import os
import json
import sys
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)
import torch.nn.functional as F
from example.common.security.path import get_valid_write_path, get_valid_read_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 msmodelslim import logger
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_anti_dataset(tokenizer, anti_dict, device="npu"):
anti_dataset = []
max_len = 0
anti_list = [anti_dict['prompt']]
for anti_data in anti_list:
inputs = tokenizer(anti_data, return_tensors='pt')
anti_dataset.append(inputs.data['input_ids'].to(device))
max_len = max(max_len, inputs.data['input_ids'].shape[1])
for i, _ in enumerate(anti_dataset):
anti_dataset[i] = F.pad(anti_dataset[i], (0, max_len - anti_dataset[i].shape[1]), value=0)
return torch.cat(anti_dataset)
def get_calib_dataset(tokenizer, calib_dict, device="npu"):
calib_dataset = []
calib_list = [calib_dict['prompt']]
for calib_data in calib_list:
inputs = tokenizer(calib_data, return_tensors='pt').to(device)
calib_dataset.append([inputs.data['input_ids']])
return calib_dataset
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=None)
parser.add_argument(
'--calib_texts',
type=str,
nargs='+',
default=None)
parser.add_argument(
'--calib_file',
type=str,
help='A jsonl file contains calibration data.',
default=os.path.join(os.path.dirname(os.path.dirname(__file__)), 'common', 'teacher_qualification.jsonl'))
parser.add_argument(
'--anti_file',
type=str,
default=os.path.join(os.path.dirname(os.path.dirname(__file__)), 'common', 'mix_dataset_glm.json'))
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=CPU)
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('--tp_size', type=int, default=1)
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='{"padding_side":"left","pad_token":"<unk>"}',
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,
device_map=device_map,
trust_remote_code=self.trust_remote_code
)
tokenizer_args = kwargs.get("tokenizer_args", {})
self.tokenizer = safe_generator.get_tokenizer_from_pretrained(
self.model_path_or_name,
use_fast=False,
trust_remote_code=self.trust_remote_code,
legacy=False,
**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_calib_data, tokenized_anti_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_anti_data, cfg=self.anti_outlier_config)
anti_outlier.process()
calibrator = Calibrator(self.model,
self.quant_config,
calib_data=tokenized_calib_data,
disable_level=disable_level)
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()
try:
rank: int = int(os.getenv("RANK", "0"))
except ValueError as e:
logger.warning(f"Error converting 'RANK' environment variable to integer: {e}")
logger.info("Defaulting to 0.")
rank: int = 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_layers
disable_names = args.disable_names
if not disable_names and args.a_bit == 8:
disable_names = [f"transformer.encoder.layers.{i}.mlp.dense_4h_to_h"
for i in range(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)
tokenizer_args = parse_tokenizer_args(
args.tokenizer_args,
default={"padding_side": "left", "pad_token": "<unk>"}
)
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
)
anti_file_path = get_valid_read_path(args.anti_file, "json", is_dir=False)
calib_file_path = get_valid_read_path(args.calib_file, "json", is_dir=False)
with open(anti_file_path, 'r') as f:
anti_prompts = json.load(f)
with open(calib_file_path, 'r') as f:
calib_prompts = json.load(f)
anti_dataset = []
for anti_prompt in anti_prompts:
tokenizer_anti_prompt = get_anti_dataset(quantifier.tokenizer, anti_prompt, args.device_type)
anti_dataset.append([tokenizer_anti_prompt])
calib_dataset = []
for clib_prompt in calib_prompts:
tokenizer_calib_prompt = get_calib_dataset(quantifier.tokenizer, clib_prompt, args.device_type)
calib_dataset += (tokenizer_calib_prompt)
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(calib_dataset,
anti_dataset, save_directory,
args.disable_level,
part_file_size=args.part_file_size)
quant_type = f"w{args.w_bit}a{args.a_bit}"
is_sparseCompress = args.w_bit == 4 and args.a_bit == 8 and (args.co_sparse or args.is_lowbit)
if is_sparseCompress:
quant_type = "w8a8s"
is_w8a8_dynamic = args.w_bit == 8 and args.a_bit == 8 and args.is_dynamic
if is_w8a8_dynamic:
quant_type = "w8a8_dynamic"
auto_config = checker.get_config_from_pretrained(model_path, trust_remote_code=args.trust_remote_code)
checker.modify_config(model_path, save_directory, auto_config.torch_dtype,
quant_type, args)
checker.copy_tokenizer_files(model_path, save_directory)