Source code for opacus.utils.batch_memory_manager

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import math
from typing import List

import numpy as np
from opacus.optimizers import DPOptimizer
from opacus.utils.uniform_sampler import (
    DistributedUniformWithReplacementSampler,
    UniformWithReplacementSampler,
)
from torch.utils.data import BatchSampler, DataLoader, Sampler


[docs]class BatchSplittingSampler(Sampler[List[int]]): """ Samples according to the underlying instance of ``Sampler``, but splits the index sequences into smaller chunks. Used to split large logical batches into physocal batches of a smaller size, while coordinating with DPOptimizer when the logical batch has ended. """ def __init__( self, *, sampler: Sampler[List[int]], max_batch_size: int, optimizer: DPOptimizer, ): """ Args: sampler: Wrapped Sampler instance max_batch_size: Max size of emitted chunk of indices optimizer: optimizer instance to notify when the logical batch is over """ self.sampler = sampler self.max_batch_size = max_batch_size self.optimizer = optimizer def __iter__(self): for batch_idxs in self.sampler: split_idxs = np.array_split( batch_idxs, math.ceil(len(batch_idxs) / self.max_batch_size) ) split_idxs = [s.tolist() for s in split_idxs] for x in split_idxs[:-1]: self.optimizer.signal_skip_step(do_skip=True) yield x self.optimizer.signal_skip_step(do_skip=False) yield split_idxs[-1] def __len__(self): if isinstance(self.sampler, BatchSampler): return int( len(self.sampler) * (self.sampler.batch_size / self.max_batch_size) ) elif isinstance(self.sampler, UniformWithReplacementSampler) or isinstance( self.sampler, DistributedUniformWithReplacementSampler ): expected_batch_size = self.sampler.sample_rate * self.sampler.num_samples return int(len(self.sampler) * (expected_batch_size / self.max_batch_size)) return len(self.sampler)
[docs]def wrap_data_loader( *, data_loader: DataLoader, max_batch_size: int, optimizer: DPOptimizer ): """ Replaces batch_sampler in the input data loader with ``BatchSplittingSampler`` Args: data_loader: Wrapper DataLoader max_batch_size: max physical batch size we want to emit optimizer: DPOptimizer instance used for training Returns: New DataLoader instance with batch_sampler wrapped in ``BatchSplittingSampler`` """ return DataLoader( dataset=data_loader.dataset, batch_sampler=BatchSplittingSampler( sampler=data_loader.batch_sampler, max_batch_size=max_batch_size, optimizer=optimizer, ), num_workers=data_loader.num_workers, collate_fn=data_loader.collate_fn, pin_memory=data_loader.pin_memory, timeout=data_loader.timeout, worker_init_fn=data_loader.worker_init_fn, multiprocessing_context=data_loader.multiprocessing_context, generator=data_loader.generator, prefetch_factor=data_loader.prefetch_factor, persistent_workers=data_loader.persistent_workers, )
[docs]class BatchMemoryManager(object): """ Context manager to manage memory consumption during training. Allows setting hard limit on the physical batch size as a just one line code change. Can be used both for simulating large logical batches with limited memory and for safeguarding against occasinal large batches produced by :class:`~opacus.utils.uniform_sampler.UniformWithReplacementSampler`. Note that it doesn't modify the input DataLoader, you'd need to use new DataLoader returned by the context manager. BatchSplittingSampler will split large logical batches into smaller sub-batches with certain maximum size. On every step optimzer will check if the batch was the last physical batch comprising a logical one, and will change behaviour accordignly. If it was not the last, ``optimizer.step()`` will only clip per sample gradients and sum them into ``p.summed_grad`.` ``optimizer.zero_grad()`` will clear ``p.grad_sample``, but will leave ``p.grad`` and ``p.summed_grad`` If the batch was the last one of the current logical batch, then ``optimizer.step()`` and ``optimizer.zero_grad()`` will behave normally. Example: >>> # Assuming you've initialized you objects and passed them to PrivacyEngine. >>> # For this example we assume data_loader is initalized with batch_size=4 >>> model, optimizer, data_loader = _init_private_training() >>> criterion = nn.CrossEntropyLoss() >>> with BatchMemoryManager( ... data_loader=data_loader, max_physical_batch_size=2, optimizer=optimizer ... ) as new_data_loader: ... for data, label in new_data_loader: ... assert len(data) <= 2 # physical batch is no more than 2 ... output = model(data) ... loss = criterion(output, label) ... loss.backward() ... # optimizer won't actually make a step unless logical batch is over ... optimizer.step() ... # optimizer won't actually clear gradients unless logical batch is over ... optimizer.zero_grad() """ def __init__( self, *, data_loader: DataLoader, max_physical_batch_size: int, optimizer: DPOptimizer, ): self.data_loader = data_loader self.optimizer = optimizer self.max_physical_batch_size = max_physical_batch_size def __enter__(self): return wrap_data_loader( data_loader=self.data_loader, max_batch_size=self.max_physical_batch_size, optimizer=self.optimizer, ) def __exit__(self, type, value, traceback): pass