cc @muellerzr

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Thank you for the work on this, @shijie-wu!

It may seem like a little PR to some, but this would be a huge step to bring transformers closer to parity with projects like gpt-neox for large-scale training.

Gentle ping @shijie-wu :)

Found that self.accelerator.clip_grad_norm_ will return None if we are using DeepSpeed with Trainer. In DeepSpeed we should use model.get_global_grad_norm() to get grad_norm:

_grad_norm = self.accelerator.clip_grad_norm_(
    model.parameters(),
    args.max_grad_norm,
)
if self.accelerator.distributed_type == DistributedType.DEEPSPEED:
    grad_norm = model.get_global_grad_norm()
else:
    grad_norm = _grad_norm.item() if _grad_norm is not None else None

sorry for the delay! PTAL @muellerzr @mjbommar

Gentle ping @muellerzr @mjbommar :)

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cc @amyeroberts for final review :)

not sure if this was mentioned anywhere, but this PR breaks training checkpoint saving because

1. the grad norm is added to TrainerState.log_history as a tensor
2. TrainerState.save_to_json attempts to jsonify that tensor, which naturally errors out as Tensors can't be jsonified

my fix for this was to patch save_to_json to the following:

    def save_to_json(self, json_path: str):
        """Save the content of this instance in JSON format inside `json_path`."""
        selfd = dataclasses.asdict(self)
        for d in selfd['log_history']:
            if 'grad_norm' in d: d['grad_norm'] = d['grad_norm'].item()
        json_string = json.dumps(selfd, indent=2, sort_keys=True) + "\n"
        with open(json_path, "w", encoding="utf-8") as f: f.write(json_string)

but this is probably not the best approach to doing this

@152334H it does convert grad_norm to number before passing it into _maybe_log_save_evaluate

same for deepspeed

https://github.com/microsoft/DeepSpeed/blob/bcc617a0009dd27b4e144de59979bd7770eaf57c/deepspeed/runtime/engine.py#L448-L458

what backend were you using?

Deepspeed zero2.

Seems likely that the type hint is not universally correct. The value returned in scaled_global_norm for zero2 is a tensor scalar. That value subsequently assigns _global_grad_norm without any .item().

not sure if this was mentioned anywhere, but this PR breaks training checkpoint saving because

1. the grad norm is added to TrainerState.log_history as a tensor
2. TrainerState.save_to_json attempts to jsonify that tensor, which naturally errors out as Tensors can't be jsonified

I'm facing the same issue with deepspeed stage 1, can you please fix this. I need to use v4.38.0 for a different fix?

Can you all try installing with pip install git+https://github.com/huggingface/transformers@muellerzr-deepspeed-item?

This PR may have fixed this too as well: #29444

Can you all try installing with pip install git+https://github.com/huggingface/transformers@muellerzr-deepspeed-item?

That fixed it for me! Thanks a lot

same error here:

11%|████████████████████████▏ | 800/7050 [4:07:59<32:10:41, 18.53s/it]Trainer is attempting to log a value of "2.204314947128296" of type <class 'torch.Tensor'> for key "train/grad_norm" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.

Please tell me how to fix it?