| | |
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| | | wandb_model_log_interval: int |
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| | | use_pai: bool |
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| | | oss_bucket: Union[oss2.Bucket, None] |
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| | | |
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| | | batch_interval: int |
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| | | |
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| | | class Trainer: |
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| | | """Trainer having a optimizer. |
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| | |
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| | | logging.warning("No keep_nbest_models is given. Change to [1]") |
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| | | trainer_options.keep_nbest_models = [1] |
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| | | keep_nbest_models = trainer_options.keep_nbest_models |
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| | | |
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| | | |
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| | | #assert batch_interval is set and >0 |
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| | | assert trainer_options.batch_interval > 0 |
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| | | |
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| | | output_dir = Path(trainer_options.output_dir) |
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| | | reporter = Reporter() |
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| | | if trainer_options.use_amp: |
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| | |
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| | | # [For distributed] Because iteration counts are not always equals between |
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| | | # processes, send stop-flag to the other processes if iterator is finished |
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| | | iterator_stop = torch.tensor(0).to("cuda" if ngpu > 0 else "cpu") |
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| | | |
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| | | |
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| | | #get the rank |
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| | | rank = distributed_option.dist_rank |
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| | | #get the num batch updates |
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| | | num_batch_updates = 0 |
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| | | #ouput dir |
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| | | output_dir = Path(options.output_dir) |
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| | | #batch interval |
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| | | batch_interval = options.batch_interval |
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| | | |
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| | | start_time = time.perf_counter() |
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| | | for iiter, (_, batch) in enumerate( |
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| | | reporter.measure_iter_time(iterator, "iter_time"), 1 |
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| | | ): |
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| | | assert isinstance(batch, dict), type(batch) |
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| | | |
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| | | if batch_interval > 0 and (not distributed_option.distributed or rank == 0): |
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| | | if hasattr(model, "num_updates") or (hasattr(model, "module") and hasattr(model.module, "num_updates")): |
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| | | num_batch_updates = model.get_num_updates() if hasattr(model,"num_updates") else model.module.get_num_updates() |
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| | | if num_batch_updates % batch_interval == 0: |
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| | | if options.use_pai and options.oss_bucket is not None: |
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| | | buffer = BytesIO() |
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| | | torch.save(model.state_dict(), buffer) |
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| | | options.oss_bucket.put_object(os.path.join(output_dir, f"{num_batch_updates}step.pb"), buffer.getvalue()) |
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| | | else: |
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| | | torch.save(model.state_dict(), os.path.join(output_dir, f"{num_batch_updates}step.pb")) |
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| | | |
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| | | if distributed: |
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| | | torch.distributed.all_reduce(iterator_stop, ReduceOp.SUM) |
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| | |
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| | | else: |
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| | | if distributed: |
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| | | iterator_stop.fill_(1) |
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| | | torch.distributed.all_reduce(iterator_stop, ReduceOp.SUM) |
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| | | torch.distributed.all_reduce(iterator_stop, ReduceOp.SUM) |
|---|
