Move LLaMA 4-bit into a separate file

This commit is contained in:
oobabooga 2023-03-12 11:12:34 -03:00
parent 28fd4fc970
commit fed3617f07
2 changed files with 64 additions and 50 deletions

View File

@ -42,7 +42,7 @@ def load_model(model_name):
shared.is_RWKV = model_name.lower().startswith('rwkv-')
# Default settings
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):
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]):
if any(size in shared.model_name.lower() for size in ('13b', '20b', '30b')):
model = AutoModelForCausalLM.from_pretrained(Path(f"models/{shared.model_name}"), device_map='auto', load_in_8bit=True)
else:
@ -88,56 +88,10 @@ def load_model(model_name):
return model, tokenizer
# 4-bit LLaMA
elif shared.args.llama_bits>0 or shared.args.load_in_4bit:
sys.path.insert(0, os.path.abspath(Path("repositories/GPTQ-for-LLaMa")))
if shared.args.load_in_4bit:
bits = 4
else:
bits = shared.args.llama_bits
elif shared.args.llama_bits > 0 or shared.args.load_in_4bit:
from modules.quantized_LLaMA import load_quantized_LLaMA
from llama import load_quant
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, pt_path, bits)
# Multi-GPU setup
if shared.args.gpu_memory:
import accelerate
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'))
model = load_quantized_LLaMA(model_name)
# Custom
else:

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@ -0,0 +1,60 @@
import os
import sys
from pathlib import Path
import accelerate
import torch
import modules.shared as shared
sys.path.insert(0, os.path.abspath(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.llama_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, pt_path, bits)
# Multi-GPU setup
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