Source code for torch.ao.nn.intrinsic.qat.modules.conv_fused

# mypy: allow-untyped-defs
import math
from typing import ClassVar, Optional, Type

import torch
import torch.ao.nn.intrinsic as nni
import torch.ao.nn.qat as nnqat
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import init
from torch.nn.modules.utils import _pair, _single, _triple
from torch.nn.parameter import Parameter
from torch.nn.utils import fuse_conv_bn_weights


__all__ = [
    "ConvBn1d",
    "ConvBnReLU1d",
    "ConvReLU1d",
    "ConvBn2d",
    "ConvBnReLU2d",
    "ConvReLU2d",
    "ConvBn3d",
    "ConvBnReLU3d",
    "ConvReLU3d",
    "update_bn_stats",
    "freeze_bn_stats",
]
_BN_CLASS_MAP = {
    1: nn.BatchNorm1d,
    2: nn.BatchNorm2d,
    3: nn.BatchNorm3d,
}


class _ConvBnNd(nn.modules.conv._ConvNd, nni._FusedModule):
    _version = 2
    _FLOAT_MODULE: ClassVar[Type[nn.modules.conv._ConvNd]]

    def __init__(
        self,
        # ConvNd args
        in_channels,
        out_channels,
        kernel_size,
        stride,
        padding,
        dilation,
        transposed,
        output_padding,
        groups,
        bias,
        padding_mode,
        # BatchNormNd args
        # num_features: out_channels
        eps=1e-05,
        momentum=0.1,
        # affine: True
        # track_running_stats: True
        # Args for this module
        freeze_bn=False,
        qconfig=None,
        dim=2,
    ):
        nn.modules.conv._ConvNd.__init__(
            self,
            in_channels,
            out_channels,
            kernel_size,
            stride,
            padding,
            dilation,
            transposed,
            output_padding,
            groups,
            False,
            padding_mode,
        )
        assert qconfig, "qconfig must be provided for QAT module"
        self.qconfig = qconfig
        self.freeze_bn = freeze_bn if self.training else True
        self.bn = _BN_CLASS_MAP[dim](out_channels, eps, momentum, True, True)
        self.weight_fake_quant = self.qconfig.weight()
        if bias:
            self.bias = Parameter(torch.empty(out_channels))
        else:
            self.register_parameter("bias", None)
        self.reset_bn_parameters()

        # this needs to be called after reset_bn_parameters,
        # as they modify the same state
        if self.training:
            if freeze_bn:
                self.freeze_bn_stats()
            else:
                self.update_bn_stats()
        else:
            self.freeze_bn_stats()

        self._enable_slow_path_for_better_numerical_stability = False

    def reset_running_stats(self):
        self.bn.reset_running_stats()

    def reset_bn_parameters(self):
        self.bn.reset_running_stats()
        init.uniform_(self.bn.weight)
        init.zeros_(self.bn.bias)
        # note: below is actually for conv, not BN
        if self.bias is not None:
            fan_in, _ = init._calculate_fan_in_and_fan_out(self.weight)
            bound = 1 / math.sqrt(fan_in)
            init.uniform_(self.bias, -bound, bound)

    def reset_parameters(self):
        super().reset_parameters()

    def update_bn_stats(self):
        self.freeze_bn = False
        self.bn.training = True
        return self

    def freeze_bn_stats(self):
        self.freeze_bn = True
        self.bn.training = False
        return self

    def _forward(self, input):
        if self._enable_slow_path_for_better_numerical_stability:
            return self._forward_slow(input)
        return self._forward_approximate(input)

    def _forward_approximate(self, input):
        """Approximated method to fuse conv and bn. It requires only one forward pass.
        conv_orig = conv / scale_factor where scale_factor = bn.weight / running_std
        """
        assert self.bn.running_var is not None
        running_std = torch.sqrt(self.bn.running_var + self.bn.eps)
        scale_factor = self.bn.weight / running_std
        weight_shape = [1] * len(self.weight.shape)
        weight_shape[0] = -1
        bias_shape = [1] * len(self.weight.shape)
        bias_shape[1] = -1
        scaled_weight = self.weight_fake_quant(
            self.weight * scale_factor.reshape(weight_shape)
        )
        # using zero bias here since the bias for original conv
        # will be added later
        if self.bias is not None:
            zero_bias = torch.zeros_like(self.bias, dtype=input.dtype)
        else:
            zero_bias = torch.zeros(
                self.out_channels, device=scaled_weight.device, dtype=input.dtype
            )
        conv = self._conv_forward(input, scaled_weight, zero_bias)
        conv_orig = conv / scale_factor.reshape(bias_shape)
        if self.bias is not None:
            conv_orig = conv_orig + self.bias.reshape(bias_shape)
        conv = self.bn(conv_orig)
        return conv

    def _forward_slow(self, input):
        """
        A more accurate but slow method to compute conv bn fusion, following https://arxiv.org/pdf/1806.08342.pdf
        It requires two forward passes but handles the case bn.weight == 0

        Conv: Y = WX + B_c
        Conv without bias: Y0 = WX = Y - B_c, Y = Y0 + B_c

        Batch statistics:
          mean_Y = Y.mean()
                 = Y0.mean() + B_c
          var_Y = (Y - mean_Y)^2.mean()
                = (Y0 - Y0.mean())^2.mean()
        BN (r: bn.weight, beta: bn.bias):
          Z = r * (Y - mean_Y) / sqrt(var_Y + eps) + beta
            = r * (Y0 - Y0.mean()) / sqrt(var_Y + eps) + beta

        Fused Conv BN training (std_Y = sqrt(var_Y + eps)):
          Z = (r * W / std_Y) * X + r * (B_c - mean_Y) / std_Y + beta
            = (r * W / std_Y) * X - r * Y0.mean() / std_Y + beta

        Fused Conv BN inference (running_std = sqrt(running_var + eps)):
          Z = (r * W / running_std) * X - r * (running_mean - B_c) / running_std + beta

        QAT with fused conv bn:
          Z_train = fake_quant(r * W / running_std) * X * (running_std / std_Y) - r * Y0.mean() / std_Y + beta
                  = conv(X, fake_quant(r * W / running_std)) * (running_std / std_Y) - r * Y0.mean() / std_Y + beta
          Z_inference = conv(X, fake_quant(r * W / running_std)) - r * (running_mean - B_c) / running_std + beta
        """

        assert self.bn.running_var is not None
        assert self.bn.running_mean is not None

        # using zero bias here since the bias for original conv
        # will be added later
        zero_bias = torch.zeros(
            self.out_channels, device=self.weight.device, dtype=input.dtype
        )

        weight_shape = [1] * len(self.weight.shape)
        weight_shape[0] = -1
        bias_shape = [1] * len(self.weight.shape)
        bias_shape[1] = -1

        if self.bn.training:
            # needed to compute batch mean/std
            conv_out = self._conv_forward(input, self.weight, zero_bias)
            # update bn statistics
            with torch.no_grad():
                conv_out_bias = (
                    conv_out
                    if self.bias is None
                    else conv_out + self.bias.reshape(bias_shape)
                )
                self.bn(conv_out_bias)

            # fused conv + bn without bias using bn running statistics
            running_std = torch.sqrt(self.bn.running_var + self.bn.eps)
            scale_factor = self.bn.weight / running_std
            scaled_weight = self.weight_fake_quant(
                self.weight * scale_factor.reshape(weight_shape)
            )
            # fused conv without bias for inference: (r * W / running_std) * X
            conv_bn = self._conv_forward(input, scaled_weight, zero_bias)

            avg_dims = [0] + list(range(2, len(self.weight.shape)))
            batch_mean = conv_out.mean(avg_dims)
            batch_var = torch.square(conv_out - batch_mean.reshape(bias_shape)).mean(
                avg_dims
            )
            batch_std = torch.sqrt(batch_var + self.bn.eps)

            # scale to use batch std in training mode
            # conv(X, r * W / std_Y) = conv(X, r * W / running_std) * (running_std / std_Y)
            unscale_factor = running_std / batch_std
            conv_bn *= unscale_factor.reshape(bias_shape)

            fused_mean = batch_mean
            fused_std = batch_std
        else:
            # fused conv + bn without bias using bn running statistics
            running_std = torch.sqrt(self.bn.running_var + self.bn.eps)
            scale_factor = self.bn.weight / running_std
            scaled_weight = self.weight_fake_quant(
                self.weight * scale_factor.reshape(weight_shape)
            )
            # fused conv without bias for inference: (r * W / running_std) * X
            conv_bn = self._conv_forward(input, scaled_weight, zero_bias)

            fused_mean = self.bn.running_mean - (
                self.bias if self.bias is not None else 0
            )
            fused_std = running_std

        # fused bias = beta - r * mean / std
        fused_bias = self.bn.bias - self.bn.weight * fused_mean / fused_std
        conv_bn += fused_bias.reshape(bias_shape)

        # HACK to let conv bias participate in loss to avoid DDP error (parameters
        #   were not used in producing loss)
        if self.bias is not None:
            conv_bn += (self.bias - self.bias).reshape(bias_shape)

        return conv_bn

    def extra_repr(self):
        # TODO(jerryzh): extend
        return super().extra_repr()

    def forward(self, input):
        return self._forward(input)

    def train(self, mode=True):
        """
        Batchnorm's training behavior is using the self.training flag. Prevent
        changing it if BN is frozen. This makes sure that calling `model.train()`
        on a model with a frozen BN will behave properly.
        """
        self.training = mode
        if not self.freeze_bn:
            for module in self.children():
                module.train(mode)
        return self

    # ===== Serialization version history =====
    #
    # Version 1/None
    #   self
    #   |--- weight : Tensor
    #   |--- bias : Tensor
    #   |--- gamma : Tensor
    #   |--- beta : Tensor
    #   |--- running_mean : Tensor
    #   |--- running_var : Tensor
    #   |--- num_batches_tracked : Tensor
    #
    # Version 2
    #   self
    #   |--- weight : Tensor
    #   |--- bias : Tensor
    #   |--- bn : Module
    #        |--- weight : Tensor (moved from v1.self.gamma)
    #        |--- bias : Tensor (moved from v1.self.beta)
    #        |--- running_mean : Tensor (moved from v1.self.running_mean)
    #        |--- running_var : Tensor (moved from v1.self.running_var)
    #        |--- num_batches_tracked : Tensor (moved from v1.self.num_batches_tracked)
    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 == 1:
            # BN related parameters and buffers were moved into the BN module for v2
            v2_to_v1_names = {
                "bn.weight": "gamma",
                "bn.bias": "beta",
                "bn.running_mean": "running_mean",
                "bn.running_var": "running_var",
                "bn.num_batches_tracked": "num_batches_tracked",
            }
            for v2_name, v1_name in v2_to_v1_names.items():
                if prefix + v1_name in state_dict:
                    state_dict[prefix + v2_name] = state_dict[prefix + v1_name]
                    state_dict.pop(prefix + v1_name)
                elif prefix + v2_name in state_dict:
                    # there was a brief period where forward compatibility
                    # for this module was broken (between
                    # https://github.com/pytorch/pytorch/pull/38478
                    # and https://github.com/pytorch/pytorch/pull/38820)
                    # and modules emitted the v2 state_dict format while
                    # specifying that version == 1. This patches the forward
                    # compatibility issue by allowing the v2 style entries to
                    # be used.
                    pass
                elif strict:
                    missing_keys.append(prefix + v2_name)

        super()._load_from_state_dict(
            state_dict,
            prefix,
            local_metadata,
            strict,
            missing_keys,
            unexpected_keys,
            error_msgs,
        )

    @classmethod
    def from_float(cls, mod, use_precomputed_fake_quant=False):
        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
        """
        # The ignore is because _FLOAT_MODULE is a TypeVar here where the bound
        # has no __name__ (code is fine though)
        assert type(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"
        qconfig = mod.qconfig
        conv, bn = mod[0], mod[1]  # type: ignore[index]
        qat_convbn = cls(
            conv.in_channels,
            conv.out_channels,
            conv.kernel_size,
            conv.stride,
            conv.padding,
            conv.dilation,
            conv.groups,
            conv.bias is not None,
            conv.padding_mode,
            bn.eps,
            bn.momentum,
            False,
            qconfig,
        )
        qat_convbn.weight = conv.weight
        qat_convbn.bias = conv.bias
        qat_convbn.bn.weight = bn.weight
        qat_convbn.bn.bias = bn.bias
        qat_convbn.bn.running_mean = bn.running_mean
        qat_convbn.bn.running_var = bn.running_var
        # mypy error: Cannot determine type of 'num_batches_tracked'
        qat_convbn.bn.num_batches_tracked = bn.num_batches_tracked
        return qat_convbn

    def to_float(self):
        cls = type(self)
        conv = cls._FLOAT_CONV_MODULE(  # type: ignore[attr-defined]
            self.in_channels,
            self.out_channels,
            self.kernel_size,
            self.stride,
            self.padding,
            self.dilation,
            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())

        if cls._FLOAT_BN_MODULE:  # type: ignore[attr-defined]
            # fuse bn into conv
            assert self.bn.running_var is not None and self.bn.running_mean is not None
            conv.weight, conv.bias = fuse_conv_bn_weights(
                conv.weight,
                conv.bias,
                self.bn.running_mean,
                self.bn.running_var,
                self.bn.eps,
                self.bn.weight,
                self.bn.bias,
            )

        if cls._FLOAT_RELU_MODULE:  # type: ignore[attr-defined]
            modules = []
            modules.append(conv)
            relu = cls._FLOAT_RELU_MODULE()  # type: ignore[attr-defined]
            modules.append(relu)
            conv_relu = cls._FUSED_FLOAT_MODULE(*modules)  # type: ignore[attr-defined]
            conv_relu.train(self.training)
            return conv_relu
        else:
            conv.train(self.training)
            return conv


[docs]class ConvBn1d(_ConvBnNd, nn.Conv1d):
    r"""
    A ConvBn1d module is a module fused from Conv1d and BatchNorm1d,
    attached with FakeQuantize modules for weight,
    used in quantization aware training.

    We combined the interface of :class:`torch.nn.Conv1d` and
    :class:`torch.nn.BatchNorm1d`.

    Similar to :class:`torch.nn.Conv1d`, with FakeQuantize modules initialized
    to default.

    Attributes:
        freeze_bn:
        weight_fake_quant: fake quant module for weight

    """
    _FLOAT_BN_MODULE: ClassVar[Type[nn.BatchNorm1d]] = nn.BatchNorm1d
    _FLOAT_RELU_MODULE: ClassVar[Optional[Type[nn.Module]]] = None
    _FLOAT_MODULE: ClassVar[Type[nn.Module]] = nni.ConvBn1d  # type: ignore[assignment]
    _FLOAT_CONV_MODULE: ClassVar[Type[nn.Conv1d]] = nn.Conv1d

    def __init__(
        self,
        # Conv1d args
        in_channels,
        out_channels,
        kernel_size,
        stride=1,
        padding=0,
        dilation=1,
        groups=1,
        bias=None,
        padding_mode="zeros",
        # BatchNorm1d args
        # num_features: out_channels
        eps=1e-05,
        momentum=0.1,
        # affine: True
        # track_running_stats: True
        # Args for this module
        freeze_bn=False,
        qconfig=None,
    ):
        kernel_size = _single(kernel_size)
        stride = _single(stride)
        padding = _single(padding)
        dilation = _single(dilation)
        _ConvBnNd.__init__(
            self,
            in_channels,
            out_channels,
            kernel_size,
            stride,
            padding,
            dilation,
            False,
            _single(0),
            groups,
            bias,
            padding_mode,
            eps,
            momentum,
            freeze_bn,
            qconfig,
            dim=1,
        )


[docs]class ConvBnReLU1d(ConvBn1d):
    r"""
    A ConvBnReLU1d module is a module fused from Conv1d, BatchNorm1d and ReLU,
    attached with FakeQuantize modules for weight,
    used in quantization aware training.

    We combined the interface of :class:`torch.nn.Conv1d` and
    :class:`torch.nn.BatchNorm1d` and :class:`torch.nn.ReLU`.

    Similar to `torch.nn.Conv1d`, with FakeQuantize modules initialized to
    default.

    Attributes:
        weight_fake_quant: fake quant module for weight

    """
    # base class defines _FLOAT_MODULE as "ConvBn1d"
    _FLOAT_MODULE: ClassVar[Type[nn.Module]] = nni.ConvBnReLU1d
    _FLOAT_CONV_MODULE: ClassVar[Type[nn.Conv1d]] = nn.Conv1d
    _FLOAT_BN_MODULE: ClassVar[Type[nn.BatchNorm1d]] = nn.BatchNorm1d
    _FLOAT_RELU_MODULE: ClassVar[Optional[Type[nn.Module]]] = nn.ReLU
    # module class after fusing bn into conv
    _FUSED_FLOAT_MODULE: ClassVar[Optional[Type[nn.Module]]] = nni.ConvReLU1d

    def __init__(
        self,
        # Conv1d args
        in_channels,
        out_channels,
        kernel_size,
        stride=1,
        padding=0,
        dilation=1,
        groups=1,
        bias=None,
        padding_mode="zeros",
        # BatchNorm1d args
        # num_features: out_channels
        eps=1e-05,
        momentum=0.1,
        # affine: True
        # track_running_stats: True
        # Args for this module
        freeze_bn=False,
        qconfig=None,
    ):
        super().__init__(
            in_channels,
            out_channels,
            kernel_size,
            stride,
            padding,
            dilation,
            groups,
            bias,
            padding_mode,
            eps,
            momentum,
            freeze_bn,
            qconfig,
        )

    def forward(self, input):
        return F.relu(self._forward(input))

    @classmethod
    def from_float(cls, mod, use_precomputed_fake_quant=False):
        return super().from_float(mod, use_precomputed_fake_quant)


class ConvReLU1d(nnqat.Conv1d, nni._FusedModule):
    r"""A ConvReLU1d module is a fused module of Conv1d and ReLU, attached with
    FakeQuantize modules for weight for
    quantization aware training.

    We combined the interface of :class:`~torch.nn.Conv1d` and
    :class:`~torch.nn.BatchNorm1d`.

    Attributes:
        weight_fake_quant: fake quant module for weight

    """
    _FLOAT_MODULE: ClassVar[Type[nni.ConvReLU1d]] = nni.ConvReLU1d  # type: ignore[assignment]
    _FLOAT_CONV_MODULE: ClassVar[Type[nn.Conv1d]] = nn.Conv1d
    _FLOAT_BN_MODULE: ClassVar[Optional[Type[nn.Module]]] = None
    _FLOAT_RELU_MODULE: ClassVar[Optional[Type[nn.Module]]] = nn.ReLU

    def __init__(
        self,
        in_channels,
        out_channels,
        kernel_size,
        stride=1,
        padding=0,
        dilation=1,
        groups=1,
        bias=True,
        padding_mode="zeros",
        qconfig=None,
    ):
        super().__init__(
            in_channels,
            out_channels,
            kernel_size,
            stride=stride,
            padding=padding,
            dilation=dilation,
            groups=groups,
            bias=bias,
            padding_mode=padding_mode,
            qconfig=qconfig,
        )
        assert qconfig, "qconfig must be provided for QAT module"
        self.qconfig = qconfig
        self.weight_fake_quant = self.qconfig.weight()

    def forward(self, input):
        return F.relu(
            self._conv_forward(input, self.weight_fake_quant(self.weight), self.bias)
        )

    @classmethod
    def from_float(cls, mod, use_precomputed_fake_quant=False):
        return super().from_float(
            mod, use_precomputed_fake_quant=use_precomputed_fake_quant
        )


[docs]class ConvBn2d(_ConvBnNd, nn.Conv2d):
    r"""
    A ConvBn2d module is a module fused from Conv2d and BatchNorm2d,
    attached with FakeQuantize modules for weight,
    used in quantization aware training.

    We combined the interface of :class:`torch.nn.Conv2d` and
    :class:`torch.nn.BatchNorm2d`.

    Similar to :class:`torch.nn.Conv2d`, with FakeQuantize modules initialized
    to default.

    Attributes:
        freeze_bn:
        weight_fake_quant: fake quant module for weight

    """
    _FLOAT_MODULE: ClassVar[Type[nni.ConvBn2d]] = nni.ConvBn2d  # type: ignore[assignment]
    _FLOAT_CONV_MODULE: ClassVar[Type[nn.Conv2d]] = nn.Conv2d
    _FLOAT_BN_MODULE: ClassVar[Optional[Type[nn.Module]]] = nn.BatchNorm2d
    _FLOAT_RELU_MODULE: ClassVar[Optional[Type[nn.Module]]] = None

    def __init__(
        self,
        # ConvNd args
        in_channels,
        out_channels,
        kernel_size,
        stride=1,
        padding=0,
        dilation=1,
        groups=1,
        bias=None,
        padding_mode="zeros",
        # BatchNorm2d args
        # num_features: out_channels
        eps=1e-05,
        momentum=0.1,
        # affine: True
        # track_running_stats: True
        # Args for this module
        freeze_bn=False,
        qconfig=None,
    ):
        kernel_size = _pair(kernel_size)
        stride = _pair(stride)
        padding = _pair(padding)
        dilation = _pair(dilation)
        _ConvBnNd.__init__(
            self,
            in_channels,
            out_channels,
            kernel_size,
            stride,
            padding,
            dilation,
            False,
            _pair(0),
            groups,
            bias,
            padding_mode,
            eps,
            momentum,
            freeze_bn,
            qconfig,
            dim=2,
        )


[docs]class ConvBnReLU2d(ConvBn2d):
    r"""
    A ConvBnReLU2d module is a module fused from Conv2d, BatchNorm2d and ReLU,
    attached with FakeQuantize modules for weight,
    used in quantization aware training.

    We combined the interface of :class:`torch.nn.Conv2d` and
    :class:`torch.nn.BatchNorm2d` and :class:`torch.nn.ReLU`.

    Similar to `torch.nn.Conv2d`, with FakeQuantize modules initialized to
    default.

    Attributes:
        weight_fake_quant: fake quant module for weight

    """
    # base class defines _FLOAT_MODULE as "ConvBn2d"
    _FLOAT_MODULE: ClassVar[Type[nni.ConvBnReLU2d]] = nni.ConvBnReLU2d  # type: ignore[assignment]
    _FLOAT_CONV_MODULE: ClassVar[Type[nn.Conv2d]] = nn.Conv2d
    _FLOAT_BN_MODULE: ClassVar[Type[nn.BatchNorm2d]] = nn.BatchNorm2d
    _FLOAT_RELU_MODULE: ClassVar[Optional[Type[nn.Module]]] = nn.ReLU
    # module class after fusing bn into conv
    _FUSED_FLOAT_MODULE: ClassVar[Optional[Type[nni.ConvReLU2d]]] = nni.ConvReLU2d

    def __init__(
        self,
        # Conv2d args
        in_channels,
        out_channels,
        kernel_size,
        stride=1,
        padding=0,
        dilation=1,
        groups=1,
        bias=None,
        padding_mode="zeros",
        # BatchNorm2d args
        # num_features: out_channels
        eps=1e-05,
        momentum=0.1,
        # affine: True
        # track_running_stats: True
        # Args for this module
        freeze_bn=False,
        qconfig=None,
    ):
        super().__init__(
            in_channels,
            out_channels,
            kernel_size,
            stride,
            padding,
            dilation,
            groups,
            bias,
            padding_mode,
            eps,
            momentum,
            freeze_bn,
            qconfig,
        )

    def forward(self, input):
        return F.relu(self._forward(input))

    @classmethod
    def from_float(cls, mod, use_precomputed_fake_quant=False):
        return super().from_float(mod, use_precomputed_fake_quant)


[docs]class ConvReLU2d(nnqat.Conv2d, nni._FusedModule):
    r"""A ConvReLU2d module is a fused module of Conv2d and ReLU, attached with
    FakeQuantize modules for weight for
    quantization aware training.

    We combined the interface of :class:`~torch.nn.Conv2d` and
    :class:`~torch.nn.BatchNorm2d`.

    Attributes:
        weight_fake_quant: fake quant module for weight

    """
    _FLOAT_MODULE: ClassVar[Type[nn.Module]] = nni.ConvReLU2d  # type: ignore[assignment]
    _FLOAT_CONV_MODULE: ClassVar[Type[nn.Conv2d]] = nn.Conv2d
    _FLOAT_BN_MODULE: ClassVar[Optional[Type[nn.Module]]] = None
    _FLOAT_RELU_MODULE: ClassVar[Optional[Type[nn.Module]]] = nn.ReLU

    def __init__(
        self,
        in_channels,
        out_channels,
        kernel_size,
        stride=1,
        padding=0,
        dilation=1,
        groups=1,
        bias=True,
        padding_mode="zeros",
        qconfig=None,
    ):
        super().__init__(
            in_channels,
            out_channels,
            kernel_size,
            stride=stride,
            padding=padding,
            dilation=dilation,
            groups=groups,
            bias=bias,
            padding_mode=padding_mode,
            qconfig=qconfig,
        )
        assert qconfig, "qconfig must be provided for QAT module"
        self.qconfig = qconfig
        self.weight_fake_quant = self.qconfig.weight()

    def forward(self, input):
        return F.relu(
            self._conv_forward(input, self.weight_fake_quant(self.weight), self.bias)
        )

    @classmethod
    def from_float(cls, mod, use_precomputed_fake_quant=False):
        return super().from_float(
            mod, use_precomputed_fake_quant=use_precomputed_fake_quant
        )


[docs]class ConvBn3d(_ConvBnNd, nn.Conv3d):
    r"""
    A ConvBn3d module is a module fused from Conv3d and BatchNorm3d,
    attached with FakeQuantize modules for weight,
    used in quantization aware training.

    We combined the interface of :class:`torch.nn.Conv3d` and
    :class:`torch.nn.BatchNorm3d`.

    Similar to :class:`torch.nn.Conv3d`, with FakeQuantize modules initialized
    to default.

    Attributes:
        freeze_bn:
        weight_fake_quant: fake quant module for weight

    """
    _FLOAT_MODULE: ClassVar[Type[nni.ConvBn3d]] = nni.ConvBn3d  # type: ignore[assignment]
    _FLOAT_CONV_MODULE: ClassVar[Type[nn.Conv3d]] = nn.Conv3d
    _FLOAT_BN_MODULE: ClassVar[Optional[Type[nn.Module]]] = nn.BatchNorm3d
    _FLOAT_RELU_MODULE: ClassVar[Optional[Type[nn.Module]]] = None

    def __init__(
        self,
        # ConvNd args
        in_channels,
        out_channels,
        kernel_size,
        stride=1,
        padding=0,
        dilation=1,
        groups=1,
        bias=None,
        padding_mode="zeros",
        # BatchNorm3d args
        # num_features: out_channels
        eps=1e-05,
        momentum=0.1,
        # affine: True
        # track_running_stats: True
        # Args for this module
        freeze_bn=False,
        qconfig=None,
    ):
        kernel_size = _triple(kernel_size)
        stride = _triple(stride)
        padding = _triple(padding)
        dilation = _triple(dilation)
        _ConvBnNd.__init__(
            self,
            in_channels,
            out_channels,
            kernel_size,
            stride,
            padding,
            dilation,
            False,
            _triple(0),
            groups,
            bias,
            padding_mode,
            eps,
            momentum,
            freeze_bn,
            qconfig,
            dim=3,
        )


[docs]class ConvBnReLU3d(ConvBn3d):
    r"""
    A ConvBnReLU3d module is a module fused from Conv3d, BatchNorm3d and ReLU,
    attached with FakeQuantize modules for weight,
    used in quantization aware training.

    We combined the interface of :class:`torch.nn.Conv3d` and
    :class:`torch.nn.BatchNorm3d` and :class:`torch.nn.ReLU`.

    Similar to `torch.nn.Conv3d`, with FakeQuantize modules initialized to
    default.

    Attributes:
        weight_fake_quant: fake quant module for weight

    """
    _FLOAT_MODULE: ClassVar[Type[nni.ConvBnReLU3d]] = nni.ConvBnReLU3d  # type: ignore[assignment]
    _FLOAT_CONV_MODULE: ClassVar[Type[nn.Conv3d]] = nn.Conv3d
    _FLOAT_BN_MODULE: ClassVar[Type[nn.BatchNorm3d]] = nn.BatchNorm3d
    _FLOAT_RELU_MODULE: ClassVar[Optional[Type[nn.ReLU]]] = nn.ReLU
    # module class after fusing bn into conv
    _FUSED_FLOAT_MODULE: ClassVar[Optional[Type[nni.ConvReLU3d]]] = nni.ConvReLU3d

    def __init__(
        self,
        # Conv3d args
        in_channels,
        out_channels,
        kernel_size,
        stride=1,
        padding=0,
        dilation=1,
        groups=1,
        bias=None,
        padding_mode="zeros",
        # BatchNorm3d args
        # num_features: out_channels
        eps=1e-05,
        momentum=0.1,
        # affine: True
        # track_running_stats: True
        # Args for this module
        freeze_bn=False,
        qconfig=None,
    ):
        super().__init__(
            in_channels,
            out_channels,
            kernel_size,
            stride,
            padding,
            dilation,
            groups,
            bias,
            padding_mode,
            eps,
            momentum,
            freeze_bn,
            qconfig,
        )

    def forward(self, input):
        return F.relu(ConvBn3d._forward(self, input))

    @classmethod
    def from_float(cls, mod, use_precomputed_fake_quant=False):
        return super().from_float(
            mod, use_precomputed_fake_quant=use_precomputed_fake_quant
        )


[docs]class ConvReLU3d(nnqat.Conv3d, nni._FusedModule):
    r"""A ConvReLU3d module is a fused module of Conv3d and ReLU, attached with
    FakeQuantize modules for weight for
    quantization aware training.

    We combined the interface of :class:`~torch.nn.Conv3d` and
    :class:`~torch.nn.BatchNorm3d`.

    Attributes:
        weight_fake_quant: fake quant module for weight

    """
    _FLOAT_MODULE: ClassVar[Type[nni.ConvReLU3d]] = nni.ConvReLU3d  # type: ignore[assignment]
    _FLOAT_CONV_MODULE: ClassVar[Type[nn.Conv3d]] = nn.Conv3d
    _FLOAT_BN_MODULE: ClassVar[Optional[Type[nn.Module]]] = None
    _FLOAT_RELU_MODULE: ClassVar[Optional[Type[nn.Module]]] = nn.ReLU

    def __init__(
        self,
        in_channels,
        out_channels,
        kernel_size,
        stride=1,
        padding=0,
        dilation=1,
        groups=1,
        bias=True,
        padding_mode="zeros",
        qconfig=None,
    ):
        super().__init__(
            in_channels,
            out_channels,
            kernel_size,
            stride=stride,
            padding=padding,
            dilation=dilation,
            groups=groups,
            bias=bias,
            padding_mode=padding_mode,
            qconfig=qconfig,
        )
        assert qconfig, "qconfig must be provided for QAT module"
        self.qconfig = qconfig
        self.weight_fake_quant = self.qconfig.weight()

    def forward(self, input):
        return F.relu(
            self._conv_forward(input, self.weight_fake_quant(self.weight), self.bias)
        )

    @classmethod
    def from_float(cls, mod, use_precomputed_fake_quant=False):
        return super().from_float(
            mod, use_precomputed_fake_quant=use_precomputed_fake_quant
        )


[docs]def update_bn_stats(mod):
    if type(mod) in {
        ConvBnReLU1d,
        ConvBnReLU2d,
        ConvBnReLU3d,
        ConvBn1d,
        ConvBn2d,
        ConvBn3d,
    }:
        mod.update_bn_stats()


[docs]def freeze_bn_stats(mod):
    if type(mod) in {
        ConvBnReLU1d,
        ConvBnReLU2d,
        ConvBnReLU3d,
        ConvBn1d,
        ConvBn2d,
        ConvBn3d,
    }:
        mod.freeze_bn_stats()
Source code for torch.ao.nn.intrinsic.qat.modules.conv_fused

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