wehub-resource-sync
361 lines
14 KiB
Python
361 lines
14 KiB
Python
import logging
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from abc import ABC, abstractmethod
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from typing import Any, Union, List, Tuple, Dict, Literal, Optional
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import multiprocessing as mp
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from multiprocessing import Queue
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import math
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import gc
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import torch
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import numpy as np
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from tqdm import tqdm, trange
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from transformers import is_torch_npu_available
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try:
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import torch_musa
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except Exception:
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pass
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logger = logging.getLogger(__name__)
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class AbsReranker(ABC):
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"""
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Base class for Reranker.
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Extend this class and implement :meth:`compute_score_single_gpu` for custom rerankers.
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Args:
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model_name_or_path (str): If it's a path to a local model, it loads the model from the path. Otherwise tries to download and
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load a model from HuggingFace Hub with the name.
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use_fp16 (bool, optional): If true, use half-precision floating-point to speed up computation with a slight performance
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degradation. Defaults to :data:`False`.
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query_instruction_for_rerank: (Optional[str], optional): Query instruction for reranking, which will be used with
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with :attr:`query_instruction_format`. Defaults to :data:`None`.
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query_instruction_format: (str, optional): The template for :attr:`query_instruction_for_rerank`. Defaults to :data:`"{}{}"`.
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passage_instruction_for_rerank (Optional[str], optional): Passage instruction for reranking. Defaults to :data:`None`.
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passage_instruction_format (str, optional): Passage instruction format when using :attr:`passage_instruction_for_rerank`.
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Defaults to :data:`"{}{}"`.
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devices (Optional[Union[str, int, List[str], List[int]]], optional): Devices to use for model inference. Defaults to :data:`None`.
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batch_size (int, optional): Batch size for inference. Defaults to :data:`128`.
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query_max_length (int, optional): Maximum length for query. Defaults to :data:`None`.
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max_length (int, optional): Maximum length. Defaults to :data:`512`.
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normalize (bool, optional): If true, normalize the result. Defaults to :data:`False`.
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kwargs (Dict[Any], optional): Additional parameters for HuggingFace Transformers config or children classes.
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"""
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def __init__(
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self,
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model_name_or_path: str,
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use_fp16: bool = False,
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query_instruction_for_rerank: Optional[str] = None,
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query_instruction_format: str = "{}{}", # specify the format of query_instruction_for_rerank
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passage_instruction_for_rerank: Optional[str] = None,
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passage_instruction_format: str = "{}{}", # specify the format of passage_instruction_for_rerank
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devices: Optional[Union[str, int, List[str], List[int]]] = None,
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# inference
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batch_size: int = 128,
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query_max_length: Optional[int] = None,
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max_length: int = 512,
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normalize: bool = False,
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**kwargs: Any,
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):
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self.model_name_or_path = model_name_or_path
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self.use_fp16 = use_fp16
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self.query_instruction_for_rerank = query_instruction_for_rerank
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self.query_instruction_format = query_instruction_format
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self.passage_instruction_for_rerank = passage_instruction_for_rerank
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self.passage_instruction_format = passage_instruction_format
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self.target_devices = self.get_target_devices(devices)
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self.batch_size = batch_size
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self.query_max_length = query_max_length
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self.max_length = max_length
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self.normalize = normalize
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for k in kwargs:
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setattr(self, k, kwargs[k])
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self.kwargs = kwargs
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# tokenizer and model are initialized in the child class
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self.model = None
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self.tokenizer = None
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self.pool = None
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def stop_self_pool(self):
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if self.pool is not None:
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self.stop_multi_process_pool(self.pool)
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self.pool = None
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try:
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self.model.to('cpu')
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torch.cuda.empty_cache()
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except:
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pass
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if gc is not None and callable(gc.collect):
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gc.collect()
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@staticmethod
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def get_target_devices(devices: Union[str, int, List[str], List[int]]) -> List[str]:
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"""
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Args:
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devices (Union[str, int, List[str], List[int]]): Specified devices, can be `str`, `int`, list of `str`, or list of `int`.
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Raises:
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ValueError: Devices should be a string or an integer or a list of strings or a list of integers.
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Returns:
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List[str]: A list of target devices in format
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"""
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if devices is None:
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if torch.cuda.is_available():
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return [f"cuda:{i}" for i in range(torch.cuda.device_count())]
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elif is_torch_npu_available():
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return [f"npu:{i}" for i in range(torch.npu.device_count())]
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elif hasattr(torch, "musa") and torch.musa.is_available():
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return [f"musa:{i}" for i in range(torch.musa.device_count())]
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elif torch.backends.mps.is_available():
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return ["mps"]
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else:
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return ["cpu"]
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elif isinstance(devices, str):
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return [devices]
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elif isinstance(devices, int):
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if hasattr(torch, "musa") and torch.musa.is_available():
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return [f"musa:{devices}"]
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else:
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return [f"cuda:{devices}"]
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elif isinstance(devices, list):
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if isinstance(devices[0], str):
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return devices
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elif isinstance(devices[0], int):
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if hasattr(torch, "musa") and torch.musa.is_available():
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return [f"musa:{device}" for device in devices]
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else:
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return [f"cuda:{device}" for device in devices]
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else:
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raise ValueError("devices should be a string or an integer or a list of strings or a list of integers.")
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else:
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raise ValueError("devices should be a string or an integer or a list of strings or a list of integers.")
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def get_detailed_instruct(self, instruction_format: str, instruction: str, sentence: str):
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"""Combine the instruction and sentence along with the instruction format.
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Args:
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instruction_format (str): Format for instruction.
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instruction (str): The text of instruction.
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sentence (str): The sentence to concatenate with.
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Returns:
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str: The complete sentence with instruction
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"""
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if "\\n" in instruction_format:
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instruction_format = instruction_format.replace("\\n", "\n")
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return instruction_format.format(instruction, sentence)
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def get_detailed_inputs(self, sentence_pairs: Union[str, List[str]]):
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"""get detailed instruct for all the inputs
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Args:
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sentence_pairs (Union[str, List[str]]): Input sentence pairs
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Returns:
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list[list[str]]: The complete sentence pairs with instruction
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"""
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if isinstance(sentence_pairs, str):
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sentence_pairs = [sentence_pairs]
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if self.query_instruction_for_rerank is not None:
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if self.passage_instruction_for_rerank is None:
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return [
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[
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self.get_detailed_instruct(self.query_instruction_format, self.query_instruction_for_rerank, sentence_pair[0]),
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sentence_pair[1]
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] for sentence_pair in sentence_pairs
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]
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else:
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return [
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[
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self.get_detailed_instruct(self.query_instruction_format, self.query_instruction_for_rerank, sentence_pair[0]),
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self.get_detailed_instruct(self.passage_instruction_format, self.passage_instruction_for_rerank, sentence_pair[1])
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] for sentence_pair in sentence_pairs
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]
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else:
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if self.passage_instruction_for_rerank is None:
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return [
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[
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sentence_pair[0],
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sentence_pair[1]
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] for sentence_pair in sentence_pairs
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]
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else:
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return [
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[
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sentence_pair[0],
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self.get_detailed_instruct(self.passage_instruction_format, self.passage_instruction_for_rerank, sentence_pair[1])
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] for sentence_pair in sentence_pairs
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]
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def compute_score(
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self,
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sentence_pairs: Union[List[Tuple[str, str]], Tuple[str, str]],
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**kwargs
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):
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"""Compute score for each sentence pair
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Args:
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sentence_pairs (Union[List[Tuple[str, str]], Tuple[str, str]]): Input sentence pairs to compute.
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Returns:
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numpy.ndarray: scores of all the sentence pairs.
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"""
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if isinstance(sentence_pairs[0], str):
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sentence_pairs = [sentence_pairs]
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sentence_pairs = self.get_detailed_inputs(sentence_pairs)
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if isinstance(sentence_pairs, str) or len(self.target_devices) == 1:
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return self.compute_score_single_gpu(
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sentence_pairs,
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device=self.target_devices[0],
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**kwargs
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)
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if self.pool is None:
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self.pool = self.start_multi_process_pool()
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scores = self.encode_multi_process(sentence_pairs,
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self.pool,
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**kwargs)
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return scores
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def __del__(self):
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self.stop_self_pool()
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@abstractmethod
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def compute_score_single_gpu(
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self,
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sentence_pairs: Union[List[Tuple[str, str]], Tuple[str, str]],
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batch_size: int = 256,
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query_max_length: Optional[int] = None,
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max_length: int = 512,
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normalize: bool = False,
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device: Optional[str] = None,
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**kwargs: Any,
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):
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"""
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This method should compute the scores of sentence_pair and return scores.
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"""
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pass
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# copied from https://github.com/UKPLab/sentence-transformers/blob/1802076d4eae42ff0a5629e1b04e75785d4e193b/sentence_transformers/SentenceTransformer.py#L857
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def start_multi_process_pool(self) -> Dict[Literal["input", "output", "processes"], Any]:
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"""
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Starts a multi-process pool to process the encoding with several independent processes
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via :meth:`SentenceTransformer.encode_multi_process <sentence_transformers.SentenceTransformer.encode_multi_process>`.
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This method is recommended if you want to encode on multiple GPUs or CPUs. It is advised
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to start only one process per GPU. This method works together with encode_multi_process
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and stop_multi_process_pool.
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Returns:
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Dict[str, Any]: A dictionary with the target processes, an input queue, and an output queue.
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"""
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logger.info("Start multi-process pool on devices: {}".format(", ".join(map(str, self.target_devices))))
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self.model.to("cpu")
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self.model.share_memory()
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ctx = mp.get_context("spawn")
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input_queue = ctx.Queue()
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output_queue = ctx.Queue()
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processes = []
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for device_id in tqdm(self.target_devices, desc='initial target device'):
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p = ctx.Process(
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target=AbsReranker._encode_multi_process_worker,
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args=(device_id, self, input_queue, output_queue),
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daemon=True,
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)
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p.start()
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processes.append(p)
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return {"input": input_queue, "output": output_queue, "processes": processes}
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# copied from https://github.com/UKPLab/sentence-transformers/blob/1802076d4eae42ff0a5629e1b04e75785d4e193b/sentence_transformers/SentenceTransformer.py#L857
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def encode_multi_process(
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self,
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sentence_pairs: List,
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pool: Dict[Literal["input", "output", "processes"], Any],
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**kwargs
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) -> np.ndarray:
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chunk_size = math.ceil(len(sentence_pairs) / len(pool["processes"]))
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input_queue = pool["input"]
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last_chunk_id = 0
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chunk = []
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for sentence_pair in sentence_pairs:
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chunk.append(sentence_pair)
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if len(chunk) >= chunk_size:
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input_queue.put(
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[last_chunk_id, chunk, kwargs]
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)
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last_chunk_id += 1
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chunk = []
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if len(chunk) > 0:
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input_queue.put([last_chunk_id, chunk, kwargs])
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last_chunk_id += 1
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output_queue = pool["output"]
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results_list = sorted(
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[output_queue.get() for _ in trange(last_chunk_id, desc="Chunks")],
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key=lambda x: x[0],
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)
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scores = np.concatenate([result[1] for result in results_list])
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return scores
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# copied from https://github.com/UKPLab/sentence-transformers/blob/1802076d4eae42ff0a5629e1b04e75785d4e193b/sentence_transformers/SentenceTransformer.py#L857
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@staticmethod
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def _encode_multi_process_worker(
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target_device: str, model: 'AbsReranker', input_queue: Queue, results_queue: Queue
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) -> None:
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"""
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Internal working process to encode sentences in multi-process setup
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"""
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while True:
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try:
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chunk_id, sentences, kwargs = (
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input_queue.get()
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)
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embeddings = model.compute_score_single_gpu(
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sentences,
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device=target_device,
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**kwargs
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)
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results_queue.put([chunk_id, embeddings])
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except:
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break
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# copied from https://github.com/UKPLab/sentence-transformers/blob/1802076d4eae42ff0a5629e1b04e75785d4e193b/sentence_transformers/SentenceTransformer.py#L857
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@staticmethod
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def stop_multi_process_pool(pool: Dict[Literal["input", "output", "processes"], Any]) -> None:
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"""
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Stops all processes started with start_multi_process_pool.
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Args:
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pool (Dict[str, object]): A dictionary containing the input queue, output queue, and process list.
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Returns:
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None
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"""
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for p in pool["processes"]:
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p.terminate()
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for p in pool["processes"]:
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p.join()
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p.close()
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pool["input"].close()
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pool["output"].close()
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