File size: 3,340 Bytes
4198d45 | 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 | #!/usr/bin/env python3
# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
"""Run released pre-decoders on the five T0-T4 simulated noise tasks."""
from __future__ import annotations
import argparse
import sys
from pathlib import Path
CODE_ROOT = Path(__file__).resolve().parents[1]
if str(CODE_ROOT) not in sys.path:
sys.path.insert(0, str(CODE_ROOT))
from scripts.qadapt_example_utils import ( # noqa: E402
InferenceJob,
TASK_CONFIGS,
add_common_inference_args,
build_paired_command,
parse_gpus,
run_jobs,
)
TASK_BY_ID = {
f"T{index}": (task_key, config_name)
for index, (task_key, config_name) in enumerate(TASK_CONFIGS)
}
def parse_distances(value: str) -> list[int]:
result = [int(item.strip()) for item in value.split(",") if item.strip()]
if not result or result != sorted(set(result)):
raise argparse.ArgumentTypeError(
"distances must be a non-empty, increasing comma-separated list"
)
return result
def parse_tasks(value: str) -> list[str]:
result = [item.strip().upper() for item in value.split(",") if item.strip()]
if not result or len(result) != len(set(result)):
raise argparse.ArgumentTypeError(
"tasks must be a non-empty comma-separated subset of T0,T1,T2,T3,T4"
)
unknown = [item for item in result if item not in TASK_BY_ID]
if unknown:
raise argparse.ArgumentTypeError(f"unknown task(s): {','.join(unknown)}")
return result
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument(
"--distances",
type=parse_distances,
default=[9],
help=(
"Comma-separated distances. Use 7,9 with --tasks T0 for the "
"paper's mapped-noise geometry; the default is release coverage at d=9."
),
)
parser.add_argument(
"--tasks",
type=parse_tasks,
default=list(TASK_BY_ID),
help="Comma-separated task subset; defaults to T0,T1,T2,T3,T4.",
)
parser.add_argument("--n-rounds", type=int, default=9)
add_common_inference_args(
parser,
default_output_dir=Path("outputs/examples/released_models/t0_t4"),
)
return parser.parse_args()
def main() -> None:
args = parse_args()
jobs = []
for distance in args.distances:
for task_id in args.tasks:
task_key, config_name = TASK_BY_ID[task_id]
label = f"d{distance}_{task_key}"
output_path = args.output_dir / f"d{distance}" / f"{task_key}.json"
jobs.append(
InferenceJob(
label=label,
command=build_paired_command(
args,
config_name=config_name,
output_path=output_path,
distance=distance,
n_rounds=args.n_rounds,
),
output_path=output_path,
)
)
run_jobs(
jobs,
gpus=parse_gpus(args.gpus),
parallelism=args.parallelism,
resume=args.resume,
dry_run=args.dry_run,
)
if __name__ == "__main__":
main()
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