diff --git a/.github/workflows/ci.yaml b/.github/workflows/ci.yaml index a54ffe4..8e49c87 100644 --- a/.github/workflows/ci.yaml +++ b/.github/workflows/ci.yaml @@ -10,7 +10,14 @@ jobs: strategy: matrix: os: ["ubuntu-latest", "windows-latest"] - python-version: ["3.10"] + python-version: ["3.9", "3.10", "3.11", "3.12"] + exclude: + - os: windows-latest + python-version: "3.9" + - os: windows-latest + python-version: "3.11" + - os: windows-latest + python-version: "3.12" fail-fast: false steps: @@ -42,8 +49,7 @@ jobs: # Install dependencies using uv pip - name: Install dependencies - run: make install - # run: uv pip install -e ".[pytest]" + run: make install-no-pre-commit # Run tests with coverage - name: Run tests under coverage diff --git a/.gitignore b/.gitignore index 6503faf..0f614e1 100644 --- a/.gitignore +++ b/.gitignore @@ -168,7 +168,6 @@ models checkpoints/* features/* model2vec_models -results/* counts/* results_old/* local/* diff --git a/Makefile b/Makefile index f27bf9e..2861817 100644 --- a/Makefile +++ b/Makefile @@ -8,6 +8,9 @@ install: uv sync --all-extras uv run pre-commit install +install-no-pre-commit: + uv pip install ".[dev,distill]" + install-base: uv sync --extra dev diff --git a/README.md b/README.md index a634689..5601d13 100644 --- a/README.md +++ b/README.md @@ -7,14 +7,15 @@
-

Distill a Small Fast Model from any Sentence Transformer

+

Distill a Small Static Model from any Sentence Transformer

🤗 Models | 📚 Tutorials | - 📖 Website + 📖 Blog | + 🏆 Results

@@ -37,17 +38,26 @@ +<<<<<<< HEAD +======= -Model2Vec is a technique to turn any sentence transformer into a really small fast model, reducing model size by 15x and making the models up to 500x faster, with a small drop in performance. See our results [here](#classification-and-speed-benchmarks), or dive in to see how it works. +Model2Vec is a technique to turn any sentence transformer into a really small static model, reducing model size by 15x and making the models up to 500x faster, with a small drop in performance. See our results [here](results/README.md), or dive in to see how it works. +>>>>>>> 54a64605fb896b247d8fbb9917d89059210aa28f + + +## Updates & Announcements + +- **30/10/2024**: We released three new models: [potion-base-8M](https://huggingface.co/minishlab/potion-base-8M), [potion-base-4M](https://huggingface.co/minishlab/potion-base-4M), and [potion-base-2M](https://huggingface.co/minishlab/potion-base-2M). These models are trained using [Tokenlearn](https://github.com/MinishLab/tokenlearn). Find out more in our [blog post](https://minishlab.github.io/tokenlearn_blogpost/). NOTE: for users of any of our old English M2V models, we recommend switching to these new models as they [perform better on all tasks](https://github.com/MinishLab/model2vec/tree/main/results). ## Table of Contents - [Quickstart](#quickstart) -- [What is Model2Vec?](#what-is-model2vec) - [Main Features](#main-features) +- [What is Model2Vec?](#what-is-model2vec) - [Usage](#usage) - - [Distilling a Model2Vec model](#distilling-a-model2vec-model) - - [Inferencing a Model2Vec model](#inference-with-a-model2vec-model) - - [Evaluating a Model2Vec model](#evaluating-a-model2vec-model) + - [Distillation](#distillation) + - [Inference](#inference) + - [Evaluation](#evaluation) + - [Integrations](#integrations) - [Model List](#model-list) - [Results](#results) - [Related Work](#related-work) @@ -55,7 +65,13 @@ Model2Vec is a technique to turn any sentence transformer into a really small fa ## Quickstart -Install the package with: +Install the package and all required extras with: +```bash +pip install model2vec[distill] +``` + +If you want a light-weight version of the package which only requires `numpy`, omit the `distill` extra. This means you can't distill your own models, but you can use pre-trained models. This is useful for inference pipelines. + ```bash pip install model2vec ``` @@ -64,8 +80,8 @@ The easiest way to get started with Model2Vec is to download one of our [flagshi ```python from model2vec import StaticModel -# Load a model from the HuggingFace hub (in this case the M2V_base_output model) -model_name = "minishlab/M2V_base_output" +# Load a model from the HuggingFace hub (in this case the potion-base-8M model) +model_name = "minishlab/potion-base-8M" model = StaticModel.from_pretrained(model_name) # Make embeddings @@ -99,7 +115,7 @@ from sentence_transformers import SentenceTransformer from sentence_transformers.models import StaticEmbedding # Initialize a StaticEmbedding module -static_embedding = StaticEmbedding.from_model2vec("minishlab/M2V_base_output") +static_embedding = StaticEmbedding.from_model2vec("minishlab/potion-base-8M") model = SentenceTransformer(modules=[static_embedding]) embeddings = model.encode(["It's dangerous to go alone!", "It's a secret to everybody."]) ``` @@ -115,23 +131,12 @@ embeddings = model.encode(["It's dangerous to go alone!", "It's a secret to ever ``` For more documentation, please refer to the [Sentence Transformers documentation](https://sbert.net/docs/package_reference/sentence_transformer/models.html#sentence_transformers.models.StaticEmbedding). -## What is Model2Vec? - -Model2vec creates a small, fast, and powerful model that outperforms other static embedding models by a large margin on all tasks we could find, while being much faster to create than traditional static embedding models such as GloVe. Like BPEmb, it can create subword embeddings, but with much better performance. Best of all, you don't need _any_ data to distill a model using Model2Vec. - -It works by passing a vocabulary through a sentence transformer model, then reducing the dimensionality of the resulting embeddings using PCA, and finally weighting the embeddings using zipf weighting. During inference, we simply take the mean of all token embeddings occurring in a sentence. - -Model2vec has 3 modes: -- **Output**: behaves much like a real sentence transformer, i.e., it uses a subword tokenizer and simply encodes all wordpieces in its vocab. This is really quick to create (30 seconds on a CPU), very small (30 MB in float32), but might be less performant on some tasks. -- **Vocab (word level)**: creates a word-level tokenizer and only encodes words that are in the vocabulary. This is a bit slower to create and creates a larger model, but might be more performant on some tasks. Note that this model can go out-of-vocabulary, which might be beneficial if your domain is very noisy -- **Vocab (subword)**: a combination of the two methods above. In this mode, you can pass your own vocabulary, but it also uses the subword vocabulary to create representations for words not in the passed vocabulary. - ## Main Features -Model2Vec is: +Model2Vec has the following features: -- **Small**: reduces the size of a Sentence Transformer model by a factor of 15, from 120M params, down to 7.5M (30 MB on disk!). -- **Static, but better**: smaller than GLoVe, but much more performant, even with the same vocabulary. +- **Small**: reduces the size of a Sentence Transformer model by a factor of 15, from 120M params, down to 7.5M (30 MB on disk, making it the smallest model on [MTEB](https://huggingface.co/spaces/mteb/leaderboard)!). +- **Static, but better**: smaller than GLoVe and BPEmb, but [much more performant](results/README.md), even with the same vocabulary. - **Fast distillation**: make your own model in 30 seconds. - **Fast inference**: up to 500 times faster on CPU than the original model. Go green or go home. - **No data needed**: Distillation happens directly on the token level, so no dataset is needed. @@ -143,11 +148,35 @@ Model2Vec is: - **Tightly integrated with HuggingFace hub**: easily share and load models from the HuggingFace hub, using the familiar `from_pretrained` and `push_to_hub`. Our own models can be found [here](https://huggingface.co/minishlab). Feel free to share your own. - **Easy Evaluation**: evaluate your models on MTEB and some of our own tasks to measure the performance of the distilled model. Model2Vec models work out of the box on [MTEB](https://huggingface.co/spaces/mteb/leaderboard). +## What is Model2Vec? + +Model2vec creates a small, fast, and powerful model that outperforms other static embedding models by a large margin on all tasks we could find, while being much faster to create than traditional static embedding models such as GloVe. Like BPEmb, it can create subword embeddings, but with much better performance. Best of all, you don't need _any_ data to distill a model using Model2Vec. + +It works by passing a vocabulary through a sentence transformer model, then reducing the dimensionality of the resulting embeddings using PCA, and finally weighting the embeddings using zipf weighting. During inference, we simply take the mean of all token embeddings occurring in a sentence. + +Model2vec has 3 modes: +- **Output**: behaves much like a real sentence transformer, i.e., it uses a subword tokenizer and simply encodes all wordpieces in its vocab. This is really quick to create (30 seconds on a CPU), very small (30 MB in float32), but might be less performant on some tasks. +- **Vocab (word level)**: creates a word-level tokenizer and only encodes words that are in the vocabulary. This is a bit slower to create and creates a larger model, but might be more performant on some tasks. Note that this model can go out-of-vocabulary, which might be beneficial if your domain is very noisy +- **Vocab (subword)**: a combination of the two methods above. In this mode, you can pass your own vocabulary, but it also uses the subword vocabulary to create representations for words not in the passed vocabulary. + +For a technical deepdive into Model2Vec, please refer to our [blog post](https://huggingface.co/blog/Pringled/model2vec). + +### Tokenlearn + +Our flagship POTION models are pre-trained using [Tokenlearn](https://github.com/MinishLab/tokenlearn). This method is described in our [Tokenlearn blogpost](https://minishlab.github.io/tokenlearn_blogpost/). + + ## Usage +<<<<<<< HEAD ### Distilling a Model2Vec model +======= + +### Distillation + +>>>>>>> 54a64605fb896b247d8fbb9917d89059210aa28f
Distilling from a Sentence Transformer
@@ -234,6 +263,7 @@ m2v_model.push_to_hub("my_organization/my_model", token=" Inference a pretrained model +======= + +```python +m2v_model = distill(model_name=model_name, vocabulary=vocabulary, use_subword=False) +``` + +**Important note:** we assume the passed vocabulary is sorted in rank frequency. i.e., we don't care about the actual word frequencies, but do assume that the most frequent word is first, and the least frequent word is last. If you're not sure whether this is case, set `apply_zipf` to `False`. This disables the weighting, but will also make performance a little bit worse. + +
+ +### Inference + +
+ Inference using pretrained model +>>>>>>> 54a64605fb896b247d8fbb9917d89059210aa28f
Inference works as follows. The example shows one of our own models, but you can also just load a local one, or another one from the hub. @@ -265,7 +310,7 @@ Inference works as follows. The example shows one of our own models, but you can from model2vec import StaticModel # Load a model from the HuggingFace hub, or a local one. -model_name = "minishlab/M2V_base_output" +model_name = "minishlab/potion-base-8M" # You can optionally pass a token if you're loading a private model model = StaticModel.from_pretrained(model_name, token=None) @@ -279,7 +324,11 @@ token_embeddings = model.encode_as_sequence(["It's dangerous to go alone!", "It'
+<<<<<<< HEAD Inference with the Sentence Transformers library +======= + Inference using the Sentence Transformers library +>>>>>>> 54a64605fb896b247d8fbb9917d89059210aa28f
The following code snippet shows how to use a Model2Vec model in the [Sentence Transformers](https://github.com/UKPLab/sentence-transformers) library. This is useful if you want to use the model in a Sentence Transformers pipeline. @@ -289,17 +338,28 @@ from sentence_transformers import SentenceTransformer from sentence_transformers.models import StaticEmbedding # Initialize a StaticEmbedding module +<<<<<<< HEAD static_embedding = StaticEmbedding.from_model2vec("minishlab/M2V_base_output") +======= +static_embedding = StaticEmbedding.from_model2vec("minishlab/potion-base-8M") +>>>>>>> 54a64605fb896b247d8fbb9917d89059210aa28f model = SentenceTransformer(modules=[static_embedding]) embeddings = model.encode(["It's dangerous to go alone!", "It's a secret to everybody."]) ```
+<<<<<<< HEAD ### Evaluating a Model2Vec model +======= + +### Evaluation + + +>>>>>>> 54a64605fb896b247d8fbb9917d89059210aa28f
Installation
@@ -307,7 +367,7 @@ embeddings = model.encode(["It's dangerous to go alone!", "It's a secret to ever Our models can be evaluated using our [evaluation package](https://github.com/MinishLab/evaluation). Install the evaluation package with: ```bash -pip install evaluation@git+https://github.com MinishLab/evaluation@main +pip install git+https://github.com/MinishLab/evaluation.git@main ```
@@ -348,8 +408,12 @@ print(make_leaderboard(task_scores)) ```
-## Model List +### Integrations +
+ Sentence Transformers +
+<<<<<<< HEAD We provide a number of models that can be used out of the box. These models are available on the [HuggingFace hub](https://huggingface.co/collections/minishlab/model2vec-base-models-66fd9dd9b7c3b3c0f25ca90e) and can be loaded using the `from_pretrained` method. The models are listed below. | Model | Language | Vocab | Sentence Transformer | Tokenizer Type | Params | @@ -361,72 +425,111 @@ We provide a number of models that can be used out of the box. These models are ## Results +======= +Model2Vec can be used directly in [Sentence Transformers](https://github.com/UKPLab/sentence-transformers) using the `StaticEmbedding` module. + +The following code snippet shows how to load a Model2Vec model into a Sentence Transformer model: +```python +from sentence_transformers import SentenceTransformer +from sentence_transformers.models import StaticEmbedding +>>>>>>> 54a64605fb896b247d8fbb9917d89059210aa28f -### Main Results +# Initialize a StaticEmbedding module +static_embedding = StaticEmbedding.from_model2vec("minishlab/potion-base-8M") +model = SentenceTransformer(modules=[static_embedding]) +embeddings = model.encode(["It's dangerous to go alone!", "It's a secret to everybody."]) +``` -Model2Vec is evaluated on MTEB, as well as two additional tasks: [PEARL](https://github.com/tigerchen52/PEARL) (a phrase representation task) and WordSim (a collection of _word_ similarity tasks). The results are shown in the table below. +The following code snippet shows how to distill a model directly into a Sentence Transformer model: +```python +from sentence_transformers import SentenceTransformer +from sentence_transformers.models import StaticEmbedding +static_embedding = StaticEmbedding.from_distillation("BAAI/bge-base-en-v1.5", device="cpu", pca_dims=256) +model = SentenceTransformer(modules=[static_embedding]) +embeddings = model.encode(["It's dangerous to go alone!", "It's a secret to everybody."]) +``` -| Model | Avg (All) | Avg (MTEB) | Class | Clust | PairClass | Rank | Ret | STS | Sum | Pearl | WordSim | -|:-----------------------|:---------:|:----------:|:------:|:------:|:---------:|:------:|:------:|:------:|:------:|:------:|:-------:| -| all-MiniLM-L6-v2 | 56.08 | 56.09 | 62.62 | 41.94 | 82.37 | 58.04 | 41.95 | 78.90 | 30.81 | 60.83 | 49.91 | -| M2V_base_glove_subword | 49.06 | 46.69 | 61.27 | 30.03 | 74.71 | 49.15 | 27.16 | 69.09 | 30.08 | 56.82 | 57.99 | -| M2V_base_glove | 48.58 | 47.60 | 61.35 | 30.52 | 75.34 | 48.50 | 29.26 | 70.31 | 31.50 | 50.28 | 54.29 | -| M2V_base_output | 46.79 | 45.34 | 61.25 | 25.58 | 74.90 | 47.63 | 26.14 | 68.58 | 29.20 | 54.02 | 49.18 | -| GloVe_300d | 42.84 | 42.36 | 57.31 | 27.66 | 72.48 | 43.30 | 22.78 | 61.90 | 28.81 | 45.65 | 43.05 | -| BPEmb_50k_300d | 39.34 | 37.78 | 55.76 | 23.35 | 57.86 | 43.21 | 17.50 | 55.10 | 29.74 | 47.56 | 41.28 | -| WL256* | 48.88 | 49.36 | 58.98 | 33.34 | 74.00 | 52.03 | 33.12 | 73.34 | 29.05 | 48.81 | 45.16 | +
- Task Abbreviations - -For readability, the MTEB task names are abbreviated as follows: -- Class: Classification -- Clust: Clustering -- PairClass: PairClassification -- Rank: Reranking -- Ret: Retrieval -- STS: Semantic Textual Similarity -- Sum: Summarization -
+ Transformers.js + +
+ +To use a Model2Vec model in [transformers.js](https://github.com/huggingface/transformers.js), the following code snippet can be used as a starting point: + +```javascript +import { AutoModel, AutoTokenizer, Tensor } from '@huggingface/transformers'; + +const modelName = 'minishlab/potion-base-8M'; -\ -\* WL256, introduced in the [WordLlama](https://github.com/dleemiller/WordLlama/tree/main) package is included for comparison due to its similarities to Model2Vec. However, we believe it is heavily overfit to the MTEB dataset since it is trained on datasets used in MTEB itself. This can be seen by the fact that the WL256 model performs much worse on the non-MTEB tasks (PEARL and WordSim) than our models and GLoVe. The results shown in the [Classification and Speed Benchmarks](#classification-and-speed-benchmarks) further support this. +const modelConfig = { + config: { model_type: 'model2vec' }, + dtype: 'fp32', + revision: 'refs/pr/1' +}; +const tokenizerConfig = { + revision: 'refs/pr/2' +}; -### Classification and Speed Benchmarks +const model = await AutoModel.from_pretrained(modelName, modelConfig); +const tokenizer = await AutoTokenizer.from_pretrained(modelName, tokenizerConfig); -In addition to the MTEB evaluation, we evaluate Model2Vec on a number of classification datasets. These are used as additional evidence to avoid overfitting to the MTEB dataset and to benchmark the speed of the model. The results are shown in the table below. +const texts = ['hello', 'hello world']; +const { input_ids } = await tokenizer(texts, { add_special_tokens: false, return_tensor: false }); +const cumsum = arr => arr.reduce((acc, num, i) => [...acc, num + (acc[i - 1] || 0)], []); +const offsets = [0, ...cumsum(input_ids.slice(0, -1).map(x => x.length))]; -| Model | Average | SST2 | IMDB | TREC | AG News | -|:-----------------------|:-------:|:------:|:-----:|:------:|:-------:| -| bge-base-en-v1.5 | 90.00 | 91.54 | 91.88 | 85.16 | 91.45 | -| all-MiniLM-L6-v2 | 84.10 | 83.95 | 81.36 | 81.31 | 89.77 | -| M2V_base_output | 82.23 | 80.92 | 84.56 | 75.27 | 88.17 | -| M2V_base_glove_subword | 81.95 | 82.84 | 85.96 | 70.51 | 88.49 | -| BPEmb_50k_300d | 81.15 | 80.42 | 84.04 | 71.25 | 88.92 | -| M2V_base_glove | 80.76 | 83.07 | 85.24 | 66.12 | 88.61 | -| WL256 | 78.48 | 76.88 | 80.12 | 69.23 | 87.68 | -| GloVe_300d | 77.77 | 81.68 | 84.00 | 55.67 | 89.71 | +const flattened_input_ids = input_ids.flat(); +const modelInputs = { + input_ids: new Tensor('int64', flattened_input_ids, [flattened_input_ids.length]), + offsets: new Tensor('int64', offsets, [offsets.length]) +}; +const { embeddings } = await model(modelInputs); +console.log(embeddings.tolist()); // output matches python version +``` + +Note that this requires that the Model2Vec has a `model.onnx` file and several required tokenizers file. To generate these for a model that does not have them yet, the following code snippet can be used: + +```bash +python scripts/export_to_onnx.py --model_path --save_path "" +``` -As can be seen, Model2Vec models outperform the GloVe, BPEmb, and WL256 models on all classification tasks, and are competitive with the all-MiniLM-L6-v2 model, while being much faster. -The figure below shows the relationship between the number of sentences per second and the average classification score. The circle sizes correspond to the number of parameters in the models (larger = more parameters). -This plot shows that the Model2Vec models are much faster than the other models, while still being competitive in terms of classification performance with the [all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) model. +
+ + + +## Model List + +We provide a number of models that can be used out of the box. These models are available on the [HuggingFace hub](https://huggingface.co/collections/minishlab/model2vec-base-models-66fd9dd9b7c3b3c0f25ca90e) and can be loaded using the `from_pretrained` method. The models are listed below. + + +| Model | Language | Vocab | Sentence Transformer | Tokenizer Type | Params | Tokenlearn | +|-----------------------------------------------------------------------|-------------|------------------|-----------------------------------------------------------------|----------------|---------|------------| +| [potion-base-8M](https://huggingface.co/minishlab/potion-base-8M) | English | Output | [bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | Subword | 7.5M | True | +| [potion-base-4M](https://huggingface.co/minishlab/potion-base-4M) | English | Output | [bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | Subword | 3.7M | True | +| [potion-base-2M](https://huggingface.co/minishlab/potion-base-2M) | English | Output | [bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | Subword | 1.8M | True | +| [M2V_multilingual_output](https://huggingface.co/minishlab/M2V_multilingual_output) | Multilingual | Output | [LaBSE](https://huggingface.co/sentence-transformers/LaBSE) | Subword | 471M | False | + + +## Results -| ![Description](assets/images/speed_vs_accuracy_v3.png) | -|:--:| -|*Figure: The average accuracy over all classification datasets plotted against sentence per second. The circle size indicates model size.*| +We have performed extensive experiments to evaluate the performance of Model2Vec models. The results are documented in the [results](results/README.md) folder. The results are presented in the following sections: +- [MTEB Results](results/README.md#mteb-results) +- [Ablations](results/README.md#ablations) ## Related work If you are interested in fast small models, also consider looking at these techniques: * [BPEmb](https://bpemb.h-its.org/): GLoVE embeddings trained on BPE-encoded Wikipedias. Huge inspiration to this project, multilingual, very fast. If you don't find a sentence transformer in the language you need, check this out. * [fast-sentence-transformers](https://github.com/davidberenstein1957/fast-sentence-transformers): distillation using Model2Vec comes at a cost. If that cost is too steep for you, and you have access to a GPU, this package is for you. It automates the quantization and optimization of sentence transformers without loss of performance. -* [wordllama](https://github.com/dleemiller/WordLlama): Uses the _input_ embeddings of a LLama2 model and then performs contrastive learning on these embeddings. As we show above, we think this is a bit overfit on MTEB, as the model is trained on MTEB datasets, and only evaluated on MTEB. It provides an interesting point of comparison to Model2Vec, and, fun fact, was invented at the same time. +* [wordllama](https://github.com/dleemiller/WordLlama): Uses the _input_ embeddings of a LLama2 model and then performs contrastive learning on these embeddings. We think this is a bit overfit on MTEB, as the model is trained on MTEB datasets, and only evaluated on MTEB. Fun fact: this was invented at the same time as Model2Vec. If you find other related work, please let us know. diff --git a/assets/images/speed_vs_accuracy_v4.png b/assets/images/speed_vs_accuracy_v4.png new file mode 100644 index 0000000..e63461d Binary files /dev/null and b/assets/images/speed_vs_accuracy_v4.png differ diff --git a/assets/images/speed_vs_mteb_score.png b/assets/images/speed_vs_mteb_score.png new file mode 100644 index 0000000..f9d9c01 Binary files /dev/null and b/assets/images/speed_vs_mteb_score.png differ diff --git a/assets/images/speed_vs_mteb_score_v2.png b/assets/images/speed_vs_mteb_score_v2.png new file mode 100644 index 0000000..4c4d11b Binary files /dev/null and b/assets/images/speed_vs_mteb_score_v2.png differ diff --git a/model2vec/__init__.py b/model2vec/__init__.py index e3b3e53..7112ba0 100644 --- a/model2vec/__init__.py +++ b/model2vec/__init__.py @@ -1,4 +1,4 @@ -from model2vec.distill import distill from model2vec.model import StaticModel +from model2vec.version import __version__ -__all__ = ["distill", "StaticModel"] +__all__ = ["StaticModel", "__version__"] diff --git a/model2vec/distill/__init__.py b/model2vec/distill/__init__.py index 93abcf8..fbc59f1 100644 --- a/model2vec/distill/__init__.py +++ b/model2vec/distill/__init__.py @@ -1,3 +1,10 @@ +from model2vec.utils import get_package_extras, importable + +_REQUIRED_EXTRA = "distill" + +for extra_dependency in get_package_extras("model2vec", _REQUIRED_EXTRA): + importable(extra_dependency, _REQUIRED_EXTRA) + from model2vec.distill.distillation import distill, distill_from_model __all__ = ["distill", "distill_from_model"] diff --git a/model2vec/distill/__main__.py b/model2vec/distill/__main__.py deleted file mode 100644 index 3193ce6..0000000 --- a/model2vec/distill/__main__.py +++ /dev/null @@ -1,53 +0,0 @@ -import logging -from pathlib import Path -from typing import Annotated, Optional - -import typer - -from model2vec.distill.distillation import distill -from model2vec.utils import setup_logging - -logger = logging.getLogger(__name__) - -app = typer.Typer() - - -@app.command() -def main( - model_name: Annotated[str, typer.Option(help="The model name to initialize the embedder model with.")], - save_path: Annotated[str, typer.Option(help="The folder to save the model to.")], - vocabulary_path: Annotated[ - Optional[str], - typer.Option( - help="The path to the vocabulary file, which is a .txt file with one word per line. If this is not passed, we use the model's vocabulary." - ), - ] = None, - device: Annotated[str, typer.Option(help="The device to train the model on.")] = "cpu", - pca_dims: Annotated[ - int | None, typer.Option(help="The PCA dimensionality to use. If this is None, no PCA is applied.") - ] = 256, - apply_zipf: Annotated[bool, typer.Option(help="Whether to apply Zipf weighting.")] = True, - use_subword: Annotated[ - bool, typer.Option(help="Whether to use subword tokenization. If this is False, you must pass a vocabulary.") - ] = True, -) -> None: - """Creates output embeddings for a sentencetransformer.""" - if vocabulary_path is not None: - vocabulary = open(vocabulary_path).read().splitlines() - else: - vocabulary = None - - model = distill( - model_name=model_name, - vocabulary=vocabulary, - device=device, - pca_dims=pca_dims, - apply_zipf=apply_zipf, - use_subword=use_subword, - ) - model.save_pretrained(Path(save_path)) - - -if __name__ == "__main__": - setup_logging() - typer.run(main) diff --git a/model2vec/distill/distillation.py b/model2vec/distill/distillation.py index 8870f53..da32bba 100644 --- a/model2vec/distill/distillation.py +++ b/model2vec/distill/distillation.py @@ -1,5 +1,7 @@ +from __future__ import annotations + import logging -from typing import Literal +from typing import Literal, Union import numpy as np from huggingface_hub import model_info @@ -26,7 +28,7 @@ logger = logging.getLogger(__name__) -PCADimType = int | None | Literal["auto"] +PCADimType = Union[int, None, Literal["auto"]] def distill_from_model( @@ -214,9 +216,17 @@ def _post_process_embeddings(embeddings: np.ndarray, pca_dims: PCADimType, apply elif pca_dims <= embeddings.shape[1]: logger.info(f"Applying PCA with n_components {pca_dims}") + orig_dims = embeddings.shape[1] p = PCA(n_components=pca_dims, whiten=False) embeddings = p.fit_transform(embeddings) + if embeddings.shape[1] < orig_dims: + explained_variance_ratio = np.sum(p.explained_variance_ratio_) + explained_variance = np.sum(p.explained_variance_) + logger.info(f"Reduced dimensionality from {orig_dims} to {embeddings.shape[1]}.") + logger.info(f"Explained variance ratio: {explained_variance_ratio:.3f}.") + logger.info(f"Explained variance: {explained_variance:.3f}.") + if apply_zipf: logger.info("Applying Zipf weighting") embeddings *= np.log(1 + np.arange(embeddings.shape[0]))[:, None] diff --git a/model2vec/distill/inference.py b/model2vec/distill/inference.py index 400c0ac..a8dbfcc 100644 --- a/model2vec/distill/inference.py +++ b/model2vec/distill/inference.py @@ -1,8 +1,10 @@ # -*- coding: utf-8 -*- +from __future__ import annotations + import inspect import logging from pathlib import Path -from typing import Protocol +from typing import Protocol, Union import numpy as np import torch @@ -13,7 +15,7 @@ logger = logging.getLogger(__name__) -PathLike = str | Path +PathLike = Union[Path, str] _DEFAULT_BATCH_SIZE = 1024 @@ -113,7 +115,15 @@ def create_output_embeddings_from_model_name( :return: The tokens and output embeddings. """ model = model.to(device) - ids = torch.arange(tokenizer.vocab_size) + + # Quick check to see if the tokenizer is consistent. + vocab_length = len(tokenizer.get_vocab()) + if vocab_length != tokenizer.vocab_size: + logger.warning( + f"Reported vocab size {tokenizer.vocab_size} is inconsistent with the vocab size {vocab_length}." + ) + + ids = torch.arange(vocab_length) # Work-around to get the eos and bos token ids without having to go into tokenizer internals. dummy_encoding = tokenizer.encode("A") @@ -122,7 +132,8 @@ def create_output_embeddings_from_model_name( bos = torch.full([len(ids)], fill_value=bos_token_id) eos = torch.full([len(ids)], fill_value=eos_token_id) - stacked = torch.stack([bos, ids, eos], dim=1) + # NOTE: reversing the bos and eos tokens works better on our benchmarks. + stacked = torch.stack([eos, ids, bos], dim=1) intermediate_weights: list[np.ndarray] = [] for batch_idx in tqdm(range(0, len(stacked), _DEFAULT_BATCH_SIZE)): diff --git a/model2vec/distill/tokenizer.py b/model2vec/distill/tokenizer.py index 8022b76..e6c3087 100644 --- a/model2vec/distill/tokenizer.py +++ b/model2vec/distill/tokenizer.py @@ -2,6 +2,7 @@ import json import logging +from typing import Any from tokenizers import Tokenizer @@ -36,11 +37,11 @@ def remove_tokens(tokenizer: Tokenizer, tokens_to_remove: list[str]) -> Tokenize logger.info("No tokens to remove.") return Tokenizer.from_str(tokenizer.to_str()) - tokenizer_data = json.loads(tokenizer.to_str()) + tokenizer_data: dict[str, Any] = json.loads(tokenizer.to_str()) # Find all added tokens - added_tokens = tokenizer_data["added_tokens"] - added_tokens_str = {token["content"] for token in added_tokens} + added_tokens: list[dict[str, Any]] = tokenizer_data.get("added_tokens", []) + added_tokens_str: set[str] = {token["content"] for token in added_tokens} # Remove all added tokens from the list of tokens to remove. # Things will go bad if we keep them. @@ -49,34 +50,36 @@ def remove_tokens(tokenizer: Tokenizer, tokens_to_remove: list[str]) -> Tokenize # Load the vocabulary. model_type = tokenizer_data["model"]["type"] - match model_type: - case "WordPiece": - # Vocab is a dictionary. - vocab: dict[str, int] = tokenizer_data["model"]["vocab"] - n_tokens = len(vocab) - - # Remove the tokens. - for token in tokens_to_remove: - if vocab.pop(token, None) is None: - logger.warning(f"Token {token} was not in the vocabulary.") - - n_removed = n_tokens - len(vocab) - logger.info(f"Removed {n_removed} tokens from the vocabulary.") - - # Reindex the vocabulary so that it is contiguous. - reindexed = {token: idx for idx, (token, _) in enumerate(sorted(vocab.items(), key=lambda x: x[1]))} - tokenizer_data["model"]["vocab"] = reindexed - case "Unigram": - raise ValueError("Removing tokens from a unigram tokenizer is not supported.") - case "BPE": - raise ValueError("Removing tokens from a bpe tokenizer is not supported.") - case _: - raise ValueError(f"Unknown model type {model_type}") + if model_type == "WordPiece": + # Vocab is a dictionary. + vocab: dict[str, int] = tokenizer_data["model"]["vocab"] + n_tokens = len(vocab) + + # Remove the tokens. + for token in tokens_to_remove: + if vocab.pop(token, None) is None: + logger.warning(f"Token {token} was not in the vocabulary.") + + n_removed = n_tokens - len(vocab) + logger.info(f"Removed {n_removed} tokens from the vocabulary.") + + # Reindex the vocabulary so that it is contiguous. + reindexed = {token: idx for idx, (token, _) in enumerate(sorted(vocab.items(), key=lambda x: x[1]))} + tokenizer_data["model"]["vocab"] = reindexed + + elif model_type == "Unigram": + raise ValueError("Removing tokens from a unigram tokenizer is not supported.") + + elif model_type == "BPE": + raise ValueError("Removing tokens from a BPE tokenizer is not supported.") + + else: + raise ValueError(f"Unknown model type {model_type}") # Reindex the special tokens (i.e., CLS and SEP for BertTokenizers.) - special_tokens_post_processor: dict[str, dict] = tokenizer_data["post_processor"]["special_tokens"] - for token, token_data in special_tokens_post_processor.items(): - token_data["ids"] = [reindexed[token] for token in token_data["tokens"]] + added_tokens = tokenizer_data.get("added_tokens", []) + for token_data in added_tokens: + token_data["id"] = reindexed[token_data["content"]] # Reinitialize the tokenizer from the json. tokenizer = Tokenizer.from_str(json.dumps(tokenizer_data)) @@ -97,18 +100,20 @@ def add_tokens(tokenizer: Tokenizer, tokens_to_add: list[str]) -> Tokenizer: model = data["model"]["type"] - match model: - case "WordPiece": - wordpiece_vocab: dict[str, int] = data["model"]["vocab"] - for token in tokens_to_add: - if token not in wordpiece_vocab: - wordpiece_vocab[token] = len(wordpiece_vocab) - case "Unigram": - raise ValueError("Adding tokens to a unigram tokenizer is not supported.") - case "BPE": - raise ValueError("Adding tokens to a bpe tokenizer is not supported.") - case _: - raise ValueError(f"Unknown model type {model}") + if model == "WordPiece": + wordpiece_vocab: dict[str, int] = data["model"]["vocab"] + for token in tokens_to_add: + if token not in wordpiece_vocab: + wordpiece_vocab[token] = len(wordpiece_vocab) + + elif model == "Unigram": + raise ValueError("Adding tokens to a unigram tokenizer is not supported.") + + elif model == "BPE": + raise ValueError("Adding tokens to a BPE tokenizer is not supported.") + + else: + raise ValueError(f"Unknown model type {model}") tokenizer = Tokenizer.from_str(json.dumps(data)) diff --git a/model2vec/distill/utils.py b/model2vec/distill/utils.py index cf16977..3e81004 100644 --- a/model2vec/distill/utils.py +++ b/model2vec/distill/utils.py @@ -1,3 +1,5 @@ +from __future__ import annotations + from logging import getLogger import torch diff --git a/model2vec/hf_utils.py b/model2vec/hf_utils.py new file mode 100644 index 0000000..0fa3514 --- /dev/null +++ b/model2vec/hf_utils.py @@ -0,0 +1,198 @@ +from __future__ import annotations + +import json +import logging +from pathlib import Path +from typing import Any, cast + +import huggingface_hub +import huggingface_hub.errors +import numpy as np +import safetensors +from huggingface_hub import ModelCard, ModelCardData +from safetensors.numpy import save_file +from tokenizers import Tokenizer + +from model2vec.utils import SafeOpenProtocol + +logger = logging.getLogger(__name__) + + +def save_pretrained( + folder_path: Path, + embeddings: np.ndarray, + tokenizer: Tokenizer, + config: dict[str, Any], + create_model_card: bool = True, + **kwargs: Any, +) -> None: + """ + Save a model to a folder. + + :param folder_path: The path to the folder. + :param embeddings: The embeddings. + :param tokenizer: The tokenizer. + :param config: A metadata config. + :param create_model_card: Whether to create a model card. + :param **kwargs: Any additional arguments. + """ + folder_path.mkdir(exist_ok=True, parents=True) + save_file({"embeddings": embeddings}, folder_path / "model.safetensors") + tokenizer.save(str(folder_path / "tokenizer.json")) + json.dump(config, open(folder_path / "config.json", "w")) + + logger.info(f"Saved model to {folder_path}") + + # Optionally create the model card + if create_model_card: + _create_model_card(folder_path, **kwargs) + + +def _create_model_card( + folder_path: Path, + base_model_name: str = "unknown", + license: str = "mit", + language: list[str] | None = None, + model_name: str | None = None, + **kwargs: Any, +) -> None: + """ + Create a model card and store it in the specified path. + + :param folder_path: The path where the model card will be stored. + :param base_model_name: The name of the base model. + :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 **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" + + model_card_data = ModelCardData( + model_name=model_name, + base_model=base_model_name, + license=license, + language=language, + tags=["embeddings", "static-embeddings"], + library_name="model2vec", + **kwargs, + ) + model_card = ModelCard.from_template(model_card_data, template_path=template_path) + model_card.save(folder_path / "README.md") + + +def load_pretrained( + folder_or_repo_path: str | Path, token: str | None = None +) -> tuple[np.ndarray, Tokenizer, dict[str, Any], dict[str, Any]]: + """ + Loads a pretrained model from a folder. + + :param folder_or_repo_path: The folder or repo path to load from. + - If this is a local path, we will load from the local path. + - If the local path is not found, we will attempt to load from the huggingface hub. + :param token: The huggingface token to use. + :raises: FileNotFoundError if the folder exists, but the file does not exist locally. + :return: The embeddings, tokenizer, config, and metadata. + + """ + folder_or_repo_path = Path(folder_or_repo_path) + if folder_or_repo_path.exists(): + embeddings_path = folder_or_repo_path / "model.safetensors" + if not embeddings_path.exists(): + old_embeddings_path = folder_or_repo_path / "embeddings.safetensors" + if old_embeddings_path.exists(): + logger.warning("Old embeddings file found. Please rename to `model.safetensors` and re-save.") + embeddings_path = old_embeddings_path + else: + raise FileNotFoundError(f"Embeddings file does not exist in {folder_or_repo_path}") + + config_path = folder_or_repo_path / "config.json" + if not config_path.exists(): + raise FileNotFoundError(f"Config file does not exist in {folder_or_repo_path}") + + tokenizer_path = folder_or_repo_path / "tokenizer.json" + if not tokenizer_path.exists(): + raise FileNotFoundError(f"Tokenizer file does not exist in {folder_or_repo_path}") + + # README is optional, so this is a bit finicky. + readme_path = folder_or_repo_path / "README.md" + metadata = _get_metadata_from_readme(readme_path) + + else: + logger.info("Folder does not exist locally, attempting to use huggingface hub.") + try: + embeddings_path = huggingface_hub.hf_hub_download( + folder_or_repo_path.as_posix(), "model.safetensors", token=token + ) + except huggingface_hub.utils.EntryNotFoundError as e: + try: + embeddings_path = huggingface_hub.hf_hub_download( + folder_or_repo_path.as_posix(), "embeddings.safetensors", token=token + ) + except huggingface_hub.utils.EntryNotFoundError: + # Raise original exception. + raise e + + try: + readme_path = huggingface_hub.hf_hub_download(folder_or_repo_path.as_posix(), "README.md", token=token) + metadata = _get_metadata_from_readme(Path(readme_path)) + except huggingface_hub.utils.EntryNotFoundError: + logger.info("No README found in the model folder. No model card loaded.") + metadata = {} + + config_path = huggingface_hub.hf_hub_download(folder_or_repo_path.as_posix(), "config.json", token=token) + tokenizer_path = huggingface_hub.hf_hub_download(folder_or_repo_path.as_posix(), "tokenizer.json", token=token) + + opened_tensor_file = cast(SafeOpenProtocol, safetensors.safe_open(embeddings_path, framework="numpy")) + embeddings = opened_tensor_file.get_tensor("embeddings") + + tokenizer: Tokenizer = Tokenizer.from_file(str(tokenizer_path)) + config = json.load(open(config_path)) + + if len(tokenizer.get_vocab()) != len(embeddings): + logger.warning( + f"Number of tokens does not match number of embeddings: `{len(tokenizer.get_vocab())}` vs `{len(embeddings)}`" + ) + + return embeddings, tokenizer, config, metadata + + +def _get_metadata_from_readme(readme_path: Path) -> dict[str, Any]: + """Get metadata from a README file.""" + if not readme_path.exists(): + logger.info(f"README file not found in {readme_path}. No model card loaded.") + return {} + model_card = ModelCard.load(readme_path) + data: dict[str, Any] = model_card.data.to_dict() + if not data: + logger.info("File README.md exists, but was empty. No model card loaded.") + return data + + +def push_folder_to_hub(folder_path: Path, repo_id: str, private: bool, token: str | None) -> None: + """ + Push a model folder to the huggingface hub, including model card. + + :param folder_path: The path to the folder. + :param repo_id: The repo name. + :param private: Whether the repo is private. + :param token: The huggingface token. + """ + if not huggingface_hub.repo_exists(repo_id=repo_id, token=token): + huggingface_hub.create_repo(repo_id, token=token, private=private) + + # Push model card and all model files to the Hugging Face hub + huggingface_hub.upload_folder(repo_id=repo_id, folder_path=folder_path, token=token) + + # Check if the model card exists, and push it if available + model_card_path = folder_path / "README.md" + if model_card_path.exists(): + card = ModelCard.load(model_card_path) + card.push_to_hub(repo_id=repo_id, token=token) + logger.info(f"Pushed model card to {repo_id}") + else: + logger.warning(f"Model card README.md not found in {folder_path}. Skipping model card upload.") + + logger.info(f"Pushed model to {repo_id}") diff --git a/model2vec/model.py b/model2vec/model.py index 017207c..cb2013e 100644 --- a/model2vec/model.py +++ b/model2vec/model.py @@ -1,26 +1,24 @@ from __future__ import annotations +import math from logging import getLogger from pathlib import Path from tempfile import TemporaryDirectory -from typing import Any, Iterator +from typing import Any, Iterator, Union import numpy as np -import torch from tokenizers import Encoding, Tokenizer -from torch import nn -from torch.nn import Embedding, EmbeddingBag from tqdm import tqdm -from model2vec.utils import load_pretrained, push_folder_to_hub, save_pretrained +from model2vec.utils import load_local_model -PathLike = Path | str +PathLike = Union[Path, str] logger = getLogger(__name__) -class StaticModel(nn.Module): +class StaticModel: def __init__( self, vectors: np.ndarray, @@ -44,13 +42,8 @@ def __init__( super().__init__() tokens, _ = zip(*sorted(tokenizer.get_vocab().items(), key=lambda x: x[1])) self.tokens = tokens - tensor = torch.from_numpy(vectors) - # NOTE: We make two embedding modules, but since they share the same memory, - # there's no overhead. - # Gradients are also integrated into both. - self.embedding_bag = EmbeddingBag.from_pretrained(tensor, mode="mean") - self.embedding = Embedding.from_pretrained(tensor) + self.embedding = vectors if len(tokens) != vectors.shape[0]: raise ValueError(f"Number of tokens ({len(tokens)}) does not match number of vectors ({vectors.shape[0]})") @@ -60,12 +53,16 @@ def __init__( if hasattr(self.tokenizer.model, "unk_token") and self.tokenizer.model.unk_token is not None: self.unk_token_id = tokenizer.get_vocab()[self.tokenizer.model.unk_token] else: - self.unk_token_id = None + self.unk_token_id = None # pragma: no cover # Doesn't actually happen, but can happen. self.median_token_length = int(np.median([len(token) for token in self.tokens])) self.config = config or {} self.base_model_name = base_model_name self.language = language + if hasattr(self.tokenizer, "encode_batch_fast"): + self._can_encode_fast = True + else: + self._can_encode_fast = False if normalize is not None: self.normalize = normalize @@ -75,12 +72,7 @@ def __init__( @property def dim(self) -> int: """Get the dimension of the model.""" - return self.embedding.weight.shape[1] - - @property - def device(self) -> torch.device: - """Get the device of the model.""" - return next(self.parameters()).device + return self.embedding.shape[1] @property def normalize(self) -> bool: @@ -109,9 +101,11 @@ def save_pretrained(self, path: PathLike, model_name: str | None = None) -> None :param path: The path to save to. :param model_name: The model name to use in the Model Card. """ + from model2vec.hf_utils import save_pretrained + save_pretrained( folder_path=Path(path), - embeddings=self.embedding.weight.numpy(), + embeddings=self.embedding, tokenizer=self.tokenizer, config=self.config, base_model_name=self.base_model_name, @@ -119,31 +113,7 @@ def save_pretrained(self, path: PathLike, model_name: str | None = None) -> None model_name=model_name, ) - def forward(self, X: tuple[torch.Tensor, torch.Tensor]) -> torch.Tensor: - """ - Helper method to facilitate training. - - :param X: a tuple of ids and offsets. - :return: A padded output tensor. - """ - ids, offsets = X - return self.embedding_bag(ids, offsets) - - def forward_mean(self, ids: torch.Tensor, offsets: torch.Tensor) -> torch.Tensor: - """ - Forward pass of the model. - - :param ids: The input tensor. - :param offsets: The offsets tensor. - :return: The output tensor. - """ - means = self.embedding_bag(ids, offsets) - - if self.normalize: - return torch.nn.functional.normalize(means) - return means - - def tokenize(self, sentences: list[str], max_length: int | None = None) -> tuple[torch.Tensor, torch.Tensor]: + def tokenize(self, sentences: list[str], max_length: int | None = None) -> list[int]: """ Tokenize a sentence. @@ -155,7 +125,11 @@ def tokenize(self, sentences: list[str], max_length: int | None = None) -> tuple m = max_length * self.median_token_length sentences = [sentence[:m] for sentence in sentences] - encodings: list[Encoding] = self.tokenizer.encode_batch(sentences, add_special_tokens=False) + if self._can_encode_fast: + encodings: list[Encoding] = self.tokenizer.encode_batch_fast(sentences, add_special_tokens=False) + else: + encodings = self.tokenizer.encode_batch(sentences, add_special_tokens=False) + encodings_ids = [encoding.ids for encoding in encodings] if self.unk_token_id is not None: @@ -166,10 +140,7 @@ def tokenize(self, sentences: list[str], max_length: int | None = None) -> tuple if max_length is not None: encodings_ids = [token_ids[:max_length] for token_ids in encodings_ids] - offsets = torch.from_numpy(np.cumsum([0] + [len(token_ids) for token_ids in encodings_ids[:-1]])) - ids = torch.tensor([token_id for token_ids in encodings_ids for token_id in token_ids], dtype=torch.long) - - return ids, offsets + return encodings_ids @classmethod def from_pretrained( @@ -186,6 +157,8 @@ def from_pretrained( :param token: The huggingface token to use. :return: A StaticModel """ + from model2vec.hf_utils import load_pretrained + embeddings, tokenizer, config, metadata = load_pretrained(path, token=token) return cls( @@ -193,7 +166,11 @@ def from_pretrained( ) def encode_as_sequence( - self, sentences: list[str] | str, max_length: int | None = None + self, + sentences: list[str] | str, + max_length: int | None = None, + batch_size: int = 1024, + show_progress_bar: bool = False, ) -> list[np.ndarray] | np.ndarray: """ Encode a list of sentences as a list of numpy arrays of tokens. @@ -207,38 +184,44 @@ def encode_as_sequence( :param sentences: The list of sentences to encode. :param max_length: The maximum length of the sentences. Any tokens beyond this length will be truncated. If this is None, no truncation is done. + :param batch_size: The batch size to use. + :param show_progress_bar: Whether to show the progress bar. :return: The encoded sentences with an embedding per token. """ was_single = False if isinstance(sentences, str): - was_single = True sentences = [sentences] + was_single = True - ids, offsets = self.tokenize(sentences=sentences, max_length=max_length) - ids = ids.to(self.device) - offsets = offsets.to(self.device) - - out = [tensor.cpu().numpy() for tensor in self._sub_encode_as_sequence(ids, offsets)] + out_array: list[np.ndarray] = [] + for batch in tqdm( + self._batch(sentences, batch_size), + total=math.ceil(len(sentences) / batch_size), + disable=not show_progress_bar, + ): + out_array.extend(self._encode_batch_as_sequence(batch, max_length)) if was_single: - return out[0] + return out_array[0] - return out + return out_array - def _sub_encode_as_sequence(self, ids: torch.Tensor, offsets: torch.Tensor) -> list[torch.Tensor]: - """Helper function to reduce deduplication.""" - out = [] - for x in range(len(offsets) - 1): - start, end = offsets[x], offsets[x + 1] - out.append(self.embedding(ids[start:end])) - out.append(self.embedding(ids[offsets[-1] :])) + def _encode_batch_as_sequence(self, sentences: list[str], max_length: int | None) -> list[np.ndarray]: + """Encode a batch of sentences as a sequence.""" + ids = self.tokenize(sentences=sentences, max_length=max_length) + out: list[np.ndarray] = [] + for id_list in ids: + if id_list: + out.append(self.embedding[id_list]) + else: + out.append(np.zeros((0, self.dim))) return out def encode( self, sentences: list[str] | str, - show_progressbar: bool = False, + show_progress_bar: bool = False, max_length: int | None = 512, batch_size: int = 1024, **kwargs: Any, @@ -250,7 +233,7 @@ def encode( For ease of use, we don't batch sentences together. :param sentences: The list of sentences to encode. You can also pass a single sentence. - :param show_progressbar: Whether to show the progress bar. + :param show_progress_bar: Whether to show the progress bar. :param max_length: The maximum length of the sentences. Any tokens beyond this length will be truncated. If this is None, no truncation is done. :param batch_size: The batch size to use. @@ -264,7 +247,9 @@ def encode( out_arrays: list[np.ndarray] = [] for batch in tqdm( - self._batch(sentences, batch_size), total=(len(sentences) // batch_size) + 1, disable=not show_progressbar + self._batch(sentences, batch_size), + total=math.ceil(len(sentences) / batch_size), + disable=not show_progress_bar, ): out_arrays.append(self._encode_batch(batch, max_length)) @@ -275,13 +260,22 @@ def encode( return out_array - @torch.no_grad() def _encode_batch(self, sentences: list[str], max_length: int | None) -> np.ndarray: """Encode a batch of sentences.""" - ids, offsets = self.tokenize(sentences, max_length) - ids = ids.to(self.device) - offsets = offsets.to(self.device) - return self.forward_mean(ids, offsets).cpu().numpy() + ids = self.tokenize(sentences=sentences, max_length=max_length) + out: list[np.ndarray] = [] + for id_list in ids: + if id_list: + out.append(self.embedding[id_list].mean(0)) + else: + out.append(np.zeros(self.dim)) + + out_array = np.stack(out) + if self.normalize: + norm = np.linalg.norm(out_array, axis=1, keepdims=True) + 1e-32 + out_array = out_array / norm + + return out_array @staticmethod def _batch(sentences: list[str], batch_size: int) -> Iterator[list[str]]: @@ -299,6 +293,33 @@ def push_to_hub(self, repo_id: str, private: bool = False, token: str | None = N If the repo already exists, this doesn't change the visibility. :param token: The huggingface token to use. """ + from model2vec.hf_utils import push_folder_to_hub + with TemporaryDirectory() as temp_dir: self.save_pretrained(temp_dir, model_name=repo_id) push_folder_to_hub(Path(temp_dir), repo_id, private, token) + + @classmethod + def load_local(cls: type[StaticModel], path: PathLike) -> StaticModel: + """ + Loads a model from a local path. + + You should only use this code path if you are concerned with start-up time. + Loading via the `from_pretrained` method is safer, and auto-downloads, but + also means we import a whole bunch of huggingface code that we don't need. + + Additionally, huggingface will check the most recent version of the model, + which can be slow. + + :param path: The path to load the model from. The path is a directory saved by the + `save_pretrained` method. + :return: A StaticModel + :raises: ValueError if the path is not a directory. + """ + path = Path(path) + if not path.is_dir(): + raise ValueError(f"Path {path} is not a directory.") + + embeddings, tokenizer, config = load_local_model(path) + + return StaticModel(embeddings, tokenizer, config) diff --git a/model2vec/utils.py b/model2vec/utils.py index 7ec8ee7..b77c45a 100644 --- a/model2vec/utils.py +++ b/model2vec/utils.py @@ -1,17 +1,13 @@ # -*- coding: utf-8 -*- import json import logging +from importlib import import_module +from importlib.metadata import metadata from pathlib import Path -from typing import Any, Protocol, cast +from typing import Iterator, Protocol, cast -import click -import huggingface_hub -import huggingface_hub.errors import numpy as np import safetensors -from huggingface_hub import ModelCard, ModelCardData -from rich.logging import RichHandler -from safetensors.numpy import save_file from tokenizers import Tokenizer logger = logging.getLogger(__name__) @@ -22,194 +18,71 @@ class SafeOpenProtocol(Protocol): def get_tensor(self, key: str) -> np.ndarray: """Get a tensor.""" - ... + ... # pragma: no cover + + +_MODULE_MAP = (("scikit-learn", "sklearn"),) + + +def get_package_extras(package: str, extra: str) -> Iterator[str]: + """Get the extras of the package.""" + try: + message = metadata(package) + except Exception as e: + raise ImportError(f"Could not retrieve metadata for package '{package}': {e}") + + all_packages = message.get_all("Requires-Dist") or [] + for package in all_packages: + name, *rest = package.split(";", maxsplit=1) + if rest: + # Extract and clean the extra requirement + found_extra = rest[0].split("==")[-1].strip(" \"'") + if found_extra == extra: + yield name.strip() + + +def importable(module: str, extra: str) -> None: + """Check if a module is importable.""" + module = dict(_MODULE_MAP).get(module, module) + try: + import_module(module) + except ImportError: + raise ImportError( + f"`{module}`, is required. Please reinstall model2vec with the `distill` extra. `pip install model2vec[{extra}]`" + ) def setup_logging() -> None: """Simple logging setup.""" + from rich.logging import RichHandler + logging.basicConfig( level="INFO", format="%(name)s - %(message)s", datefmt="%Y-%m-%d %H:%M:%S", - handlers=[RichHandler(rich_tracebacks=True, tracebacks_suppress=[click])], + handlers=[RichHandler(rich_tracebacks=True)], ) -def save_pretrained( - folder_path: Path, - embeddings: np.ndarray, - tokenizer: Tokenizer, - config: dict[str, Any], - create_model_card: bool = True, - **kwargs: Any, -) -> None: - """ - Save a model to a folder. - - :param folder_path: The path to the folder. - :param embeddings: The embeddings. - :param tokenizer: The tokenizer. - :param config: A metadata config. - :param create_model_card: Whether to create a model card. - :param **kwargs: Any additional arguments. - """ - folder_path.mkdir(exist_ok=True, parents=True) - save_file({"embeddings": embeddings}, folder_path / "model.safetensors") - tokenizer.save(str(folder_path / "tokenizer.json")) - json.dump(config, open(folder_path / "config.json", "w")) - - logger.info(f"Saved model to {folder_path}") - - # Optionally create the model card - if create_model_card: - _create_model_card(folder_path, **kwargs) - - -def _create_model_card( - folder_path: Path, - base_model_name: str = "unknown", - license: str = "mit", - language: list[str] | None = None, - model_name: str | None = None, - **kwargs: Any, -) -> None: - """ - Create a model card and store it in the specified path. - - :param folder_path: The path where the model card will be stored. - :param base_model_name: The name of the base model. - :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 **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" - - model_card_data = ModelCardData( - model_name=model_name, - base_model=base_model_name, - license=license, - language=language, - tags=["embeddings", "static-embeddings"], - library_name="model2vec", - **kwargs, - ) - model_card = ModelCard.from_template(model_card_data, template_path=template_path) - model_card.save(folder_path / "README.md") - - -def load_pretrained( - folder_or_repo_path: str | Path, token: str | None = None -) -> tuple[np.ndarray, Tokenizer, dict[str, Any], dict[str, Any]]: - """ - Loads a pretrained model from a folder. - - :param folder_or_repo_path: The folder or repo path to load from. - - If this is a local path, we will load from the local path. - - If the local path is not found, we will attempt to load from the huggingface hub. - :param token: The huggingface token to use. - :raises: FileNotFoundError if the folder exists, but the file does not exist locally. - :return: The embeddings, tokenizer, config, and metadata. - - """ - folder_or_repo_path = Path(folder_or_repo_path) - if folder_or_repo_path.exists(): - embeddings_path = folder_or_repo_path / "model.safetensors" - if not embeddings_path.exists(): - old_embeddings_path = folder_or_repo_path / "embeddings.safetensors" - if old_embeddings_path.exists(): - logger.warning("Old embeddings file found. Please rename to `model.safetensors` and re-save.") - embeddings_path = old_embeddings_path - else: - raise FileNotFoundError(f"Embeddings file does not exist in {folder_or_repo_path}") - - config_path = folder_or_repo_path / "config.json" - if not config_path.exists(): - raise FileNotFoundError(f"Config file does not exist in {folder_or_repo_path}") - - tokenizer_path = folder_or_repo_path / "tokenizer.json" - if not tokenizer_path.exists(): - raise FileNotFoundError(f"Tokenizer file does not exist in {folder_or_repo_path}") - - # README is optional, so this is a bit finicky. - readme_path = folder_or_repo_path / "README.md" - metadata = _get_metadata_from_readme(readme_path) - - else: - logger.info("Folder does not exist locally, attempting to use huggingface hub.") - try: - embeddings_path = huggingface_hub.hf_hub_download( - folder_or_repo_path.as_posix(), "model.safetensors", token=token - ) - except huggingface_hub.utils.EntryNotFoundError as e: - try: - embeddings_path = huggingface_hub.hf_hub_download( - folder_or_repo_path.as_posix(), "embeddings.safetensors", token=token - ) - except huggingface_hub.utils.EntryNotFoundError: - # Raise original exception. - raise e - - try: - readme_path = huggingface_hub.hf_hub_download(folder_or_repo_path.as_posix(), "README.md", token=token) - metadata = _get_metadata_from_readme(Path(readme_path)) - except huggingface_hub.utils.EntryNotFoundError: - logger.info("No README found in the model folder. No model card loaded.") - metadata = {} - - config_path = huggingface_hub.hf_hub_download(folder_or_repo_path.as_posix(), "config.json", token=token) - tokenizer_path = huggingface_hub.hf_hub_download(folder_or_repo_path.as_posix(), "tokenizer.json", token=token) +def load_local_model(folder: Path) -> tuple[np.ndarray, Tokenizer, dict[str, str]]: + """Load a local model.""" + embeddings_path = folder / "model.safetensors" + tokenizer_path = folder / "tokenizer.json" + config_path = folder / "config.json" opened_tensor_file = cast(SafeOpenProtocol, safetensors.safe_open(embeddings_path, framework="numpy")) embeddings = opened_tensor_file.get_tensor("embeddings") + if config_path.exists(): + config = json.load(open(config_path)) + else: + config = {} + tokenizer: Tokenizer = Tokenizer.from_file(str(tokenizer_path)) - config = json.load(open(config_path)) if len(tokenizer.get_vocab()) != len(embeddings): logger.warning( f"Number of tokens does not match number of embeddings: `{len(tokenizer.get_vocab())}` vs `{len(embeddings)}`" ) - return embeddings, tokenizer, config, metadata - - -def _get_metadata_from_readme(readme_path: Path) -> dict[str, Any]: - """Get metadata from a README file.""" - if not readme_path.exists(): - logger.info(f"README file not found in {readme_path}. No model card loaded.") - return {} - model_card = ModelCard.load(readme_path) - data: dict[str, Any] = model_card.data.to_dict() - if not data: - logger.info("File README.md exists, but was empty. No model card loaded.") - return data - - -def push_folder_to_hub(folder_path: Path, repo_id: str, private: bool, token: str | None) -> None: - """ - Push a model folder to the huggingface hub, including model card. - - :param folder_path: The path to the folder. - :param repo_id: The repo name. - :param private: Whether the repo is private. - :param token: The huggingface token. - """ - if not huggingface_hub.repo_exists(repo_id=repo_id, token=token): - huggingface_hub.create_repo(repo_id, token=token, private=private) - - # Push model card and all model files to the Hugging Face hub - huggingface_hub.upload_folder(repo_id=repo_id, folder_path=folder_path, token=token) - - # Check if the model card exists, and push it if available - model_card_path = folder_path / "README.md" - if model_card_path.exists(): - card = ModelCard.load(model_card_path) - card.push_to_hub(repo_id=repo_id, token=token) - logger.info(f"Pushed model card to {repo_id}") - else: - logger.warning(f"Model card README.md not found in {folder_path}. Skipping model card upload.") - - logger.info(f"Pushed model to {repo_id}") + return embeddings, tokenizer, config diff --git a/model2vec/version.py b/model2vec/version.py new file mode 100644 index 0000000..0c3f13b --- /dev/null +++ b/model2vec/version.py @@ -0,0 +1,2 @@ +__version_triple__ = (0, 3, 2) +__version__ = ".".join(map(str, __version_triple__)) diff --git a/pyproject.toml b/pyproject.toml index ae7af19..625a5fd 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,11 +1,11 @@ [project] name = "model2vec" -description = "Distill a Small Fast Model from any Sentence Transformer" +description = "Distill a Small Static Model from any Sentence Transformer" readme = { file = "README.md", content-type = "text/markdown" } license = { file = "LICENSE" } -version = "0.2.4" -requires-python = ">=3.10" +requires-python = ">=3.9" authors = [{ name = "Stéphan Tulkens", email = "stephantul@gmail.com"}, {name = "Thomas van Dongen", email = "thomas123@live.nl"}] +dynamic = ["version"] classifiers = [ "Development Status :: 4 - Beta", @@ -15,6 +15,7 @@ classifiers = [ "Topic :: Software Development :: Libraries", "License :: OSI Approved :: MIT License", "Programming Language :: Python :: 3 :: Only", + "Programming Language :: Python :: 3.9", "Programming Language :: Python :: 3.10", "Programming Language :: Python :: 3.11", "Programming Language :: Python :: 3.12", @@ -22,15 +23,11 @@ classifiers = [ ] dependencies = [ - "click", "numpy", "rich", "tqdm", - "typer", - "transformers", - "torch", - "tokenizers", - "scikit-learn", + "tokenizers>=0.20", + "safetensors", "setuptools", ] @@ -55,14 +52,15 @@ dev = [ "pytest-coverage", "ruff", ] +distill = ["torch", "transformers", "scikit-learn"] +onnx = ["onnx", "torch"] [project.urls] "Homepage" = "https://github.com/MinishLab" "Bug Reports" = "https://github.com/MinishLab/model2vec/issues" "Source" = "https://github.com/MinishLab/model2vec" - [tool.ruff] exclude = [".venv/"] line-length = 120 @@ -108,3 +106,6 @@ ignore_missing_imports = true [tool.setuptools_scm] # can be empty if no extra settings are needed, presence enables setuptools_scm + +[tool.setuptools.dynamic] +version = {attr = "model2vec.version.__version__"} diff --git a/results/README.md b/results/README.md new file mode 100644 index 0000000..aa6d89c --- /dev/null +++ b/results/README.md @@ -0,0 +1,66 @@ +# Results + +This page contains the experiments results of the Model2Vec project. The results are presented in the following sections: +- [MTEB Results](#mteb-results) +- [Ablations](#ablations) + +## MTEB Results + +Model2Vec is evaluated on MTEB, as well as two additional tasks: [PEARL](https://github.com/tigerchen52/PEARL) (a phrase representation task) and WordSim (a collection of _word_ similarity tasks). The results are shown in the table below. + + +| Model | Avg (All) | Avg (MTEB) | Class | Clust | PairClass | Rank | Ret | STS | Sum | Pearl | WordSim | +|:-----------------------|------------:|-------------:|--------:|--------:|------------:|-------:|-------:|-------:|-------:|--------:|----------:| +| all-MiniLM-L6-v2 | 56.08 | 56.09 | 62.62 | 41.94 | 82.37 | 58.04 | 41.95 | 78.90 | 30.81 | 60.83 | 49.91 | +| potion-base-8M | 50.54 | 50.03 | 64.44 | 32.93 | 76.62 | 49.73 | 31.71 | 73.24 | 29.28 | 53.54 | 50.75 | +| M2V_base_glove_subword | 49.06 | 46.69 | 61.27 | 30.03 | 74.71 | 49.15 | 27.16 | 69.09 | 30.08 | 56.82 | 57.99 | +| potion-base-4M | 48.87 | 48.23 | 62.19 | 31.47 | 75.37 | 48.75 | 29.11 | 72.19 | 28.89 | 52.55 | 49.21 | +| M2V_base_glove | 48.58 | 47.6 | 61.35 | 30.52 | 75.34 | 48.5 | 29.26 | 70.31 | 31.5 | 50.28 | 54.29 | +| M2V_base_output | 46.79 | 45.34 | 61.25 | 25.58 | 74.9 | 47.63 | 26.14 | 68.58 | 29.2 | 54.02 | 49.18 | +| potion-base-2M | 45.52 | 44.77 | 58.45 | 27.5 | 73.72 | 46.82 | 24.13 | 70.14 | 31.51 | 50.82 | 44.72 | +| GloVe_300d | 42.84 | 42.36 | 57.31 | 27.66 | 72.48 | 43.3 | 22.78 | 61.9 | 28.81 | 45.65 | 43.05 | +| BPEmb_50k_300d | 39.34 | 37.78 | 55.76 | 23.35 | 57.86 | 43.21 | 17.5 | 55.1 | 29.74 | 47.56 | 41.28 | + + +
+ Task Abbreviations + +For readability, the MTEB task names are abbreviated as follows: +- Class: Classification +- Clust: Clustering +- PairClass: PairClassification +- Rank: Reranking +- Ret: Retrieval +- STS: Semantic Textual Similarity +- Sum: Summarization +
+ +The figure below shows the relationship between the number of sentences per second and the average MTEB score. The circle sizes correspond to the number of parameters in the models (larger = more parameters). +This plot shows that the Model2Vec models are much faster than the other models, while still being competitive in terms of performance with the [all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) model. + +| ![Description](../assets/images/speed_vs_mteb_score_v2.png) | +|:--:| +|*Figure: The average MTEB score plotted against sentences per second. The circle size indicates model size.*| + + +## Ablations + +To better understand the factors contributing to the performance of Model2Vec, we conducted a comprehensive set of ablation studies, covering various aspects of the model's architecture and preprocessing methods. In these studies, we examined the impact of key elements such as PCA, Zipf weighting, and the use of Sentence Transformers versus regular transformer models. We also compared the performance of input embeddings versus output embeddings, since it would seem plausible that these should also work well. The results are shown in the table below. + + +| Model | Avg (All) | Avg (MTEB) | Class | Clust | PairClass | Rank | Ret | STS | Sum | Pearl | WordSim | +|:-----------------------------|------------:|-------------:|--------:|--------:|------------:|-------:|------:|------:|------:|--------:|----------:| +| M2V_base_output | 46.79 | 45.34 | 61.25 | 25.58 | 74.9 | 47.63 | 26.14 | 68.58 | 29.2 | 54.02 | 49.18 | +| M2V_base_output_nopca | 44.04 | 42.31 | 61.42 | 20.15 | 68.21 | 44.67 | 25.25 | 61.87 | 29.85 | 51.02 | 48.96 | +| M2V_base_output_nozipf | 43.61 | 41.52 | 60.44 | 21.62 | 72.15 | 45.57 | 20.35 | 62.71 | 30.66 | 52.28 | 49.17 | +| M2V_base_input_nozipf_nopca | 40.97 | 39.55 | 54.16 | 18.62 | 68.3 | 43.65 | 23.63 | 59.38 | 32.04 | 50.19 | 40.52 | +| M2V_base_output_nozipf_nopca | 40.8 | 38.44 | 59.78 | 19.31 | 62.39 | 42.26 | 19.01 | 55.16 | 30 | 49.09 | 48.97 | +| M2V_base_input | 40.74 | 39.93 | 60.35 | 22.66 | 59.63 | 43.02 | 25.47 | 50.05 | 29.35 | 50.61 | 34.47 | +| M2V_bert_output_nozipf_nopca | 35.54 | 34.82 | 55.69 | 15.42 | 58.68 | 39.87 | 12.92 | 55.24 | 30.15 | 46.9 | 26.72 | + + +There's four main findings in these results: +1. Non-Sentence Transformers do not work well. This can be seen by comparing `M2V_bert_output_nozipf_nopca` (which uses [BERT](https://huggingface.co/google-bert/bert-base-uncased), a non-Sentence Transformer) and `M2V_base_output_nozipf_nopca` (which uses [BGE-base](https://huggingface.co/BAAI/bge-base-en-v1.5), a Sentence Transformer). Using a Sentence Transformer gives a ~5.2% increase in performance. +2. PCA is crucial for performance. This can be seen by comparing `M2V_base_output_nozipf_nopca` and `M2V_base_output_nozipf` which gives a ~2.8% increase in performance. Furthermore, PCA improves performance on _all_ tasks. +3. Zipf weighting is crucial for performance. This can be seen by comparing `M2V_base_output_nozipf_nopca` and `M2V_base_output_nopca` which gives a ~3.1% increase in performance. +4. Output embeddings outperform input embeddings. This can be seen by comparing `M2V_base_input` and `M2V_base_output` which gives a ~6.1% increase in performance. Note that input embeddings do work well for some tasks. We hypothesize that this is because input embeddings are inherently normalized. diff --git a/scripts/export_to_onnx.py b/scripts/export_to_onnx.py new file mode 100644 index 0000000..aa1bfae --- /dev/null +++ b/scripts/export_to_onnx.py @@ -0,0 +1,208 @@ +from model2vec.utils import get_package_extras, importable + +# Define the optional dependency group name +_REQUIRED_EXTRA = "onnx" + +# Check if each dependency for the "onnx" group is importable +for extra_dependency in get_package_extras("model2vec", _REQUIRED_EXTRA): + importable(extra_dependency, _REQUIRED_EXTRA) + +import argparse +import json +import logging +from pathlib import Path + +import torch +from tokenizers import Tokenizer +from transformers import AutoTokenizer, PreTrainedTokenizerFast + +from model2vec import StaticModel + +logging.basicConfig(level=logging.INFO) +logger = logging.getLogger(__name__) + + +class TorchStaticModel(torch.nn.Module): + def __init__(self, model: StaticModel) -> None: + """Initialize the TorchStaticModel with a StaticModel instance.""" + super().__init__() + # Convert NumPy embeddings to a torch.nn.EmbeddingBag + embeddings = torch.tensor(model.embedding, dtype=torch.float32) + self.embedding_bag = torch.nn.EmbeddingBag.from_pretrained(embeddings, mode="mean", freeze=True) + self.normalize = model.normalize + # Save tokenizer attributes + self.tokenizer = model.tokenizer + self.unk_token_id = model.unk_token_id + self.median_token_length = model.median_token_length + + def forward(self, input_ids: torch.Tensor, offsets: torch.Tensor) -> torch.Tensor: + """ + Forward pass of the model. + + :param input_ids: The input token ids. + :param offsets: The offsets to compute the mean pooling. + :return: The embeddings. + """ + # Perform embedding lookup and mean pooling + embeddings = self.embedding_bag(input_ids, offsets) + # Normalize if required + if self.normalize: + embeddings = torch.nn.functional.normalize(embeddings, p=2, dim=-1) + return embeddings + + def tokenize(self, sentences: list[str], max_length: int | None = None) -> tuple[torch.Tensor, torch.Tensor]: + """ + Tokenize the input sentences. + + :param sentences: The input sentences. + :param max_length: The maximum length of the input_ids. + :return: The input_ids and offsets. + """ + # Tokenization logic similar to your StaticModel + if max_length is not None: + m = max_length * self.median_token_length + sentences = [sentence[:m] for sentence in sentences] + encodings = self.tokenizer.encode_batch(sentences, add_special_tokens=False) + encodings_ids = [encoding.ids for encoding in encodings] + if self.unk_token_id is not None: + # Remove unknown tokens + encodings_ids = [ + [token_id for token_id in token_ids if token_id != self.unk_token_id] for token_ids in encodings_ids + ] + if max_length is not None: + encodings_ids = [token_ids[:max_length] for token_ids in encodings_ids] + # Flatten input_ids and compute offsets + offsets = torch.tensor([0] + [len(ids) for ids in encodings_ids[:-1]], dtype=torch.long).cumsum(dim=0) + input_ids = torch.tensor( + [token_id for token_ids in encodings_ids for token_id in token_ids], + dtype=torch.long, + ) + return input_ids, offsets + + +def export_model_to_onnx(model_path: str, save_path: Path) -> None: + """ + Export the StaticModel to ONNX format and save tokenizer files. + + :param model_path: The path to the pretrained StaticModel. + :param save_path: The directory to save the model and related files. + """ + save_path.mkdir(parents=True, exist_ok=True) + + # Load the StaticModel + model = StaticModel.from_pretrained(model_path) + torch_model = TorchStaticModel(model) + + # Save the model using save_pretrained + model.save_pretrained(save_path) + + # Prepare dummy input data + texts = ["hello", "hello world"] + input_ids, offsets = torch_model.tokenize(texts) + + # Export the model to ONNX + onnx_model_path = save_path / "onnx/model.onnx" + onnx_model_path.parent.mkdir(parents=True, exist_ok=True) + torch.onnx.export( + torch_model, + (input_ids, offsets), + str(onnx_model_path), + export_params=True, + opset_version=14, + do_constant_folding=True, + input_names=["input_ids", "offsets"], + output_names=["embeddings"], + dynamic_axes={ + "input_ids": {0: "num_tokens"}, + "offsets": {0: "batch_size"}, + "embeddings": {0: "batch_size"}, + }, + ) + + logger.info(f"Model has been successfully exported to {onnx_model_path}") + + # Save the tokenizer files required for transformers.js + save_tokenizer(model.tokenizer, save_path) + logger.info(f"Tokenizer files have been saved to {save_path}") + + +def save_tokenizer(tokenizer: Tokenizer, save_directory: Path) -> None: + """ + Save tokenizer files in a format compatible with Transformers. + + :param tokenizer: The tokenizer from the StaticModel. + :param save_directory: The directory to save the tokenizer files. + :raises FileNotFoundError: If config.json is not found in save_directory. + :raises FileNotFoundError: If tokenizer_config.json is not found in save_directory. + :raises ValueError: If tokenizer_name is not found in config.json. + """ + tokenizer_json_path = save_directory / "tokenizer.json" + tokenizer.save(str(tokenizer_json_path)) + + # Save vocab.txt + vocab = tokenizer.get_vocab() + vocab_path = save_directory / "vocab.txt" + with open(vocab_path, "w", encoding="utf-8") as vocab_file: + for token in sorted(vocab, key=vocab.get): + vocab_file.write(f"{token}\n") + + # Load config.json to get tokenizer_name + config_path = save_directory / "config.json" + if config_path.exists(): + with open(config_path, "r", encoding="utf-8") as f: + config = json.load(f) + else: + raise FileNotFoundError(f"config.json not found in {save_directory}") + + tokenizer_name = config.get("tokenizer_name") + if not tokenizer_name: + raise ValueError("tokenizer_name not found in config.json") + + # Load the original tokenizer + original_tokenizer = AutoTokenizer.from_pretrained(tokenizer_name) + + # Extract special tokens and tokenizer class + special_tokens = original_tokenizer.special_tokens_map + tokenizer_class = original_tokenizer.__class__.__name__ + + # Load the tokenizer using PreTrainedTokenizerFast with special tokens + fast_tokenizer = PreTrainedTokenizerFast( + tokenizer_file=str(tokenizer_json_path), + **special_tokens, + ) + + # Save the tokenizer files + fast_tokenizer.save_pretrained(str(save_directory)) + # Modify tokenizer_config.json to set the correct tokenizer_class + tokenizer_config_path = save_directory / "tokenizer_config.json" + if tokenizer_config_path.exists(): + with open(tokenizer_config_path, "r", encoding="utf-8") as f: + tokenizer_config = json.load(f) + else: + raise FileNotFoundError(f"tokenizer_config.json not found in {save_directory}") + + # Update the tokenizer_class field + tokenizer_config["tokenizer_class"] = tokenizer_class + + # Write the updated tokenizer_config.json back to disk + with open(tokenizer_config_path, "w", encoding="utf-8") as f: + json.dump(tokenizer_config, f, indent=4, sort_keys=True) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Export StaticModel to ONNX format") + parser.add_argument( + "--model_path", + type=str, + required=True, + help="Path to the pretrained StaticModel", + ) + parser.add_argument( + "--save_path", + type=str, + required=True, + help="Directory to save the exported model and files", + ) + args = parser.parse_args() + + export_model_to_onnx(args.model_path, Path(args.save_path)) diff --git a/tests/conftest.py b/tests/conftest.py index f7d2222..ced1abc 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -1,6 +1,5 @@ from __future__ import annotations -from pathlib import Path from typing import Any import numpy as np @@ -25,7 +24,7 @@ def mock_tokenizer() -> Tokenizer: return tokenizer -@pytest.fixture +@pytest.fixture(scope="function") def mock_berttokenizer() -> AutoTokenizer: """Load the real BertTokenizerFast from the provided tokenizer.json file.""" return AutoTokenizer.from_pretrained("tests/data/test_tokenizer") diff --git a/tests/test_distillation.py b/tests/test_distillation.py index 36da266..c0c3707 100644 --- a/tests/test_distillation.py +++ b/tests/test_distillation.py @@ -1,3 +1,5 @@ +from __future__ import annotations + import json from importlib import import_module from unittest.mock import MagicMock, patch @@ -90,7 +92,7 @@ def test_distill_from_model( use_subword=use_subword, ) - assert static_model.embedding.weight.shape == static_model2.embedding.weight.shape + assert static_model.embedding.shape == static_model2.embedding.shape assert static_model.config == static_model2.config assert json.loads(static_model.tokenizer.to_str()) == json.loads(static_model2.tokenizer.to_str()) assert static_model.base_model_name == static_model2.base_model_name @@ -159,7 +161,7 @@ def test_distill( # Assert the model is correctly generated assert isinstance(static_model, StaticModel) - assert static_model.embedding.weight.shape == expected_shape + assert static_model.embedding.shape == expected_shape assert "mock-model" in static_model.config["tokenizer_name"] assert static_model.tokenizer is not None diff --git a/tests/test_model.py b/tests/test_model.py index f6d8810..aaed373 100644 --- a/tests/test_model.py +++ b/tests/test_model.py @@ -1,7 +1,9 @@ from pathlib import Path +from tempfile import TemporaryDirectory import numpy as np import pytest +import safetensors from tokenizers import Tokenizer from model2vec import StaticModel @@ -10,7 +12,7 @@ def test_initialization(mock_vectors: np.ndarray, mock_tokenizer: Tokenizer, mock_config: dict[str, str]) -> None: """Test successful initialization of StaticModel.""" model = StaticModel(vectors=mock_vectors, tokenizer=mock_tokenizer, config=mock_config) - assert model.embedding.weight.shape == (5, 2) + assert model.embedding.shape == (5, 2) assert len(model.tokens) == 5 assert model.tokenizer == mock_tokenizer assert model.config == mock_config @@ -23,6 +25,27 @@ def test_initialization_token_vector_mismatch(mock_tokenizer: Tokenizer, mock_co StaticModel(vectors=mock_vectors, tokenizer=mock_tokenizer, config=mock_config) +def test_tokenize(mock_vectors: np.ndarray, mock_tokenizer: Tokenizer, mock_config: dict[str, str]) -> None: + """Test tokenization of a sentence.""" + model = StaticModel(vectors=mock_vectors, tokenizer=mock_tokenizer, config=mock_config) + model._can_encode_fast = True + tokens_fast = model.tokenize(["word1 word2"]) + model._can_encode_fast = False + tokens_slow = model.tokenize(["word1 word2"]) + + assert tokens_fast == tokens_slow + + +def test_encode_batch_fast( + mock_vectors: np.ndarray, mock_berttokenizer: Tokenizer, mock_config: dict[str, str] +) -> None: + """Test tokenization of a sentence.""" + if hasattr(mock_berttokenizer, "encode_batch_fast"): + del mock_berttokenizer.encode_batch_fast + model = StaticModel(vectors=mock_vectors, tokenizer=mock_berttokenizer, config=mock_config) + assert not model._can_encode_fast + + def test_encode_single_sentence( mock_vectors: np.ndarray, mock_tokenizer: Tokenizer, mock_config: dict[str, str] ) -> None: @@ -32,6 +55,17 @@ def test_encode_single_sentence( assert encoded.shape == (2,) +def test_encode_single_sentence_empty( + mock_vectors: np.ndarray, mock_tokenizer: Tokenizer, mock_config: dict[str, str] +) -> None: + """Test encoding of a single empty sentence.""" + model = StaticModel(vectors=mock_vectors, tokenizer=mock_tokenizer, config=mock_config) + model.normalize = True + encoded = model.encode("") + assert not np.isnan(encoded).any() + assert np.all(encoded == 0) + + def test_encode_multiple_sentences( mock_vectors: np.ndarray, mock_tokenizer: Tokenizer, mock_config: dict[str, str] ) -> None: @@ -60,21 +94,11 @@ def test_encode_as_tokens_empty( """Test encoding of an empty list of sentences.""" model = StaticModel(vectors=mock_vectors, tokenizer=mock_tokenizer, config=mock_config) encoded = model.encode_as_sequence("") - assert np.array_equal(encoded, np.zeros(shape=(0, 2), dtype=model.embedding.weight.numpy().dtype)) + assert np.array_equal(encoded, np.zeros(shape=(0, 2), dtype=model.embedding.dtype)) encoded = model.encode_as_sequence(["", ""]) - out = [np.zeros(shape=(0, 2), dtype=model.embedding.weight.numpy().dtype) for _ in range(2)] - assert np.array_equal(encoded, out) - - -def test_forward(mock_vectors: np.ndarray, mock_tokenizer: Tokenizer, mock_config: dict[str, str]) -> None: - """Test forward pass of the model.""" - model = StaticModel(vectors=mock_vectors, tokenizer=mock_tokenizer, config=mock_config) - encoded = model.forward(model.tokenize(["word1 word2"])) - assert encoded.shape == (1, model.embedding.weight.shape[1]) - - encoded = model.forward(model.tokenize(["word1", "word1 word2 word1"])) - assert encoded.shape == (2, model.embedding.weight.shape[1]) + out = [np.zeros(shape=(0, 2), dtype=model.embedding.dtype) for _ in range(2)] + assert [np.array_equal(x, y) for x, y in zip(encoded, out)] def test_encode_empty_sentence( @@ -130,7 +154,7 @@ def test_load_pretrained( loaded_model = StaticModel.from_pretrained(save_path) # Assert that the loaded model has the same properties as the original one - np.testing.assert_array_equal(loaded_model.embedding.weight.numpy(), mock_vectors) + np.testing.assert_array_equal(loaded_model.embedding, mock_vectors) assert loaded_model.tokenizer.get_vocab() == mock_tokenizer.get_vocab() assert loaded_model.config == mock_config @@ -166,4 +190,24 @@ def test_dim(mock_vectors: np.ndarray, mock_tokenizer: Tokenizer, mock_config: d """Tests the dimensionality of the model.""" model = StaticModel(mock_vectors, mock_tokenizer, mock_config) assert model.dim == 2 - assert model.dim == model.embedding.weight.shape[1] + assert model.dim == model.embedding.shape[1] + + +def test_local_load_from_model(mock_tokenizer: Tokenizer) -> None: + """Test local load from a model.""" + x = np.ones((mock_tokenizer.get_vocab_size(), 2)) + with TemporaryDirectory() as tempdir: + tempdir_path = Path(tempdir) + safetensors.numpy.save_file({"embeddings": x}, Path(tempdir) / "model.safetensors") + mock_tokenizer.save(str(Path(tempdir) / "tokenizer.json")) + + model = StaticModel.load_local(tempdir_path) + assert model.embedding.shape == x.shape + assert model.tokenizer.to_str() == mock_tokenizer.to_str() + assert model.config == {"normalize": False} + + +def test_local_load_from_model_no_folder() -> None: + """Test local load from a model with no folder.""" + with pytest.raises(ValueError): + StaticModel.load_local("woahbuddy_relax_this_is_just_a_test") diff --git a/tests/test_utils.py b/tests/test_utils.py index d6219e0..f7910ec 100644 --- a/tests/test_utils.py +++ b/tests/test_utils.py @@ -1,11 +1,20 @@ +from __future__ import annotations + +import json from pathlib import Path -from tempfile import NamedTemporaryFile +from tempfile import NamedTemporaryFile, TemporaryDirectory +from typing import Any from unittest.mock import patch +import numpy as np import pytest +import safetensors +import safetensors.numpy +from tokenizers import Tokenizer from model2vec.distill.utils import select_optimal_device -from model2vec.utils import _get_metadata_from_readme +from model2vec.hf_utils import _get_metadata_from_readme +from model2vec.utils import get_package_extras, importable, load_local_model def test__get_metadata_from_readme_not_exists() -> None: @@ -50,3 +59,63 @@ def test_select_optimal_device(device: str | None, expected: str, cuda: bool, mp patch("torch.backends.mps.is_available", return_value=mps), ): assert select_optimal_device(device) == expected + + +def test_importable() -> None: + """Test the importable function.""" + with pytest.raises(ImportError): + importable("clown", "clown") + + importable("os", "clown") + + +def test_get_package_extras() -> None: + """Test package extras.""" + extras = set(get_package_extras("model2vec", "distill")) + assert extras == {"torch", "transformers", "scikit-learn"} + + +def test_get_package_extras_empty() -> None: + """Test package extras with an empty package.""" + assert not list(get_package_extras("tqdm", "")) + + +@pytest.mark.parametrize( + "config, expected", + [ + ({"dog": "cat"}, {"dog": "cat"}), + ({}, {}), + (None, {}), + ], +) +def test_local_load(mock_tokenizer: Tokenizer, config: dict[str, Any], expected: dict[str, Any]) -> None: + """Test local loading.""" + x = np.ones((mock_tokenizer.get_vocab_size(), 2)) + + with TemporaryDirectory() as tempdir: + tempdir_path = Path(tempdir) + safetensors.numpy.save_file({"embeddings": x}, Path(tempdir) / "model.safetensors") + mock_tokenizer.save(str(Path(tempdir) / "tokenizer.json")) + if config is not None: + json.dump(config, open(tempdir_path / "config.json", "w")) + arr, tokenizer, config = load_local_model(tempdir_path) + assert config == expected + assert tokenizer.to_str() == mock_tokenizer.to_str() + assert arr.shape == x.shape + + +def test_local_load_mismatch(mock_tokenizer: Tokenizer, caplog: pytest.LogCaptureFixture) -> None: + """Test local loading.""" + x = np.ones((10, 2)) + + with TemporaryDirectory() as tempdir: + tempdir_path = Path(tempdir) + safetensors.numpy.save_file({"embeddings": x}, Path(tempdir) / "model.safetensors") + mock_tokenizer.save(str(Path(tempdir) / "tokenizer.json")) + + load_local_model(tempdir_path) + expected = ( + f"Number of tokens does not match number of embeddings: `{len(mock_tokenizer.get_vocab())}` vs `{len(x)}`" + ) + assert len(caplog.records) == 1 + assert caplog.records[0].message == expected diff --git a/tutorials/semantic_deduplication.ipynb b/tutorials/semantic_deduplication.ipynb index 82c0b62..e3b30a5 100644 --- a/tutorials/semantic_deduplication.ipynb +++ b/tutorials/semantic_deduplication.ipynb @@ -110,7 +110,7 @@ ], "source": [ "# Encode texts into embeddings\n", - "embedding_matrix = model.encode(texts, show_progressbar=True)" + "embedding_matrix = model.encode(texts)" ] }, { @@ -407,8 +407,8 @@ "texts_test = ds_test['text']\n", "\n", "# Encode texts into embeddings\n", - "embedding_matrix_train = model.encode(texts_train, show_progressbar=True)\n", - "embedding_matrix_test = model.encode(texts_test, show_progressbar=True)\n", + "embedding_matrix_train = model.encode(texts_train)\n", + "embedding_matrix_test = model.encode(texts_test)\n", "\n", "def deduplicate_across_datasets(embedding_matrix_1: np.ndarray, embedding_matrix_2: np.ndarray, threshold: float, batch_size: int = 1024) -> tuple[list[int], dict[int, int]]:\n", " \"\"\"\n", diff --git a/uv.lock b/uv.lock index 30ee1f4..68a3406 100644 --- a/uv.lock +++ b/uv.lock @@ -1,11 +1,5 @@ version = 1 -requires-python = ">=3.10" -resolution-markers = [ - "python_full_version < '3.11'", - "python_full_version == '3.11.*'", - "python_full_version == '3.12.*'", - "python_full_version >= '3.13'", -] +requires-python = ">=3.9" [[package]] name = "asttokens" @@ -21,7 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