File size: 6,258 Bytes
6f7a484 | 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 | from __future__ import annotations
import argparse
import json
import sys
from collections import OrderedDict
from pathlib import Path
from typing import Any, Iterable, TextIO
from .constants import (
DEFAULT_DOCUMENT_MAX_LENGTH,
DEFAULT_ENCODER_CHUNK_SIZE,
DEFAULT_MAX_MODEL_LEN,
DEFAULT_QUERY_MAX_LENGTH,
MODEL_ID,
SAMPLE_DOCUMENTS,
SAMPLE_QUERY,
SUPPORTED_ENCODER_CHUNK_SIZES,
parse_encoder_chunk_size,
)
from .reranker import KaLMVLLMReranker
def build_parser() -> argparse.ArgumentParser:
parser = argparse.ArgumentParser(
description="Offline KaLM-Reranker-V1-Nano scoring with vLLM 0.19.1.",
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
)
parser.add_argument("--input-jsonl", type=Path)
parser.add_argument("--output-jsonl", type=Path)
parser.add_argument("--model", default=MODEL_ID)
parser.add_argument("--query-max-length", type=int, default=DEFAULT_QUERY_MAX_LENGTH)
parser.add_argument(
"--document-max-length", type=int, default=DEFAULT_DOCUMENT_MAX_LENGTH
)
parser.add_argument(
"--encoder-chunk-size",
default=str(DEFAULT_ENCODER_CHUNK_SIZE),
help=f"One of {sorted(SUPPORTED_ENCODER_CHUNK_SIZES)}.",
)
parser.add_argument("--max-model-len", type=int, default=DEFAULT_MAX_MODEL_LEN)
parser.add_argument("--batch-size", type=int, default=32)
parser.add_argument("--return-margin", action="store_true")
parser.add_argument("--top-k", type=int)
parser.add_argument("--dtype", default="bfloat16")
parser.add_argument("--gpu-memory-utilization", type=float, default=0.85)
parser.add_argument("--tensor-parallel-size", type=int, default=1)
return parser
def _read_jsonl(path: Path) -> list[dict[str, Any]]:
rows: list[dict[str, Any]] = []
with path.open("r", encoding="utf-8") as handle:
for line_no, line in enumerate(handle, start=1):
if not line.strip():
continue
try:
row = json.loads(line)
except json.JSONDecodeError as error:
raise ValueError(f"{path}:{line_no}: invalid JSON.") from error
if not isinstance(row, dict):
raise ValueError(f"{path}:{line_no}: expected a JSON object.")
if not isinstance(row.get("query"), str):
raise ValueError(f"{path}:{line_no}: 'query' must be a string.")
if not isinstance(row.get("document"), str):
raise ValueError(f"{path}:{line_no}: 'document' must be a string.")
if "instruction" in row and not isinstance(row["instruction"], str):
raise ValueError(f"{path}:{line_no}: 'instruction' must be a string.")
rows.append(row)
return rows
def _write_jsonl(rows: Iterable[dict[str, Any]], output: TextIO) -> None:
for row in rows:
output.write(json.dumps(row, ensure_ascii=False) + "\n")
output.flush()
def _score_rows(
reranker: KaLMVLLMReranker,
rows: list[dict[str, Any]],
*,
return_margin: bool,
top_k: int | None,
) -> list[dict[str, Any]]:
grouped: OrderedDict[str | None, list[tuple[int, dict[str, Any]]]] = OrderedDict()
for index, row in enumerate(rows):
grouped.setdefault(row.get("instruction"), []).append((index, row))
scored: dict[int, dict[str, Any]] = {}
for instruction, items in grouped.items():
predictions = reranker.predict(
[(row["query"], row["document"]) for _, row in items],
instruction=instruction,
return_margin=True,
)
for (index, row), prediction in zip(items, predictions):
assert isinstance(prediction, dict)
result = {
"id": row.get("id", index),
"query": row["query"],
"document": row["document"],
"score": prediction["score"],
}
if "instruction" in row:
result["instruction"] = row["instruction"]
if return_margin:
result["margin"] = prediction["margin"]
scored[index] = result
ordered = [scored[index] for index in range(len(rows))]
if top_k is None:
return ordered
if top_k < 0:
raise ValueError("--top-k must be non-negative.")
by_query: OrderedDict[str, list[dict[str, Any]]] = OrderedDict()
for row in ordered:
by_query.setdefault(str(row["query"]), []).append(row)
output: list[dict[str, Any]] = []
for group in by_query.values():
group.sort(key=lambda item: float(item["score"]), reverse=True)
output.extend(group[:top_k])
return output
def main() -> int:
args = build_parser().parse_args()
with KaLMVLLMReranker(
args.model,
query_max_length=args.query_max_length,
document_max_length=args.document_max_length,
encoder_chunk_size=parse_encoder_chunk_size(args.encoder_chunk_size),
max_model_len=args.max_model_len,
batch_size=args.batch_size,
dtype=args.dtype,
gpu_memory_utilization=args.gpu_memory_utilization,
tensor_parallel_size=args.tensor_parallel_size,
) as reranker:
if args.input_jsonl is None:
rankings = reranker.rank(
SAMPLE_QUERY,
SAMPLE_DOCUMENTS,
top_k=args.top_k,
return_margin=args.return_margin,
)
rows = [
{
"query": SAMPLE_QUERY,
"document": SAMPLE_DOCUMENTS[int(item["corpus_id"])],
**item,
}
for item in rankings
]
else:
rows = _score_rows(
reranker,
_read_jsonl(args.input_jsonl),
return_margin=args.return_margin,
top_k=args.top_k,
)
if args.output_jsonl is None:
_write_jsonl(rows, sys.stdout)
else:
args.output_jsonl.parent.mkdir(parents=True, exist_ok=True)
with args.output_jsonl.open("w", encoding="utf-8") as handle:
_write_jsonl(rows, handle)
return 0
if __name__ == "__main__":
raise SystemExit(main())
|