80955935
 tangwang
Reranker 补充 qwen3...
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)
max_length = int(self._config.get("max_length", 8192))
batch_size = int(self._config.get("batch_size", 64))
use_fp16 = bool(self._config.get("use_fp16", True))
device = self._config.get("device")
attn_impl = self._config.get("attn_implementation") # e.g. "flash_attention_2"
self._model_name = model_name
self._batch_size = batch_size
logger.info(
"[Qwen3_Transformers] Loading model %s (max_length=%s, batch=%s, fp16=%s)",
model_name,
max_length,
batch_size,
use_fp16,
)
self._tokenizer = AutoTokenizer.from_pretrained(model_name, padding_side="left")
self._tokenizer.pad_token = self._tokenizer.eos_token
# Prefix/suffix from official reference
prefix = "<|im_start|>system\nJudge whether the Document meets the requirements based on the Query and the Instruct provided. Note that the answer can only be \"yes\" or \"no\".<|im_end|>\n<|im_start|>user\n"
suffix = "<|im_end|>\n<|im_start|>assistant\n<think>\n\n</think>\n\n"
self._prefix_tokens = self._tokenizer.encode(prefix, add_special_tokens=False)
self._suffix_tokens = self._tokenizer.encode(suffix, add_special_tokens=False)
self._max_length = max_length
self._effective_max_len = max_length - len(self._prefix_tokens) - len(self._suffix_tokens)
self._token_true_id = self._tokenizer.convert_tokens_to_ids("yes")
self._token_false_id = self._tokenizer.convert_tokens_to_ids("no")
kwargs = {}
if use_fp16 and torch.cuda.is_available():
kwargs["torch_dtype"] = torch.float16
if attn_impl:
kwargs["attn_implementation"] = attn_impl
self._model = AutoModelForCausalLM.from_pretrained(model_name, **kwargs).eval()
if device is not None:
self._model = self._model.to(device)
elif torch.cuda.is_available():
self._model = self._model.cuda()
logger.info(
"[Qwen3_Transformers] Model ready | model=%s device=%s",
model_name,
next(self._model.parameters()).device,
)
def _process_inputs(self, pairs: List[str]) -> Dict[str, torch.Tensor]:
"""Tokenize pairs and add prefix/suffix tokens. Returns batched tensors on model device."""
inputs = self._tokenizer(
pairs,
padding=False,
truncation="longest_first",
return_attention_mask=False,
max_length=self._effective_max_len,
)
for i, ele in enumerate(inputs["input_ids"]):
inputs["input_ids"][i] = self._prefix_tokens + ele + self._suffix_tokens
inputs = self._tokenizer.pad(
inputs,
padding=True,
return_tensors="pt",
)
for key in inputs:
inputs[key] = inputs[key].to(self._model.device)
return inputs
@torch.no_grad()
def _compute_scores(self, pairs: List[str]) -> List[float]:
"""Run forward pass and compute yes/no probability per pair."""
if not pairs:
return []
inputs = self._process_inputs(pairs)
outputs = self._model(**inputs)
batch_scores = outputs.logits[:, -1, :]
true_vector = batch_scores[:, self._token_true_id]
false_vector = batch_scores[:, self._token_false_id]
batch_scores = torch.stack([false_vector, true_vector], dim=1)
batch_scores = torch.nn.functional.log_softmax(batch_scores, dim=1)
scores = batch_scores[:, 1].exp().tolist()
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()
total_docs = len(docs) if docs else 0
output_scores: List[float] = [0.0] * total_docs
query = "" if query is None else str(query).strip()
indexed: List[Tuple[int, str]] = []
for i, doc in enumerate(docs or []):
if doc is None:
continue
text = str(doc).strip()
if not text:
continue
indexed.append((i, text))
if not query or not indexed:
elapsed_ms = (time.time() - start_ts) * 1000.0
return output_scores, {
"input_docs": total_docs,
"usable_docs": len(indexed),
"unique_docs": 0,
"dedup_ratio": 0.0,
"elapsed_ms": round(elapsed_ms, 3),
"model": self._model_name,
"backend": "qwen3_transformers",
"normalize": normalize,
}
# Deduplicate by text, keep mapping to original indices
unique_texts: List[str] = []
position_to_unique: List[int] = []
prev: Optional[str] = None
for _idx, text in indexed:
if text != prev:
unique_texts.append(text)
prev = text
position_to_unique.append(len(unique_texts) - 1)
pairs = [
_format_instruction(self._instruction, query, t)
for t in unique_texts
]
# Batch inference
unique_scores: List[float] = []
for i in range(0, len(pairs), self._batch_size):
batch = pairs[i : i + self._batch_size]
batch_scores = self._compute_scores(batch)
unique_scores.extend(batch_scores)
for (orig_idx, _), unique_idx in zip(indexed, 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:
dedup_ratio = 1.0 - (len(unique_texts) / float(len(indexed)))
meta = {
"input_docs": total_docs,
"usable_docs": len(indexed),
"unique_docs": len(unique_texts),
"dedup_ratio": round(dedup_ratio, 4),
"elapsed_ms": round(elapsed_ms, 3),
"model": self._model_name,
"backend": "qwen3_transformers",
"normalize": normalize,
}
return output_scores, meta
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