for n, s in zip(self.recorder.metric_names, self.recorder.log)
if n not in ["train_loss", "epoch", "time"]
})
def after_fit(self):
wandb.log({}) # ensure sync of last step
self.run.finish()
# my model
if c.my_model:
sys.path.insert(0, os.path.abspath(".."))
from modeling.model import ModelForDiscriminator
from hyperparameter import electra_hparam_from_hf
hparam = electra_hparam_from_hf(electra_config, hf_tokenizer)
hparam.update(hparam_update)
# Path
Path("./datasets").mkdir(exist_ok=True)
Path("./checkpoints/glue").mkdir(exist_ok=True, parents=True)
Path("./test_outputs/glue").mkdir(exist_ok=True, parents=True)
c.pretrained_ckp_path = Path(
f"./checkpoints/pretrain/{c.pretrained_checkpoint}")
if c.group_name is None:
if c.pretrained_checkpoint:
c.group_name = c.pretrained_checkpoint[:-4]
elif c.pretrained_checkpoint is None:
c.group_name = f"{c.size}++"
# Print info
book_cache_dir = Path("./datasets/bookcorpus/plain_text/1.0.0")
wbdl_cache_dir = Path("./datasets/wikibook_dl")
wbdl_cache_dir.mkdir(exist_ok=True)
# Print info
print(f"process id: {os.getpid()}")
print(c)
print(hparam_update)
# %%
if c.my_model:
sys.path.insert(0, os.path.abspath(".."))
from modeling.model import ModelForGenerator,ModelForDiscriminator
from hyperparameter import electra_hparam_from_hf
gen_hparam = electra_hparam_from_hf(gen_config, hf_tokenizer)
gen_hparam.update(hparam_update)
disc_hparam = electra_hparam_from_hf(disc_config, hf_tokenizer)
disc_hparam.update(hparam_update)
# %% [markdown]
# # 1. Load Data
# %%
if c.size in ['small', 'base']:
# wiki
if (wiki_cache_dir/f"wiki_electra_{c.max_length}.arrow").exists():
print('loading the electra data (wiki)')
wiki = nlp.Dataset.from_file(str(wiki_cache_dir/f"wiki_electra_{c.max_length}.arrow"))
else: