| | |
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| | | use_pai: bool |
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| | | oss_bucket: Union[oss2.Bucket, None] |
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| | | batch_interval: int |
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| | | bias_grad_times: float |
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| | | |
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| | | class Trainer: |
|---|
| | | """Trainer having a optimizer. |
|---|
| | |
|---|
| | | 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 |
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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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| | | output_dir = Path(trainer_options.output_dir) |
|---|
| | | reporter = Reporter() |
|---|
| | |
|---|
| | | no_forward_run = options.no_forward_run |
|---|
| | | ngpu = options.ngpu |
|---|
| | | use_wandb = options.use_wandb |
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| | | bias_grad_times = options.bias_grad_times |
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| | | distributed = distributed_option.distributed |
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| | | |
|---|
| | | if bias_grad_times != 1.0: |
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| | | logging.warning("Using bias_grad_times: {} for gradient scaling".format(bias_grad_times)) |
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| | | if log_interval is None: |
|---|
| | | try: |
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| | | log_interval = max(len(iterator) // 20, 10) |
|---|
| | |
|---|
| | | #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 |
|---|
| | | assert batch_interval > 0 |
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| | | batch_interval = options.batch_interval |
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| | | |
|---|
| | | start_time = time.perf_counter() |
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| | | for iiter, (_, batch) in enumerate( |
|---|
| | |
|---|
| | | ): |
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| | | assert isinstance(batch, dict), type(batch) |
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| | | |
|---|
| | | if rank == 0: |
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| | | if batch_interval > 0 and (not distributed_option.distributed or rank == 0): |
|---|
| | | if hasattr(model, "num_updates") or (hasattr(model, "module") and hasattr(model.module, "num_updates")): |
|---|
| | | num_batch_updates = model.get_num_updates() if hasattr(model,"num_updates") else 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() |
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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}batch.pth"), buffer.getvalue()) |
|---|
| | | |
|---|
| | | if num_batch_updates % batch_interval == 0: |
|---|
| | | if options.use_pai and options.oss_bucket is not None: |
|---|
| | | buffer = BytesIO() |
|---|
| | | if hasattr(model, "module"): |
|---|
| | | torch.save(model.module.state_dict(), buffer) |
|---|
| | | else: |
|---|
| | | torch.save(model.state_dict(), buffer) |
|---|
| | | options.oss_bucket.put_object(os.path.join(output_dir, f"{num_batch_updates}step.pb"), buffer.getvalue()) |
|---|
| | | else: |
|---|
| | | if hasattr(model, "module"): |
|---|
| | | torch.save(model.module.state_dict(), os.path.join(output_dir, f"{num_batch_updates}step.pb")) |
|---|
| | | else: |
|---|
| | | torch.save(model.state_dict(), os.path.join(output_dir, f"{num_batch_updates}step.pb")) |
|---|
| | | |
|---|
| | | if distributed: |
|---|
| | | torch.distributed.all_reduce(iterator_stop, ReduceOp.SUM) |
|---|
| | | if iterator_stop > 0: |
|---|
| | |
|---|
| | | scale_factor=0.55, |
|---|
| | | ) |
|---|
| | | |
|---|
| | | # for contextual training |
|---|
| | | if bias_grad_times != 1.0: |
|---|
| | | # contextual related parameter names |
|---|
| | | cr_pnames = ["bias_encoder", "bias_embed", "decoder.bias_decoder", "decoder.bias_output"] |
|---|
| | | for name, param in model.named_parameters(): |
|---|
| | | for cr_pname in cr_pnames: |
|---|
| | | if cr_pname in name: |
|---|
| | | param.grad *= bias_grad_times |
|---|
| | | continue |
|---|
| | | |
|---|
| | | # compute the gradient norm to check if it is normal or not |
|---|
| | | grad_norm = torch.nn.utils.clip_grad_norm_( |
|---|
| | | model.parameters(), |
|---|
