text-generation-webui/modules/models.py
2023-03-09 21:54:50 -03:00

188 lines
8.2 KiB
Python

import json
import os
import sys
import time
import zipfile
from pathlib import Path
import numpy as np
import torch
import transformers
from transformers import AutoModelForCausalLM, AutoTokenizer
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, str2bool)
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 = model_name.lower().startswith('rwkv-')
# Default settings
if not (shared.args.cpu or shared.args.load_in_8bit 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 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:
model = AutoModelForCausalLM.from_pretrained(Path(f"models/{shared.model_name}"), low_cpu_mem_usage=True, torch_dtype=torch.bfloat16 if shared.args.bf16 else torch.float16).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, "models", policy)
# DeepSpeed ZeRO-3
elif shared.args.deepspeed:
model = AutoModelForCausalLM.from_pretrained(Path(f"models/{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'models/{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('models'))
return model, tokenizer
# 4-bit LLaMA
elif shared.args.load_in_4bit:
sys.path.insert(0, os.path.abspath(Path("repositories/GPTQ-for-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 = 'llama-7b-4bit.pt'
if path_to_model.name.lower().startswith('llama-13b'):
pt_model = 'llama-13b-4bit.pt'
if path_to_model.name.lower().startswith('llama-30b'):
pt_model = 'llama-30b-4bit.pt'
if not Path(f"models/{pt_model}").exists():
print(f"Could not find models/{pt_model}, exiting...")
exit()
elif pt_model == '':
print(f"Could not find the .pt model for {model_name}, exiting...")
exit()
model = load_quant(path_to_model, Path(f"models/{pt_model}"), 4)
model = model.to(torch.device('cuda:0'))
# Custom
else:
command = "AutoModelForCausalLM.from_pretrained"
params = ["low_cpu_mem_usage=True"]
if not shared.args.cpu and not torch.cuda.is_available():
print("Warning: no GPU has been detected.\nFalling back to CPU mode.\n")
shared.args.cpu = True
if shared.args.cpu:
params.append("low_cpu_mem_usage=True")
params.append("torch_dtype=torch.float32")
else:
params.append("device_map='auto'")
params.append("load_in_8bit=True" if shared.args.load_in_8bit else "torch_dtype=torch.bfloat16" if shared.args.bf16 else "torch_dtype=torch.float16")
if shared.args.gpu_memory:
memory_map = shared.args.gpu_memory
max_memory = f"max_memory={{0: '{memory_map[0]}GiB'"
for i in range(1, len(memory_map)):
max_memory += (f", {i}: '{memory_map[i]}GiB'")
max_memory += (f", 'cpu': '{shared.args.cpu_memory or '99'}GiB'}}")
params.append(max_memory)
elif not shared.args.load_in_8bit:
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")
params.append(f"max_memory={{0: '{suggestion}GiB', 'cpu': '{shared.args.cpu_memory or '99'}GiB'}}")
if shared.args.disk:
params.append(f"offload_folder='{shared.args.disk_cache_dir}'")
command = f"{command}(Path(f'models/{shared.model_name}'), {', '.join(set(params))})"
model = eval(command)
# Loading the tokenizer
if shared.model_name.lower().startswith(('gpt4chan', 'gpt-4chan', '4chan')) and Path("models/gpt-j-6B/").exists():
tokenizer = AutoTokenizer.from_pretrained(Path("models/gpt-j-6B/"))
else:
tokenizer = AutoTokenizer.from_pretrained(Path(f"models/{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