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aituner/runs/frontier-decode-batch-grid-v0/materialize_decode_batch.py

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2.2 KiB
Python
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#!/usr/bin/env python3
"""Create simultaneous, prefix-disjoint requests for a fixed decode batch."""
from __future__ import annotations
import argparse
import json
from pathlib import Path
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser()
parser.add_argument("--model", type=Path, required=True)
parser.add_argument("--batch", type=int, required=True)
parser.add_argument("--input-tokens", type=int, default=2048)
parser.add_argument("--output-tokens", type=int, default=128)
parser.add_argument("--output", type=Path, required=True)
return parser.parse_args()
def main() -> None:
args = parse_args()
if min(args.batch, args.input_tokens, args.output_tokens) <= 0:
raise ValueError("batch and token counts must be positive")
if args.input_tokens + args.output_tokens > 40960:
raise ValueError("request exceeds the server max model length")
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(args.model, trust_remote_code=True)
special = set(tokenizer.all_special_ids)
candidates = [
token for token in range(tokenizer.vocab_size) if token not in special
]
if len(candidates) < args.batch + 1:
raise ValueError("tokenizer has too few non-special token IDs")
base = candidates[0]
args.output.parent.mkdir(parents=True, exist_ok=True)
with args.output.open("w") as output:
for index in range(args.batch):
row = {
"source_index": index,
"arrived_at": 0.0,
"input_length": args.input_tokens,
"output_length": args.output_tokens,
"session_id": index,
"runtime_block_ids": [],
"body": {
"prompt": [
candidates[index + 1],
*([base] * (args.input_tokens - 1)),
],
"min_tokens": args.output_tokens,
"max_tokens": args.output_tokens,
"ignore_eos": True,
},
}
output.write(json.dumps(row, separators=(",", ":")) + "\n")
if __name__ == "__main__":
main()