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55c92b3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 | #!/usr/bin/env python
"""Console script for lm-quant-toolkit."""
import argparse
import sys
from timeit import default_timer as timer
from lm_quant_toolkit.prep.fnorm import calc_fnorm_for_model
from lm_quant_toolkit.prep.sensitivity import measure_sensitivity
from lm_quant_toolkit.prep.wdist import calculate_kurtosis_llm
from lm_quant_toolkit.utils.hub import (
LLAMA_MODELS,
get_hf_model_storge_base_dir,
)
def get_parser_args():
parser = argparse.ArgumentParser()
subparsers = parser.add_subparsers()
parser_sensi = subparsers.add_parser(
"sensi", help="Evaluate and dump sensitivity data"
)
parser_sensi.set_defaults(which="sensi")
parser_sensi.add_argument(
"--model",
type=str,
nargs="+",
default="1",
help="Model to evaluate",
)
parser_sensi.add_argument(
"--quant-method",
type=str,
choices=[
"hqq",
"rtn",
"bnb",
],
default="hqq",
help="Output file location",
)
parser_sensi.add_argument(
"--output-file",
type=str,
default="sensi.csv",
help="Output file location",
)
parser_sensi.add_argument(
"--config",
default=None,
type=str,
nargs="+",
help="bit-group configurations",
)
parser_sensi.add_argument(
"--calib-dataset",
default=None,
type=str,
nargs="+",
help="calibration dataset(s) to use",
)
parser_fnorm = subparsers.add_parser("fnorm", help="Evaluate and dump FNorm data")
parser_fnorm.set_defaults(which="fnorm")
parser_fnorm.add_argument(
"--model",
type=str,
nargs="+",
help="Model to evaluate",
)
parser_fnorm.add_argument(
"--output-dir",
type=str,
default="data",
help="Output directory",
)
parser_kurt = subparsers.add_parser(
"kurtosis", help="Evaluate and dump model kurtosis data"
)
parser_kurt.set_defaults(which="kurtosis")
parser_kurt.add_argument(
"--model",
type=str,
nargs="+",
help="Model to evaluate",
)
parser_kurt.add_argument(
"--output-dir",
type=str,
default="data",
help="Output directory",
)
args = parser.parse_args()
return parser, args
def main():
parser, base = get_parser_args()
print(base)
if not hasattr(base, "which"):
parser.print_help()
return 2
try:
if base.which == "sensi":
main_sensi(base)
elif base.which == "fnorm":
main_fnorm(base)
elif base.which == "kurtosis":
main_kurt(base)
except Exception as e:
print(e)
return 1
return 0
def main_sensi(args):
csv_fp = args.output_file
models = args.model
cfgs = args.config
calib_ds = args.calib_dataset
quant_method = args.quant_method
measure_sensitivity(models, quant_method, cfgs, calib_ds, csv_fp)
def main_fnorm(args):
if not args.model or len(args.model) < 1:
raise ValueError("At least one model is required")
output_dir = args.output_dir
for model_id in args.model:
model = LLAMA_MODELS[model_id]
if not model:
raise ValueError(f"Unsupported model: {model_id}")
t1 = timer()
base_dir = model.get("base_dir", None)
model_base_dir = get_hf_model_storge_base_dir(model_id, base_dir)
calc_fnorm_for_model(
model_id,
model_base_dir,
model["layers"],
output_dir,
)
t2 = timer()
print(f"Finished {model_id} Frobenius norm metrics calc in {t2 - t1} seconds")
def main_kurt(args):
if not args.model or len(args.model) < 1:
raise ValueError("At least one model is required")
output_dir = args.output_dir
for model_id in args.model:
model = LLAMA_MODELS[model_id]
print(model)
if not model:
raise ValueError(f"Unsupported model: {model_id}")
t1 = timer()
base_dir = model.get("base_dir", None)
# print(base_dir)
model_base_dir = get_hf_model_storge_base_dir(model_id, base_dir)
print(model_base_dir)
calculate_kurtosis_llm(
model_id,
model_base_dir,
model["layers"],
output_dir,
)
t2 = timer()
print(f"Finished {model_id} Kurtosis metrics calc in {t2 - t1} seconds")
if __name__ == "__main__":
sys.exit(main()) # pragma: no cover
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