d90e7428
 tangwang
补充重排
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"""
Minimal BGE reranker for pairwise scoring (query, doc).
Features:
- Model loading with optional FP16
- Length-based sorting to reduce padding waste
- Deduplication to avoid redundant inference
- Scores returned in original doc order
"""
import logging
import math
import threading
import time
from typing import Any, Dict, List, Optional, Tuple
import torch
from modelscope import AutoModelForSequenceClassification, AutoTokenizer
logger = logging.getLogger("reranker.core")
class BGEReranker:
def __init__(
self,
model_name: str = "BAAI/bge-reranker-v2-m3",
device: Optional[str] = None,
batch_size: int = 64,
use_fp16: bool = True,
max_length: int = 512,
cache_dir: str = "./model_cache",
enable_warmup: bool = True,
) -> None:
self.model_name = model_name
self.device = device or ("cuda" if torch.cuda.is_available() else "cpu")
self.batch_size = max(1, int(batch_size))
self.max_length = int(max_length)
self.use_fp16 = bool(use_fp16 and self.device == "cuda")
self._lock = threading.Lock()
logger.info(
"[BGE_RERANKER] Loading model %s on %s (fp16=%s)",
self.model_name,
self.device,
self.use_fp16,
)
self.tokenizer = AutoTokenizer.from_pretrained(
self.model_name, trust_remote_code=True, cache_dir=cache_dir
)
self.model = AutoModelForSequenceClassification.from_pretrained(
self.model_name, trust_remote_code=True, cache_dir=cache_dir
)
self.model = self.model.to(self.device)
self.model.eval()
if self.use_fp16:
self.model = self.model.half()
if self.device == "cuda":
torch.backends.cudnn.benchmark = True
if enable_warmup:
self._warmup()
logger.info(
"[BGE_RERANKER] Model ready | model=%s device=%s fp16=%s batch=%s max_len=%s",
self.model_name,
self.device,
self.use_fp16,
self.batch_size,
self.max_length,
)
def _warmup(self) -> None:
try:
with torch.inference_mode():
pairs = [["warmup", "warmup"]]
inputs = self.tokenizer(
pairs,
padding=True,
truncation=True,
return_tensors="pt",
max_length=self.max_length,
)
inputs = {k: v.to(self.device) for k, v in inputs.items()}
if self.use_fp16:
inputs = {
k: (v.half() if v.dtype == torch.float32 else v)
for k, v in inputs.items()
}
_ = self.model(**inputs, return_dict=True).logits
if self.device == "cuda":
torch.cuda.synchronize()
except Exception as exc:
logger.warning("[BGE_RERANKER] Warmup failed: %s", exc)
def score(self, query: str, docs: List[str], normalize: bool = True) -> List[float]:
scores, _meta = self.score_with_meta(query, docs, normalize=normalize)
return scores
def score_with_meta(
self, query: str, docs: List[str], normalize: bool = True
) -> Tuple[List[float], Dict[str, Any]]:
start_ts = time.time()
if docs is None:
docs = []
query = "" if query is None else str(query).strip()
total_docs = len(docs)
output_scores: List[float] = [0.0] * total_docs
indexed_docs: List[Tuple[int, str]] = []
for i, doc in enumerate(docs):
if doc is None:
continue
text = str(doc).strip()
if not text:
continue
indexed_docs.append((i, text))
if not query or not indexed_docs:
elapsed_ms = (time.time() - start_ts) * 1000.0
return output_scores, {
"input_docs": total_docs,
"usable_docs": len(indexed_docs),
"unique_docs": 0,
"dedup_ratio": 0.0,
"elapsed_ms": round(elapsed_ms, 3),
}
# Sort by estimated length + text to cluster similar lengths
indexed_docs.sort(key=lambda x: (len(x[1]), x[1]))
unique_texts: List[str] = []
position_to_unique: List[int] = []
prev_text: Optional[str] = None
for _idx, text in indexed_docs:
if text != prev_text:
unique_texts.append(text)
prev_text = text
position_to_unique.append(len(unique_texts) - 1)
logger.debug(
"[BGE_RERANKER] Preprocess | input=%d usable=%d unique=%d",
total_docs,
len(indexed_docs),
len(unique_texts),
)
unique_scores = self._score_unique(
query=query, passages=unique_texts, normalize=normalize
)
for (orig_idx, _text), unique_idx in zip(indexed_docs, position_to_unique):
output_scores[orig_idx] = float(unique_scores[unique_idx])
elapsed_ms = (time.time() - start_ts) * 1000.0
dedup_ratio = 0.0
if indexed_docs:
dedup_ratio = 1.0 - (len(unique_texts) / float(len(indexed_docs)))
meta = {
"input_docs": total_docs,
"usable_docs": len(indexed_docs),
"unique_docs": len(unique_texts),
"dedup_ratio": round(dedup_ratio, 4),
"elapsed_ms": round(elapsed_ms, 3),
"model": self.model_name,
"device": self.device,
"fp16": self.use_fp16,
"batch_size": self.batch_size,
"max_length": self.max_length,
"normalize": normalize,
}
logger.info(
"[BGE_RERANKER] Done | input=%d usable=%d unique=%d dedup=%s elapsed_ms=%s",
meta["input_docs"],
meta["usable_docs"],
meta["unique_docs"],
meta["dedup_ratio"],
meta["elapsed_ms"],
)
return output_scores, meta
def _compute_optimal_batch_size(self, total: int) -> int:
if total <= 0:
return 1
current_batch_size = self.batch_size + 8
current_batch_count = math.ceil(total / current_batch_size)
optimal_batch_size = current_batch_size
test_batch_size = current_batch_size - 4
while test_batch_size > 0:
test_batch_count = math.ceil(total / test_batch_size)
if test_batch_count <= current_batch_count:
optimal_batch_size = test_batch_size
test_batch_size -= 4
else:
break
return max(1, optimal_batch_size)
def _score_unique(
self, query: str, passages: List[str], normalize: bool = True
) -> List[float]:
if not passages:
return []
optimal_batch_size = self._compute_optimal_batch_size(len(passages))
logger.info(
"[BGE_RERANKER] Reranking %d unique passages | batch=%d | device=%s | fp16=%s",
len(passages),
optimal_batch_size,
self.device,
self.use_fp16,
)
scores: List[float] = []
with self._lock:
for i in range(0, len(passages), optimal_batch_size):
batch_passages = passages[i : i + optimal_batch_size]
pairs = [[query, passage] for passage in batch_passages]
with torch.inference_mode():
inputs = self.tokenizer(
pairs,
padding=True,
truncation=True,
return_tensors="pt",
max_length=self.max_length,
add_special_tokens=True,
)
inputs = {k: v.to(self.device) for k, v in inputs.items()}
if self.use_fp16:
inputs = {
k: (v.half() if v.dtype == torch.float32 else v)
for k, v in inputs.items()
}
logits = self.model(**inputs, return_dict=True).logits.view(-1).float()
if normalize:
logits = torch.sigmoid(logits)
batch_scores = logits.detach().cpu().numpy().tolist()
scores.extend(batch_scores)
return scores
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