text-generation-webui/modules/models.py
2023-03-30 11:22:37 +01:00

205 lines
9.2 KiB
Python

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, AutoModelForCausalLM, AutoTokenizer,
BitsAndBytesConfig)
import modules.shared as shared
transformers.logging.set_verbosity_error()
local_rank = None
if shared.args.flexgen:
from flexgen.flex_opt import CompressionConfig, ExecutionEnv, OptLM, Policy
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 = model_name.lower().startswith('llamacpp-')
# Default settings
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]):
if any(size in shared.model_name.lower() for size in ('13b', '20b', '30b')):
model = AutoModelForCausalLM.from_pretrained(Path(f"{shared.args.model_dir}/{shared.model_name}"), device_map='auto', load_in_8bit=True)
else:
model = AutoModelForCausalLM.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)
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 = AutoModelForCausalLM.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 import LlamaCppModel
model, tokenizer = LlamaCppModel.from_pretrained(Path(f'models/{model_name}/ggml-model-q4_0.bin'))
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'
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 = AutoModelForCausalLM.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 = AutoModelForCausalLM.from_pretrained(checkpoint, **params)
# 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/"))
else:
tokenizer = AutoTokenizer.from_pretrained(Path(f"{shared.args.model_dir}/{shared.model_name}/"))
tokenizer.truncation_side = 'left'
print(f"Loaded the model in {(time.time()-t0):.2f} seconds.")
return model, tokenizer
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