mirror of
https://github.com/oobabooga/text-generation-webui.git
synced 2024-10-01 01:26:03 -04:00
244 lines
10 KiB
Python
244 lines
10 KiB
Python
import gc
|
|
import json
|
|
import os
|
|
import re
|
|
import time
|
|
import zipfile
|
|
from pathlib import Path
|
|
|
|
import numpy as np
|
|
import torch
|
|
import transformers
|
|
from accelerate import infer_auto_device_map, init_empty_weights
|
|
from transformers import (AutoConfig, AutoModel, AutoModelForCausalLM,
|
|
AutoTokenizer, BitsAndBytesConfig, LlamaTokenizer)
|
|
|
|
import modules.shared as shared
|
|
from modules import llama_attn_hijack
|
|
|
|
transformers.logging.set_verbosity_error()
|
|
|
|
if shared.args.flexgen:
|
|
from flexgen.flex_opt import CompressionConfig, ExecutionEnv, OptLM, Policy
|
|
|
|
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
|
|
|
|
|
|
def load_model(model_name):
|
|
print(f"Loading {model_name}...")
|
|
t0 = time.time()
|
|
|
|
shared.is_RWKV = 'rwkv-' in model_name.lower()
|
|
shared.is_llamacpp = len(list(Path(f'{shared.args.model_dir}/{model_name}').glob('ggml*.bin'))) > 0
|
|
if 'chatglm' in model_name.lower():
|
|
LoaderClass = AutoModel
|
|
trust_remote_code = shared.args.trust_remote_code
|
|
else:
|
|
LoaderClass = AutoModelForCausalLM
|
|
trust_remote_code = False
|
|
|
|
# Load the model in simple 16-bit mode by default
|
|
if not any([shared.args.cpu, shared.args.load_in_8bit, shared.args.wbits, 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, shared.is_llamacpp]):
|
|
model = LoaderClass.from_pretrained(Path(f"{shared.args.model_dir}/{shared.model_name}"), low_cpu_mem_usage=True, torch_dtype=torch.bfloat16 if shared.args.bf16 else torch.float16, trust_remote_code=trust_remote_code)
|
|
if torch.has_mps:
|
|
device = torch.device('mps')
|
|
model = model.to(device)
|
|
else:
|
|
model = model.cuda()
|
|
|
|
# FlexGen
|
|
elif shared.args.flexgen:
|
|
# 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/{shared.model_name}", env, shared.args.model_dir, policy)
|
|
|
|
# DeepSpeed ZeRO-3
|
|
elif shared.args.deepspeed:
|
|
model = LoaderClass.from_pretrained(Path(f"{shared.args.model_dir}/{shared.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
|
|
print(f"DeepSpeed ZeRO-3 is enabled: {is_deepspeed_zero3_enabled()}")
|
|
|
|
# RMKV model (not on HuggingFace)
|
|
elif shared.is_RWKV:
|
|
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
|
|
|
|
# Quantized model
|
|
elif shared.args.wbits > 0:
|
|
from modules.GPTQ_loader import load_quantized
|
|
|
|
model = load_quantized(model_name)
|
|
|
|
# llamacpp model
|
|
elif shared.is_llamacpp:
|
|
from modules.llamacpp_model_alternative import LlamaCppModel
|
|
|
|
model_file = list(Path(f'{shared.args.model_dir}/{model_name}').glob('ggml*.bin'))[0]
|
|
print(f"llama.cpp weights detected: {model_file}\n")
|
|
|
|
model, tokenizer = LlamaCppModel.from_pretrained(model_file)
|
|
return model, tokenizer
|
|
|
|
# Custom
|
|
else:
|
|
params = {"low_cpu_mem_usage": True}
|
|
if not any((shared.args.cpu, torch.cuda.is_available(), torch.has_mps)):
|
|
print("Warning: torch.cuda.is_available() returned False.\nThis means that no GPU has been detected.\nFalling back to CPU mode.\n")
|
|
shared.args.cpu = True
|
|
|
|
if shared.args.cpu:
|
|
params["torch_dtype"] = torch.float32
|
|
else:
|
|
params["device_map"] = 'auto'
|
|
params["trust_remote_code"] = trust_remote_code
|
|
if 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
|
|
|
|
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
|
|
params['max_memory'] = max_memory
|
|
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))
|
|
print(f"\033[1;32;1mAuto-assiging --gpu-memory {suggestion} for your GPU to try to prevent out-of-memory errors.\nYou can manually set other values.\033[0;37;0m")
|
|
|
|
max_memory = {0: f'{suggestion}GiB', 'cpu': f'{shared.args.cpu_memory or 99}GiB'}
|
|
params['max_memory'] = max_memory
|
|
|
|
if shared.args.disk:
|
|
params["offload_folder"] = shared.args.disk_cache_dir
|
|
|
|
checkpoint = Path(f'{shared.args.model_dir}/{shared.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)
|
|
with init_empty_weights():
|
|
model = LoaderClass.from_config(config)
|
|
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)
|
|
|
|
# Hijack attention with xformers
|
|
if any((shared.args.xformers, shared.args.sdp_attention)):
|
|
llama_attn_hijack.hijack_llama_attention()
|
|
|
|
# Loading the tokenizer
|
|
if any((k in shared.model_name.lower() for k in ['gpt4chan', 'gpt-4chan'])) 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 type(model) is transformers.LlamaForCausalLM:
|
|
tokenizer = LlamaTokenizer.from_pretrained(Path(f"{shared.args.model_dir}/{shared.model_name}/"), clean_up_tokenization_spaces=True)
|
|
# Leaving this here until the LLaMA tokenizer gets figured out.
|
|
# For some people this fixes things, for others it causes an error.
|
|
try:
|
|
tokenizer.eos_token_id = 2
|
|
tokenizer.bos_token_id = 1
|
|
tokenizer.pad_token_id = 0
|
|
except:
|
|
pass
|
|
else:
|
|
tokenizer = AutoTokenizer.from_pretrained(Path(f"{shared.args.model_dir}/{shared.model_name}/"), trust_remote_code=trust_remote_code)
|
|
|
|
print(f"Loaded the model in {(time.time()-t0):.2f} seconds.")
|
|
return model, tokenizer
|
|
|
|
|
|
def clear_torch_cache():
|
|
gc.collect()
|
|
if not shared.args.cpu:
|
|
torch.cuda.empty_cache()
|
|
|
|
|
|
def unload_model():
|
|
shared.model = shared.tokenizer = None
|
|
clear_torch_cache()
|
|
|
|
|
|
def reload_model():
|
|
unload_model()
|
|
shared.model, shared.tokenizer = load_model(shared.model_name)
|
|
|
|
|
|
def load_soft_prompt(name):
|
|
if name == 'None':
|
|
shared.soft_prompt = False
|
|
shared.soft_prompt_tensor = None
|
|
else:
|
|
with zipfile.ZipFile(Path(f'softprompts/{name}.zip')) as zf:
|
|
zf.extract('tensor.npy')
|
|
zf.extract('meta.json')
|
|
j = json.loads(open('meta.json', 'r').read())
|
|
print(f"\nLoading the softprompt \"{name}\".")
|
|
for field in j:
|
|
if field != 'name':
|
|
if type(j[field]) is list:
|
|
print(f"{field}: {', '.join(j[field])}")
|
|
else:
|
|
print(f"{field}: {j[field]}")
|
|
print()
|
|
tensor = np.load('tensor.npy')
|
|
Path('tensor.npy').unlink()
|
|
Path('meta.json').unlink()
|
|
tensor = torch.Tensor(tensor).to(device=shared.model.device, dtype=shared.model.dtype)
|
|
tensor = torch.reshape(tensor, (1, tensor.shape[0], tensor.shape[1]))
|
|
|
|
shared.soft_prompt = True
|
|
shared.soft_prompt_tensor = tensor
|
|
|
|
return name
|