From 4a8f20b25f917acf60438cbf81366cb133fc39de Mon Sep 17 00:00:00 2001 From: "synacktra.work@gmail.com" Date: Sun, 17 Dec 2023 20:25:19 +0530 Subject: [PATCH] [chore] initial commit --- .gitignore | 160 +++++++++ LICENSE | 201 +++++++++++ contexts | 10 + poetry.lock | 751 ++++++++++++++++++++++++++++++++++++++++++ pyproject.toml | 24 ++ readme.md | 185 +++++++++++ swiftrank/__init__.py | 2 + swiftrank/__main__.py | 65 ++++ swiftrank/ranker.py | 173 ++++++++++ swiftrank/settings.py | 49 +++ 10 files changed, 1620 insertions(+) create mode 100644 .gitignore create mode 100644 LICENSE create mode 100644 contexts create mode 100644 poetry.lock create mode 100644 pyproject.toml create mode 100644 readme.md create mode 100644 swiftrank/__init__.py create mode 100644 swiftrank/__main__.py create mode 100644 swiftrank/ranker.py create mode 100644 swiftrank/settings.py diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..68bc17f --- /dev/null +++ b/.gitignore @@ -0,0 +1,160 @@ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ +cover/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +.pybuilder/ +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +# For a library or package, you might want to ignore these files since the code is +# intended to run in multiple environments; otherwise, check them in: +# .python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# poetry +# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. +# This is especially recommended for binary packages to ensure reproducibility, and is more +# commonly ignored for libraries. +# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control +#poetry.lock + +# pdm +# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. +#pdm.lock +# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it +# in version control. +# https://pdm.fming.dev/#use-with-ide +.pdm.toml + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# pytype static type analyzer +.pytype/ + +# Cython debug symbols +cython_debug/ + +# PyCharm +# JetBrains specific template is maintained in a separate JetBrains.gitignore that can +# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore +# and can be added to the global gitignore or merged into this file. 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+ SwiftRank GIF +
+ +--- + +

Streamlined, Light-Weight, Ultra-Fast State-of-the-Art Reranker, Engineered for Both Retrieval Pipelines and Terminal Applications.

+ +> Re-write version of [FlashRank](https://github.com/PrithivirajDamodaran/FlashRank) with additional features, more flexibility and optimizations. +--- + +### Features 🌀 + +🌟 **Light Weight**: +- **No Torch or Transformers**: Operable solely on CPU. +- Boasts the **tiniest reranking model in the world, ~4MB**. + +⚡ **Ultra Fast**: +- Reranking efficiency depends on the **total token count in contexts and queries, plus the depth of the model (number of layers)**. +- For illustration, the duration for the process using the standard model is exemplified in the following test: + + +🎯 **Based on SoTA Cross-encoders and other models**: +- How good are Zero-shot rerankers? => [Reference](https://github.com/PrithivirajDamodaran/FlashRank/blob/main/README.md#references). +- Supported Models :- + * `ms-marco-TinyBERT-L-2-v2` (default) + * `ms-marco-MiniLM-L-12-v2` + * `ms-marco-MultiBERT-L-12` (Multi-lingual, [supports 100+ languages](https://github.com/google-research/bert/blob/master/multilingual.md#list-of-languages)) + * `rank-T5-flan` (Best non cross-encoder reranker) +- Why only sleeker models? Reranking is the final leg of larger retrieval pipelines, idea is to avoid any extra overhead especially for user-facing scenarios. To that end models with really small footprint that doesn't need any specialised hardware and yet offer competitive performance are chosen. Feel free to raise issues to add support for a new models as you see fit. + +🔧 **Versatile Configuration**: +- Implements a structured pipeline for the reranking process. `Ranker` and `Tokenizer` instances are passed to create the pipeline. +- Supports complex dictionary objects handling. +- Includes a customizable threshold parameter to filter contexts, ensuring only those with a value equal to or exceeding the threshold are selected. + +⌨️ **Terminal Integration**: +- Pipe your output into `swiftrank` cli tool and get reranked output + +--- + +### 🚀 Installation + +```sh +pip install swiftrank +``` + +### Library Usage 🤗 + +- Create `Ranker` and `Tokenizer` instance. + ```py + from swiftrank import Ranker, Tokenizer + ranker = Ranker(model_id="ms-marco-TinyBERT-L-2-v2") + tokenizer = Tokenizer(model_id="ms-marco-TinyBERT-L-2-v2") + ``` + +- Build a `ReRankPipeline` instance + ```py + from swiftrank import ReRankPipeline + reranker = ReRankPipeline(ranker=ranker, tokenizer=tokenizer) + ``` + +- Evaluate the pipeline + ```py + contexts = [ + "Introduce *lookahead decoding*: - a parallel decoding algo to accelerate LLM inference - w/o the need for a draft model or a data store - linearly decreases # decoding steps relative to log(FLOPs) used per decoding step.", + "LLM inference efficiency will be one of the most crucial topics for both industry and academia, simply because the more efficient you are, the more $$$ you will save. vllm project is a must-read for this direction, and now they have just released the paper", + "There are many ways to increase LLM inference throughput (tokens/second) and decrease memory footprint, sometimes at the same time. Here are a few methods I’ve found effective when working with Llama 2. These methods are all well-integrated with Hugging Face. This list is far from exhaustive; some of these techniques can be used in combination with each other and there are plenty of others to try. - Bettertransformer (Optimum Library): Simply call `model.to_bettertransformer()` on your Hugging Face model for a modest improvement in tokens per second. - Fp4 Mixed-Precision (Bitsandbytes): Requires minimal configuration and dramatically reduces the model's memory footprint. - AutoGPTQ: Time-consuming but leads to a much smaller model and faster inference. The quantization is a one-time cost that pays off in the long run.", + "Ever want to make your LLM inference go brrrrr but got stuck at implementing speculative decoding and finding the suitable draft model? No more pain! Thrilled to unveil Medusa, a simple framework that removes the annoying draft model while getting 2x speedup.", + "vLLM is a fast and easy-to-use library for LLM inference and serving. vLLM is fast with: State-of-the-art serving throughput Efficient management of attention key and value memory with PagedAttention Continuous batching of incoming requests Optimized CUDA kernels" + ] + for mapping in reranker.invoke( + query="Tricks to accelerate LLM inference", contexts=contexts + ): + print(mapping) + ``` + ``` + {'score': 0.9977508, 'context': 'Introduce *lookahead decoding*: - a parallel decoding algo to accelerate LLM inference - w/o the need for a draft model or a data store - linearly decreases # decoding steps relative to log(FLOPs) used per decoding step.'} + {'score': 0.9415497, 'context': "There are many ways to increase LLM inference throughput (tokens/second) and decrease memory footprint, sometimes at the same time. Here are a few methods I’ve found effective when working with Llama 2. These methods are all well-integrated with Hugging Face. This list is far from exhaustive; some of these techniques can be used in combination with each other and there are plenty of others to try. - Bettertransformer (Optimum Library): Simply call `model.to_bettertransformer()` on your Hugging Face model for a modest improvement in tokens per second. - Fp4 Mixed-Precision (Bitsandbytes): Requires minimal configuration and dramatically reduces the model's memory footprint. - AutoGPTQ: Time-consuming but leads to a much smaller model and faster inference. The quantization is a one-time cost that pays off in the long run."} + {'score': 0.47455463, 'context': 'vLLM is a fast and easy-to-use library for LLM inference and serving. vLLM is fast with: State-of-the-art serving throughput Efficient management of attention key and value memory with PagedAttention Continuous batching of incoming requests Optimized CUDA kernels'} + {'score': 0.43783104, 'context': 'LLM inference efficiency will be one of the most crucial topics for both industry and academia, simply because the more efficient you are, the more $$$ you will save. vllm project is a must-read for this direction, and now they have just released the paper'} + {'score': 0.043041725, 'context': 'Ever want to make your LLM inference go brrrrr but got stuck at implementing speculative decoding and finding the suitable draft model? No more pain! Thrilled to unveil Medusa, a simple framework that removes the annoying draft model while getting 2x speedup.'} + ``` + +- Want to filter contexts? Utilize `threshold` parameter. + ```py + for mapping in reranker.invoke( + query="Tricks to accelerate LLM inference", contexts=contexts, threshold=0.8 + ): + print(mapping) + ``` + ``` + {'score': 0.9977508, 'context': 'Introduce *lookahead decoding*: - a parallel decoding algo to accelerate LLM inference - w/o the need for a draft model or a data store - linearly decreases # decoding steps relative to log(FLOPs) used per decoding step.'} + {'score': 0.9415497, 'context': "There are many ways to increase LLM inference throughput (tokens/second) and decrease memory footprint, sometimes at the same time. Here are a few methods I’ve found effective when working with Llama 2. These methods are all well-integrated with Hugging Face. This list is far from exhaustive; some of these techniques can be used in combination with each other and there are plenty of others to try. - Bettertransformer (Optimum Library): Simply call `model.to_bettertransformer()` on your Hugging Face model for a modest improvement in tokens per second. - Fp4 Mixed-Precision (Bitsandbytes): Requires minimal configuration and dramatically reduces the model's memory footprint. - AutoGPTQ: Time-consuming but leads to a much smaller model and faster inference. The quantization is a one-time cost that pays off in the long run."} + + ``` + +- Have complex dictionary object? Utilize `key` parameter. + ```py + contexts = [ + {"id": 1, "content": "Introduce *lookahead decoding*: - a parallel decoding algo to accelerate LLM inference - w/o the need for a draft model or a data store - linearly decreases # decoding steps relative to log(FLOPs) used per decoding step."}, + {"id": 2, "content": "LLM inference efficiency will be one of the most crucial topics for both industry and academia, simply because the more efficient you are, the more $$$ you will save. vllm project is a must-read for this direction, and now they have just released the paper"}, + {"id": 3, "content": "There are many ways to increase LLM inference throughput (tokens/second) and decrease memory footprint, sometimes at the same time. Here are a few methods I’ve found effective when working with Llama 2. These methods are all well-integrated with Hugging Face. This list is far from exhaustive; some of these techniques can be used in combination with each other and there are plenty of others to try. - Bettertransformer (Optimum Library): Simply call `model.to_bettertransformer()` on your Hugging Face model for a modest improvement in tokens per second. - Fp4 Mixed-Precision (Bitsandbytes): Requires minimal configuration and dramatically reduces the model's memory footprint. - AutoGPTQ: Time-consuming but leads to a much smaller model and faster inference. The quantization is a one-time cost that pays off in the long run."}, + {"id": 4, "content": "Ever want to make your LLM inference go brrrrr but got stuck at implementing speculative decoding and finding the suitable draft model? No more pain! Thrilled to unveil Medusa, a simple framework that removes the annoying draft model while getting 2x speedup."}, + {"id": 5, "content": "vLLM is a fast and easy-to-use library for LLM inference and serving. vLLM is fast with: State-of-the-art serving throughput Efficient management of attention key and value memory with PagedAttention Continuous batching of incoming requests Optimized CUDA kernels"} + ] + for mapping in reranker.invoke( + query="Tricks to accelerate LLM inference", contexts=contexts, key=lambda x: x['content'] + ): + print(mapping) + ``` + ``` + {'score': 0.9977508, 'context': {'id': 1, 'content': 'Introduce *lookahead decoding*: - a parallel decoding algo to accelerate LLM inference - w/o the need for a draft model or a data store - linearly decreases # decoding steps relative to log(FLOPs) used per decoding step.'}} + {'score': 0.9415497, 'context': {'id': 3, 'content': "There are many ways to increase LLM inference throughput (tokens/second) and decrease memory footprint, sometimes at the same time. Here are a few methods I’ve found effective when working with Llama 2. These methods are all well-integrated with Hugging Face. This list is far from exhaustive; some of these techniques can be used in combination with each other and there are plenty of others to try. - Bettertransformer (Optimum Library): Simply call `model.to_bettertransformer()` on your Hugging Face model for a modest improvement in tokens per second. - Fp4 Mixed-Precision (Bitsandbytes): Requires minimal configuration and dramatically reduces the model's memory footprint. - AutoGPTQ: Time-consuming but leads to a much smaller model and faster inference. The quantization is a one-time cost that pays off in the long run."}} + {'score': 0.47455463, 'context': {'id': 5, 'content': 'vLLM is a fast and easy-to-use library for LLM inference and serving. vLLM is fast with: State-of-the-art serving throughput Efficient management of attention key and value memory with PagedAttention Continuous batching of incoming requests Optimized CUDA kernels'}} + {'score': 0.43783104, 'context': {'id': 2, 'content': 'LLM inference efficiency will be one of the most crucial topics for both industry and academia, simply because the more efficient you are, the more $$$ you will save. vllm project is a must-read for this direction, and now they have just released the paper'}} + {'score': 0.043041725, 'context': {'id': 4, 'content': 'Ever want to make your LLM inference go brrrrr but got stuck at implementing speculative decoding and finding the suitable draft model? No more pain! Thrilled to unveil Medusa, a simple framework that removes the annoying draft model while getting 2x speedup.'}} + ``` + +### CLI Usage 🤗 + +``` + Usage: swiftrank [OPTIONS] + + Rerank contexts provided on stdin. + +╭─ Options ───────────────────────────────────────────────────────────────────────────────────────────────────────╮ +│ * --query -q TEXT query for reranking evaluation. [required] │ +│ --threshold -t FLOAT filter contexts using threshold. │ +│ --first -f get most relevant context. │ +│ --help -h Show this message and exit. │ +╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯ +``` + +> Note: It only supports string data for now. I am planning to add support for more complex data structures (json, jsonl, yaml, ...). + +- Print most relevant context + ```sh + cat contexts | swiftrank -q "Monogatari Series: Season 2" -f + ``` + ``` + Monogatari Series: Second Season + ``` + +- Filtering using threshold + > piping the output to `fzf` provides with a selection menu + ```sh + cat contexts | swiftrank -q "Monogatari Series: Season 2" -t 0.8 | fzf + ``` + ``` + Monogatari Series: Second Season + Ore Monogatari!! + Umi Monogatari: Anata ga Ite Kureta Koto + ``` + +- Using different model by setting `SWIFTRANK_MODEL` environment variable + - Shell + ```sh + SWIFTRANK_MODEL="ms-marco-MiniLM-L-12-v2" + ``` + - Powershell + ```powershell + $env:SWIFTRANK_MODEL = "ms-marco-MiniLM-L-12-v2" + ``` + ```sh + cat contexts | swiftrank -q "Monogatari Series: Season 2" + ``` + ``` + Monogatari Series: Second Season + Umi Monogatari: Anata ga Ite Kureta Koto + Ore Monogatari!! + Owarimonogatari 2nd Season + Kizumonogatari III: Reiketsu-hen + Nisemonogatari + Kizumonogatari II: Nekketsu-hen + Hanamonogatari + Nekomonogatari: Kuro + Kizumonogatari I: Tekketsu-hen + ``` + +--- + +#### Acknowledgment of Original Repository + +This project is derived from [FlashRank](https://github.com/PrithivirajDamodaran/FlashRank), which is licensed under the Apache License 2.0. We extend our gratitude to the original authors and contributors for their work. The original repository provided a foundational framework for the development of our project, and we have built upon it with additional features and improvements. \ No newline at end of file diff --git a/swiftrank/__init__.py b/swiftrank/__init__.py new file mode 100644 index 0000000..878b317 --- /dev/null +++ b/swiftrank/__init__.py @@ -0,0 +1,2 @@ +from . import settings +from .ranker import Ranker, Tokenizer, ReRankPipeline \ No newline at end of file diff --git a/swiftrank/__main__.py b/swiftrank/__main__.py new file mode 100644 index 0000000..bd252ff --- /dev/null +++ b/swiftrank/__main__.py @@ -0,0 +1,65 @@ +import sys + +from typer import Typer, Option + +cli = Typer( + rich_help_panel='rich', + add_completion=False, + context_settings={"help_option_names": ["-h", "--help"]} +) + +try: + from signal import signal, SIGPIPE, SIG_DFL + signal(SIGPIPE, SIG_DFL) +except ImportError: + pass + +def read_stdin(verify_tty: bool = False): + """ + Read values from standard input (stdin). + If `verify_tty` is True, exit if no input has been piped. + """ + if verify_tty and sys.stdin.isatty(): + return + try: + for line in sys.stdin: + if line: + yield line.strip() + except KeyboardInterrupt: + return + +@cli.command( + name="swiftrank", help="Rerank contexts provided on stdin." +) +def __cli__( + query: str = Option( + ..., "--query", "-q", help="query for reranking evaluation.", show_default=False), + threshold: float = Option( + None, "--threshold", "-t", help="filter contexts using threshold.", show_default=False), + first: bool = Option( + False, "--first", "-f", help="get most relevant context.", is_flag=True), +): + from typer import echo + + contexts = list(read_stdin()) + if not contexts: + echo("No contexts found on stdin", err=True) + return + + from . import settings + from .ranker import Ranker, Tokenizer, ReRankPipeline + + _model = settings.DEFAULT_MODEL + pipeline = ReRankPipeline( + ranker=Ranker(_model), tokenizer=Tokenizer(_model) + ) + + reranked = list(pipeline.invoke( + query=query, contexts=contexts, threshold=threshold + )) + if reranked and first: + echo(reranked[0]['context']) + return + + for mapping in reranked: + echo(mapping["context"]) \ No newline at end of file diff --git a/swiftrank/ranker.py b/swiftrank/ranker.py new file mode 100644 index 0000000..0770be2 --- /dev/null +++ b/swiftrank/ranker.py @@ -0,0 +1,173 @@ +import json +from pathlib import Path +from collections import OrderedDict +from typing import ( + overload, Any, Optional, Iterable, Protocol, Callable, TypeVar, TypedDict, Generator +) + +import numpy as np +import onnxruntime as ort +from tokenizers import AddedToken, Tokenizer as TokenizerLoader + +from . import settings + + +class SupportsGetItem(Protocol): + def __getitem__(self, key) -> str: ... + +_T = TypeVar("_T", bound=SupportsGetItem) + +class Mapping(TypedDict): + score: float + context: str + + +class Ranker: + """Load Ranker from available models.""" + def __init__( + self, model_id: str = settings.DEFAULT_MODEL, max_length: int = 512 + ) -> None: + self.model_id = model_id + model_file = settings.MODEL_MAP.get(self.model_id) + if model_file is None: + raise LookupError(f"{self.model_id!r} model not available.") + self.instance = ort.InferenceSession( + settings.get_model_path(model_id=self.model_id) / model_file + ) + + +class Tokenizer: + """Load Tokenizer from available models.""" + def __init__( + self, model_id: str = settings.DEFAULT_MODEL, max_length: int = 512 + ) -> None: + self.model_id = model_id + self.model_dir = settings.get_model_path(model_id=self.model_id) + self.max_length = max_length + self.instance = self.__load() + + def __file_handler(self, filename: str, read_json: bool = True) -> dict[str, Any] | Path: + """Json file handler. If read_json is true, returns loaded object else path is returned""" + path = self.model_dir / filename + if not path.exists(): + raise FileNotFoundError(f"{filename!r} file missing from {self.model_dir!r}") + if read_json: + return json.loads(path.read_bytes()) + return path + + def __load_vocab(self, vocab_file: Path): + """Load vocab file""" + vocab, ids_to_tokens = OrderedDict(), OrderedDict() + with vocab_file.open(encoding="utf-8") as handler: + tokens = handler.readlines() + + for idx, tok in enumerate(tokens): + tok = tok.rstrip("\n") + vocab[tok], ids_to_tokens[idx] = idx, tok + + return vocab, ids_to_tokens + + def __load(self): + """Load tokenizer""" + config = self.__file_handler("config.json") + tokenizer_config = self.__file_handler("tokenizer_config.json") + tokens_map = self.__file_handler("special_tokens_map.json") + + tokenizer: TokenizerLoader = TokenizerLoader.from_file(str( + self.__file_handler("tokenizer.json", read_json=False) + )) + tokenizer.enable_truncation(max_length=min(tokenizer_config["model_max_length"], self.max_length)) + tokenizer.enable_padding(pad_id=config["pad_token_id"], pad_token=tokenizer_config["pad_token"]) + + for token in tokens_map.values(): + if isinstance(token, str): + tokenizer.add_special_tokens([token]) + elif isinstance(token, dict): + tokenizer.add_special_tokens([AddedToken(**token)]) + + vocab_file = self.model_dir / "vocab.txt" + if vocab_file.exists(): + tokenizer.vocab, tokenizer.ids_to_tokens = self.__load_vocab(vocab_file) + + return tokenizer + + +class ReRankPipeline: + """ + Pipeline for reranking task. + :param ranker: `Ranker` class instance + :param tokenizer: `Tokenizer` class instance + + >>> from flashrank import ReRankPipeline + >>> pipeline = ReRankPipeline(ranker=ranker, tokenizer=tokenizer) + >>> pipeline.invoke( + ... query="", contexts=["", "", ...] + ... ) + """ + def __init__(self, ranker: Ranker, tokenizer: Tokenizer) -> None: + self.ranker = ranker.instance + self.tokenizer = tokenizer.instance + + def __create_attr_array(self, tokenized, attr: str): + """Create array of tokenized attribute values.""" + return np.array([getattr(_, attr) for _ in tokenized], dtype=np.int64) + + @overload + def invoke( + self, query: str, contexts: Iterable[str], threshold: Optional[float] = None + ) -> Generator[Mapping, None, None]: + """ + Rerank contexts based on query. + :param query: The query to use for reranking evaluation. + :param contexts: The contexts to rerank. + :param threshold: Get contexts that are equal or higher than threshold value. + """ + + @overload + def invoke( + self, query: str, contexts: Iterable[_T], threshold: Optional[float] = None, *, key: Callable[[_T], str] + ) -> Generator[Mapping, None, None]: + """ + Rerank contexts based on query. + :param query: The query to use for reranking evaluation. + :param contexts: The contexts object. + :param threshold: Get contexts that are equal or higher than threshold value. + :param key: function/method to use for getting fields from contexts object. + """ + + def invoke( + self, + query: str, + contexts: Iterable[str] | Iterable[_T], + threshold: Optional[float] = None, + *, + key: Callable[[_T], str] = None + ) -> Generator[Mapping, None, None]: + + processor = (lambda _:_) if key is None else key + tokenized = self.tokenizer.encode_batch( + [(query, processor(context)) for context in contexts] + ) + + onnx_input = { + "input_ids": self.__create_attr_array(tokenized, 'ids'), + "attention_mask": self.__create_attr_array(tokenized, 'attention_mask'), + } + token_type_ids = self.__create_attr_array(tokenized, 'type_ids') + use_type_ids = not np.all(token_type_ids == 0) + if use_type_ids: + onnx_input = onnx_input | {'token_type_ids': token_type_ids} + + output = self.ranker.run(None, onnx_input)[0] + scores = list(1 / (1 + np.exp( + -(output[:, 1] if output.shape[1] > 1 else output.flatten()))) + ) + + combined = sorted(zip(scores, contexts), key=lambda x: x[0], reverse=True) + + if threshold is None: + return ({'score': sc, "context": ctx} for sc, ctx in combined) + + return ( + {'score': sc, "context": ctx} for sc, ctx in combined if sc >= threshold + ) \ No newline at end of file diff --git a/swiftrank/settings.py b/swiftrank/settings.py new file mode 100644 index 0000000..d62d554 --- /dev/null +++ b/swiftrank/settings.py @@ -0,0 +1,49 @@ +import os +import zipfile +import requests +from pathlib import Path + +from tqdm import tqdm + +DEFAULT_CACHE_DIR = Path(os.getenv( + "SWIFTRANK_CACHE", + default=Path("~").expanduser() / ".cache" / "swiftrank" +)) +DEFAULT_CACHE_DIR.mkdir(parents=True, exist_ok=True) + +MODEL_MAP = { + "ms-marco-TinyBERT-L-2-v2": "flashrank-TinyBERT-L-2-v2.onnx", + "ms-marco-MiniLM-L-12-v2": "flashrank-MiniLM-L-12-v2_Q.onnx", + "ms-marco-MultiBERT-L-12": "flashrank-MultiBERT-L12_Q.onnx", + "rank-T5-flan": "flashrank-rankt5_Q.onnx" +} + +DEFAULT_MODEL = os.getenv("SWIFTRANK_MODEL", "ms-marco-TinyBERT-L-2-v2") +"""Default Model to use""" + +def get_model_path(model_id: str) -> Path: + model_dir = DEFAULT_CACHE_DIR / model_id + if model_dir.exists(): + return model_dir + + local_zip_file = str(DEFAULT_CACHE_DIR / f"{model_id}.zip") + model_url = f"https://storage.googleapis.com/flashrank/{model_id}.zip" + + with requests.get(model_url, stream=True) as r: + r.raise_for_status() + total_size = int(r.headers.get('content-length', 0)) + progress_bar = tqdm( + desc=model_id, total=total_size, unit='iB', unit_scale=True, unit_divisor=1024, + ) + with open(local_zip_file, 'wb') as f: + for chunk in r.iter_content(chunk_size=8192): + progress_bar.update(f.write(chunk)) + + progress_bar.desc = local_zip_file + progress_bar.close() + + with zipfile.ZipFile(local_zip_file, 'r') as zip_ref: + zip_ref.extractall(DEFAULT_CACHE_DIR) + + os.remove(local_zip_file) + return model_dir