diff --git a/README.md b/README.md index baad76a3..c593d9cd 100644 --- a/README.md +++ b/README.md @@ -54,6 +54,7 @@ Model2Vec is a technique to turn any sentence transformer into a really small st - [Distillation](#distillation) - [Inference](#inference) - [Evaluation](#evaluation) + - [Integrations](#integrations) - [Model List](#model-list) - [Results](#results) - [Related Work](#related-work) @@ -356,6 +357,89 @@ print(make_leaderboard(task_scores)) ``` +### Integrations +
+ Sentence Transformers +
+ +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 + +# 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."]) +``` + +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."]) +``` + +
+ + +
+ 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'; + +const modelConfig = { + config: { model_type: 'model2vec' }, + dtype: 'fp32', + revision: 'refs/pr/1' +}; +const tokenizerConfig = { + revision: 'refs/pr/2' +}; + +const model = await AutoModel.from_pretrained(modelName, modelConfig); +const tokenizer = await AutoTokenizer.from_pretrained(modelName, tokenizerConfig); + +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))]; + +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 "" +``` + + +
+
+ + ## 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. diff --git a/pyproject.toml b/pyproject.toml index 7209a95d..625a5fd4 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -54,6 +54,8 @@ dev = [ ] distill = ["torch", "transformers", "scikit-learn"] +onnx = ["onnx", "torch"] + [project.urls] "Homepage" = "https://github.com/MinishLab" "Bug Reports" = "https://github.com/MinishLab/model2vec/issues" diff --git a/scripts/export_to_onnx.py b/scripts/export_to_onnx.py new file mode 100644 index 00000000..aa1bfae0 --- /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/uv.lock b/uv.lock index b331640b..68a34068 100644 --- a/uv.lock +++ b/uv.lock @@ -531,6 +531,10 @@ distill = [ { name = "torch" }, { name = "transformers" }, ] +onnx = [ + { name 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