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Trainer: delegate default generation values to generation_config (#25987)

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This commit is contained in:
Joao Gante
2023-09-05 14:47:00 +01:00
committed by GitHub
parent aea761499f
commit 9a70d6e56f
2 changed files with 32 additions and 21 deletions
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13
examples/pytorch/question-answering/trainer_seq2seq_qa.py
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@@ -46,12 +46,13 @@ class QuestionAnsweringSeq2SeqTrainer(Seq2SeqTrainer):
**gen_kwargs, **gen_kwargs,
) -> Dict[str, float]: ) -> Dict[str, float]:
gen_kwargs = gen_kwargs.copy() gen_kwargs = gen_kwargs.copy()
gen_kwargs["max_length"] = (
gen_kwargs["max_length"] if gen_kwargs.get("max_length") is not None else self.args.generation_max_length # Use legacy argument setting if a) the option is not explicitly passed; and b) the argument is set in the
) # training args
gen_kwargs["num_beams"] = ( if gen_kwargs.get("max_length") is None and self.args.generation_max_length is not None:
gen_kwargs["num_beams"] if gen_kwargs.get("num_beams") is not None else self.args.generation_num_beams gen_kwargs["max_length"] = self.args.generation_max_length
) if gen_kwargs.get("num_beams") is None and self.args.generation_num_beams is not None:
gen_kwargs["num_beams"] = self.args.generation_num_beams
self._gen_kwargs = gen_kwargs self._gen_kwargs = gen_kwargs
eval_dataset = self.eval_dataset if eval_dataset is None else eval_dataset eval_dataset = self.eval_dataset if eval_dataset is None else eval_dataset

40
src/transformers/trainer_seq2seq.py
View File

@@ -149,11 +149,17 @@ class Seq2SeqTrainer(Trainer):
""" """
gen_kwargs = gen_kwargs.copy() gen_kwargs = gen_kwargs.copy()
if gen_kwargs.get("max_length") is None and gen_kwargs.get("max_new_tokens") is None:
# Use legacy argument setting if a) the option is not explicitly passed; and b) the argument is set in the
# training args
if (
gen_kwargs.get("max_length") is None
and gen_kwargs.get("max_new_tokens") is None
and self.args.generation_max_length is not None
):
gen_kwargs["max_length"] = self.args.generation_max_length gen_kwargs["max_length"] = self.args.generation_max_length
gen_kwargs["num_beams"] = ( if gen_kwargs.get("num_beams") is None and self.args.generation_num_beams is not None:
gen_kwargs["num_beams"] if gen_kwargs.get("num_beams") is not None else self.args.generation_num_beams gen_kwargs["num_beams"] = self.args.generation_num_beams
)
self._gen_kwargs = gen_kwargs self._gen_kwargs = gen_kwargs
return super().evaluate(eval_dataset, ignore_keys=ignore_keys, metric_key_prefix=metric_key_prefix) return super().evaluate(eval_dataset, ignore_keys=ignore_keys, metric_key_prefix=metric_key_prefix)
@@ -206,11 +212,17 @@ class Seq2SeqTrainer(Trainer):
""" """
gen_kwargs = gen_kwargs.copy() gen_kwargs = gen_kwargs.copy()
if gen_kwargs.get("max_length") is None and gen_kwargs.get("max_new_tokens") is None:
# Use legacy argument setting if a) the option is not explicitly passed; and b) the argument is set in the
# training args
if (
gen_kwargs.get("max_length") is None
and gen_kwargs.get("max_new_tokens") is None
and self.args.generation_max_length is not None
):
gen_kwargs["max_length"] = self.args.generation_max_length gen_kwargs["max_length"] = self.args.generation_max_length
gen_kwargs["num_beams"] = ( if gen_kwargs.get("num_beams") is None and self.args.generation_num_beams is not None:
gen_kwargs["num_beams"] if gen_kwargs.get("num_beams") is not None else self.args.generation_num_beams gen_kwargs["num_beams"] = self.args.generation_num_beams
)
self._gen_kwargs = gen_kwargs self._gen_kwargs = gen_kwargs
return super().predict(test_dataset, ignore_keys=ignore_keys, metric_key_prefix=metric_key_prefix) return super().predict(test_dataset, ignore_keys=ignore_keys, metric_key_prefix=metric_key_prefix)
@@ -256,16 +268,14 @@ class Seq2SeqTrainer(Trainer):
# XXX: adapt synced_gpus for fairscale as well # XXX: adapt synced_gpus for fairscale as well
# Priority (handled in generate): # Priority (handled in generate):
# gen_kwargs > model.generation_config > default GenerationConfig() # non-`None` gen_kwargs > model.generation_config > default GenerationConfig()
if len(gen_kwargs) == 0 and hasattr(self, "_gen_kwargs"): if len(gen_kwargs) == 0 and hasattr(self, "_gen_kwargs"):
gen_kwargs = self._gen_kwargs.copy() gen_kwargs = self._gen_kwargs.copy()
if "num_beams" in gen_kwargs and gen_kwargs["num_beams"] is None:
gen_kwargs.pop("num_beams")
if "max_length" in gen_kwargs and gen_kwargs["max_length"] is None:
gen_kwargs.pop("max_length")
if gen_kwargs.get("max_length") is None and gen_kwargs.get("max_new_tokens") is None:
gen_kwargs["max_length"] = self.model.config.max_length
gen_kwargs["num_beams"] = (
gen_kwargs["num_beams"] if gen_kwargs.get("num_beams") is not None else self.model.config.num_beams
)
default_synced_gpus = True if is_deepspeed_zero3_enabled() else False default_synced_gpus = True if is_deepspeed_zero3_enabled() else False
gen_kwargs["synced_gpus"] = ( gen_kwargs["synced_gpus"] = (
gen_kwargs["synced_gpus"] if gen_kwargs.get("synced_gpus") is not None else default_synced_gpus gen_kwargs["synced_gpus"] if gen_kwargs.get("synced_gpus") is not None else default_synced_gpus