mirror of
https://github.com/oobabooga/text-generation-webui.git
synced 2024-10-01 01:26:03 -04:00
349 lines
13 KiB
Python
349 lines
13 KiB
Python
import gc
|
|
import os
|
|
import re
|
|
import time
|
|
from pathlib import Path
|
|
import hashlib
|
|
|
|
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,
|
|
'FlexGen': flexgen_loader,
|
|
'RWKV': RWKV_loader,
|
|
'ExLlama': ExLlama_loader,
|
|
'ExLlama_HF': ExLlama_HF_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]):
|
|
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.has_mps:
|
|
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.has_mps)):
|
|
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
|
|
)
|
|
|
|
model = LoaderClass.from_pretrained(checkpoint, **params)
|
|
|
|
return model
|
|
|
|
|
|
def flexgen_loader(model_name):
|
|
from flexgen.flex_opt import CompressionConfig, ExecutionEnv, OptLM, Policy
|
|
|
|
# Initialize environment
|
|
env = ExecutionEnv.create(shared.args.disk_cache_dir)
|
|
|
|
# Offloading policy
|
|
policy = Policy(1, 1,
|
|
shared.args.percent[0], shared.args.percent[1],
|
|
shared.args.percent[2], shared.args.percent[3],
|
|
shared.args.percent[4], shared.args.percent[5],
|
|
overlap=True, sep_layer=True, pin_weight=shared.args.pin_weight,
|
|
cpu_cache_compute=False, attn_sparsity=1.0,
|
|
compress_weight=shared.args.compress_weight,
|
|
comp_weight_config=CompressionConfig(
|
|
num_bits=4, group_size=64,
|
|
group_dim=0, symmetric=False),
|
|
compress_cache=False,
|
|
comp_cache_config=CompressionConfig(
|
|
num_bits=4, group_size=64,
|
|
group_dim=2, symmetric=False))
|
|
|
|
model = OptLM(f"facebook/{model_name}", env, shared.args.model_dir, policy)
|
|
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}\n")
|
|
model, tokenizer = LlamaCppModel.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)
|