torch.nn.modules.batchnorm — PyTorch 2.7 documentation (original) (raw)

mypy: allow-untyped-defs

from typing import Any, Optional

import torch from torch import Tensor from torch.nn import functional as F, init from torch.nn.parameter import Parameter, UninitializedBuffer, UninitializedParameter

from ._functions import SyncBatchNorm as sync_batch_norm from .lazy import LazyModuleMixin from .module import Module

all = [ "BatchNorm1d", "LazyBatchNorm1d", "BatchNorm2d", "LazyBatchNorm2d", "BatchNorm3d", "LazyBatchNorm3d", "SyncBatchNorm", ]

class _NormBase(Module): """Common base of _InstanceNorm and _BatchNorm."""

_version = 2
__constants__ = ["track_running_stats", "momentum", "eps", "num_features", "affine"]
num_features: int
eps: float
momentum: Optional[float]
affine: bool
track_running_stats: bool
# WARNING: weight and bias purposely not defined here.
# See https://github.com/pytorch/pytorch/issues/39670

def __init__(
    self,
    num_features: int,
    eps: float = 1e-5,
    momentum: Optional[float] = 0.1,
    affine: bool = True,
    track_running_stats: bool = True,
    device=None,
    dtype=None,
) -> None:
    factory_kwargs = {"device": device, "dtype": dtype}
    super().__init__()
    self.num_features = num_features
    self.eps = eps
    self.momentum = momentum
    self.affine = affine
    self.track_running_stats = track_running_stats
    if self.affine:
        self.weight = Parameter(torch.empty(num_features, **factory_kwargs))
        self.bias = Parameter(torch.empty(num_features, **factory_kwargs))
    else:
        self.register_parameter("weight", None)
        self.register_parameter("bias", None)
    if self.track_running_stats:
        self.register_buffer(
            "running_mean", torch.zeros(num_features, **factory_kwargs)
        )
        self.register_buffer(
            "running_var", torch.ones(num_features, **factory_kwargs)
        )
        self.running_mean: Optional[Tensor]
        self.running_var: Optional[Tensor]
        self.register_buffer(
            "num_batches_tracked",
            torch.tensor(
                0,
                dtype=torch.long,
                **{k: v for k, v in factory_kwargs.items() if k != "dtype"},
            ),
        )
        self.num_batches_tracked: Optional[Tensor]
    else:
        self.register_buffer("running_mean", None)
        self.register_buffer("running_var", None)
        self.register_buffer("num_batches_tracked", None)
    self.reset_parameters()

def reset_running_stats(self) -> None:
    if self.track_running_stats:
        # running_mean/running_var/num_batches... are registered at runtime depending
        # if self.track_running_stats is on
        self.running_mean.zero_()  # type: ignore[union-attr]
        self.running_var.fill_(1)  # type: ignore[union-attr]
        self.num_batches_tracked.zero_()  # type: ignore[union-attr,operator]

def reset_parameters(self) -> None:
    self.reset_running_stats()
    if self.affine:
        init.ones_(self.weight)
        init.zeros_(self.bias)

def _check_input_dim(self, input):
    raise NotImplementedError

def extra_repr(self):
    return (
        "{num_features}, eps={eps}, momentum={momentum}, affine={affine}, "
        "track_running_stats={track_running_stats}".format(**self.__dict__)
    )

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) and self.track_running_stats:
        # at version 2: added num_batches_tracked buffer
        #               this should have a default value of 0
        num_batches_tracked_key = prefix + "num_batches_tracked"
        if num_batches_tracked_key not in state_dict:
            state_dict[num_batches_tracked_key] = (
                self.num_batches_tracked
                if self.num_batches_tracked is not None
                and self.num_batches_tracked.device != torch.device("meta")
                else torch.tensor(0, dtype=torch.long)
            )

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

class _BatchNorm(_NormBase): def init( self, num_features: int, eps: float = 1e-5, momentum: Optional[float] = 0.1, affine: bool = True, track_running_stats: bool = True, device=None, dtype=None, ) -> None: factory_kwargs = {"device": device, "dtype": dtype} super().init( num_features, eps, momentum, affine, track_running_stats, **factory_kwargs )

def forward(self, input: Tensor) -> Tensor:
    self._check_input_dim(input)

    # exponential_average_factor is set to self.momentum
    # (when it is available) only so that it gets updated
    # in ONNX graph when this node is exported to ONNX.
    if self.momentum is None:
        exponential_average_factor = 0.0
    else:
        exponential_average_factor = self.momentum

    if self.training and self.track_running_stats:
        # TODO: if statement only here to tell the jit to skip emitting this when it is None
        if self.num_batches_tracked is not None:  # type: ignore[has-type]
            self.num_batches_tracked.add_(1)  # type: ignore[has-type]
            if self.momentum is None:  # use cumulative moving average
                exponential_average_factor = 1.0 / float(self.num_batches_tracked)
            else:  # use exponential moving average
                exponential_average_factor = self.momentum

    r"""
    Decide whether the mini-batch stats should be used for normalization rather than the buffers.
    Mini-batch stats are used in training mode, and in eval mode when buffers are None.
    """
    if self.training:
        bn_training = True
    else:
        bn_training = (self.running_mean is None) and (self.running_var is None)

    r"""
    Buffers are only updated if they are to be tracked and we are in training mode. Thus they only need to be
    passed when the update should occur (i.e. in training mode when they are tracked), or when buffer stats are
    used for normalization (i.e. in eval mode when buffers are not None).
    """
    return F.batch_norm(
        input,
        # If buffers are not to be tracked, ensure that they won't be updated
        self.running_mean
        if not self.training or self.track_running_stats
        else None,
        self.running_var if not self.training or self.track_running_stats else None,
        self.weight,
        self.bias,
        bn_training,
        exponential_average_factor,
        self.eps,
    )

class _LazyNormBase(LazyModuleMixin, _NormBase): weight: UninitializedParameter # type: ignore[assignment] bias: UninitializedParameter # type: ignore[assignment]

def __init__(
    self,
    eps=1e-5,
    momentum=0.1,
    affine=True,
    track_running_stats=True,
    device=None,
    dtype=None,
) -> None:
    factory_kwargs = {"device": device, "dtype": dtype}
    super().__init__(
        # affine and track_running_stats are hardcoded to False to
        # avoid creating tensors that will soon be overwritten.
        0,
        eps,
        momentum,
        False,
        False,
        **factory_kwargs,
    )
    self.affine = affine
    self.track_running_stats = track_running_stats
    if self.affine:
        self.weight = UninitializedParameter(**factory_kwargs)
        self.bias = UninitializedParameter(**factory_kwargs)
    if self.track_running_stats:
        self.running_mean = UninitializedBuffer(**factory_kwargs)
        self.running_var = UninitializedBuffer(**factory_kwargs)
        self.num_batches_tracked = torch.tensor(
            0,
            dtype=torch.long,
            **{k: v for k, v in factory_kwargs.items() if k != "dtype"},
        )

def reset_parameters(self) -> None:
    if not self.has_uninitialized_params() and self.num_features != 0:
        super().reset_parameters()

def initialize_parameters(self, input) -> None:  # type: ignore[override]
    if self.has_uninitialized_params():
        self.num_features = input.shape[1]
        if self.affine:
            assert isinstance(self.weight, UninitializedParameter)
            assert isinstance(self.bias, UninitializedParameter)
            self.weight.materialize((self.num_features,))
            self.bias.materialize((self.num_features,))
        if self.track_running_stats:
            self.running_mean.materialize(  # type:ignore[union-attr]
                (self.num_features,)
            )
            self.running_var.materialize(  # type:ignore[union-attr]
                (self.num_features,)
            )
        self.reset_parameters()

[docs]class BatchNorm1d(_BatchNorm): r"""Applies Batch Normalization over a 2D or 3D input.

Method described in the paper
`Batch Normalization: Accelerating Deep Network Training by Reducing
Internal Covariate Shift <https://arxiv.org/abs/1502.03167>`__ .

.. math::

    y = \frac{x - \mathrm{E}[x]}{\sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta

The mean and standard-deviation are calculated per-dimension over
the mini-batches and :math:`\gamma` and :math:`\beta` are learnable parameter vectors
of size `C` (where `C` is the number of features or channels of the input). By default, the
elements of :math:`\gamma` are set to 1 and the elements of :math:`\beta` are set to 0.
At train time in the forward pass, the variance is calculated via the biased estimator,
equivalent to ``torch.var(input, unbiased=False)``. However, the value stored in the
moving average of the variance is calculated via the unbiased  estimator, equivalent to
``torch.var(input, unbiased=True)``.

Also by default, during training this layer keeps running estimates of its
computed mean and variance, which are then used for normalization during
evaluation. The running estimates are kept with a default :attr:`momentum`
of 0.1.

If :attr:`track_running_stats` is set to ``False``, this layer then does not
keep running estimates, and batch statistics are instead used during
evaluation time as well.

.. note::
    This :attr:`momentum` argument is different from one used in optimizer
    classes and the conventional notion of momentum. Mathematically, the
    update rule for running statistics here is
    :math:`\hat{x}_\text{new} = (1 - \text{momentum}) \times \hat{x} + \text{momentum} \times x_t`,
    where :math:`\hat{x}` is the estimated statistic and :math:`x_t` is the
    new observed value.

Because the Batch Normalization is done over the `C` dimension, computing statistics
on `(N, L)` slices, it's common terminology to call this Temporal Batch Normalization.

Args:
    num_features: number of features or channels :math:`C` of the input
    eps: a value added to the denominator for numerical stability.
        Default: 1e-5
    momentum: the value used for the running_mean and running_var
        computation. Can be set to ``None`` for cumulative moving average
        (i.e. simple average). Default: 0.1
    affine: a boolean value that when set to ``True``, this module has
        learnable affine parameters. Default: ``True``
    track_running_stats: a boolean value that when set to ``True``, this
        module tracks the running mean and variance, and when set to ``False``,
        this module does not track such statistics, and initializes statistics
        buffers :attr:`running_mean` and :attr:`running_var` as ``None``.
        When these buffers are ``None``, this module always uses batch statistics.
        in both training and eval modes. Default: ``True``

Shape:
    - Input: :math:`(N, C)` or :math:`(N, C, L)`, where :math:`N` is the batch size,
      :math:`C` is the number of features or channels, and :math:`L` is the sequence length
    - Output: :math:`(N, C)` or :math:`(N, C, L)` (same shape as input)

Examples::

    >>> # With Learnable Parameters
    >>> m = nn.BatchNorm1d(100)
    >>> # Without Learnable Parameters
    >>> m = nn.BatchNorm1d(100, affine=False)
    >>> input = torch.randn(20, 100)
    >>> output = m(input)
"""

def _check_input_dim(self, input):
    if input.dim() != 2 and input.dim() != 3:
        raise ValueError(f"expected 2D or 3D input (got {input.dim()}D input)")

[docs]class LazyBatchNorm1d(_LazyNormBase, _BatchNorm): r"""A :class:torch.nn.BatchNorm1d module with lazy initialization.

Lazy initialization based on the ``num_features`` argument of the :class:`BatchNorm1d` that is inferred
from the ``input.size(1)``.
The attributes that will be lazily initialized are `weight`, `bias`,
`running_mean` and `running_var`.

Check the :class:`torch.nn.modules.lazy.LazyModuleMixin` for further documentation
on lazy modules and their limitations.

Args:
    eps: a value added to the denominator for numerical stability.
        Default: 1e-5
    momentum: the value used for the running_mean and running_var
        computation. Can be set to ``None`` for cumulative moving average
        (i.e. simple average). Default: 0.1
    affine: a boolean value that when set to ``True``, this module has
        learnable affine parameters. Default: ``True``
    track_running_stats: a boolean value that when set to ``True``, this
        module tracks the running mean and variance, and when set to ``False``,
        this module does not track such statistics, and initializes statistics
        buffers :attr:`running_mean` and :attr:`running_var` as ``None``.
        When these buffers are ``None``, this module always uses batch statistics.
        in both training and eval modes. Default: ``True``
"""

cls_to_become = BatchNorm1d  # type: ignore[assignment]

def _check_input_dim(self, input):
    if input.dim() != 2 and input.dim() != 3:
        raise ValueError(f"expected 2D or 3D input (got {input.dim()}D input)")

[docs]class BatchNorm2d(_BatchNorm): r"""Applies Batch Normalization over a 4D input.

4D is a mini-batch of 2D inputs
with additional channel dimension. Method described in the paper
`Batch Normalization: Accelerating Deep Network Training by Reducing
Internal Covariate Shift <https://arxiv.org/abs/1502.03167>`__ .

.. math::

    y = \frac{x - \mathrm{E}[x]}{ \sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta

The mean and standard-deviation are calculated per-dimension over
the mini-batches and :math:`\gamma` and :math:`\beta` are learnable parameter vectors
of size `C` (where `C` is the input size). By default, the elements of :math:`\gamma` are set
to 1 and the elements of :math:`\beta` are set to 0. At train time in the forward pass, the
standard-deviation is calculated via the biased estimator, equivalent to
``torch.var(input, unbiased=False)``. However, the value stored in the moving average of the
standard-deviation is calculated via the unbiased  estimator, equivalent to
``torch.var(input, unbiased=True)``.

Also by default, during training this layer keeps running estimates of its
computed mean and variance, which are then used for normalization during
evaluation. The running estimates are kept with a default :attr:`momentum`
of 0.1.

If :attr:`track_running_stats` is set to ``False``, this layer then does not
keep running estimates, and batch statistics are instead used during
evaluation time as well.

.. note::
    This :attr:`momentum` argument is different from one used in optimizer
    classes and the conventional notion of momentum. Mathematically, the
    update rule for running statistics here is
    :math:`\hat{x}_\text{new} = (1 - \text{momentum}) \times \hat{x} + \text{momentum} \times x_t`,
    where :math:`\hat{x}` is the estimated statistic and :math:`x_t` is the
    new observed value.

Because the Batch Normalization is done over the `C` dimension, computing statistics
on `(N, H, W)` slices, it's common terminology to call this Spatial Batch Normalization.

Args:
    num_features: :math:`C` from an expected input of size
        :math:`(N, C, H, W)`
    eps: a value added to the denominator for numerical stability.
        Default: 1e-5
    momentum: the value used for the running_mean and running_var
        computation. Can be set to ``None`` for cumulative moving average
        (i.e. simple average). Default: 0.1
    affine: a boolean value that when set to ``True``, this module has
        learnable affine parameters. Default: ``True``
    track_running_stats: a boolean value that when set to ``True``, this
        module tracks the running mean and variance, and when set to ``False``,
        this module does not track such statistics, and initializes statistics
        buffers :attr:`running_mean` and :attr:`running_var` as ``None``.
        When these buffers are ``None``, this module always uses batch statistics.
        in both training and eval modes. Default: ``True``

Shape:
    - Input: :math:`(N, C, H, W)`
    - Output: :math:`(N, C, H, W)` (same shape as input)

Examples::

    >>> # With Learnable Parameters
    >>> m = nn.BatchNorm2d(100)
    >>> # Without Learnable Parameters
    >>> m = nn.BatchNorm2d(100, affine=False)
    >>> input = torch.randn(20, 100, 35, 45)
    >>> output = m(input)
"""

def _check_input_dim(self, input):
    if input.dim() != 4:
        raise ValueError(f"expected 4D input (got {input.dim()}D input)")

[docs]class LazyBatchNorm2d(_LazyNormBase, _BatchNorm): r"""A :class:torch.nn.BatchNorm2d module with lazy initialization.

Lazy initialization is done for the ``num_features`` argument of the :class:`BatchNorm2d` that is inferred
from the ``input.size(1)``.
The attributes that will be lazily initialized are `weight`, `bias`,
`running_mean` and `running_var`.

Check the :class:`torch.nn.modules.lazy.LazyModuleMixin` for further documentation
on lazy modules and their limitations.

Args:
    eps: a value added to the denominator for numerical stability.
        Default: 1e-5
    momentum: the value used for the running_mean and running_var
        computation. Can be set to ``None`` for cumulative moving average
        (i.e. simple average). Default: 0.1
    affine: a boolean value that when set to ``True``, this module has
        learnable affine parameters. Default: ``True``
    track_running_stats: a boolean value that when set to ``True``, this
        module tracks the running mean and variance, and when set to ``False``,
        this module does not track such statistics, and initializes statistics
        buffers :attr:`running_mean` and :attr:`running_var` as ``None``.
        When these buffers are ``None``, this module always uses batch statistics.
        in both training and eval modes. Default: ``True``
"""

cls_to_become = BatchNorm2d  # type: ignore[assignment]

def _check_input_dim(self, input):
    if input.dim() != 4:
        raise ValueError(f"expected 4D input (got {input.dim()}D input)")

[docs]class BatchNorm3d(_BatchNorm): r"""Applies Batch Normalization over a 5D input.

5D is a mini-batch of 3D inputs with additional channel dimension as described in the paper
`Batch Normalization: Accelerating Deep Network Training by Reducing
Internal Covariate Shift <https://arxiv.org/abs/1502.03167>`__ .

.. math::

    y = \frac{x - \mathrm{E}[x]}{ \sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta

The mean and standard-deviation are calculated per-dimension over
the mini-batches and :math:`\gamma` and :math:`\beta` are learnable parameter vectors
of size `C` (where `C` is the input size). By default, the elements of :math:`\gamma` are set
to 1 and the elements of :math:`\beta` are set to 0. At train time in the forward pass, the
standard-deviation is calculated via the biased estimator, equivalent to
``torch.var(input, unbiased=False)``. However, the value stored in the moving average of the
standard-deviation is calculated via the unbiased  estimator, equivalent to
``torch.var(input, unbiased=True)``.

Also by default, during training this layer keeps running estimates of its
computed mean and variance, which are then used for normalization during
evaluation. The running estimates are kept with a default :attr:`momentum`
of 0.1.

If :attr:`track_running_stats` is set to ``False``, this layer then does not
keep running estimates, and batch statistics are instead used during
evaluation time as well.

.. note::
    This :attr:`momentum` argument is different from one used in optimizer
    classes and the conventional notion of momentum. Mathematically, the
    update rule for running statistics here is
    :math:`\hat{x}_\text{new} = (1 - \text{momentum}) \times \hat{x} + \text{momentum} \times x_t`,
    where :math:`\hat{x}` is the estimated statistic and :math:`x_t` is the
    new observed value.

Because the Batch Normalization is done over the `C` dimension, computing statistics
on `(N, D, H, W)` slices, it's common terminology to call this Volumetric Batch Normalization
or Spatio-temporal Batch Normalization.

Args:
    num_features: :math:`C` from an expected input of size
        :math:`(N, C, D, H, W)`
    eps: a value added to the denominator for numerical stability.
        Default: 1e-5
    momentum: the value used for the running_mean and running_var
        computation. Can be set to ``None`` for cumulative moving average
        (i.e. simple average). Default: 0.1
    affine: a boolean value that when set to ``True``, this module has
        learnable affine parameters. Default: ``True``
    track_running_stats: a boolean value that when set to ``True``, this
        module tracks the running mean and variance, and when set to ``False``,
        this module does not track such statistics, and initializes statistics
        buffers :attr:`running_mean` and :attr:`running_var` as ``None``.
        When these buffers are ``None``, this module always uses batch statistics.
        in both training and eval modes. Default: ``True``

Shape:
    - Input: :math:`(N, C, D, H, W)`
    - Output: :math:`(N, C, D, H, W)` (same shape as input)

Examples::

    >>> # With Learnable Parameters
    >>> m = nn.BatchNorm3d(100)
    >>> # Without Learnable Parameters
    >>> m = nn.BatchNorm3d(100, affine=False)
    >>> input = torch.randn(20, 100, 35, 45, 10)
    >>> output = m(input)
"""

def _check_input_dim(self, input):
    if input.dim() != 5:
        raise ValueError(f"expected 5D input (got {input.dim()}D input)")

[docs]class LazyBatchNorm3d(_LazyNormBase, _BatchNorm): r"""A :class:torch.nn.BatchNorm3d module with lazy initialization.

Lazy initialization is done for the ``num_features`` argument of the :class:`BatchNorm3d` that is inferred
from the ``input.size(1)``.
The attributes that will be lazily initialized are `weight`, `bias`,
`running_mean` and `running_var`.

Check the :class:`torch.nn.modules.lazy.LazyModuleMixin` for further documentation
on lazy modules and their limitations.

Args:
    eps: a value added to the denominator for numerical stability.
        Default: 1e-5
    momentum: the value used for the running_mean and running_var
        computation. Can be set to ``None`` for cumulative moving average
        (i.e. simple average). Default: 0.1
    affine: a boolean value that when set to ``True``, this module has
        learnable affine parameters. Default: ``True``
    track_running_stats: a boolean value that when set to ``True``, this
        module tracks the running mean and variance, and when set to ``False``,
        this module does not track such statistics, and initializes statistics
        buffers :attr:`running_mean` and :attr:`running_var` as ``None``.
        When these buffers are ``None``, this module always uses batch statistics.
        in both training and eval modes. Default: ``True``
"""

cls_to_become = BatchNorm3d  # type: ignore[assignment]

def _check_input_dim(self, input):
    if input.dim() != 5:
        raise ValueError(f"expected 5D input (got {input.dim()}D input)")

[docs]class SyncBatchNorm(_BatchNorm): r"""Applies Batch Normalization over a N-Dimensional input.

The N-D input is a mini-batch of [N-2]D inputs with additional channel dimension) as described in the paper
`Batch Normalization: Accelerating Deep Network Training by Reducing
Internal Covariate Shift <https://arxiv.org/abs/1502.03167>`__ .

.. math::

    y = \frac{x - \mathrm{E}[x]}{ \sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta

The mean and standard-deviation are calculated per-dimension over all
mini-batches of the same process groups. :math:`\gamma` and :math:`\beta`
are learnable parameter vectors of size `C` (where `C` is the input size).
By default, the elements of :math:`\gamma` are sampled from
:math:`\mathcal{U}(0, 1)` and the elements of :math:`\beta` are set to 0.
The standard-deviation is calculated via the biased estimator, equivalent to
`torch.var(input, unbiased=False)`.

Also by default, during training this layer keeps running estimates of its
computed mean and variance, which are then used for normalization during
evaluation. The running estimates are kept with a default :attr:`momentum`
of 0.1.

If :attr:`track_running_stats` is set to ``False``, this layer then does not
keep running estimates, and batch statistics are instead used during
evaluation time as well.

.. note::
    This :attr:`momentum` argument is different from one used in optimizer
    classes and the conventional notion of momentum. Mathematically, the
    update rule for running statistics here is
    :math:`\hat{x}_\text{new} = (1 - \text{momentum}) \times \hat{x} + \text{momentum} \times x_t`,
    where :math:`\hat{x}` is the estimated statistic and :math:`x_t` is the
    new observed value.

Because the Batch Normalization is done for each channel in the ``C`` dimension, computing
statistics on ``(N, +)`` slices, it's common terminology to call this Volumetric Batch
Normalization or Spatio-temporal Batch Normalization.

Currently :class:`SyncBatchNorm` only supports
:class:`~torch.nn.DistributedDataParallel` (DDP) with single GPU per process. Use
:meth:`torch.nn.SyncBatchNorm.convert_sync_batchnorm()` to convert
:attr:`BatchNorm*D` layer to :class:`SyncBatchNorm` before wrapping
Network with DDP.

Args:
    num_features: :math:`C` from an expected input of size
        :math:`(N, C, +)`
    eps: a value added to the denominator for numerical stability.
        Default: ``1e-5``
    momentum: the value used for the running_mean and running_var
        computation. Can be set to ``None`` for cumulative moving average
        (i.e. simple average). Default: 0.1
    affine: a boolean value that when set to ``True``, this module has
        learnable affine parameters. Default: ``True``
    track_running_stats: a boolean value that when set to ``True``, this
        module tracks the running mean and variance, and when set to ``False``,
        this module does not track such statistics, and initializes statistics
        buffers :attr:`running_mean` and :attr:`running_var` as ``None``.
        When these buffers are ``None``, this module always uses batch statistics.
        in both training and eval modes. Default: ``True``
    process_group: synchronization of stats happen within each process group
        individually. Default behavior is synchronization across the whole
        world

Shape:
    - Input: :math:`(N, C, +)`
    - Output: :math:`(N, C, +)` (same shape as input)

.. note::
    Synchronization of batchnorm statistics occurs only while training, i.e.
    synchronization is disabled when ``model.eval()`` is set or if
    ``self.training`` is otherwise ``False``.

Examples::

    >>> # xdoctest: +SKIP
    >>> # With Learnable Parameters
    >>> m = nn.SyncBatchNorm(100)
    >>> # creating process group (optional)
    >>> # ranks is a list of int identifying rank ids.
    >>> ranks = list(range(8))
    >>> r1, r2 = ranks[:4], ranks[4:]
    >>> # Note: every rank calls into new_group for every
    >>> # process group created, even if that rank is not
    >>> # part of the group.
    >>> process_groups = [torch.distributed.new_group(pids) for pids in [r1, r2]]
    >>> process_group = process_groups[0 if dist.get_rank() <= 3 else 1]
    >>> # Without Learnable Parameters
    >>> m = nn.BatchNorm3d(100, affine=False, process_group=process_group)
    >>> input = torch.randn(20, 100, 35, 45, 10)
    >>> output = m(input)

    >>> # network is nn.BatchNorm layer
    >>> sync_bn_network = nn.SyncBatchNorm.convert_sync_batchnorm(network, process_group)
    >>> # only single gpu per process is currently supported
    >>> ddp_sync_bn_network = torch.nn.parallel.DistributedDataParallel(
    >>>                         sync_bn_network,
    >>>                         device_ids=[args.local_rank],
    >>>                         output_device=args.local_rank)
"""

def __init__(
    self,
    num_features: int,
    eps: float = 1e-5,
    momentum: Optional[float] = 0.1,
    affine: bool = True,
    track_running_stats: bool = True,
    process_group: Optional[Any] = None,
    device=None,
    dtype=None,
) -> None:
    factory_kwargs = {"device": device, "dtype": dtype}
    super().__init__(
        num_features, eps, momentum, affine, track_running_stats, **factory_kwargs
    )
    self.process_group = process_group

def _check_input_dim(self, input):
    if input.dim() < 2:
        raise ValueError(f"expected at least 2D input (got {input.dim()}D input)")

def _check_non_zero_input_channels(self, input):
    if input.size(1) == 0:
        raise ValueError(
            "SyncBatchNorm number of input channels should be non-zero"
        )

def forward(self, input: Tensor) -> Tensor:
    self._check_input_dim(input)
    self._check_non_zero_input_channels(input)

    # exponential_average_factor is set to self.momentum
    # (when it is available) only so that it gets updated
    # in ONNX graph when this node is exported to ONNX.
    if self.momentum is None:
        exponential_average_factor = 0.0
    else:
        exponential_average_factor = self.momentum

    if self.training and self.track_running_stats:
        assert self.num_batches_tracked is not None
        self.num_batches_tracked.add_(1)
        if self.momentum is None:  # use cumulative moving average
            exponential_average_factor = 1.0 / self.num_batches_tracked.item()
        else:  # use exponential moving average
            exponential_average_factor = self.momentum

    r"""
    Decide whether the mini-batch stats should be used for normalization rather than the buffers.
    Mini-batch stats are used in training mode, and in eval mode when buffers are None.
    """
    if self.training:
        bn_training = True
    else:
        bn_training = (self.running_mean is None) and (self.running_var is None)

    r"""
    Buffers are only updated if they are to be tracked and we are in training mode. Thus they only need to be
    passed when the update should occur (i.e. in training mode when they are tracked), or when buffer stats are
    used for normalization (i.e. in eval mode when buffers are not None).
    """
    # If buffers are not to be tracked, ensure that they won't be updated
    running_mean = (
        self.running_mean if not self.training or self.track_running_stats else None
    )
    running_var = (
        self.running_var if not self.training or self.track_running_stats else None
    )

    # Don't sync batchnorm stats in inference mode (model.eval()).
    need_sync = (
        bn_training
        and self.training
        and torch.distributed.is_available()
        and torch.distributed.is_initialized()
    )
    if need_sync:
        # currently only GPU/PrivateUse1 input is supported
        if input.device.type not in [
            "cuda",
            "xpu",
            torch._C._get_privateuse1_backend_name(),
        ]:
            raise ValueError(
                "SyncBatchNorm expected input tensor to be on GPU or XPU or "
                f"{torch._C._get_privateuse1_backend_name()}"
            )

        process_group = torch.distributed.group.WORLD
        if self.process_group:
            process_group = self.process_group
        world_size = torch.distributed.get_world_size(process_group)
        need_sync = world_size > 1

    # fallback to framework BN when synchronization is not necessary
    if not need_sync:
        return F.batch_norm(
            input,
            running_mean,
            running_var,
            self.weight,
            self.bias,
            bn_training,
            exponential_average_factor,
            self.eps,
        )
    else:
        assert bn_training
        return sync_batch_norm.apply(
            input,
            self.weight,
            self.bias,
            running_mean,
            running_var,
            self.eps,
            exponential_average_factor,
            process_group,  # type: ignore[possibly-undefined]
            world_size,  # type: ignore[possibly-undefined]
        )

[docs] @classmethod def convert_sync_batchnorm(cls, module, process_group=None): r"""Converts all :attr:BatchNorm*D layers in the model to :class:torch.nn.SyncBatchNorm layers.

    Args:
        module (nn.Module): module containing one or more :attr:`BatchNorm*D` layers
        process_group (optional): process group to scope synchronization,
            default is the whole world

    Returns:
        The original :attr:`module` with the converted :class:`torch.nn.SyncBatchNorm`
        layers. If the original :attr:`module` is a :attr:`BatchNorm*D` layer,
        a new :class:`torch.nn.SyncBatchNorm` layer object will be returned
        instead.

    Example::

        >>> # Network with nn.BatchNorm layer
        >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA)
        >>> module = torch.nn.Sequential(
        >>>            torch.nn.Linear(20, 100),
        >>>            torch.nn.BatchNorm1d(100),
        >>>          ).cuda()
        >>> # creating process group (optional)
        >>> # ranks is a list of int identifying rank ids.
        >>> ranks = list(range(8))
        >>> r1, r2 = ranks[:4], ranks[4:]
        >>> # Note: every rank calls into new_group for every
        >>> # process group created, even if that rank is not
        >>> # part of the group.
        >>> # xdoctest: +SKIP("distributed")
        >>> process_groups = [torch.distributed.new_group(pids) for pids in [r1, r2]]
        >>> process_group = process_groups[0 if dist.get_rank() <= 3 else 1]
        >>> sync_bn_module = torch.nn.SyncBatchNorm.convert_sync_batchnorm(module, process_group)

    """
    module_output = module
    if isinstance(module, torch.nn.modules.batchnorm._BatchNorm):
        module_output = torch.nn.SyncBatchNorm(
            module.num_features,
            module.eps,
            module.momentum,
            module.affine,
            module.track_running_stats,
            process_group,
        )
        if module.affine:
            with torch.no_grad():
                module_output.weight = module.weight
                module_output.bias = module.bias
        module_output.running_mean = module.running_mean
        module_output.running_var = module.running_var
        module_output.num_batches_tracked = module.num_batches_tracked
        module_output.training = module.training
        if hasattr(module, "qconfig"):
            module_output.qconfig = module.qconfig
    for name, child in module.named_children():
        module_output.add_module(
            name, cls.convert_sync_batchnorm(child, process_group)
        )
    del module
    return module_output