Source code for opacus.distributed

#!/usr/bin/env python3
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import torch
import torch.nn as nn


[docs]def average_gradients(model: nn.Module) -> None: """ For all parameters of a given ``model`` averages gradients over all workers Args: model: model Returns: None """ world_size = torch.distributed.get_world_size() for param in model.parameters(): if not param.requires_grad: continue torch.distributed.all_reduce(param.grad, op=torch.distributed.ReduceOp.SUM) param.grad /= world_size
[docs]class DifferentiallyPrivateDistributedDataParallel(nn.Module): """ Implements distributed data parallelism that is based on ``torch.distributed`` package at the module level. """ def __init__(self, model: nn.Module): super().__init__() # Synchronize the model params = list(model.parameters()) with torch.no_grad(): for p in params: torch.distributed.broadcast(p.data, 0) self.module = model
[docs] def forward(self, *args, **kwargs): return self.module(*args, **kwargs)