import os
import sys
import functools
import torch
current_directory = os.path.dirname(os.path.abspath(__file__))
parent_directory = os.path.abspath(os.path.join(current_directory, '..', ".."))
sys.path.append(parent_directory)
from example.common.security.path import get_valid_write_path
from example.common.utils import SafeGenerator, ArgumentParser, StringArgumentValidator, MAX_KEY_LENGTH, \
MAX_JSON_LENGTH, cmd_bool, parse_tokenizer_args
from msmodelslim.pytorch.llm_ptq.anti_outlier import AntiOutlier, AntiOutlierConfig
from msmodelslim.pytorch.llm_ptq.llm_ptq_tools import Calibrator, QuantConfig
from example.common.copy_config_files import copy_config_files, modify_config_json
CPU = "cpu"
NPU = "npu"
def cmd_bool(cmd_arg):
if cmd_arg == "True":
return True
elif cmd_arg == "False":
return False
raise ValueError(f"{cmd_arg} should be True or False")
def get_disable_names(num_layers: int) -> list:
return [f"model.layers.{i}.mlp.gate.wg" for i in range(num_layers)]
def custom_hook(model_config):
model_config["quantize"] = "w8a8"
model_config["moe_quantize"] = "w8a8_dynamic"
def parse_arguments():
parser = ArgumentParser()
parser.add_argument('--model_path', type=str, help="model and tokenizer path")
parser.add_argument('--save_directory', type=str)
parser.add_argument('--part_file_size', type=int, default=5)
parser.add_argument('--w_bit', type=int, default=8)
parser.add_argument('--a_bit', type=int, default=8)
parser.add_argument('--disable_names', type=str, nargs='+', default=None)
parser.add_argument('--device_type', type=str, choices=[CPU, NPU], default=NPU)
parser.add_argument('--fraction', type=float, default=0.01)
parser.add_argument("--act_method", type=int, choices=[1, 2, 3], default=1,
help=" 1: MinMax, 2: Histogram, 3: Auto")
parser.add_argument('--co_sparse', type=cmd_bool, default=False)
parser.add_argument('--anti_method', type=str, default='')
parser.add_argument('--disable_level', type=str, default='L0')
parser.add_argument('--do_smooth', type=cmd_bool, default=False)
parser.add_argument('--use_sigma', type=cmd_bool, default=False)
parser.add_argument('--use_reduce_quant', type=cmd_bool, default=False)
parser.add_argument('--sigma_factor', type=float, default=3.0)
parser.add_argument('--is_lowbit', type=cmd_bool, default=False)
parser.add_argument('--mm_tensor', type=cmd_bool, default=True)
parser.add_argument('--w_sym', type=cmd_bool, default=True)
parser.add_argument('--use_kvcache_quant', type=cmd_bool, default=False)
parser.add_argument('--use_fa_quant', type=cmd_bool, default=False)
parser.add_argument('--fa_amp', type=int, default=0)
parser.add_argument('--open_outlier', type=cmd_bool, default=True)
parser.add_argument('--group_size', type=int, default=64)
parser.add_argument('--is_dynamic', type=cmd_bool, default=False)
parser.add_argument('--input_ids_name', type=str, default='input_ids',
validator=StringArgumentValidator(min_length=1, max_length=MAX_KEY_LENGTH))
parser.add_argument('--attention_mask_name', type=str, default='attention_mask',
validator=StringArgumentValidator(min_length=1, max_length=MAX_KEY_LENGTH))
parser.add_argument('--tokenizer_args', type=str, default='{}',
validator=StringArgumentValidator(min_length=2, max_length=MAX_JSON_LENGTH))
parser.add_argument('--disable_last_linear', type=cmd_bool, default=True)
parser.add_argument('--model_name', type=str, default=None,
validator=StringArgumentValidator(min_length=1, max_length=MAX_KEY_LENGTH, allow_none=True))
parser.add_argument('--trust_remote_code', type=cmd_bool, default=False)
parser.add_argument('--mindie_format', action="store_true", help="Compatible with quantization formats \
supported by before 2.1.RC1 version of MindIE")
return parser.parse_args()
class Quantifier:
def __init__(self, model_path_or_name, quant_config=None,
anti_outlier_config=None, device_type='cpu', trust_remote_code=False, **kwargs):
safe_generator = SafeGenerator()
self.device_type = device_type
device_map = CPU if self.device_type == CPU else "auto"
self.trust_remote_code = trust_remote_code
self.quant_config = quant_config
self.anti_outlier_config = anti_outlier_config
self.model_path_or_name = model_path_or_name
self.config = safe_generator.get_config_from_pretrained(
self.model_path_or_name,
trust_remote_code=self.trust_remote_code
)
self.dtype = self.config.torch_dtype if self.device_type == NPU else torch.float32
self.model = safe_generator.get_model_from_pretrained(
self.model_path_or_name,
low_cpu_mem_usage=True,
torch_dtype=self.dtype,
trust_remote_code=self.trust_remote_code,
device_map={
"model.embed_tokens": 0,
"model.layers": "cpu",
"model.norm": "cpu",
"lm_head": 0,
}
)
tokenizer_args = kwargs.get("tokenizer_args", {})
self.tokenizer = safe_generator.get_tokenizer_from_pretrained(
self.model_path_or_name,
use_fast=True,
trust_remote_code=self.trust_remote_code,
add_eos_token=True,
**tokenizer_args
)
self.model_name = kwargs.get("model_name", None)
def get_tokenized_data(self, input_texts,
input_ids_name='input_ids',
attention_mask_name='attention_mask'):
tokenized_data = []
for input_text in input_texts:
inputs = self.tokenizer(input_text, return_tensors='pt', padding=True).to(self.device_type)
tokenized_data.append(
[inputs.data[input_ids_name], inputs.data[attention_mask_name]])
return tokenized_data
def convert(self, tokenized_data, save_path, disable_level, part_file_size=None):
if self.device_type == NPU:
torch.npu.set_compile_mode(jit_compile=False)
if self.anti_outlier_config is not None:
anti_outlier = AntiOutlier(self.model, calib_data=tokenized_data, cfg=self.anti_outlier_config)
anti_outlier.process()
mix_cfg = {
"*.experts.*": "w8a8_dynamic",
"*": "w8a8"
}
calibrator = Calibrator(
self.model,
self.quant_config,
calib_data=tokenized_data,
disable_level=disable_level,
mix_cfg=mix_cfg
)
calibrator.run()
save_type = "safe_tensor" if args.mindie_format else "ascendV1"
calibrator.save(save_path, save_type=[save_type], part_file_size=part_file_size)
if __name__ == '__main__':
args = parse_arguments()
checker = SafeGenerator()
rank: int = int(os.getenv("RANK", "0"))
model_path = args.model_path
save_directory = args.save_directory
num_layers = checker.get_config_from_pretrained(
model_path,
trust_remote_code=args.trust_remote_code
).num_hidden_layers
disable_names = args.disable_names
if not disable_names:
disable_names = get_disable_names(num_layers)
quant_conf = QuantConfig(
w_bit=args.w_bit,
a_bit=args.a_bit,
disable_names=disable_names,
dev_type=args.device_type,
dev_id=rank,
act_method=args.act_method,
w_sym=args.w_sym,
mm_tensor=False,
co_sparse=args.co_sparse,
fraction=args.fraction,
sigma_factor=args.sigma_factor,
use_sigma=args.use_sigma,
is_lowbit=args.is_lowbit,
do_smooth=args.do_smooth,
open_outlier=args.open_outlier,
group_size=args.group_size,
use_kvcache_quant=args.use_kvcache_quant,
is_dynamic=args.is_dynamic,
disable_last_linear=args.disable_last_linear,
)
if args.use_fa_quant:
quant_conf = quant_conf.fa_quant(fa_amp=args.fa_amp)
anti_outlier_config_val = None
if args.anti_method == 'm3':
anti_outlier_config_val = AntiOutlierConfig(a_bit=args.a_bit, w_bit=args.w_bit,
anti_method=args.anti_method, w_sym=args.w_sym,
dev_type=args.device_type, dev_id=rank)
elif args.anti_method:
anti_outlier_config_val = AntiOutlierConfig(anti_method=args.anti_method,
dev_type=args.device_type, dev_id=rank)
tokenizer_args = parse_tokenizer_args(
args.tokenizer_args,
default={}
)
quantifier = Quantifier(
model_path, quant_conf, anti_outlier_config_val,
device_type=args.device_type, tokenizer_args=tokenizer_args,
model_name=args.model_name, trust_remote_code=args.trust_remote_code
)
tokenized_calib_data = None
calib_texts = [
"Where is the capital of China?",
"Please make a poem:",
"I want to learn python, how should I learn it?",
"Please help me write a job report on large model inference optimization:",
"What are the most worth visiting scenic spots in China?"
]
if calib_texts is not None:
tokenized_calib_data = quantifier.get_tokenized_data(
calib_texts,
input_ids_name=args.input_ids_name,
attention_mask_name=args.attention_mask_name
)
if not os.path.exists(save_directory):
os.makedirs(save_directory, mode=0o750, exist_ok=True)
save_directory = get_valid_write_path(save_directory, is_dir=True)
quantifier.convert(tokenized_calib_data, save_directory, args.disable_level, part_file_size=args.part_file_size)
custom_hooks = {
'config.json': functools.partial(modify_config_json, custom_hook=custom_hook)
}
copy_config_files(
input_path=model_path,
output_path=save_directory,
quant_config=quant_conf,
mindie_format=args.mindie_format,
custom_hooks=custom_hooks
)
checker.copy_tokenizer_files(model_path, save_directory)
