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import gc
import hashlib
import os
import re
import time
from pathlib import Path
import torch
import transformers
from accelerate import infer_auto_device_map, init_empty_weights
from transformers import (
AutoConfig,
AutoModel,
AutoModelForCausalLM,
AutoModelForSeq2SeqLM,
AutoTokenizer,
BitsAndBytesConfig
)
import modules.shared as shared
from modules import llama_attn_hijack, sampler_hijack
from modules.logging_colors import logger
from modules.models_settings import infer_loader
transformers.logging.set_verbosity_error()
local_rank = None
if shared.args.deepspeed:
import deepspeed
from transformers.deepspeed import (
HfDeepSpeedConfig,
is_deepspeed_zero3_enabled
)
from modules.deepspeed_parameters import generate_ds_config
# Distributed setup
local_rank = shared.args.local_rank if shared.args.local_rank is not None else int(os.getenv("LOCAL_RANK", "0"))
world_size = int(os.getenv("WORLD_SIZE", "1"))
torch.cuda.set_device(local_rank)
deepspeed.init_distributed()
ds_config = generate_ds_config(shared.args.bf16, 1 * world_size, shared.args.nvme_offload_dir)
dschf = HfDeepSpeedConfig(ds_config) # Keep this object alive for the Transformers integration
sampler_hijack.hijack_samplers()
def load_model(model_name, loader=None):
logger.info(f"Loading {model_name}...")
t0 = time.time()
shared.is_seq2seq = False
load_func_map = {
'Transformers': huggingface_loader,
'AutoGPTQ': AutoGPTQ_loader,
'GPTQ-for-LLaMa': GPTQ_loader,
'llama.cpp': llamacpp_loader,
'llamacpp_HF': llamacpp_HF_loader,
'RWKV': RWKV_loader,
'ExLlama': ExLlama_loader,
'ExLlama_HF': ExLlama_HF_loader,
'ctransformers': ctransformers_loader,
}
p = Path(model_name)
if p.exists():
model_name = p.parts[-1]
if loader is None:
if shared.args.loader is not None:
loader = shared.args.loader
else:
loader = infer_loader(model_name)
if loader is None:
logger.error('The path to the model does not exist. Exiting.')
return None, None
shared.args.loader = loader
output = load_func_map[loader](model_name)
if type(output) is tuple:
model, tokenizer = output
else:
model = output
if model is None:
return None, None
else:
tokenizer = load_tokenizer(model_name, model)
# Hijack attention with xformers
if any((shared.args.xformers, shared.args.sdp_attention)):
llama_attn_hijack.hijack_llama_attention()
logger.info(f"Loaded the model in {(time.time()-t0):.2f} seconds.\n")
return model, tokenizer
def load_tokenizer(model_name, model):
tokenizer = None
path_to_model = Path(f"{shared.args.model_dir}/{model_name}/")
if any(s in model_name.lower() for s in ['gpt-4chan', 'gpt4chan']) and Path(f"{shared.args.model_dir}/gpt-j-6B/").exists():
tokenizer = AutoTokenizer.from_pretrained(Path(f"{shared.args.model_dir}/gpt-j-6B/"))
elif path_to_model.exists():
try:
tokenizer = AutoTokenizer.from_pretrained(
path_to_model,
trust_remote_code=shared.args.trust_remote_code,
use_fast=False
)
except ValueError:
tokenizer = AutoTokenizer.from_pretrained(
path_to_model,
trust_remote_code=shared.args.trust_remote_code,
use_fast=True
)
if tokenizer.__class__.__name__ == 'LlamaTokenizer':
pairs = [
['tokenizer_config.json', '516c6167c884793a738c440e29ccb80c15e1493ffc965affc69a1a8ddef4572a'],
['special_tokens_map.json', 'ff3b4a612c4e447acb02d40071bddd989fe0da87eb5b7fe0dbadfc4f74de7531']
]
for pair in pairs:
p = path_to_model / pair[0]
if p.exists():
with open(p, "rb") as f:
bytes = f.read()
file_hash = hashlib.sha256(bytes).hexdigest()
if file_hash != pair[1]:
logger.warning(f"{p} is different from the original LlamaTokenizer file. It is either customized or outdated.")
return tokenizer
def huggingface_loader(model_name):
path_to_model = Path(f'{shared.args.model_dir}/{model_name}')
if 'chatglm' in model_name.lower():
LoaderClass = AutoModel
else:
config = AutoConfig.from_pretrained(path_to_model, trust_remote_code=shared.args.trust_remote_code)
if config.to_dict().get("is_encoder_decoder", False):
LoaderClass = AutoModelForSeq2SeqLM
shared.is_seq2seq = True
else:
LoaderClass = AutoModelForCausalLM
# Load the model in simple 16-bit mode by default
if not any([shared.args.cpu, shared.args.load_in_8bit, shared.args.load_in_4bit, shared.args.auto_devices, shared.args.disk, shared.args.deepspeed, shared.args.gpu_memory is not None, shared.args.cpu_memory is not None, shared.args.compress_pos_emb > 1, shared.args.alpha_value > 1]):
model = LoaderClass.from_pretrained(Path(f"{shared.args.model_dir}/{model_name}"), low_cpu_mem_usage=True, torch_dtype=torch.bfloat16 if shared.args.bf16 else torch.float16, trust_remote_code=shared.args.trust_remote_code)
if torch.backends.mps.is_available():
device = torch.device('mps')
model = model.to(device)
else:
model = model.cuda()
# DeepSpeed ZeRO-3
elif shared.args.deepspeed:
model = LoaderClass.from_pretrained(Path(f"{shared.args.model_dir}/{model_name}"), torch_dtype=torch.bfloat16 if shared.args.bf16 else torch.float16)
model = deepspeed.initialize(model=model, config_params=ds_config, model_parameters=None, optimizer=None, lr_scheduler=None)[0]
model.module.eval() # Inference
logger.info(f"DeepSpeed ZeRO-3 is enabled: {is_deepspeed_zero3_enabled()}")
# Custom
else:
params = {
"low_cpu_mem_usage": True,
"trust_remote_code": shared.args.trust_remote_code
}
if not any((shared.args.cpu, torch.cuda.is_available(), torch.backends.mps.is_available())):
logger.warning("torch.cuda.is_available() returned False. This means that no GPU has been detected. Falling back to CPU mode.")
shared.args.cpu = True
if shared.args.cpu:
params["torch_dtype"] = torch.float32
else:
params["device_map"] = 'auto'
if shared.args.load_in_4bit:
# See https://github.com/huggingface/transformers/pull/23479/files
# and https://huggingface.co/blog/4bit-transformers-bitsandbytes
quantization_config_params = {
'load_in_4bit': True,
'bnb_4bit_compute_dtype': eval("torch.{}".format(shared.args.compute_dtype)) if shared.args.compute_dtype in ["bfloat16", "float16", "float32"] else None,
'bnb_4bit_quant_type': shared.args.quant_type,
'bnb_4bit_use_double_quant': shared.args.use_double_quant,
}
logger.warning("Using the following 4-bit params: " + str(quantization_config_params))
params['quantization_config'] = BitsAndBytesConfig(**quantization_config_params)
elif shared.args.load_in_8bit and any((shared.args.auto_devices, shared.args.gpu_memory)):
params['quantization_config'] = BitsAndBytesConfig(load_in_8bit=True, llm_int8_enable_fp32_cpu_offload=True)
elif shared.args.load_in_8bit:
params['quantization_config'] = BitsAndBytesConfig(load_in_8bit=True)
elif shared.args.bf16:
params["torch_dtype"] = torch.bfloat16
else:
params["torch_dtype"] = torch.float16
params['max_memory'] = get_max_memory_dict()
if shared.args.disk:
params["offload_folder"] = shared.args.disk_cache_dir
checkpoint = Path(f'{shared.args.model_dir}/{model_name}')
if shared.args.load_in_8bit and params.get('max_memory', None) is not None and params['device_map'] == 'auto':
config = AutoConfig.from_pretrained(checkpoint, trust_remote_code=shared.args.trust_remote_code)
with init_empty_weights():
model = LoaderClass.from_config(config, trust_remote_code=shared.args.trust_remote_code)
model.tie_weights()
params['device_map'] = infer_auto_device_map(
model,
dtype=torch.int8,
max_memory=params['max_memory'],
no_split_module_classes=model._no_split_modules
)
if shared.args.compress_pos_emb > 1:
params['rope_scaling'] = {'type': 'linear', 'factor': shared.args.compress_pos_emb}
elif shared.args.alpha_value > 1:
params['rope_scaling'] = {'type': 'dynamic', 'factor': shared.args.alpha_value}
model = LoaderClass.from_pretrained(checkpoint, **params)
return model
def RWKV_loader(model_name):
from modules.RWKV import RWKVModel, RWKVTokenizer
model = RWKVModel.from_pretrained(Path(f'{shared.args.model_dir}/{model_name}'), dtype="fp32" if shared.args.cpu else "bf16" if shared.args.bf16 else "fp16", device="cpu" if shared.args.cpu else "cuda")
tokenizer = RWKVTokenizer.from_pretrained(Path(shared.args.model_dir))
return model, tokenizer
def llamacpp_loader(model_name):
from modules.llamacpp_model import LlamaCppModel
path = Path(f'{shared.args.model_dir}/{model_name}')
if path.is_file():
model_file = path
else:
model_file = list(Path(f'{shared.args.model_dir}/{model_name}').glob('*ggml*.bin'))[0]
logger.info(f"llama.cpp weights detected: {model_file}")
model, tokenizer = LlamaCppModel.from_pretrained(model_file)
return model, tokenizer
def llamacpp_HF_loader(model_name):
from modules.llamacpp_hf import LlamacppHF
for fname in ["oobabooga_llama-tokenizer", "llama-tokenizer"]:
path = Path(f'{shared.args.model_dir}/{fname}')
if path.exists():
break
else:
logger.error("Could not load the model because a tokenizer in transformers format was not found. Please download oobabooga/llama-tokenizer.")
return None, None
tokenizer = AutoTokenizer.from_pretrained(
path,
trust_remote_code=shared.args.trust_remote_code,
use_fast=False
)
model = LlamacppHF.from_pretrained(model_name)
return model, tokenizer
def ctransformers_loader(model_name):
from modules.ctransformers_model import CtransformersModel
path = Path(f'{shared.args.model_dir}/{model_name}')
ctrans = CtransformersModel()
if ctrans.model_type_is_auto():
model_file = path
else:
if path.is_file():
model_file = path
else:
model_file = list(Path(f'{shared.args.model_dir}/{model_name}').glob('*.bin'))[0]
logger.info(f'ctransformers weights detected: {model_file}')
model, tokenizer = ctrans.from_pretrained(model_file)
return model, tokenizer
def GPTQ_loader(model_name):
# Monkey patch
if shared.args.monkey_patch:
logger.warning("Applying the monkey patch for using LoRAs with GPTQ models. It may cause undefined behavior outside its intended scope.")
from modules.monkey_patch_gptq_lora import load_model_llama
model, _ = load_model_llama(model_name)
# No monkey patch
else:
import modules.GPTQ_loader
model = modules.GPTQ_loader.load_quantized(model_name)
return model
def AutoGPTQ_loader(model_name):
import modules.AutoGPTQ_loader
return modules.AutoGPTQ_loader.load_quantized(model_name)
def ExLlama_loader(model_name):
from modules.exllama import ExllamaModel
model, tokenizer = ExllamaModel.from_pretrained(model_name)
return model, tokenizer
def ExLlama_HF_loader(model_name):
from modules.exllama_hf import ExllamaHF
return ExllamaHF.from_pretrained(model_name)
def get_max_memory_dict():
max_memory = {}
if shared.args.gpu_memory:
memory_map = list(map(lambda x: x.strip(), shared.args.gpu_memory))
for i in range(len(memory_map)):
max_memory[i] = f'{memory_map[i]}GiB' if not re.match('.*ib$', memory_map[i].lower()) else memory_map[i]
max_cpu_memory = shared.args.cpu_memory.strip() if shared.args.cpu_memory is not None else '99GiB'
max_memory['cpu'] = f'{max_cpu_memory}GiB' if not re.match('.*ib$', max_cpu_memory.lower()) else max_cpu_memory
# If --auto-devices is provided standalone, try to get a reasonable value
# for the maximum memory of device :0
elif shared.args.auto_devices:
total_mem = (torch.cuda.get_device_properties(0).total_memory / (1024 * 1024))
suggestion = round((total_mem - 1000) / 1000) * 1000
if total_mem - suggestion < 800:
suggestion -= 1000
suggestion = int(round(suggestion / 1000))
logger.warning(f"Auto-assiging --gpu-memory {suggestion} for your GPU to try to prevent out-of-memory errors. You can manually set other values.")
max_memory = {0: f'{suggestion}GiB', 'cpu': f'{shared.args.cpu_memory or 99}GiB'}
return max_memory if len(max_memory) > 0 else None
def clear_torch_cache():
gc.collect()
if not shared.args.cpu:
torch.cuda.empty_cache()
def unload_model():
shared.model = shared.tokenizer = None
shared.lora_names = []
shared.model_dirty_from_training = False
clear_torch_cache()
def reload_model():
unload_model()
shared.model, shared.tokenizer = load_model(shared.model_name)
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