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[trainer] --model_parallel hasn't been implemented for most models #9347

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merged 6 commits into from
Jan 5, 2021
Merged

[trainer] --model_parallel hasn't been implemented for most models #9347

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d73e5e6
--model_parallel hasn't been implemented for most models
stas00 Dec 29, 2020
1553f61
make the help clear as well
stas00 Dec 29, 2020
0f1297f
Merge remote-tracking branch 'origin/master' into mp-check
stas00 Jan 4, 2021
3859e77
implement is_parallelizable; use it
stas00 Jan 4, 2021
6f3799c
oops
stas00 Jan 4, 2021
d4f60ea
remove property
stas00 Jan 4, 2021
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8 changes: 8 additions & 0 deletions src/transformers/trainer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -241,6 +241,14 @@ def __init__(
if model is None and model_init is not None:
model = self.call_model_init()

if self.args.model_parallel:
# XXX: ideally this register should be maintained elsewhere so that the trainer could just do
# if model.model_parallel_is_supported()
mp_supported = ["gpt2", "t5"]
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Maybe we can check like this for now:

if not hasattr(model, "model_parallel"):
    raise ValueError(f"{model.config.model_type} implementation currently doesn't support model parallelism, therefore --model_parallel cl arg cannot be used")

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I like that check.

assert (
model.config.model_type in mp_supported
), f"{model.config.model_type} implementation currently doesn't support model parallelism, therefore --model_parallel cl arg cannot be used"

# Model parallel
if model is not None and not self.args.model_parallel:
model = model.to(args.device)
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4 changes: 2 additions & 2 deletions src/transformers/training_args.py
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Expand Up @@ -207,8 +207,8 @@ class TrainingArguments:
:obj:`"eval_loss"`.
- :obj:`False` if :obj:`metric_for_best_model` is not set, or set to :obj:`"loss"` or :obj:`"eval_loss"`.
model_parallel (:obj:`bool`, `optional`, defaults to :obj:`False`):
If there is more than one device, whether to use model parallelism to distribute the model's modules across
devices or not.
If the model supports model parallelism and there is more than one device, whether to use model parallelism
to distribute the model's modules across devices or not.
ignore_skip_data (:obj:`bool`, `optional`, defaults to :obj:`False`):
When resuming training, whether or not to skip the epochs and batches to get the data loading at the same
stage as in the previous training. If set to :obj:`True`, the training will begin faster (as that skipping
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