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align docstring of make_private_with_epsilon with args #526

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align docstring of make_private_with_epsilon with args #526

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14 changes: 8 additions & 6 deletions opacus/privacy_engine.py
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Original file line number Diff line number Diff line change
Expand Up @@ -463,9 +463,6 @@ def make_private_with_epsilon(
module: PyTorch module to be used for training
optimizer: Optimizer to be used for training
data_loader: DataLoader to be used for training
noise_multiplier: The ratio of the standard deviation of the Gaussian noise to
the L2-sensitivity of the function to which the noise is added
(How much noise to add)
max_grad_norm: The maximum norm of the per-sample gradients. Any gradient with norm
higher than this will be clipped to this value.
batch_first: Flag to indicate if the input tensor to the corresponding module
Expand All @@ -474,26 +471,31 @@ def make_private_with_epsilon(
``[K, batch_size, ...]``
loss_reduction: Indicates if the loss reduction (for aggregating the gradients)
is a sum or a mean operation. Can take values "sum" or "mean"
noise_seed: Seed to be used for random noise generation
poisson_sampling: ``True`` if you want to use standard sampling required
for DP guarantees. Setting ``False`` will leave provided data_loader
unchanged. Technically this doesn't fit the assumptions made by
privacy accounting mechanism, but it can be a good approximation when
using Poisson sampling is unfeasible.
clipping: Per sample gradient clipping mechanism ("flat" or "per_layer").
clipping: Per sample gradient clipping mechanism ("flat" or "per_layer" or "adaptive").
Flat clipping calculates the norm of the entire gradient over
all parameters, per layer clipping sets individual norms for
every parameter tensor, and adaptive clipping updates clipping bound per iteration.
Flat clipping is usually preferred, but using per layer clipping in combination
with distributed training can provide notable performance gains.
noise_generator: torch.Generator() object used as a source of randomness for
the noise
grad_sample_mode: mode for computing per sample gradients. Determines the
implementation class for the wrapped ``module``. See
:class:`~opacus.grad_sample.gsm_base.AbstractGradSampleModule` for more
details

Returns:
Tuple of (model, optimizer, data_loader).

Model is a wrapper around the original model that also computes per sample
gradients
Optimizer is a wrapper around the original optimizer that also does
gradient clipping and adding noise to the gradients
gradient clipping and noise addition to the gradients
DataLoader is a brand new DataLoader object, constructed to behave as
equivalent to the original data loader, possibly with updated
sampling mechanism. Points to the same dataset object.
Expand Down