text-generation-webui/modules/quantized_LLaMA.py
2023-03-13 00:20:02 -03:00

60 lines
1.8 KiB
Python

import sys
from pathlib import Path
import accelerate
import torch
import modules.shared as shared
sys.path.insert(0, str(Path("repositories/GPTQ-for-LLaMa")))
from llama import load_quant
# 4-bit LLaMA
def load_quantized_LLaMA(model_name):
if shared.args.load_in_4bit:
bits = 4
else:
bits = shared.args.gptq_bits
path_to_model = Path(f'models/{model_name}')
pt_model = ''
if path_to_model.name.lower().startswith('llama-7b'):
pt_model = f'llama-7b-{bits}bit.pt'
elif path_to_model.name.lower().startswith('llama-13b'):
pt_model = f'llama-13b-{bits}bit.pt'
elif path_to_model.name.lower().startswith('llama-30b'):
pt_model = f'llama-30b-{bits}bit.pt'
elif path_to_model.name.lower().startswith('llama-65b'):
pt_model = f'llama-65b-{bits}bit.pt'
else:
pt_model = f'{model_name}-{bits}bit.pt'
# Try to find the .pt both in models/ and in the subfolder
pt_path = None
for path in [Path(p) for p in [f"models/{pt_model}", f"{path_to_model}/{pt_model}"]]:
if path.exists():
pt_path = path
if not pt_path:
print(f"Could not find {pt_model}, exiting...")
exit()
model = load_quant(path_to_model, str(pt_path), bits)
# Multiple GPUs or GPU+CPU
if shared.args.gpu_memory:
max_memory = {}
for i in range(len(shared.args.gpu_memory)):
max_memory[i] = f"{shared.args.gpu_memory[i]}GiB"
max_memory['cpu'] = f"{shared.args.cpu_memory or '99'}GiB"
device_map = accelerate.infer_auto_device_map(model, max_memory=max_memory, no_split_module_classes=["LLaMADecoderLayer"])
model = accelerate.dispatch_model(model, device_map=device_map)
# Single GPU
else:
model = model.to(torch.device('cuda:0'))
return model