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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")))
import llama
import opt
def load_quantized(model_name):
if not shared.args.gptq_model_type:
# Try to determine model type from model name
model_type = model_name.split('-')[0].lower()
if model_type not in ('llama', 'opt'):
print("Can't determine model type from model name. Please specify it manually using --gptq-model-type "
"argument")
exit()
else:
model_type = shared.args.gptq_model_type.lower()
if model_type == 'llama':
load_quant = llama.load_quant
elif model_type == 'opt':
load_quant = opt.load_quant
else:
print("Unknown pre-quantized model type specified. Only 'llama' and 'opt' are supported")
exit()
path_to_model = Path(f'models/{model_name}')
if path_to_model.name.lower().startswith('llama-7b'):
pt_model = f'llama-7b-{shared.args.gptq_bits}bit.pt'
elif path_to_model.name.lower().startswith('llama-13b'):
pt_model = f'llama-13b-{shared.args.gptq_bits}bit.pt'
elif path_to_model.name.lower().startswith('llama-30b'):
pt_model = f'llama-30b-{shared.args.gptq_bits}bit.pt'
elif path_to_model.name.lower().startswith('llama-65b'):
pt_model = f'llama-65b-{shared.args.gptq_bits}bit.pt'
else:
pt_model = f'{model_name}-{shared.args.gptq_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(str(path_to_model), str(pt_path), shared.args.gptq_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
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