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from typing import Any, ClassVar
try:
from typing import Self
except ImportError:
from typing_extensions import Self
import torch
from sentence_transformers.base.modules import InputModule
from transformers import AutoConfig, AutoModelForSeq2SeqLM, AutoTokenizer
from .kalm_reranker_utils import (
DEFAULT_INSTRUCTION,
DEFAULT_SYSTEM_INSTRUCTION,
answer_token_id,
build_decoder_text,
cast_floating_parameters,
extract_yes_no_logits,
forward_reranker_model,
normalize_requested_dtype,
validate_text_pairs,
)
class KaLMCrossEncoderModule(InputModule):
"""Sentence Transformers input module for KaLM encoder-decoder rerankers."""
config_file_name = "kalm_cross_encoder_config.json"
config_keys: ClassVar[list[str]] = [
"query_max_length",
"document_max_length",
"encoder_chunk_size",
"system_instruction",
]
save_in_root = True
def __init__(
self,
model_name_or_path: str,
*,
query_max_length: int = 512,
document_max_length: int = 1024,
encoder_chunk_size: int | None = 4,
system_instruction: str = DEFAULT_SYSTEM_INSTRUCTION,
model_kwargs: dict[str, Any] | None = None,
processor_kwargs: dict[str, Any] | None = None,
config_kwargs: dict[str, Any] | None = None,
backend: str = "torch",
) -> None:
super().__init__()
if backend != "torch":
raise ValueError(
"KaLMCrossEncoderModule only supports backend='torch'; "
f"received {backend!r}."
)
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 not isinstance(query_max_length, int) or query_max_length <= 0:
raise ValueError("query_max_length must be a positive integer.")
if not isinstance(document_max_length, int) or document_max_length <= 0:
raise ValueError("document_max_length must be a positive integer.")
if encoder_chunk_size is not None and (
not isinstance(encoder_chunk_size, int) or encoder_chunk_size <= 0
):
raise ValueError("encoder_chunk_size must be a positive integer or None.")
if not isinstance(system_instruction, str):
raise TypeError("system_instruction must be a string.")
self.query_max_length = query_max_length
self.max_seq_length = document_max_length
self.encoder_chunk_size = encoder_chunk_size
self.system_instruction = system_instruction
self.backend = backend
model_kwargs = dict(model_kwargs or {})
processor_kwargs = dict(processor_kwargs or {})
config_kwargs = dict(config_kwargs or {})
num_labels = config_kwargs.pop("num_labels", 1)
if num_labels != 1:
raise ValueError(
"KaLM reranking produces one relevance score; num_labels must be 1."
)
config = AutoConfig.from_pretrained(model_name_or_path, **config_kwargs)
self.tokenizer = AutoTokenizer.from_pretrained(
model_name_or_path, **processor_kwargs
)
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.processor = self.tokenizer
requested_dtype = normalize_requested_dtype(
model_kwargs.get("dtype", model_kwargs.get("torch_dtype"))
)
self.model = AutoModelForSeq2SeqLM.from_pretrained(
model_name_or_path,
config=config,
**model_kwargs,
)
cast_floating_parameters(self.model, requested_dtype)
self.yes_token_id = answer_token_id(self.tokenizer, "yes")
self.no_token_id = answer_token_id(self.tokenizer, "no")
@property
def document_max_length(self) -> int:
return self.max_seq_length
@document_max_length.setter
def document_max_length(self, value: int) -> None:
if not isinstance(value, int) or value <= 0:
raise ValueError("document_max_length must be a positive integer.")
self.max_seq_length = value
@property
def encoder_chunk_size(self) -> int | None:
return self._encoder_chunk_size
@encoder_chunk_size.setter
def encoder_chunk_size(self, value: int | None) -> None:
if value is not None and (not isinstance(value, int) or value <= 0):
raise ValueError("encoder_chunk_size must be a positive integer or None.")
self._encoder_chunk_size = value
@property
def chunk_size(self) -> int | None:
"""Alias for the encoder token mean-pooling compression rate."""
return self.encoder_chunk_size
@chunk_size.setter
def chunk_size(self, value: int | None) -> None:
self.encoder_chunk_size = value
def preprocess(
self,
inputs: list[Any],
prompt: str | None = None,
**kwargs: Any,
) -> dict[str, torch.Tensor]:
pairs = validate_text_pairs(inputs)
if not pairs:
return {}
instruction = DEFAULT_INSTRUCTION if prompt is None else prompt
if not isinstance(instruction, str):
raise TypeError("prompt must be a string or None.")
encoder_texts = [f"<Document>: {document}" for _, document in pairs]
decoder_texts = [
build_decoder_text(
self.tokenizer,
query,
instruction,
self.system_instruction,
self.query_max_length,
)
for query, _ in pairs
]
encoder_batch = self.tokenizer(
encoder_texts,
padding=True,
truncation=True,
max_length=self.document_max_length,
add_special_tokens=False,
return_tensors="pt",
)
decoder_batch = self.tokenizer(
decoder_texts,
padding=True,
pad_to_multiple_of=8,
add_special_tokens=False,
return_tensors="pt",
)
return {
"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"],
}
def forward(
self,
features: dict[str, torch.Tensor | Any],
**kwargs: Any,
) -> dict[str, torch.Tensor | Any]:
outputs = forward_reranker_model(
self.model,
input_ids=features["input_ids"],
attention_mask=features["attention_mask"],
decoder_input_ids=features["decoder_input_ids"],
decoder_attention_mask=features["decoder_attention_mask"],
encoder_chunk_size=self.chunk_size,
)
yes_no_logits = extract_yes_no_logits(
outputs.logits,
features["decoder_attention_mask"],
self.yes_token_id,
self.no_token_id,
)
features["scores"] = (yes_no_logits[:, 0] - yes_no_logits[:, 1]).unsqueeze(1)
return features
def save(
self,
output_path: str,
*args: Any,
safe_serialization: bool = True,
**kwargs: Any,
) -> None:
self.model.save_pretrained(output_path, safe_serialization=safe_serialization)
self.tokenizer.save_pretrained(output_path)
self.save_config(output_path)
@classmethod
def load(
cls,
model_name_or_path: str,
subfolder: str = "",
token: bool | str | None = None,
cache_folder: str | None = None,
revision: str | None = None,
local_files_only: bool = False,
trust_remote_code: bool = False,
model_kwargs: dict[str, Any] | None = None,
processor_kwargs: dict[str, Any] | None = None,
config_kwargs: dict[str, Any] | None = None,
backend: str = "torch",
**kwargs: Any,
) -> Self:
module_config = cls.load_config(
model_name_or_path,
subfolder=subfolder,
token=token,
cache_folder=cache_folder,
revision=revision,
local_files_only=local_files_only,
)
supplied_model_kwargs = dict(model_kwargs or {})
supplied_config_kwargs = dict(config_kwargs or {})
supplied_module_kwargs = dict(kwargs)
chunk_size_values: list[tuple[str, int | None]] = []
for source_name, source in (
("model_kwargs", supplied_model_kwargs),
("config_kwargs", supplied_config_kwargs),
("module kwargs", supplied_module_kwargs),
):
for key in ("chunk_size", "encoder_chunk_size"):
if key in source:
chunk_size_values.append((f"{source_name}.{key}", source.pop(key)))
if chunk_size_values:
first_name, first_value = chunk_size_values[0]
for current_name, current_value in chunk_size_values[1:]:
if current_value != first_value:
raise ValueError(
"Conflicting encoder chunk sizes: "
f"{first_name}={first_value!r}, "
f"{current_name}={current_value!r}."
)
module_config["encoder_chunk_size"] = first_value
hub_kwargs = {
"subfolder": subfolder,
"token": token,
"cache_dir": cache_folder,
"revision": revision,
"local_files_only": local_files_only,
"trust_remote_code": trust_remote_code,
}
effective_model_kwargs = {**hub_kwargs, **supplied_model_kwargs}
effective_processor_kwargs = {**hub_kwargs, **(processor_kwargs or {})}
effective_config_kwargs = {**hub_kwargs, **supplied_config_kwargs}
if "model_max_length" in effective_processor_kwargs:
module_config["document_max_length"] = effective_processor_kwargs[
"model_max_length"
]
return cls(
model_name_or_path,
model_kwargs=effective_model_kwargs,
processor_kwargs=effective_processor_kwargs,
config_kwargs=effective_config_kwargs,
backend=backend,
**module_config,
)
__all__ = ["KaLMCrossEncoderModule"]
|