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# SyncBatchNorm

class torch.nn.SyncBatchNorm(num_features, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True, process_group=None, device=None, dtype=None)

  Applies Batch Normalization over a N-Dimensional input (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 .

  y=x−E[x]V a r[x]+ϵ∗γ+β y = \frac{x - \mathrm{E}[x]}{ \sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta y=Var[x]+ϵ
  ​x−E[x]​∗γ+β

  The mean and standard-deviation are calculated per-dimension over all mini-batches of the same process groups. γ\gamma γ and β\beta β are learnable parameter vectors of size C (where C is the input size). By default, the elements of γ\gamma γ are sampled from U(0,1)\mathcal{U}(0, 1)U(0,1) and the elements of β\beta β 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 `momentum` of 0.1.

  If `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 `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 x^new=(1−momentum)×x^+momentum×x t\hat{x}_\text{new} = (1 - \text{momentum}) \times \hat{x} + \text{momentum} \times x_t x^new​=(1−momentum)×x^+momentum×x t​, where x^\hat{x}x^ is the estimated statistic and x t x_t 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 `SyncBatchNorm` only supports `DistributedDataParallel` (DDP) with single GPU per process. Use `torch.nn.SyncBatchNorm.convert_sync_batchnorm()` to convert `BatchNorm*D` layer to `SyncBatchNorm` before wrapping Network with DDP.

  Parameters:

    - num_features (int) – C C C from an expected input of size (N,C,+)(N, C, +)(N,C,+)

    - eps (float) – a value added to the denominator for numerical stability. Default: `1e-5`

    - momentum (float) – 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 (bool) – a boolean value that when set to `True`, this module has learnable affine parameters. Default: `True`

    - track_running_stats (bool) – 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 `running_mean` and `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 (Optional[Any]) – synchronization of stats happen within each process group individually. Default behavior is synchronization across the whole world

  Shape:

    - Input: (N,C,+)(N, C, +)(N,C,+)

    - Output: (N,C,+)(N, C, +)(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:

      >>> # 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)

  classmethod convert_sync_batchnorm(module, process_group=None)

    Helper function to convert all `BatchNorm*D` layers in the model to `torch.nn.SyncBatchNorm` layers.

    Parameters:

      - module (nn.Module) – module containing one or more `BatchNorm*D` layers

      - process_group (optional) – process group to scope synchronization, default is the whole world

    Returns:

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

    Example:

        >>> # Network with nn.BatchNorm layer
        >>> 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.
        >>> 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)

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