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Refactor model loading function

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oobabooga 2023-01-09 16:28:04 -03:00
parent 96a75b616b
commit 00a12889e9
2 changed files with 10 additions and 11 deletions
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21
server.py
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@ -36,15 +36,18 @@ def load_model(model_name):
if not args.cpu and Path(f"torch-dumps/{model_name}.pt").exists(): if not args.cpu and Path(f"torch-dumps/{model_name}.pt").exists():
print("Loading in .pt format...") print("Loading in .pt format...")
model = torch.load(Path(f"torch-dumps/{model_name}.pt")) model = torch.load(Path(f"torch-dumps/{model_name}.pt"))
elif model_name.lower().startswith(('gpt-neo', 'opt-', 'galactica')): elif model_name.lower().startswith(('gpt-neo', 'opt-', 'galactica')) and any(size in model_name.lower() for size in ('13b', '20b', '30b')):
if any(size in model_name.lower() for size in ('13b', '20b', '30b')): model = AutoModelForCausalLM.from_pretrained(Path(f"models/{model_name}"), device_map='auto', load_in_8bit=True)
model = AutoModelForCausalLM.from_pretrained(Path(f"models/{model_name}"), device_map='auto', load_in_8bit=True)
else:
model = AutoModelForCausalLM.from_pretrained(Path(f"models/{model_name}"), low_cpu_mem_usage=True, torch_dtype=dtype)
elif model_name in ['flan-t5', 't5-large']: elif model_name in ['flan-t5', 't5-large']:
model = T5ForConditionalGeneration.from_pretrained(Path(f"models/{model_name}")) if args.cpu:
model = T5ForConditionalGeneration.from_pretrained(Path(f"models/{model_name}"))
else:
model = T5ForConditionalGeneration.from_pretrained(Path(f"models/{model_name}")).cuda()
else: else:
model = AutoModelForCausalLM.from_pretrained(Path(f"models/{model_name}"), low_cpu_mem_usage=True, torch_dtype=dtype) if args.cpu:
model = AutoModelForCausalLM.from_pretrained(Path(f"models/{model_name}"), low_cpu_mem_usage=True, torch_dtype=dtype)
else:
model = AutoModelForCausalLM.from_pretrained(Path(f"models/{model_name}"), low_cpu_mem_usage=True, torch_dtype=dtype).cuda()
# Loading the tokenizer # Loading the tokenizer
if model_name.lower().startswith('gpt4chan') and Path(f"models/gpt-j-6B/").exists(): if model_name.lower().startswith('gpt4chan') and Path(f"models/gpt-j-6B/").exists():
@ -54,10 +57,6 @@ def load_model(model_name):
else: else:
tokenizer = AutoTokenizer.from_pretrained(Path(f"models/{model_name}/")) tokenizer = AutoTokenizer.from_pretrained(Path(f"models/{model_name}/"))
# Sending to the GPU
if not (args.cpu or any(size in model_name.lower() for size in ('13b', '20b', '30b'))):
model = model.cuda()
print(f"Loaded the model in {(time.time()-t0):.2f} seconds.") print(f"Loaded the model in {(time.time()-t0):.2f} seconds.")
return model, tokenizer return model, tokenizer

0
torch-dumps/place-your-pt-models-here.txt
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