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import importlib.metadata
import math
import os
from pathlib import Path
from typing import Any, Iterable, Optional, Sequence
from .constants import (
ARCHITECTURE,
DEFAULT_DECODER_PAD_TO_MULTIPLE_OF,
DEFAULT_DOCUMENT_MAX_LENGTH,
DEFAULT_ENCODER_CHUNK_SIZE,
DEFAULT_INSTRUCTION,
DEFAULT_MAX_MODEL_LEN,
DEFAULT_QUERY_MAX_LENGTH,
DEFAULT_SYSTEM_INSTRUCTION,
MODEL_ID,
NO_TOKEN_ID,
PLUGIN_NAME,
SUPPORTED_ENCODER_CHUNK_SIZES,
TEXT_MODALITY,
YES_TOKEN_ID,
decoder_text,
encoder_text,
parse_encoder_chunk_size,
validate_answer_tokens,
)
TESTED_VLLM_VERSION = "0.19.1"
def _positive_int(value: int, name: str) -> int:
parsed = int(value)
if parsed <= 0:
raise ValueError(f"{name} must be a positive integer.")
return parsed
def _sigmoid(value: float) -> float:
if value >= 0:
scale = math.exp(-value)
return 1.0 / (1.0 + scale)
scale = math.exp(value)
return scale / (1.0 + scale)
def build_hf_overrides(encoder_chunk_size: int) -> dict[str, object]:
return {
"architectures": [ARCHITECTURE],
"num_labels": 1,
"yes_token_id": YES_TOKEN_ID,
"no_token_id": NO_TOKEN_ID,
"encoder_chunk_size": encoder_chunk_size,
"decoder_pad_to_multiple_of": DEFAULT_DECODER_PAD_TO_MULTIPLE_OF,
"problem_type": "regression",
}
def _enable_plugin() -> None:
allowed = os.environ.get("VLLM_PLUGINS")
if allowed is None:
os.environ["VLLM_PLUGINS"] = PLUGIN_NAME
return
names = {item.strip() for item in allowed.split(",") if item.strip()}
if PLUGIN_NAME not in names:
raise RuntimeError(
f"VLLM_PLUGINS={allowed!r} excludes {PLUGIN_NAME!r}. Add the plugin "
"name or unset VLLM_PLUGINS."
)
def check_runtime() -> None:
installed_vllm = importlib.metadata.version("vllm")
if installed_vllm != TESTED_VLLM_VERSION:
raise RuntimeError(
f"This adapter requires vLLM {TESTED_VLLM_VERSION}; "
f"found {installed_vllm}."
)
discovered = {
entry.name: entry.value
for entry in importlib.metadata.entry_points(group="vllm.general_plugins")
}
if PLUGIN_NAME not in discovered:
raise RuntimeError(
f"vLLM plugin {PLUGIN_NAME!r} is not installed. Install the "
"vllm_support package before creating the reranker."
)
from vllm.model_executor.models import ModelRegistry
from vllm.plugins import load_general_plugins
load_general_plugins()
if ARCHITECTURE not in set(ModelRegistry.get_supported_archs()):
raise RuntimeError(f"{ARCHITECTURE} was not registered in ModelRegistry.")
class KaLMVLLMReranker:
"""Single-GPU vLLM adapter preserving the original KaLM score contract."""
def __init__(
self,
model: str | Path = MODEL_ID,
*,
query_max_length: int = DEFAULT_QUERY_MAX_LENGTH,
document_max_length: int = DEFAULT_DOCUMENT_MAX_LENGTH,
encoder_chunk_size: object = DEFAULT_ENCODER_CHUNK_SIZE,
dtype: str = "bfloat16",
tensor_parallel_size: int = 1,
max_model_len: int = DEFAULT_MAX_MODEL_LEN,
gpu_memory_utilization: float = 0.85,
batch_size: int = 32,
instruction: str = DEFAULT_INSTRUCTION,
system_instruction: str = DEFAULT_SYSTEM_INSTRUCTION,
skip_runtime_check: bool = False,
) -> None:
self.model = str(model)
self.query_max_length = _positive_int(
query_max_length, "query_max_length"
)
self.document_max_length = _positive_int(
document_max_length, "document_max_length"
)
self.encoder_chunk_size = parse_encoder_chunk_size(encoder_chunk_size)
self.dtype = str(dtype)
self.tensor_parallel_size = _positive_int(
tensor_parallel_size, "tensor_parallel_size"
)
if self.tensor_parallel_size != 1:
raise ValueError(
"The published adapter supports tensor_parallel_size=1 only."
)
self.max_model_len = _positive_int(max_model_len, "max_model_len")
self.gpu_memory_utilization = float(gpu_memory_utilization)
if not 0 < self.gpu_memory_utilization <= 1:
raise ValueError("gpu_memory_utilization must be in the interval (0, 1].")
self.batch_size = _positive_int(batch_size, "batch_size")
if not isinstance(instruction, str) or not isinstance(
system_instruction, str
):
raise TypeError("instruction and system_instruction must be strings.")
self.instruction = instruction
self.system_instruction = system_instruction
_enable_plugin()
if not skip_runtime_check:
check_runtime()
from transformers import AutoTokenizer
self.tokenizer = AutoTokenizer.from_pretrained(
self.model,
trust_remote_code=True,
)
validate_answer_tokens(self.tokenizer)
from vllm import LLM
from vllm.pooling_params import PoolingParams
self.pooling_params = PoolingParams(use_activation=False)
self.llm = LLM(
model=self.model,
runner="pooling",
trust_remote_code=True,
hf_overrides=build_hf_overrides(self.encoder_chunk_size),
dtype=self.dtype,
tensor_parallel_size=1,
max_model_len=self.max_model_len,
gpu_memory_utilization=self.gpu_memory_utilization,
enforce_eager=True,
limit_mm_per_prompt={TEXT_MODALITY: 1},
)
def close(self) -> None:
llm = getattr(self, "llm", None)
if llm is None:
return
engine = getattr(llm, "llm_engine", None)
engine_core = getattr(engine, "engine_core", None)
shutdown = getattr(engine_core, "shutdown", None)
if callable(shutdown):
shutdown()
self.llm = None
def __enter__(self) -> "KaLMVLLMReranker":
return self
def __exit__(self, exc_type, exc_value, traceback) -> None:
self.close()
def _encoder_ids(self, document: str) -> tuple[str, list[int]]:
text = encoder_text(document)
token_ids = self.tokenizer(
text,
add_special_tokens=False,
truncation=True,
max_length=self.document_max_length,
)["input_ids"]
if not token_ids:
raise ValueError("Encoded document prompt is empty.")
return text, list(token_ids)
def _decoder_ids(self, query: str, instruction: str) -> tuple[str, list[int]]:
text = decoder_text(
self.tokenizer,
query,
instruction=instruction,
system_instruction=self.system_instruction,
query_max_length=self.query_max_length,
)
token_ids = self.tokenizer.encode(text, add_special_tokens=False)
if not token_ids:
raise ValueError("Encoded decoder prompt is empty.")
return text, list(token_ids)
def _prompt(self, query: str, document: str, instruction: str):
from vllm.inputs import ExplicitEncoderDecoderPrompt, TokensPrompt
encoder_prompt, encoder_ids = self._encoder_ids(document)
decoder_prompt, decoder_ids = self._decoder_ids(query, instruction)
return ExplicitEncoderDecoderPrompt(
encoder_prompt=TokensPrompt(
prompt_token_ids=encoder_ids,
prompt=encoder_prompt,
multi_modal_data={TEXT_MODALITY: [encoder_prompt]},
),
decoder_prompt=TokensPrompt(
prompt_token_ids=decoder_ids,
prompt=decoder_prompt,
),
)
@staticmethod
def _validate_pairs(
pairs: Sequence[tuple[str, str]],
) -> list[tuple[str, str]]:
if isinstance(pairs, (str, bytes)) or not isinstance(pairs, Sequence):
raise TypeError("pairs must be a sequence of (query, document) pairs.")
validated: list[tuple[str, str]] = []
for index, pair in enumerate(pairs):
if (
isinstance(pair, (str, bytes))
or not isinstance(pair, Sequence)
or len(pair) != 2
):
raise ValueError(f"pairs[{index}] must contain exactly two strings.")
query, document = pair
if not isinstance(query, str) or not isinstance(document, str):
raise TypeError(f"pairs[{index}] must contain exactly two strings.")
validated.append((query, document))
return validated
@staticmethod
def _margins_from_outputs(outputs: Iterable[Any]) -> list[float]:
margins: list[float] = []
for output in outputs:
values = output.outputs.probs
if len(values) != 1:
raise RuntimeError(f"Expected one raw margin, got {values}.")
margin = float(values[0])
if not math.isfinite(margin):
raise RuntimeError(f"vLLM returned a non-finite margin: {margin}.")
margins.append(margin)
return margins
def predict(
self,
pairs: Sequence[tuple[str, str]],
*,
instruction: Optional[str] = None,
return_margin: bool = False,
) -> list[float] | list[dict[str, float]]:
validated_pairs = self._validate_pairs(pairs)
if not validated_pairs:
return []
effective_instruction = self.instruction if instruction is None else instruction
if not isinstance(effective_instruction, str):
raise TypeError("instruction must be a string or None.")
margins: list[float] = []
for start in range(0, len(validated_pairs), self.batch_size):
batch = validated_pairs[start : start + self.batch_size]
prompts = [
self._prompt(query, document, effective_instruction)
for query, document in batch
]
if self.llm is None:
raise RuntimeError("The reranker has been closed.")
outputs = self.llm.classify(
prompts,
pooling_params=self.pooling_params,
use_tqdm=False,
)
margins.extend(self._margins_from_outputs(outputs))
scores = [_sigmoid(margin) for margin in margins]
if return_margin:
return [
{"score": score, "margin": margin}
for score, margin in zip(scores, margins)
]
return scores
def rank(
self,
query: str,
documents: Sequence[str],
*,
instruction: Optional[str] = None,
top_k: Optional[int] = None,
return_margin: bool = False,
) -> list[dict[str, float | int]]:
if not isinstance(query, str):
raise TypeError("query must be a string.")
if isinstance(documents, (str, bytes)) or not isinstance(documents, Sequence):
raise TypeError("documents must be a sequence of strings.")
if any(not isinstance(document, str) for document in documents):
raise TypeError("every document must be a string.")
if top_k is not None:
top_k = int(top_k)
if top_k < 0:
raise ValueError("top_k must be non-negative or None.")
predictions = self.predict(
[(query, document) for document in documents],
instruction=instruction,
return_margin=return_margin,
)
rankings: list[dict[str, float | int]] = []
for corpus_id, prediction in enumerate(predictions):
if return_margin:
assert isinstance(prediction, dict)
item: dict[str, float | int] = {
"corpus_id": corpus_id,
"score": prediction["score"],
"margin": prediction["margin"],
}
else:
assert isinstance(prediction, float)
item = {"corpus_id": corpus_id, "score": prediction}
rankings.append(item)
rankings.sort(key=lambda item: float(item["score"]), reverse=True)
return rankings if top_k is None else rankings[:top_k]
KaLMVLLMOfflineReranker = KaLMVLLMReranker
__all__ = [
"KaLMVLLMOfflineReranker",
"KaLMVLLMReranker",
"SUPPORTED_ENCODER_CHUNK_SIZES",
"build_hf_overrides",
"check_runtime",
"parse_encoder_chunk_size",
]
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