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df724f2 63e14b4 df724f2 | 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 | #!/usr/bin/env python3
"""
Validate JSONL rows against what training/sft_train.py will actually use.
Read-only; does not run training.
"""
from __future__ import annotations
import argparse
import json
import statistics
import sys
from pathlib import Path
# Mirror training/sft_train._extract_completions (do not import to avoid heavy deps at import time)
def _extract_completions(rec: dict) -> list[str]:
completion = rec.get("completion")
if isinstance(completion, str) and completion.strip():
return [completion.strip()]
conversation = rec.get("conversation", [])
candidates: list[str] = []
if isinstance(conversation, list):
for turn in conversation:
if not isinstance(turn, dict):
continue
role = str(turn.get("role", "")).lower()
content = str(turn.get("content", "")).strip()
if role == "negotiator" and content:
candidates.append(content)
return candidates
def _approx_tokens(text: str) -> float:
"""Rough token estimate without tokenizer (good enough for preflight OOM risk)."""
if not text:
return 0.0
return len(text) / 4.0
def main() -> None:
parser = argparse.ArgumentParser(description="Validate SFT JSONL against sft_train.py expectations")
parser.add_argument("--data", type=str, default="data/episodes.jsonl", help="Path to JSONL file")
args = parser.parse_args()
path = Path(args.data)
if not path.is_file():
print(f"File not found: {path.resolve()}")
print("Run: python -m training.generate_data --episodes 80 --output data/episodes.jsonl")
return
usable_rows = 0
skipped = 0
prompt_tok: list[float] = []
completion_tok: list[float] = []
first_bad_line: int | None = None
with path.open("r", encoding="utf-8") as f:
for line_no, line in enumerate(f, 1):
line = line.strip()
if not line:
continue
try:
rec = json.loads(line)
except json.JSONDecodeError:
skipped += 1
if first_bad_line is None:
first_bad_line = line_no
continue
if not isinstance(rec, dict):
skipped += 1
continue
prompt = str(rec.get("prompt", "")).strip()
if not prompt:
skipped += 1
continue
completions = _extract_completions(rec)
if not completions:
skipped += 1
continue
usable_rows += 1
p_t = _approx_tokens(prompt)
prompt_tok.append(p_t)
for c in completions:
completion_tok.append(_approx_tokens(c))
sft_trains_one_row_per_completion = "sft_train.py expands one dataset row per negotiator line"
print("SFT data validator (vs training/sft_train.py load_sft_dataset / _extract_completions)")
print(f" File: {path.resolve()}")
print(f" Note: {sft_trains_one_row_per_completion} when 'completion' is absent.")
print()
print(f" JSONL records usable (has non-empty 'prompt' and completion or negotiator text): {usable_rows}")
print(f" Records / rows SKIPPED: {skipped}")
if first_bad_line is not None:
print(f" (includes malformed JSONL starting around line {first_bad_line} if any)")
print()
if not prompt_tok and not completion_tok:
print("No prompt/completion lengths to summarize (all skipped).")
else:
def _summary(vals: list[float], label: str) -> None:
if not vals:
print(f" {label}: (empty)")
return
print(
f" {label} (approx. tokens, len/4): "
f"min={min(vals):.1f} max={max(vals):.1f} mean={statistics.mean(vals):.1f} "
f"std={(statistics.pstdev(vals) if len(vals) > 1 else 0.0):.1f}"
)
_summary(prompt_tok, "Prompt length")
_summary(completion_tok, "Completion length (each negotiator / completion string)")
if prompt_tok and statistics.mean(prompt_tok) > 2048.0:
print(
" FLAG: Mean prompt length > 2048 (approx. tokens) - may OOM or truncate with "
"SFTConfig max_length=2048 in sft_train.py on small GPUs."
)
print()
print(f" {usable_rows} records usable for SFT, {skipped} will be skipped (at record level; negotiator")
print(" expansion in sft_train can still multiply rows for usable records).")
if usable_rows < 50:
print(" WARNING: May be insufficient for SFT. Generate more data first.")
if usable_rows == 0:
sys.exit(1)
if __name__ == "__main__":
main()
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