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from typing import Any, Dict, List, Optional, Sequence, Tuple, Union
import numpy as np
import torch
import torch.nn.functional as F
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
from transformers.modeling_outputs import BaseModelOutput
DEFAULT_INSTRUCTION = "Given a query, retrieve documents that answer the query."
DEFAULT_SYSTEM_INSTRUCTION = (
"Judge whether the Document meets the requirements based on the Query and "
'the Instruct provided. Note that the answer can only be "yes" or "no".'
)
class KaLMReranker:
"""Score query-document relevance with a KaLM encoder-decoder reranker.
The returned score is ``P(yes)`` after applying a two-class softmax to the
model's ``yes`` and ``no`` logits.
"""
def __init__(
self,
model_name_or_path: str,
*,
device: Optional[Union[str, torch.device]] = None,
dtype: Optional[Union[str, torch.dtype]] = None,
batch_size: int = 32,
query_max_length: int = 512,
max_length: int = 1024,
chunk_size: Optional[int] = 4,
instruction: str = DEFAULT_INSTRUCTION,
system_instruction: str = DEFAULT_SYSTEM_INSTRUCTION,
**model_kwargs: Any,
) -> None:
if not isinstance(model_name_or_path, str) or not model_name_or_path:
raise ValueError("model_name_or_path must be a non-empty string.")
if batch_size <= 0:
raise ValueError("batch_size must be positive.")
if query_max_length <= 0 or max_length <= 0:
raise ValueError("query_max_length and max_length must be positive.")
if chunk_size is not None and chunk_size <= 0:
raise ValueError("chunk_size must be positive or None.")
if not isinstance(instruction, str) or not isinstance(system_instruction, str):
raise TypeError("instruction and system_instruction must be strings.")
self.device = self._resolve_device(device)
self.dtype = self._resolve_dtype(dtype, self.device)
self.batch_size = batch_size
self.query_max_length = query_max_length
self.max_length = max_length
self.chunk_size = chunk_size
self.instruction = instruction
self.system_instruction = system_instruction
self.tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
if self.tokenizer.pad_token_id is None:
if self.tokenizer.eos_token_id is None:
raise ValueError("The tokenizer must define a pad token or an EOS token.")
self.tokenizer.pad_token = self.tokenizer.eos_token
self.tokenizer.padding_side = "right"
self.model = AutoModelForSeq2SeqLM.from_pretrained(
model_name_or_path,
dtype=self.dtype,
**model_kwargs,
)
for parameter in self.model.parameters():
if parameter.is_floating_point() and parameter.dtype != self.dtype:
parameter.data = parameter.data.to(dtype=self.dtype)
self.model.to(device=self.device)
self.model.eval()
self.yes_token_id = self._answer_token_id("yes")
self.no_token_id = self._answer_token_id("no")
@staticmethod
def _resolve_device(device: Optional[Union[str, torch.device]]) -> torch.device:
if device is None:
device = "cuda" if torch.cuda.is_available() else "cpu"
resolved = torch.device(device)
if resolved.type == "cuda" and not torch.cuda.is_available():
raise RuntimeError("CUDA was requested, but no CUDA device is available.")
return resolved
@staticmethod
def _resolve_dtype(
dtype: Optional[Union[str, torch.dtype]], device: torch.device
) -> torch.dtype:
if dtype is None:
return torch.bfloat16 if device.type == "cuda" else torch.float32
if isinstance(dtype, torch.dtype):
return dtype
if not isinstance(dtype, str):
raise TypeError("dtype must be a torch.dtype or a string such as 'bfloat16'.")
normalized = dtype.lower().removeprefix("torch.")
supported = {
"bfloat16": torch.bfloat16,
"bf16": torch.bfloat16,
"float16": torch.float16,
"fp16": torch.float16,
"float32": torch.float32,
"fp32": torch.float32,
}
if normalized not in supported:
raise ValueError(f"Unsupported dtype: {dtype!r}.")
return supported[normalized]
def _answer_token_id(self, answer: str) -> int:
token_ids = self.tokenizer(answer, add_special_tokens=False)["input_ids"]
if not token_ids:
raise ValueError(f"Failed to tokenize the answer {answer!r}.")
return token_ids[-1]
def _get_encoder(self):
if hasattr(self.model, "get_encoder"):
return self.model.get_encoder()
if hasattr(self.model, "encoder"):
return self.model.encoder
raise AttributeError(f"Cannot find the encoder on {type(self.model).__name__}.")
@staticmethod
def _pool_encoder_chunks(
hidden_states: torch.Tensor,
attention_mask: torch.Tensor,
chunk_size: int,
) -> Tuple[torch.Tensor, torch.Tensor]:
batch_size, sequence_length, hidden_size = hidden_states.shape
num_chunks = (sequence_length + chunk_size - 1) // chunk_size
padded_length = num_chunks * chunk_size
pad_length = padded_length - sequence_length
if pad_length:
hidden_states = F.pad(hidden_states, (0, 0, 0, pad_length))
attention_mask = F.pad(attention_mask, (0, pad_length))
hidden_states = hidden_states.view(
batch_size, num_chunks, chunk_size, hidden_size
)
chunk_mask = attention_mask.view(batch_size, num_chunks, chunk_size)
expanded_mask = chunk_mask.unsqueeze(-1).to(hidden_states.dtype)
pooled_hidden = (hidden_states * expanded_mask).sum(dim=2)
pooled_hidden = pooled_hidden / chunk_mask.sum(dim=2).clamp(min=1).unsqueeze(-1)
pooled_mask = (chunk_mask.sum(dim=2) > 0).to(attention_mask.dtype)
return pooled_hidden, pooled_mask
def _decoder_text(self, query: str, instruction: str) -> str:
query_ids = self.tokenizer(
query,
add_special_tokens=False,
truncation=True,
max_length=self.query_max_length,
)["input_ids"]
truncated_query = self.tokenizer.decode(
query_ids,
skip_special_tokens=False,
clean_up_tokenization_spaces=False,
)
return (
"<bos><start_of_turn>user\n"
f"{self.system_instruction}\n\n"
f"<Instruct>: {instruction}\n"
f"<Query>: {truncated_query}<end_of_turn>\n"
"<start_of_turn>model\n\n\n\n"
)
@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
@torch.inference_mode()
def _predict_batch(
self, pairs: Sequence[Tuple[str, str]], instruction: str
) -> List[float]:
encoder_texts = [f"<Document>: {document}" for _, document in pairs]
decoder_texts = [self._decoder_text(query, instruction) for query, _ in pairs]
encoder_batch = self.tokenizer(
encoder_texts,
padding=True,
truncation=True,
max_length=self.max_length,
add_special_tokens=False,
return_tensors="pt",
).to(self.device)
decoder_batch = self.tokenizer(
decoder_texts,
padding=True,
pad_to_multiple_of=8,
add_special_tokens=False,
return_tensors="pt",
).to(self.device)
if self.chunk_size is None:
outputs = self.model(
input_ids=encoder_batch["input_ids"],
attention_mask=encoder_batch["attention_mask"],
decoder_input_ids=decoder_batch["input_ids"],
decoder_attention_mask=decoder_batch["attention_mask"],
return_dict=True,
)
else:
encoder_outputs = self._get_encoder()(
input_ids=encoder_batch["input_ids"],
attention_mask=encoder_batch["attention_mask"],
return_dict=True,
)
pooled_hidden, pooled_mask = self._pool_encoder_chunks(
encoder_outputs.last_hidden_state,
encoder_batch["attention_mask"],
self.chunk_size,
)
outputs = self.model(
encoder_outputs=BaseModelOutput(last_hidden_state=pooled_hidden),
attention_mask=pooled_mask,
decoder_input_ids=decoder_batch["input_ids"],
decoder_attention_mask=decoder_batch["attention_mask"],
return_dict=True,
)
sequence_lengths = decoder_batch["attention_mask"].sum(dim=1) - 1
batch_indices = torch.arange(outputs.logits.shape[0], device=self.device)
last_logits = outputs.logits[batch_indices, sequence_lengths]
yes_no_logits = torch.stack(
(
last_logits[:, self.yes_token_id],
last_logits[:, self.no_token_id],
),
dim=-1,
).float()
if not torch.isfinite(yes_no_logits).all():
bad_count = (~torch.isfinite(yes_no_logits).all(dim=-1)).sum().item()
raise RuntimeError(
f"The model produced non-finite yes/no logits for {bad_count} input(s). "
"Use bfloat16 or float32 instead of float16."
)
return torch.softmax(yes_no_logits, dim=-1)[:, 0].cpu().tolist()
def predict(
self,
pairs: Sequence[Tuple[str, str]],
*,
instruction: Optional[str] = None,
batch_size: Optional[int] = None,
) -> List[float]:
"""Return ``P(yes)`` scores in the same order as ``pairs``."""
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.")
effective_batch_size = self.batch_size if batch_size is None else batch_size
if not isinstance(effective_batch_size, int) or effective_batch_size <= 0:
raise ValueError("batch_size must be a positive integer.")
length_sorted_indices = np.argsort(
[-(len(query) + len(document)) for query, document in validated_pairs]
)
sorted_pairs = [validated_pairs[index] for index in length_sorted_indices]
tested_batch_size = effective_batch_size
first_batch_scores: List[float] = []
while tested_batch_size > 1:
try:
first_batch_scores = self._predict_batch(
sorted_pairs[: min(len(sorted_pairs), tested_batch_size)],
effective_instruction,
)
break
except torch.cuda.OutOfMemoryError:
if torch.cuda.is_available():
torch.cuda.empty_cache()
tested_batch_size = max(1, tested_batch_size * 3 // 4)
sorted_scores: List[float] = list(first_batch_scores)
try:
for start in range(tested_batch_size, len(sorted_pairs), tested_batch_size):
sorted_scores.extend(
self._predict_batch(
sorted_pairs[start : start + tested_batch_size],
effective_instruction,
)
)
except torch.cuda.OutOfMemoryError as error:
if torch.cuda.is_available():
torch.cuda.empty_cache()
raise RuntimeError(
"CUDA ran out of memory during reranking. Retry with a smaller batch_size "
"or shorter max_length."
) from error
inverse_indices = np.argsort(length_sorted_indices)
return [sorted_scores[index] for index in inverse_indices]
def rank(
self,
query: str,
documents: Sequence[str],
*,
instruction: Optional[str] = None,
top_k: Optional[int] = None,
batch_size: Optional[int] = None,
) -> List[Dict[str, Union[int, float]]]:
"""Rank documents and return ``corpus_id``/``score`` dictionaries."""
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 and (not isinstance(top_k, int) or top_k < 0):
raise ValueError("top_k must be a non-negative integer or None.")
scores = self.predict(
[(query, document) for document in documents],
instruction=instruction,
batch_size=batch_size,
)
rankings: List[Dict[str, Union[int, float]]] = [
{"corpus_id": corpus_id, "score": score}
for corpus_id, score in enumerate(scores)
]
rankings.sort(key=lambda item: item["score"], reverse=True)
return rankings if top_k is None else rankings[:top_k]
__all__ = ["KaLMReranker"]
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