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fb5b18e | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 | from __future__ import annotations
import argparse
import os
import re
import sys
from pathlib import Path
from typing import Any, Dict, Tuple
import torch
THIS_DIR = Path(__file__).resolve().parent
REPO_ROOT = THIS_DIR.parents[2]
if str(REPO_ROOT) not in sys.path:
sys.path.insert(0, str(REPO_ROOT))
from quantization.escnet.data.calib import build_calib_loader
from quantization.escnet.methods.registry import METHODS
from quantization.escnet.quant.calibrate import run_calibration
from quantization.escnet.quant.config import QuantConfig
from quantization.escnet.quant.prepare import finalize_act_calibration
from quantization.escnet.utils.io import save_quant_checkpoint
from quantization.escnet.utils.seed import set_seed
def _import_load_config():
from model_sources.escnet.ESCNet_model.config import load_config
return load_config
def _import_escnet():
try:
from model_sources.escnet.ESCNet_model.models.ESCNet import ESCNet # type: ignore
return ESCNet
except Exception as e:
raise RuntimeError(
"Failed to import ESCNet. Run this from repo root and ensure ./model_sources/escnet/ESCNet_model exists."
) from e
def _sanitize_state_dict(sd: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]:
# handle common wrappers
if "state_dict" in sd and isinstance(sd["state_dict"], dict):
sd = sd["state_dict"]
if "model" in sd and isinstance(sd["model"], dict):
sd = sd["model"]
# strip 'module.' from DataParallel
keys = list(sd.keys())
if keys and keys[0].startswith("module."):
sd = {k[len("module."):]: v for k, v in sd.items()}
return sd
def _none_if_negative(v: float) -> float | None:
return None if float(v) < 0 else float(v)
def _apply_recipe(args: argparse.Namespace) -> None:
recipe = str(getattr(args, "recipe", "custom")).lower()
if recipe == "legacy_our":
args.method = "ptq_qdq"
args.w_bits = 4
args.a_bits = 4
args.w_mode = "per_channel"
args.a_mode = "per_token_group"
args.group_size = 32
args.dstg_clip_percentile = -1.0
args.dcrp_tau = 1.0
args.dcrp_zero_ratio = 0.02
args.ep_residual_topk = 0
args.calib_batches = 32
args.calib_batch_size = 4
def _legacy_escnet_qcfg_dict(qcfg: QuantConfig) -> Dict[str, Any]:
return {
"w_bits": int(qcfg.w_bits),
"a_bits": int(qcfg.a_bits),
"w_mode": "per_channel",
"a_mode": "per_token_group",
"group_size": 32,
"token_clip_percentile": None,
"star_tau": 1.0,
"star_zero_ratio": 0.02,
"ep_residual_topk": 0,
"calib_batches": 32,
"calib_batch_size": 4,
"exclude_name_regex": None,
"verbose": False,
}
def main() -> None:
parser = argparse.ArgumentParser("ESCNet PTQ export (QDQ)")
parser.add_argument("--config", type=str, default="configs/escnet/main/config.yaml")
parser.add_argument("--ckpt", type=str, required=True, help="Float checkpoint (.pth)")
parser.add_argument("--out", type=str, required=True, help="Output quant checkpoint (.pt)")
parser.add_argument("--method", type=str, default="ptq_qdq", choices=sorted(METHODS.keys()))
parser.add_argument(
"--recipe",
type=str,
default="custom",
choices=["custom", "legacy_our"],
help="legacy_our uses the released COD-TDQ W4A4 settings.",
)
# quant settings
parser.add_argument("--w_bits", type=int, default=4, choices=[2, 3, 4, 8])
parser.add_argument("--a_bits", type=int, default=4, choices=[2, 3, 4, 8])
parser.add_argument("--a_mode", type=str, default="per_dstg_group",
choices=["per_tensor", "per_channel", "per_dstg_group", "per_token_group"])
parser.add_argument("--w_mode", type=str, default="per_channel", choices=["per_tensor", "per_channel"])
parser.add_argument("--group_size", type=int, default=32)
parser.add_argument("--dstg_clip_percentile", "--token_clip_percentile", dest="dstg_clip_percentile", type=float, default=-1.0,
help="e.g. 0.99; <0 means None (absmax).")
# DCRP
parser.add_argument("--dcrp_tau", "--star_tau", dest="dcrp_tau", type=float, default=-1.0, help="DCRP tau; <0 disables")
parser.add_argument("--dcrp_zero_ratio", "--star_zero_ratio", dest="dcrp_zero_ratio", type=float, default=-1.0, help="DCRP zero_ratio; <0 disables")
# EP residual
parser.add_argument("--ep_residual_topk", type=int, default=0)
# calibration
parser.add_argument("--calib_dir", type=str, default="", help="Dataset dir containing Imgs/GT. Empty->random.")
parser.add_argument("--calib_batches", type=int, default=32)
parser.add_argument("--calib_batch_size", type=int, default=4)
parser.add_argument("--calib_samples", type=int, default=128, help="Used only for random fallback.")
parser.add_argument("--num_workers", type=int, default=4)
parser.add_argument("--device", type=str, default="cuda:0")
parser.add_argument("--exclude_name_regex", type=str, default="", help="Regex to skip wrapping certain modules.")
parser.add_argument("--verbose", action="store_true")
parser.add_argument("--seed", type=int, default=42)
args = parser.parse_args()
_apply_recipe(args)
load_config = _import_load_config()
ESCNet = _import_escnet()
cfg = load_config(args.config)
set_seed(args.seed if args.seed is not None else getattr(cfg, "rand_seed", 42))
device = torch.device(args.device if torch.cuda.is_available() else "cpu")
qcfg = QuantConfig(
w_bits=args.w_bits,
a_bits=args.a_bits,
w_mode=args.w_mode,
a_mode=args.a_mode, # type: ignore
group_size=args.group_size,
dstg_group_size=args.group_size,
dstg_clip_percentile=None if args.dstg_clip_percentile < 0 else float(args.dstg_clip_percentile),
token_clip_percentile=None if args.dstg_clip_percentile < 0 else float(args.dstg_clip_percentile),
dcrp_tau=_none_if_negative(args.dcrp_tau),
dcrp_zero_ratio=_none_if_negative(args.dcrp_zero_ratio),
star_tau=_none_if_negative(args.dcrp_tau),
star_zero_ratio=_none_if_negative(args.dcrp_zero_ratio),
ep_residual_topk=int(args.ep_residual_topk),
calib_batches=int(args.calib_batches),
calib_batch_size=int(args.calib_batch_size),
exclude_name_regex=args.exclude_name_regex or None,
verbose=bool(args.verbose),
)
# build float model (do not autoload backbone weights; we load full ckpt)
model = ESCNet(cfg, pretrained=False)
model.eval()
ckpt = torch.load(args.ckpt, map_location="cpu")
ckpt = _sanitize_state_dict(ckpt)
missing, unexpected = model.load_state_dict(ckpt, strict=False)
if args.verbose:
print(f"[load] missing={len(missing)}, unexpected={len(unexpected)}")
# wrap
preparer = METHODS[args.method]
report = preparer(model, qcfg)
if args.verbose:
print(f"[wrap] {report}")
model.to(device)
# build loader
calib_dir = args.calib_dir if args.calib_dir else None
loader = build_calib_loader(
config_obj=cfg,
calib_dir=calib_dir,
img_size=int(getattr(cfg, "img_size", 416)),
batch_size=qcfg.calib_batch_size,
num_workers=int(args.num_workers),
num_samples=int(args.calib_samples),
seed=args.seed,
)
# calibration + finalize (static modes)
run_calibration(model, loader, device=device, num_batches=qcfg.calib_batches)
finalize_act_calibration(model, device=device)
meta = {
"float_ckpt": os.path.abspath(args.ckpt),
"config": os.path.abspath(args.config),
"method": args.method,
"device": str(device),
}
qcfg_override = _legacy_escnet_qcfg_dict(qcfg) if str(args.recipe).lower() == "legacy_our" else None
save_quant_checkpoint(args.out, model.state_dict(), qcfg, meta, qcfg_override=qcfg_override)
print(f"[done] saved quant checkpoint -> {args.out}")
if __name__ == "__main__":
main()
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