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Add --revision command line flag

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Gnome Ann
2022-05-10 22:14:56 -04:00
parent 937d9ee06a
commit b97b2a02d6
2 changed files with 42 additions and 41 deletions
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30
tpu_mtj_backend.py
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@@ -1251,39 +1251,39 @@ def load_model(path: str, driver_version="tpu_driver0.1_dev20210607", hf_checkpo
with torch_lazy_loader.use_lazy_torch_load(callback=callback, dematerialized_modules=True):
if(os.path.isdir(vars.custmodpth)):
try:
tokenizer = AutoTokenizer.from_pretrained(vars.custmodpth, cache_dir="cache")
tokenizer = AutoTokenizer.from_pretrained(vars.custmodpth, revision=vars.revision, cache_dir="cache")
except Exception as e:
try:
tokenizer = GPT2TokenizerFast.from_pretrained(vars.custmodpth, cache_dir="cache")
tokenizer = GPT2TokenizerFast.from_pretrained(vars.custmodpth, revision=vars.revision, cache_dir="cache")
except Exception as e:
tokenizer = GPT2TokenizerFast.from_pretrained("gpt2", cache_dir="cache")
tokenizer = GPT2TokenizerFast.from_pretrained("gpt2", revision=vars.revision, cache_dir="cache")
try:
model = AutoModelForCausalLM.from_pretrained(vars.custmodpth, cache_dir="cache")
model = AutoModelForCausalLM.from_pretrained(vars.custmodpth, revision=vars.revision, cache_dir="cache")
except Exception as e:
model = GPTNeoForCausalLM.from_pretrained(vars.custmodpth, cache_dir="cache")
model = GPTNeoForCausalLM.from_pretrained(vars.custmodpth, revision=vars.revision, cache_dir="cache")
elif(os.path.isdir("models/{}".format(vars.model.replace('/', '_')))):
try:
tokenizer = AutoTokenizer.from_pretrained("models/{}".format(vars.model.replace('/', '_')), cache_dir="cache")
tokenizer = AutoTokenizer.from_pretrained("models/{}".format(vars.model.replace('/', '_')), revision=vars.revision, cache_dir="cache")
except Exception as e:
try:
tokenizer = GPT2TokenizerFast.from_pretrained("models/{}".format(vars.model.replace('/', '_')), cache_dir="cache")
tokenizer = GPT2TokenizerFast.from_pretrained("models/{}".format(vars.model.replace('/', '_')), revision=vars.revision, cache_dir="cache")
except Exception as e:
tokenizer = GPT2TokenizerFast.from_pretrained("gpt2", cache_dir="cache")
tokenizer = GPT2TokenizerFast.from_pretrained("gpt2", revision=vars.revision, cache_dir="cache")
try:
model = AutoModelForCausalLM.from_pretrained("models/{}".format(vars.model.replace('/', '_')), cache_dir="cache")
model = AutoModelForCausalLM.from_pretrained("models/{}".format(vars.model.replace('/', '_')), revision=vars.revision, cache_dir="cache")
except Exception as e:
model = GPTNeoForCausalLM.from_pretrained("models/{}".format(vars.model.replace('/', '_')), cache_dir="cache")
model = GPTNeoForCausalLM.from_pretrained("models/{}".format(vars.model.replace('/', '_')), revision=vars.revision, cache_dir="cache")
else:
try:
tokenizer = AutoTokenizer.from_pretrained(vars.model, cache_dir="cache")
tokenizer = AutoTokenizer.from_pretrained(vars.model, revision=vars.revision, cache_dir="cache")
except Exception as e:
try:
tokenizer = GPT2TokenizerFast.from_pretrained(vars.model, cache_dir="cache")
tokenizer = GPT2TokenizerFast.from_pretrained(vars.model, revision=vars.revision, cache_dir="cache")
except Exception as e:
tokenizer = GPT2TokenizerFast.from_pretrained("gpt2", cache_dir="cache")
tokenizer = GPT2TokenizerFast.from_pretrained("gpt2", revision=vars.revision, cache_dir="cache")
try:
model = AutoModelForCausalLM.from_pretrained(vars.model, cache_dir="cache")
model = AutoModelForCausalLM.from_pretrained(vars.model, revision=vars.revision, cache_dir="cache")
except Exception as e:
model = GPTNeoForCausalLM.from_pretrained(vars.model, cache_dir="cache")
model = GPTNeoForCausalLM.from_pretrained(vars.model, revision=vars.revision, cache_dir="cache")
#network.state = network.move_xmap(network.state, np.zeros(cores_per_replica))