Source code for opacus.privacy_engine

#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved

import os
import types
import warnings
from typing import List, Optional, Tuple, Union

import torch
import torchcsprng as csprng
from torch import nn

from . import privacy_analysis as tf_privacy
from .dp_model_inspector import DPModelInspector
from .per_sample_gradient_clip import PerSampleGradientClipper
from .utils import clipping

[docs]class PrivacyEngine: r""" The main component of Opacus is the ``PrivacyEngine``. To train a model with differential privacy, all you need to do is to define a ``PrivacyEngine`` and later attach it to your optimizer before running. Example: This example shows how to define a ``PrivacyEngine`` and to attach it to your optimizer. >>> import torch >>> model = torch.nn.Linear(16, 32) # An example model >>> optimizer = torch.optim.SGD(model.parameters(), lr=0.05) >>> privacy_engine = PrivacyEngine(model, batch_size, sample_size, alphas=range(2,32), noise_multiplier=1.3, max_grad_norm=1.0) >>> privacy_engine.attach(optimizer) # That's it! Now it's business as usual. """ def __init__( self, module: nn.Module, batch_size: int, sample_size: int, alphas: List[float], noise_multiplier: float, max_grad_norm: Union[float, List[float]], secure_rng: bool = False, grad_norm_type: int = 2, batch_first: bool = True, target_delta: float = 1e-6, loss_reduction: str = "mean", **misc_settings, ): r""" Args: module: The Pytorch module to which we are attaching the privacy engine batch_size: Training batch size. Used in the privacy accountant. sample_size: The size of the sample (dataset). Used in the privacy accountant. alphas: A list of RDP orders 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 max_grad_norm: The maximum norm of the per-sample gradients. Any gradient with norm higher than this will be clipped to this value. secure_rng: If on, it will use ``torchcsprng`` for secure random number generation. Comes with a significant performance cost, therefore it's recommended that you turn it off when just experimenting. grad_norm_type: The order of the norm. For instance, 2 represents L-2 norm, while 1 represents L-1 norm. batch_first: Flag to indicate if the input tensor to the corresponding module has the first dimension representing the batch. If set to True, dimensions on input tensor will be ``[batch_size, ..., ...]``. target_delta: The target delta loss_reduction: Indicates if the loss reduction (for aggregating the gradients) is a sum or a mean operation. Can take values "sum" or "mean" **misc_settings: Other arguments to the init """ self.steps = 0 self.module = module self.secure_rng = secure_rng self.alphas = alphas self.device = next(module.parameters()).device self.batch_size = batch_size self.sample_rate = batch_size / sample_size self.noise_multiplier = noise_multiplier self.max_grad_norm = max_grad_norm self.grad_norm_type = grad_norm_type self.batch_first = batch_first self.target_delta = target_delta if self.sample_rate > 1.0: raise ValueError( f"PrivacyEngine received a dataset sample size of {sample_size} " f"but a batch of size {batch_size}. For correct privacy accounting " f"the batch size must be less than the sample size." ) if self.secure_rng: self.seed = None self.random_number_generator = csprng.create_random_device_generator( "/dev/urandom" ) else: warnings.warn( "Secure RNG turned off. This is perfectly fine for experimentation as it allows " "for much faster training performance, but remember to turn it on and retrain " "one last time before production with ``secure_rng`` turned on." ) with warnings.catch_warnings(): warnings.simplefilter("ignore") self.seed = int.from_bytes(os.urandom(8), byteorder="big", signed=True) self.random_number_generator = self._set_seed(self.seed) self.validator = DPModelInspector() self.clipper = None # lazy initialization in attach self.misc_settings = misc_settings self.loss_reduction = loss_reduction
[docs] def detach(self): r""" Detaches the privacy engine from optimizer. To detach the ``PrivacyEngine`` from optimizer, this method returns the model and the optimizer to their original states (i.e. all added attributes/methods will be removed). """ optim = self.optimizer optim.privacy_engine = None self.clipper.close() optim.step = types.MethodType(optim.original_step, optim) del optim.virtual_step
[docs] def attach(self, optimizer: torch.optim.Optimizer): r""" Attaches the privacy engine to the optimizer. Attaches to the ``PrivacyEngine`` an optimizer object,and injects itself into the optimizer's step. To do that it, 1. Validates that the model does not have unsupported layers. 2. Adds a pointer to this object (the ``PrivacyEngine``) inside the optimizer. 3. Moves optimizer's original ``step()`` function to ``original_step()``. 4. Monkeypatches the optimizer's ``step()`` function to call ``step()`` on the query engine automatically whenever it would call ``step()`` for itself. Args: optimizer: The optimizer to which the privacy engine will attach """ self.validator.validate(self.module) norm_clipper = ( # pyre-fixme[6]: Expected `float` for 1st param but got # `Union[List[float], float]`. clipping.ConstantFlatClipper(self.max_grad_norm) if not isinstance(self.max_grad_norm, list) # pyre-fixme[6]: Expected `List[float]` for 1st param but got # `Union[List[float], float]`. else clipping.ConstantPerLayerClipper(self.max_grad_norm) ) if self.misc_settings.get("experimental", False): norm_clipper = clipping._Dynamic_Clipper_( # pyre-fixme[6]: Expected `List[float]` for 1st param but got # `List[Union[List[float], float]]`. [self.max_grad_norm], self.misc_settings.get("clip_per_layer", False), self.misc_settings.get( "clipping_method", clipping.ClippingMethod.STATIC ), self.misc_settings.get("clipping_ratio", 0.0), self.misc_settings.get("clipping_momentum", 0.0), ) self.clipper = PerSampleGradientClipper( self.module, norm_clipper, self.batch_first, self.loss_reduction, ) def dp_zero_grad(self): self.privacy_engine.zero_grad() self.original_zero_grad() def dp_step(self, closure=None): self.privacy_engine.step() self.original_step(closure) # Pyre doesn't like monkeypatching. But we'll do it anyway :) optimizer.privacy_engine = self # pyre-ignore optimizer.original_step = optimizer.step # pyre-ignore optimizer.step = types.MethodType(dp_step, optimizer) # pyre-ignore optimizer.original_zero_grad = optimizer.zero_grad # pyre-ignore optimizer.zero_grad = types.MethodType(dp_zero_grad, optimizer) # pyre-ignore def virtual_step(self): self.privacy_engine.virtual_step() # pyre-ignore optimizer.virtual_step = types.MethodType(virtual_step, optimizer) # create a cross reference for detaching self.optimizer = optimizer # pyre-ignore
def get_renyi_divergence(self): rdp = torch.tensor( tf_privacy.compute_rdp( self.sample_rate, self.noise_multiplier, 1, self.alphas ) ) return rdp
[docs] def get_privacy_spent( self, target_delta: Optional[float] = None ) -> Tuple[float, float]: """ Computes the (epsilon, delta) privacy budget spent so far. This method converts from an (alpha, epsilon)-DP guarantee for all alphas that the ``PrivacyEngine`` was initialized with. It returns the optimal alpha together with the best epsilon. Args: target_delta: The Target delta. If None, it will default to the privacy engine's target delta. Returns: Pair of epsilon and optimal order alpha. """ if target_delta is None: target_delta = self.target_delta rdp = self.get_renyi_divergence() * self.steps return tf_privacy.get_privacy_spent(self.alphas, rdp, target_delta)
[docs] def zero_grad(self): """ Resets clippers status. Clipper keeps internal gradient per sample in the batch in each ``forward`` call of the module, they need to be cleaned before the next round. If these variables are not cleaned the per sample gradients keep being concatenated accross batches. If accumulating gradients is intented behavious, e.g. simulating a large batch, prefer using ``virtual_step()`` function. """ if self.clipper is not None: self.clipper.zero_grad()
[docs] def step(self): """ Takes a step for the privacy engine. Notes: You should not call this method directly. Rather, by attaching your ``PrivacyEngine`` to the optimizer, the ``PrivacyEngine`` would have the optimizer call this method for you. Raises: ValueError: If the last batch of training epoch is greater than others. This ensures the clipper consumed the right amount of gradients. In the last batch of a training epoch, we might get a batch that is smaller than others but we should never get a batch that is too large """ self.steps += 1 self.clipper.clip_and_accumulate() clip_values, batch_size = self.clipper.pre_step() if batch_size > self.batch_size: raise ValueError( f"PrivacyEngine expected a batch of size {self.batch_size} " f"but received a batch of size {batch_size}" ) if batch_size < self.batch_size: warnings.warn( f"PrivacyEngine expected a batch of size {self.batch_size} " f"but the last step received a batch of size {batch_size}. " "This means that the privacy analysis will be a bit more " "pessimistic. You can set `drop_last = True` in your PyTorch " "dataloader to avoid this problem completely" ) params = (p for p in self.module.parameters() if p.requires_grad) for p, clip_value in zip(params, clip_values): noise = self._generate_noise(clip_value, p) if self.loss_reduction == "mean": noise /= batch_size p.grad += noise
[docs] def to(self, device: Union[str, torch.device]): """ Moves the privacy engine to the target device. Args: device : The device on which Pytorch Tensors are allocated. See: Example: This example shows the usage of this method, on how to move the model after instantiating the ``PrivacyEngine``. >>> model = torch.nn.Linear(16, 32) # An example model. Default device is CPU >>> privacy_engine = PrivacyEngine(model, batch_size, sample_size, alphas=range(5,64), noise_multiplier=0.8, max_grad_norm=0.5) >>> device = "cuda:3" # GPU >>> # If we move the model to GPU, we should call the to() method of the privacy engine (next line) >>> Returns: The current ``PrivacyEngine`` """ self.device = device return self
[docs] def virtual_step(self): r""" Takes a virtual step. Virtual batches enable training with arbitrary large batch sizes, while keeping the memory consumption constant. This is beneficial, when training models with larger batch sizes than standard models. Example: Imagine you want to train a model with batch size of 2048, but you can only fit batch size of 128 in your GPU. Then, you can do the following: >>> for i, (X, y) in enumerate(dataloader): >>> logits = model(X) >>> loss = criterion(logits, y) >>> loss.backward() >>> if i % 16 == 15: >>> optimizer.step() # this will call privacy engine's step() >>> optimizer.zero_grad() >>> else: >>> optimizer.virtual_step() # this will call privacy engine's virtual_step() The rough idea of virtual step is as follows: 1. Calling ``loss.backward()`` repeatedly stores the per-sample gradients for all mini-batches. If we call ``loss.backward()`` ``N`` times on mini-batches of size ``B``, then each weight's ``.grad_sample`` field will contain ``NxB`` gradients. Then, when calling ``step()``, the privacy engine clips all ``NxB`` gradients and computes the average gradient for an effective batch of size ``NxB``. A call to ``optimizer.zero_grad()`` erases the per-sample gradients. 2. By calling ``virtual_step()`` after ``loss.backward()``,the ``B`` per-sample gradients for this mini-batch are clipped and summed up into a gradient accumulator. The per-sample gradients can then be discarded. After ``N`` iterations (alternating calls to ``loss.backward()`` and ``virtual_step()``), a call to ``step()`` will compute the average gradient for an effective batch of size ``NxB``. The advantage here is that this is memory-efficient: it discards the per-sample gradients after every mini-batch. We can thus handle batches of arbitrary size. """ self.clipper.clip_and_accumulate()
def _generate_noise( self, max_grad_norm: float, reference: nn.parameter.Parameter ) -> torch.Tensor: r""" Generates a tensor of Gaussian noise of the same shape as ``reference``. The generated tensor has zero mean and standard deviation sigma = ``noise_multiplier x max_grad_norm `` Args: max_grad_norm : The maximum norm of the per-sample gradients. reference : The reference, based on which the dimention of the noise tensor will be determined Returns: the generated noise with noise zero and standard deviation of ``noise_multiplier x max_grad_norm `` """ if self.noise_multiplier > 0 and max_grad_norm > 0: return torch.normal( 0, self.noise_multiplier * max_grad_norm, # pyre-fixme[16]: nn.parameter.Parameter has no attribute grad reference.grad.shape, device=self.device, generator=self.random_number_generator, ) return torch.zeros(reference.grad.shape, device=self.device) def _set_seed(self, seed: int): r""" Allows to manually set the seed allowing for a deterministic run. Useful if you want to debug. WARNING: MANUALLY SETTING THE SEED BREAKS THE GUARANTEE OF SECURE RNG. For this reason, this method will raise a ValueError if you had ``secure_rng`` turned on. Args: seed : The **unsecure** seed """ if self.secure_rng: raise ValueError( "Seed was manually set on a ``PrivacyEngine`` with ``secure_rng`` turned on." "This fundamentally breaks secure_rng, and cannot be allowed. " "If you do need reproducibility with a fixed seed, first instantiate the PrivacyEngine " "with ``secure_seed`` turned off." ) self.seed = seed return ( torch.random.manual_seed(self.seed) if self.device.type == "cpu" else torch.cuda.manual_seed(self.seed) )