From 4078a3bf009ab5fc7343e9e92fe06ca2dead241a Mon Sep 17 00:00:00 2001 From: stephantul Date: Sun, 22 Dec 2024 12:38:01 +0100 Subject: [PATCH 01/57] Fix tokenizer issue --- model2vec/distill/distillation.py | 50 ++++++++++++++++++++------ model2vec/distill/tokenizer.py | 6 ++-- model2vec/version.py | 2 +- tests/test_distillation.py | 60 +++++++++++++++++++++++++++++++ uv.lock | 36 ++++++++++--------- 5 files changed, 124 insertions(+), 30 deletions(-) diff --git a/model2vec/distill/distillation.py b/model2vec/distill/distillation.py index 0c55d2b7..ea9b2880 100644 --- a/model2vec/distill/distillation.py +++ b/model2vec/distill/distillation.py @@ -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 = "\[unused\d+\]", ) -> 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 @@ -157,6 +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, diff --git a/model2vec/distill/tokenizer.py b/model2vec/distill/tokenizer.py index e6c30871..174523d2 100644 --- a/model2vec/distill/tokenizer.py +++ b/model2vec/distill/tokenizer.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}") diff --git a/model2vec/version.py b/model2vec/version.py index 9bfefb0c..f0768802 100644 --- a/model2vec/version.py +++ b/model2vec/version.py @@ -1,2 +1,2 @@ -__version_triple__ = (0, 3, 3) +__version_triple__ = (0, 3, 4) __version__ = ".".join(map(str, __version_triple__)) diff --git a/tests/test_distillation.py b/tests/test_distillation.py index c0c37076..168404f2 100644 --- a/tests/test_distillation.py +++ b/tests/test_distillation.py @@ -1,6 +1,7 @@ from __future__ import annotations import json +import re from importlib import import_module from unittest.mock import MagicMock, patch @@ -98,6 +99,65 @@ def test_distill_from_model( assert static_model.base_model_name == static_model2.base_model_name +@patch.object(import_module("model2vec.distill.distillation"), "model_info") +@patch("transformers.AutoModel.from_pretrained") +def test_distill_removal_pattern( + mock_auto_model: MagicMock, + mock_model_info: MagicMock, + mock_berttokenizer: BertTokenizerFast, + mock_transformer: AutoModel, +) -> None: + """Test the removal pattern.""" + # Mock the return value of model_info to avoid calling the Hugging Face API + mock_model_info.return_value = type("ModelInfo", (object,), {"cardData": {"language": "en"}}) + + # Patch the tokenizers and models to return the real BertTokenizerFast and mock model instances + # mock_auto_tokenizer.return_value = mock_berttokenizer + mock_auto_model.return_value = mock_transformer + + vocab_size = mock_berttokenizer.vocab_size + + static_model = distill_from_model( + model=mock_transformer, + tokenizer=mock_berttokenizer, + vocabulary=None, + device="cpu", + token_remove_pattern=None, + ) + + assert len(static_model.embedding) == vocab_size + + # No tokens removed, nonsensical pattern + static_model = distill_from_model( + model=mock_transformer, + tokenizer=mock_berttokenizer, + vocabulary=None, + device="cpu", + token_remove_pattern="£££££££££££££££££", + ) + + assert len(static_model.embedding) == vocab_size + + with pytest.raises(ValueError): + static_model = distill_from_model( + model=mock_transformer, + tokenizer=mock_berttokenizer, + vocabulary=None, + device="cpu", + token_remove_pattern="[...papapa", + ) + + # Remove all tokens + with pytest.raises(ValueError): + static_model = distill_from_model( + model=mock_transformer, + tokenizer=mock_berttokenizer, + vocabulary=None, + device="cpu", + token_remove_pattern=".*", + ) + + @pytest.mark.parametrize( "vocabulary, use_subword, pca_dims, apply_zipf, expected_shape", [ diff --git a/uv.lock b/uv.lock index 6f76a02a..948b7819 100644 --- a/uv.lock +++ b/uv.lock @@ -1,5 +1,9 @@ version = 1 requires-python = ">=3.9" +resolution-markers = [ + "python_full_version >= '3.12'", + "python_full_version < '3.12'", +] [[package]] name = "asttokens" @@ -158,7 +162,7 @@ name = "click" version = "8.1.7" source = { registry = "https://pypi.org/simple" } dependencies = [ - { name = "colorama", marker = "platform_system == 'Windows'" }, + { name = "colorama", marker = "sys_platform == 'win32'" }, ] sdist = { url = "https://files.pythonhosted.org/packages/96/d3/f04c7bfcf5c1862a2a5b845c6b2b360488cf47af55dfa79c98f6a6bf98b5/click-8.1.7.tar.gz", hash = "sha256:ca9853ad459e787e2192211578cc907e7594e294c7ccc834310722b41b9ca6de", size = 336121 } wheels = [ @@ -505,7 +509,7 @@ wheels = [ [[package]] name = "model2vec" -version = "0.3.2" +version = "0.3.4" source = { editable = "." } dependencies = [ { name = "jinja2" }, @@ -1511,21 +1515,21 @@ dependencies = [ { name = "fsspec" }, { name = "jinja2" }, { name = "networkx" }, - { name = "nvidia-cublas-cu12", marker = "platform_machine == 'x86_64' and platform_system == 'Linux'" }, - { name = "nvidia-cuda-cupti-cu12", marker = "platform_machine == 'x86_64' and platform_system == 'Linux'" }, - { name = "nvidia-cuda-nvrtc-cu12", marker = "platform_machine == 'x86_64' and platform_system == 'Linux'" }, - { name = "nvidia-cuda-runtime-cu12", marker = "platform_machine == 'x86_64' and platform_system == 'Linux'" }, - { name = "nvidia-cudnn-cu12", marker = "platform_machine == 'x86_64' and platform_system == 'Linux'" }, - { name = "nvidia-cufft-cu12", marker = "platform_machine == 'x86_64' and platform_system == 'Linux'" }, - { name = "nvidia-curand-cu12", marker = "platform_machine == 'x86_64' and platform_system == 'Linux'" }, - { name = "nvidia-cusolver-cu12", marker = "platform_machine == 'x86_64' and platform_system == 'Linux'" }, - { name = "nvidia-cusparse-cu12", marker = "platform_machine == 'x86_64' and platform_system == 'Linux'" }, - { name = "nvidia-nccl-cu12", marker = "platform_machine == 'x86_64' and platform_system == 'Linux'" }, - { name = "nvidia-nvjitlink-cu12", marker = "platform_machine == 'x86_64' and platform_system == 'Linux'" }, - { name = "nvidia-nvtx-cu12", marker = "platform_machine == 'x86_64' and platform_system == 'Linux'" }, + { name = "nvidia-cublas-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "nvidia-cuda-cupti-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "nvidia-cuda-nvrtc-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "nvidia-cuda-runtime-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "nvidia-cudnn-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "nvidia-cufft-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "nvidia-curand-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "nvidia-cusolver-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "nvidia-cusparse-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "nvidia-nccl-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "nvidia-nvjitlink-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "nvidia-nvtx-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, { name = "setuptools", marker = "python_full_version >= '3.12'" }, { name = "sympy" }, - { name = "triton", marker = "python_full_version < '3.13' and platform_machine == 'x86_64' and platform_system == 'Linux'" }, + { name = "triton", marker = "python_full_version < '3.13' and platform_machine == 'x86_64' and sys_platform == 'linux'" }, { name = "typing-extensions" }, ] wheels = [ @@ -1553,7 +1557,7 @@ name = "tqdm" version = "4.66.5" source = { registry = "https://pypi.org/simple" } dependencies = [ - { name = "colorama", marker = "platform_system == 'Windows'" }, + { name = "colorama", marker = "sys_platform == 'win32'" }, ] sdist = { url = "https://files.pythonhosted.org/packages/58/83/6ba9844a41128c62e810fddddd72473201f3eacde02046066142a2d96cc5/tqdm-4.66.5.tar.gz", hash = "sha256:e1020aef2e5096702d8a025ac7d16b1577279c9d63f8375b63083e9a5f0fcbad", size = 169504 } wheels = [ From 09f888d68773cf8eb2e086240124e7034965d3d3 Mon Sep 17 00:00:00 2001 From: stephantul Date: Sun, 22 Dec 2024 12:40:55 +0100 Subject: [PATCH 02/57] fix issue with warning --- model2vec/distill/distillation.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/model2vec/distill/distillation.py b/model2vec/distill/distillation.py index ea9b2880..866edb7e 100644 --- a/model2vec/distill/distillation.py +++ b/model2vec/distill/distillation.py @@ -41,7 +41,7 @@ def distill_from_model( pca_dims: PCADimType = 256, apply_zipf: bool = True, use_subword: bool = True, - token_remove_pattern: str | None = "\[unused\d+\]", + token_remove_pattern: str | None = r"\[unused\d+\]", ) -> StaticModel: """ Distill a staticmodel from a sentence transformer. 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13:14:32 +0100 Subject: [PATCH 05/57] Try to not select 2.5.1 --- pyproject.toml | 4 ++-- uv.lock | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 3bc11aaa..aa4d72cc 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -53,9 +53,9 @@ dev = [ "pytest-cov", "ruff", ] -distill = ["torch", "transformers", "scikit-learn"] +distill = ["torch!=2.5.1", "transformers", "scikit-learn"] -onnx = ["onnx", "torch"] +onnx = ["onnx", "torch!=2.5.1"] [project.urls] "Homepage" = "https://github.com/MinishLab" diff --git a/uv.lock b/uv.lock index 6d38e37a..541052e3 100644 --- a/uv.lock +++ b/uv.lock @@ -587,8 +587,8 @@ requires-dist = [ { name = "scikit-learn", marker = "extra == 'distill'" }, { name = "setuptools" }, { name = "tokenizers", specifier = ">=0.20" }, - { name = "torch", marker = "extra == 'distill'" }, - { name = "torch", marker = "extra == 'onnx'" }, + { name = "torch", marker = "extra == 'distill'", specifier = "!=2.5.1" }, + { name = "torch", marker = "extra == 'onnx'", specifier = "!=2.5.1" }, { name = "tqdm" }, { name = "transformers", marker = "extra == 'distill'" }, ] From 3e6866982aeb61d957cd260cb733e2c1fec70490 Mon Sep 17 00:00:00 2001 From: stephantul Date: Sun, 22 Dec 2024 13:24:26 +0100 Subject: [PATCH 06/57] fix: issue with dividers in utils --- model2vec/utils.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/model2vec/utils.py b/model2vec/utils.py index 0e4899ff..b2f02ef4 100644 --- a/model2vec/utils.py +++ b/model2vec/utils.py @@ -1,6 +1,7 @@ # -*- coding: utf-8 -*- import json import logging +import re from importlib import import_module from importlib.metadata import metadata from pathlib import Path @@ -22,6 +23,7 @@ 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 +40,7 @@ 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: - prefix, *_ = name.split("==") + prefix, *_ = _DIVIDERS.split(name) yield prefix.strip() From 1ae4d61b4465628269d2b071bb0b1b663e7a4457 Mon Sep 17 00:00:00 2001 From: stephantul Date: Sun, 22 Dec 2024 13:36:17 +0100 Subject: [PATCH 07/57] Try to not select 2.5.0 --- pyproject.toml | 4 ++-- uv.lock | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index aa4d72cc..031d0cd7 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -53,9 +53,9 @@ dev = [ "pytest-cov", "ruff", ] -distill = ["torch!=2.5.1", "transformers", "scikit-learn"] +distill = ["torch<2.5.0", "transformers", "scikit-learn"] -onnx = ["onnx", "torch!=2.5.1"] +onnx = ["onnx", "torch<2.5.0"] [project.urls] "Homepage" = "https://github.com/MinishLab" diff --git a/uv.lock b/uv.lock index 541052e3..7287b01f 100644 --- a/uv.lock +++ b/uv.lock @@ -587,8 +587,8 @@ requires-dist = [ { name = "scikit-learn", marker = "extra == 'distill'" }, { name = "setuptools" }, { name = "tokenizers", specifier = ">=0.20" }, - { name = "torch", marker = "extra == 'distill'", specifier = "!=2.5.1" }, - { name = "torch", marker = "extra == 'onnx'", specifier = "!=2.5.1" }, + { name = "torch", marker = "extra == 'distill'", specifier = "<2.5.0" }, + { name = "torch", marker = "extra == 'onnx'", specifier = "<2.5.0" }, { name = "tqdm" }, { name = "transformers", marker = "extra == 'distill'" }, ] From 1349b0c184d7cb83468638a5e7b3ed359fc37cd1 Mon Sep 17 00:00:00 2001 From: stephantul Date: Sun, 22 Dec 2024 18:25:25 +0100 Subject: [PATCH 08/57] fix: do not up version --- model2vec/version.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/model2vec/version.py b/model2vec/version.py index f0768802..9bfefb0c 100644 --- a/model2vec/version.py +++ b/model2vec/version.py @@ -1,2 +1,2 @@ -__version_triple__ = (0, 3, 4) +__version_triple__ = (0, 3, 3) __version__ = ".".join(map(str, __version_triple__)) From 4b83d5913ca00327d8270417ce7f938d539f4170 Mon Sep 17 00:00:00 2001 From: stephantul Date: Sun, 22 Dec 2024 18:26:57 +0100 Subject: [PATCH 09/57] Attempt special fix --- Makefile | 1 + pyproject.toml | 5 ++--- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/Makefile b/Makefile index 28618177..ef0f2d3e 100644 --- a/Makefile +++ b/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 diff --git a/pyproject.toml b/pyproject.toml index 031d0cd7..fa1beab8 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -53,9 +53,8 @@ dev = [ "pytest-cov", "ruff", ] -distill = ["torch<2.5.0", "transformers", "scikit-learn"] - -onnx = ["onnx", "torch<2.5.0"] +distill = ["torch", "transformers", "scikit-learn"] +onnx = ["onnx", "torch"] [project.urls] "Homepage" = "https://github.com/MinishLab" From dfd865b26b4ab3993e86818997883e07517bb55f Mon Sep 17 00:00:00 2001 From: stephantul Date: Mon, 23 Dec 2024 14:31:31 +0100 Subject: [PATCH 10/57] feat: add training --- model2vec/train/__init__.py | 6 + model2vec/train/base.py | 172 ++++++++++++++++++++++++++ model2vec/train/classifier.py | 93 ++++++++++++++ model2vec/train/train_loop.py | 224 ++++++++++++++++++++++++++++++++++ 4 files changed, 495 insertions(+) create mode 100644 model2vec/train/__init__.py create mode 100644 model2vec/train/base.py create mode 100644 model2vec/train/classifier.py create mode 100644 model2vec/train/train_loop.py diff --git a/model2vec/train/__init__.py b/model2vec/train/__init__.py new file mode 100644 index 00000000..2e9bea2d --- /dev/null +++ b/model2vec/train/__init__.py @@ -0,0 +1,6 @@ +from model2vec.utils import get_package_extras, importable + +_REQUIRED_EXTRA = "train" + +for extra_dependency in get_package_extras("model2vec", _REQUIRED_EXTRA): + importable(extra_dependency, _REQUIRED_EXTRA) diff --git a/model2vec/train/base.py b/model2vec/train/base.py new file mode 100644 index 00000000..1f0d2169 --- /dev/null +++ b/model2vec/train/base.py @@ -0,0 +1,172 @@ +from __future__ import annotations + +from typing import Any, TypeVar + +import torch +from tokenizers import Encoding, Tokenizer +from torch import nn +from torch.nn.utils.rnn import pad_sequence +from torch.utils.data import DataLoader, Dataset + +from model2vec import StaticModel + + +class FinetunableStaticModel(nn.Module): + def __init__(self, *, vectors: torch.Tensor, tokenizer: Tokenizer, out_dim: int, pad_id: int = 0) -> None: + """ + Initialize a trainable StaticModel from a StaticModel. + + :param vectors: The embeddings of the staticmodel. + :param tokenizer: The tokenizer. + :param out_dim: The output dimension of the head. + :param pad_id: The padding id. This is set to 0 in almost all model2vec models + """ + super().__init__() + self.pad_id = pad_id + self.out_dim = out_dim + self.embed_dim = vectors.shape[1] + + self.embeddings = nn.Embedding.from_pretrained(vectors.clone(), freeze=False, padding_idx=pad_id) + self.head = self.construct_head() + + # Weights for + weights = torch.ones(len(vectors)) + weights[pad_id] = 0 + self.w = nn.Parameter(weights) + self.tokenizer = tokenizer + + def construct_head(self) -> nn.Module: + """Method should be overridden for various other classes.""" + return nn.Linear(self.embed_dim, self.out_dim) + + @classmethod + def from_pretrained( + cls: type[ModelType], out_dim: int, model_name: str = "minishlab/potion-base-8m", **kwargs: Any + ) -> ModelType: + """Load the model from a pretrained model2vec model.""" + model = StaticModel.from_pretrained(model_name) + return cls.from_static_model(model, out_dim, **kwargs) + + @classmethod + def from_static_model(cls: type[ModelType], model: StaticModel, out_dim: int, **kwargs: Any) -> ModelType: + """Load the model from a static model.""" + embeddings_converted = torch.from_numpy(model.embedding) + return cls( + vectors=embeddings_converted, + pad_id=model.tokenizer.token_to_id("[PAD]"), + out_dim=out_dim, + tokenizer=model.tokenizer, + **kwargs, + ) + + def _encode(self, input_ids: torch.Tensor) -> torch.Tensor: + """ + A forward pass and mean pooling. + + This function is analogous to `StaticModel.encode`, but reimplemented to allow gradients + to pass through. + + :param input_ids: A 2D tensor of input ids. All input ids are have to be within bounds. + :return: The mean over the input ids, weighted by token weights. + """ + w = self.w[input_ids] + zeros = (input_ids != self.pad_id).float() + length = zeros.sum(1) + embedded = self.embeddings(input_ids) + # Simulate actual mean + # Zero out the padding + embedded = embedded * zeros[:, :, None] + embedded = (embedded * w[:, :, None]).sum(1) / w.sum(1)[:, None] + embedded = embedded / length[:, None] + + return torch.nn.functional.normalize(embedded) + + def forward(self, input_ids: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: + """Forward pass through the mean, and a classifier layer after.""" + encoded = self._encode(input_ids) + return self.head(encoded), encoded + + def tokenize(self, texts: list[str], max_length: int | None = 512) -> torch.Tensor: + """ + Tokenize a bunch of strings into a single padded 2D tensor. + + Note that this is not used during training. + + :param texts: The texts to tokenize. + :param max_length: If this is None, the sequence lengths are truncated to 512. + :return: A 2D padded tensor + """ + encoded: list[Encoding] = self.tokenizer.encode_batch_fast(texts, add_special_tokens=False) + encoded_ids: list[torch.Tensor] = [torch.Tensor(encoding.ids[:max_length]).long() for encoding in encoded] + return pad_sequence(encoded_ids, batch_first=True) + + @property + def device(self) -> str: + """Get the device of the model.""" + return self.embeddings.weight.device + + def to_static_model(self, config: dict[str, Any] | None = None) -> StaticModel: + """ + Convert the model to a static model. + + This is useful if you want to discard your head, and consolidate the information learned by + the model to use it in a downstream task. + + :param config: The config used in the StaticModel. If this is set to None, it will have no config. + :return: A static model. + """ + # Perform the forward pass on the selected device. + with torch.no_grad(): + all_indices = torch.arange(len(self.embeddings.weight))[:, None].to(self.device) + vectors = self._encode(all_indices).cpu().numpy() + + new_model = StaticModel(vectors=vectors, tokenizer=self.tokenizer, config=config) + + return new_model + + +class TextDataset(Dataset): + def __init__(self, texts: list[str], targets: torch.Tensor, tokenizer: Tokenizer) -> None: + """ + A dataset of texts. + + This dataset tokenizes the texts and stores them as Tensors, which are then padded in the collation function. + + :param texts: The texts to tokenize. + :param targets: The targets. + :param tokenizer: The tokenizer to use. + :raises ValueError: If the number of labels does not match the number of texts. + """ + if len(targets) != len(texts): + raise ValueError("Number of labels does not match number of texts.") + self.texts = texts + self.tokenized_texts: list[list[int]] = [ + encoding.ids for encoding in tokenizer.encode_batch_fast(self.texts, add_special_tokens=False) + ] + self.targets = targets + self.tokenizer = tokenizer + + def __len__(self) -> int: + """Return the length of the dataset.""" + return len(self.tokenized_texts) + + def __getitem__(self, index: int) -> tuple[list[int], torch.Tensor]: + """Gets an item.""" + return self.tokenized_texts[index], self.targets[index] + + @staticmethod + def collate_fn(batch: list[tuple[list[list[int]], int]]) -> tuple[torch.Tensor, torch.Tensor]: + """Collate function.""" + texts, targets = zip(*batch) + + tensors = [torch.LongTensor(x) for x in texts] + padded = pad_sequence(tensors, batch_first=True, padding_value=0) + + return padded, torch.stack(targets) + + def to_dataloader(self, shuffle: bool, batch_size: int = 32) -> DataLoader: + """Convert the dataset to a DataLoader.""" + return DataLoader(self, collate_fn=self.collate_fn, shuffle=shuffle, batch_size=batch_size) + + +ModelType = TypeVar("ModelType", bound=FinetunableStaticModel) diff --git a/model2vec/train/classifier.py b/model2vec/train/classifier.py new file mode 100644 index 00000000..60682d74 --- /dev/null +++ b/model2vec/train/classifier.py @@ -0,0 +1,93 @@ +from typing import Any + +import torch +from tokenizers import Tokenizer +from torch import nn + +from model2vec.train.base import FinetunableStaticModel, TextDataset +from model2vec.train.train_loop import train_supervised + + +class ClassificationStaticModel(FinetunableStaticModel): + def __init__( + self, + *, + vectors: torch.Tensor, + tokenizer: Tokenizer, + n_layers: int, + hidden_dim: int, + out_dim: int, + pad_id: int = 0, + ) -> None: + """Initialize a standard classifier model.""" + self.n_layers = n_layers + self.hidden_dim = hidden_dim + # Alias: Follows scikit-learn. + self.classes_: list[str] = [] + super().__init__(vectors=vectors, out_dim=out_dim, pad_id=pad_id, tokenizer=tokenizer) + + @property + def classes(self) -> list[str]: + """Return all clasess in the correct order.""" + return self.classes_ + + def construct_head(self) -> nn.Module: + """Constructs a simple classifier head.""" + if self.n_layers == 0: + return nn.Linear(self.embed_dim, self.out_dim) + modules = [nn.Linear(self.embed_dim, self.hidden_dim), nn.ReLU()] + for _ in range(self.n_layers - 1): + modules.extend([nn.Linear(self.hidden_dim, self.hidden_dim), nn.ReLU()]) + modules.append(nn.Linear(self.hidden_dim, self.out_dim)) + + return nn.Sequential(*modules) + + def predict(self, texts: list[str]) -> list[str]: + """Predict a class for a set of texts.""" + logits = self._predict(texts) + + return [self.classes[idx] for idx in logits.argmax(1)] + + def _predict(self, texts: list[str]) -> torch.Tensor: + input_ids = self.tokenize(texts) + vectors, _ = self.forward(input_ids) + return vectors + + def predict_proba(self, texts: list[str]) -> torch.Tensor: + """Predict the probability of each class.""" + logits = self._predict(texts) + + return torch.softmax(logits, dim=1) + + def loss_calculator( + self, head_out: torch.Tensor, embedding_out: torch.Tensor, y: torch.Tensor + ) -> dict[str, torch.Tensor]: + """Calculates the loss for this specific task.""" + # Separate loss components + loss = nn.functional.cross_entropy(head_out, y.to(self.device)).mean() + return {"loss": loss} + + def fit( + self, + train_texts: list[str], + train_labels: list[str], + validation_texts: list[str], + validation_labels: list[str], + **kwargs: Any, + ) -> FinetunableStaticModel: + """Fit a model.""" + classes = sorted(set(train_labels) | set(validation_labels)) + self.classes_ = classes + + if len(self.classes) != self.out_dim: + self.out_dim = len(self.classes) + self.head = self.construct_head() + + label_mapping = {label: idx for idx, label in enumerate(self.classes)} + # Turn labels into a LongTensor + train_labels_tensor = torch.Tensor([label_mapping[label] for label in train_labels]).long() + train_dataset = TextDataset(train_texts, train_labels_tensor, self.tokenizer) + val_labels_tensor = torch.Tensor([label_mapping[label] for label in validation_labels]).long() + val_dataset = TextDataset(validation_texts, val_labels_tensor, self.tokenizer) + + return train_supervised(self, train_dataset, val_dataset, self.loss_calculator, **kwargs) diff --git a/model2vec/train/train_loop.py b/model2vec/train/train_loop.py new file mode 100644 index 00000000..60556bc6 --- /dev/null +++ b/model2vec/train/train_loop.py @@ -0,0 +1,224 @@ +from __future__ import annotations + +import logging +from collections import defaultdict +from typing import Callable + +import numpy as np +import torch +from tqdm import tqdm + +from model2vec.train.base import FinetunableStaticModel, TextDataset + +logger = logging.getLogger(__name__) + +# Try to import wandb for logging +try: + import wandb + + _WANDB_INSTALLED = True +except ImportError: + _WANDB_INSTALLED = False + + +def _init_wandb(project_name: str, config: dict | None = None) -> None: + """Initialize Weights & Biases for tracking experiments if wandb is installed.""" + if _WANDB_INSTALLED: + wandb.init(project=project_name, config=config) + logger.info(f"W&B initialized with project: {project_name}") + else: + logger.info("Skipping W&B initialization since wandb is not installed.") + + +def train_supervised( # noqa: C901 + model: FinetunableStaticModel, + train_dataset: TextDataset, + validation_dataset: TextDataset | None, + loss_calculator: Callable, + max_epochs: int = 50, + min_epochs: int = 1, + patience: int | None = 5, + patience_min_delta: float = 0.001, + batch_size: int = 256, + wandb_project_name: str | None = None, + wandb_config: dict | None = None, + lr_scheduler_patience: int = 3, + lr_scheduler_min_delta: float = 0.03, + lr_model: float = 0.001, + lr_head: float = 0.001, +) -> FinetunableStaticModel: + """ + Train a StaticModel. + + :param model: The model to train. + :param train_dataset: The training dataset. + :param validation_dataset: The validation dataset. If this is None and patience is set, + early stopping is performed on the training set. + :param loss_calculator: A function that calculates the loss. + :param max_epochs: The maximum number of epochs to train. + :param min_epochs: The minimum number of epochs to train. + :param patience: The number of epochs to wait before early stopping. + :param patience_min_delta: The minimum delta for early stopping. + :param batch_size: The batch size. + :param wandb_project_name: The name of the project for W&B. + :param wandb_config: The configuration for W&B. + :param lr_scheduler_patience: The patience for the learning rate scheduler. + :param lr_scheduler_min_delta: The minimum delta for the learning rate scheduler. + :param lr_model: The learning rate for the model. + :param lr_head: The learning rate for the head. + :return: The trained model. + """ + if wandb_config is None: + wandb_config = { + "batch_size": batch_size, + "max_epochs": max_epochs, + "min_epochs": min_epochs, + "patience": patience, + "lr_scheduler_patience": lr_scheduler_patience, + "lr_scheduler_min_delta": lr_scheduler_min_delta, + "lr_model": lr_model, + "lr_linear": lr_head, + } + + # Initialize W&B only if wandb is installed and project name is provided + if _WANDB_INSTALLED and wandb_project_name: + _init_wandb(project_name=wandb_project_name, config=wandb_config) + wandb_initialized = True + else: + wandb_initialized = False + + train_dataloader = train_dataset.to_dataloader(shuffle=True, batch_size=batch_size) + if validation_dataset is not None: + validation_dataloader = validation_dataset.to_dataloader(shuffle=False, batch_size=batch_size) + else: + validation_dataloader = None + + # Separate parameters for model and linear layer + model_params = list(model.embeddings.parameters()) + [model.w] + head_params = model.head.parameters() + + # Create optimizer with separate parameter groups + optimizer = torch.optim.Adam([{"params": model_params, "lr": lr_model}, {"params": head_params, "lr": lr_head}]) + + # Initialize the learning rate scheduler + scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau( + optimizer, + mode="min", + factor=0.5, + patience=lr_scheduler_patience, + verbose=True, + min_lr=1e-6, + threshold=lr_scheduler_min_delta, + threshold_mode="rel", + ) + + lowest_loss = float("inf") + param_dict = model.state_dict() + curr_patience = patience + + try: + for epoch in range(max_epochs): + logger.info(f"Epoch {epoch}") + model.train() + + # Track train loss separately + tracked_losses = defaultdict(list) + barred_train = tqdm(train_dataloader, desc=f"Epoch {epoch:03d} [Train]") + + for x, y in barred_train: + optimizer.zero_grad() + x = x.to(model.device) + head_out, emb_out = model(x) + losses: dict[str, torch.Tensor] = loss_calculator(head_out, emb_out, y) + + train_loss = losses["loss"] + train_loss.backward() + + optimizer.step() + + for loss_name, loss in losses.items(): + tracked_losses[loss_name].append(loss.item()) + + barred_train.set_description_str(f"Train Loss: {np.mean(tracked_losses['loss'][-10:]):.3f}") + + # Calculate average losses + avg_train_loss = float(np.mean(tracked_losses["loss"])) + + # Step the scheduler with the current training loss + scheduler.step(avg_train_loss) + + # Get current learning rates + current_lr_model = optimizer.param_groups[0]["lr"] + current_lr_linear = optimizer.param_groups[1]["lr"] + + # Log training loss and learning rates to wandb + if wandb_initialized: + wandb.log( + { + "epoch": epoch, + "learning_rate_model": current_lr_model, + "learning_rate_linear": current_lr_linear, + **{k: np.mean(v) for k, v in tracked_losses.items()}, + } + ) + + logger.info(f"Training loss: {avg_train_loss:.3f}") + + patience_loss = avg_train_loss + if validation_dataloader is not None: + model.eval() + avg_validation_loss: list[float] = [] + for x, y in validation_dataloader: + optimizer.zero_grad() + x = x.to(model.device) + head_out, emb_out = model(x) + losses = loss_calculator(head_out, emb_out, y) + + val_loss: float = losses["loss"].item() + avg_validation_loss.append(val_loss) + + patience_loss = float(np.mean(avg_validation_loss)) + logger.info(f"Validation loss: {patience_loss:.3f}") + + # Early stopping logic based on training loss + curr_patience, lowest_loss = _track_patience( + patience, curr_patience, epoch, min_epochs, patience_min_delta, patience_loss, lowest_loss + ) + if curr_patience is not None and curr_patience == 0: + break + + model.train() + + except KeyboardInterrupt: + logger.info("Training interrupted") + + model.eval() + # Load best model based on training loss + model.load_state_dict(param_dict) + + return model + + +def _track_patience( + patience: None | int, + curr_patience: int | None, + epoch: int, + min_epochs: int, + patience_min_delta: float, + curr_loss: float, + lowest_loss: float, +) -> tuple[int | None, float]: + if patience is not None and curr_patience is not None and epoch >= min_epochs: + patience_str = "🌝" * curr_patience + logger.info(f"Patience level: {patience_str}") + logger.info(f"Lowest train loss: {lowest_loss:.3f}") + if (lowest_loss - curr_loss) > patience_min_delta: + curr_patience = patience + lowest_loss = curr_loss + return patience, lowest_loss + else: + curr_patience -= 1 + return curr_patience, lowest_loss + else: + # We shouldn't ever stop + return patience, float("inf") From 4713bfa8dd4ed4840ff4d374cbd679c4baa9dc6b Mon Sep 17 00:00:00 2001 From: stephantul Date: Tue, 24 Dec 2024 12:45:58 +0100 Subject: [PATCH 11/57] fix: no grad --- model2vec/train/classifier.py | 1 + 1 file changed, 1 insertion(+) diff --git a/model2vec/train/classifier.py b/model2vec/train/classifier.py index 60682d74..24157333 100644 --- a/model2vec/train/classifier.py +++ b/model2vec/train/classifier.py @@ -48,6 +48,7 @@ def predict(self, texts: list[str]) -> list[str]: return [self.classes[idx] for idx in logits.argmax(1)] + @torch.no_grad() def _predict(self, texts: list[str]) -> torch.Tensor: input_ids = self.tokenize(texts) vectors, _ = self.forward(input_ids) From e8058bbdc9f3dfad95887534d5854caf2be3b080 Mon Sep 17 00:00:00 2001 From: stephantul Date: Tue, 24 Dec 2024 12:48:32 +0100 Subject: [PATCH 12/57] use numpy --- model2vec/train/classifier.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/model2vec/train/classifier.py b/model2vec/train/classifier.py index 24157333..80f47396 100644 --- a/model2vec/train/classifier.py +++ b/model2vec/train/classifier.py @@ -1,5 +1,6 @@ from typing import Any +import numpy as np import torch from tokenizers import Tokenizer from torch import nn @@ -54,11 +55,11 @@ def _predict(self, texts: list[str]) -> torch.Tensor: vectors, _ = self.forward(input_ids) return vectors - def predict_proba(self, texts: list[str]) -> torch.Tensor: + def predict_proba(self, texts: list[str]) -> np.ndarray: """Predict the probability of each class.""" logits = self._predict(texts) - return torch.softmax(logits, dim=1) + return torch.softmax(logits, dim=1).numpy() def loss_calculator( self, head_out: torch.Tensor, embedding_out: torch.Tensor, y: torch.Tensor From a59127e7ba8f851c04918d455f67ab5d862d42ed Mon Sep 17 00:00:00 2001 From: stephantul Date: Tue, 24 Dec 2024 12:51:31 +0100 Subject: [PATCH 13/57] Add train_test_split --- model2vec/train/classifier.py | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/model2vec/train/classifier.py b/model2vec/train/classifier.py index 80f47396..1829f8ec 100644 --- a/model2vec/train/classifier.py +++ b/model2vec/train/classifier.py @@ -2,6 +2,7 @@ import numpy as np import torch +from sklearn.model_selection import train_test_split from tokenizers import Tokenizer from torch import nn @@ -71,14 +72,12 @@ def loss_calculator( def fit( self, - train_texts: list[str], - train_labels: list[str], - validation_texts: list[str], - validation_labels: list[str], + texts: list[str], + labels: list[str], **kwargs: Any, ) -> FinetunableStaticModel: """Fit a model.""" - classes = sorted(set(train_labels) | set(validation_labels)) + classes = sorted(set(labels)) self.classes_ = classes if len(self.classes) != self.out_dim: @@ -86,6 +85,8 @@ def fit( self.head = self.construct_head() label_mapping = {label: idx for idx, label in enumerate(self.classes)} + train_texts, validation_texts, train_labels, validation_labels = train_test_split(texts, labels, test_size=0.1) + # Turn labels into a LongTensor train_labels_tensor = torch.Tensor([label_mapping[label] for label in train_labels]).long() train_dataset = TextDataset(train_texts, train_labels_tensor, self.tokenizer) From 310fbb5a6748b3435320ddc0b77a01715188a9ad Mon Sep 17 00:00:00 2001 From: stephantul Date: Tue, 24 Dec 2024 13:08:27 +0100 Subject: [PATCH 14/57] fix: issue with fit not resetting --- model2vec/train/base.py | 1 + model2vec/train/classifier.py | 3 ++- 2 files changed, 3 insertions(+), 1 deletion(-) diff --git a/model2vec/train/base.py b/model2vec/train/base.py index 1f0d2169..932b6a81 100644 --- a/model2vec/train/base.py +++ b/model2vec/train/base.py @@ -26,6 +26,7 @@ def __init__(self, *, vectors: torch.Tensor, tokenizer: Tokenizer, out_dim: int, self.out_dim = out_dim self.embed_dim = vectors.shape[1] + self.vectors = vectors self.embeddings = nn.Embedding.from_pretrained(vectors.clone(), freeze=False, padding_idx=pad_id) self.head = self.construct_head() diff --git a/model2vec/train/classifier.py b/model2vec/train/classifier.py index 1829f8ec..9cc385db 100644 --- a/model2vec/train/classifier.py +++ b/model2vec/train/classifier.py @@ -82,7 +82,8 @@ def fit( if len(self.classes) != self.out_dim: self.out_dim = len(self.classes) - self.head = self.construct_head() + self.head = self.construct_head() + self.embeddings = nn.Embedding.from_pretrained(self.vectors.clone(), freeze=False, padding_idx=self.pad_id) label_mapping = {label: idx for idx, label in enumerate(self.classes)} train_texts, validation_texts, train_labels, validation_labels = train_test_split(texts, labels, test_size=0.1) From b1899d1156c3a76144f86f8520c1276b6748de51 Mon Sep 17 00:00:00 2001 From: stephantul Date: Sat, 28 Dec 2024 20:52:41 +0100 Subject: [PATCH 15/57] feat: add lightning --- model2vec/train/base.py | 31 +- model2vec/train/classifier.py | 142 +++++++-- model2vec/train/train_loop.py | 224 ------------- pyproject.toml | 2 +- uv.lock | 572 +++++++++++++++++++++++++++++++++- 5 files changed, 703 insertions(+), 268 deletions(-) delete mode 100644 model2vec/train/train_loop.py diff --git a/model2vec/train/base.py b/model2vec/train/base.py index 932b6a81..90cf0e58 100644 --- a/model2vec/train/base.py +++ b/model2vec/train/base.py @@ -26,13 +26,13 @@ def __init__(self, *, vectors: torch.Tensor, tokenizer: Tokenizer, out_dim: int, self.out_dim = out_dim self.embed_dim = vectors.shape[1] - self.vectors = vectors + self.vectors = torch.randn_like(vectors) self.embeddings = nn.Embedding.from_pretrained(vectors.clone(), freeze=False, padding_idx=pad_id) self.head = self.construct_head() # Weights for - weights = torch.ones(len(vectors)) - weights[pad_id] = 0 + weights = torch.zeros(len(vectors)) + weights[pad_id] = -10_000 self.w = nn.Parameter(weights) self.tokenizer = tokenizer @@ -71,16 +71,18 @@ def _encode(self, input_ids: torch.Tensor) -> torch.Tensor: :return: The mean over the input ids, weighted by token weights. """ w = self.w[input_ids] + w = torch.softmax(w, dim=1) zeros = (input_ids != self.pad_id).float() - length = zeros.sum(1) + w = w * zeros + # Add a small epsilon to avoid division by zero + length = zeros.sum(1) + 1e-16 embedded = self.embeddings(input_ids) # Simulate actual mean # Zero out the padding - embedded = embedded * zeros[:, :, None] - embedded = (embedded * w[:, :, None]).sum(1) / w.sum(1)[:, None] + embedded = torch.bmm(w[:, None, :], embedded).squeeze(1) embedded = embedded / length[:, None] - return torch.nn.functional.normalize(embedded) + return nn.functional.normalize(embedded) def forward(self, input_ids: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: """Forward pass through the mean, and a classifier layer after.""" @@ -127,25 +129,18 @@ def to_static_model(self, config: dict[str, Any] | None = None) -> StaticModel: class TextDataset(Dataset): - def __init__(self, texts: list[str], targets: torch.Tensor, tokenizer: Tokenizer) -> None: + def __init__(self, tokenized_texts: list[list[int]], targets: torch.Tensor) -> None: """ A dataset of texts. - This dataset tokenizes the texts and stores them as Tensors, which are then padded in the collation function. - - :param texts: The texts to tokenize. + :param tokenized_texts: The tokenized texts. Each text is a list of token ids. :param targets: The targets. - :param tokenizer: The tokenizer to use. :raises ValueError: If the number of labels does not match the number of texts. """ - if len(targets) != len(texts): + if len(targets) != len(tokenized_texts): raise ValueError("Number of labels does not match number of texts.") - self.texts = texts - self.tokenized_texts: list[list[int]] = [ - encoding.ids for encoding in tokenizer.encode_batch_fast(self.texts, add_special_tokens=False) - ] + self.tokenized_texts = tokenized_texts self.targets = targets - self.tokenizer = tokenizer def __len__(self) -> int: """Return the length of the dataset.""" diff --git a/model2vec/train/classifier.py b/model2vec/train/classifier.py index 9cc385db..9799bd13 100644 --- a/model2vec/train/classifier.py +++ b/model2vec/train/classifier.py @@ -1,13 +1,20 @@ +from __future__ import annotations + +import logging +from collections import Counter from typing import Any +import lightning as pl import numpy as np import torch +from lightning.pytorch.callbacks import Callback, EarlyStopping from sklearn.model_selection import train_test_split from tokenizers import Tokenizer from torch import nn from model2vec.train.base import FinetunableStaticModel, TextDataset -from model2vec.train.train_loop import train_supervised + +logger = logging.getLogger(__name__) class ClassificationStaticModel(FinetunableStaticModel): @@ -37,18 +44,33 @@ def construct_head(self) -> nn.Module: """Constructs a simple classifier head.""" if self.n_layers == 0: return nn.Linear(self.embed_dim, self.out_dim) - modules = [nn.Linear(self.embed_dim, self.hidden_dim), nn.ReLU()] + modules = [ + nn.Dropout(0.5), + nn.Linear(self.embed_dim, self.hidden_dim), + nn.LayerNorm(self.hidden_dim), + nn.ReLU(), + ] for _ in range(self.n_layers - 1): - modules.extend([nn.Linear(self.hidden_dim, self.hidden_dim), nn.ReLU()]) - modules.append(nn.Linear(self.hidden_dim, self.out_dim)) + modules.extend( + [nn.Dropout(0.5), nn.Linear(self.hidden_dim, self.hidden_dim), nn.LayerNorm(self.hidden_dim), nn.ReLU()] + ) + modules.extend([nn.Linear(self.hidden_dim, self.out_dim)]) + + for module in modules: + if isinstance(module, nn.Linear): + nn.init.kaiming_normal_(module.weight) + nn.init.zeros_(module.bias) return nn.Sequential(*modules) def predict(self, texts: list[str]) -> list[str]: """Predict a class for a set of texts.""" - logits = self._predict(texts) + pred: list[str] = [] + for batch in range(0, len(texts), 1024): + logits = self._predict(texts[batch : batch + 1024]) + pred.extend([self.classes[idx] for idx in logits.argmax(1)]) - return [self.classes[idx] for idx in logits.argmax(1)] + return pred @torch.no_grad() def _predict(self, texts: list[str]) -> torch.Tensor: @@ -58,40 +80,116 @@ def _predict(self, texts: list[str]) -> torch.Tensor: def predict_proba(self, texts: list[str]) -> np.ndarray: """Predict the probability of each class.""" - logits = self._predict(texts) - - return torch.softmax(logits, dim=1).numpy() + pred: list[np.ndarray] = [] + for batch in range(0, len(texts), 1024): + logits = self._predict(texts[batch : batch + 1024]) + pred.append(torch.softmax(logits, dim=1).numpy()) - def loss_calculator( - self, head_out: torch.Tensor, embedding_out: torch.Tensor, y: torch.Tensor - ) -> dict[str, torch.Tensor]: - """Calculates the loss for this specific task.""" - # Separate loss components - loss = nn.functional.cross_entropy(head_out, y.to(self.device)).mean() - return {"loss": loss} + return np.concatenate(pred) def fit( self, texts: list[str], labels: list[str], **kwargs: Any, - ) -> FinetunableStaticModel: + ) -> ClassificationStaticModel: """Fit a model.""" + pl.seed_everything(42) classes = sorted(set(labels)) self.classes_ = classes if len(self.classes) != self.out_dim: self.out_dim = len(self.classes) + self.head = self.construct_head() self.embeddings = nn.Embedding.from_pretrained(self.vectors.clone(), freeze=False, padding_idx=self.pad_id) label_mapping = {label: idx for idx, label in enumerate(self.classes)} - train_texts, validation_texts, train_labels, validation_labels = train_test_split(texts, labels, test_size=0.1) + label_counts = Counter(labels) + if min(label_counts.values()) < 2: + logger.info("Some classes have less than 2 samples. Stratification is disabled.") + train_texts, validation_texts, train_labels, validation_labels = train_test_split( + texts, labels, test_size=0.1, random_state=42, shuffle=True + ) + else: + train_texts, validation_texts, train_labels, validation_labels = train_test_split( + texts, labels, test_size=0.1, random_state=42, shuffle=True, stratify=labels + ) # Turn labels into a LongTensor + train_tokenized: list[list[int]] = [ + encoding.ids for encoding in self.tokenizer.encode_batch_fast(train_texts, add_special_tokens=False) + ] train_labels_tensor = torch.Tensor([label_mapping[label] for label in train_labels]).long() - train_dataset = TextDataset(train_texts, train_labels_tensor, self.tokenizer) - val_labels_tensor = torch.Tensor([label_mapping[label] for label in validation_labels]).long() - val_dataset = TextDataset(validation_texts, val_labels_tensor, self.tokenizer) + train_dataset = TextDataset(train_tokenized, train_labels_tensor) - return train_supervised(self, train_dataset, val_dataset, self.loss_calculator, **kwargs) + val_tokenized: list[list[int]] = [ + encoding.ids for encoding in self.tokenizer.encode_batch_fast(validation_texts, add_special_tokens=False) + ] + val_labels_tensor = torch.Tensor([label_mapping[label] for label in validation_labels]).long() + val_dataset = TextDataset(val_tokenized, val_labels_tensor) + + c = ClassifierLightningModule(self) + + batch_size = 32 + n_train_batches = len(train_dataset) // batch_size + callbacks: list[Callback] = [EarlyStopping(monitor="val_accuracy", mode="max", patience=5)] + if n_train_batches < 250: + trainer = pl.Trainer(max_epochs=500, callbacks=callbacks, check_val_every_n_epoch=1) + else: + val_check_interval = max(250, 2 * len(val_dataset) // batch_size) + trainer = pl.Trainer( + max_epochs=500, callbacks=callbacks, val_check_interval=val_check_interval, check_val_every_n_epoch=None + ) + + trainer.fit( + c, + train_dataloaders=train_dataset.to_dataloader(shuffle=True, batch_size=batch_size), + val_dataloaders=val_dataset.to_dataloader(shuffle=False, batch_size=batch_size), + ) + best_model_path = trainer.checkpoint_callback.best_model_path # type: ignore + + state_dict = { + k.removeprefix("model."): v for k, v in torch.load(best_model_path, weights_only=True)["state_dict"].items() + } + self.load_state_dict(state_dict) + + self.eval() + + return self + + +class ClassifierLightningModule(pl.LightningModule): + def __init__(self, model: ClassificationStaticModel) -> None: + """Initialize the lightningmodule.""" + super().__init__() + self.model = model + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """Simple forward pass.""" + return self.model(x) + + def training_step(self, batch: tuple[torch.Tensor, torch.Tensor], batch_idx: int) -> torch.Tensor: + """Simple training step using cross entropy loss.""" + x, y = batch + head_out, _ = self.model(x) + loss = nn.functional.cross_entropy(head_out, y).mean() + + self.log("train_loss", loss) + return loss + + def validation_step(self, batch: tuple[torch.Tensor, torch.Tensor], batch_idx: int) -> torch.Tensor: + """Simple validation step using cross entropy loss and accuracy.""" + x, y = batch + head_out, _ = self.model(x) + loss = nn.functional.cross_entropy(head_out, y).mean() + accuracy = (head_out.argmax(1) == y).float().mean() + + self.log("val_loss", loss) + self.log("val_accuracy", accuracy, prog_bar=True) + + return loss + + def configure_optimizers(self) -> torch.optim.Optimizer: + """Simple Adam optimizer.""" + return torch.optim.Adam(self.model.parameters(), lr=1e-3) diff --git a/model2vec/train/train_loop.py b/model2vec/train/train_loop.py deleted file mode 100644 index 60556bc6..00000000 --- a/model2vec/train/train_loop.py +++ /dev/null @@ -1,224 +0,0 @@ -from __future__ import annotations - -import logging -from collections import defaultdict -from typing import Callable - -import numpy as np -import torch -from tqdm import tqdm - -from model2vec.train.base import FinetunableStaticModel, TextDataset - -logger = logging.getLogger(__name__) - -# Try to import wandb for logging -try: - import wandb - - _WANDB_INSTALLED = True -except ImportError: - _WANDB_INSTALLED = False - - -def _init_wandb(project_name: str, config: dict | None = None) -> None: - """Initialize Weights & Biases for tracking experiments if wandb is installed.""" - if _WANDB_INSTALLED: - wandb.init(project=project_name, config=config) - logger.info(f"W&B initialized with project: {project_name}") - else: - logger.info("Skipping W&B initialization since wandb is not installed.") - - -def train_supervised( # noqa: C901 - model: FinetunableStaticModel, - train_dataset: TextDataset, - validation_dataset: TextDataset | None, - loss_calculator: Callable, - max_epochs: int = 50, - min_epochs: int = 1, - patience: int | None = 5, - patience_min_delta: float = 0.001, - batch_size: int = 256, - wandb_project_name: str | None = None, - wandb_config: dict | None = None, - lr_scheduler_patience: int = 3, - lr_scheduler_min_delta: float = 0.03, - lr_model: float = 0.001, - lr_head: float = 0.001, -) -> FinetunableStaticModel: - """ - Train a StaticModel. - - :param model: The model to train. - :param train_dataset: The training dataset. - :param validation_dataset: The validation dataset. If this is None and patience is set, - early stopping is performed on the training set. - :param loss_calculator: A function that calculates the loss. - :param max_epochs: The maximum number of epochs to train. - :param min_epochs: The minimum number of epochs to train. - :param patience: The number of epochs to wait before early stopping. - :param patience_min_delta: The minimum delta for early stopping. - :param batch_size: The batch size. - :param wandb_project_name: The name of the project for W&B. - :param wandb_config: The configuration for W&B. - :param lr_scheduler_patience: The patience for the learning rate scheduler. - :param lr_scheduler_min_delta: The minimum delta for the learning rate scheduler. - :param lr_model: The learning rate for the model. - :param lr_head: The learning rate for the head. - :return: The trained model. - """ - if wandb_config is None: - wandb_config = { - "batch_size": batch_size, - "max_epochs": max_epochs, - "min_epochs": min_epochs, - "patience": patience, - "lr_scheduler_patience": lr_scheduler_patience, - "lr_scheduler_min_delta": lr_scheduler_min_delta, - "lr_model": lr_model, - "lr_linear": lr_head, - } - - # Initialize W&B only if wandb is installed and project name is provided - if _WANDB_INSTALLED and wandb_project_name: - _init_wandb(project_name=wandb_project_name, config=wandb_config) - wandb_initialized = True - else: - wandb_initialized = False - - train_dataloader = train_dataset.to_dataloader(shuffle=True, batch_size=batch_size) - if validation_dataset is not None: - validation_dataloader = validation_dataset.to_dataloader(shuffle=False, batch_size=batch_size) - else: - validation_dataloader = None - - # Separate parameters for model and linear layer - model_params = list(model.embeddings.parameters()) + [model.w] - head_params = model.head.parameters() - - # Create optimizer with separate parameter groups - optimizer = torch.optim.Adam([{"params": model_params, "lr": lr_model}, {"params": head_params, "lr": lr_head}]) - - # Initialize the learning rate scheduler - scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau( - optimizer, - mode="min", - factor=0.5, - patience=lr_scheduler_patience, - verbose=True, - min_lr=1e-6, - threshold=lr_scheduler_min_delta, - threshold_mode="rel", - ) - - lowest_loss = float("inf") - param_dict = model.state_dict() - curr_patience = patience - - try: - for epoch in range(max_epochs): - logger.info(f"Epoch {epoch}") - model.train() - - # Track train loss separately - tracked_losses = defaultdict(list) - barred_train = tqdm(train_dataloader, desc=f"Epoch {epoch:03d} [Train]") - - for x, y in barred_train: - optimizer.zero_grad() - x = x.to(model.device) - head_out, emb_out = model(x) - losses: dict[str, torch.Tensor] = loss_calculator(head_out, emb_out, y) - - train_loss = losses["loss"] - train_loss.backward() - - optimizer.step() - - for loss_name, loss in losses.items(): - tracked_losses[loss_name].append(loss.item()) - - barred_train.set_description_str(f"Train Loss: {np.mean(tracked_losses['loss'][-10:]):.3f}") - - # Calculate average losses - avg_train_loss = float(np.mean(tracked_losses["loss"])) - - # Step the scheduler with the current training loss - scheduler.step(avg_train_loss) - - # Get current learning rates - current_lr_model = optimizer.param_groups[0]["lr"] - current_lr_linear = optimizer.param_groups[1]["lr"] - - # Log training loss and learning rates to wandb - if wandb_initialized: - wandb.log( - { - "epoch": epoch, - "learning_rate_model": current_lr_model, - "learning_rate_linear": current_lr_linear, - **{k: np.mean(v) for k, v in tracked_losses.items()}, - } - ) - - logger.info(f"Training loss: {avg_train_loss:.3f}") - - patience_loss = avg_train_loss - if validation_dataloader is not None: - model.eval() - avg_validation_loss: list[float] = [] - for x, y in validation_dataloader: - optimizer.zero_grad() - x = x.to(model.device) - head_out, emb_out = model(x) - losses = loss_calculator(head_out, emb_out, y) - - val_loss: float = losses["loss"].item() - avg_validation_loss.append(val_loss) - - patience_loss = float(np.mean(avg_validation_loss)) - logger.info(f"Validation loss: {patience_loss:.3f}") - - # Early stopping logic based on training loss - curr_patience, lowest_loss = _track_patience( - patience, curr_patience, epoch, min_epochs, patience_min_delta, patience_loss, lowest_loss - ) - if curr_patience is not None and curr_patience == 0: - break - - model.train() - - except KeyboardInterrupt: - logger.info("Training interrupted") - - model.eval() - # Load best model based on training loss - model.load_state_dict(param_dict) - - return model - - -def _track_patience( - patience: None | int, - curr_patience: int | None, - epoch: int, - min_epochs: int, - patience_min_delta: float, - curr_loss: float, - lowest_loss: float, -) -> tuple[int | None, float]: - if patience is not None and curr_patience is not None and epoch >= min_epochs: - patience_str = "🌝" * curr_patience - logger.info(f"Patience level: {patience_str}") - logger.info(f"Lowest train loss: {lowest_loss:.3f}") - if (lowest_loss - curr_loss) > patience_min_delta: - curr_patience = patience - lowest_loss = curr_loss - return patience, lowest_loss - else: - curr_patience -= 1 - return curr_patience, lowest_loss - else: - # We shouldn't ever stop - return patience, float("inf") diff --git a/pyproject.toml b/pyproject.toml index 659e207f..40202331 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -56,7 +56,7 @@ dev = [ distill = ["torch", "transformers", "scikit-learn"] onnx = ["onnx", "torch"] -train = ["torch"] +train = ["torch", "lightning"] [project.urls] "Homepage" = "https://github.com/MinishLab" diff --git a/uv.lock b/uv.lock index 7287b01f..1fed4244 100644 --- a/uv.lock +++ b/uv.lock @@ -6,6 +6,120 @@ resolution-markers = [ "python_full_version < '3.10'", ] +[[package]] +name = "aiohappyeyeballs" +version = "2.4.4" +source = { registry = "https://pypi.org/simple" 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__init__(self, *, vectors: torch.Tensor, tokenizer: Tokenizer, out_dim: int, self.pad_id = pad_id self.out_dim = out_dim self.embed_dim = vectors.shape[1] + self.vectors = vectors - self.vectors = torch.randn_like(vectors) self.embeddings = nn.Embedding.from_pretrained(vectors.clone(), freeze=False, padding_idx=pad_id) self.head = self.construct_head() - # Weights for weights = torch.zeros(len(vectors)) weights[pad_id] = -10_000 self.w = nn.Parameter(weights) @@ -71,7 +70,7 @@ def _encode(self, input_ids: torch.Tensor) -> torch.Tensor: :return: The mean over the input ids, weighted by token weights. """ w = self.w[input_ids] - w = torch.softmax(w, dim=1) + w = torch.sigmoid(w) zeros = (input_ids != self.pad_id).float() w = w * zeros # Add a small epsilon to avoid division by zero @@ -80,6 +79,7 @@ def _encode(self, input_ids: torch.Tensor) -> torch.Tensor: # Simulate actual mean # Zero out the padding embedded = torch.bmm(w[:, None, :], embedded).squeeze(1) + # embedded = embedded.sum(1) embedded = embedded / length[:, None] return nn.functional.normalize(embedded) diff --git a/model2vec/train/classifier.py b/model2vec/train/classifier.py index 9799bd13..a73b8965 100644 --- a/model2vec/train/classifier.py +++ b/model2vec/train/classifier.py @@ -8,6 +8,7 @@ import numpy as np import torch from lightning.pytorch.callbacks import Callback, EarlyStopping +from lightning.pytorch.utilities.types import OptimizerLRScheduler from sklearn.model_selection import train_test_split from tokenizers import Tokenizer from torch import nn @@ -31,8 +32,8 @@ def __init__( """Initialize a standard classifier model.""" self.n_layers = n_layers self.hidden_dim = hidden_dim - # Alias: Follows scikit-learn. - self.classes_: list[str] = [] + # Alias: Follows scikit-learn. Set to dummy classes + self.classes_: list[str] = [str(x) for x in range(out_dim)] super().__init__(vectors=vectors, out_dim=out_dim, pad_id=pad_id, tokenizer=tokenizer) @property @@ -45,57 +46,53 @@ def construct_head(self) -> nn.Module: if self.n_layers == 0: return nn.Linear(self.embed_dim, self.out_dim) modules = [ - nn.Dropout(0.5), nn.Linear(self.embed_dim, self.hidden_dim), - nn.LayerNorm(self.hidden_dim), nn.ReLU(), ] for _ in range(self.n_layers - 1): - modules.extend( - [nn.Dropout(0.5), nn.Linear(self.hidden_dim, self.hidden_dim), nn.LayerNorm(self.hidden_dim), nn.ReLU()] - ) + modules.extend([nn.Linear(self.hidden_dim, self.hidden_dim), nn.ReLU()]) modules.extend([nn.Linear(self.hidden_dim, self.out_dim)]) for module in modules: if isinstance(module, nn.Linear): - nn.init.kaiming_normal_(module.weight) + nn.init.kaiming_uniform_(module.weight) nn.init.zeros_(module.bias) return nn.Sequential(*modules) - def predict(self, texts: list[str]) -> list[str]: + def predict(self, X: list[str]) -> list[str]: """Predict a class for a set of texts.""" pred: list[str] = [] - for batch in range(0, len(texts), 1024): - logits = self._predict(texts[batch : batch + 1024]) + for batch in range(0, len(X), 1024): + logits = self._predict(X[batch : batch + 1024]) pred.extend([self.classes[idx] for idx in logits.argmax(1)]) return pred @torch.no_grad() - def _predict(self, texts: list[str]) -> torch.Tensor: - input_ids = self.tokenize(texts) + def _predict(self, X: list[str]) -> torch.Tensor: + input_ids = self.tokenize(X) vectors, _ = self.forward(input_ids) return vectors - def predict_proba(self, texts: list[str]) -> np.ndarray: + def predict_proba(self, X: list[str]) -> np.ndarray: """Predict the probability of each class.""" pred: list[np.ndarray] = [] - for batch in range(0, len(texts), 1024): - logits = self._predict(texts[batch : batch + 1024]) + for batch in range(0, len(X), 1024): + logits = self._predict(X[batch : batch + 1024]) pred.append(torch.softmax(logits, dim=1).numpy()) return np.concatenate(pred) def fit( self, - texts: list[str], - labels: list[str], + X: list[str], + y: list[str], **kwargs: Any, ) -> ClassificationStaticModel: """Fit a model.""" pl.seed_everything(42) - classes = sorted(set(labels)) + classes = sorted(set(y)) self.classes_ = classes if len(self.classes) != self.out_dim: @@ -105,15 +102,15 @@ def fit( self.embeddings = nn.Embedding.from_pretrained(self.vectors.clone(), freeze=False, padding_idx=self.pad_id) label_mapping = {label: idx for idx, label in enumerate(self.classes)} - label_counts = Counter(labels) + label_counts = Counter(y) if min(label_counts.values()) < 2: logger.info("Some classes have less than 2 samples. Stratification is disabled.") train_texts, validation_texts, train_labels, validation_labels = train_test_split( - texts, labels, test_size=0.1, random_state=42, shuffle=True + X, y, test_size=0.1, random_state=42, shuffle=True ) else: train_texts, validation_texts, train_labels, validation_labels = train_test_split( - texts, labels, test_size=0.1, random_state=42, shuffle=True, stratify=labels + X, y, test_size=0.1, random_state=42, shuffle=True, stratify=y ) # Turn labels into a LongTensor @@ -190,6 +187,18 @@ def validation_step(self, batch: tuple[torch.Tensor, torch.Tensor], batch_idx: i return loss - def configure_optimizers(self) -> torch.optim.Optimizer: + def configure_optimizers(self) -> OptimizerLRScheduler: """Simple Adam optimizer.""" - return torch.optim.Adam(self.model.parameters(), lr=1e-3) + optimizer = torch.optim.Adam(self.model.parameters(), lr=1e-3) + scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau( + optimizer, + mode="min", + factor=0.5, + patience=3, + verbose=True, + min_lr=1e-6, + threshold=0.03, + threshold_mode="rel", + ) + + return {"optimizer": optimizer, "lr_scheduler": {"scheduler": scheduler, "monitor": "val_loss"}} From 839d88a6fdeb79d31e211d149d753d8e493b7173 Mon Sep 17 00:00:00 2001 From: stephantul Date: Sun, 5 Jan 2025 16:07:37 +0100 Subject: [PATCH 17/57] fix: reviewer comments --- model2vec/train/base.py | 19 ------ model2vec/train/classifier.py | 105 ++++++++++++++++++++-------------- 2 files changed, 61 insertions(+), 63 deletions(-) diff --git a/model2vec/train/base.py b/model2vec/train/base.py index dd6a90d9..eee2b5ed 100644 --- a/model2vec/train/base.py +++ b/model2vec/train/base.py @@ -108,25 +108,6 @@ def device(self) -> str: """Get the device of the model.""" return self.embeddings.weight.device - def to_static_model(self, config: dict[str, Any] | None = None) -> StaticModel: - """ - Convert the model to a static model. - - This is useful if you want to discard your head, and consolidate the information learned by - the model to use it in a downstream task. - - :param config: The config used in the StaticModel. If this is set to None, it will have no config. - :return: A static model. - """ - # Perform the forward pass on the selected device. - with torch.no_grad(): - all_indices = torch.arange(len(self.embeddings.weight))[:, None].to(self.device) - vectors = self._encode(all_indices).cpu().numpy() - - new_model = StaticModel(vectors=vectors, tokenizer=self.tokenizer, config=config) - - return new_model - class TextDataset(Dataset): def __init__(self, tokenized_texts: list[list[int]], targets: torch.Tensor) -> None: diff --git a/model2vec/train/classifier.py b/model2vec/train/classifier.py index a73b8965..89a9a86d 100644 --- a/model2vec/train/classifier.py +++ b/model2vec/train/classifier.py @@ -88,56 +88,42 @@ def fit( self, X: list[str], y: list[str], - **kwargs: Any, + learning_rate: float = 1e-3, + batch_size: int = 32, + early_stopping_patience: int | None = 25, + test_size: float = 0.1, ) -> ClassificationStaticModel: """Fit a model.""" pl.seed_everything(42) - classes = sorted(set(y)) - self.classes_ = classes - - if len(self.classes) != self.out_dim: - self.out_dim = len(self.classes) - - self.head = self.construct_head() - self.embeddings = nn.Embedding.from_pretrained(self.vectors.clone(), freeze=False, padding_idx=self.pad_id) - - label_mapping = {label: idx for idx, label in enumerate(self.classes)} - label_counts = Counter(y) - if min(label_counts.values()) < 2: - logger.info("Some classes have less than 2 samples. Stratification is disabled.") - train_texts, validation_texts, train_labels, validation_labels = train_test_split( - X, y, test_size=0.1, random_state=42, shuffle=True - ) - else: - train_texts, validation_texts, train_labels, validation_labels = train_test_split( - X, y, test_size=0.1, random_state=42, shuffle=True, stratify=y - ) + self._initialize(y) - # Turn labels into a LongTensor - train_tokenized: list[list[int]] = [ - encoding.ids for encoding in self.tokenizer.encode_batch_fast(train_texts, add_special_tokens=False) - ] - train_labels_tensor = torch.Tensor([label_mapping[label] for label in train_labels]).long() - train_dataset = TextDataset(train_tokenized, train_labels_tensor) + train_texts, validation_texts, train_labels, validation_labels = self._train_test_split( + X, y, test_size=test_size + ) - val_tokenized: list[list[int]] = [ - encoding.ids for encoding in self.tokenizer.encode_batch_fast(validation_texts, add_special_tokens=False) - ] - val_labels_tensor = torch.Tensor([label_mapping[label] for label in validation_labels]).long() - val_dataset = TextDataset(val_tokenized, val_labels_tensor) + train_dataset = self._prepare_dataset(train_texts, train_labels) + val_dataset = self._prepare_dataset(validation_texts, validation_labels) - c = ClassifierLightningModule(self) + c = ClassifierLightningModule(self, learning_rate=learning_rate) - batch_size = 32 n_train_batches = len(train_dataset) // batch_size - callbacks: list[Callback] = [EarlyStopping(monitor="val_accuracy", mode="max", patience=5)] + callbacks: list[Callback] = [] + if early_stopping_patience is not None: + callback = EarlyStopping(monitor="val_accuracy", mode="max", patience=early_stopping_patience) + callbacks.append(callback) + if n_train_batches < 250: - trainer = pl.Trainer(max_epochs=500, callbacks=callbacks, check_val_every_n_epoch=1) + val_check_interval = None + check_val_every_epoch = True else: val_check_interval = max(250, 2 * len(val_dataset) // batch_size) - trainer = pl.Trainer( - max_epochs=500, callbacks=callbacks, val_check_interval=val_check_interval, check_val_every_n_epoch=None - ) + check_val_every_epoch = False + trainer = pl.Trainer( + max_epochs=500, + callbacks=callbacks, + val_check_interval=val_check_interval, + check_val_every_n_epoch=check_val_every_epoch, + ) trainer.fit( c, @@ -145,22 +131,53 @@ def fit( val_dataloaders=val_dataset.to_dataloader(shuffle=False, batch_size=batch_size), ) best_model_path = trainer.checkpoint_callback.best_model_path # type: ignore + best_model_weights = torch.load(best_model_path, weights_only=True) - state_dict = { - k.removeprefix("model."): v for k, v in torch.load(best_model_path, weights_only=True)["state_dict"].items() - } - self.load_state_dict(state_dict) + state_dict = {} + for weight_name, weight in best_model_weights["state_dict"].items(): + state_dict[weight_name.removeprefix("model.")] = weight + self.load_state_dict(state_dict) self.eval() return self + def _initialize(self, y: list[str]) -> None: + """Sets the out dimensionality, the classes and initializes the head.""" + classes = sorted(set(y)) + self.classes_ = classes + + if len(self.classes) != self.out_dim: + self.out_dim = len(self.classes) + + self.head = self.construct_head() + self.embeddings = nn.Embedding.from_pretrained(self.vectors.clone(), freeze=False, padding_idx=self.pad_id) + + def _prepare_dataset(self, X: list[str], y: list[str]) -> TextDataset: + """Prepare a dataset.""" + tokenized: list[list[int]] = [ + encoding.ids for encoding in self.tokenizer.encode_batch_fast(X, add_special_tokens=False) + ] + labels_tensor = torch.Tensor([self.classes.index(label) for label in y]).long() + return TextDataset(tokenized, labels_tensor) + + def _train_test_split( + self, X: list[str], y: list[str], test_size: float + ) -> tuple[list[str], list[str], list[str], list[str]]: + """Split the data.""" + label_counts = Counter(y) + if min(label_counts.values()) < 2: + logger.info("Some classes have less than 2 samples. Stratification is disabled.") + return train_test_split(X, y, test_size=0.1, random_state=42, shuffle=True) + return train_test_split(X, y, test_size=0.1, random_state=42, shuffle=True, stratify=y) + class ClassifierLightningModule(pl.LightningModule): - def __init__(self, model: ClassificationStaticModel) -> None: + def __init__(self, model: ClassificationStaticModel, learning_rate: float) -> None: """Initialize the lightningmodule.""" super().__init__() self.model = model + self.learning_rate = learning_rate def forward(self, x: torch.Tensor) -> torch.Tensor: """Simple forward pass.""" From 8457357c392f907c10f6accd6410ad9d61795f25 Mon Sep 17 00:00:00 2001 From: stephantul Date: Sun, 5 Jan 2025 16:28:35 +0100 Subject: [PATCH 18/57] fix train issue --- model2vec/train/classifier.py | 1 + 1 file changed, 1 insertion(+) diff --git a/model2vec/train/classifier.py b/model2vec/train/classifier.py index 89a9a86d..75b5de84 100644 --- a/model2vec/train/classifier.py +++ b/model2vec/train/classifier.py @@ -152,6 +152,7 @@ def _initialize(self, y: list[str]) -> None: self.head = self.construct_head() self.embeddings = nn.Embedding.from_pretrained(self.vectors.clone(), freeze=False, padding_idx=self.pad_id) + self.train() def _prepare_dataset(self, X: list[str], y: list[str]) -> TextDataset: """Prepare a dataset.""" From a750709548b49dfa2783da94f0fc9f8160a0dc99 Mon Sep 17 00:00:00 2001 From: stephantul Date: Tue, 7 Jan 2025 17:21:12 +0100 Subject: [PATCH 19/57] fix issue with trainer --- model2vec/train/classifier.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/model2vec/train/classifier.py b/model2vec/train/classifier.py index 75b5de84..70a71395 100644 --- a/model2vec/train/classifier.py +++ b/model2vec/train/classifier.py @@ -114,10 +114,10 @@ def fit( if n_train_batches < 250: val_check_interval = None - check_val_every_epoch = True + check_val_every_epoch = 1 else: val_check_interval = max(250, 2 * len(val_dataset) // batch_size) - check_val_every_epoch = False + check_val_every_epoch = None trainer = pl.Trainer( max_epochs=500, callbacks=callbacks, From e83c54e1985dcc615c0d61701851764b1837394c Mon Sep 17 00:00:00 2001 From: stephantul Date: Tue, 7 Jan 2025 17:40:27 +0100 Subject: [PATCH 20/57] fix: truncate during training --- model2vec/train/classifier.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/model2vec/train/classifier.py b/model2vec/train/classifier.py index 70a71395..4450cb17 100644 --- a/model2vec/train/classifier.py +++ b/model2vec/train/classifier.py @@ -2,7 +2,6 @@ import logging from collections import Counter -from typing import Any import lightning as pl import numpy as np @@ -12,6 +11,7 @@ from sklearn.model_selection import train_test_split from tokenizers import Tokenizer from torch import nn +from tqdm import tqdm from model2vec.train.base import FinetunableStaticModel, TextDataset @@ -157,7 +157,7 @@ def _initialize(self, y: list[str]) -> None: def _prepare_dataset(self, X: list[str], y: list[str]) -> TextDataset: """Prepare a dataset.""" tokenized: list[list[int]] = [ - encoding.ids for encoding in self.tokenizer.encode_batch_fast(X, add_special_tokens=False) + encoding.ids[:512] for encoding in tqdm(self.tokenizer.encode_batch_fast(X, add_special_tokens=False)) ] labels_tensor = torch.Tensor([self.classes.index(label) for label in y]).long() return TextDataset(tokenized, labels_tensor) From 803565db9e607143c9f149dab4c0a8db7a44ac9d Mon Sep 17 00:00:00 2001 From: stephantul Date: Tue, 7 Jan 2025 20:40:09 +0100 Subject: [PATCH 21/57] feat: tokenize maximum length truncation --- model2vec/train/classifier.py | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/model2vec/train/classifier.py b/model2vec/train/classifier.py index 4450cb17..6fae8645 100644 --- a/model2vec/train/classifier.py +++ b/model2vec/train/classifier.py @@ -11,7 +11,6 @@ from sklearn.model_selection import train_test_split from tokenizers import Tokenizer from torch import nn -from tqdm import tqdm from model2vec.train.base import FinetunableStaticModel, TextDataset @@ -95,13 +94,16 @@ def fit( ) -> ClassificationStaticModel: """Fit a model.""" pl.seed_everything(42) + logger.info("Re-initializing model.") self._initialize(y) train_texts, validation_texts, train_labels, validation_labels = self._train_test_split( X, y, test_size=test_size ) + logger.info("Prepating train dataset.") train_dataset = self._prepare_dataset(train_texts, train_labels) + logger.info("Prepating validation dataset.") val_dataset = self._prepare_dataset(validation_texts, validation_labels) c = ClassifierLightningModule(self, learning_rate=learning_rate) @@ -156,8 +158,10 @@ def _initialize(self, y: list[str]) -> None: def _prepare_dataset(self, X: list[str], y: list[str]) -> TextDataset: """Prepare a dataset.""" + # Truncate texts to pre-specified maximum length. + X = [x[:3072] for x in X] tokenized: list[list[int]] = [ - encoding.ids[:512] for encoding in tqdm(self.tokenizer.encode_batch_fast(X, add_special_tokens=False)) + encoding.ids[:512] for encoding in self.tokenizer.encode_batch_fast(X, add_special_tokens=False) ] labels_tensor = torch.Tensor([self.classes.index(label) for label in y]).long() return TextDataset(tokenized, labels_tensor) From 9052806ed20b1390065dc1d99f3b752d7b3e7670 Mon Sep 17 00:00:00 2001 From: stephantul Date: Wed, 8 Jan 2025 09:57:20 +0100 Subject: [PATCH 22/57] fixes --- model2vec/train/base.py | 8 +++++++- model2vec/train/classifier.py | 34 +++++++++++++++++++++++++++------- 2 files changed, 34 insertions(+), 8 deletions(-) diff --git a/model2vec/train/base.py b/model2vec/train/base.py index eee2b5ed..9b5f8f05 100644 --- a/model2vec/train/base.py +++ b/model2vec/train/base.py @@ -32,9 +32,15 @@ def __init__(self, *, vectors: torch.Tensor, tokenizer: Tokenizer, out_dim: int, weights = torch.zeros(len(vectors)) weights[pad_id] = -10_000 - self.w = nn.Parameter(weights) + self.w = self.construct_weights() self.tokenizer = tokenizer + def construct_weights(self) -> nn.Parameter: + """Construct the weights for the model.""" + weights = torch.zeros(len(self.vectors)) + weights[self.pad_id] = -10_000 + return nn.Parameter(weights) + def construct_head(self) -> nn.Module: """Method should be overridden for various other classes.""" return nn.Linear(self.embed_dim, self.out_dim) diff --git a/model2vec/train/classifier.py b/model2vec/train/classifier.py index 6fae8645..2e3cc731 100644 --- a/model2vec/train/classifier.py +++ b/model2vec/train/classifier.py @@ -92,7 +92,23 @@ def fit( early_stopping_patience: int | None = 25, test_size: float = 0.1, ) -> ClassificationStaticModel: - """Fit a model.""" + """ + Fit a model. + + This function creates a Lightning Trainer object and fits the model to the data. + We use early stopping. After training, the weigths of the best model are loaded back into the model. + + This function seeds everything with a seed of 42, so the results are reproducible. + It also splits the data into a train and validation set, again with a random seed. + + :param X: The texts to train on. + :param y: The labels to train on. + :param learning_rate: The learning rate. + :param batch_size: The batch size. + :param early_stopping_patience: The patience for early stopping. + :param test_size: The test size for the train-test split. + :return: The fitted model. + """ pl.seed_everything(42) logger.info("Re-initializing model.") self._initialize(y) @@ -154,16 +170,20 @@ def _initialize(self, y: list[str]) -> None: self.head = self.construct_head() self.embeddings = nn.Embedding.from_pretrained(self.vectors.clone(), freeze=False, padding_idx=self.pad_id) + self.w = self.construct_weights() self.train() - def _prepare_dataset(self, X: list[str], y: list[str]) -> TextDataset: + def _prepare_dataset(self, X: list[str], y: list[str], max_length: int = 512) -> TextDataset: """Prepare a dataset.""" - # Truncate texts to pre-specified maximum length. - X = [x[:3072] for x in X] + # This is a speed optimization. + # assumes a mean token length of 10, which is really high, so safe. + truncate_length = max_length * 10 + X = [x[:truncate_length] for x in X] tokenized: list[list[int]] = [ - encoding.ids[:512] for encoding in self.tokenizer.encode_batch_fast(X, add_special_tokens=False) + encoding.ids[:max_length] for encoding in self.tokenizer.encode_batch_fast(X, add_special_tokens=False) ] labels_tensor = torch.Tensor([self.classes.index(label) for label in y]).long() + return TextDataset(tokenized, labels_tensor) def _train_test_split( @@ -173,8 +193,8 @@ def _train_test_split( label_counts = Counter(y) if min(label_counts.values()) < 2: logger.info("Some classes have less than 2 samples. Stratification is disabled.") - return train_test_split(X, y, test_size=0.1, random_state=42, shuffle=True) - return train_test_split(X, y, test_size=0.1, random_state=42, shuffle=True, stratify=y) + return train_test_split(X, y, test_size=test_size, random_state=42, shuffle=True) + return train_test_split(X, y, test_size=test_size, random_state=42, shuffle=True, stratify=y) class ClassifierLightningModule(pl.LightningModule): From 2f9fbf4993faa3df551b3b4d8d73241b8f2804a5 Mon Sep 17 00:00:00 2001 From: stephantul Date: Wed, 8 Jan 2025 10:03:13 +0100 Subject: [PATCH 23/57] typo --- model2vec/train/classifier.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/model2vec/train/classifier.py b/model2vec/train/classifier.py index 2e3cc731..2f3b21e3 100644 --- a/model2vec/train/classifier.py +++ b/model2vec/train/classifier.py @@ -117,9 +117,9 @@ def fit( X, y, test_size=test_size ) - logger.info("Prepating train dataset.") + logger.info("Preparing train dataset.") train_dataset = self._prepare_dataset(train_texts, train_labels) - logger.info("Prepating validation dataset.") + logger.info("Preparing validation dataset.") val_dataset = self._prepare_dataset(validation_texts, validation_labels) c = ClassifierLightningModule(self, learning_rate=learning_rate) From f1e08c3840c745bac1dd95af0088b6d94c5970e2 Mon Sep 17 00:00:00 2001 From: stephantul Date: Wed, 8 Jan 2025 13:40:21 +0100 Subject: [PATCH 24/57] Add progressbar --- model2vec/train/classifier.py | 15 ++++++++------- 1 file changed, 8 insertions(+), 7 deletions(-) diff --git a/model2vec/train/classifier.py b/model2vec/train/classifier.py index 2f3b21e3..0283e9a7 100644 --- a/model2vec/train/classifier.py +++ b/model2vec/train/classifier.py @@ -11,6 +11,7 @@ from sklearn.model_selection import train_test_split from tokenizers import Tokenizer from torch import nn +from tqdm import trange from model2vec.train.base import FinetunableStaticModel, TextDataset @@ -59,26 +60,26 @@ def construct_head(self) -> nn.Module: return nn.Sequential(*modules) - def predict(self, X: list[str]) -> list[str]: + def predict(self, X: list[str], show_progress_bar: bool = False, batch_size: int = 1024) -> list[str]: """Predict a class for a set of texts.""" pred: list[str] = [] - for batch in range(0, len(X), 1024): - logits = self._predict(X[batch : batch + 1024]) + for batch in trange(0, len(X), batch_size, disable=not show_progress_bar): + logits = self._predict_single_batch(X[batch : batch + batch_size]) pred.extend([self.classes[idx] for idx in logits.argmax(1)]) return pred @torch.no_grad() - def _predict(self, X: list[str]) -> torch.Tensor: + def _predict_single_batch(self, X: list[str]) -> torch.Tensor: input_ids = self.tokenize(X) vectors, _ = self.forward(input_ids) return vectors - def predict_proba(self, X: list[str]) -> np.ndarray: + def predict_proba(self, X: list[str], show_progress_bar: bool = False, batch_size: int = 1024) -> np.ndarray: """Predict the probability of each class.""" pred: list[np.ndarray] = [] - for batch in range(0, len(X), 1024): - logits = self._predict(X[batch : batch + 1024]) + for batch in trange(0, len(X), batch_size, disable=not show_progress_bar): + logits = self._predict_single_batch(X[batch : batch + batch_size]) pred.append(torch.softmax(logits, dim=1).numpy()) return np.concatenate(pred) From bb54a769b70f3fe3313aab742c890ac73331a13b Mon Sep 17 00:00:00 2001 From: stephantul Date: Wed, 8 Jan 2025 14:54:35 +0100 Subject: [PATCH 25/57] small code changes, add docs --- model2vec/train/README.md | 105 ++++++++++++++++++++++++++++++++++ model2vec/train/__init__.py | 4 ++ model2vec/train/base.py | 6 +- model2vec/train/classifier.py | 18 +++--- 4 files changed, 121 insertions(+), 12 deletions(-) create mode 100644 model2vec/train/README.md diff --git a/model2vec/train/README.md b/model2vec/train/README.md new file mode 100644 index 00000000..dc153347 --- /dev/null +++ b/model2vec/train/README.md @@ -0,0 +1,105 @@ +# Training + +Aside from [distillation](../../README.md#distillation), `model2vec` also supports training simple classifiers on top of static models, using [pytorch](https://pytorch.org/) and [lightning](https://lightning.ai/). + +# Installation + +To train, make sure you install the training extra: + +``` +pip install model2vec[training] +``` + +# Quickstart + +To train a model, simply initialize it using a `StaticModel`, or from a pre-trained model, as follows: + +```python +from model2vec.distill import distill +from model2vec.train import StaticModelForClassification + +# From a distilled model +distilled_model = distill("baai/bge-base-en-v1.5") +classifier = StaticModelForClassification.from_static_model(distilled_model) + +# From a pre-trained model: potion is the default +classifier = StaticModelForClassification.from_pretrained(model_name="minishlab/potion-base-8m") +``` + +This creates a very simple classifier: a StaticModel with a single 512-unit hidden layer on top. You can adjust the number of hidden layers and the number units through some parameters on both functions. Note that the default for `from_pretrained` is [potion-base-8m](https://huggingface.co/minishlab/potion-base-8M), our best model to date. This is our recommended path if you're working on general English data. + +Now that you have created the classifier, let's just train a model. This assumes you have the [`datasets`](https://github.com/huggingface/datasets) library installed. + +```python +import numpy as np +from datasets import load_dataset + +ds = load_dataset("setfit/subj") +train = ds["train"] +test = ds["test"] + +s = perf_counter() +classifier = classifier.fit(train["text"], train["label"]) + +predicted = classifier.predict(test["text"]) +print(f"Training took {int(perf_counter() - s)} seconds.") +# Training took 81 seconds +accuracy = np.mean([x == y for x, y in zip(predicted, test["label"])]) * 100 +print(f"Achieved {accuracy} test accuracy") +# Achieved 91.0 test accuracy +``` + +As you can see, we got a pretty nice accuracy. + +The training loop is handled by [`lightning`](https://pypi.org/project/lightning/). It trains on 90% of the data, using a stratified split to create a 10% validation set. By default, it runs with early stopping on the validation set accuracy, with a really high patience. + +Note that this model is as fast as you're used to from us: + +```python +from time import perf_counter + +s = perf_counter() +classifier.predict(test["text"]) +print(f"Took {int((perf_counter() - s) * 1000)} milliseconds for {len(test)} instances on CPU.") +# Took 67 milliseconds for 2000 instances on CPU. +``` + +# Results + +The main results are detailed in our training blogpost, but we'll do a comparison with vanilla model2vec here. In a vanilla model2vec classifier, you just put a scikit-learn `LogisticRegressionCV` on top of the model encoder. In contrast, training a `StaticModelForClassification` fine-tunes the full model, including the `StaticModel` weights. + +We use 14 classification datasets, using 1000 examples from the train set, and the full test set. No parameters were tuned on any validation set. All datasets were taken from the [Setfit organization on Hugging Face](https://huggingface.co/datasets/SetFit). + +| dataset_name | logreg | full finetune | +|:---------------------------|-----------:|---------------:| +| 20_newgroups | 0.545312 | 0.555459 | +| ade | 0.715725 | 0.740307 | +| ag_news | 0.860154 | 0.858304 | +| amazon_counterfactual | 0.637754 | 0.744288 | +| bbc | 0.955719 | 0.965018 | +| emotion | 0.516267 | 0.586328 | +| enron_spam | 0.951975 | 0.964994 | +| hatespeech_offensive | 0.543758 | 0.592587 | +| imdb | 0.839002 | 0.846198 | +| massive_scenario | 0.797779 | 0.822825 | +| senteval_cr | 0.743436 | 0.745863 | +| sst5 | 0.290249 | 0.363071 | +| student | 0.806069 | 0.837581 | +| subj | 0.878394 | 0.88941 | +| tweet_sentiment_extraction | 0.638664 | 0.632009 | + +As you can see, full fine-tuning brings modest performance improvements in some cases, but very large ones in other cases. Our advice is to test both if you can use `potion-base-8m`, and to use full fine-tuning if you are starting from another base model. + +# Bring your own architecture + +Our training architecture is set up to be extensible, with each task having a specific class. Right now, we only offer `StaticModelForClassification`, but in the future we'll also offer regression, etc. + +The core functionality of the `StaticModelForClassification` is contained in a couple of functions: + +* `construct_head`: This function constructs the classifier on top of the staticmodel. For example, if you want to create a model that has LayerNorm, just subclass, and replace this function. This should be the main function to update if you want to change model behavior. +* `train_test_split`: governs the train test split before classification. +* `prepare_dataset`: Selects the `torch.Dataset` that will be used in the `Dataloader` during training. +* `_encode`: The encoding function used in the model. +* `fit`: contains all the lightning-related fitting logic. + +The training of the model is done in a `lighting.LightningModule`, which can be modified but is very basic. diff --git a/model2vec/train/__init__.py b/model2vec/train/__init__.py index 2e9bea2d..c70f8039 100644 --- a/model2vec/train/__init__.py +++ b/model2vec/train/__init__.py @@ -4,3 +4,7 @@ for extra_dependency in get_package_extras("model2vec", _REQUIRED_EXTRA): importable(extra_dependency, _REQUIRED_EXTRA) + +from model2vec.train.classifier import StaticModelForClassification + +__all__ = ["StaticModelForClassification"] diff --git a/model2vec/train/base.py b/model2vec/train/base.py index 9b5f8f05..d07ca8cb 100644 --- a/model2vec/train/base.py +++ b/model2vec/train/base.py @@ -12,7 +12,7 @@ class FinetunableStaticModel(nn.Module): - def __init__(self, *, vectors: torch.Tensor, tokenizer: Tokenizer, out_dim: int, pad_id: int = 0) -> None: + def __init__(self, *, vectors: torch.Tensor, tokenizer: Tokenizer, out_dim: int = 2, pad_id: int = 0) -> None: """ Initialize a trainable StaticModel from a StaticModel. @@ -47,14 +47,14 @@ def construct_head(self) -> nn.Module: @classmethod def from_pretrained( - cls: type[ModelType], out_dim: int, model_name: str = "minishlab/potion-base-8m", **kwargs: Any + cls: type[ModelType], out_dim: int = 2, model_name: str = "minishlab/potion-base-8m", **kwargs: Any ) -> ModelType: """Load the model from a pretrained model2vec model.""" model = StaticModel.from_pretrained(model_name) return cls.from_static_model(model, out_dim, **kwargs) @classmethod - def from_static_model(cls: type[ModelType], model: StaticModel, out_dim: int, **kwargs: Any) -> ModelType: + def from_static_model(cls: type[ModelType], model: StaticModel, out_dim: int = 2, **kwargs: Any) -> ModelType: """Load the model from a static model.""" embeddings_converted = torch.from_numpy(model.embedding) return cls( diff --git a/model2vec/train/classifier.py b/model2vec/train/classifier.py index 0283e9a7..92c1673d 100644 --- a/model2vec/train/classifier.py +++ b/model2vec/train/classifier.py @@ -18,15 +18,15 @@ logger = logging.getLogger(__name__) -class ClassificationStaticModel(FinetunableStaticModel): +class StaticModelForClassification(FinetunableStaticModel): def __init__( self, *, vectors: torch.Tensor, tokenizer: Tokenizer, - n_layers: int, - hidden_dim: int, - out_dim: int, + n_layers: int = 1, + hidden_dim: int = 512, + out_dim: int = 2, pad_id: int = 0, ) -> None: """Initialize a standard classifier model.""" @@ -90,9 +90,9 @@ def fit( y: list[str], learning_rate: float = 1e-3, batch_size: int = 32, - early_stopping_patience: int | None = 25, + early_stopping_patience: int | None = 5, test_size: float = 0.1, - ) -> ClassificationStaticModel: + ) -> StaticModelForClassification: """ Fit a model. @@ -123,7 +123,7 @@ def fit( logger.info("Preparing validation dataset.") val_dataset = self._prepare_dataset(validation_texts, validation_labels) - c = ClassifierLightningModule(self, learning_rate=learning_rate) + c = _ClassifierLightningModule(self, learning_rate=learning_rate) n_train_batches = len(train_dataset) // batch_size callbacks: list[Callback] = [] @@ -198,8 +198,8 @@ def _train_test_split( return train_test_split(X, y, test_size=test_size, random_state=42, shuffle=True, stratify=y) -class ClassifierLightningModule(pl.LightningModule): - def __init__(self, model: ClassificationStaticModel, learning_rate: float) -> None: +class _ClassifierLightningModule(pl.LightningModule): + def __init__(self, model: StaticModelForClassification, learning_rate: float) -> None: """Initialize the lightningmodule.""" super().__init__() self.model = model From 69ee4ee289c00f6f99b10a456147c3856f77964e Mon Sep 17 00:00:00 2001 From: stephantul Date: Wed, 8 Jan 2025 15:05:06 +0100 Subject: [PATCH 26/57] fix training comments --- model2vec/train/README.md | 17 +++++++++++------ 1 file changed, 11 insertions(+), 6 deletions(-) diff --git a/model2vec/train/README.md b/model2vec/train/README.md index dc153347..2575558f 100644 --- a/model2vec/train/README.md +++ b/model2vec/train/README.md @@ -26,14 +26,15 @@ classifier = StaticModelForClassification.from_static_model(distilled_model) classifier = StaticModelForClassification.from_pretrained(model_name="minishlab/potion-base-8m") ``` -This creates a very simple classifier: a StaticModel with a single 512-unit hidden layer on top. You can adjust the number of hidden layers and the number units through some parameters on both functions. Note that the default for `from_pretrained` is [potion-base-8m](https://huggingface.co/minishlab/potion-base-8M), our best model to date. This is our recommended path if you're working on general English data. +This creates a very simple classifier: a StaticModel with a single 512-unit hidden layer on top. You can adjust the number of hidden layers and the number units through some parameters on both functions. Note that the default for `from_pretrained` is [potion-base-8m](https://huggingface.co/minishlab/potion-base-8M), our best model to date. This is our recommended path if you're working with general English data. -Now that you have created the classifier, let's just train a model. This assumes you have the [`datasets`](https://github.com/huggingface/datasets) library installed. +Now that you have created the classifier, let's just train a model. The example below assumes you have the [`datasets`](https://github.com/huggingface/datasets) library installed. ```python import numpy as np from datasets import load_dataset +# Load the subj dataset ds = load_dataset("setfit/subj") train = ds["train"] test = ds["test"] @@ -49,9 +50,9 @@ print(f"Achieved {accuracy} test accuracy") # Achieved 91.0 test accuracy ``` -As you can see, we got a pretty nice accuracy. +As you can see, we got a pretty nice 91% accuracy, with only 81 seconds of training. -The training loop is handled by [`lightning`](https://pypi.org/project/lightning/). It trains on 90% of the data, using a stratified split to create a 10% validation set. By default, it runs with early stopping on the validation set accuracy, with a really high patience. +The training loop is handled by [`lightning`](https://pypi.org/project/lightning/). By default the training loop splits the data into a train and validation split, with 90% of the data being used for training and 10% for validation. By default, it runs with early stopping on the validation set accuracy, with a patience of 5. Note that this model is as fast as you're used to from us: @@ -70,7 +71,7 @@ The main results are detailed in our training blogpost, but we'll do a compariso We use 14 classification datasets, using 1000 examples from the train set, and the full test set. No parameters were tuned on any validation set. All datasets were taken from the [Setfit organization on Hugging Face](https://huggingface.co/datasets/SetFit). -| dataset_name | logreg | full finetune | +| dataset name | logistic regression head | full finetune | |:---------------------------|-----------:|---------------:| | 20_newgroups | 0.545312 | 0.555459 | | ade | 0.715725 | 0.740307 | @@ -88,7 +89,11 @@ We use 14 classification datasets, using 1000 examples from the train set, and t | subj | 0.878394 | 0.88941 | | tweet_sentiment_extraction | 0.638664 | 0.632009 | -As you can see, full fine-tuning brings modest performance improvements in some cases, but very large ones in other cases. Our advice is to test both if you can use `potion-base-8m`, and to use full fine-tuning if you are starting from another base model. +| | logreg | full finetune | +|:---------------------------|-----------:|---------------:| +| average | 0.714 | 0.742 | + +As you can see, full fine-tuning brings modest performance improvements in some cases, but very large ones in other cases, leading to a pretty large increase in average score. Our advice is to test both if you can use `potion-base-8m`, and to use full fine-tuning if you are starting from another base model. # Bring your own architecture From ffec235e9dcedd6e592e6e390f8247ca17711219 Mon Sep 17 00:00:00 2001 From: stephantul Date: Thu, 16 Jan 2025 06:54:02 +0100 Subject: [PATCH 27/57] Add pipeline saving --- model2vec/train/base.py | 11 +++++++++-- model2vec/train/classifier.py | 26 ++++++++++++++++++++++++-- 2 files changed, 33 insertions(+), 4 deletions(-) diff --git a/model2vec/train/base.py b/model2vec/train/base.py index d07ca8cb..2f08af78 100644 --- a/model2vec/train/base.py +++ b/model2vec/train/base.py @@ -41,9 +41,9 @@ def construct_weights(self) -> nn.Parameter: weights[self.pad_id] = -10_000 return nn.Parameter(weights) - def construct_head(self) -> nn.Module: + def construct_head(self) -> nn.Sequential: """Method should be overridden for various other classes.""" - return nn.Linear(self.embed_dim, self.out_dim) + return nn.Sequential(nn.Linear(self.embed_dim, self.out_dim)) @classmethod def from_pretrained( @@ -114,6 +114,13 @@ def device(self) -> str: """Get the device of the model.""" return self.embeddings.weight.device + def to_static_model(self) -> StaticModel: + """Convert the model to a static model.""" + emb = self.embeddings.weight.detach().cpu().numpy() + w = torch.sigmoid(self.w).detach().cpu().numpy() + + return StaticModel(emb * w[:, None], self.tokenizer) + class TextDataset(Dataset): def __init__(self, tokenized_texts: list[list[int]], targets: torch.Tensor) -> None: diff --git a/model2vec/train/classifier.py b/model2vec/train/classifier.py index 92c1673d..4c3c6669 100644 --- a/model2vec/train/classifier.py +++ b/model2vec/train/classifier.py @@ -9,6 +9,9 @@ from lightning.pytorch.callbacks import Callback, EarlyStopping from lightning.pytorch.utilities.types import OptimizerLRScheduler from sklearn.model_selection import train_test_split +from sklearn.neural_network import MLPClassifier +from sklearn.pipeline import Pipeline +from sklearn.preprocessing import FunctionTransformer from tokenizers import Tokenizer from torch import nn from tqdm import trange @@ -41,10 +44,10 @@ def classes(self) -> list[str]: """Return all clasess in the correct order.""" return self.classes_ - def construct_head(self) -> nn.Module: + def construct_head(self) -> nn.Sequential: """Constructs a simple classifier head.""" if self.n_layers == 0: - return nn.Linear(self.embed_dim, self.out_dim) + return nn.Sequential(nn.Linear(self.embed_dim, self.out_dim)) modules = [ nn.Linear(self.embed_dim, self.hidden_dim), nn.ReLU(), @@ -197,6 +200,25 @@ def _train_test_split( return train_test_split(X, y, test_size=test_size, random_state=42, shuffle=True) return train_test_split(X, y, test_size=test_size, random_state=42, shuffle=True, stratify=y) + def to_pipeline(self) -> Pipeline: + """Convert the model to an sklearn pipeline.""" + static_model = self.to_static_model() + encoding_step = FunctionTransformer(static_model.encode) + + random_state = np.random.RandomState(42) + n_items = len(self.classes) + X = random_state.randn(n_items, static_model.dim) + y = np.arange(n_items) + + converted = MLPClassifier(hidden_layer_sizes=(self.hidden_dim,) * self.n_layers) + converted.fit(X, y) + + for index, layer in enumerate([module for module in self.head if isinstance(module, nn.Linear)]): + converted.coefs_[index] = layer.weight.detach().cpu().numpy().T + converted.intercepts_[index] = layer.bias.detach().cpu().numpy() + + return Pipeline([("model2vec", encoding_step), ("head", converted)]) + class _ClassifierLightningModule(pl.LightningModule): def __init__(self, model: StaticModelForClassification, learning_rate: float) -> None: From 0af84fcb0e298d883d93948f943c9898d57a2226 Mon Sep 17 00:00:00 2001 From: stephantul Date: Thu, 16 Jan 2025 07:06:23 +0100 Subject: [PATCH 28/57] fix bug --- model2vec/model.py | 5 +++-- model2vec/train/base.py | 2 +- 2 files changed, 4 insertions(+), 3 deletions(-) diff --git a/model2vec/model.py b/model2vec/model.py index 5d60d8b7..ae419048 100644 --- a/model2vec/model.py +++ b/model2vec/model.py @@ -66,9 +66,10 @@ def __init__( self._can_encode_fast = False if normalize is not None: - self.normalize = normalize + self._normalize = normalize + self.config["normalize"] = normalize else: - self.normalize = self.config.get("normalize", False) + self._normalize = self.config.get("normalize", False) @property def dim(self) -> int: diff --git a/model2vec/train/base.py b/model2vec/train/base.py index 2f08af78..53a0966a 100644 --- a/model2vec/train/base.py +++ b/model2vec/train/base.py @@ -119,7 +119,7 @@ def to_static_model(self) -> StaticModel: emb = self.embeddings.weight.detach().cpu().numpy() w = torch.sigmoid(self.w).detach().cpu().numpy() - return StaticModel(emb * w[:, None], self.tokenizer) + return StaticModel(emb * w[:, None], self.tokenizer, normalize=True) class TextDataset(Dataset): From c829745d07c9c50cfe85aa7900ccfc2c25ee65c2 Mon Sep 17 00:00:00 2001 From: stephantul Date: Thu, 16 Jan 2025 10:19:03 +0100 Subject: [PATCH 29/57] fix issue with normalize test --- model2vec/model.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/model2vec/model.py b/model2vec/model.py index ae419048..49736274 100644 --- a/model2vec/model.py +++ b/model2vec/model.py @@ -69,7 +69,7 @@ def __init__( self._normalize = normalize self.config["normalize"] = normalize else: - self._normalize = self.config.get("normalize", False) + self.normalize = self.config.get("normalize", False) @property def dim(self) -> int: From 9ce65a1705f7fa16ef62c047f272b570107ab1af Mon Sep 17 00:00:00 2001 From: stephantul Date: Fri, 17 Jan 2025 16:16:23 +0100 Subject: [PATCH 30/57] change default batch size --- model2vec/train/classifier.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/model2vec/train/classifier.py b/model2vec/train/classifier.py index 4c3c6669..fc97517c 100644 --- a/model2vec/train/classifier.py +++ b/model2vec/train/classifier.py @@ -92,7 +92,7 @@ def fit( X: list[str], y: list[str], learning_rate: float = 1e-3, - batch_size: int = 32, + batch_size: int = 512, early_stopping_patience: int | None = 5, test_size: float = 0.1, ) -> StaticModelForClassification: From e1169fb630178d4451029d2fbad60184c59224da Mon Sep 17 00:00:00 2001 From: stephantul Date: Mon, 20 Jan 2025 13:44:15 +0100 Subject: [PATCH 31/57] feat: add sklearn skops pipeline --- model2vec/train/classifier.py | 10 ++- model2vec/trained_model/__init__.py | 10 +++ model2vec/trained_model/trained.py | 98 +++++++++++++++++++++++++++++ pyproject.toml | 1 + uv.lock | 34 +++++++++- 5 files changed, 145 insertions(+), 8 deletions(-) create mode 100644 model2vec/trained_model/__init__.py create mode 100644 model2vec/trained_model/trained.py diff --git a/model2vec/train/classifier.py b/model2vec/train/classifier.py index fc97517c..d15616e8 100644 --- a/model2vec/train/classifier.py +++ b/model2vec/train/classifier.py @@ -10,13 +10,12 @@ from lightning.pytorch.utilities.types import OptimizerLRScheduler from sklearn.model_selection import train_test_split from sklearn.neural_network import MLPClassifier -from sklearn.pipeline import Pipeline -from sklearn.preprocessing import FunctionTransformer from tokenizers import Tokenizer from torch import nn from tqdm import trange from model2vec.train.base import FinetunableStaticModel, TextDataset +from model2vec.trained_model import StaticModelPipeline logger = logging.getLogger(__name__) @@ -200,15 +199,14 @@ def _train_test_split( return train_test_split(X, y, test_size=test_size, random_state=42, shuffle=True) return train_test_split(X, y, test_size=test_size, random_state=42, shuffle=True, stratify=y) - def to_pipeline(self) -> Pipeline: + def to_pipeline(self) -> StaticModelPipeline: """Convert the model to an sklearn pipeline.""" static_model = self.to_static_model() - encoding_step = FunctionTransformer(static_model.encode) random_state = np.random.RandomState(42) n_items = len(self.classes) X = random_state.randn(n_items, static_model.dim) - y = np.arange(n_items) + y = self.classes converted = MLPClassifier(hidden_layer_sizes=(self.hidden_dim,) * self.n_layers) converted.fit(X, y) @@ -217,7 +215,7 @@ def to_pipeline(self) -> Pipeline: converted.coefs_[index] = layer.weight.detach().cpu().numpy().T converted.intercepts_[index] = layer.bias.detach().cpu().numpy() - return Pipeline([("model2vec", encoding_step), ("head", converted)]) + return StaticModelPipeline(static_model, converted) class _ClassifierLightningModule(pl.LightningModule): diff --git a/model2vec/trained_model/__init__.py b/model2vec/trained_model/__init__.py new file mode 100644 index 00000000..ad562014 --- /dev/null +++ b/model2vec/trained_model/__init__.py @@ -0,0 +1,10 @@ +from model2vec.utils import get_package_extras, importable + +_REQUIRED_EXTRA = "trained_model" + +for extra_dependency in get_package_extras("model2vec", _REQUIRED_EXTRA): + importable(extra_dependency, _REQUIRED_EXTRA) + +from model2vec.trained_model.trained import StaticModelPipeline + +__all__ = ["StaticModelPipeline"] diff --git a/model2vec/trained_model/trained.py b/model2vec/trained_model/trained.py new file mode 100644 index 00000000..8a1b1ade --- /dev/null +++ b/model2vec/trained_model/trained.py @@ -0,0 +1,98 @@ +from __future__ import annotations + +import re +from pathlib import Path +from tempfile import TemporaryDirectory + +import huggingface_hub +import numpy as np +import skops.io +from sklearn.pipeline import Pipeline + +from model2vec.model import PathLike, StaticModel + +_DEFAULT_TRUST_PATTERN = re.compile("sklearn\..+") + + +class StaticModelPipeline: + def __init__(self, model: StaticModel, head: Pipeline) -> None: + """Create a pipeline in which the model is the encoder.""" + self.model = model + self.head = head + + @classmethod + def from_pretrained( + cls: type[StaticModelPipeline], path: PathLike, token: str | None = None + ) -> StaticModelPipeline: + """Load the pipeline from the trained model.""" + model, head = _load_pipeline(path, token) + + return cls(model, head) + + def save_pretrained(self, path: str) -> None: + """Push the pipeline to the hub.""" + save_pipeline(self, path) + + def push_to_hub(self, repo_id: str, token: str, private: bool = False) -> None: + """Push the pipeline to the hub.""" + from model2vec.hf_utils import push_folder_to_hub + + with TemporaryDirectory() as temp_dir: + save_pipeline(self, temp_dir) + self.model.save_pretrained(temp_dir) + push_folder_to_hub(Path(temp_dir), repo_id, private, token) + + def predict(self, X: list[str] | str) -> list[str]: + """Predict the labels of the input.""" + encoded = self.model.encode(X) + if np.ndim(encoded) == 1: + encoded = encoded[None, :] + + return self.head.predict(encoded) + + def predict_proba(self, X: list[str] | str) -> np.ndarray: + """Predict the probabilities of the labels of the input.""" + encoded = self.model.encode(X) + if np.ndim(encoded) == 1: + encoded = encoded[None, :] + + return self.head.predict_proba(encoded) + + +def _load_pipeline( + folder_or_repo_path: PathLike, token: str | None = None, trust_remote_code: bool = False +) -> Pipeline: + """Load the pipeline from the trained model.""" + folder_or_repo_path = Path(folder_or_repo_path) + model_filename = "pipeline.skops" + if folder_or_repo_path.exists(): + head_pipeline_path = folder_or_repo_path / model_filename + if not head_pipeline_path.exists(): + raise FileNotFoundError(f"Pipeline file does not exist in {folder_or_repo_path}") + else: + head_pipeline_path = huggingface_hub.hf_hub_download( + folder_or_repo_path.as_posix(), model_filename, token=token + ) + + model = StaticModel.from_pretrained(folder_or_repo_path) + + unknown_types = skops.io.get_untrusted_types(file=head_pipeline_path) + # If the user does not trust remote code, we should check that the unknown types are trusted. + # By default, we trust everything coming from scikit-learn. + if not trust_remote_code: + for t in unknown_types: + if not _DEFAULT_TRUST_PATTERN.match(t): + raise ValueError(f"Untrusted type {t}.") + head = skops.io.load(head_pipeline_path, trusted=unknown_types) + + return model, head + + +def save_pipeline(pipeline: StaticModelPipeline, folder_or_repo_path: str | Path) -> None: + """Saves a pipeline to a folder.""" + folder_or_repo_path = Path(folder_or_repo_path) + folder_or_repo_path.mkdir(parents=True, exist_ok=True) + model_filename = "pipeline.skops" + head_pipeline_path = folder_or_repo_path / model_filename + skops.io.dump(pipeline.head, head_pipeline_path) + pipeline.model.save_pretrained(folder_or_repo_path) diff --git a/pyproject.toml b/pyproject.toml index 34a9f2f2..4ed4eb1b 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -58,6 +58,7 @@ dev = [ distill = ["torch", "transformers", "scikit-learn"] onnx = ["onnx", "torch"] train = ["torch", "lightning"] +trained_model = ["scikit-learn", "skops"] [project.urls] "Homepage" = "https://github.com/MinishLab" diff --git a/uv.lock b/uv.lock index 18996e7d..9093d823 100644 --- a/uv.lock +++ b/uv.lock @@ -791,7 +791,7 @@ wheels = [ [[package]] name = "model2vec" -version = "0.3.5" +version = "0.3.6" source = { editable = "." } dependencies = [ { name = "jinja2" }, @@ -829,14 +829,18 @@ train = [ { name = "lightning" }, { name = "torch" }, ] +trained-model = [ + { name = "scikit-learn" }, + { name = "skops" }, +] [package.metadata] requires-dist = [ { name = "black", marker = "extra == 'dev'" }, { name = "ipython", marker = "extra == 'dev'" }, { name = "jinja2" }, - { name = "lightning", marker = "extra == 'train'" }, { name = "joblib" }, + { name = "lightning", marker = "extra == 'train'" }, { name = "mypy", marker = "extra == 'dev'" }, { name = "numpy" }, { name = "onnx", marker = "extra == 'onnx'" }, @@ -847,7 +851,9 @@ requires-dist = [ { name = "ruff", marker = "extra == 'dev'" }, { name = "safetensors" }, { name = "scikit-learn", marker = "extra == 'distill'" }, + { name = "scikit-learn", marker = "extra == 'trained-model'" }, { name = "setuptools" }, + { name = "skops", marker = "extra == 'trained-model'" }, { name = "tokenizers", specifier = ">=0.20" }, { name = "torch", marker = "extra == 'distill'" }, { name = "torch", marker = "extra == 'onnx'" }, @@ -1974,6 +1980,21 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/55/21/47d163f615df1d30c094f6c8bbb353619274edccf0327b185cc2493c2c33/setuptools-75.6.0-py3-none-any.whl", hash = "sha256:ce74b49e8f7110f9bf04883b730f4765b774ef3ef28f722cce7c273d253aaf7d", size = 1224032 }, ] +[[package]] +name = "skops" +version = "0.11.0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "huggingface-hub" }, + { name = "packaging" }, + { name = "scikit-learn" }, + { name = "tabulate" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/80/15/5718ee04c70425b083e070aa4da651292e1527144ea87b3ed07591d729a9/skops-0.11.0.tar.gz", hash = "sha256:229c867fbc5e669a1c6a88661c3883a14f3591abd9bfa6073df308d63ae1fa3a", size = 610701 } +wheels = [ + { url = "https://files.pythonhosted.org/packages/f4/5f/a3ec074e67b5dcce2de290bb5b2edb60f78c9304d86485dc1570bca22c2d/skops-0.11.0-py3-none-any.whl", hash = "sha256:8c6109e27e4d762948cad7d21de008034bd14e15f111e9405c7930e74a7fe8c1", size = 146956 }, +] + [[package]] name = "stack-data" version = "0.6.3" @@ -2000,6 +2021,15 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/b2/fe/81695a1aa331a842b582453b605175f419fe8540355886031328089d840a/sympy-1.13.1-py3-none-any.whl", hash = "sha256:db36cdc64bf61b9b24578b6f7bab1ecdd2452cf008f34faa33776680c26d66f8", size = 6189177 }, ] +[[package]] +name = "tabulate" +version = "0.9.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/ec/fe/802052aecb21e3797b8f7902564ab6ea0d60ff8ca23952079064155d1ae1/tabulate-0.9.0.tar.gz", hash = "sha256:0095b12bf5966de529c0feb1fa08671671b3368eec77d7ef7ab114be2c068b3c", size = 81090 } +wheels = [ + { url = "https://files.pythonhosted.org/packages/40/44/4a5f08c96eb108af5cb50b41f76142f0afa346dfa99d5296fe7202a11854/tabulate-0.9.0-py3-none-any.whl", hash = "sha256:024ca478df22e9340661486f85298cff5f6dcdba14f3813e8830015b9ed1948f", size = 35252 }, +] + [[package]] name = "threadpoolctl" version = "3.5.0" From f09682492e0b0932b28d91d2611366fe932f1b0d Mon Sep 17 00:00:00 2001 From: stephantul Date: Mon, 20 Jan 2025 20:31:08 +0100 Subject: [PATCH 32/57] Device handling and automatic batch size --- model2vec/train/classifier.py | 13 ++++++++++++- 1 file changed, 12 insertions(+), 1 deletion(-) diff --git a/model2vec/train/classifier.py b/model2vec/train/classifier.py index d15616e8..33e6fbda 100644 --- a/model2vec/train/classifier.py +++ b/model2vec/train/classifier.py @@ -91,9 +91,10 @@ def fit( X: list[str], y: list[str], learning_rate: float = 1e-3, - batch_size: int = 512, + batch_size: int | None = None, early_stopping_patience: int | None = 5, test_size: float = 0.1, + device: str = "auto", ) -> StaticModelForClassification: """ Fit a model. @@ -108,8 +109,11 @@ def fit( :param y: The labels to train on. :param learning_rate: The learning rate. :param batch_size: The batch size. + If this is None, a good batch size is chosen automatically. :param early_stopping_patience: The patience for early stopping. + If this is None, early stopping is disabled. :param test_size: The test size for the train-test split. + :param device: The device to train on. If this is "auto", the device is chosen automatically. :return: The fitted model. """ pl.seed_everything(42) @@ -120,6 +124,10 @@ def fit( X, y, test_size=test_size ) + if batch_size is None: + batch_size = max(min(32, len(train_texts) // 10), 512) + logger.info("Batch size automatically set to %d.", batch_size) + logger.info("Preparing train dataset.") train_dataset = self._prepare_dataset(train_texts, train_labels) logger.info("Preparing validation dataset.") @@ -133,6 +141,8 @@ def fit( callback = EarlyStopping(monitor="val_accuracy", mode="max", patience=early_stopping_patience) callbacks.append(callback) + # If the dataset is small, we check the validation set every epoch. + # If the dataset is large, we check the validation set every 250 batches. if n_train_batches < 250: val_check_interval = None check_val_every_epoch = 1 @@ -144,6 +154,7 @@ def fit( callbacks=callbacks, val_check_interval=val_check_interval, check_val_every_n_epoch=check_val_every_epoch, + accelerator=device, ) trainer.fit( From ff3ebdf59af48c47e52b8123af38390947ee4f97 Mon Sep 17 00:00:00 2001 From: stephantul Date: Mon, 20 Jan 2025 20:32:34 +0100 Subject: [PATCH 33/57] Add docstrings, defaults --- model2vec/trained_model/trained.py | 77 +++++++++++++++++++++++------- 1 file changed, 59 insertions(+), 18 deletions(-) diff --git a/model2vec/trained_model/trained.py b/model2vec/trained_model/trained.py index 8a1b1ade..19a28552 100644 --- a/model2vec/trained_model/trained.py +++ b/model2vec/trained_model/trained.py @@ -12,11 +12,12 @@ from model2vec.model import PathLike, StaticModel _DEFAULT_TRUST_PATTERN = re.compile("sklearn\..+") +_DEFAULT_MODEL_FILENAME = "pipeline.skops" class StaticModelPipeline: def __init__(self, model: StaticModel, head: Pipeline) -> None: - """Create a pipeline in which the model is the encoder.""" + """Create a pipeline with a StaticModel encoder.""" self.model = model self.head = head @@ -24,17 +25,29 @@ def __init__(self, model: StaticModel, head: Pipeline) -> None: def from_pretrained( cls: type[StaticModelPipeline], path: PathLike, token: str | None = None ) -> StaticModelPipeline: - """Load the pipeline from the trained model.""" + """ + Load the pipeline from the trained model. + + :param path: The path to the folder containing the pipeline. + :param token: The token to use to download the pipeline from the hub. + :return: The loaded pipeline. + """ model, head = _load_pipeline(path, token) return cls(model, head) def save_pretrained(self, path: str) -> None: - """Push the pipeline to the hub.""" + """Save the model to a folder.""" save_pipeline(self, path) def push_to_hub(self, repo_id: str, token: str, private: bool = False) -> None: - """Push the pipeline to the hub.""" + """ + Save a model to a folder, and then push that folder to the hf hub. + + :param repo_id: The id of the repository to push to. + :param token: The token to use to push to the hub. + :param private: Whether the repository should be private. + """ from model2vec.hf_utils import push_folder_to_hub with TemporaryDirectory() as temp_dir: @@ -42,19 +55,23 @@ def push_to_hub(self, repo_id: str, token: str, private: bool = False) -> None: self.model.save_pretrained(temp_dir) push_folder_to_hub(Path(temp_dir), repo_id, private, token) - def predict(self, X: list[str] | str) -> list[str]: - """Predict the labels of the input.""" + def _predict_and_coerce_to_2d(self, X: list[str] | str) -> np.ndarray: + """Predict the labels of the input and coerce the output to a matrix.""" encoded = self.model.encode(X) if np.ndim(encoded) == 1: encoded = encoded[None, :] + return encoded + + def predict(self, X: list[str] | str) -> list[str]: + """Predict the labels of the input.""" + encoded = self._predict_and_coerce_to_2d(X) + return self.head.predict(encoded) def predict_proba(self, X: list[str] | str) -> np.ndarray: """Predict the probabilities of the labels of the input.""" - encoded = self.model.encode(X) - if np.ndim(encoded) == 1: - encoded = encoded[None, :] + encoded = self._predict_and_coerce_to_2d(X) return self.head.predict_proba(encoded) @@ -62,9 +79,28 @@ def predict_proba(self, X: list[str] | str) -> np.ndarray: def _load_pipeline( folder_or_repo_path: PathLike, token: str | None = None, trust_remote_code: bool = False ) -> Pipeline: - """Load the pipeline from the trained model.""" + """ + Load the pipeline from the trained model. + + This assumes the following files are present in the repo: + - `pipeline.skops`: The head of the pipeline. + - `config.json`: The configuration of the model. + - `model.safetensors`: The weights of the model. + - `tokenizer.json`: The tokenizer of the model. + + :param folder_or_repo_path: The path to the folder containing the pipeline. + :param token: The token to use to download the pipeline from the hub. If this is None, you will only + be able to load the pipeline from a local folder, public repository, or a repository that you have access to + because you are logged in. + :param trust_remote_code: Whether to trust the remote code. If this is False, + we will only load components coming from `sklearn`. If this is True, we will load all components. + If you set this to True, you are responsible for whatever happens. + :return: The loaded pipeline. + :raises FileNotFoundError: If the pipeline file does not exist in the folder. + :raises ValueError: If an untrusted type is found in the pipeline, and `trust_remote_code` is False. + """ folder_or_repo_path = Path(folder_or_repo_path) - model_filename = "pipeline.skops" + model_filename = _DEFAULT_MODEL_FILENAME if folder_or_repo_path.exists(): head_pipeline_path = folder_or_repo_path / model_filename if not head_pipeline_path.exists(): @@ -88,11 +124,16 @@ def _load_pipeline( return model, head -def save_pipeline(pipeline: StaticModelPipeline, folder_or_repo_path: str | Path) -> None: - """Saves a pipeline to a folder.""" - folder_or_repo_path = Path(folder_or_repo_path) - folder_or_repo_path.mkdir(parents=True, exist_ok=True) - model_filename = "pipeline.skops" - head_pipeline_path = folder_or_repo_path / model_filename +def save_pipeline(pipeline: StaticModelPipeline, folder_path: str | Path) -> None: + """ + Save a pipeline to a folder. + + :param pipeline: The pipeline to save. + :param folder_path: The path to the folder to save the pipeline to. + """ + folder_path = Path(folder_path) + folder_path.mkdir(parents=True, exist_ok=True) + model_filename = _DEFAULT_MODEL_FILENAME + head_pipeline_path = folder_path / model_filename skops.io.dump(pipeline.head, head_pipeline_path) - pipeline.model.save_pretrained(folder_or_repo_path) + pipeline.model.save_pretrained(folder_path) From b4e966a04ba1c0bdad4a99c234693be6d4fd1846 Mon Sep 17 00:00:00 2001 From: stephantul Date: Mon, 20 Jan 2025 21:42:57 +0100 Subject: [PATCH 34/57] docs --- model2vec/train/README.md | 27 +++++++++++++++++++++++++++ model2vec/trained_model/README.md | 18 ++++++++++++++++++ 2 files changed, 45 insertions(+) create mode 100644 model2vec/trained_model/README.md diff --git a/model2vec/train/README.md b/model2vec/train/README.md index 2575558f..a9f57408 100644 --- a/model2vec/train/README.md +++ b/model2vec/train/README.md @@ -65,6 +65,33 @@ print(f"Took {int((perf_counter() - s) * 1000)} milliseconds for {len(test)} ins # Took 67 milliseconds for 2000 instances on CPU. ``` +# Persistence + +You can turn a classifier into a scikit-learn compatible pipeline, as follows: + +```python +pipeline = classifier.to_pipeline() +``` + +This pipeline object can be persisted using standard pickle-based methods, such as [joblib](https://joblib.readthedocs.io/en/stable/). This makes it easy to use your model in inferene pipelines (no installing torch!). + +If you want to persist your pipeline to the Hugging Face hub, you can use our built-in functions: + +```python +pipeline.save_pretrained(path) +pipeline.push_to_hub("my_cool/project") +``` + +Later, you can load these as follows: + +```python +from model2vec.trained_model import StaticModelPipeline + +pipeline = StaticModelPipeline.from_pretrained("my_cool/project") +``` + +Loading pipelines in this way is _extremely_ fast. It takes only 30ms to load a pipeline from disk. + # Results The main results are detailed in our training blogpost, but we'll do a comparison with vanilla model2vec here. In a vanilla model2vec classifier, you just put a scikit-learn `LogisticRegressionCV` on top of the model encoder. In contrast, training a `StaticModelForClassification` fine-tunes the full model, including the `StaticModel` weights. diff --git a/model2vec/trained_model/README.md b/model2vec/trained_model/README.md new file mode 100644 index 00000000..94a6ecd8 --- /dev/null +++ b/model2vec/trained_model/README.md @@ -0,0 +1,18 @@ +# Trained + +This subpackage mainly contains helper functions for working with trained models that have been exported to `scikit-learn` compatible pipelines. + +If you're looking for information on how to train a model, see [here](../train/README.md). + +# Usage + +Let's assume you're using our `potion-edu classifier`. + +```python +from model2vec.trained_model import StaticModelPipeline + +s = StaticModelPipeline.from_pretrained("minishlab/potion-edu-classifier") +label = s.predict("Attitudes towards cattle in the Alps: a study in letting go.") +``` + +This should just work. From 8f65bfd8c5d9ea246395104aa73d978a198d67fc Mon Sep 17 00:00:00 2001 From: stephantul Date: Tue, 21 Jan 2025 11:42:29 +0100 Subject: [PATCH 35/57] fix: rename --- model2vec/{trained_model => inference}/README.md | 6 +++--- model2vec/{trained_model => inference}/__init__.py | 2 +- model2vec/{trained_model => inference}/trained.py | 7 ++++--- model2vec/train/README.md | 2 +- model2vec/train/base.py | 2 ++ model2vec/train/classifier.py | 2 +- 6 files changed, 12 insertions(+), 9 deletions(-) rename model2vec/{trained_model => inference}/README.md (62%) rename model2vec/{trained_model => inference}/__init__.py (79%) rename model2vec/{trained_model => inference}/trained.py (96%) diff --git a/model2vec/trained_model/README.md b/model2vec/inference/README.md similarity index 62% rename from model2vec/trained_model/README.md rename to model2vec/inference/README.md index 94a6ecd8..ffa4cc24 100644 --- a/model2vec/trained_model/README.md +++ b/model2vec/inference/README.md @@ -1,6 +1,6 @@ -# Trained +# Inference -This subpackage mainly contains helper functions for working with trained models that have been exported to `scikit-learn` compatible pipelines. +This subpackage mainly contains helper functions for inference with trained models that have been exported to `scikit-learn` compatible pipelines. If you're looking for information on how to train a model, see [here](../train/README.md). @@ -9,7 +9,7 @@ If you're looking for information on how to train a model, see [here](../train/R Let's assume you're using our `potion-edu classifier`. ```python -from model2vec.trained_model import StaticModelPipeline +from model2vec.inference import StaticModelPipeline s = StaticModelPipeline.from_pretrained("minishlab/potion-edu-classifier") label = s.predict("Attitudes towards cattle in the Alps: a study in letting go.") diff --git a/model2vec/trained_model/__init__.py b/model2vec/inference/__init__.py similarity index 79% rename from model2vec/trained_model/__init__.py rename to model2vec/inference/__init__.py index ad562014..6961390b 100644 --- a/model2vec/trained_model/__init__.py +++ b/model2vec/inference/__init__.py @@ -5,6 +5,6 @@ for extra_dependency in get_package_extras("model2vec", _REQUIRED_EXTRA): importable(extra_dependency, _REQUIRED_EXTRA) -from model2vec.trained_model.trained import StaticModelPipeline +from model2vec.inference.trained import StaticModelPipeline __all__ = ["StaticModelPipeline"] diff --git a/model2vec/trained_model/trained.py b/model2vec/inference/trained.py similarity index 96% rename from model2vec/trained_model/trained.py rename to model2vec/inference/trained.py index 19a28552..65e73fb7 100644 --- a/model2vec/trained_model/trained.py +++ b/model2vec/inference/trained.py @@ -33,6 +33,7 @@ def from_pretrained( :return: The loaded pipeline. """ model, head = _load_pipeline(path, token) + model.embedding = np.nan_to_num(model.embedding) return cls(model, head) @@ -78,9 +79,9 @@ def predict_proba(self, X: list[str] | str) -> np.ndarray: def _load_pipeline( folder_or_repo_path: PathLike, token: str | None = None, trust_remote_code: bool = False -) -> Pipeline: +) -> tuple[StaticModel, Pipeline]: """ - Load the pipeline from the trained model. + Load a model and an sklearn pipeline. This assumes the following files are present in the repo: - `pipeline.skops`: The head of the pipeline. @@ -95,7 +96,7 @@ def _load_pipeline( :param trust_remote_code: Whether to trust the remote code. If this is False, we will only load components coming from `sklearn`. If this is True, we will load all components. If you set this to True, you are responsible for whatever happens. - :return: The loaded pipeline. + :return: The encoder model and the loaded head :raises FileNotFoundError: If the pipeline file does not exist in the folder. :raises ValueError: If an untrusted type is found in the pipeline, and `trust_remote_code` is False. """ diff --git a/model2vec/train/README.md b/model2vec/train/README.md index a9f57408..fc137d22 100644 --- a/model2vec/train/README.md +++ b/model2vec/train/README.md @@ -85,7 +85,7 @@ pipeline.push_to_hub("my_cool/project") Later, you can load these as follows: ```python -from model2vec.trained_model import StaticModelPipeline +from model2vec.inference import StaticModelPipeline pipeline = StaticModelPipeline.from_pretrained("my_cool/project") ``` diff --git a/model2vec/train/base.py b/model2vec/train/base.py index 53a0966a..f887253a 100644 --- a/model2vec/train/base.py +++ b/model2vec/train/base.py @@ -2,6 +2,7 @@ from typing import Any, TypeVar +import numpy as np import torch from tokenizers import Encoding, Tokenizer from torch import nn @@ -56,6 +57,7 @@ def from_pretrained( @classmethod def from_static_model(cls: type[ModelType], model: StaticModel, out_dim: int = 2, **kwargs: Any) -> ModelType: """Load the model from a static model.""" + model.embedding = np.nan_to_num(model.embedding) embeddings_converted = torch.from_numpy(model.embedding) return cls( vectors=embeddings_converted, diff --git a/model2vec/train/classifier.py b/model2vec/train/classifier.py index 33e6fbda..bf4b94a3 100644 --- a/model2vec/train/classifier.py +++ b/model2vec/train/classifier.py @@ -14,8 +14,8 @@ from torch import nn from tqdm import trange +from model2vec.inference import StaticModelPipeline from model2vec.train.base import FinetunableStaticModel, TextDataset -from model2vec.trained_model import StaticModelPipeline logger = logging.getLogger(__name__) From 8cdb6684cdcbbb25de84d4de9b8331c69422552e Mon Sep 17 00:00:00 2001 From: stephantul Date: Tue, 21 Jan 2025 11:46:20 +0100 Subject: [PATCH 36/57] fix: rename --- model2vec/inference/README.md | 2 +- model2vec/inference/__init__.py | 2 +- model2vec/inference/{trained.py => model.py} | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) rename model2vec/inference/{trained.py => model.py} (98%) diff --git a/model2vec/inference/README.md b/model2vec/inference/README.md index ffa4cc24..6f859e51 100644 --- a/model2vec/inference/README.md +++ b/model2vec/inference/README.md @@ -11,7 +11,7 @@ Let's assume you're using our `potion-edu classifier`. ```python from model2vec.inference import StaticModelPipeline -s = StaticModelPipeline.from_pretrained("minishlab/potion-edu-classifier") +s = StaticModelPipeline.from_pretrained("minishlab/potion-8m-edu-classifier") label = s.predict("Attitudes towards cattle in the Alps: a study in letting go.") ``` diff --git a/model2vec/inference/__init__.py b/model2vec/inference/__init__.py index 6961390b..7571fcf6 100644 --- a/model2vec/inference/__init__.py +++ b/model2vec/inference/__init__.py @@ -5,6 +5,6 @@ for extra_dependency in get_package_extras("model2vec", _REQUIRED_EXTRA): importable(extra_dependency, _REQUIRED_EXTRA) -from model2vec.inference.trained import StaticModelPipeline +from model2vec.inference.model import StaticModelPipeline __all__ = ["StaticModelPipeline"] diff --git a/model2vec/inference/trained.py b/model2vec/inference/model.py similarity index 98% rename from model2vec/inference/trained.py rename to model2vec/inference/model.py index 65e73fb7..bedfdba7 100644 --- a/model2vec/inference/trained.py +++ b/model2vec/inference/model.py @@ -41,7 +41,7 @@ def save_pretrained(self, path: str) -> None: """Save the model to a folder.""" save_pipeline(self, path) - def push_to_hub(self, repo_id: str, token: str, private: bool = False) -> None: + def push_to_hub(self, repo_id: str, token: str | None = None, private: bool = False) -> None: """ Save a model to a folder, and then push that folder to the hf hub. From e96a72ad3beaa09d67fb3a6125cb2438a4c8082c Mon Sep 17 00:00:00 2001 From: stephantul Date: Tue, 21 Jan 2025 11:56:48 +0100 Subject: [PATCH 37/57] fix installation --- model2vec/inference/__init__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/model2vec/inference/__init__.py b/model2vec/inference/__init__.py index 7571fcf6..de94e18d 100644 --- a/model2vec/inference/__init__.py +++ b/model2vec/inference/__init__.py @@ -1,6 +1,6 @@ from model2vec.utils import get_package_extras, importable -_REQUIRED_EXTRA = "trained_model" +_REQUIRED_EXTRA = "inference" for extra_dependency in get_package_extras("model2vec", _REQUIRED_EXTRA): importable(extra_dependency, _REQUIRED_EXTRA) From 3e76083348eee35497bfc2f5bdec86b24e5d74e9 Mon Sep 17 00:00:00 2001 From: stephantul Date: Tue, 21 Jan 2025 12:00:39 +0100 Subject: [PATCH 38/57] rename --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 4ed4eb1b..faa34e9e 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -58,7 +58,7 @@ dev = [ distill = ["torch", "transformers", "scikit-learn"] onnx = ["onnx", "torch"] train = ["torch", "lightning"] -trained_model = ["scikit-learn", "skops"] +inference = ["scikit-learn", "skops"] [project.urls] "Homepage" = "https://github.com/MinishLab" From 9f1cb5a88f7e48c7dd560d981111597e390b1075 Mon Sep 17 00:00:00 2001 From: stephantul Date: Thu, 23 Jan 2025 12:10:22 +0100 Subject: [PATCH 39/57] Add training tutorial --- tutorials/train_classifier.ipynb | 248 +++++++++++++++++++++++++++++++ 1 file changed, 248 insertions(+) create mode 100644 tutorials/train_classifier.ipynb diff --git a/tutorials/train_classifier.ipynb b/tutorials/train_classifier.ipynb new file mode 100644 index 00000000..a4fddc57 --- /dev/null +++ b/tutorials/train_classifier.ipynb @@ -0,0 +1,248 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Training a classifier using model2vec\n", + "\n", + "Model2Vec supports built-in classifier training with an easy, scikit-learn-based syntax. Just give the model your data in `.fit`, and you'll have a trained model!\n", + "\n", + "How it works:\n", + "* We load a base `StaticModel` using as a torch module. By default we use [potion-base-8m](https://huggingface.co/minishlab/potion-base-8M).\n", + "* We add a one-layer MLP with 512 hidden units and `ReLU` activation as a head.\n", + "* We train the model using cross-entropy, using [`pytorch-lightning`](https://lightning.ai/docs/pytorch/stable/) as a training framework.\n", + "\n", + "After training, you can export the model using regular torch tools, such as `torch.save` and `torch.load`, or you can export the model to a `scikit-learn` pipeline. The latter option leads to a really small footprint during inference, as there is no longer a need to use `torch`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "vscode": { + "languageId": "plaintext" + } + }, + "outputs": [], + "source": [ + "# Install the necessary libraries\n", + "!uv pip install \"model2vec[train,inference]\"\n", + "!uv pip install \"datasets\"\n", + "!uv pip install \"scikit-learn\"\n", + "\n", + "# Import the necessary libraries\n", + "from model2vec.train import StaticModelForClassification\n", + "from model2vec.inference import StaticModelPipeline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To demonstrate how to train a model, we'll be using the `subjectivity` dataset, which contains short utterances and whether they are subjective or objective." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from datasets import load_dataset\n", + "\n", + "dataset = load_dataset(\"setfit/20_newsgroups\")\n", + "print(dataset)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's take a look at the first five training samples:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# First 5 training samples:\n", + "for record in dataset[\"train\"].to_list()[:5]:\n", + " print(f\"TEXT: {record['text']} LABEL: {record['label_text']}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Define the staticmodel\n", + "model = StaticModelForClassification.from_pretrained()\n", + "# Optional arguments:\n", + "# model_name: the name of the base model (defaults to potion-base-8m)\n", + "# n_layers: the number of layers in the MLP (defaults to 1)\n", + "# hidden_dim: the number of hidden units (defaults to 512)\n", + "print(model)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's train the model on a subset of examples. We pick the first 1000 examples to train on." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import time\n", + "# Fit the model on the first 1000 records\n", + "subset = dataset[\"train\"].select(range(1000))\n", + "s = time.time()\n", + "model = model.fit(subset[\"text\"], subset[\"label_text\"])\n", + "print(f\"training took {time.time() - s} seconds\")\n", + "# Fit takes many many arguments, check them out!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We have trained a classifier in five seconds. Nice!\n", + "\n", + "Let's take a look at how good it is." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.metrics import classification_report\n", + "\n", + "predictions = model.predict(dataset[\"test\"][\"text\"])\n", + "\n", + "print(classification_report(dataset[\"test\"][\"label_text\"], predictions))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Our model scores 0.55 accuracy. But what does this mean? Let's compare it to a `tf-idf` pipeline from `scikit-learn`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.feature_extraction.text import TfidfVectorizer\n", + "from sklearn.pipeline import make_pipeline\n", + "\n", + "sklearn_pipeline = make_pipeline(TfidfVectorizer(), LogisticRegression())\n", + "sklearn_pipeline.fit(subset[\"text\"], subset[\"label_text\"])\n", + "predictions = sklearn_pipeline.predict(dataset[\"test\"][\"text\"])\n", + "\n", + "print(classification_report(dataset[\"test\"][\"label_text\"], predictions))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Pretty good! We outperform the tf-idf pipeline by a wide margin.\n", + "\n", + "We can now export the model to scikit-learn, and push it to the hub. But first, let's verify whether the predictions of this model and the original model match." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pipeline = model.to_pipeline()\n", + "\n", + "predictions = pipeline.predict(dataset[\"test\"][\"text\"])\n", + "\n", + "print(classification_report(dataset[\"test\"][\"label_text\"], predictions))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Ok, so let's save the model locally, or push it to the hub!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pipeline.save_pretrained(\"my_cool_model\")\n", + "# Fill in your own org\n", + "# pipeline.push_to_hub(\"my_org/my_model\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This saves a model to a local folder. The model can then be loaded as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "new_model = StaticModelPipeline.from_pretrained(\"my_cool_model\")\n", + "# Or from the hub\n", + "# model = StaticModelPipeline.from_pretrained(\"my_org/my_model\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "One reason to work like this is that the `StaticModelPipeline` does not require torch to be installed at all, leading to really fast cold start predictions, smaller images, and a lot less hassle overall.\n", + "\n", + "And that's it! Super fast, super small, super good classifiers." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.4" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} From e2d92b9c60d7efb9f264b30fdb12d5c5ac99d43c Mon Sep 17 00:00:00 2001 From: stephantul Date: Thu, 23 Jan 2025 13:28:36 +0100 Subject: [PATCH 40/57] Add tutorial link --- tutorials/README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/tutorials/README.md b/tutorials/README.md index fc1992e7..bf8628cf 100644 --- a/tutorials/README.md +++ b/tutorials/README.md @@ -14,3 +14,4 @@ This is a list of all our tutorials. They are all self-contained ipython noteboo | **Recipe search** 🍝 | Learn how to do lightning-fast semantic search by distilling a small model. Compare a really tiny model to a larger with one with a better vocabulary. Learn what Fattoush is (delicious). | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/minishlab/model2vec/blob/master/tutorials/recipe_search.ipynb) | | **Semantic deduplication** 🧹 | Learn how Model2Vec can be used to detect duplicate texts. Clean your dataset efficiently by finding both exact and semantic duplicates. Detect train-test leakage. | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/minishlab/model2vec/blob/master/tutorials/semantic_deduplication.ipynb) | | **Semantic chunking** 🧩 | Learn how to chunk your text into meaningful segments with [Chonkie](https://github.com/bhavnicksm/chonkie) at lightning-speed. Efficiently query your chunks with [Vicinity](https://github.com/MinishLab/vicinity). | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/minishlab/model2vec/blob/master/tutorials/semantic_chunking.ipynb) | +| **Training a classifier** 🧩 | Learn how to train a classifier using model2vec. Lightning fast, great performance, especially on small datasets | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/minishlab/model2vec/blob/master/tutorials/train_classifier.ipynb) | From 773009fd68009e90723bbb2de313db4d7069a409 Mon Sep 17 00:00:00 2001 From: stephantul Date: Fri, 24 Jan 2025 15:46:11 +0100 Subject: [PATCH 41/57] test: add tests --- model2vec/inference/model.py | 5 +- model2vec/train/base.py | 5 +- model2vec/train/classifier.py | 16 ++-- tests/conftest.py | 31 +++++++- tests/test_inference.py | 50 ++++++++++++ tests/test_trainable.py | 139 ++++++++++++++++++++++++++++++++++ uv.lock | 42 +++++----- 7 files changed, 253 insertions(+), 35 deletions(-) create mode 100644 tests/test_inference.py create mode 100644 tests/test_trainable.py diff --git a/model2vec/inference/model.py b/model2vec/inference/model.py index bedfdba7..ab2c8864 100644 --- a/model2vec/inference/model.py +++ b/model2vec/inference/model.py @@ -7,11 +7,12 @@ import huggingface_hub import numpy as np import skops.io +from sklearn.neural_network import MLPClassifier from sklearn.pipeline import Pipeline from model2vec.model import PathLike, StaticModel -_DEFAULT_TRUST_PATTERN = re.compile("sklearn\..+") +_DEFAULT_TRUST_PATTERN = re.compile(r"sklearn\..+") _DEFAULT_MODEL_FILENAME = "pipeline.skops" @@ -64,7 +65,7 @@ def _predict_and_coerce_to_2d(self, X: list[str] | str) -> np.ndarray: return encoded - def predict(self, X: list[str] | str) -> list[str]: + def predict(self, X: list[str] | str) -> np.ndarray: """Predict the labels of the input.""" encoded = self._predict_and_coerce_to_2d(X) diff --git a/model2vec/train/base.py b/model2vec/train/base.py index f887253a..65f4b45e 100644 --- a/model2vec/train/base.py +++ b/model2vec/train/base.py @@ -28,11 +28,8 @@ def __init__(self, *, vectors: torch.Tensor, tokenizer: Tokenizer, out_dim: int self.embed_dim = vectors.shape[1] self.vectors = vectors - self.embeddings = nn.Embedding.from_pretrained(vectors.clone(), freeze=False, padding_idx=pad_id) + self.embeddings = nn.Embedding.from_pretrained(vectors.clone().float(), freeze=False, padding_idx=pad_id) self.head = self.construct_head() - - weights = torch.zeros(len(vectors)) - weights[pad_id] = -10_000 self.w = self.construct_weights() self.tokenizer = tokenizer diff --git a/model2vec/train/classifier.py b/model2vec/train/classifier.py index bf4b94a3..66791bb2 100644 --- a/model2vec/train/classifier.py +++ b/model2vec/train/classifier.py @@ -10,6 +10,7 @@ from lightning.pytorch.utilities.types import OptimizerLRScheduler from sklearn.model_selection import train_test_split from sklearn.neural_network import MLPClassifier +from sklearn.pipeline import make_pipeline from tokenizers import Tokenizer from torch import nn from tqdm import trange @@ -62,14 +63,14 @@ def construct_head(self) -> nn.Sequential: return nn.Sequential(*modules) - def predict(self, X: list[str], show_progress_bar: bool = False, batch_size: int = 1024) -> list[str]: + def predict(self, X: list[str], show_progress_bar: bool = False, batch_size: int = 1024) -> np.ndarray: """Predict a class for a set of texts.""" pred: list[str] = [] for batch in trange(0, len(X), batch_size, disable=not show_progress_bar): logits = self._predict_single_batch(X[batch : batch + batch_size]) pred.extend([self.classes[idx] for idx in logits.argmax(1)]) - return pred + return np.asarray(pred) @torch.no_grad() def _predict_single_batch(self, X: list[str]) -> torch.Tensor: @@ -200,8 +201,9 @@ def _prepare_dataset(self, X: list[str], y: list[str], max_length: int = 512) -> return TextDataset(tokenized, labels_tensor) + @staticmethod def _train_test_split( - self, X: list[str], y: list[str], test_size: float + X: list[str], y: list[str], test_size: float ) -> tuple[list[str], list[str], list[str], list[str]]: """Split the data.""" label_counts = Counter(y) @@ -219,12 +221,14 @@ def to_pipeline(self) -> StaticModelPipeline: X = random_state.randn(n_items, static_model.dim) y = self.classes - converted = MLPClassifier(hidden_layer_sizes=(self.hidden_dim,) * self.n_layers) + converted = make_pipeline(MLPClassifier(hidden_layer_sizes=(self.hidden_dim,) * self.n_layers)) converted.fit(X, y) + mlp_head: MLPClassifier = converted[-1] for index, layer in enumerate([module for module in self.head if isinstance(module, nn.Linear)]): - converted.coefs_[index] = layer.weight.detach().cpu().numpy().T - converted.intercepts_[index] = layer.bias.detach().cpu().numpy() + mlp_head.coefs_[index] = layer.weight.detach().cpu().numpy().T + mlp_head.intercepts_[index] = layer.bias.detach().cpu().numpy() + mlp_head.n_outputs_ = self.out_dim return StaticModelPipeline(static_model, converted) diff --git a/tests/conftest.py b/tests/conftest.py index ced1abca..bd5107b7 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -5,13 +5,19 @@ import numpy as np import pytest import torch +from sklearn.neural_network import MLPClassifier +from sklearn.pipeline import make_pipeline from tokenizers import Tokenizer from tokenizers.models import WordLevel from tokenizers.pre_tokenizers import Whitespace from transformers import AutoModel, AutoTokenizer +from model2vec.inference import StaticModelPipeline +from model2vec.model import StaticModel +from model2vec.train import StaticModelForClassification -@pytest.fixture + +@pytest.fixture(scope="session") def mock_tokenizer() -> Tokenizer: """Create a mock tokenizer.""" vocab = ["word1", "word2", "word3", "[UNK]", "[PAD]"] @@ -62,7 +68,7 @@ def __call__(self, *args: Any, **kwargs: Any) -> Any: return MockPreTrainedModel() -@pytest.fixture +@pytest.fixture(scope="session") def mock_vectors() -> np.ndarray: """Create mock vectors.""" return np.array([[0.1, 0.2], [0.2, 0.3], [0.3, 0.4], [0.0, 0.0], [0.0, 0.0]]) @@ -72,3 +78,24 @@ def mock_vectors() -> np.ndarray: def mock_config() -> dict[str, str]: """Create a mock config.""" return {"some_config": "value"} + + +@pytest.fixture(scope="session") +def mock_inference_pipeline(mock_vectors: np.ndarray, mock_tokenizer: Tokenizer) -> StaticModelPipeline: + """Mock pipeline.""" + encoder = StaticModel(vectors=mock_vectors, tokenizer=mock_tokenizer, config={}) + encoded = encoder.encode(["dog", "cat"]) + labels = ["a", "b"] + head = make_pipeline(MLPClassifier(random_state=12)).fit(encoded, labels) + + return StaticModelPipeline(encoder, head=head) + + +@pytest.fixture(scope="session") +def mock_trained_pipeline(mock_vectors: np.ndarray, mock_tokenizer: Tokenizer) -> StaticModelForClassification: + """Mock staticmodelforclassification.""" + vectors_torched = torch.from_numpy(mock_vectors).float() + s = StaticModelForClassification(vectors=vectors_torched, tokenizer=mock_tokenizer).to("cpu") + s.fit(["dog", "cat"], ["a", "b"], device="cpu") + + return s diff --git a/tests/test_inference.py b/tests/test_inference.py new file mode 100644 index 00000000..29c0d730 --- /dev/null +++ b/tests/test_inference.py @@ -0,0 +1,50 @@ +import os +import re +from tempfile import TemporaryDirectory +from unittest.mock import patch + +import pytest + +from model2vec.inference import StaticModelPipeline + + +def test_init_predict(mock_inference_pipeline: StaticModelPipeline) -> None: + """Test successful initialization of StaticModelPipeline.""" + assert mock_inference_pipeline.predict("dog").tolist() == ["a"] + assert mock_inference_pipeline.predict(["dog"]).tolist() == ["a"] + + +def test_init_predict_proba(mock_inference_pipeline: StaticModelPipeline) -> None: + """Test successful initialization of StaticModelPipeline.""" + assert mock_inference_pipeline.predict_proba("dog").argmax() == 0 + assert mock_inference_pipeline.predict_proba(["dog"]).argmax(1).tolist() == [0] + + +def test_roundtrip_save(mock_inference_pipeline: StaticModelPipeline) -> None: + """Test saving and loading the pipeline.""" + with TemporaryDirectory() as temp_dir: + mock_inference_pipeline.save_pretrained(temp_dir) + loaded = StaticModelPipeline.from_pretrained(temp_dir) + assert loaded.predict("dog") == ["a"] + assert loaded.predict(["dog"]) == ["a"] + assert loaded.predict_proba("dog").argmax() == 0 + assert loaded.predict_proba(["dog"]).argmax(1).tolist() == [0] + + +@patch("model2vec.inference.model._DEFAULT_TRUST_PATTERN", re.compile("torch")) +def test_roundtrip_save_mock_trust_pattern(mock_inference_pipeline: StaticModelPipeline) -> None: + """Test saving and loading the pipeline.""" + with TemporaryDirectory() as temp_dir: + mock_inference_pipeline.save_pretrained(temp_dir) + with pytest.raises(ValueError): + StaticModelPipeline.from_pretrained(temp_dir) + + +def test_roundtrip_save_file_gone(mock_inference_pipeline: StaticModelPipeline) -> None: + """Test saving and loading the pipeline.""" + with TemporaryDirectory() as temp_dir: + mock_inference_pipeline.save_pretrained(temp_dir) + # Rename the file to abc.pipeline, so that it looks like it was downloaded from the hub + os.unlink(os.path.join(temp_dir, "pipeline.skops")) + with pytest.raises(FileNotFoundError): + StaticModelPipeline.from_pretrained(temp_dir) diff --git a/tests/test_trainable.py b/tests/test_trainable.py new file mode 100644 index 00000000..4d5b3946 --- /dev/null +++ b/tests/test_trainable.py @@ -0,0 +1,139 @@ +from tempfile import TemporaryDirectory + +import numpy as np +import pytest +import torch +from tokenizers import Tokenizer + +from model2vec.model import StaticModel +from model2vec.train import StaticModelForClassification +from model2vec.train.base import FinetunableStaticModel, TextDataset + + +@pytest.mark.parametrize("n_layers", [0, 1, 2, 3]) +def test_init_predict(n_layers: int, mock_vectors: np.ndarray, mock_tokenizer: Tokenizer) -> None: + """Test successful initialization of StaticModelForClassification.""" + vectors_torched = torch.from_numpy(mock_vectors) + s = StaticModelForClassification(vectors=vectors_torched, tokenizer=mock_tokenizer, n_layers=n_layers) + assert s.vectors.shape == mock_vectors.shape + assert s.w.shape[0] == mock_vectors.shape[0] + assert s.classes == s.classes_ + assert s.classes == ["0", "1"] + + head = s.construct_head() + assert head[0].in_features == mock_vectors.shape[1] + head = s.construct_head() + assert head[0].in_features == mock_vectors.shape[1] + assert head[-1].out_features == 2 + + +def test_init_base_class(mock_vectors: np.ndarray, mock_tokenizer: Tokenizer) -> None: + """Test successful initialization of the base class.""" + vectors_torched = torch.from_numpy(mock_vectors) + s = FinetunableStaticModel(vectors=vectors_torched, tokenizer=mock_tokenizer) + assert s.vectors.shape == mock_vectors.shape + assert s.w.shape[0] == mock_vectors.shape[0] + + head = s.construct_head() + assert head[0].in_features == mock_vectors.shape[1] + + +def test_init_base_from_model(mock_vectors: np.ndarray, mock_tokenizer: Tokenizer) -> None: + """Test initializion from a static model.""" + model = StaticModel(vectors=mock_vectors, tokenizer=mock_tokenizer) + s = FinetunableStaticModel.from_static_model(model) + assert s.vectors.shape == mock_vectors.shape + assert s.w.shape[0] == mock_vectors.shape[0] + + with TemporaryDirectory() as temp_dir: + model.save_pretrained(temp_dir) + s = FinetunableStaticModel.from_pretrained(model_name=temp_dir) + assert s.vectors.shape == mock_vectors.shape + assert s.w.shape[0] == mock_vectors.shape[0] + + +def test_init_classifier_from_model(mock_vectors: np.ndarray, mock_tokenizer: Tokenizer) -> None: + """Test initializion from a static model.""" + model = StaticModel(vectors=mock_vectors, tokenizer=mock_tokenizer) + s = StaticModelForClassification.from_static_model(model) + assert s.vectors.shape == mock_vectors.shape + assert s.w.shape[0] == mock_vectors.shape[0] + + with TemporaryDirectory() as temp_dir: + model.save_pretrained(temp_dir) + s = StaticModelForClassification.from_pretrained(model_name=temp_dir) + assert s.vectors.shape == mock_vectors.shape + assert s.w.shape[0] == mock_vectors.shape[0] + + +def test_encode(mock_trained_pipeline: StaticModelForClassification) -> None: + """Test the encode function.""" + result = mock_trained_pipeline._encode(torch.tensor([[0, 1], [1, 0]]).long()) + assert result.shape == (2, 2) + assert torch.allclose(result[0], result[1]) + + +def test_tokenize(mock_trained_pipeline: StaticModelForClassification) -> None: + """Test the encode function.""" + result = mock_trained_pipeline.tokenize(["dog dog", "cat"]) + assert result.shape == torch.Size([2, 2]) + assert result[1, 1] == 0 + + +def test_device(mock_trained_pipeline: StaticModelForClassification) -> None: + """Get the device.""" + assert mock_trained_pipeline.device == torch.device(type="cpu") # type: ignore # False positive + assert mock_trained_pipeline.device == mock_trained_pipeline.w.device + + +def test_conversion(mock_trained_pipeline: StaticModelForClassification) -> None: + """Test the conversion to numpy.""" + staticmodel = mock_trained_pipeline.to_static_model() + with torch.no_grad(): + result_1 = mock_trained_pipeline._encode(torch.tensor([[0, 1], [1, 0]]).long()).numpy() + result_2 = staticmodel.embedding[[[0, 1], [1, 0]]].mean(0) + result_2 /= np.linalg.norm(result_2, axis=1, keepdims=True) + + assert np.allclose(result_1, result_2) + + +def test_textdataset_init() -> None: + """Test the textdataset init.""" + dataset = TextDataset([[0], [1]], torch.arange(2)) + assert len(dataset) == 2 + + +def test_textdataset_init_incorrect() -> None: + """Test the textdataset init.""" + with pytest.raises(ValueError): + TextDataset([[0]], torch.arange(2)) + + +def test_predict(mock_trained_pipeline: StaticModelForClassification) -> None: + """Test the predict function.""" + result = mock_trained_pipeline.predict(["dog dog", "cat"]).tolist() + assert result == ["a", "a"] + + +def test_predict_proba(mock_trained_pipeline: StaticModelForClassification) -> None: + """Test the predict function.""" + result = mock_trained_pipeline.predict_proba(["dog dog", "cat"]) + assert result.shape == (2, 2) + + +def test_convert_to_pipeline(mock_trained_pipeline: StaticModelForClassification) -> None: + """Convert a model to a pipeline.""" + mock_trained_pipeline.eval() + pipeline = mock_trained_pipeline.to_pipeline() + a = pipeline.predict(["dog dog", "cat"]).tolist() + b = mock_trained_pipeline.predict(["dog dog", "cat"]).tolist() + assert a == b + + +def test_train_test_split() -> None: + """Test the train test split function.""" + a, b, c, d = StaticModelForClassification._train_test_split(["0", "1", "2", "3"], ["1", "1", "0", "0"], 0.5) + assert len(a) == 2 + assert len(b) == 2 + assert len(c) == len(a) + assert len(d) == len(b) diff --git a/uv.lock b/uv.lock index de0b5ca7..495b1560 100644 --- a/uv.lock +++ b/uv.lock @@ -292,7 +292,7 @@ name = "click" version = "8.1.8" source = { registry = "https://pypi.org/simple" } dependencies = [ - { name = "colorama", marker = "platform_system == 'Windows'" }, + { name = "colorama", marker = "sys_platform == 'win32'" }, ] sdist = { url = "https://files.pythonhosted.org/packages/b9/2e/0090cbf739cee7d23781ad4b89a9894a41538e4fcf4c31dcdd705b78eb8b/click-8.1.8.tar.gz", hash = "sha256:ed53c9d8990d83c2a27deae68e4ee337473f6330c040a31d4225c9574d16096a", size = 226593 } wheels = [ @@ -791,7 +791,7 @@ wheels = [ [[package]] name = "model2vec" -version = "0.3.6" +version = "0.3.7" source = { editable = "." } dependencies = [ { name = "jinja2" }, @@ -821,6 +821,10 @@ distill = [ { name = "torch" }, { name = "transformers" }, ] +inference = [ + { name = "scikit-learn" }, + { name = "skops" }, +] onnx = [ { name = "onnx" }, { name = "torch" }, @@ -829,10 +833,6 @@ train = [ { name = "lightning" }, { name = "torch" }, ] -trained-model = [ - { name = "scikit-learn" }, - { name = "skops" }, -] [package.metadata] requires-dist = [ @@ -851,9 +851,9 @@ requires-dist = [ { name = "ruff", marker = "extra == 'dev'" }, { name = "safetensors" }, { name = "scikit-learn", marker = "extra == 'distill'" }, - { name = "scikit-learn", marker = "extra == 'trained-model'" }, + { name = "scikit-learn", marker = "extra == 'inference'" }, { name = "setuptools" }, - { name = "skops", marker = "extra == 'trained-model'" }, + { name = "skops", marker = "extra == 'inference'" }, { name = "tokenizers", specifier = ">=0.20" }, { name = "torch", marker = "extra == 'distill'" }, { name = "torch", marker = "extra == 'onnx'" }, @@ -2113,19 +2113,19 @@ dependencies = [ { name = "jinja2" }, { name = "networkx", version = "3.2.1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.10'" }, { name = "networkx", version = "3.4.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.10'" }, - { name = "nvidia-cublas-cu12", marker = "platform_machine == 'x86_64' and platform_system == 'Linux'" }, - { name = "nvidia-cuda-cupti-cu12", marker = "platform_machine == 'x86_64' and platform_system == 'Linux'" }, - { name = "nvidia-cuda-nvrtc-cu12", marker = "platform_machine == 'x86_64' and platform_system == 'Linux'" }, - { name = "nvidia-cuda-runtime-cu12", marker = "platform_machine == 'x86_64' and platform_system == 'Linux'" }, - { name = "nvidia-cudnn-cu12", marker = "platform_machine == 'x86_64' and platform_system == 'Linux'" }, - { name = "nvidia-cufft-cu12", marker = "platform_machine == 'x86_64' and platform_system == 'Linux'" }, - { name = "nvidia-curand-cu12", marker = "platform_machine == 'x86_64' and platform_system == 'Linux'" }, - { name = "nvidia-cusolver-cu12", marker = "platform_machine == 'x86_64' and platform_system == 'Linux'" }, - { name = "nvidia-cusparse-cu12", marker = "platform_machine == 'x86_64' and platform_system == 'Linux'" }, - { name = "nvidia-nccl-cu12", marker = "platform_machine == 'x86_64' and platform_system == 'Linux'" }, - { name = "nvidia-nvtx-cu12", marker = "platform_machine == 'x86_64' and platform_system == 'Linux'" }, + { name = "nvidia-cublas-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "nvidia-cuda-cupti-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "nvidia-cuda-nvrtc-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "nvidia-cuda-runtime-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "nvidia-cudnn-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "nvidia-cufft-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "nvidia-curand-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "nvidia-cusolver-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "nvidia-cusparse-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "nvidia-nccl-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "nvidia-nvtx-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, { name = "sympy" }, - { name = "triton", marker = "python_full_version < '3.13' and platform_machine == 'x86_64' and platform_system == 'Linux'" }, + { name = "triton", marker = "python_full_version < '3.13' and platform_machine == 'x86_64' and sys_platform == 'linux'" }, { name = "typing-extensions" }, ] wheels = [ @@ -2168,7 +2168,7 @@ name = "tqdm" version = "4.67.1" source = { registry = "https://pypi.org/simple" } dependencies = [ - { name = "colorama", marker = "platform_system == 'Windows'" }, + { name = "colorama", marker = "sys_platform == 'win32'" }, ] sdist = { url = "https://files.pythonhosted.org/packages/a8/4b/29b4ef32e036bb34e4ab51796dd745cdba7ed47ad142a9f4a1eb8e0c744d/tqdm-4.67.1.tar.gz", hash = "sha256:f8aef9c52c08c13a65f30ea34f4e5aac3fd1a34959879d7e59e63027286627f2", size = 169737 } wheels = [ From 7015341ef898bfa1849efd70ab58cc25260b3f30 Mon Sep 17 00:00:00 2001 From: stephantul Date: Fri, 24 Jan 2025 15:59:26 +0100 Subject: [PATCH 42/57] fix tests --- Makefile | 2 +- model2vec/utils.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/Makefile b/Makefile index ef0f2d3e..be38ccb6 100644 --- a/Makefile +++ b/Makefile @@ -9,7 +9,7 @@ install: uv run pre-commit install install-no-pre-commit: - uv pip install ".[dev,distill]" + uv pip install ".[dev,distill,inference,train]" uv pip install "torch<2.5.0" install-base: diff --git a/model2vec/utils.py b/model2vec/utils.py index 235c3d91..f11f079b 100644 --- a/model2vec/utils.py +++ b/model2vec/utils.py @@ -88,7 +88,7 @@ def importable(module: str, extra: str) -> None: import_module(module) except ImportError: raise ImportError( - f"`{module}`, is required. Please reinstall model2vec with the `distill` extra. `pip install model2vec[{extra}]`" + f"`{module}`, is required. Please reinstall model2vec with the `{extra}` extra. `pip install model2vec[{extra}]`" ) From 8ab8456f4c37edbefcb4d89910755dabe65c85b7 Mon Sep 17 00:00:00 2001 From: stephantul Date: Fri, 24 Jan 2025 19:15:31 +0100 Subject: [PATCH 43/57] tests: fix tests --- model2vec/train/classifier.py | 39 ++++++++++++++++++++++------------- tests/conftest.py | 19 +++++++---------- tests/test_inference.py | 16 +++++++------- tests/test_trainable.py | 18 ++++++++++------ 4 files changed, 53 insertions(+), 39 deletions(-) diff --git a/model2vec/train/classifier.py b/model2vec/train/classifier.py index 66791bb2..63e60449 100644 --- a/model2vec/train/classifier.py +++ b/model2vec/train/classifier.py @@ -2,6 +2,7 @@ import logging from collections import Counter +from tempfile import TemporaryDirectory import lightning as pl import numpy as np @@ -150,21 +151,24 @@ def fit( else: val_check_interval = max(250, 2 * len(val_dataset) // batch_size) check_val_every_epoch = None - trainer = pl.Trainer( - max_epochs=500, - callbacks=callbacks, - val_check_interval=val_check_interval, - check_val_every_n_epoch=check_val_every_epoch, - accelerator=device, - ) - trainer.fit( - c, - train_dataloaders=train_dataset.to_dataloader(shuffle=True, batch_size=batch_size), - val_dataloaders=val_dataset.to_dataloader(shuffle=False, batch_size=batch_size), - ) - best_model_path = trainer.checkpoint_callback.best_model_path # type: ignore - best_model_weights = torch.load(best_model_path, weights_only=True) + with TemporaryDirectory() as tempdir: + trainer = pl.Trainer( + max_epochs=500, + callbacks=callbacks, + val_check_interval=val_check_interval, + check_val_every_n_epoch=check_val_every_epoch, + accelerator=device, + default_root_dir=tempdir, + ) + + trainer.fit( + c, + train_dataloaders=train_dataset.to_dataloader(shuffle=True, batch_size=batch_size), + val_dataloaders=val_dataset.to_dataloader(shuffle=False, batch_size=batch_size), + ) + best_model_path = trainer.checkpoint_callback.best_model_path # type: ignore + best_model_weights = torch.load(best_model_path, weights_only=True) state_dict = {} for weight_name, weight in best_model_weights["state_dict"].items(): @@ -228,7 +232,14 @@ def to_pipeline(self) -> StaticModelPipeline: for index, layer in enumerate([module for module in self.head if isinstance(module, nn.Linear)]): mlp_head.coefs_[index] = layer.weight.detach().cpu().numpy().T mlp_head.intercepts_[index] = layer.bias.detach().cpu().numpy() + # Below is necessary to ensure that the converted model works correctly. + # In scikit-learn, a binary classifier only has a single vector of output coefficients + # and a single intercept. We use two output vectors. + # To convert correctly, we need to set the outputs correctly, and fix the activation function. + # Make sure n_outputs is set to > 1. mlp_head.n_outputs_ = self.out_dim + # Set to softmax + mlp_head.out_activation_ = "softmax" return StaticModelPipeline(static_model, converted) diff --git a/tests/conftest.py b/tests/conftest.py index bd5107b7..62203292 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -20,7 +20,7 @@ @pytest.fixture(scope="session") def mock_tokenizer() -> Tokenizer: """Create a mock tokenizer.""" - vocab = ["word1", "word2", "word3", "[UNK]", "[PAD]"] + vocab = ["[PAD]", "word1", "word2", "word3", "[UNK]"] unk_token = "[UNK]" model = WordLevel(vocab={word: idx for idx, word in enumerate(vocab)}, unk_token=unk_token) @@ -81,21 +81,18 @@ def mock_config() -> dict[str, str]: @pytest.fixture(scope="session") -def mock_inference_pipeline(mock_vectors: np.ndarray, mock_tokenizer: Tokenizer) -> StaticModelPipeline: +def mock_inference_pipeline(mock_trained_pipeline: StaticModelForClassification) -> StaticModelPipeline: """Mock pipeline.""" - encoder = StaticModel(vectors=mock_vectors, tokenizer=mock_tokenizer, config={}) - encoded = encoder.encode(["dog", "cat"]) - labels = ["a", "b"] - head = make_pipeline(MLPClassifier(random_state=12)).fit(encoded, labels) - - return StaticModelPipeline(encoder, head=head) + return mock_trained_pipeline.to_pipeline() @pytest.fixture(scope="session") -def mock_trained_pipeline(mock_vectors: np.ndarray, mock_tokenizer: Tokenizer) -> StaticModelForClassification: +def mock_trained_pipeline() -> StaticModelForClassification: """Mock staticmodelforclassification.""" - vectors_torched = torch.from_numpy(mock_vectors).float() - s = StaticModelForClassification(vectors=vectors_torched, tokenizer=mock_tokenizer).to("cpu") + tokenizer = AutoTokenizer.from_pretrained("tests/data/test_tokenizer").backend_tokenizer + torch.random.manual_seed(42) + vectors_torched = torch.randn(len(tokenizer.get_vocab()), 12) + s = StaticModelForClassification(vectors=vectors_torched, tokenizer=tokenizer, hidden_dim=12).to("cpu") s.fit(["dog", "cat"], ["a", "b"], device="cpu") return s diff --git a/tests/test_inference.py b/tests/test_inference.py index 29c0d730..9f4618df 100644 --- a/tests/test_inference.py +++ b/tests/test_inference.py @@ -10,14 +10,14 @@ def test_init_predict(mock_inference_pipeline: StaticModelPipeline) -> None: """Test successful initialization of StaticModelPipeline.""" - assert mock_inference_pipeline.predict("dog").tolist() == ["a"] - assert mock_inference_pipeline.predict(["dog"]).tolist() == ["a"] + assert mock_inference_pipeline.predict("dog").tolist() == ["b"] + assert mock_inference_pipeline.predict(["dog"]).tolist() == ["b"] def test_init_predict_proba(mock_inference_pipeline: StaticModelPipeline) -> None: """Test successful initialization of StaticModelPipeline.""" - assert mock_inference_pipeline.predict_proba("dog").argmax() == 0 - assert mock_inference_pipeline.predict_proba(["dog"]).argmax(1).tolist() == [0] + assert mock_inference_pipeline.predict_proba("dog").argmax() == 1 + assert mock_inference_pipeline.predict_proba(["dog"]).argmax(1).tolist() == [1] def test_roundtrip_save(mock_inference_pipeline: StaticModelPipeline) -> None: @@ -25,10 +25,10 @@ def test_roundtrip_save(mock_inference_pipeline: StaticModelPipeline) -> None: with TemporaryDirectory() as temp_dir: mock_inference_pipeline.save_pretrained(temp_dir) loaded = StaticModelPipeline.from_pretrained(temp_dir) - assert loaded.predict("dog") == ["a"] - assert loaded.predict(["dog"]) == ["a"] - assert loaded.predict_proba("dog").argmax() == 0 - assert loaded.predict_proba(["dog"]).argmax(1).tolist() == [0] + assert loaded.predict("dog") == ["b"] + assert loaded.predict(["dog"]) == ["b"] + assert loaded.predict_proba("dog").argmax() == 1 + assert loaded.predict_proba(["dog"]).argmax(1).tolist() == [1] @patch("model2vec.inference.model._DEFAULT_TRUST_PATTERN", re.compile("torch")) diff --git a/tests/test_trainable.py b/tests/test_trainable.py index 4d5b3946..dc9bb811 100644 --- a/tests/test_trainable.py +++ b/tests/test_trainable.py @@ -69,7 +69,7 @@ def test_init_classifier_from_model(mock_vectors: np.ndarray, mock_tokenizer: To def test_encode(mock_trained_pipeline: StaticModelForClassification) -> None: """Test the encode function.""" result = mock_trained_pipeline._encode(torch.tensor([[0, 1], [1, 0]]).long()) - assert result.shape == (2, 2) + assert result.shape == (2, 12) assert torch.allclose(result[0], result[1]) @@ -111,13 +111,13 @@ def test_textdataset_init_incorrect() -> None: def test_predict(mock_trained_pipeline: StaticModelForClassification) -> None: """Test the predict function.""" - result = mock_trained_pipeline.predict(["dog dog", "cat"]).tolist() - assert result == ["a", "a"] + result = mock_trained_pipeline.predict(["dog cat", "dog"]).tolist() + assert result == ["b", "b"] def test_predict_proba(mock_trained_pipeline: StaticModelForClassification) -> None: """Test the predict function.""" - result = mock_trained_pipeline.predict_proba(["dog dog", "cat"]) + result = mock_trained_pipeline.predict_proba(["dog cat", "dog"]) assert result.shape == (2, 2) @@ -125,9 +125,15 @@ def test_convert_to_pipeline(mock_trained_pipeline: StaticModelForClassification """Convert a model to a pipeline.""" mock_trained_pipeline.eval() pipeline = mock_trained_pipeline.to_pipeline() - a = pipeline.predict(["dog dog", "cat"]).tolist() - b = mock_trained_pipeline.predict(["dog dog", "cat"]).tolist() + encoded_pipeline = pipeline.model.encode(["dog cat", "dog"]) + encoded_model = mock_trained_pipeline(mock_trained_pipeline.tokenize(["dog cat", "dog"]))[1].detach().numpy() + assert np.allclose(encoded_pipeline, encoded_model) + a = pipeline.predict(["dog cat", "dog"]).tolist() + b = mock_trained_pipeline.predict(["dog cat", "dog"]).tolist() assert a == b + p1 = pipeline.predict_proba(["dog cat", "dog"]) + p2 = mock_trained_pipeline.predict_proba(["dog cat", "dog"]) + assert np.allclose(p1, p2) def test_train_test_split() -> None: From e21e61f9eeef4a5e2559e2009b443e60d4a92017 Mon Sep 17 00:00:00 2001 From: stephantul Date: Sun, 26 Jan 2025 13:26:48 +0100 Subject: [PATCH 44/57] Address comments --- model2vec/inference/model.py | 6 ++++-- model2vec/train/README.md | 38 +++++++++++++++++------------------ model2vec/train/classifier.py | 6 ++++-- 3 files changed, 27 insertions(+), 23 deletions(-) diff --git a/model2vec/inference/model.py b/model2vec/inference/model.py index ab2c8864..62bb6424 100644 --- a/model2vec/inference/model.py +++ b/model2vec/inference/model.py @@ -27,9 +27,11 @@ def from_pretrained( cls: type[StaticModelPipeline], path: PathLike, token: str | None = None ) -> StaticModelPipeline: """ - Load the pipeline from the trained model. + Load a StaticModel from a local path or huggingface hub path. - :param path: The path to the folder containing the pipeline. + NOTE: if you load a private model from the huggingface hub, you need to pass a token. + + :param path: The path to the folder containing the pipeline, or a repository on the Hugging Face Hub :param token: The token to use to download the pipeline from the hub. :return: The loaded pipeline. """ diff --git a/model2vec/train/README.md b/model2vec/train/README.md index fc137d22..e4c9d1ad 100644 --- a/model2vec/train/README.md +++ b/model2vec/train/README.md @@ -1,6 +1,6 @@ # Training -Aside from [distillation](../../README.md#distillation), `model2vec` also supports training simple classifiers on top of static models, using [pytorch](https://pytorch.org/) and [lightning](https://lightning.ai/). +Aside from [distillation](../../README.md#distillation), `model2vec` also supports training simple classifiers on top of static models, using [pytorch](https://pytorch.org/), [lightning](https://lightning.ai/) and [scikit-learn](https://scikit-learn.org/stable/index.html). # Installation @@ -73,7 +73,7 @@ You can turn a classifier into a scikit-learn compatible pipeline, as follows: pipeline = classifier.to_pipeline() ``` -This pipeline object can be persisted using standard pickle-based methods, such as [joblib](https://joblib.readthedocs.io/en/stable/). This makes it easy to use your model in inferene pipelines (no installing torch!). +This pipeline object can be persisted using standard pickle-based methods, such as [joblib](https://joblib.readthedocs.io/en/stable/). This makes it easy to use your model in inferene pipelines (no installing torch!), although `joblib` and `pickle` should not be used to share models outside of your organization. If you want to persist your pipeline to the Hugging Face hub, you can use our built-in functions: @@ -98,23 +98,23 @@ The main results are detailed in our training blogpost, but we'll do a compariso We use 14 classification datasets, using 1000 examples from the train set, and the full test set. No parameters were tuned on any validation set. All datasets were taken from the [Setfit organization on Hugging Face](https://huggingface.co/datasets/SetFit). -| dataset name | logistic regression head | full finetune | -|:---------------------------|-----------:|---------------:| -| 20_newgroups | 0.545312 | 0.555459 | -| ade | 0.715725 | 0.740307 | -| ag_news | 0.860154 | 0.858304 | -| amazon_counterfactual | 0.637754 | 0.744288 | -| bbc | 0.955719 | 0.965018 | -| emotion | 0.516267 | 0.586328 | -| enron_spam | 0.951975 | 0.964994 | -| hatespeech_offensive | 0.543758 | 0.592587 | -| imdb | 0.839002 | 0.846198 | -| massive_scenario | 0.797779 | 0.822825 | -| senteval_cr | 0.743436 | 0.745863 | -| sst5 | 0.290249 | 0.363071 | -| student | 0.806069 | 0.837581 | -| subj | 0.878394 | 0.88941 | -| tweet_sentiment_extraction | 0.638664 | 0.632009 | +| dataset_name | model2vec logreg | setfit | model2vec full finetune | +|:---------------------------|---------------------------------------------:|-------------------------------------------------:|--------------------------------------:| +| 20_newgroups | 0.545312 | 0.595426 | 0.555459 | +| ade | 0.715725 | 0.788789 | 0.740307 | +| ag_news | 0.860154 | 0.880142 | 0.858304 | +| amazon_counterfactual | 0.637754 | 0.873249 | 0.744288 | +| bbc | 0.955719 | 0.965823 | 0.965018 | +| emotion | 0.516267 | 0.598852 | 0.586328 | +| enron_spam | 0.951975 | 0.974498 | 0.964994 | +| hatespeech_offensive | 0.543758 | 0.659873 | 0.592587 | +| imdb | 0.839002 | 0.860037 | 0.846198 | +| massive_scenario | 0.797779 | 0.814601 | 0.822825 | +| senteval_cr | 0.743436 | 0.8526 | 0.745863 | +| sst5 | 0.290249 | 0.393179 | 0.363071 | +| student | 0.806069 | 0.889399 | 0.837581 | +| subj | 0.878394 | 0.937955 | 0.88941 | +| tweet_sentiment_extraction | 0.638664 | 0.755296 | 0.632009 | | | logreg | full finetune | |:---------------------------|-----------:|---------------:| diff --git a/model2vec/train/classifier.py b/model2vec/train/classifier.py index 63e60449..7155164f 100644 --- a/model2vec/train/classifier.py +++ b/model2vec/train/classifier.py @@ -21,6 +21,8 @@ logger = logging.getLogger(__name__) +_RANDOM_SEED = 42 + class StaticModelForClassification(FinetunableStaticModel): def __init__( @@ -118,7 +120,7 @@ def fit( :param device: The device to train on. If this is "auto", the device is chosen automatically. :return: The fitted model. """ - pl.seed_everything(42) + pl.seed_everything(_RANDOM_SEED) logger.info("Re-initializing model.") self._initialize(y) @@ -220,7 +222,7 @@ def to_pipeline(self) -> StaticModelPipeline: """Convert the model to an sklearn pipeline.""" static_model = self.to_static_model() - random_state = np.random.RandomState(42) + random_state = np.random.RandomState(_RANDOM_SEED) n_items = len(self.classes) X = random_state.randn(n_items, static_model.dim) y = self.classes From ff75af9dd3f848c51e67c56a394cf501bb9c405e Mon Sep 17 00:00:00 2001 From: stephantul Date: Sun, 26 Jan 2025 13:27:23 +0100 Subject: [PATCH 45/57] Add inference reqs to train reqs --- pyproject.toml | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index faa34e9e..1f4efec8 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -57,7 +57,8 @@ dev = [ distill = ["torch", "transformers", "scikit-learn"] onnx = ["onnx", "torch"] -train = ["torch", "lightning"] +# train also installs inference +train = ["torch", "lightning", "scikit-learn", "skops"] inference = ["scikit-learn", "skops"] [project.urls] From 87de7c42be28b8866e391b206d8f3f09ea95c354 Mon Sep 17 00:00:00 2001 From: stephantul Date: Sun, 26 Jan 2025 13:30:12 +0100 Subject: [PATCH 46/57] fix normalize --- model2vec/model.py | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/model2vec/model.py b/model2vec/model.py index 82152494..5b19d2eb 100644 --- a/model2vec/model.py +++ b/model2vec/model.py @@ -66,8 +66,7 @@ def __init__( self._can_encode_fast = False if normalize is not None: - self._normalize = normalize - self.config["normalize"] = normalize + self.normalize = normalize else: self.normalize = self.config.get("normalize", False) @@ -88,7 +87,7 @@ def normalize(self) -> bool: @normalize.setter def normalize(self, value: bool) -> None: """Update the config if the value of normalize changes.""" - config_normalize = self.config.get("normalize", False) + config_normalize = self.config.get("normalize") self._normalize = value if config_normalize is not None and value != config_normalize: logger.warning( From 1fb33f19bcc2a8e324c33ee0a856e65d0967d226 Mon Sep 17 00:00:00 2001 From: stephantul Date: Sun, 26 Jan 2025 13:34:14 +0100 Subject: [PATCH 47/57] update lock file --- uv.lock | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/uv.lock b/uv.lock index 495b1560..3d05a811 100644 --- a/uv.lock +++ b/uv.lock @@ -831,6 +831,8 @@ onnx = [ ] train = [ { name = "lightning" }, + { name = "scikit-learn" }, + { name = "skops" }, { name = "torch" }, ] @@ -852,8 +854,10 @@ requires-dist = [ { name = "safetensors" }, { name = "scikit-learn", marker = "extra == 'distill'" }, { name = "scikit-learn", marker = "extra == 'inference'" }, + { name = "scikit-learn", marker = "extra == 'train'" }, { name = "setuptools" }, { name = "skops", marker = "extra == 'inference'" }, + { name = "skops", marker = "extra == 'train'" }, { name = "tokenizers", specifier = ">=0.20" }, { name = "torch", marker = "extra == 'distill'" }, { name = "torch", marker = "extra == 'onnx'" }, From 261a9b49acc40768b1a688bb903ad0b35cf7f9ce Mon Sep 17 00:00:00 2001 From: stephantul Date: Mon, 3 Feb 2025 08:50:41 +0100 Subject: [PATCH 48/57] fix: move modelcards --- model2vec/hf_utils.py | 6 ++- model2vec/inference/model.py | 15 +++++- model2vec/modelcards/classifier_template.md | 49 +++++++++++++++++++ .../{ => modelcards}/model_card_template.md | 2 +- pyproject.toml | 2 +- 5 files changed, 69 insertions(+), 5 deletions(-) create mode 100644 model2vec/modelcards/classifier_template.md rename model2vec/{ => modelcards}/model_card_template.md (85%) diff --git a/model2vec/hf_utils.py b/model2vec/hf_utils.py index 44d0b5e9..a24fdd31 100644 --- a/model2vec/hf_utils.py +++ b/model2vec/hf_utils.py @@ -60,6 +60,7 @@ def _create_model_card( license: str = "mit", language: list[str] | None = None, model_name: str | None = None, + template_path: str = "modelcards/model_card_template.md", **kwargs: Any, ) -> None: """ @@ -70,11 +71,12 @@ def _create_model_card( :param license: The license to use. :param language: The language of the model. :param model_name: The name of the model to use in the Model Card. + :param template_path: The path to the template. :param **kwargs: Additional metadata for the model card (e.g., model_name, base_model, etc.). """ folder_path = Path(folder_path) model_name = model_name or folder_path.name - template_path = Path(__file__).parent / "model_card_template.md" + full_path = Path(__file__).parent / template_path model_card_data = ModelCardData( model_name=model_name, @@ -85,7 +87,7 @@ def _create_model_card( library_name="model2vec", **kwargs, ) - model_card = ModelCard.from_template(model_card_data, template_path=template_path) + model_card = ModelCard.from_template(model_card_data, template_path=full_path) model_card.save(folder_path / "README.md") diff --git a/model2vec/inference/model.py b/model2vec/inference/model.py index 62bb6424..7fd33dab 100644 --- a/model2vec/inference/model.py +++ b/model2vec/inference/model.py @@ -7,9 +7,9 @@ import huggingface_hub import numpy as np import skops.io -from sklearn.neural_network import MLPClassifier from sklearn.pipeline import Pipeline +from model2vec.hf_utils import _create_model_card from model2vec.model import PathLike, StaticModel _DEFAULT_TRUST_PATTERN = re.compile(r"sklearn\..+") @@ -141,3 +141,16 @@ def save_pipeline(pipeline: StaticModelPipeline, folder_path: str | Path) -> Non head_pipeline_path = folder_path / model_filename skops.io.dump(pipeline.head, head_pipeline_path) pipeline.model.save_pretrained(folder_path) + base_model_name = pipeline.model.base_model_name + if isinstance(base_model_name, list) and base_model_name: + name = base_model_name[0] + elif isinstance(base_model_name, str): + name = base_model_name + else: + name = "unknown" + _create_model_card( + folder_path, + base_model_name=name, + language=pipeline.model.language, + template_path="modelcards/classifier_template.md", + ) diff --git a/model2vec/modelcards/classifier_template.md b/model2vec/modelcards/classifier_template.md new file mode 100644 index 00000000..7f38d272 --- /dev/null +++ b/model2vec/modelcards/classifier_template.md @@ -0,0 +1,49 @@ +--- +{{ card_data }} +--- + +# {{ model_name }} Model Card + +This [Model2Vec](https://github.com/MinishLab/model2vec) model is a fine-tuned version of {% if base_model %}the [{{ base_model }}](https://huggingface.co/{{ base_model }}){% else %}a{% endif %} Model2Vec model. It also includes a classifier head on top. + +## Installation + +Install model2vec using pip: +``` +pip install model2vec[inference] +``` + +## Usage +Load this model using the `from_pretrained` method: +```python +from model2vec.inference import StaticModelPipeline + +# Load a pretrained Model2Vec model +model = StaticModelPipeline.from_pretrained("{{ model_name }}") + +# Predict labels +predicted = model.predict(["Example sentence"]) +``` + +## Additional Resources + +- [All Model2Vec models on the hub](https://huggingface.co/models?library=model2vec) +- [Model2Vec Repo](https://github.com/MinishLab/model2vec) +- [Model2Vec Results](https://github.com/MinishLab/model2vec?tab=readme-ov-file#results) +- [Model2Vec Tutorials](https://github.com/MinishLab/model2vec/tree/main/tutorials) + +## Library Authors + +Model2Vec was developed by the [Minish Lab](https://github.com/MinishLab) team consisting of [Stephan Tulkens](https://github.com/stephantul) and [Thomas van Dongen](https://github.com/Pringled). + +## Citation + +Please cite the [Model2Vec repository](https://github.com/MinishLab/model2vec) if you use this model in your work. +``` +@software{minishlab2024model2vec, + authors = {Stephan Tulkens, Thomas van Dongen}, + title = {Model2Vec: Turn any Sentence Transformer into a Small Fast Model}, + year = {2024}, + url = {https://github.com/MinishLab/model2vec}, +} +``` diff --git a/model2vec/model_card_template.md b/model2vec/modelcards/model_card_template.md similarity index 85% rename from model2vec/model_card_template.md rename to model2vec/modelcards/model_card_template.md index bfd80e41..b304d7ee 100644 --- a/model2vec/model_card_template.md +++ b/model2vec/modelcards/model_card_template.md @@ -4,7 +4,7 @@ # {{ model_name }} Model Card -This [Model2Vec](https://github.com/MinishLab/model2vec) model is a distilled version of {% if base_model %}the [{{ base_model }}](https://huggingface.co/{{ base_model }}){% else %}a{% endif %} Sentence Transformer. It uses static embeddings, allowing text embeddings to be computed orders of magnitude faster on both GPU and CPU. It is designed for applications where computational resources are limited or where real-time performance is critical. +This [Model2Vec](https://github.com/MinishLab/model2vec) model is a distilled version of {% if base_model %}the {{ base_model }}(https://huggingface.co/{{ base_model }}){% else %}a{% endif %} Sentence Transformer. It uses static embeddings, allowing text embeddings to be computed orders of magnitude faster on both GPU and CPU. It is designed for applications where computational resources are limited or where real-time performance is critical. ## Installation diff --git a/pyproject.toml b/pyproject.toml index 1f4efec8..c1c56d3a 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -42,7 +42,7 @@ packages = ["model2vec"] include-package-data = true [tool.setuptools.package-data] -model2vec = ["assets/model_card_template.md"] +model2vec = ["assets/modelcards/model_card_template.md", "assets/modelcards/classifier_template.md"] [project.optional-dependencies] dev = [ From e1d53ac4e31995f9194967123788e367660ad32e Mon Sep 17 00:00:00 2001 From: stephantul Date: Mon, 3 Feb 2025 09:19:38 +0100 Subject: [PATCH 49/57] fix: batch size --- model2vec/train/classifier.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/model2vec/train/classifier.py b/model2vec/train/classifier.py index 7155164f..34a225d4 100644 --- a/model2vec/train/classifier.py +++ b/model2vec/train/classifier.py @@ -129,7 +129,9 @@ def fit( ) if batch_size is None: - batch_size = max(min(32, len(train_texts) // 10), 512) + # Set to a multiple of 32 + base_number = int(min(max(1, (len(train_texts) / 30) // 32), 16)) + batch_size = int(base_number * 32) logger.info("Batch size automatically set to %d.", batch_size) logger.info("Preparing train dataset.") From 6b5f99174fba042ff9cae34759fb7d231956b332 Mon Sep 17 00:00:00 2001 From: stephantul Date: Mon, 3 Feb 2025 09:41:09 +0100 Subject: [PATCH 50/57] update lock file --- uv.lock | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/uv.lock b/uv.lock index 3d05a811..f7d37b8c 100644 --- a/uv.lock +++ b/uv.lock @@ -791,7 +791,7 @@ wheels = [ [[package]] name = "model2vec" -version = "0.3.7" +version = "0.3.8" source = { editable = "." } dependencies = [ { name = "jinja2" }, From 759b96ccbd971aee68fb6a8701e9704daaa90eb8 Mon Sep 17 00:00:00 2001 From: Stephan Tulkens Date: Fri, 7 Feb 2025 20:07:45 +0100 Subject: [PATCH 51/57] Update model2vec/inference/README.md Co-authored-by: Thomas van Dongen --- model2vec/inference/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/model2vec/inference/README.md b/model2vec/inference/README.md index 6f859e51..8e12960d 100644 --- a/model2vec/inference/README.md +++ b/model2vec/inference/README.md @@ -6,7 +6,7 @@ If you're looking for information on how to train a model, see [here](../train/R # Usage -Let's assume you're using our `potion-edu classifier`. +Let's assume you're using our [potion-edu classifier](https://huggingface.co/minishlab/potion-8m-edu-classifier). ```python from model2vec.inference import StaticModelPipeline From 7caf9bcf737d858d3b335633dd9a2ed165f1bd4a Mon Sep 17 00:00:00 2001 From: Stephan Tulkens Date: Fri, 7 Feb 2025 20:07:54 +0100 Subject: [PATCH 52/57] Update model2vec/inference/README.md Co-authored-by: Thomas van Dongen --- model2vec/inference/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/model2vec/inference/README.md b/model2vec/inference/README.md index 8e12960d..5ec985ae 100644 --- a/model2vec/inference/README.md +++ b/model2vec/inference/README.md @@ -11,7 +11,7 @@ Let's assume you're using our [potion-edu classifier](https://huggingface.co/min ```python from model2vec.inference import StaticModelPipeline -s = StaticModelPipeline.from_pretrained("minishlab/potion-8m-edu-classifier") +classifier = StaticModelPipeline.from_pretrained("minishlab/potion-8m-edu-classifier") label = s.predict("Attitudes towards cattle in the Alps: a study in letting go.") ``` From c7b68b6dddf546b7d7b98c9ef59324403345762d Mon Sep 17 00:00:00 2001 From: Stephan Tulkens Date: Fri, 7 Feb 2025 20:08:02 +0100 Subject: [PATCH 53/57] Update model2vec/inference/README.md Co-authored-by: Thomas van Dongen --- model2vec/inference/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/model2vec/inference/README.md b/model2vec/inference/README.md index 5ec985ae..a14c3e1d 100644 --- a/model2vec/inference/README.md +++ b/model2vec/inference/README.md @@ -12,7 +12,7 @@ Let's assume you're using our [potion-edu classifier](https://huggingface.co/min from model2vec.inference import StaticModelPipeline classifier = StaticModelPipeline.from_pretrained("minishlab/potion-8m-edu-classifier") -label = s.predict("Attitudes towards cattle in the Alps: a study in letting go.") +label = classifier.predict("Attitudes towards cattle in the Alps: a study in letting go.") ``` This should just work. From be7baa1ea548156a4e265369cdc50d2ea152ed09 Mon Sep 17 00:00:00 2001 From: Stephan Tulkens Date: Fri, 7 Feb 2025 20:08:12 +0100 Subject: [PATCH 54/57] Update model2vec/train/classifier.py Co-authored-by: Thomas van Dongen --- model2vec/train/classifier.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/model2vec/train/classifier.py b/model2vec/train/classifier.py index 34a225d4..9ec30ee0 100644 --- a/model2vec/train/classifier.py +++ b/model2vec/train/classifier.py @@ -282,7 +282,7 @@ def validation_step(self, batch: tuple[torch.Tensor, torch.Tensor], batch_idx: i def configure_optimizers(self) -> OptimizerLRScheduler: """Simple Adam optimizer.""" - optimizer = torch.optim.Adam(self.model.parameters(), lr=1e-3) + optimizer = torch.optim.Adam(self.model.parameters(), lr=self.learning_rate) scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau( optimizer, mode="min", From cc746182bd5f4fccae7817dcb93620708e296517 Mon Sep 17 00:00:00 2001 From: stephantul Date: Fri, 7 Feb 2025 20:12:40 +0100 Subject: [PATCH 55/57] fix: encode args --- model2vec/inference/model.py | 57 ++++++++++++++++++++++++++++++++---- 1 file changed, 51 insertions(+), 6 deletions(-) diff --git a/model2vec/inference/model.py b/model2vec/inference/model.py index 7fd33dab..5619db75 100644 --- a/model2vec/inference/model.py +++ b/model2vec/inference/model.py @@ -59,23 +59,68 @@ def push_to_hub(self, repo_id: str, token: str | None = None, private: bool = Fa self.model.save_pretrained(temp_dir) push_folder_to_hub(Path(temp_dir), repo_id, private, token) - def _predict_and_coerce_to_2d(self, X: list[str] | str) -> np.ndarray: + def _predict_and_coerce_to_2d( + self, + X: list[str] | str, + show_progress_bar: bool, + max_length: int | None, + batch_size: int, + use_multiprocessing: bool, + multiprocessing_threshold: int, + ) -> np.ndarray: """Predict the labels of the input and coerce the output to a matrix.""" - encoded = self.model.encode(X) + encoded = self.model.encode( + X, + show_progress_bar=show_progress_bar, + max_length=max_length, + batch_size=batch_size, + use_multiprocessing=use_multiprocessing, + multiprocessing_threshold=multiprocessing_threshold, + ) if np.ndim(encoded) == 1: encoded = encoded[None, :] return encoded - def predict(self, X: list[str] | str) -> np.ndarray: + def predict( + self, + X: list[str] | str, + show_progress_bar: bool = False, + max_length: int | None = 512, + batch_size: int = 1024, + use_multiprocessing: bool = True, + multiprocessing_threshold: int = 10_000, + ) -> np.ndarray: """Predict the labels of the input.""" - encoded = self._predict_and_coerce_to_2d(X) + encoded = self._predict_and_coerce_to_2d( + X, + show_progress_bar=show_progress_bar, + max_length=max_length, + batch_size=batch_size, + use_multiprocessing=use_multiprocessing, + multiprocessing_threshold=multiprocessing_threshold, + ) return self.head.predict(encoded) - def predict_proba(self, X: list[str] | str) -> np.ndarray: + def predict_proba( + self, + X: list[str] | str, + show_progress_bar: bool = False, + max_length: int | None = 512, + batch_size: int = 1024, + use_multiprocessing: bool = True, + multiprocessing_threshold: int = 10_000, + ) -> np.ndarray: """Predict the probabilities of the labels of the input.""" - encoded = self._predict_and_coerce_to_2d(X) + encoded = self._predict_and_coerce_to_2d( + X, + show_progress_bar=show_progress_bar, + max_length=max_length, + batch_size=batch_size, + use_multiprocessing=use_multiprocessing, + multiprocessing_threshold=multiprocessing_threshold, + ) return self.head.predict_proba(encoded) From a4d8d6c24c560439941eeb2e01b34910b85cb184 Mon Sep 17 00:00:00 2001 From: stephantul Date: Fri, 7 Feb 2025 20:14:34 +0100 Subject: [PATCH 56/57] fix: trust_remote_code --- model2vec/inference/model.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/model2vec/inference/model.py b/model2vec/inference/model.py index 5619db75..5b08dad8 100644 --- a/model2vec/inference/model.py +++ b/model2vec/inference/model.py @@ -24,7 +24,7 @@ def __init__(self, model: StaticModel, head: Pipeline) -> None: @classmethod def from_pretrained( - cls: type[StaticModelPipeline], path: PathLike, token: str | None = None + cls: type[StaticModelPipeline], path: PathLike, token: str | None = None, trust_remote_code: bool = False ) -> StaticModelPipeline: """ Load a StaticModel from a local path or huggingface hub path. @@ -33,9 +33,10 @@ def from_pretrained( :param path: The path to the folder containing the pipeline, or a repository on the Hugging Face Hub :param token: The token to use to download the pipeline from the hub. + :param trust_remote_code: Whether to trust the remote code. If this is False, we will only load components coming from `sklearn`. :return: The loaded pipeline. """ - model, head = _load_pipeline(path, token) + model, head = _load_pipeline(path, token, trust_remote_code) model.embedding = np.nan_to_num(model.embedding) return cls(model, head) From a0d56d5f87e70b0b9c67f55d458db032c2deda5d Mon Sep 17 00:00:00 2001 From: stephantul Date: Fri, 7 Feb 2025 20:23:13 +0100 Subject: [PATCH 57/57] fix notebook --- tutorials/train_classifier.ipynb | 600 +++++++++++++++++++++++++++++-- 1 file changed, 579 insertions(+), 21 deletions(-) diff --git a/tutorials/train_classifier.ipynb b/tutorials/train_classifier.ipynb index a4fddc57..988007d6 100644 --- a/tutorials/train_classifier.ipynb +++ b/tutorials/train_classifier.ipynb @@ -18,13 +18,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": { "vscode": { "languageId": "plaintext" } }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[2mUsing Python 3.11.4 environment at: /Users/stephantulkens/Documents/GitHub/model2vec/.venv\u001b[0m\n", + "\u001b[2mAudited \u001b[1m1 package\u001b[0m \u001b[2min 4ms\u001b[0m\u001b[0m\n", + "\u001b[2mUsing Python 3.11.4 environment at: /Users/stephantulkens/Documents/GitHub/model2vec/.venv\u001b[0m\n", + "\u001b[2mAudited \u001b[1m1 package\u001b[0m \u001b[2min 8ms\u001b[0m\u001b[0m\n", + "\u001b[2mUsing Python 3.11.4 environment at: /Users/stephantulkens/Documents/GitHub/model2vec/.venv\u001b[0m\n", + "\u001b[2mAudited \u001b[1m1 package\u001b[0m \u001b[2min 3ms\u001b[0m\u001b[0m\n" + ] + } + ], "source": [ "# Install the necessary libraries\n", "!uv pip install \"model2vec[train,inference]\"\n", @@ -40,14 +53,38 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "To demonstrate how to train a model, we'll be using the `subjectivity` dataset, which contains short utterances and whether they are subjective or objective." + "To demonstrate how to train a model, we'll be using the `20_newsgroups` dataset, which contains posts from 1 of 20 newsgroups." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Repo card metadata block was not found. Setting CardData to empty.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "DatasetDict({\n", + " train: Dataset({\n", + " features: ['text', 'label', 'label_text'],\n", + " num_rows: 11314\n", + " })\n", + " test: Dataset({\n", + " features: ['text', 'label', 'label_text'],\n", + " num_rows: 7532\n", + " })\n", + "})\n" + ] + } + ], "source": [ "from datasets import load_dataset\n", "\n", @@ -64,9 +101,78 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "TEXT: I was wondering if anyone out there could enlighten me on this car I saw\n", + "the other day. It was a 2-door sports car, looked to be from the late 60s/\n", + "early 70s. It was called a Bricklin. The doors were really small. In addition,\n", + "the front bumper was separate from the rest of the body. This is \n", + "all I know. If anyone can tellme a model name, engine specs, years\n", + "of production, where this car is made, history, or whatever info you\n", + "have on this funky looking car, please e-mail. LABEL: rec.autos\n", + "TEXT: A fair number of brave souls who upgraded their SI clock oscillator have\n", + "shared their experiences for this poll. Please send a brief message detailing\n", + "your experiences with the procedure. Top speed attained, CPU rated speed,\n", + "add on cards and adapters, heat sinks, hour of usage per day, floppy disk\n", + "functionality with 800 and 1.4 m floppies are especially requested.\n", + "\n", + "I will be summarizing in the next two days, so please add to the network\n", + "knowledge base if you have done the clock upgrade and haven't answered this\n", + "poll. Thanks. LABEL: comp.sys.mac.hardware\n", + "TEXT: well folks, my mac plus finally gave up the ghost this weekend after\n", + "starting life as a 512k way back in 1985. sooo, i'm in the market for a\n", + "new machine a bit sooner than i intended to be...\n", + "\n", + "i'm looking into picking up a powerbook 160 or maybe 180 and have a bunch\n", + "of questions that (hopefully) somebody can answer:\n", + "\n", + "* does anybody know any dirt on when the next round of powerbook\n", + "introductions are expected? i'd heard the 185c was supposed to make an\n", + "appearence \"this summer\" but haven't heard anymore on it - and since i\n", + "don't have access to macleak, i was wondering if anybody out there had\n", + "more info...\n", + "\n", + "* has anybody heard rumors about price drops to the powerbook line like the\n", + "ones the duo's just went through recently?\n", + "\n", + "* what's the impression of the display on the 180? i could probably swing\n", + "a 180 if i got the 80Mb disk rather than the 120, but i don't really have\n", + "a feel for how much \"better\" the display is (yea, it looks great in the\n", + "store, but is that all \"wow\" or is it really that good?). could i solicit\n", + "some opinions of people who use the 160 and 180 day-to-day on if its worth\n", + "taking the disk size and money hit to get the active display? (i realize\n", + "this is a real subjective question, but i've only played around with the\n", + "machines in a computer store breifly and figured the opinions of somebody\n", + "who actually uses the machine daily might prove helpful).\n", + "\n", + "* how well does hellcats perform? ;)\n", + "\n", + "thanks a bunch in advance for any info - if you could email, i'll post a\n", + "summary (news reading time is at a premium with finals just around the\n", + "corner... :( )\n", + "--\n", + "Tom Willis \\ twillis@ecn.purdue.edu \\ Purdue Electrical Engineering LABEL: comp.sys.mac.hardware\n", + "TEXT: \n", + "Do you have Weitek's address/phone number? I'd like to get some information\n", + "about this chip.\n", + " LABEL: comp.graphics\n", + "TEXT: From article , by tombaker@world.std.com (Tom A Baker):\n", + "\n", + "\n", + "My understanding is that the 'expected errors' are basically\n", + "known bugs in the warning system software - things are checked\n", + "that don't have the right values in yet because they aren't\n", + "set till after launch, and suchlike. Rather than fix the code\n", + "and possibly introduce new bugs, they just tell the crew\n", + "'ok, if you see a warning no. 213 before liftoff, ignore it'. LABEL: sci.space\n" + ] + } + ], "source": [ "# First 5 training samples:\n", "for record in dataset[\"train\"].to_list()[:5]:\n", @@ -75,9 +181,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "StaticModelForClassification(\n", + " (embeddings): Embedding(29528, 256, padding_idx=0)\n", + " (head): Sequential(\n", + " (0): Linear(in_features=256, out_features=512, bias=True)\n", + " (1): ReLU()\n", + " (2): Linear(in_features=512, out_features=2, bias=True)\n", + " )\n", + ")\n" + ] + } + ], "source": [ "# Define the staticmodel\n", "model = StaticModelForClassification.from_pretrained()\n", @@ -97,9 +218,344 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Seed set to 42\n", + "GPU available: True (mps), used: True\n", + "TPU available: False, using: 0 TPU cores\n", + "HPU available: False, using: 0 HPUs\n", + "/Users/stephantulkens/Documents/GitHub/model2vec/.venv/lib/python3.11/site-packages/lightning/pytorch/trainer/connectors/logger_connector/logger_connector.py:76: Starting from v1.9.0, `tensorboardX` has been removed as a dependency of the `lightning.pytorch` package, due to potential conflicts with other packages in the ML ecosystem. For this reason, `logger=True` will use `CSVLogger` as the default logger, unless the `tensorboard` or `tensorboardX` packages are found. Please `pip install lightning[extra]` or one of them to enable TensorBoard support by default\n", + "/Users/stephantulkens/Documents/GitHub/model2vec/.venv/lib/python3.11/site-packages/torch/optim/lr_scheduler.py:60: UserWarning: The verbose parameter is deprecated. Please use get_last_lr() to access the learning rate.\n", + " warnings.warn(\n", + "\n", + " | Name | Type | Params | Mode \n", + "---------------------------------------------------------------\n", + "0 | model | StaticModelForClassification | 7.7 M | train\n", + "---------------------------------------------------------------\n", + "7.7 M Trainable params\n", + "0 Non-trainable params\n", + "7.7 M Total params\n", + "30.922 Total estimated model params size (MB)\n", + "6 Modules in train mode\n", + "0 Modules in eval mode\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "2351ba8c0b53458fb680e8d29e0f0a6c", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Sanity Checking: | | 0/? [00:00