mirror of
https://github.com/oobabooga/text-generation-webui.git
synced 2024-09-20 18:45:09 +02:00
84 lines
3.3 KiB
Python
84 lines
3.3 KiB
Python
import re
|
|
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 llama_inference_offload
|
|
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':
|
|
if not shared.args.gptq_pre_layer:
|
|
load_quant = llama.load_quant
|
|
else:
|
|
load_quant = llama_inference_offload.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()
|
|
|
|
# qwopqwop200's offload
|
|
if shared.args.gptq_pre_layer:
|
|
model = load_quant(str(path_to_model), str(pt_path), shared.args.gptq_bits, shared.args.gptq_pre_layer)
|
|
else:
|
|
model = load_quant(str(path_to_model), str(pt_path), shared.args.gptq_bits)
|
|
|
|
# accelerate offload (doesn't work properly)
|
|
if shared.args.gpu_memory:
|
|
memory_map = list(map(lambda x : x.strip(), shared.args.gpu_memory))
|
|
max_cpu_memory = shared.args.cpu_memory.strip() if shared.args.cpu_memory is not None else '99GiB'
|
|
max_memory = {}
|
|
for i in range(len(memory_map)):
|
|
max_memory[i] = f'{memory_map[i]}GiB' if not re.match('.*ib$', memory_map[i].lower()) else memory_map[i]
|
|
max_memory['cpu'] = max_cpu_memory
|
|
|
|
device_map = accelerate.infer_auto_device_map(model, max_memory=max_memory, no_split_module_classes=["LlamaDecoderLayer"])
|
|
print("Using the following device map for the 4-bit model:", device_map)
|
|
# https://huggingface.co/docs/accelerate/package_reference/big_modeling#accelerate.dispatch_model
|
|
model = accelerate.dispatch_model(model, device_map=device_map, offload_buffers=True)
|
|
|
|
# No offload
|
|
elif not shared.args.cpu:
|
|
model = model.to(torch.device('cuda:0'))
|
|
|
|
return model
|