
-        * **zero_point** - quantization zero point of the output, type: long.
-
-    """
-
-    def __init__(self, normalized_shape, weight, bias, scale, zero_point, eps=1e-5,
-                 elementwise_affine=True, device=None, dtype=None) -> None:
-        factory_kwargs = {'device': device, 'dtype': dtype}
-        super().__init__(normalized_shape, eps=eps, elementwise_affine=elementwise_affine,
-                         **factory_kwargs)
-        self.weight = weight
-        self.bias = bias
-        self.register_buffer('scale', torch.tensor(scale, **factory_kwargs))
-        self.register_buffer('zero_point', torch.tensor(zero_point, **factory_kwargs))
-
-    def forward(self, input):
-        return torch.ops.quantized.layer_norm(
-            input, self.normalized_shape, weight=self.weight, bias=self.bias,
-            eps=self.eps, output_scale=self.scale, output_zero_point=self.zero_point)
-
-    def _get_name(self):
-        return 'QuantizedLayerNorm'
-
-    @classmethod
-    def from_float(cls, mod):
-        scale, zero_point = mod.activation_post_process.calculate_qparams()
-        new_mod = cls(
-            mod.normalized_shape, mod.weight, mod.bias, float(scale),
-            int(zero_point), mod.eps, mod.elementwise_affine)
-        return new_mod
-
-    @classmethod
-    def from_reference(cls, mod, scale, zero_point):
-        return cls(
-            mod.normalized_shape, mod.weight, mod.bias, float(scale),
-            int(zero_point), mod.eps, mod.elementwise_affine)

-
-
[docs]class GroupNorm(torch.nn.GroupNorm):
-    r"""This is the quantized version of :class:`~torch.nn.GroupNorm`.
-
-    Additional args:
-        * **scale** - quantization scale of the output, type: double.
-        * **zero_point** - quantization zero point of the output, type: long.
-
-    """
-    __constants__ = ['num_groups', 'num_channels', 'eps', 'affine']
-
-    def __init__(self, num_groups, num_channels, weight, bias, scale, zero_point, eps=1e-5,
-                 affine=True, device=None, dtype=None) -> None:
-        factory_kwargs = {'device': device, 'dtype': dtype}
-        super().__init__(num_groups, num_channels, eps, affine, **factory_kwargs)
-        self.weight = weight
-        self.bias = bias
-        self.register_buffer('scale', torch.tensor(scale, **factory_kwargs))
-        self.register_buffer('zero_point', torch.tensor(zero_point, **factory_kwargs))
-
-    def forward(self, input):
-        return torch.ops.quantized.group_norm(
-            input, self.num_groups, self.weight, self.bias, self.eps, self.scale,
-            self.zero_point)
-
-    def _get_name(self):
-        return 'QuantizedGroupNorm'
-
-    @classmethod
-    def from_float(cls, mod):
-        scale, zero_point = mod.activation_post_process.calculate_qparams()
-        new_mod = cls(
-            mod.num_groups, mod.num_channels, mod.weight, mod.bias, float(scale), int(zero_point),
-            mod.eps, mod.affine)
-        return new_mod

-
-
[docs]class InstanceNorm1d(torch.nn.InstanceNorm1d):
-    r"""This is the quantized version of :class:`~torch.nn.InstanceNorm1d`.
-
-    Additional args:
-        * **scale** - quantization scale of the output, type: double.
-        * **zero_point** - quantization zero point of the output, type: long.
-
-    """
-    def __init__(self, num_features, weight, bias, scale, zero_point,
-                 eps=1e-5, momentum=0.1, affine=False,
-                 track_running_stats=False, device=None, dtype=None) -> None:
-        factory_kwargs = {'device': device, 'dtype': dtype}
-        super().__init__(num_features, eps, momentum, affine, track_running_stats, **factory_kwargs)
-        self.weight = weight
-        self.bias = bias
-        self.register_buffer('scale', torch.tensor(scale, **factory_kwargs))
-        self.register_buffer('zero_point', torch.tensor(zero_point, **factory_kwargs))
-
-    def forward(self, input):
-        return torch.ops.quantized.instance_norm(
-            input, self.weight, self.bias, self.eps, self.scale,
-            self.zero_point)
-
-    def _get_name(self):
-        return 'QuantizedInstanceNorm1d'
-
-    @classmethod
-    def from_float(cls, mod):
-        scale, zero_point = mod.activation_post_process.calculate_qparams()
-        new_mod = cls(
-            mod.num_features, mod.weight, mod.bias, float(scale), int(zero_point),
-            mod.eps, mod.affine)
-        return new_mod
-
-    @classmethod
-    def from_reference(cls, mod, scale, zero_point):
-        return cls(
-            mod.num_features, mod.weight, mod.bias, float(scale), int(zero_point),
-            mod.eps, mod.affine)

-
-
[docs]class InstanceNorm2d(torch.nn.InstanceNorm2d):
-    r"""This is the quantized version of :class:`~torch.nn.InstanceNorm2d`.
-
-    Additional args:
-        * **scale** - quantization scale of the output, type: double.
-        * **zero_point** - quantization zero point of the output, type: long.
-
-    """
-    def __init__(self, num_features, weight, bias, scale, zero_point,
-                 eps=1e-5, momentum=0.1, affine=False,
-                 track_running_stats=False, device=None, dtype=None) -> None:
-        factory_kwargs = {'device': device, 'dtype': dtype}
-        super().__init__(num_features, eps, momentum, affine, track_running_stats, **factory_kwargs)
-        self.weight = weight
-        self.bias = bias
-        self.register_buffer('scale', torch.tensor(scale, **factory_kwargs))
-        self.register_buffer('zero_point', torch.tensor(zero_point, **factory_kwargs))
-
-    def forward(self, input):
-        return torch.ops.quantized.instance_norm(
-            input, self.weight, self.bias, self.eps, self.scale,
-            self.zero_point)
-
-    def _get_name(self):
-        return 'QuantizedInstanceNorm2d'
-
-    @classmethod
-    def from_float(cls, mod):
-        scale, zero_point = mod.activation_post_process.calculate_qparams()
-        new_mod = cls(
-            mod.num_features, mod.weight, mod.bias, float(scale), int(zero_point),
-            mod.eps, mod.affine)
-        return new_mod
-
-    @classmethod
-    def from_reference(cls, mod, scale, zero_point):
-        return cls(
-            mod.num_features, mod.weight, mod.bias, float(scale), int(zero_point),
-            mod.eps, mod.affine)

-
-
[docs]class InstanceNorm3d(torch.nn.InstanceNorm3d):
-    r"""This is the quantized version of :class:`~torch.nn.InstanceNorm3d`.
-
-    Additional args:
-        * **scale** - quantization scale of the output, type: double.
-        * **zero_point** - quantization zero point of the output, type: long.
-
-    """
-    def __init__(self, num_features, weight, bias, scale, zero_point,
-                 eps=1e-5, momentum=0.1, affine=False,
-                 track_running_stats=False, device=None, dtype=None) -> None:
-        factory_kwargs = {'device': device, 'dtype': dtype}
-        super().__init__(num_features, eps, momentum, affine, track_running_stats, **factory_kwargs)
-        self.weight = weight
-        self.bias = bias
-        self.register_buffer('scale', torch.tensor(scale, **factory_kwargs))
-        self.register_buffer('zero_point', torch.tensor(zero_point, **factory_kwargs))
-
-    def forward(self, input):
-        return torch.ops.quantized.instance_norm(
-            input, self.weight, self.bias, self.eps, self.scale,
-            self.zero_point)
-
-    def _get_name(self):
-        return 'QuantizedInstanceNorm3d'
-
-    @classmethod
-    def from_float(cls, mod):
-        scale, zero_point = mod.activation_post_process.calculate_qparams()
-        new_mod = cls(
-            mod.num_features, mod.weight, mod.bias, float(scale), int(zero_point),
-            mod.eps, mod.affine)
-        return new_mod
-
-    @classmethod
-    def from_reference(cls, mod, scale, zero_point):
-        return cls(
-            mod.num_features, mod.weight, mod.bias, float(scale), int(zero_point),
-            mod.eps, mod.affine)

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Source code for torch.ao.quantization.backend_config.backend_config

-from __future__ import annotations
-from dataclasses import dataclass
-from typing import Any, Callable, Dict, List, Optional, Type, Union
-
-import torch
-from torch.ao.quantization.utils import Pattern
-from enum import Enum
-
-
-__all__ = [
-    "BackendConfig",
-    "BackendPatternConfig",
-    "DTypeConfig",
-    "DTypeWithConstraints",
-    "ObservationType",
-]
-
-
-# DTypeConfig dict keys
-INPUT_DTYPE_DICT_KEY = "input_dtype"
-OUTPUT_DTYPE_DICT_KEY = "output_dtype"
-WEIGHT_DTYPE_DICT_KEY = "weight_dtype"
-BIAS_DTYPE_DICT_KEY = "bias_dtype"
-IS_DYNAMIC_DICT_KEY = "is_dynamic"
-
-# BackendConfig dict keys
-NAME_DICT_KEY = "name"
-CONFIGS_DICT_KEY = "configs"
-
-# BackendPatternConfig dict keys
-PATTERN_DICT_KEY = "pattern"
-PATTERN_COMPLEX_FORMAT_DICT_KEY = "pattern_complex_format"
-OBSERVATION_TYPE_DICT_KEY = "observation_type"
-DTYPE_CONFIGS_DICT_KEY = "dtype_configs"
-ROOT_MODULE_DICT_KEY = "root_module"
-QAT_MODULE_DICT_KEY = "qat_module"
-REFERENCE_QUANTIZED_MODULE_DICT_KEY = "reference_quantized_module_for_root"
-FUSED_MODULE_DICT_KEY = "fused_module"
-FUSER_METHOD_DICT_KEY = "fuser_method"
-ROOT_NODE_GETTER_DICT_KEY = "root_node_getter"
-EXTRA_INPUTS_GETTER_DICT_KEY = "extra_inputs_getter"
-NUM_TENSOR_ARGS_TO_OBSERVATION_TYPE_DICT_KEY = "num_tensor_args_to_observation_type"
-INPUT_TYPE_TO_INDEX_DICT_KEY = "input_type_to_index"
-
-
-# TODO: maybe rename this to something that's not related to observer
-# e.g. QParamsType
-
[docs]class ObservationType(Enum):
-    """ An enum that represents different ways of how an operator/operator pattern
-    should be observed
-    """
-
-    OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT = 0
-    """this means input and output are observed with different observers, based
-    on qconfig.activation
-    example: conv, linear, softmax
-    """
-
-    OUTPUT_SHARE_OBSERVER_WITH_INPUT = 1
-    """this means the output will use the same observer instance as input, based
-    on qconfig.activation
-    example: torch.cat, maxpool
-    """
-
-    INPUT_OUTPUT_NOT_OBSERVED = 2
-    """this means the input and output are never observed
-    example: x.shape, x.size
-    """

-
-
-
[docs]@dataclass
-class DTypeWithConstraints:
-    """
-    Config for specifying additional constraints for a given dtype, such as quantization
-    value ranges, scale value ranges, and fixed quantization params, to be used in
-    :class:`~torch.ao.quantization.backend_config.DTypeConfig`.
-
-    The constraints currently supported are:
-
-    * `quant_min_lower_bound` and `quant_max_upper_bound`: Lower and upper
-      bounds for the minimum and maximum quantized values respectively. If
-      the QConfig’s `quant_min` and `quant_max` fall outside this range,
-      then the QConfig will be ignored.
-
-    * `scale_min_lower_bound` and `scale_max_upper_bound`: Lower and upper
-      bounds for the minimum and maximum scale values respectively. If the
-      QConfig’s minimum scale value (currently exposed as `eps`) falls below
-      the lower bound, then the QConfig will be ignored. Note that the upper
-      bound is currently not enforced.
-
-    * `scale_exact_match` and `zero_point_exact_match`: Exact match requirements
-      for scale and zero point, to be used for operators with fixed quantization
-      parameters such as sigmoid and tanh. If the observer specified in the QConfig
-      is neither `FixedQParamsObserver` nor `FixedQParamsFakeQuantize`, or if
-      the quantization parameters don't match, then the QConfig will be ignored.
-    """
-    dtype: Optional[torch.dtype] = None
-    quant_min_lower_bound: Union[int, float, None] = None
-    quant_max_upper_bound: Union[int, float, None] = None
-    scale_min_lower_bound: Union[int, float, None] = None
-    scale_max_upper_bound: Union[int, float, None] = None
-    scale_exact_match: Optional[float] = None
-    zero_point_exact_match: Optional[int] = None

-
-
-
[docs]@dataclass
-class DTypeConfig:
-    """
-    Config object that specifies the supported data types passed as arguments to
-    quantize ops in the reference model spec, for input and output activations,
-    weights, and biases.
-
-    For example, consider the following reference model:
-
-      quant1 - [dequant1 - fp32_linear - quant2] - dequant2
-
-    The pattern in the square brackets refers to the reference pattern of
-    statically quantized linear. Setting the input dtype as `torch.quint8`
-    in the DTypeConfig means we pass in `torch.quint8` as the dtype argument
-    to the first quantize op (quant1). Similarly, setting the output dtype as
-    `torch.quint8` means we pass in `torch.quint8` as the dtype argument to
-    the second quantize op (quant2).
-
-    Note that the dtype here does not refer to the interface dtypes of the
-    op. For example, the "input dtype" here is not the dtype of the input
-    tensor passed to the quantized linear op. Though it can still be the
-    same as the interface dtype, this is not always the case, e.g. the
-    interface dtype is fp32 in dynamic quantization but the "input dtype"
-    specified in the DTypeConfig would still be quint8. The semantics of
-    dtypes here are the same as the semantics of the dtypes specified in
-    the observers.
-
-    These dtypes are matched against the ones specified in the user’s
-    QConfig. If there is a match, and the QConfig satisfies the constraints
-    specified in the DTypeConfig (if any), then we will quantize the given
-    pattern using this DTypeConfig. Otherwise, the QConfig is ignored and
-    the pattern will not be quantized.
-
-    Example usage::
-
-        >>> # xdoctest: +SKIP(failing)
-        >>> dtype_config1 = DTypeConfig(
-        ...     input_dtype=torch.quint8,
-        ...     output_dtype=torch.quint8,
-        ...     weight_dtype=torch.qint8,
-        ...     bias_dtype=torch.float)
-
-        >>> dtype_config2 = DTypeConfig(
-        ...     input_dtype=DTypeWithConstraints(
-        ...         dtype=torch.quint8,
-        ...         quant_min_lower_bound=0,
-        ...         quant_max_upper_bound=255,
-        ...     ),
-        ...     output_dtype=DTypeWithConstraints(
-        ...         dtype=torch.quint8,
-        ...         quant_min_lower_bound=0,
-        ...         quant_max_upper_bound=255,
-        ...     ),
-        ...     weight_dtype=DTypeWithConstraints(
-        ...         dtype=torch.qint8,
-        ...         quant_min_lower_bound=-128,
-        ...         quant_max_upper_bound=127,
-        ...     ),
-        ...     bias_dtype=torch.float)
-
-        >>> dtype_config1.input_dtype
-        torch.quint8
-
-        >>> dtype_config2.input_dtype
-        torch.quint8
-
-        >>> dtype_config2.input_dtype_with_constraints
-        DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=0, quant_max_upper_bound=255, \
-scale_min_lower_bound=None, scale_max_upper_bound=None)
-    """
-    input_dtype_with_constraints: DTypeWithConstraints
-    output_dtype_with_constraints: DTypeWithConstraints
-    weight_dtype_with_constraints: DTypeWithConstraints
-    bias_dtype: Optional[torch.dtype]
-    is_dynamic: Optional[bool]
-
-    def __init__(
-        self,
-        input_dtype: Union[torch.dtype, DTypeWithConstraints, None] = None,
-        output_dtype: Union[torch.dtype, DTypeWithConstraints, None] = None,
-        weight_dtype: Union[torch.dtype, DTypeWithConstraints, None] = None,
-        bias_dtype: Optional[torch.dtype] = None,
-        is_dynamic: Optional[bool] = None,
-    ):
-        if isinstance(input_dtype, DTypeWithConstraints):
-            self.input_dtype_with_constraints = input_dtype
-        else:
-            self.input_dtype_with_constraints = DTypeWithConstraints(dtype=input_dtype)
-
-        if isinstance(output_dtype, DTypeWithConstraints):
-            self.output_dtype_with_constraints = output_dtype
-        else:
-            self.output_dtype_with_constraints = DTypeWithConstraints(dtype=output_dtype)
-
-        if isinstance(weight_dtype, DTypeWithConstraints):
-            self.weight_dtype_with_constraints = weight_dtype
-        else:
-            self.weight_dtype_with_constraints = DTypeWithConstraints(dtype=weight_dtype)
-
-        self.bias_dtype = bias_dtype
-        self.is_dynamic = is_dynamic
-
-    @property
-    def input_dtype(self) -> Optional[torch.dtype]:
-        return self.input_dtype_with_constraints.dtype
-
-    @property
-    def output_dtype(self) -> Optional[torch.dtype]:
-        return self.output_dtype_with_constraints.dtype
-
-    @property
-    def weight_dtype(self) -> Optional[torch.dtype]:
-        return self.weight_dtype_with_constraints.dtype
-
-
[docs]    @classmethod
-    def from_dict(cls, dtype_config_dict: Dict[str, Any]) -> DTypeConfig:
-        """
-        Create a ``DTypeConfig`` from a dictionary with the following items (all optional):
-            "input_dtype": torch.dtype or ``DTypeWithConstraints``
-            "output_dtype": torch.dtype or ``DTypeWithConstraints``
-            "weight_dtype": torch.dtype or ``DTypeWithConstraints``
-            "bias_type": torch.dtype
-            "is_dynamic": bool
-        """
-        input_dtype = dtype_config_dict.get(INPUT_DTYPE_DICT_KEY, None)
-        if input_dtype is not None and not isinstance(input_dtype, (torch.dtype, DTypeWithConstraints)):
-            raise ValueError("Expected input_dtype to be a torch.dtype or DTypeWithConstraints")
-        output_dtype = dtype_config_dict.get(OUTPUT_DTYPE_DICT_KEY, None)
-        if output_dtype is not None and not isinstance(output_dtype, (torch.dtype, DTypeWithConstraints)):
-            raise ValueError("Expected output_dtype to be a torch.dtype or DTypeWithConstraints")
-        weight_dtype = dtype_config_dict.get(WEIGHT_DTYPE_DICT_KEY, None)
-        if weight_dtype is not None and not isinstance(weight_dtype, (torch.dtype, DTypeWithConstraints)):
-            raise ValueError("Expected weight_dtype to be a torch.dtype or DTypeWithConstraints")
-        bias_dtype = dtype_config_dict.get(BIAS_DTYPE_DICT_KEY, None)
-        is_dynamic = dtype_config_dict.get(IS_DYNAMIC_DICT_KEY, None)
-        return cls(input_dtype, output_dtype, weight_dtype, bias_dtype, is_dynamic)

-
-
[docs]    def to_dict(self) -> Dict[str, Any]:
-        """
-        Convert this ``DTypeConfig`` to a dictionary with the items described in
-        :func:`~torch.ao.quantization.backend_config.DTypeConfig.from_dict`.
-        """
-        dtype_config_dict: Dict[str, Any] = {}
-        if self.input_dtype is not None:
-            dtype_config_dict[INPUT_DTYPE_DICT_KEY] = self.input_dtype_with_constraints
-        if self.output_dtype is not None:
-            dtype_config_dict[OUTPUT_DTYPE_DICT_KEY] = self.output_dtype_with_constraints
-        if self.weight_dtype is not None:
-            dtype_config_dict[WEIGHT_DTYPE_DICT_KEY] = self.weight_dtype_with_constraints
-        if self.bias_dtype is not None:
-            dtype_config_dict[BIAS_DTYPE_DICT_KEY] = self.bias_dtype
-        if self.is_dynamic is not None:
-            dtype_config_dict[IS_DYNAMIC_DICT_KEY] = self.is_dynamic
-        return dtype_config_dict

-
-
-
[docs]class BackendConfig:
-    # TODO: refer to NativeBackendConfig once that is implemented
-    """Config that defines the set of patterns that can be quantized on a given backend, and how reference
-    quantized models can be produced from these patterns.
-
-    A pattern in this context refers to a module, a functional, an operator, or a directed acyclic graph
-    of the above. Each pattern supported on the target backend can be individually configured through
-    :class:`~torch.ao.quantization.backend_config.BackendPatternConfig` in terms of:
-
-    (1) The supported input/output activation, weight, and bias data types
-
-    (2) How observers and quant/dequant ops are inserted in order to construct the reference pattern, and
-
-    (3) (Optionally) Fusion, QAT, and reference module mappings.
-
-    The format of the patterns is described in:
-    https://github.com/pytorch/pytorch/blob/master/torch/ao/quantization/backend_config/README.md
-
-    Example usage::
-
-        import torch
-        from torch.ao.quantization.backend_config import (
-            BackendConfig,
-            BackendPatternConfig,
-            DTypeConfig,
-            ObservationType,
-        )
-
-        weighted_int8_dtype_config = DTypeConfig(
-            input_dtype=torch.quint8,
-            output_dtype=torch.quint8,
-            weight_dtype=torch.qint8,
-            bias_dtype=torch.float)
-
-        def fuse_conv2d_relu(is_qat, conv, relu):
-            return torch.ao.nn.intrinsic.ConvReLU2d(conv, relu)
-
-        # For quantizing Linear
-        linear_config = BackendPatternConfig(torch.nn.Linear) \
-            .set_observation_type(ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT) \
-            .add_dtype_config(weighted_int8_dtype_config) \
-            .set_root_module(torch.nn.Linear) \
-            .set_qat_module(torch.ao.nn.qat.Linear) \
-            .set_reference_quantized_module(torch.ao.nn.quantized.reference.Linear)
-
-        # For fusing Conv2d + ReLU into ConvReLU2d
-        conv_relu_config = BackendPatternConfig((torch.nn.Conv2d, torch.nn.ReLU)) \
-            .set_observation_type(ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT) \
-            .add_dtype_config(weighted_int8_dtype_config) \
-            .set_fused_module(torch.ao.nn.intrinsic.ConvReLU2d) \
-            .set_fuser_method(fuse_conv2d_relu)
-
-        # For quantizing ConvReLU2d
-        fused_conv_relu_config = BackendPatternConfig(torch.ao.nn.intrinsic.ConvReLU2d) \
-            .set_observation_type(ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT) \
-            .add_dtype_config(weighted_int8_dtype_config) \
-            .set_root_module(torch.nn.Conv2d) \
-            .set_qat_module(torch.ao.nn.intrinsic.qat.ConvReLU2d) \
-            .set_reference_quantized_module(torch.ao.nn.quantized.reference.Conv2d)
-
-        backend_config = BackendConfig("my_backend") \
-            .set_backend_pattern_config(linear_config) \
-            .set_backend_pattern_config(conv_relu_config) \
-            .set_backend_pattern_config(fused_conv_relu_config)
-
-    """
-    def __init__(self, name: str = ""):
-        self.name = name
-        # Store all BackendPatternConfigs in a map to handle duplicates
-        # Note: the key in this map uses the complex reversed tuple format.
-        # This is intended only for internal use; users who wish to access
-        # the original patterns should go through `self.configs` instead.
-        self._pattern_complex_format_to_config: Dict[Pattern, BackendPatternConfig] = {}
-
-    def __repr__(self):
-        return f"BackendConfig({self.__dict__})"
-
-
[docs]    def set_name(self, name: str) -> BackendConfig:
-        """
-        Set the name of the target backend.
-        """
-        self.name = name
-        return self

-
-
[docs]    def set_backend_pattern_config(self, config: BackendPatternConfig) -> BackendConfig:
-        """
-        Set the config for an pattern that can be run on the target backend.
-        This overrides any existing config for the given pattern.
-        """
-        # Avoid circular dependencies
-        pattern_complex_format = torch.ao.quantization.backend_config.utils \
-            ._get_pattern_in_reversed_nested_tuple_format(config)  # type: ignore[attr-defined]
-        self._pattern_complex_format_to_config[pattern_complex_format] = config
-        return self

-
-
[docs]    def set_backend_pattern_configs(self, configs: List[BackendPatternConfig]) -> BackendConfig:
-        """
-        Set the configs for patterns that can be run on the target backend.
-        This overrides any existing config for a given pattern if it was previously registered already.
-        """
-        for conf in configs:
-            self.set_backend_pattern_config(conf)
-        return self

-
-    @property
-    def configs(self) -> List[BackendPatternConfig]:
-        """
-        Return a copy of the list of configs set in this `BackendConfig`.
-        """
-        return list(self._pattern_complex_format_to_config.values())
-
-
[docs]    @classmethod
-    def from_dict(cls, backend_config_dict: Dict[str, Any]) -> BackendConfig:
-        """
-        Create a ``BackendConfig`` from a dictionary with the following items:
-
-            "name": the name of the target backend
-
-            "configs": a list of dictionaries that each represents a `BackendPatternConfig`
-
-        """
-        conf = cls(backend_config_dict.get(NAME_DICT_KEY, ""))
-        for d in backend_config_dict.get(CONFIGS_DICT_KEY, []):
-            if isinstance(d, BackendPatternConfig):
-                conf.set_backend_pattern_config(d)
-            elif isinstance(d, Dict):
-                conf.set_backend_pattern_config(BackendPatternConfig.from_dict(d))
-            else:
-                raise ValueError("Expected backend_config_dict['%s'] to be a dictionary" % CONFIGS_DICT_KEY)
-        return conf

-
-
[docs]    def to_dict(self) -> Dict[str, Any]:
-        """
-        Convert this ``BackendConfig`` to a dictionary with the items described in
-        :func:`~torch.ao.quantization.backend_config.BackendConfig.from_dict`.
-        """
-        return {
-            NAME_DICT_KEY: self.name,
-            CONFIGS_DICT_KEY: [c.to_dict() for c in self.configs],
-        }

-
-
-
[docs]class BackendPatternConfig:
-    """
-    Config object that specifies quantization behavior for a given operator pattern.
-    For a detailed example usage, see :class:`~torch.ao.quantization.backend_config.BackendConfig`.
-    """
-    def __init__(self, pattern: Optional[Pattern] = None):
-        self.pattern: Optional[Pattern] = pattern
-        self.observation_type = ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT
-        self.dtype_configs: List[DTypeConfig] = []
-        self.root_module: Optional[Type[torch.nn.Module]] = None
-        self.qat_module: Optional[Type[torch.nn.Module]] = None
-        self.reference_quantized_module: Optional[Type[torch.nn.Module]] = None
-        self.fused_module: Optional[Type[torch.nn.Module]] = None
-        self.fuser_method: Optional[Callable] = None
-
-        # Temporary/internal configs
-        self._root_node_getter: Optional[Callable] = None
-        self._extra_inputs_getter: Optional[Callable] = None
-        self._num_tensor_args_to_observation_type: Dict[int, ObservationType] = {}
-        self._input_type_to_index: Dict[str, int] = {}
-        self._pattern_complex_format: Optional[Pattern] = None
-
-    def __repr__(self):
-        dict_nonempty = {
-            k: v for k, v in self.__dict__.items()
-            if (
-                (not isinstance(v, (list, dict)) and v is not None)
-                or (isinstance(v, (list, dict)) and len(v) > 0)
-            )
-        }
-        return f"BackendPatternConfig({dict_nonempty})"
-
-
[docs]    def set_pattern(self, pattern: Pattern) -> BackendPatternConfig:
-        """
-        Set the pattern to configure.
-
-        The pattern can be a float module, functional operator, pytorch operator, or a tuple
-        combination of the above. Tuple patterns are treated as sequential patterns, and
-        currently only tuples of 2 or 3 elements are supported.
-        """
-        if self._pattern_complex_format is not None:
-            raise ValueError("Only one of 'pattern' or 'pattern_complex_format' can be set")
-        self.pattern = pattern
-        return self

-
-
[docs]    def set_observation_type(self, observation_type: ObservationType) -> BackendPatternConfig:
-        """
-        Set how observers should be inserted in the graph for this pattern.
-
-        Observation type here refers to how observers (or quant-dequant ops) will be placed
-        in the graph. This is used to produce the desired reference patterns understood by
-        the backend. Weighted ops such as linear and conv require different observers
-        (or quantization parameters passed to quantize ops in the reference model) for the
-        input and the output.
-
-        There are two observation types:
-
-            `OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT` (default): the output observer instance
-            will be different from the input. This is the most common observation type.
-
-            `OUTPUT_SHARE_OBSERVER_WITH_INPUT`: the output observer instance will be the
-            same as the input. This is useful for operators like `cat`.
-
-        Note: This will be renamed in the near future, since we will soon insert QuantDeQuantStubs
-        with observers (and fake quantizes) attached instead of observers themselves.
-        """
-        self.observation_type = observation_type
-        return self

-
-
[docs]    def add_dtype_config(self, dtype_config: DTypeConfig) -> BackendPatternConfig:
-        """
-        Add a set of supported data types passed as arguments to quantize ops in the
-        reference model spec.
-        """
-        self.dtype_configs.append(dtype_config)
-        return self

-
-
[docs]    def set_dtype_configs(self, dtype_configs: List[DTypeConfig]) -> BackendPatternConfig:
-        """
-        Set the supported data types passed as arguments to quantize ops in the
-        reference model spec, overriding all previously registered data types.
-        """
-        self.dtype_configs = dtype_configs
-        return self

-
-
[docs]    def set_root_module(self, root_module: Type[torch.nn.Module]) -> BackendPatternConfig:
-        """
-        Set the module that represents the root for this pattern.
-
-        When we construct the reference quantized model during the convert phase,
-        the root modules (e.g. torch.nn.Linear for torch.ao.nn.intrinsic.LinearReLU)
-        will be swapped to the corresponding reference quantized modules (e.g.
-        torch.ao.nn.reference.quantized.Linear). This allows custom backends to
-        specify custom reference quantized module implementations to match the
-        numerics of their lowered operators. Since this is a one-to-one mapping,
-        both the root module and the reference quantized module must be specified
-        in the same BackendPatternConfig in order for the conversion to take place.
-        """
-        self.root_module = root_module
-        return self

-
-
[docs]    def set_qat_module(self, qat_module: Type[torch.nn.Module]) -> BackendPatternConfig:
-        """
-        Set the module that represents the QAT implementation for this pattern.
-        """
-        self.qat_module = qat_module
-        return self

-
-
[docs]    def set_reference_quantized_module(self, reference_quantized_module: Type[torch.nn.Module]) -> BackendPatternConfig:
-        """
-        Set the module that represents the reference quantized implementation for
-        this pattern's root module.
-
-        For more detail, see :func:`~torch.ao.quantization.backend_config.BackendPatternConfig.set_root_module`.
-        """
-        self.reference_quantized_module = reference_quantized_module
-        return self

-
-
[docs]    def set_fused_module(self, fused_module: Type[torch.nn.Module]) -> BackendPatternConfig:
-        """
-        Set the module that represents the fused implementation for this pattern.
-        """
-        self.fused_module = fused_module
-        return self

-
-
[docs]    def set_fuser_method(self, fuser_method: Callable) -> BackendPatternConfig:
-        """
-        Set the function that specifies how to fuse this BackendPatternConfig's pattern.
-
-        The first argument of this function should be `is_qat`, and the rest of the arguments
-        should be the items in the tuple pattern. The return value of this function should be
-        the resulting fused module.
-
-        For example, the fuser method for the pattern `(torch.nn.Linear, torch.nn.ReLU)` can be:
-
-            def fuse_linear_relu(is_qat, linear, relu):
-                return torch.ao.nn.intrinsic.LinearReLU(linear, relu)
-
-        For a more complicated example, see https://gist.github.com/jerryzh168/8bea7180a8ba3c279f2c9b050f2a69a6.
-        """
-        self.fuser_method = fuser_method
-        return self

-
-    def _set_root_node_getter(self, root_node_getter: Callable) -> BackendPatternConfig:
-        self._root_node_getter = root_node_getter
-        return self
-
-    def _set_extra_inputs_getter(self, extra_inputs_getter: Callable) -> BackendPatternConfig:
-        self._extra_inputs_getter = extra_inputs_getter
-        return self
-
-    def _set_num_tensor_args_to_observation_type(
-            self, num_tensor_args_to_observation_type: Dict[int, ObservationType]) -> BackendPatternConfig:
-        self._num_tensor_args_to_observation_type = num_tensor_args_to_observation_type
-        return self
-
-    def _set_input_type_to_index(self, input_type_to_index: Dict[str, int]) -> BackendPatternConfig:
-        self._input_type_to_index = input_type_to_index
-        return self
-
-    def _set_pattern_complex_format(self, pattern: Pattern) -> BackendPatternConfig:
-        """
-        Set the pattern to configure, using the reversed nested tuple format.
-
-        See the BackendConfig README for more detail:
-        https://github.com/pytorch/pytorch/blob/master/torch/ao/quantization/backend_config/README.md#advanced-pattern-specification
-        """
-        if self.pattern is not None:
-            raise ValueError("Only one of 'pattern' or 'pattern_complex_format' can be set")
-        self._pattern_complex_format = pattern
-        return self
-
-
[docs]    @classmethod
-    def from_dict(cls, backend_pattern_config_dict: Dict[str, Any]) -> BackendPatternConfig:
-        """
-        Create a ``BackendPatternConfig`` from a dictionary with the following items:
-
-            "pattern": the pattern being configured
-            "observation_type": the :class:`~torch.ao.quantization.backend_config.ObservationType` that specifies how
-            observers should be inserted for this pattern
-            "dtype_configs": a list of dictionaries that represents :class:`~torch.ao.quantization.backend_config.DTypeConfig` s
-            "root_module": a :class:`torch.nn.Module` that represents the root for this pattern
-            "qat_module": a :class:`torch.nn.Module` that represents the QAT implementation for this pattern
-            "reference_quantized_module": a :class:`torch.nn.Module` that represents the reference quantized
-            implementation for this pattern's root module.
-            "fused_module": a :class:`torch.nn.Module` that represents the fused implementation for this pattern
-            "fuser_method": a function that specifies how to fuse the pattern for this pattern
-            "pattern_complex_format": the pattern specified in the reversed nested tuple format (deprecated)
-
-        """
-        def _get_dtype_config(obj: Any) -> DTypeConfig:
-            """
-            Convert the given object into a ``DTypeConfig`` if possible, else throw an exception.
-            """
-            if isinstance(obj, DTypeConfig):
-                return obj
-            if isinstance(obj, Dict):
-                return DTypeConfig.from_dict(obj)
-            raise ValueError("Expected a list of DTypeConfigs in backend_pattern_config_dict[\"%s\"], got '%s'" %
-                             (DTYPE_CONFIGS_DICT_KEY, type(obj)))
-
-        conf = cls()
-        if PATTERN_DICT_KEY in backend_pattern_config_dict:
-            conf.set_pattern(backend_pattern_config_dict[PATTERN_DICT_KEY])
-        if OBSERVATION_TYPE_DICT_KEY in backend_pattern_config_dict:
-            conf.set_observation_type(backend_pattern_config_dict[OBSERVATION_TYPE_DICT_KEY])
-        for d in backend_pattern_config_dict.get(DTYPE_CONFIGS_DICT_KEY, []):
-            conf.add_dtype_config(_get_dtype_config(d))
-        conf.set_root_module(backend_pattern_config_dict.get(ROOT_MODULE_DICT_KEY, None))
-        conf.set_qat_module(backend_pattern_config_dict.get(QAT_MODULE_DICT_KEY, None))
-        conf.set_reference_quantized_module(backend_pattern_config_dict.get(REFERENCE_QUANTIZED_MODULE_DICT_KEY, None))
-        conf.set_fused_module(backend_pattern_config_dict.get(FUSED_MODULE_DICT_KEY, None))
-        conf.set_fuser_method(backend_pattern_config_dict.get(FUSER_METHOD_DICT_KEY, None))
-        conf._set_root_node_getter(backend_pattern_config_dict.get(ROOT_NODE_GETTER_DICT_KEY, None))
-        conf._set_extra_inputs_getter(backend_pattern_config_dict.get(EXTRA_INPUTS_GETTER_DICT_KEY, None))
-        conf._set_num_tensor_args_to_observation_type(
-            backend_pattern_config_dict.get(NUM_TENSOR_ARGS_TO_OBSERVATION_TYPE_DICT_KEY, {}))
-        conf._set_input_type_to_index(backend_pattern_config_dict.get(INPUT_TYPE_TO_INDEX_DICT_KEY, {}))
-        if PATTERN_COMPLEX_FORMAT_DICT_KEY in backend_pattern_config_dict:
-            conf._set_pattern_complex_format(backend_pattern_config_dict[PATTERN_COMPLEX_FORMAT_DICT_KEY])
-        return conf

-
-
[docs]    def to_dict(self) -> Dict[str, Any]:
-        """
-        Convert this ``BackendPatternConfig`` to a dictionary with the items described in
-        :func:`~torch.ao.quantization.backend_config.BackendPatternConfig.from_dict`.
-        """
-        backend_pattern_config_dict: Dict[str, Any] = {
-            OBSERVATION_TYPE_DICT_KEY: self.observation_type,
-            DTYPE_CONFIGS_DICT_KEY: [c.to_dict() for c in self.dtype_configs],
-        }
-        if self.pattern is not None:
-            backend_pattern_config_dict[PATTERN_DICT_KEY] = self.pattern
-        if self.root_module is not None:
-            backend_pattern_config_dict[ROOT_MODULE_DICT_KEY] = self.root_module
-        if self.qat_module is not None:
-            backend_pattern_config_dict[QAT_MODULE_DICT_KEY] = self.qat_module
-        if self.reference_quantized_module is not None:
-            backend_pattern_config_dict[REFERENCE_QUANTIZED_MODULE_DICT_KEY] = self.reference_quantized_module
-        if self.fused_module is not None:
-            backend_pattern_config_dict[FUSED_MODULE_DICT_KEY] = self.fused_module
-        if self.fuser_method is not None:
-            backend_pattern_config_dict[FUSER_METHOD_DICT_KEY] = self.fuser_method
-        if self._root_node_getter is not None:
-            backend_pattern_config_dict[ROOT_NODE_GETTER_DICT_KEY] = self._root_node_getter
-        if self._extra_inputs_getter is not None:
-            backend_pattern_config_dict[EXTRA_INPUTS_GETTER_DICT_KEY] = self._extra_inputs_getter
-        if len(self._num_tensor_args_to_observation_type) > 0:
-            backend_pattern_config_dict[NUM_TENSOR_ARGS_TO_OBSERVATION_TYPE_DICT_KEY] = self._num_tensor_args_to_observation_type
-        if len(self._input_type_to_index) > 0:
-            backend_pattern_config_dict[INPUT_TYPE_TO_INDEX_DICT_KEY] = self._input_type_to_index
-        if self._pattern_complex_format is not None:
-            backend_pattern_config_dict[PATTERN_COMPLEX_FORMAT_DICT_KEY] = self._pattern_complex_format
-        return backend_pattern_config_dict

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- - - - - - - - \ No newline at end of file diff --git a/master/_modules/torch/ao/quantization/fake_quantize.html b/master/_modules/torch/ao/quantization/fake_quantize.html deleted file mode 120000 index 8574c2b8c740..000000000000 --- a/master/_modules/torch/ao/quantization/fake_quantize.html +++ /dev/null @@ -1 +0,0 @@ -../../../../__redirect__.html \ No newline at end of file diff --git a/master/_modules/torch/ao/quantization/fuse_modules.html b/master/_modules/torch/ao/quantization/fuse_modules.html deleted file mode 120000 index 8574c2b8c740..000000000000 --- a/master/_modules/torch/ao/quantization/fuse_modules.html +++ /dev/null @@ -1 +0,0 @@ -../../../../__redirect__.html \ No newline at end of file diff --git a/master/_modules/torch/ao/quantization/fx/custom_config.html b/master/_modules/torch/ao/quantization/fx/custom_config.html deleted file mode 100644 index aa76f4d6c933..000000000000 --- a/master/_modules/torch/ao/quantization/fx/custom_config.html +++ /dev/null @@ -1,1234 +0,0 @@ - - - - - - - - - - - - - torch.ao.quantization.fx.custom_config — PyTorch master documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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- Table of Contents - -
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Source code for torch.ao.quantization.fx.custom_config

-from __future__ import annotations
-from dataclasses import dataclass
-from typing import Any, Dict, List, Optional, Tuple, Type
-
-from torch.ao.quantization import QConfigMapping
-from torch.ao.quantization.backend_config import BackendConfig
-from torch.ao.quantization.quant_type import QuantType, _quant_type_from_str, _get_quant_type_to_str
-
-
-__all__ = [
-    "ConvertCustomConfig",
-    "FuseCustomConfig",
-    "PrepareCustomConfig",
-    "StandaloneModuleConfigEntry",
-]
-
-
-# TODO: replace all usages with these constants
-STANDALONE_MODULE_NAME_DICT_KEY = "standalone_module_name"
-STANDALONE_MODULE_CLASS_DICT_KEY = "standalone_module_class"
-FLOAT_TO_OBSERVED_DICT_KEY = "float_to_observed_custom_module_class"
-OBSERVED_TO_QUANTIZED_DICT_KEY = "observed_to_quantized_custom_module_class"
-NON_TRACEABLE_MODULE_NAME_DICT_KEY = "non_traceable_module_name"
-NON_TRACEABLE_MODULE_CLASS_DICT_KEY = "non_traceable_module_class"
-INPUT_QUANTIZED_INDEXES_DICT_KEY = "input_quantized_idxs"
-OUTPUT_QUANTIZED_INDEXES_DICT_KEY = "output_quantized_idxs"
-PRESERVED_ATTRIBUTES_DICT_KEY = "preserved_attributes"
-
-
-
[docs]@dataclass
-class StandaloneModuleConfigEntry:
-    # qconfig_mapping for the prepare function called in the submodule,
-    # None means use qconfig from parent qconfig_mapping
-    qconfig_mapping: Optional[QConfigMapping]
-    example_inputs: Tuple[Any, ...]
-    prepare_custom_config: Optional[PrepareCustomConfig]
-    backend_config: Optional[BackendConfig]

-
-
-
[docs]class PrepareCustomConfig:
-    """
-    Custom configuration for :func:`~torch.ao.quantization.quantize_fx.prepare_fx` and
-    :func:`~torch.ao.quantization.quantize_fx.prepare_qat_fx`.
-
-    Example usage::
-
-        prepare_custom_config = PrepareCustomConfig() \
-            .set_standalone_module_name("module1", qconfig_mapping, example_inputs, \
-                child_prepare_custom_config, backend_config) \
-            .set_standalone_module_class(MyStandaloneModule, qconfig_mapping, example_inputs, \
-                child_prepare_custom_config, backend_config) \
-            .set_float_to_observed_mapping(FloatCustomModule, ObservedCustomModule) \
-            .set_non_traceable_module_names(["module2", "module3"]) \
-            .set_non_traceable_module_classes([NonTraceableModule1, NonTraceableModule2]) \
-            .set_input_quantized_indexes([0]) \
-            .set_output_quantized_indexes([0]) \
-            .set_preserved_attributes(["attr1", "attr2"])
-    """
-    def __init__(self):
-        self.standalone_module_names: Dict[str, StandaloneModuleConfigEntry] = {}
-        self.standalone_module_classes: Dict[Type, StandaloneModuleConfigEntry] = {}
-        self.float_to_observed_mapping: Dict[QuantType, Dict[Type, Type]] = {}
-        self.non_traceable_module_names: List[str] = []
-        self.non_traceable_module_classes: List[Type] = []
-        self.input_quantized_indexes: List[int] = []
-        self.output_quantized_indexes: List[int] = []
-        self.preserved_attributes: List[str] = []
-
-    def __repr__(self):
-        dict_nonempty = {
-            k: v for k, v in self.__dict__.items()
-            if len(v) > 0
-        }
-        return f"PrepareCustomConfig({dict_nonempty})"
-
-
[docs]    def set_standalone_module_name(
-            self,
-            module_name: str,
-            qconfig_mapping: Optional[QConfigMapping],
-            example_inputs: Tuple[Any, ...],
-            prepare_custom_config: Optional[PrepareCustomConfig],
-            backend_config: Optional[BackendConfig]) -> PrepareCustomConfig:
-        """
-        Set the configuration for running a standalone module identified by ``module_name``.
-
-        If ``qconfig_mapping`` is None, the parent ``qconfig_mapping`` will be used instead.
-        If ``prepare_custom_config`` is None, an empty ``PrepareCustomConfig`` will be used.
-        If ``backend_config`` is None, the parent ``backend_config`` will be used instead.
-        """
-        self.standalone_module_names[module_name] = \
-            StandaloneModuleConfigEntry(qconfig_mapping, example_inputs, prepare_custom_config, backend_config)
-        return self

-
-
[docs]    def set_standalone_module_class(
-            self,
-            module_class: Type,
-            qconfig_mapping: Optional[QConfigMapping],
-            example_inputs: Tuple[Any, ...],
-            prepare_custom_config: Optional[PrepareCustomConfig],
-            backend_config: Optional[BackendConfig]) -> PrepareCustomConfig:
-        """
-        Set the configuration for running a standalone module identified by ``module_class``.
-
-        If ``qconfig_mapping`` is None, the parent ``qconfig_mapping`` will be used instead.
-        If ``prepare_custom_config`` is None, an empty ``PrepareCustomConfig`` will be used.
-        If ``backend_config`` is None, the parent ``backend_config`` will be used instead.
-        """
-        self.standalone_module_classes[module_class] = \
-            StandaloneModuleConfigEntry(qconfig_mapping, example_inputs, prepare_custom_config, backend_config)
-        return self

-
-
[docs]    def set_float_to_observed_mapping(
-            self,
-            float_class: Type,
-            observed_class: Type,
-            quant_type: QuantType = QuantType.STATIC) -> PrepareCustomConfig:
-        """
-        Set the mapping from a custom float module class to a custom observed module class.
-
-        The observed module class must have a ``from_float`` class method that converts the float module class
-        to the observed module class. This is currently only supported for static quantization.
-        """
-        if quant_type != QuantType.STATIC:
-            raise ValueError("set_float_to_observed_mapping is currently only supported for static quantization")
-        if quant_type not in self.float_to_observed_mapping:
-            self.float_to_observed_mapping[quant_type] = {}
-        self.float_to_observed_mapping[quant_type][float_class] = observed_class
-        return self

-
-
[docs]    def set_non_traceable_module_names(self, module_names: List[str]) -> PrepareCustomConfig:
-        """
-        Set the modules that are not symbolically traceable, identified by name.
-        """
-        self.non_traceable_module_names = module_names
-        return self

-
-
[docs]    def set_non_traceable_module_classes(self, module_classes: List[Type]) -> PrepareCustomConfig:
-        """
-        Set the modules that are not symbolically traceable, identified by class.
-        """
-        self.non_traceable_module_classes = module_classes
-        return self

-
-
[docs]    def set_input_quantized_indexes(self, indexes: List[int]) -> PrepareCustomConfig:
-        """
-        Set the indexes of the inputs of the graph that should be quantized.
-        Inputs are otherwise assumed to be in fp32 by default instead.
-        """
-        self.input_quantized_indexes = indexes
-        return self

-
-
[docs]    def set_output_quantized_indexes(self, indexes: List[int]) -> PrepareCustomConfig:
-        """
-        Set the indexes of the outputs of the graph that should be quantized.
-        Outputs are otherwise assumed to be in fp32 by default instead.
-        """
-        self.output_quantized_indexes = indexes
-        return self

-
-
[docs]    def set_preserved_attributes(self, attributes: List[str]) -> PrepareCustomConfig:
-        """
-        Set the names of the attributes that will persist in the graph module even if they are not used in
-        the model's ``forward`` method.
-        """
-        self.preserved_attributes = attributes
-        return self

-
-    # TODO: remove this
-
[docs]    @classmethod
-    def from_dict(cls, prepare_custom_config_dict: Dict[str, Any]) -> PrepareCustomConfig:
-        """
-        Create a ``PrepareCustomConfig`` from a dictionary with the following items:
-
-            "standalone_module_name": a list of (module_name, qconfig_mapping, example_inputs,
-            child_prepare_custom_config, backend_config) tuples
-
-            "standalone_module_class" a list of (module_class, qconfig_mapping, example_inputs,
-            child_prepare_custom_config, backend_config) tuples
-
-            "float_to_observed_custom_module_class": a nested dictionary mapping from quantization
-            mode to an inner mapping from float module classes to observed module classes, e.g.
-            {"static": {FloatCustomModule: ObservedCustomModule}}
-
-            "non_traceable_module_name": a list of modules names that are not symbolically traceable
-            "non_traceable_module_class": a list of module classes that are not symbolically traceable
-            "input_quantized_idxs": a list of indexes of graph inputs that should be quantized
-            "output_quantized_idxs": a list of indexes of graph outputs that should be quantized
-            "preserved_attributes": a list of attributes that persist even if they are not used in ``forward``
-
-        This function is primarily for backward compatibility and may be removed in the future.
-        """
-        def _get_qconfig_mapping(obj: Any, dict_key: str) -> Optional[QConfigMapping]:
-            """
-            Convert the given object into a QConfigMapping if possible, else throw an exception.
-            """
-            if isinstance(obj, QConfigMapping) or obj is None:
-                return obj
-            if isinstance(obj, Dict):
-                return QConfigMapping.from_dict(obj)
-            raise ValueError("Expected QConfigMapping in prepare_custom_config_dict[\"%s\"], got '%s'" %
-                             (dict_key, type(obj)))
-
-        def _get_prepare_custom_config(obj: Any, dict_key: str) -> Optional[PrepareCustomConfig]:
-            """
-            Convert the given object into a PrepareCustomConfig if possible, else throw an exception.
-            """
-            if isinstance(obj, PrepareCustomConfig) or obj is None:
-                return obj
-            if isinstance(obj, Dict):
-                return PrepareCustomConfig.from_dict(obj)
-            raise ValueError("Expected PrepareCustomConfig in prepare_custom_config_dict[\"%s\"], got '%s'" %
-                             (dict_key, type(obj)))
-
-        def _get_backend_config(obj: Any, dict_key: str) -> Optional[BackendConfig]:
-            """
-            Convert the given object into a BackendConfig if possible, else throw an exception.
-            """
-            if isinstance(obj, BackendConfig) or obj is None:
-                return obj
-            if isinstance(obj, Dict):
-                return BackendConfig.from_dict(obj)
-            raise ValueError("Expected BackendConfig in prepare_custom_config_dict[\"%s\"], got '%s'" %
-                             (dict_key, type(obj)))
-
-        conf = cls()
-        for (module_name, qconfig_dict, example_inputs, _prepare_custom_config_dict, backend_config_dict) in\
-                prepare_custom_config_dict.get(STANDALONE_MODULE_NAME_DICT_KEY, []):
-            qconfig_mapping = _get_qconfig_mapping(qconfig_dict, STANDALONE_MODULE_NAME_DICT_KEY)
-            prepare_custom_config = _get_prepare_custom_config(_prepare_custom_config_dict, STANDALONE_MODULE_NAME_DICT_KEY)
-            backend_config = _get_backend_config(backend_config_dict, STANDALONE_MODULE_NAME_DICT_KEY)
-            conf.set_standalone_module_name(
-                module_name, qconfig_mapping, example_inputs, prepare_custom_config, backend_config)
-        for (module_class, qconfig_dict, example_inputs, _prepare_custom_config_dict, backend_config_dict) in\
-                prepare_custom_config_dict.get(STANDALONE_MODULE_CLASS_DICT_KEY, []):
-            qconfig_mapping = _get_qconfig_mapping(qconfig_dict, STANDALONE_MODULE_CLASS_DICT_KEY)
-            prepare_custom_config = _get_prepare_custom_config(_prepare_custom_config_dict, STANDALONE_MODULE_CLASS_DICT_KEY)
-            backend_config = _get_backend_config(backend_config_dict, STANDALONE_MODULE_CLASS_DICT_KEY)
-            conf.set_standalone_module_class(
-                module_class, qconfig_mapping, example_inputs, prepare_custom_config, backend_config)
-        for quant_type_name, custom_module_mapping in prepare_custom_config_dict.get(FLOAT_TO_OBSERVED_DICT_KEY, {}).items():
-            quant_type = _quant_type_from_str(quant_type_name)
-            for float_class, observed_class in custom_module_mapping.items():
-                conf.set_float_to_observed_mapping(float_class, observed_class, quant_type)
-        conf.set_non_traceable_module_names(prepare_custom_config_dict.get(NON_TRACEABLE_MODULE_NAME_DICT_KEY, []))
-        conf.set_non_traceable_module_classes(prepare_custom_config_dict.get(NON_TRACEABLE_MODULE_CLASS_DICT_KEY, []))
-        conf.set_input_quantized_indexes(prepare_custom_config_dict.get(INPUT_QUANTIZED_INDEXES_DICT_KEY, []))
-        conf.set_output_quantized_indexes(prepare_custom_config_dict.get(OUTPUT_QUANTIZED_INDEXES_DICT_KEY, []))
-        conf.set_preserved_attributes(prepare_custom_config_dict.get(PRESERVED_ATTRIBUTES_DICT_KEY, []))
-        return conf

-
-
[docs]    def to_dict(self) -> Dict[str, Any]:
-        """
-        Convert this ``PrepareCustomConfig`` to a dictionary with the items described in
-        :func:`~torch.ao.quantization.fx.custom_config.PrepareCustomConfig.from_dict`.
-        """
-        def _make_tuple(key: Any, e: StandaloneModuleConfigEntry):
-            qconfig_dict = e.qconfig_mapping.to_dict() if e.qconfig_mapping else None
-            prepare_custom_config_dict = e.prepare_custom_config.to_dict() if e.prepare_custom_config else None
-            return (key, qconfig_dict, e.example_inputs, prepare_custom_config_dict, e.backend_config)
-
-        d: Dict[str, Any] = {}
-        for module_name, sm_config_entry in self.standalone_module_names.items():
-            if STANDALONE_MODULE_NAME_DICT_KEY not in d:
-                d[STANDALONE_MODULE_NAME_DICT_KEY] = []
-            d[STANDALONE_MODULE_NAME_DICT_KEY].append(_make_tuple(module_name, sm_config_entry))
-        for module_class, sm_config_entry in self.standalone_module_classes.items():
-            if STANDALONE_MODULE_CLASS_DICT_KEY not in d:
-                d[STANDALONE_MODULE_CLASS_DICT_KEY] = []
-            d[STANDALONE_MODULE_CLASS_DICT_KEY].append(_make_tuple(module_class, sm_config_entry))
-        for quant_type, float_to_observed_mapping in self.float_to_observed_mapping.items():
-            if FLOAT_TO_OBSERVED_DICT_KEY not in d:
-                d[FLOAT_TO_OBSERVED_DICT_KEY] = {}
-            d[FLOAT_TO_OBSERVED_DICT_KEY][_get_quant_type_to_str(quant_type)] = float_to_observed_mapping
-        if len(self.non_traceable_module_names) > 0:
-            d[NON_TRACEABLE_MODULE_NAME_DICT_KEY] = self.non_traceable_module_names
-        if len(self.non_traceable_module_classes) > 0:
-            d[NON_TRACEABLE_MODULE_CLASS_DICT_KEY] = self.non_traceable_module_classes
-        if len(self.input_quantized_indexes) > 0:
-            d[INPUT_QUANTIZED_INDEXES_DICT_KEY] = self.input_quantized_indexes
-        if len(self.output_quantized_indexes) > 0:
-            d[OUTPUT_QUANTIZED_INDEXES_DICT_KEY] = self.output_quantized_indexes
-        if len(self.preserved_attributes) > 0:
-            d[PRESERVED_ATTRIBUTES_DICT_KEY] = self.preserved_attributes
-        return d

-
-
-
[docs]class ConvertCustomConfig:
-    """
-    Custom configuration for :func:`~torch.ao.quantization.quantize_fx.convert_fx`.
-
-    Example usage::
-
-        convert_custom_config = ConvertCustomConfig() \
-            .set_observed_to_quantized_mapping(ObservedCustomModule, QuantizedCustomModule) \
-            .set_preserved_attributes(["attr1", "attr2"])
-    """
-
-    def __init__(self):
-        self.observed_to_quantized_mapping: Dict[QuantType, Dict[Type, Type]] = {}
-        self.preserved_attributes: List[str] = []
-
-    def __repr__(self):
-        dict_nonempty = {
-            k: v for k, v in self.__dict__.items()
-            if len(v) > 0
-        }
-        return f"ConvertCustomConfig({dict_nonempty})"
-
-
[docs]    def set_observed_to_quantized_mapping(
-            self,
-            observed_class: Type,
-            quantized_class: Type,
-            quant_type: QuantType = QuantType.STATIC) -> ConvertCustomConfig:
-        """
-        Set the mapping from a custom observed module class to a custom quantized module class.
-
-        The quantized module class must have a ``from_observed`` class method that converts the observed module class
-        to the quantized module class.
-        """
-        if quant_type not in self.observed_to_quantized_mapping:
-            self.observed_to_quantized_mapping[quant_type] = {}
-        self.observed_to_quantized_mapping[quant_type][observed_class] = quantized_class
-        return self

-
-
[docs]    def set_preserved_attributes(self, attributes: List[str]) -> ConvertCustomConfig:
-        """
-        Set the names of the attributes that will persist in the graph module even if they are not used in
-        the model's ``forward`` method.
-        """
-        self.preserved_attributes = attributes
-        return self

-
-    # TODO: remove this
-
[docs]    @classmethod
-    def from_dict(cls, convert_custom_config_dict: Dict[str, Any]) -> ConvertCustomConfig:
-        """
-        Create a ``ConvertCustomConfig`` from a dictionary with the following items:
-
-            "observed_to_quantized_custom_module_class": a nested dictionary mapping from quantization
-            mode to an inner mapping from observed module classes to quantized module classes, e.g.::
-            {
-            "static": {FloatCustomModule: ObservedCustomModule},
-            "dynamic": {FloatCustomModule: ObservedCustomModule},
-            "weight_only": {FloatCustomModule: ObservedCustomModule}
-            }
-            "preserved_attributes": a list of attributes that persist even if they are not used in ``forward``
-
-        This function is primarily for backward compatibility and may be removed in the future.
-        """
-        conf = cls()
-        for quant_type_name, custom_module_mapping in convert_custom_config_dict.get(OBSERVED_TO_QUANTIZED_DICT_KEY, {}).items():
-            quant_type = _quant_type_from_str(quant_type_name)
-            for observed_class, quantized_class in custom_module_mapping.items():
-                conf.set_observed_to_quantized_mapping(observed_class, quantized_class, quant_type)
-        conf.set_preserved_attributes(convert_custom_config_dict.get(PRESERVED_ATTRIBUTES_DICT_KEY, []))
-        return conf

-
-
[docs]    def to_dict(self) -> Dict[str, Any]:
-        """
-        Convert this ``ConvertCustomConfig`` to a dictionary with the items described in
-        :func:`~torch.ao.quantization.fx.custom_config.ConvertCustomConfig.from_dict`.
-        """
-        d: Dict[str, Any] = {}
-        for quant_type, observed_to_quantized_mapping in self.observed_to_quantized_mapping.items():
-            if OBSERVED_TO_QUANTIZED_DICT_KEY not in d:
-                d[OBSERVED_TO_QUANTIZED_DICT_KEY] = {}
-            d[OBSERVED_TO_QUANTIZED_DICT_KEY][_get_quant_type_to_str(quant_type)] = observed_to_quantized_mapping
-        if len(self.preserved_attributes) > 0:
-            d[PRESERVED_ATTRIBUTES_DICT_KEY] = self.preserved_attributes
-        return d

-
-
-
[docs]class FuseCustomConfig:
-    """
-    Custom configuration for :func:`~torch.ao.quantization.quantize_fx.fuse_fx`.
-
-    Example usage::
-
-        fuse_custom_config = FuseCustomConfig().set_preserved_attributes(["attr1", "attr2"])
-    """
-
-    def __init__(self):
-        self.preserved_attributes: List[str] = []
-
-    def __repr__(self):
-        dict_nonempty = {
-            k: v for k, v in self.__dict__.items()
-            if len(v) > 0
-        }
-        return f"FuseCustomConfig({dict_nonempty})"
-
-
[docs]    def set_preserved_attributes(self, attributes: List[str]) -> FuseCustomConfig:
-        """
-        Set the names of the attributes that will persist in the graph module even if they are not used in
-        the model's ``forward`` method.
-        """
-        self.preserved_attributes = attributes
-        return self

-
-    # TODO: remove this
-
[docs]    @classmethod
-    def from_dict(cls, fuse_custom_config_dict: Dict[str, Any]) -> FuseCustomConfig:
-        """
-        Create a ``ConvertCustomConfig`` from a dictionary with the following items:
-
-            "preserved_attributes": a list of attributes that persist even if they are not used in ``forward``
-
-        This function is primarily for backward compatibility and may be removed in the future.
-        """
-        conf = cls()
-        conf.set_preserved_attributes(fuse_custom_config_dict.get(PRESERVED_ATTRIBUTES_DICT_KEY, []))
-        return conf

-
-
[docs]    def to_dict(self) -> Dict[str, Any]:
-        """
-        Convert this ``FuseCustomConfig`` to a dictionary with the items described in
-        :func:`~torch.ao.quantization.fx.custom_config.ConvertCustomConfig.from_dict`.
-        """
-        d: Dict[str, Any] = {}
-        if len(self.preserved_attributes) > 0:
-            d[PRESERVED_ATTRIBUTES_DICT_KEY] = self.preserved_attributes
-        return d

-
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DIAGSYS0001:arg-format-too-verbose¶

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ToDo, experimenting diagnostics, placeholder text.

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FXE0001:fx-tracer-success¶

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FX Tracer succeeded. -The callable is successfully traced as a ‘torch.fx.GraphModule’ by one of the fx tracers.

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FXE0002:fx-tracer-failure¶

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FX Tracer failed. -The callable is not successfully traced as a ‘torch.fx.GraphModule’.

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FXE0003:fx-frontend-aotautograd¶

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FX Tracer succeeded. -The callable is successfully traced as a ‘torch.fx.GraphModule’ by one of the fx tracers.

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FXE0004:fx-pass-convert-neg-to-sigmoid¶

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A ‘fx.Interpreter’ based pass to convert all ‘torch.neg’ calls to ‘torch.sigmoid’ for -a given ‘torch.fx.GraphModule’ object.

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FXE0005:fx-ir-add-node¶

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ToDo, experimenting diagnostics, placeholder text.

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FXE0006:atenlib-symbolic-function¶

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ToDo, experimenting diagnostics, placeholder text.

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FXE0007:atenlib-fx-to-onnx¶

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ToDo, experimenting diagnostics, placeholder text.

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FXE0008:fx-node-to-onnx¶

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ToDo, experimenting diagnostics, placeholder text.

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FXE0009:fx-frontend-dynamo-make-fx¶

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ToDo, experimenting diagnostics, placeholder text.

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FXE0010:fx-pass¶

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ToDo, experimenting diagnostics, placeholder text.

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POE0001:node-missing-onnx-shape-inference¶

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Node is missing ONNX shape inference. -This usually happens when the node is not valid under standard ONNX operator spec.

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POE0002:missing-custom-symbolic-function¶

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Missing symbolic function for custom PyTorch operator, cannot translate node to ONNX.

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POE0003:missing-standard-symbolic-function¶

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Missing symbolic function for standard PyTorch operator, cannot translate node to ONNX.

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- - - - - - - - \ No newline at end of file diff --git a/master/generated/onnx_diagnostics_rules/POE0004:operator-supported-in-newer-opset-version.html b/master/generated/onnx_diagnostics_rules/POE0004:operator-supported-in-newer-opset-version.html deleted file mode 100644 index 031b06548dbb..000000000000 --- a/master/generated/onnx_diagnostics_rules/POE0004:operator-supported-in-newer-opset-version.html +++ /dev/null @@ -1,834 +0,0 @@ - - - - - - - - - - - - - - POE0004:operator-supported-in-newer-opset-version — PyTorch master documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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POE0004:operator-supported-in-newer-opset-version¶

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Operator is supported in newer opset version.

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+ + +Continue to ../../main/generated/torch.ao.quantization.qconfig_mapping.QConfigMapping.html diff --git a/master/generated/torch.ao.quantization.qconfig_mapping.get_default_qat_qconfig_mapping.html b/master/generated/torch.ao.quantization.qconfig_mapping.get_default_qat_qconfig_mapping.html deleted file mode 120000 index 674ede9dc6d6..000000000000 --- a/master/generated/torch.ao.quantization.qconfig_mapping.get_default_qat_qconfig_mapping.html +++ /dev/null @@ -1 +0,0 @@ -../__redirect__.html \ No newline at end of file diff --git a/master/generated/torch.ao.quantization.qconfig_mapping.get_default_qat_qconfig_mapping.html b/master/generated/torch.ao.quantization.qconfig_mapping.get_default_qat_qconfig_mapping.html new file mode 100644 index 000000000000..8fa1dbe72280 --- /dev/null +++ b/master/generated/torch.ao.quantization.qconfig_mapping.get_default_qat_qconfig_mapping.html @@ -0,0 +1,8 @@ + + +Redirecting… + + + + +Continue to 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Use when a versioned-docs site (Sphinx/MkDocs/Jekyll) ships byte-identical copies of the same content under multiple URL aliases (e.g., stable/ duplicating 2.11/), the site is over the GitHub Pages 10 GB artifact limit, or actions/upload-pages-artifact is dereferencing source-level symlinks into full copies. Generates ~200-byte meta-refresh stubs that preserve URLs while letting the bytes live once. +--- + +# Redirect Stubs for Versioned Docs + +Replaces duplicate version directories with `` HTML stubs so URLs keep resolving while the bytes only live once. Designed for the `actions/upload-pages-artifact` pipeline, where source-level symlinks are forcibly dereferenced via `tar --dereference --hard-dereference` and therefore can't be used for dedup. + +## When to use + +- A docs site has `stable/` (or `latest/`, `current/`, etc.) as a byte-for-byte copy of a versioned directory. +- Patch-version aliases (`1.10/`, `1.10.0/`, `1.10.1/`) all hold identical content. +- The Pages artifact tar is approaching or exceeds 10 GB. +- The user wants to keep the alias URLs working — if the aliases aren't externally linked, prefer `rm -rf` over stubs. + +## Pre-flight checks + +Before invoking the script, confirm: + +1. **The directories really are identical.** Run `diff -rq | head` and verify there's either no output or only trivial differences (e.g., `.buildinfo`). If they actually differ, stubbing will silently change served content. +2. **Find the alias's canonical mapping.** A canonical version is the one that will keep its real bytes (e.g., `2.11/` is canonical, `stable/` is the alias). +3. **Decide on the URL form.** Relative URLs (default) are portable; absolute URLs (`--url-prefix`) are needed if the site is mounted at a non-root path and you want stubs that work even when copy-pasted. +4. **Check what files are loaded directly by JS, not navigated to.** For Sphinx these include `searchindex.js`, `objects.inv`, and anything in `_static/` / `_images/`. The script keeps `searchindex.js` and `objects.inv` as real copies by default; review the `--keep` list against the specific docs generator. + +## How to invoke + +The helper script lives at `scripts/make_stubs.py` in this repo. Typical usage: + +```bash +# Dry-run first — shows what would change without writing anything +python3 scripts/make_stubs.py \ + --alias /path/to/site/stable \ + --canonical /path/to/site/2.11 \ + --dry-run + +# Run for real +python3 scripts/make_stubs.py \ + --alias /path/to/site/stable \ + --canonical /path/to/site/2.11 +``` + +The script: +- Walks `` recursively. +- For every `.html` file, overwrites it with a ~250-byte stub whose `meta http-equiv="refresh"` and `` point at the equivalent path under ``. +- Leaves files matching `--keep` patterns untouched (default: `searchindex.js`, `objects.inv`, `.buildinfo`). +- Prints a summary: files stubbed, bytes before/after, savings. + +## Workflow inside a CI / GitHub Actions pipeline + +Insert one step **between** the docs assembly step and `actions/upload-pages-artifact`: + +```yaml +- name: Replace duplicate version dirs with redirect stubs + run: | + python3 .github/scripts/make_stubs.py --alias _site/stable --canonical _site/2.11 + # Aliases that aren't externally linked can simply be removed: + rm -rf _site/1.10 _site/1.10.0 _site/1.7.0 _site/1.9.0 _site/master + +- uses: actions/upload-pages-artifact@v3 + with: + path: _site +``` + +Copy `make_stubs.py` into the docs repo (e.g., `.github/scripts/`) so the workflow doesn't depend on a per-runner skill install. + +## Verifying the result + +After running: + +1. **Sanity-check stub size**: `find -name '*.html' -size +1k` should be empty (every stub should be < 1 KB). Anything large means a non-stubbed file was missed. +2. **Spot-check a redirect**: open a stub locally in a browser; it should immediately navigate to the canonical URL. +3. **Verify search still works** in the alias: navigate to `/search.html` (which is a stub redirecting to `/search.html`); the search box should fetch `/searchindex.js` correctly. +4. **Measure impact**: `tar cf - | wc -c` before vs. after gives the exact tar savings. + +## Common pitfalls + +- **Stubbing assets** (CSS/JS/PNG/SVG): the script only touches `.html` because meta-refresh only works in HTML. Don't extend it to other extensions; instead `rm` them if you don't need them under the alias path. +- **External deep-links to alias assets**: if blog posts link directly to `stable/_images/foo.png`, deleting it 404s those references. Either keep `_images/` and `_static/` as real copies under the alias (small cost) or accept the breakage. +- **Search index in the alias**: don't stub `searchindex.js` — Sphinx's search box loads it as JSON; a stubbed HTML response will throw a JS parse error. +- **Crawler indexing**: the stub includes `` so search engines index the canonical instead of the redirect. Don't strip it. +- **Symlink-or-stub confusion**: do not also create a symlink at the alias path — `actions/upload-pages-artifact` will dereference it and undo your stubs. Stubs must be real files. diff --git a/stable b/stable deleted file mode 120000 index 372f20ba82b5..000000000000 --- a/stable +++ /dev/null @@ -1 +0,0 @@ -2.11 \ No newline at end of file diff --git a/stable/404.html b/stable/404.html new file mode 100644 index 000000000000..ddf038131659 --- /dev/null +++ b/stable/404.html @@ -0,0 +1,8 @@ + + +Redirecting… + + + + +Continue to ../2.11/404.html diff --git a/stable/_static/webpack-macros.html b/stable/_static/webpack-macros.html new file mode 100644 index 000000000000..5503188797bb --- /dev/null +++ b/stable/_static/webpack-macros.html @@ -0,0 +1,8 @@ + + +Redirecting… + + + + +Continue to ../../2.11/_static/webpack-macros.html diff --git a/stable/accelerator.html b/stable/accelerator.html new file mode 100644 index 000000000000..97b0644e15b4 --- /dev/null +++ b/stable/accelerator.html @@ -0,0 +1,8 @@ + + +Redirecting… + + + + +Continue to ../2.11/accelerator.html diff --git a/stable/accelerator/amp.html b/stable/accelerator/amp.html new 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+Redirecting… + + + + +Continue to ../2.11/benchmark_utils.html diff --git a/stable/checkpoint.html b/stable/checkpoint.html new file mode 100644 index 000000000000..db1650a0a571 --- /dev/null +++ b/stable/checkpoint.html @@ -0,0 +1,8 @@ + + +Redirecting… + + + + +Continue to ../2.11/checkpoint.html diff --git a/stable/community/build_ci_governance.html b/stable/community/build_ci_governance.html new file mode 100644 index 000000000000..39f5de07b304 --- /dev/null +++ b/stable/community/build_ci_governance.html @@ -0,0 +1,8 @@ + + +Redirecting… + + + + +Continue to ../../2.11/community/build_ci_governance.html diff --git a/stable/community/contribution_guide.html b/stable/community/contribution_guide.html new file mode 100644 index 000000000000..76d01565dfb7 --- /dev/null +++ b/stable/community/contribution_guide.html @@ -0,0 +1,8 @@ + + +Redirecting… + + + + +Continue to ../../2.11/community/contribution_guide.html diff --git a/stable/community/design.html 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+Redirecting… + + + + +Continue to ../../2.11/community/persons_of_interest.html diff --git a/stable/compile/dynamic_shapes_advanced_control_options.html b/stable/compile/dynamic_shapes_advanced_control_options.html new file mode 100644 index 000000000000..4368de0b319d --- /dev/null +++ b/stable/compile/dynamic_shapes_advanced_control_options.html @@ -0,0 +1,8 @@ + + +Redirecting… + + + + +Continue to ../../2.11/compile/dynamic_shapes_advanced_control_options.html diff --git a/stable/compile/dynamic_shapes_backed_unbacked.html b/stable/compile/dynamic_shapes_backed_unbacked.html new file mode 100644 index 000000000000..90efa099ce8f --- /dev/null +++ b/stable/compile/dynamic_shapes_backed_unbacked.html @@ -0,0 +1,8 @@ + + +Redirecting… + + + + +Continue to ../../2.11/compile/dynamic_shapes_backed_unbacked.html diff --git a/stable/compile/dynamic_shapes_beyond_the_basics.html b/stable/compile/dynamic_shapes_beyond_the_basics.html new file mode 100644 index 000000000000..8312d4b5eaa0 --- 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a/stable/generated/torch.ao.nn.qat.modules.conv.Conv1d.html b/stable/generated/torch.ao.nn.qat.modules.conv.Conv1d.html new file mode 100644 index 000000000000..d27b26125030 --- /dev/null +++ b/stable/generated/torch.ao.nn.qat.modules.conv.Conv1d.html @@ -0,0 +1,8 @@ + + +Redirecting… + + + + +Continue to ../../2.11/generated/torch.ao.nn.qat.modules.conv.Conv1d.html diff --git a/stable/generated/torch.ao.nn.qat.modules.conv.Conv2d.html b/stable/generated/torch.ao.nn.qat.modules.conv.Conv2d.html new file mode 100644 index 000000000000..e665ae62d154 --- /dev/null +++ b/stable/generated/torch.ao.nn.qat.modules.conv.Conv2d.html @@ -0,0 +1,8 @@ + + +Redirecting… + + + + +Continue to ../../2.11/generated/torch.ao.nn.qat.modules.conv.Conv2d.html diff --git a/stable/generated/torch.ao.nn.qat.modules.conv.Conv3d.html b/stable/generated/torch.ao.nn.qat.modules.conv.Conv3d.html new file mode 100644 index 000000000000..53a4847c3c03 --- /dev/null +++ b/stable/generated/torch.ao.nn.qat.modules.conv.Conv3d.html @@ -0,0 +1,8 @@ + + +Redirecting… + + + + +Continue to ../../2.11/generated/torch.ao.nn.qat.modules.conv.Conv3d.html diff --git a/stable/generated/torch.ao.nn.qat.modules.embedding_ops.Embedding.html b/stable/generated/torch.ao.nn.qat.modules.embedding_ops.Embedding.html new file mode 100644 index 000000000000..ea4f044158e7 --- /dev/null +++ b/stable/generated/torch.ao.nn.qat.modules.embedding_ops.Embedding.html @@ -0,0 +1,8 @@ + + +Redirecting… + + + + +Continue to ../../2.11/generated/torch.ao.nn.qat.modules.embedding_ops.Embedding.html diff --git a/stable/generated/torch.ao.nn.qat.modules.embedding_ops.EmbeddingBag.html b/stable/generated/torch.ao.nn.qat.modules.embedding_ops.EmbeddingBag.html new file mode 100644 index 000000000000..237d19a9b63c --- /dev/null +++ b/stable/generated/torch.ao.nn.qat.modules.embedding_ops.EmbeddingBag.html @@ -0,0 +1,8 @@ + + +Redirecting… + + + + +Continue to 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000000000000..6e8580ec53b8 --- /dev/null +++ b/stable/generated/torch.ao.nn.quantizable.MultiheadAttention.html @@ -0,0 +1,8 @@ + + +Redirecting… + + + + +Continue to ../../2.11/generated/torch.ao.nn.quantizable.MultiheadAttention.html diff --git a/stable/generated/torch.ao.nn.quantizable.modules.activation.MultiheadAttention.html b/stable/generated/torch.ao.nn.quantizable.modules.activation.MultiheadAttention.html new file mode 100644 index 000000000000..b0de40c5a71f --- /dev/null +++ b/stable/generated/torch.ao.nn.quantizable.modules.activation.MultiheadAttention.html @@ -0,0 +1,8 @@ + + +Redirecting… + + + + +Continue to ../../2.11/generated/torch.ao.nn.quantizable.modules.activation.MultiheadAttention.html diff --git a/stable/generated/torch.ao.nn.quantizable.modules.rnn.LSTM.html b/stable/generated/torch.ao.nn.quantizable.modules.rnn.LSTM.html new file mode 100644 index 000000000000..74fd30e026cb --- /dev/null +++ b/stable/generated/torch.ao.nn.quantizable.modules.rnn.LSTM.html @@ 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000000000000..4cf57125e71d --- /dev/null +++ b/stable/generated/torch.ao.quantization.fake_quantize.disable_fake_quant.html @@ -0,0 +1,8 @@ + + +Redirecting… + + + + +Continue to ../../2.11/generated/torch.ao.quantization.fake_quantize.disable_fake_quant.html diff --git a/stable/generated/torch.ao.quantization.fake_quantize.disable_observer.html b/stable/generated/torch.ao.quantization.fake_quantize.disable_observer.html new file mode 100644 index 000000000000..21dc85f7c5e9 --- /dev/null +++ b/stable/generated/torch.ao.quantization.fake_quantize.disable_observer.html @@ -0,0 +1,8 @@ + + +Redirecting… + + + + +Continue to ../../2.11/generated/torch.ao.quantization.fake_quantize.disable_observer.html diff --git a/stable/generated/torch.ao.quantization.fake_quantize.enable_fake_quant.html b/stable/generated/torch.ao.quantization.fake_quantize.enable_fake_quant.html new file mode 100644 index 000000000000..8aff4d5ca308 --- /dev/null +++ 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+Continue to ../../2.11/generated/torch.ao.quantization.fuse_modules.fuse_modules.html diff --git a/stable/generated/torch.ao.quantization.fx.custom_config.ConvertCustomConfig.html b/stable/generated/torch.ao.quantization.fx.custom_config.ConvertCustomConfig.html new file mode 100644 index 000000000000..9cf7db794c58 --- /dev/null +++ b/stable/generated/torch.ao.quantization.fx.custom_config.ConvertCustomConfig.html @@ -0,0 +1,8 @@ + + +Redirecting… + + + + +Continue to ../../2.11/generated/torch.ao.quantization.fx.custom_config.ConvertCustomConfig.html diff --git a/stable/generated/torch.ao.quantization.fx.custom_config.FuseCustomConfig.html b/stable/generated/torch.ao.quantization.fx.custom_config.FuseCustomConfig.html new file mode 100644 index 000000000000..66a9a0b993d5 --- /dev/null +++ b/stable/generated/torch.ao.quantization.fx.custom_config.FuseCustomConfig.html @@ -0,0 +1,8 @@ + + +Redirecting… + + + + +Continue to 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+Continue to ../../2.11/generated/torch.ao.quantization.fx.utils.graph_module_from_producer_nodes.html diff --git a/stable/generated/torch.ao.quantization.fx.utils.maybe_get_next_module.html b/stable/generated/torch.ao.quantization.fx.utils.maybe_get_next_module.html new file mode 100644 index 000000000000..730b73b32205 --- /dev/null +++ b/stable/generated/torch.ao.quantization.fx.utils.maybe_get_next_module.html @@ -0,0 +1,8 @@ + + +Redirecting… + + + + +Continue to ../../2.11/generated/torch.ao.quantization.fx.utils.maybe_get_next_module.html diff --git a/stable/generated/torch.ao.quantization.fx.utils.node_arg_is_bias.html b/stable/generated/torch.ao.quantization.fx.utils.node_arg_is_bias.html new file mode 100644 index 000000000000..acd9f3e5e0ae --- /dev/null +++ b/stable/generated/torch.ao.quantization.fx.utils.node_arg_is_bias.html @@ -0,0 +1,8 @@ + + +Redirecting… + + + + +Continue to ../../2.11/generated/torch.ao.quantization.fx.utils.node_arg_is_bias.html diff --git 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000000000000..b1ada53d114e --- /dev/null +++ b/stable/generated/torch.fx.experimental.proxy_tensor.is_sym_node.html @@ -0,0 +1,8 @@ + + +Redirecting… + + + + +Continue to ../../2.11/generated/torch.fx.experimental.proxy_tensor.is_sym_node.html diff --git a/stable/generated/torch.fx.experimental.proxy_tensor.make_fx.html b/stable/generated/torch.fx.experimental.proxy_tensor.make_fx.html new file mode 100644 index 000000000000..d46e9a638d88 --- /dev/null +++ b/stable/generated/torch.fx.experimental.proxy_tensor.make_fx.html @@ -0,0 +1,8 @@ + + +Redirecting… + + + + +Continue to ../../2.11/generated/torch.fx.experimental.proxy_tensor.make_fx.html diff --git a/stable/generated/torch.fx.experimental.proxy_tensor.maybe_disable_thunkify.html b/stable/generated/torch.fx.experimental.proxy_tensor.maybe_disable_thunkify.html new file mode 100644 index 000000000000..10ed8a2e5261 --- /dev/null +++ b/stable/generated/torch.fx.experimental.proxy_tensor.maybe_disable_thunkify.html @@ -0,0 +1,8 @@ + + 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../../2.11/generated/torch.fx.experimental.proxy_tensor.maybe_handle_decomp.html diff --git a/stable/generated/torch.fx.experimental.proxy_tensor.proxy_call.html b/stable/generated/torch.fx.experimental.proxy_tensor.proxy_call.html new file mode 100644 index 000000000000..93f6b3a802f6 --- /dev/null +++ b/stable/generated/torch.fx.experimental.proxy_tensor.proxy_call.html @@ -0,0 +1,8 @@ + + +Redirecting… + + + + +Continue to ../../2.11/generated/torch.fx.experimental.proxy_tensor.proxy_call.html diff --git a/stable/generated/torch.fx.experimental.proxy_tensor.set_meta.html b/stable/generated/torch.fx.experimental.proxy_tensor.set_meta.html new file mode 100644 index 000000000000..d21aa43f35fa --- /dev/null +++ b/stable/generated/torch.fx.experimental.proxy_tensor.set_meta.html @@ -0,0 +1,8 @@ + + +Redirecting… + + + + +Continue to ../../2.11/generated/torch.fx.experimental.proxy_tensor.set_meta.html diff --git a/stable/generated/torch.fx.experimental.proxy_tensor.set_original_aten_op.html b/stable/generated/torch.fx.experimental.proxy_tensor.set_original_aten_op.html new file mode 100644 index 000000000000..1d078e4e0898 --- /dev/null +++ b/stable/generated/torch.fx.experimental.proxy_tensor.set_original_aten_op.html @@ -0,0 +1,8 @@ + + +Redirecting… + + + + +Continue to ../../2.11/generated/torch.fx.experimental.proxy_tensor.set_original_aten_op.html diff --git a/stable/generated/torch.fx.experimental.proxy_tensor.set_proxy_slot.html b/stable/generated/torch.fx.experimental.proxy_tensor.set_proxy_slot.html new file mode 100644 index 000000000000..1c6eec7c9e2a --- /dev/null +++ b/stable/generated/torch.fx.experimental.proxy_tensor.set_proxy_slot.html @@ -0,0 +1,8 @@ + + +Redirecting… + + + + +Continue to ../../2.11/generated/torch.fx.experimental.proxy_tensor.set_proxy_slot.html diff --git a/stable/generated/torch.fx.experimental.proxy_tensor.snapshot_fake.html 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b/stable/generated/torch.fx.experimental.proxy_tensor.track_tensor.html @@ -0,0 +1,8 @@ + + +Redirecting… + + + + +Continue to ../../2.11/generated/torch.fx.experimental.proxy_tensor.track_tensor.html diff --git a/stable/generated/torch.fx.experimental.proxy_tensor.track_tensor_tree.html b/stable/generated/torch.fx.experimental.proxy_tensor.track_tensor_tree.html new file mode 100644 index 000000000000..39bc08e0a866 --- /dev/null +++ b/stable/generated/torch.fx.experimental.proxy_tensor.track_tensor_tree.html @@ -0,0 +1,8 @@ + + +Redirecting… + + + + +Continue to ../../2.11/generated/torch.fx.experimental.proxy_tensor.track_tensor_tree.html diff --git a/stable/generated/torch.fx.experimental.recording.record_shapeenv_event.html b/stable/generated/torch.fx.experimental.recording.record_shapeenv_event.html new file mode 100644 index 000000000000..3dbc19016134 --- /dev/null +++ b/stable/generated/torch.fx.experimental.recording.record_shapeenv_event.html @@ -0,0 +1,8 @@ + + +Redirecting… 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000000000000..b8b26f66d2c3 --- /dev/null +++ b/stable/generated/torch.fx.experimental.symbolic_shapes.EqualityConstraint.html @@ -0,0 +1,8 @@ + + +Redirecting… + + + + +Continue to ../../2.11/generated/torch.fx.experimental.symbolic_shapes.EqualityConstraint.html diff --git a/stable/generated/torch.fx.experimental.symbolic_shapes.InnerTensorKey.html b/stable/generated/torch.fx.experimental.symbolic_shapes.InnerTensorKey.html new file mode 100644 index 000000000000..4509a51657fd --- /dev/null +++ b/stable/generated/torch.fx.experimental.symbolic_shapes.InnerTensorKey.html @@ -0,0 +1,8 @@ + + +Redirecting… + + + + +Continue to ../../2.11/generated/torch.fx.experimental.symbolic_shapes.InnerTensorKey.html diff --git a/stable/generated/torch.fx.experimental.symbolic_shapes.PropagateUnbackedSymInts.html b/stable/generated/torch.fx.experimental.symbolic_shapes.PropagateUnbackedSymInts.html new file mode 100644 index 000000000000..d63cf3b90177 --- /dev/null +++ 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