File size: 6,739 Bytes
df529cc | 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 | #!/usr/bin/env python3
"""Materialize controlled full-data ChartQA OPDVR experiment recipes."""
from __future__ import annotations
import argparse
import os
import sys
from pathlib import Path
from typing import Any
import yaml
PROJECT_ROOT = Path(__file__).resolve().parents[1]
if str(PROJECT_ROOT) not in sys.path:
sys.path.insert(0, str(PROJECT_ROOT))
from config.loader import load_config, validate_config
ARMS = ("reward_gate", "reward_gate_trajectory")
def _require_student_checkpoint(path: Path) -> Path:
resolved = path.expanduser().resolve(strict=False)
if not (resolved / "config.json").is_file():
raise FileNotFoundError(f"student checkpoint lacks config.json: {resolved}")
if not any(resolved.glob("*.safetensors")) and not any(resolved.glob("*.bin")):
raise FileNotFoundError(f"student checkpoint lacks model weights: {resolved}")
return resolved
def materialize(args: argparse.Namespace) -> dict[str, Any]:
config = load_config(str(args.base_config))
student_checkpoint = _require_student_checkpoint(args.student_checkpoint)
output_dir = args.output_dir.expanduser().resolve(strict=False)
config["model"]["pretrained_model_path"] = str(student_checkpoint)
config["model"]["teacher_device_map"] = "local"
training = config["training"]
training["num_gpus"] = 4
training["num_client"] = 4
train_args = training["dyme_args"]
train_args.update(
output_dir=str(output_dir),
num_train_epochs=float(args.num_train_epochs),
learning_rate=float(args.learning_rate),
per_device_train_batch_size=2,
gradient_accumulation_steps=8,
num_generations=4,
max_completion_length=96,
temperature=0.5,
repetition_penalty=1.0,
logging_steps=1,
save_total_limit=3,
seed=42,
)
train_args.pop("resume_from_checkpoint", None)
loss = config["opsd"]["loss"]
loss.update(
loss_type="opdvr",
opsd_weight=1.0,
grpo_weight=0.0,
sft_weight=0.0,
acc_gate=False,
reward_gate={
"enabled": True,
"correct_threshold": 0.5,
"token_scope": "answer_span",
# Full-data OPDVR uses the verifier sign constraint rather than
# dropping source rows where the independent teacher was wrong.
"require_teacher_correct": False,
"require_teacher_gate_metadata": True,
# Zero means exact, unclipped log-probability ratios.
"max_abs_log_ratio": 0.0,
},
)
config["opsd"]["teacher_probe"]["enabled"] = False
config["opsd"]["teacher_trajectory"]["enabled"] = (
args.arm == "reward_gate_trajectory"
)
config["opsd"]["teacher_trajectory"]["context_providers"] = [
"visual_facts_deplot",
"format_only",
]
# Generate the four unique prompts in each rollout micro-batch together.
# This is the controlled trajectory-only difference between the arms; it
# avoids four serial 7B generate calls without changing any samples.
config["opsd"]["teacher_trajectory"]["batch_size"] = 4
config["opsd"]["teacher_trajectory"]["max_loss_rows_per_rank"] = 2
config["opsd"]["teacher_trajectory"]["loss_type"] = "fkl"
config["opsd"]["teacher_trajectory"]["weight"] = 0.5
config["opsd"]["visual_supervision"]["enabled"] = False
config["opsd"]["visual_supervision"]["checker"]["enabled"] = False
config["opsd"]["visual_supervision"]["refiner"]["enabled"] = False
teacher_gate = config["dataset"]["teacher_gate"]
teacher_gate.update(
enabled=True,
selection_mode="all",
expected_selected_rows=4576,
)
config["dataset"]["max_train_samples"] = None
config["data_validation"]["expected_samples"] = 4576
config["data_validation"]["require_qwen_rewrite"] = False
checkpoint_eval = config["checkpoint_eval"]
checkpoint_eval.update(
enabled=not args.smoke,
initial_eval=not args.smoke,
gate_steps=[10, 25, 50, 75],
regular_steps=100,
patience=3,
patience_start_step=100,
tie_policy="count",
)
if args.smoke:
train_args["max_steps"] = int(args.max_steps)
train_args["num_train_epochs"] = 1.0
train_args["save_strategy"] = "no"
config["opsd"]["debug"]["detail_every"] = 1
config["opsd"]["debug"]["distributed_trace"]["cuda_sync"] = True
else:
train_args.pop("max_steps", None)
train_args["save_strategy"] = "steps"
train_args["save_steps"] = 100
config["opsd"]["debug"]["detail_every"] = 10
config["opsd"]["debug"]["distributed_trace"]["cuda_sync"] = False
validated = validate_config(config, source=str(args.output_config))
target = args.output_config.expanduser().resolve(strict=False)
target.parent.mkdir(parents=True, exist_ok=True)
temporary = target.with_name(f".{target.name}.tmp-{os.getpid()}")
with temporary.open("w", encoding="utf-8") as handle:
yaml.safe_dump(validated, handle, allow_unicode=True, sort_keys=False)
os.replace(temporary, target)
return validated
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser()
parser.add_argument("--base-config", type=Path, required=True)
parser.add_argument("--student-checkpoint", type=Path, required=True)
parser.add_argument("--output-config", type=Path, required=True)
parser.add_argument("--output-dir", type=Path, required=True)
parser.add_argument("--arm", choices=ARMS, required=True)
parser.add_argument("--num-train-epochs", type=float, default=4.0)
parser.add_argument("--learning-rate", type=float, default=1.0e-6)
parser.add_argument("--smoke", action="store_true")
parser.add_argument("--max-steps", type=int, default=4)
return parser.parse_args()
def main() -> None:
args = parse_args()
if args.num_train_epochs <= 0:
raise ValueError("--num-train-epochs must be positive")
if args.learning_rate <= 0:
raise ValueError("--learning-rate must be positive")
if args.smoke and args.max_steps <= 0:
raise ValueError("--max-steps must be positive for smoke runs")
config = materialize(args)
print(
"Materialized ChartQA OPDVR recipe: "
f"arm={args.arm} smoke={args.smoke} "
f"trajectory={config['opsd']['teacher_trajectory']['enabled']} "
f"rows={config['dataset']['teacher_gate']['expected_selected_rows']} "
f"student={config['model']['pretrained_model_path']} "
f"output={config['training']['dyme_args']['output_dir']}",
flush=True,
)
if __name__ == "__main__":
main()
|