Merge branch 'bump-version' of https://github.com/MinishLab/model2vec into bump-version

Author: Pringled

Makefile

@@ -10,6 +10,7 @@ install:
 
 install-no-pre-commit:
 	uv pip install ".[dev,distill]"
+	uv pip install "torch<2.5.0"
 
 install-base:
 	uv sync --extra dev

README.md

@@ -1,11 +1,13 @@
 from __future__ import annotations
 
 import logging
+import re
 from typing import Literal, Union
 
 import numpy as np
 from huggingface_hub import model_info
 from sklearn.decomposition import PCA
+from tokenizers import Tokenizer
 from tokenizers.models import BPE, Unigram
 from transformers import AutoModel, AutoTokenizer, PreTrainedModel, PreTrainedTokenizerFast
 
@@ -39,6 +41,7 @@ def distill_from_model(
     pca_dims: PCADimType = 256,
     apply_zipf: bool = True,
     use_subword: bool = True,
+    token_remove_pattern: str | None = r"\[unused\d+\]",
 ) -> StaticModel:
     """
     Distill a staticmodel from a sentence transformer.
@@ -58,8 +61,12 @@ def distill_from_model(
         If this is 'auto', we don't reduce dimensionality, but still apply PCA.
     :param apply_zipf: Whether to apply Zipf weighting to the embeddings.
     :param use_subword: Whether to keep subword tokens in the vocabulary. If this is False, you must pass a vocabulary, and the returned tokenizer will only detect full words.
+    :param token_remove_pattern: If this is set to a string, we compile this into a regex. Any tokens that conform to this regex pattern will be removed from the vocabulary.
+        If the pattern is so general that it removes all tokens, we throw an error. If the pattern can't be compiled into a valid regex, we also throw an error.
     :raises: ValueError if the PCA dimension is larger than the number of dimensions in the embeddings.
     :raises: ValueError if the vocabulary contains duplicate tokens.
+    :raises: ValueError if the regex can't be compiled.
+    :raises: ValueError if the vocabulary is empty after token removal.
     :return: A StaticModel
 
     """
@@ -81,17 +88,7 @@ def distill_from_model(
     if use_subword:
         # Create the subword embeddings.
         tokens, embeddings = create_output_embeddings_from_model_name(model=model, tokenizer=tokenizer, device=device)
-
-        # Remove any unused tokens from the tokenizer and embeddings.
-        wrong_tokens = [x for x in tokens if x.startswith("[unused")]
-        vocab = tokenizer.get_vocab()
-        # Get the ids of the unused token.
-        wrong_token_ids = [vocab[token] for token in wrong_tokens]
-        # Remove the unused tokens from the tokenizer.
-        new_tokenizer = remove_tokens(tokenizer.backend_tokenizer, wrong_tokens)
-        # Remove the embeddings of the unused tokens.
-        embeddings = np.delete(embeddings, wrong_token_ids, axis=0)
-        logger.info(f"Removed {len(wrong_tokens)} unused tokens from the tokenizer and embeddings.")
+        new_tokenizer, embeddings = _remove_tokens_and_embeddings(tokenizer, token_remove_pattern, tokens, embeddings)
     else:
         # We need to keep the unk token in the tokenizer.
         unk_token = tokenizer.backend_tokenizer.model.unk_token
@@ -136,6 +133,8 @@ def distill_from_model(
     model_name = getattr(model, "name_or_path", "")
 
     config = {
+        "model_type": "model2vec",
+        "architectures": ["StaticModel"],
         "tokenizer_name": model_name,
         "apply_pca": pca_dims,
         "apply_zipf": apply_zipf,
@@ -155,6 +154,37 @@ def distill_from_model(
     )
 
 
+def _remove_tokens_and_embeddings(
+    tokenizer: PreTrainedTokenizerFast, token_remove_pattern: str | None, tokens: list[str], embeddings: np.ndarray
+) -> tuple[Tokenizer, np.ndarray]:
+    if not token_remove_pattern:
+        return tokenizer.backend_tokenizer, embeddings
+
+    try:
+        token_regex = re.compile(token_remove_pattern)
+    except re.error as e:
+        raise ValueError(f"Invalid regex pattern: {token_remove_pattern}") from e
+        # Remove any unused tokens from the tokenizer and embeddings.
+    wrong_tokens = [x for x in tokens if token_regex.match(x)]
+    vocab = tokenizer.get_vocab()
+    # Get the ids of the unused token.
+    wrong_token_ids = [vocab[token] for token in wrong_tokens]
+
+    if len(wrong_token_ids) == len(vocab):
+        raise ValueError(
+            "All tokens in the vocabulary are unused tokens. This will result in an empty tokenizer. "
+            "Please provide a valid token removal pattern. The pattern is now: {token_remove_pattern}"
+        )
+
+        # Remove the unused tokens from the tokenizer.
+    new_tokenizer = remove_tokens(tokenizer.backend_tokenizer, wrong_tokens)
+    # Remove the embeddings of the unused tokens.
+    embeddings = np.delete(embeddings, wrong_token_ids, axis=0)
+    logger.info(f"Removed {len(wrong_tokens)} unused tokens from the tokenizer and embeddings.")
+
+    return new_tokenizer, embeddings
+
+
 def distill(
     model_name: str,
     vocabulary: list[str] | None = None,

model2vec/distill/distillation.py

@@ -68,10 +68,12 @@ def remove_tokens(tokenizer: Tokenizer, tokens_to_remove: list[str]) -> Tokenize
         tokenizer_data["model"]["vocab"] = reindexed
 
     elif model_type == "Unigram":
-        raise ValueError("Removing tokens from a unigram tokenizer is not supported.")
+        logger.warning("Removing tokens from a unigram tokenizer is not supported.")
+        return tokenizer
 
     elif model_type == "BPE":
-        raise ValueError("Removing tokens from a BPE tokenizer is not supported.")
+        logger.warning("Removing tokens from a BPE tokenizer is not supported.")
+        return tokenizer
 
     else:
         raise ValueError(f"Unknown model type {model_type}")

model2vec/distill/tokenizer.py

@@ -1,20 +1,21 @@
 from __future__ import annotations
 
 import math
+import os
 from logging import getLogger
 from pathlib import Path
 from tempfile import TemporaryDirectory
 from typing import Any, Iterator, Union
 
 import numpy as np
+from joblib import delayed
 from tokenizers import Encoding, Tokenizer
 from tqdm import tqdm
 
-from model2vec.utils import load_local_model
+from model2vec.utils import ProgressParallel, load_local_model
 
 PathLike = Union[Path, str]
 
-
 logger = getLogger(__name__)
 
 
@@ -171,6 +172,8 @@ def encode_as_sequence(
         max_length: int | None = None,
         batch_size: int = 1024,
         show_progress_bar: bool = False,
+        use_multiprocessing: bool = True,
+        multiprocessing_threshold: int = 10_000,
     ) -> list[np.ndarray] | np.ndarray:
         """
         Encode a list of sentences as a list of numpy arrays of tokens.
@@ -186,24 +189,42 @@ def encode_as_sequence(
             If this is None, no truncation is done.
         :param batch_size: The batch size to use.
         :param show_progress_bar: Whether to show the progress bar.
+        :param use_multiprocessing: Whether to use multiprocessing.
+            By default, this is enabled for inputs > multiprocessing_threshold sentences and disabled otherwise.
+        :param multiprocessing_threshold: The threshold in number of sentences for using multiprocessing.
         :return: The encoded sentences with an embedding per token.
         """
         was_single = False
         if isinstance(sentences, str):
             sentences = [sentences]
             was_single = True
 
-        out_array: list[np.ndarray] = []
-        for batch in tqdm(
-            self._batch(sentences, batch_size),
-            total=math.ceil(len(sentences) / batch_size),
-            disable=not show_progress_bar,
-        ):
-            out_array.extend(self._encode_batch_as_sequence(batch, max_length))
+        # Prepare all batches
+        sentence_batches = list(self._batch(sentences, batch_size))
+        total_batches = math.ceil(len(sentences) / batch_size)
+
+        # Use joblib for multiprocessing if requested, and if we have enough sentences
+        if use_multiprocessing and len(sentences) > multiprocessing_threshold:
+            # Disable parallelism for tokenizers
+            os.environ["TOKENIZERS_PARALLELISM"] = "false"
+
+            results = ProgressParallel(n_jobs=-1, use_tqdm=show_progress_bar, total=total_batches)(
+                delayed(self._encode_batch_as_sequence)(batch, max_length) for batch in sentence_batches
+            )
+            out_array: list[np.ndarray] = []
+            for r in results:
+                out_array.extend(r)
+        else:
+            out_array = []
+            for batch in tqdm(
+                sentence_batches,
+                total=total_batches,
+                disable=not show_progress_bar,
+            ):
+                out_array.extend(self._encode_batch_as_sequence(batch, max_length))
 
         if was_single:
             return out_array[0]
-
         return out_array
 
     def _encode_batch_as_sequence(self, sentences: list[str], max_length: int | None) -> list[np.ndarray]:
@@ -224,6 +245,8 @@ def encode(
         show_progress_bar: bool = False,
         max_length: int | None = 512,
         batch_size: int = 1024,
+        use_multiprocessing: bool = True,
+        multiprocessing_threshold: int = 10_000,
         **kwargs: Any,
     ) -> np.ndarray:
         """
@@ -237,6 +260,9 @@ def encode(
         :param max_length: The maximum length of the sentences. Any tokens beyond this length will be truncated.
             If this is None, no truncation is done.
         :param batch_size: The batch size to use.
+        :param use_multiprocessing: Whether to use multiprocessing.
+            By default, this is enabled for inputs > multiprocessing_threshold sentences and disabled otherwise.
+        :param multiprocessing_threshold: The threshold in number of sentences for using multiprocessing.
         :param **kwargs: Any additional arguments. These are ignored.
         :return: The encoded sentences. If a single sentence was passed, a vector is returned.
         """
@@ -245,19 +271,32 @@ def encode(
             sentences = [sentences]
             was_single = True
 
-        out_arrays: list[np.ndarray] = []
-        for batch in tqdm(
-            self._batch(sentences, batch_size),
-            total=math.ceil(len(sentences) / batch_size),
-            disable=not show_progress_bar,
-        ):
-            out_arrays.append(self._encode_batch(batch, max_length))
+        # Prepare all batches
+        sentence_batches = list(self._batch(sentences, batch_size))
+        total_batches = math.ceil(len(sentences) / batch_size)
 
-        out_array = np.concatenate(out_arrays, axis=0)
+        # Use joblib for multiprocessing if requested, and if we have enough sentences
+        if use_multiprocessing and len(sentences) > multiprocessing_threshold:
+            # Disable parallelism for tokenizers
+            os.environ["TOKENIZERS_PARALLELISM"] = "false"
+
+            results = ProgressParallel(n_jobs=-1, use_tqdm=show_progress_bar, total=total_batches)(
+                delayed(self._encode_batch)(batch, max_length) for batch in sentence_batches
+            )
+            out_array = np.concatenate(results, axis=0)
+        else:
+            # Don't use multiprocessing
+            out_arrays: list[np.ndarray] = []
+            for batch in tqdm(
+                sentence_batches,
+                total=total_batches,
+                disable=not show_progress_bar,
+            ):
+                out_arrays.append(self._encode_batch(batch, max_length))
+            out_array = np.concatenate(out_arrays, axis=0)
 
         if was_single:
             return out_array[0]
-
         return out_array
 
     def _encode_batch(self, sentences: list[str], max_length: int | None) -> np.ndarray:

model2vec/model.py

@@ -1,18 +1,56 @@
 # -*- coding: utf-8 -*-
+from __future__ import annotations
+
 import json
 import logging
+import re
 from importlib import import_module
 from importlib.metadata import metadata
 from pathlib import Path
-from typing import Iterator, Protocol, cast
+from typing import Any, Iterator, Protocol, cast
 
 import numpy as np
 import safetensors
+from joblib import Parallel
 from tokenizers import Tokenizer
+from tqdm import tqdm
 
 logger = logging.getLogger(__name__)
 
 
+class ProgressParallel(Parallel):
+    """A drop-in replacement for joblib.Parallel that shows a tqdm progress bar."""
+
+    def __init__(self, use_tqdm: bool = True, total: int | None = None, *args: Any, **kwargs: Any) -> None:
+        """
+        Initialize the ProgressParallel object.
+
+        :param use_tqdm: Whether to show the progress bar.
+        :param total: Total number of tasks (batches) you expect to process. If None,
+                    it updates the total dynamically to the number of dispatched tasks.
+        :param *args: Additional arguments to pass to `Parallel.__init__`.
+        :param **kwargs: Additional keyword arguments to pass to `Parallel.__init__`.
+        """
+        self._use_tqdm = use_tqdm
+        self._total = total
+        super().__init__(*args, **kwargs)
+
+    def __call__(self, *args: Any, **kwargs: Any) -> Any:
+        """Create a tqdm context."""
+        with tqdm(disable=not self._use_tqdm, total=self._total) as self._pbar:
+            self._pbar = self._pbar
+            return super().__call__(*args, **kwargs)
+
+    def print_progress(self) -> None:
+        """Hook called by joblib as tasks complete. We update the tqdm bar here."""
+        if self._total is None:
+            # If no fixed total was given, we dynamically set the total
+            self._pbar.total = self.n_dispatched_tasks
+        # Move the bar to the number of completed tasks
+        self._pbar.n = self.n_completed_tasks
+        self._pbar.refresh()
+
+
 class SafeOpenProtocol(Protocol):
     """Protocol to fix safetensors safe open."""
 
@@ -22,6 +60,7 @@ def get_tensor(self, key: str) -> np.ndarray:
 
 
 _MODULE_MAP = (("scikit-learn", "sklearn"),)
+_DIVIDERS = re.compile(r"[=<>!]+")
 
 
 def get_package_extras(package: str, extra: str) -> Iterator[str]:
@@ -38,7 +77,8 @@ def get_package_extras(package: str, extra: str) -> Iterator[str]:
             # Extract and clean the extra requirement
             found_extra = rest[0].split("==")[-1].strip(" \"'")
             if found_extra == extra:
-                yield name.strip()
+                prefix, *_ = _DIVIDERS.split(name)
+                yield prefix.strip()
 
 
 def importable(module: str, extra: str) -> None:

model2vec/utils.py

@@ -24,6 +24,7 @@ classifiers = [
 
 dependencies = [
     "jinja2",
+    "joblib",
     "numpy",
     "rich",
     "safetensors",
@@ -50,11 +51,10 @@ dev = [
     "mypy",
     "pre-commit",
     "pytest",
-    "pytest-coverage",
+    "pytest-cov",
     "ruff",
 ]
 distill = ["torch", "transformers", "scikit-learn"]
-
 onnx = ["onnx", "torch"]
 
 [project.urls]