""" This is the loadable seq2seq trainer library that is in charge of training details, loss compute, and statistics. See train.py 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 train.py(one of the users of this library) for the strategy things we do. """ import time import sys import torch import traceback import onmt.utils from onmt.utils.loss import LossCompute from onmt.utils.logging import logger from onmt.utils.scoring_utils import ScoringPreparator from onmt.scorers import get_scorers_cls, build_scorers def build_trainer(opt, device_id, model, vocabs, optim, model_saver=None): """ Simplify `Trainer` creation based on user `opt`s* Args: 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" model_saver(:obj:`onmt.models.ModelSaverBase`): the utility object used to save the model """ train_loss = LossCompute.from_opts(opt, model, vocabs["tgt"]) valid_loss = LossCompute.from_opts(opt, model, vocabs["tgt"], train=False) scoring_preparator = ScoringPreparator(vocabs=vocabs, opt=opt) validset_transforms = opt.data.get("valid", {}).get("transforms", None) if validset_transforms: scoring_preparator.warm_up(validset_transforms) scorers_cls = get_scorers_cls(opt.valid_metrics) valid_scorers = build_scorers(opt, scorers_cls) trunc_size = opt.truncated_decoder # Badly named... norm_method = opt.normalization accum_count = opt.accum_count accum_steps = opt.accum_steps n_gpu = opt.world_size parallel_mode = opt.parallel_mode average_decay = opt.average_decay average_every = opt.average_every dropout = opt.dropout attention_dropout = opt.attention_dropout dropout_steps = opt.dropout_steps zero_out_prompt_loss = opt.zero_out_prompt_loss if device_id >= 0: gpu_rank = opt.gpu_ranks[device_id] else: gpu_rank = -1 n_gpu = 0 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 = Trainer( model, train_loss, valid_loss, scoring_preparator, valid_scorers, optim, trunc_size, norm_method, accum_count, accum_steps, n_gpu, gpu_rank, parallel_mode, report_manager, with_align=True if opt.lambda_align > 0 else False, model_saver=model_saver, average_decay=average_decay, average_every=average_every, model_dtype=opt.model_dtype, earlystopper=earlystopper, dropout=dropout, attention_dropout=attention_dropout, dropout_steps=dropout_steps, zero_out_prompt_loss=zero_out_prompt_loss, ) return trainer class Trainer(object): """Class that controls the training process. Args: model(:py:class:`onmt.models.model.NMTModel`): model to train train_loss(:obj:`onmt.utils.loss.LossComputeBase`): training loss computation valid_loss(:obj:`onmt.utils.loss.LossComputeBase`): training loss computation scoring_preparator(:obj:`onmt.translate.utils.ScoringPreparator`): preparator for the calculation of metrics via the _eval_handler method valid_scorers (dict): keeps in memory the current values of the validation metrics optim(:obj:`onmt.utils.optimizers.Optimizer`): the optimizer responsible for update trunc_size(int): length of truncated back propagation through time accum_count(list): accumulate gradients this many times. accum_steps(list): steps for accum gradients changes. n_gpu (int): number of gpu. gpu_rank (int): ordinal rank of the gpu in the list. report_manager(:obj:`onmt.utils.ReportMgrBase`): the object that creates reports, or None with_align (bool): whether to jointly lear alignment (Transformer) model_saver(:obj:`onmt.models.ModelSaverBase`): the saver is used to save a checkpoint. Thus nothing will be saved if this parameter is None. average_decay (float): cf opt.average_decay average_every (int): average model every x steps. model_dtype (str): fp32 or fp16. earlystopper (:obj:`onmt.utils.EarlyStopping`): add early stopping mecanism dropout (float): dropout value in RNN or FF layers. attention_dropout (float): dropaout in attention layers. dropout_steps (list): dropout values scheduling in steps. zero_out_prompt_loss (bool): whether to zero-out the prompt loss (mostly for LLM finetuning).""" def __init__( self, model, train_loss, valid_loss, scoring_preparator, valid_scorers, optim, trunc_size=0, norm_method="sents", accum_count=[1], accum_steps=[0], n_gpu=1, gpu_rank=1, parallel_mode="data_parallel", report_manager=None, with_align=False, model_saver=None, average_decay=0, average_every=1, model_dtype="fp32", earlystopper=None, dropout=[0.3], attention_dropout=[0.1], dropout_steps=[0], zero_out_prompt_loss=False, ): # Basic attributes. self.model = model self.train_loss = train_loss self.valid_loss = valid_loss self.scoring_preparator = scoring_preparator self.valid_scorers = valid_scorers self.optim = optim self.trunc_size = trunc_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.parallel_mode = parallel_mode self.report_manager = report_manager self.with_align = with_align 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.attention_dropout = attention_dropout self.dropout_steps = dropout_steps self.zero_out_prompt_loss = zero_out_prompt_loss for i in range(len(self.accum_count_l)): assert self.accum_count_l[i] > 0 # Set model in training mode. self.model.train() def _eval_handler(self, scorer, preds, texts_ref): """Trigger metrics calculations Args: scorer (:obj:``onmt.scorer.Scorer``): scorer. preds, texts_ref: outputs of the scorer's `translate` method. Returns: The metric calculated by the scorer.""" return scorer.compute_score(preds, texts_ref) 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], self.attention_dropout[i]) logger.info( "Updated dropout/attn dropout to %f %f at step %d" % (self.dropout[i], self.attention_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() normalization -= len(batch["tgt"]) # don't count for EOS else: normalization += len(batch["tgt"]) 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 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: An iterator 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: :obj:``nmt.Statistics``: training loss statistics""" if valid_iter is None: logger.info("Start training loop without validation...") valid_stats = None else: logger.info( "Start training loop and validate every %d steps...", valid_steps ) logger.info("Scoring with: {}".format(self.scoring_preparator.transform)) total_stats = onmt.utils.Statistics() report_stats = onmt.utils.Statistics() self._start_report_manager(start_time=total_stats.start_time) # Let's clean the GPUs before training loop torch.cuda.empty_cache() 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.n_gpu > 1 and self.parallel_mode == "data_parallel": 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: valid_stats = self.validate( valid_iter, moving_average=self.moving_average ) if step % valid_steps == 0 and self.gpu_rank <= 0: self._report_step( self.optim.learning_rate(), step, valid_stats=valid_stats, train_stats=total_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(): logger.info("earlystopper has_stopped!") break if self.model_saver is not None and ( save_checkpoint_steps != 0 and step % save_checkpoint_steps == 0 ): self.model_saver.save(step, moving_average=self.moving_average) if train_steps > 0 and step >= train_steps: break if self.model_saver is not None: self.model_saver.save(step, moving_average=self.moving_average) return total_stats def validate(self, valid_iter, moving_average=None): """Validate model. Args: 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(param.data) param.data = ( avg.data.half() if param.dtype == torch.float16 else avg.data ) # Set model in validating mode. valid_model.eval() # raw_srcs = [] # raw_refs = [] with torch.no_grad(): stats = onmt.utils.Statistics() start = time.time() for batch in valid_iter: src = batch["src"] src_len = batch["srclen"] tgt = batch["tgt"] with torch.cuda.amp.autocast(enabled=self.optim.amp): # F-prop through the model. model_out, attns = valid_model( src, tgt, src_len, with_align=self.with_align ) # Compute loss. _, batch_stats = self.valid_loss(batch, model_out, attns) stats.update(batch_stats) logger.info( """valid stats calculation took: {} s.""".format( time.time() - start ) ) # Compute validation metrics (at batch.dataset level) if len(self.valid_scorers) > 0: computed_metrics = {} start = time.time() preds, texts_ref = self.scoring_preparator.translate( model=self.model, gpu_rank=self.gpu_rank, step=self.optim.training_step, ) logger.info( """The translation of the valid dataset for dynamic scoring took : {} s.""".format( time.time() - start ) ) for i, metric in enumerate(self.valid_scorers): logger.info("UPDATING VALIDATION {}".format(metric)) self.valid_scorers[metric]["value"] = self._eval_handler( scorer=self.valid_scorers[metric]["scorer"], preds=preds, texts_ref=texts_ref, ) computed_metrics[metric] = self.valid_scorers[metric]["value"] logger.info( "validation {}: {}".format( metric, self.valid_scorers[metric]["value"] ) ) # Compute stats metric_stats = onmt.utils.Statistics( 0, 0, 0, 0, 0, computed_metrics ) # Update statistics. stats.update(metric_stats) if moving_average: for param_data, param in zip(model_params_data, self.model.parameters()): param.data = param_data # Set model back to training mode. valid_model.train() return stats def _gradient_accumulation( self, true_batches, normalization, total_stats, report_stats ): """Function that iterates over big batches = ``true_batches`` Perform a backward on the loss of each sub_batch and finally update the params at the end of the big batch.""" if self.accum_count > 1: self.optim.zero_grad(set_to_none=True) for k, batch in enumerate(true_batches): target_size = batch["tgt"].size(1) # Truncated BPTT: reminder not compatible with accum > 1 if self.trunc_size: trunc_size = self.trunc_size else: trunc_size = target_size src = batch["src"] src_len = batch["srclen"] if src_len is not None: report_stats.n_src_words += src_len.sum().item() total_stats.n_src_words += src_len.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(set_to_none=True) try: with torch.cuda.amp.autocast(enabled=self.optim.amp): model_out, attns = self.model( src, tgt, src_len, bptt=bptt, with_align=self.with_align ) bptt = True # 3. Compute loss. if self.zero_out_prompt_loss: # The loss of the prompt will be set to zero. batch = self.train_loss.ignore_prompt(batch) loss, batch_stats = self.train_loss( batch, model_out, attns, trunc_start=j, trunc_size=trunc_size, ) if loss is not None: loss /= normalization self.optim.backward(loss) total_stats.update(batch_stats) report_stats.update(batch_stats) except Exception as exc: trace_content = traceback.format_exc() if "CUDA out of memory" in trace_content: logger.info( "Step %d, cuda OOM - batch removed", self.optim.training_step, ) torch.cuda.empty_cache() if self.n_gpu > 1 and self.parallel_mode == "tensor_parallel": torch.distributed.destroy_process_group() sys.exit() else: traceback.print_exc() raise exc # If truncated, don't backprop fully. if self.model.decoder.state != {}: self.model.decoder.detach_state() # in case of multi step gradient accumulation, # update only after accum batches if self.n_gpu > 1 and self.parallel_mode == "data_parallel": grads = [ p.grad.data 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(self.n_gpu) ) 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_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, None if self.earlystopper is None else self.earlystopper.current_tolerance, report_stats, multigpu=self.n_gpu > 1 and self.parallel_mode == "data_parallel", ) def _report_step(self, learning_rate, step, valid_stats=None, train_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, None if self.earlystopper is None else self.earlystopper.current_tolerance, step, valid_stats=valid_stats, train_stats=train_stats, )