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add cap for NAR-len training, to avoid any weird cases in early training where it'll just mess up and generate long lengths

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This commit is contained in:
mrq 2024-08-03 21:00:32 -05:00
parent 4d2b88b164
commit ab673e0426
2 changed files with 8 additions and 8 deletions
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1
vall_e/train.py
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@ -112,6 +112,7 @@ def run_eval(engines, eval_name, dl):
resps_list = engine(text_list=batch["text"], proms_list=batch["proms"], lang_list=batch["lang"] )
elif "len" in engine.hyper_config.capabilities:
len_list = engine(text_list=batch["text"], proms_list=batch["proms"], max_steps=10 ) # don't need more than that
len_list = [ min( l, cfg.evaluation.steps ) for l in len_list ]
resps_list = engine( text_list=batch["text"], proms_list=batch["proms"], len_list=len_list, max_levels=cfg.evaluation.nar_levels )
else:
if "ar" in engine.hyper_config.capabilities:

15
vall_e/utils/trainer.py
View File

@ -127,14 +127,13 @@ def train(
engines = load_engines()
# validate if there's at least one model to train
if training:
found = False
for name, engine in engines.items():
if engine.training:
found = True
break
if not found:
raise Exception('Training, but no model loaded set to train...')
found = False
for name, engine in engines.items():
if engine.training:
found = True
break
if not found:
raise Exception('Training, but no model loaded set to train...')
"""
if is_local_leader():