Source code for onmt.trainer

    This is the loadable seq2seq trainer library that is
    in charge of training details, loss compute, and statistics.
    See for a use case of this library.

    Note: To make this a general library, we implement *only*
          mechanism things here(i.e. what to do), and leave the strategy
          things to users(i.e. how to do it). Also see of the
          users of this library) for the strategy things we do.

import torch
import traceback

import onmt.utils
from onmt.utils.logging import logger

def build_trainer(opt, device_id, model, fields, optim, model_saver=None):
    Simplify `Trainer` creation based on user `opt`s*

        opt (:obj:`Namespace`): user options (usually from argument parsing)
        model (:obj:`onmt.models.NMTModel`): the model to train
        fields (dict): dict of fields
        optim (:obj:`onmt.utils.Optimizer`): optimizer used during training
        data_type (str): string describing the type of data
            e.g. "text", "img", "audio"
        model_saver(:obj:`onmt.models.ModelSaverBase`): the utility object
            used to save the model

    tgt_field = dict(fields)["tgt"].base_field
    train_loss = onmt.utils.loss.build_loss_compute(model, tgt_field, opt)
    valid_loss = onmt.utils.loss.build_loss_compute(
        model, tgt_field, opt, train=False)

    trunc_size = opt.truncated_decoder  # Badly named...
    shard_size = opt.max_generator_batches if opt.model_dtype == 'fp32' else 0
    norm_method = opt.normalization
    accum_count = opt.accum_count
    accum_steps = opt.accum_steps
    n_gpu = opt.world_size
    average_decay = opt.average_decay
    average_every = opt.average_every
    dropout = opt.dropout
    dropout_steps = opt.dropout_steps
    if device_id >= 0:
        gpu_rank = opt.gpu_ranks[device_id]
        gpu_rank = 0
        n_gpu = 0
    gpu_verbose_level = opt.gpu_verbose_level

    earlystopper = onmt.utils.EarlyStopping(
        opt.early_stopping, scorers=onmt.utils.scorers_from_opts(opt)) \
        if opt.early_stopping > 0 else None

    report_manager = onmt.utils.build_report_manager(opt, gpu_rank)
    trainer = onmt.Trainer(model, train_loss, valid_loss, optim, trunc_size,
                           shard_size, norm_method,
                           accum_count, accum_steps,
                           n_gpu, gpu_rank,
                           gpu_verbose_level, report_manager,
                           model_saver=model_saver if gpu_rank == 0 else None,
    return trainer

[docs]class Trainer(object): """ Class that controls the training process. Args: model(:py:class:`onmt.models.model.NMTModel`): translation model to train train_loss(:obj:`onmt.utils.loss.LossComputeBase`): training loss computation valid_loss(:obj:`onmt.utils.loss.LossComputeBase`): training loss computation optim(:obj:`onmt.utils.optimizers.Optimizer`): the optimizer responsible for update trunc_size(int): length of truncated back propagation through time shard_size(int): compute loss in shards of this size for efficiency data_type(string): type of the source input: [text|img|audio] norm_method(string): normalization methods: [sents|tokens] accum_count(list): accumulate gradients this many times. accum_steps(list): steps for accum gradients changes. report_manager(:obj:`onmt.utils.ReportMgrBase`): the object that creates reports, or None model_saver(:obj:`onmt.models.ModelSaverBase`): the saver is used to save a checkpoint. Thus nothing will be saved if this parameter is None """ def __init__(self, model, train_loss, valid_loss, optim, trunc_size=0, shard_size=32, norm_method="sents", accum_count=[1], accum_steps=[0], n_gpu=1, gpu_rank=1, gpu_verbose_level=0, report_manager=None, model_saver=None, average_decay=0, average_every=1, model_dtype='fp32', earlystopper=None, dropout=[0.3], dropout_steps=[0]): # Basic attributes. self.model = model self.train_loss = train_loss self.valid_loss = valid_loss self.optim = optim self.trunc_size = trunc_size self.shard_size = shard_size self.norm_method = norm_method self.accum_count_l = accum_count self.accum_count = accum_count[0] self.accum_steps = accum_steps self.n_gpu = n_gpu self.gpu_rank = gpu_rank self.gpu_verbose_level = gpu_verbose_level self.report_manager = report_manager self.model_saver = model_saver self.average_decay = average_decay self.moving_average = None self.average_every = average_every self.model_dtype = model_dtype self.earlystopper = earlystopper self.dropout = dropout self.dropout_steps = dropout_steps for i in range(len(self.accum_count_l)): assert self.accum_count_l[i] > 0 if self.accum_count_l[i] > 1: assert self.trunc_size == 0, \ """To enable accumulated gradients, you must disable target sequence truncating.""" # Set model in training mode. self.model.train() def _accum_count(self, step): for i in range(len(self.accum_steps)): if step > self.accum_steps[i]: _accum = self.accum_count_l[i] return _accum def _maybe_update_dropout(self, step): for i in range(len(self.dropout_steps)): if step > 1 and step == self.dropout_steps[i] + 1: self.model.update_dropout(self.dropout[i])"Updated dropout to %f from step %d" % (self.dropout[i], step)) def _accum_batches(self, iterator): batches = [] normalization = 0 self.accum_count = self._accum_count(self.optim.training_step) for batch in iterator: batches.append(batch) if self.norm_method == "tokens": num_tokens = batch.tgt[1:, :, 0].ne( self.train_loss.padding_idx).sum() normalization += num_tokens.item() else: normalization += batch.batch_size if len(batches) == self.accum_count: yield batches, normalization self.accum_count = self._accum_count(self.optim.training_step) batches = [] normalization = 0 if batches: yield batches, normalization def _update_average(self, step): if self.moving_average is None: copy_params = [params.detach().float() for params in self.model.parameters()] self.moving_average = copy_params else: average_decay = max(self.average_decay, 1 - (step + 1)/(step + 10)) for (i, avg), cpt in zip(enumerate(self.moving_average), self.model.parameters()): self.moving_average[i] = \ (1 - average_decay) * avg + \ cpt.detach().float() * average_decay
[docs] def train(self, train_iter, train_steps, save_checkpoint_steps=5000, valid_iter=None, valid_steps=10000): """ The main training loop by iterating over `train_iter` and possibly running validation on `valid_iter`. Args: train_iter: A generator that returns the next training batch. train_steps: Run training for this many iterations. save_checkpoint_steps: Save a checkpoint every this many iterations. valid_iter: A generator that returns the next validation batch. valid_steps: Run evaluation every this many iterations. Returns: The gathered statistics. """ if valid_iter is None:'Start training loop without validation...') else:'Start training loop and validate every %d steps...', valid_steps) total_stats = onmt.utils.Statistics() report_stats = onmt.utils.Statistics() self._start_report_manager(start_time=total_stats.start_time) for i, (batches, normalization) in enumerate( self._accum_batches(train_iter)): step = self.optim.training_step # UPDATE DROPOUT self._maybe_update_dropout(step) if self.gpu_verbose_level > 1:"GpuRank %d: index: %d", self.gpu_rank, i) if self.gpu_verbose_level > 0:"GpuRank %d: reduce_counter: %d \ n_minibatch %d" % (self.gpu_rank, i + 1, len(batches))) if self.n_gpu > 1: normalization = sum(onmt.utils.distributed .all_gather_list (normalization)) self._gradient_accumulation( batches, normalization, total_stats, report_stats) if self.average_decay > 0 and i % self.average_every == 0: self._update_average(step) report_stats = self._maybe_report_training( step, train_steps, self.optim.learning_rate(), report_stats) if valid_iter is not None and step % valid_steps == 0: if self.gpu_verbose_level > 0:'GpuRank %d: validate step %d' % (self.gpu_rank, step)) valid_stats = self.validate( valid_iter, moving_average=self.moving_average) if self.gpu_verbose_level > 0:'GpuRank %d: gather valid stat \ step %d' % (self.gpu_rank, step)) valid_stats = self._maybe_gather_stats(valid_stats) if self.gpu_verbose_level > 0:'GpuRank %d: report stat step %d' % (self.gpu_rank, step)) self._report_step(self.optim.learning_rate(), step, valid_stats=valid_stats) # Run patience mechanism if self.earlystopper is not None: self.earlystopper(valid_stats, step) # If the patience has reached the limit, stop training if self.earlystopper.has_stopped(): break if (self.model_saver is not None and (save_checkpoint_steps != 0 and step % save_checkpoint_steps == 0)):, moving_average=self.moving_average) if train_steps > 0 and step >= train_steps: break if self.model_saver is not None:, moving_average=self.moving_average) return total_stats
[docs] def validate(self, valid_iter, moving_average=None): """ Validate model. valid_iter: validate data iterator Returns: :obj:`nmt.Statistics`: validation loss statistics """ valid_model = self.model if moving_average: # swap model params w/ moving average # (and keep the original parameters) model_params_data = [] for avg, param in zip(self.moving_average, valid_model.parameters()): model_params_data.append( = if self.optim._fp16 == "legacy" \ else # Set model in validating mode. valid_model.eval() with torch.no_grad(): stats = onmt.utils.Statistics() for batch in valid_iter: src, src_lengths = batch.src if isinstance(batch.src, tuple) \ else (batch.src, None) tgt = batch.tgt # F-prop through the model. outputs, attns = valid_model(src, tgt, src_lengths) # Compute loss. _, batch_stats = self.valid_loss(batch, outputs, attns) # Update statistics. stats.update(batch_stats) if moving_average: for param_data, param in zip(model_params_data, self.model.parameters()): = param_data # Set model back to training mode. valid_model.train() return stats
def _gradient_accumulation(self, true_batches, normalization, total_stats, report_stats): if self.accum_count > 1: self.optim.zero_grad() for k, batch in enumerate(true_batches): target_size = batch.tgt.size(0) # Truncated BPTT: reminder not compatible with accum > 1 if self.trunc_size: trunc_size = self.trunc_size else: trunc_size = target_size src, src_lengths = batch.src if isinstance(batch.src, tuple) \ else (batch.src, None) if src_lengths is not None: report_stats.n_src_words += src_lengths.sum().item() tgt_outer = batch.tgt bptt = False for j in range(0, target_size-1, trunc_size): # 1. Create truncated target. tgt = tgt_outer[j: j + trunc_size] # 2. F-prop all but generator. if self.accum_count == 1: self.optim.zero_grad() outputs, attns = self.model(src, tgt, src_lengths, bptt=bptt) bptt = True # 3. Compute loss. try: loss, batch_stats = self.train_loss( batch, outputs, attns, normalization=normalization, shard_size=self.shard_size, trunc_start=j, trunc_size=trunc_size) if loss is not None: self.optim.backward(loss) total_stats.update(batch_stats) report_stats.update(batch_stats) except Exception: traceback.print_exc()"At step %d, we removed a batch - accum %d", self.optim.training_step, k) # 4. Update the parameters and statistics. if self.accum_count == 1: # Multi GPU gradient gather if self.n_gpu > 1: grads = [ for p in self.model.parameters() if p.requires_grad and p.grad is not None] onmt.utils.distributed.all_reduce_and_rescale_tensors( grads, float(1)) self.optim.step() # If truncated, don't backprop fully. # TO CHECK # if dec_state is not None: # dec_state.detach() if self.model.decoder.state is not None: self.model.decoder.detach_state() # in case of multi step gradient accumulation, # update only after accum batches if self.accum_count > 1: if self.n_gpu > 1: grads = [ for p in self.model.parameters() if p.requires_grad and p.grad is not None] onmt.utils.distributed.all_reduce_and_rescale_tensors( grads, float(1)) self.optim.step() def _start_report_manager(self, start_time=None): """ Simple function to start report manager (if any) """ if self.report_manager is not None: if start_time is None: self.report_manager.start() else: self.report_manager.start_time = start_time def _maybe_gather_stats(self, stat): """ Gather statistics in multi-processes cases Args: stat(:obj:onmt.utils.Statistics): a Statistics object to gather or None (it returns None in this case) Returns: stat: the updated (or unchanged) stat object """ if stat is not None and self.n_gpu > 1: return onmt.utils.Statistics.all_gather_stats(stat) return stat def _maybe_report_training(self, step, num_steps, learning_rate, report_stats): """ Simple function to report training stats (if report_manager is set) see `onmt.utils.ReportManagerBase.report_training` for doc """ if self.report_manager is not None: return self.report_manager.report_training( step, num_steps, learning_rate, report_stats, multigpu=self.n_gpu > 1) def _report_step(self, learning_rate, step, train_stats=None, valid_stats=None): """ Simple function to report stats (if report_manager is set) see `onmt.utils.ReportManagerBase.report_step` for doc """ if self.report_manager is not None: return self.report_manager.report_step( learning_rate, step, train_stats=train_stats, valid_stats=valid_stats)