text stringlengths 1 93.6k |
|---|
self.ae_criterion = ae_criterion.to(self.args.device)
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# initialize meters
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self.meters = OrderedDict()
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self.meters['train_loss'] = AverageMeter()
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self.meters['train_nll_loss'] = AverageMeter()
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self.meters['valid_loss'] = AverageMeter()
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self.meters['valid_nll_loss'] = AverageMeter()
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self.meters['wps'] = TimeMeter() # words per second
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self.meters['ups'] = TimeMeter() # updates per second
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self.meters['wpb'] = AverageMeter() # words per batch
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self.meters['bsz'] = AverageMeter() # sentences per batch
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self.meters['gnorm'] = AverageMeter() # gradient norm
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self.meters['clip'] = AverageMeter() # % of updates clipped
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self.meters['oom'] = AverageMeter() # out of memory
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self.meters['wall'] = TimeMeter() # wall time in seconds
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self._buffered_stats = defaultdict(lambda: [])
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self._flat_grads = None
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self._num_updates = 0
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self._optim_history = None
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self._optimizer = None
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if optimizer is not None:
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self._optimizer = optimizer
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self.total_loss = 0.0
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self.train_score = 0.0
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self.total_norm = 0.0
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self.count_norm = 0.0
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@property
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def optimizer(self):
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if self._optimizer is None:
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self._build_optimizer()
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return self._optimizer
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def _build_optimizer(self):
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# self._optimizer = optim.build_optimizer(self.args, self.model.parameters())
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# self._optimizer =
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# self.lr_scheduler = lr_scheduler.build_lr_scheduler(self.args, self._optimizer)
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pass
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def train_step(self, sample, update_params=True):
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"""Do forward, backward and parameter update."""
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# Set seed based on args.seed and the update number so that we get
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# reproducible results when resuming from checkpoints
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# seed = self.args.seed + self.get_num_updates()
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# torch.manual_seed(seed)
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# torch.cuda.manual_seed(seed)
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# forward and backward pass
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sample = self._prepare_sample(sample)
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loss, sample_size, oom_fwd, batch_score = self._forward(sample)
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oom_bwd = self._backward(loss)
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# buffer stats and logging outputs
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# self._buffered_stats['sample_sizes'].append(sample_size)
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self._buffered_stats['sample_sizes'].append(1)
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self._buffered_stats['ooms_fwd'].append(oom_fwd)
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self._buffered_stats['ooms_bwd'].append(oom_bwd)
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# update parameters
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if update_params:
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# gather logging outputs from all replicas
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sample_sizes = self._buffered_stats['sample_sizes']
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ooms_fwd = self._buffered_stats['ooms_fwd']
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ooms_bwd = self._buffered_stats['ooms_bwd']
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ooms_fwd = sum(ooms_fwd)
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ooms_bwd = sum(ooms_bwd)
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# aggregate stats and logging outputs
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grad_denom = sum(sample_sizes)
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grad_norm = 0
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try:
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# all-reduce and rescale gradients, then take an optimization step
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grad_norm = self._all_reduce_and_rescale(grad_denom)
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self._opt()
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# update meters
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if grad_norm is not None:
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self.meters['gnorm'].update(grad_norm)
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self.meters['clip'].update(1. if grad_norm > self.args.clip_norm else 0.)
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self.meters['oom'].update(ooms_fwd + ooms_bwd)
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except OverflowError as e:
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self.zero_grad()
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print('| WARNING: overflow detected, ' + str(e))
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self.clear_buffered_stats()
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return loss, grad_norm, batch_score
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else:
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return None # buffering updates
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def _forward(self, sample, eval=False):
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# prepare model and optimizer
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if eval:
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self.model.eval()
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else:
|
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