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|
- # Copyright (c) 2017-present, Facebook, Inc.
- # All rights reserved.
- #
- # This source code is licensed under the license found in the LICENSE file in
- # the root directory of this source tree. An additional grant of patent rights
- # can be found in the PATENTS file in the same directory.
- """
- Train a network across multiple GPUs.
- """
- from collections import defaultdict, OrderedDict
- from itertools import chain
- import torch
- from fairseq import distributed_utils, optim, utils
- from fairseq.meters import AverageMeter, TimeMeter
- from fairseq.optim import lr_scheduler
- class Trainer(object):
- """Main class for data parallel training.
- This class supports data parallel training, where multiple workers each
- have a full model replica and gradients are accumulated synchronously via
- torch.distributed.all_reduce.
- """
- def __init__(self, args, model, criterion):
- if not torch.cuda.is_available():
- raise NotImplementedError('Training on CPU is not supported')
- self.args = args
- # copy model and criterion to current device
- self.model = model.cuda()
- self.criterion = criterion.cuda()
- # initialize optimizer and LR scheduler
- self._build_optimizer()
- # initialize meters
- self.meters = OrderedDict()
- self.meters['train_loss'] = AverageMeter()
- self.meters['train_nll_loss'] = AverageMeter()
- self.meters['valid_loss'] = AverageMeter()
- self.meters['valid_nll_loss'] = AverageMeter()
- self.meters['wps'] = TimeMeter() # words per second
- self.meters['ups'] = TimeMeter() # updates per second
- self.meters['wpb'] = AverageMeter() # words per batch
- self.meters['bsz'] = AverageMeter() # sentences per batch
- self.meters['gnorm'] = AverageMeter() # gradient norm
- self.meters['clip'] = AverageMeter() # % of updates clipped
- self.meters['oom'] = AverageMeter() # out of memory
- self.meters['wall'] = TimeMeter() # wall time in seconds
- self._buffered_stats = defaultdict(lambda: [])
- self._flat_grads = None
- self._num_updates = 0
- self._optim_history = None
- def _build_optimizer(self):
- self.optimizer = optim.build_optimizer(self.args, self.model.parameters())
- self.lr_scheduler = lr_scheduler.build_lr_scheduler(self.args, self.optimizer)
- def save_checkpoint(self, filename, extra_state):
- """Save all training state in a checkpoint file."""
- if distributed_utils.is_master(self.args): # only save one checkpoint
- utils.save_state(filename, self.args, self.model, self.criterion, self.optimizer,
- self.lr_scheduler, self._num_updates, self._optim_history, extra_state)
- def load_checkpoint(self, filename):
- """Load all training state from a checkpoint file."""
- extra_state, self._optim_history, last_optim_state = \
- utils.load_model_state(filename, self.model)
- if last_optim_state is not None:
- # rebuild optimizer after loading model, since params may have changed
- self._build_optimizer()
- # only reload optimizer and lr_scheduler if they match
- last_optim = self._optim_history[-1]
- if last_optim['criterion_name'] == self.criterion.__class__.__name__:
- self.lr_scheduler.load_state_dict(last_optim['lr_scheduler_state'])
- if last_optim['optimizer_name'] == self.optimizer.__class__.__name__:
- self.optimizer.load_state_dict(last_optim_state)
- self._num_updates = last_optim['num_updates']
- return extra_state
- def train_step(self, sample, update_params=True):
- """Do forward, backward and parameter update."""
- sample = self._prepare_sample(sample, volatile=False)
- # forward and backward pass
- loss, sample_size, logging_output, oom_fwd = self._forward(sample)
- oom_bwd = self._backward(loss)
- # buffer stats and logging outputs
- self._buffered_stats['sample_sizes'].append(sample_size)
- self._buffered_stats['logging_outputs'].append(logging_output)
- self._buffered_stats['ooms_fwd'].append(oom_fwd)
- self._buffered_stats['ooms_bwd'].append(oom_bwd)
- # update parameters
- if update_params:
- # gather logging outputs from all replicas
- sample_sizes = self._buffered_stats['sample_sizes']
- logging_outputs = self._buffered_stats['logging_outputs']
- ooms_fwd = self._buffered_stats['ooms_fwd']
- ooms_bwd = self._buffered_stats['ooms_bwd']
- if self.args.distributed_world_size > 1:
- sample_sizes, logging_outputs, ooms_fwd, ooms_bwd = map(
- lambda l: list(chain.from_iterable(l)),
- zip(*distributed_utils.all_gather_list(
- (sample_sizes, logging_outputs, ooms_fwd, ooms_bwd)
- ))
- )
- ooms_fwd = sum(ooms_fwd)
- ooms_bwd = sum(ooms_bwd)
- # aggregate stats and logging outputs
- ntokens = sum(log.get('ntokens', 0) for log in logging_outputs)
- nsentences = sum(log.get('nsentences', 0) for log in logging_outputs)
- agg_logging_output = self.criterion.__class__.aggregate_logging_outputs(logging_outputs)
- grad_denom = self.criterion.__class__.grad_denom(sample_sizes)
- try:
- # all-reduce and rescale gradients, then take an optimization step
- grad_norm = self._all_reduce_and_rescale(grad_denom)
- self._opt()
- # update meters
- self.meters['wps'].update(ntokens)
- self.meters['ups'].update(1.)
- self.meters['wpb'].update(ntokens)
- self.meters['bsz'].update(nsentences)
- if grad_norm is not None:
- self.meters['gnorm'].update(grad_norm)
- self.meters['clip'].update(1. if grad_norm > self.args.clip_norm else 0.)
- self.meters['oom'].update(ooms_fwd + ooms_bwd)
- # update loss meters for training
- if 'loss' in agg_logging_output:
- self.meters['train_loss'].update(agg_logging_output['loss'], grad_denom)
- # criterions can optionally log the NLL loss too
- if 'nll_loss' in agg_logging_output:
- self.meters['train_nll_loss'].update(agg_logging_output['nll_loss'], ntokens)
- except OverflowError as e:
- self.zero_grad()
- print('| WARNING: overflow detected, ' + str(e))
- self.clear_buffered_stats()
- return agg_logging_output
- else:
- return None # buffering updates
- def _forward(self, sample, eval=False):
- # prepare model and optimizer
- if eval:
- self.model.eval()
- else:
- self.model.train()
- loss = None
- sample_size = 0
- logging_output = {
- 'ntokens': sample['ntokens'] if sample is not None else 0,
- 'nsentences': sample['target'].size(0) if sample is not None else 0,
- }
- oom = 0
- if sample is not None:
- try:
- with utils.maybe_no_grad(eval):
- # calculate loss and sample size
- loss, sample_size, logging_output_ = self.criterion(self.model, sample)
- logging_output.update(logging_output_)
- except RuntimeError as e:
- if not eval and 'out of memory' in str(e):
- print('| WARNING: ran out of memory, skipping batch')
- oom = 1
- loss = None
- else:
- raise e
- return loss, sample_size, logging_output, oom
- def _backward(self, loss):
- oom = 0
- if loss is not None:
- try:
- # backward pass
- loss.backward()
- except RuntimeError as e:
- if 'out of memory' in str(e):
- print('| WARNING: ran out of memory, skipping batch')
- oom = 1
- self.zero_grad()
- else:
- raise e
- return oom
- def _all_reduce_and_rescale(self, grad_denom):
- # flatten grads into a single buffer and all-reduce
- flat_grads = self._flat_grads = self._get_flat_grads(self._flat_grads)
- if self.args.distributed_world_size > 1:
- torch.distributed.all_reduce(flat_grads)
- # rescale and clip gradients
- flat_grads.div_(grad_denom)
- grad_norm = utils.clip_grad_norm_(flat_grads, self.args.clip_norm)
- # copy grads back into model parameters
- self._set_flat_grads(flat_grads)
- return grad_norm
- def _get_grads(self):
- grads = []
- for name, p in self.model.named_parameters():
- if not p.requires_grad:
- continue
- if p.grad is None:
- raise RuntimeError('Model parameter did not receive gradient: ' + name + '. '
- 'Use the param in the forward pass or set requires_grad=False')
- grads.append(p.grad.data)
- return grads
- def _get_flat_grads(self, out=None):
- grads = self._get_grads()
- if out is None:
- grads_size = sum(g.numel() for g in grads)
- out = grads[0].new(grads_size).zero_()
- offset = 0
- for g in grads:
- numel = g.numel()
- out[offset:offset+numel].copy_(g.view(-1))
- offset += numel
- return out[:offset]
- def _set_flat_grads(self, new_grads):
- grads = self._get_grads()
- offset = 0
- for g in grads:
- numel = g.numel()
- g.copy_(new_grads[offset:offset+numel].view_as(g))
- offset += numel
- def _opt(self):
- # take an optimization step
- self.optimizer.step()
- self.zero_grad()
- self._num_updates += 1
- # update learning rate
- self.lr_scheduler.step_update(self._num_updates)
- def valid_step(self, sample):
- """Do forward pass in evaluation mode."""
- sample = self._prepare_sample(sample, volatile=True)
- # forward pass
- _loss, sample_size, logging_output, oom_fwd = self._forward(sample, eval=True)
- assert not oom_fwd, 'Ran out of memory during validation'
- # gather logging outputs from all GPUs
- if self.args.distributed_world_size > 1:
- sample_sizes, logging_outputs = zip(*distributed_utils.all_gather_list(
- (sample_size, logging_output)
- ))
- else:
- sample_sizes = [sample_size]
- logging_outputs = [logging_output]
- # aggregate stats and logging outputs
- ntokens = sum(log.get('ntokens', 0) for log in logging_outputs)
- grad_denom = self.criterion.__class__.grad_denom(sample_sizes)
- agg_logging_output = self.criterion.__class__.aggregate_logging_outputs(logging_outputs)
- # update loss meters for validation
- if 'loss' in agg_logging_output:
- self.meters['valid_loss'].update(agg_logging_output['loss'], grad_denom)
- # criterions can optionally log the NLL loss too
- if 'nll_loss' in agg_logging_output:
- self.meters['valid_nll_loss'].update(agg_logging_output['nll_loss'], ntokens)
- return agg_logging_output
- def dummy_train_step(self, dummy_batch):
- """Dummy training step for warming caching allocator."""
- self.train_step(dummy_batch, update_params=False)
- self.zero_grad()
- self.clear_buffered_stats()
- def zero_grad(self):
- self.optimizer.zero_grad()
- def clear_buffered_stats(self):
- self._buffered_stats.clear()
- def lr_step(self, epoch, val_loss=None):
- """Adjust the learning rate based on the validation loss."""
- return self.lr_scheduler.step(epoch, val_loss)
- def get_lr(self):
- """Get the current learning rate."""
- return self.optimizer.get_lr()
- def get_model(self):
- """Get the model replica."""
- return self.model
- def get_meter(self, name):
- """Get a specific meter by name."""
- if name not in self.meters:
- return None
- return self.meters[name]
- def get_num_updates(self):
- """Get the number of parameters updates."""
- return self._num_updates
- def _prepare_sample(self, sample, volatile):
- if sample is None or len(sample) == 0:
- return None
- return utils.make_variable(sample, volatile=volatile, cuda=True)
|