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https://github.com/oobabooga/text-generation-webui.git
synced 2024-10-01 01:26:03 -04:00
Move LLaMA 4-bit into a separate file
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28fd4fc970
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fed3617f07
@ -42,7 +42,7 @@ def load_model(model_name):
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shared.is_RWKV = model_name.lower().startswith('rwkv-')
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# Default settings
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if not (shared.args.cpu or shared.args.load_in_8bit or shared.args.llama_bits>0 or shared.args.load_in_4bit or shared.args.auto_devices or shared.args.disk or shared.args.gpu_memory is not None or shared.args.cpu_memory is not None or shared.args.deepspeed or shared.args.flexgen or shared.is_RWKV):
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if not any([shared.args.cpu, shared.args.load_in_8bit, shared.args.load_in_4bit, shared.args.llama_bits > 0, shared.args.auto_devices, shared.args.disk, shared.args.gpu_memory is not None, shared.args.cpu_memory is not None, shared.args.deepspeed, shared.args.flexgen, shared.is_RWKV]):
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if any(size in shared.model_name.lower() for size in ('13b', '20b', '30b')):
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model = AutoModelForCausalLM.from_pretrained(Path(f"models/{shared.model_name}"), device_map='auto', load_in_8bit=True)
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else:
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@ -88,56 +88,10 @@ def load_model(model_name):
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return model, tokenizer
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# 4-bit LLaMA
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elif shared.args.llama_bits>0 or shared.args.load_in_4bit:
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sys.path.insert(0, os.path.abspath(Path("repositories/GPTQ-for-LLaMa")))
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if shared.args.load_in_4bit:
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bits = 4
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else:
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bits = shared.args.llama_bits
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elif shared.args.llama_bits > 0 or shared.args.load_in_4bit:
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from modules.quantized_LLaMA import load_quantized_LLaMA
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from llama import load_quant
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path_to_model = Path(f'models/{model_name}')
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pt_model = ''
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if path_to_model.name.lower().startswith('llama-7b'):
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pt_model = f'llama-7b-{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-{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-{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-{bits}bit.pt'
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else:
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pt_model = f'{model_name}-{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(path_to_model, pt_path, bits)
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# Multi-GPU setup
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if shared.args.gpu_memory:
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import accelerate
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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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model = load_quantized_LLaMA(model_name)
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# Custom
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else:
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60
modules/quantized_LLaMA.py
Normal file
60
modules/quantized_LLaMA.py
Normal file
@ -0,0 +1,60 @@
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import os
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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, os.path.abspath(Path("repositories/GPTQ-for-LLaMa")))
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from llama import load_quant
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# 4-bit LLaMA
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def load_quantized_LLaMA(model_name):
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if shared.args.load_in_4bit:
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bits = 4
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else:
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bits = shared.args.llama_bits
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path_to_model = Path(f'models/{model_name}')
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pt_model = ''
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if path_to_model.name.lower().startswith('llama-7b'):
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pt_model = f'llama-7b-{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-{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-{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-{bits}bit.pt'
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else:
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pt_model = f'{model_name}-{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(path_to_model, pt_path, bits)
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# Multi-GPU setup
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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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