mirror of
https://github.com/oobabooga/text-generation-webui.git
synced 2024-10-01 01:26:03 -04:00
72 lines
2.4 KiB
Python
72 lines
2.4 KiB
Python
import sys
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from pathlib import Path
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import accelerate
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import torch
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import modules.shared as shared
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sys.path.insert(0, str(Path("repositories/GPTQ-for-LLaMa")))
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import llama
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import opt
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def load_quantized(model_name):
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if not shared.args.gptq_model_type:
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# Try to determine model type from model name
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model_type = model_name.split('-')[0].lower()
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if model_type not in ('llama', 'opt'):
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print("Can't determine model type from model name. Please specify it manually using --gptq-model-type "
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"argument")
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exit()
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else:
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model_type = shared.args.gptq_model_type.lower()
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if model_type == 'llama':
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load_quant = llama.load_quant
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elif model_type == 'opt':
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load_quant = opt.load_quant
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else:
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print("Unknown pre-quantized model type specified. Only 'llama' and 'opt' are supported")
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exit()
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path_to_model = Path(f'models/{model_name}')
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if path_to_model.name.lower().startswith('llama-7b'):
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pt_model = f'llama-7b-{shared.args.gptq_bits}bit.pt'
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elif path_to_model.name.lower().startswith('llama-13b'):
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pt_model = f'llama-13b-{shared.args.gptq_bits}bit.pt'
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elif path_to_model.name.lower().startswith('llama-30b'):
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pt_model = f'llama-30b-{shared.args.gptq_bits}bit.pt'
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elif path_to_model.name.lower().startswith('llama-65b'):
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pt_model = f'llama-65b-{shared.args.gptq_bits}bit.pt'
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else:
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pt_model = f'{model_name}-{shared.args.gptq_bits}bit.pt'
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# Try to find the .pt both in models/ and in the subfolder
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pt_path = None
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for path in [Path(p) for p in [f"models/{pt_model}", f"{path_to_model}/{pt_model}"]]:
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if path.exists():
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pt_path = path
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if not pt_path:
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print(f"Could not find {pt_model}, exiting...")
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exit()
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model = load_quant(str(path_to_model), str(pt_path), shared.args.gptq_bits)
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# Multiple GPUs or GPU+CPU
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if shared.args.gpu_memory:
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max_memory = {}
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for i in range(len(shared.args.gpu_memory)):
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max_memory[i] = f"{shared.args.gpu_memory[i]}GiB"
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max_memory['cpu'] = f"{shared.args.cpu_memory or '99'}GiB"
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device_map = accelerate.infer_auto_device_map(model, max_memory=max_memory, no_split_module_classes=["LLaMADecoderLayer"])
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model = accelerate.dispatch_model(model, device_map=device_map)
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# Single GPU
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else:
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model = model.to(torch.device('cuda:0'))
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return model
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