Merge pull request #286 from alibaba-damo-academy/dev_ncj

jmwang66

2023-03-24 78a3b75cc094a785875d8d257498b3c7438ff7ac

Merge pull request #286 from alibaba-damo-academy/dev_ncj

Dev ncj

 funasr/tasks/asr.py

@@ -412,6 +412,12 @@
            default="13_15",
            help="The range of noise decibel level.",
        )
        parser.add_argument(
            "--batch_interval",
            type=int,
            default=10000,
            help="The batch interval for saving model.",
        )

        for class_choices in cls.class_choices_list:
            # Append --<name> and --<name>_conf.

 funasr/train/trainer.py

@@ -94,7 +94,7 @@
    wandb_model_log_interval: int
    use_pai: bool
    oss_bucket: Union[oss2.Bucket, None]

    batch_interval: int

class Trainer:
    """Trainer having a optimizer.
@@ -186,7 +186,10 @@
                logging.warning("No keep_nbest_models is given. Change to [1]")
                trainer_options.keep_nbest_models = [1]
            keep_nbest_models = trainer_options.keep_nbest_models

     
        #assert batch_interval is set and >0
        assert trainer_options.batch_interval > 0
 
        output_dir = Path(trainer_options.output_dir)
        reporter = Reporter()
        if trainer_options.use_amp:
@@ -560,13 +563,30 @@
        # [For distributed] Because iteration counts are not always equals between
        # processes, send stop-flag to the other processes if iterator is finished
        iterator_stop = torch.tensor(0).to("cuda" if ngpu > 0 else "cpu")

        
        #get the rank
        rank = distributed_option.dist_rank
        #get the num batch updates
        num_batch_updates = 0
        #ouput dir
        output_dir = Path(options.output_dir)
        #batch interval
        batch_interval = options.batch_interval       
        assert batch_interval > 0
 
        start_time = time.perf_counter()
        for iiter, (_, batch) in enumerate(
            reporter.measure_iter_time(iterator, "iter_time"), 1
        ):
            assert isinstance(batch, dict), type(batch)

        
            if rank == 0 and hasattr(model.module, "num_updates"):
                num_batch_updates = model.module.get_num_updates()
                if (num_batch_updates%batch_interval == 0) and (options.oss_bucket is not None) and options.use_pai:
                    buffer = BytesIO()
                    torch.save(model.state_dict(), buffer)
                    options.oss_bucket.put_object(os.path.join(output_dir, f"{num_batch_updates}batch.pth"), buffer.getvalue())
 
            if distributed:
                torch.distributed.all_reduce(iterator_stop, ReduceOp.SUM)
                if iterator_stop > 0:
@@ -811,4 +831,4 @@
        else:
            if distributed:
                iterator_stop.fill_(1)
                torch.distributed.all_reduce(iterator_stop, ReduceOp.SUM)
                torch.distributed.all_reduce(iterator_stop, ReduceOp.SUM)