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[AIR] HuggingFaceTrainer&Predictor implementation #23876

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merged 89 commits into from
Apr 29, 2022

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@Yard1 Yard1 commented Apr 12, 2022

Why are these changes needed?

Implements HuggingFaceTrainer & HuggingFacePredictor.

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  • I've run scripts/format.sh to lint the changes in this PR.
  • I've included any doc changes needed for https://docs.ray.io/en/master/.
  • I've made sure the tests are passing. Note that there might be a few flaky tests, see the recent failures at https://flakey-tests.ray.io/
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    • This PR is not tested :(

@Yard1 Yard1 requested review from clarkzinzow and ericl April 27, 2022 21:09
@Yard1 Yard1 removed the @author-action-required The PR author is responsible for the next step. Remove tag to send back to the reviewer. label Apr 27, 2022
@Yard1 Yard1 requested a review from sven1977 as a code owner April 28, 2022 20:33
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LGTM! Just some minor nits

python/ray/ml/tests/test_huggingface_predictor.py Outdated Show resolved Hide resolved
model_config = AutoConfig.from_pretrained(model_checkpoint)
model = AutoModelForCausalLM.from_config(model_config)
predictor = HuggingFacePredictor(
pipeline=pipeline(
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Nice, loving the use of pipeline!

python/ray/ml/utils/huggingface_checkpoint_utils.py Outdated Show resolved Hide resolved
python/ray/ml/utils/huggingface_checkpoint_utils.py Outdated Show resolved Hide resolved

def get_train_dataloader(self):
if self.train_dataset is None:
raise ValueError("Trainer: training requires a train_dataset.")
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Is this error message exposed to users? If so, can we have a better message here?

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This is the same message & validation as in HuggingFace.

if resume_from_checkpoint:
self._param_dict[
"resume_from_checkpoint"
] = self._convert_directory_checkpoint_to_sync(resume_from_checkpoint)
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Ah I see we need this in both as_trainable and setup since we have to support both user-specified resuming and Tune resuming.

If we change the logic here a little bit: https://github.com/ray-project/ray/blob/master/python/ray/ml/trainer.py#L347-L352

to be like this instead

if checkpoint_dir:
    config["resume_from_checkpoint"] = Checkpoint.from_directory(checkpoint_dir)
trainer = trainer_cls(**config)

then _convert_directory_checkpoint_to_sync only needs to be called in init, right?

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Correct— my suggestion would still resolve both cases right?

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That should work, yeah. Not sure if this change should be made here though, as it would impact all trainers (possibly in unforeseen ways). Let's do that in a followup PR

@amogkam amogkam merged commit ff0ced1 into ray-project:master Apr 29, 2022
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7 participants