from __future__ import annotations import logging import os import re from typing import cast from huggingface_hub.hf_api import model_info from skeletoken import TokenizerModel from skeletoken.external.transformers import reshape_embeddings from transformers import AutoModel, AutoTokenizer, PreTrainedTokenizerFast from transformers.modeling_utils import PreTrainedModel from model2vec.distill.inference import PCADimType, PoolingMode, apply_pca, compute_weights, create_embeddings from model2vec.distill.utils import select_optimal_device from model2vec.model import StaticModel from model2vec.quantization import DType, quantize_embeddings from model2vec.tokenizer import clean_and_create_vocabulary, turn_tokens_into_ids from model2vec.vocabulary_quantization import quantize_vocabulary logger = logging.getLogger(__name__) def distill_from_model( model: PreTrainedModel, tokenizer: PreTrainedTokenizerFast, vocabulary: list[str] | None = None, device: str | None = None, pca_dims: PCADimType = 256, sif_coefficient: float | None = 1e-4, token_remove_pattern: str | None = r"\[unused\d+\]", quantize_to: DType | str = DType.Float16, vocabulary_quantization: int | None = None, pooling: PoolingMode | str = PoolingMode.MEAN, ) -> StaticModel: """Distill a staticmodel from a sentence transformer. This function creates a set of embeddings from a sentence transformer. It does this by doing either a forward pass for all subword tokens in the tokenizer, or by doing a forward pass for all tokens in a passed vocabulary. If you pass through a vocabulary, we create a custom word tokenizer for that vocabulary. If you don't pass a vocabulary, we use the model's tokenizer directly. :param model: The model to use. :param tokenizer: The tokenizer to use. :param vocabulary: The vocabulary to use. If this is None, we use the model's vocabulary. :param device: The device to use. :param pca_dims: The number of components to use for PCA. If this is None, we don't apply PCA. If this is 'auto', we don't reduce dimensionality, but still apply PCA. :param sif_coefficient: The SIF coefficient to use. If this is None, no weighting is applied. Should be a value > 0 and < 1.0. A value of 1e-4 is a good default. :param token_remove_pattern: If this is set to a string, we compile this into a regex. Any tokens that conform to this regex pattern will be removed from the vocabulary. If the pattern is so general that it removes all tokens, we throw an error. If the pattern can't be compiled into a valid regex, we also throw an error. :param quantize_to: The data type to quantize to. Can be any of the DType enum members or their string equivalents. :param vocabulary_quantization: The number of clusters to use for vocabulary quantization. If this is None, no quantization is performed. :param pooling: The pooling mode to use for creating embeddings. Can be one of: 'mean' (default): mean over all tokens. Robust and works well in most cases. 'last': use the last token's hidden state (often the [EOS] token). Common for decoder-style models. 'first': use the first token's hidden state ([CLS] token in BERT-style models). 'pooler': use the pooler output (if available). This is often a non-linear projection of the [CLS] token. :return: A StaticModel. :raises ValueError: if the vocabulary is empty after preprocessing. """ quantize_to = DType(quantize_to) sif_coefficient, token_remove_regex = _validate_parameters(sif_coefficient, token_remove_pattern) if vocabulary is None: vocabulary = [] device = select_optimal_device(device) original_tokenizer_model = TokenizerModel.from_transformers_tokenizer(tokenizer) original_tokenizer_model = original_tokenizer_model.prune_added_tokens() # Clean the vocabulary by removing duplicate tokens and tokens that are in the internal vocabulary. # Copy the original tokenizer model. tokenizer_model = original_tokenizer_model.deep_copy() if tokenizer_model.adds_prefix_space is not None: tokenizer_model.adds_prefix_space = True # Create the vocabulary in the new tokenizer. tokenizer_model = clean_and_create_vocabulary(tokenizer_model, vocabulary, token_remove_regex=token_remove_regex) # All tokens in a single list. all_tokens = tokenizer_model.sorted_vocabulary if not all_tokens: raise ValueError("The vocabulary is empty after preprocessing. Please check your token_remove_pattern.") # Turn all _new_ tokens into ids using the original tokenizer token_ids = turn_tokens_into_ids(all_tokens, original_tokenizer_model) # Reshape the transformer model = reshape_embeddings(model, original_tokenizer_model) # Create the embeddings using the ids from the original tokenizer. embeddings = create_embeddings( tokenized=token_ids, model=model, device=device, pad_token_id=original_tokenizer_model.pad_token_id or 0, pooling=pooling, ) # Apply quantization if vocabulary_quantization is not None: weights = compute_weights(len(embeddings), sif_coefficient=sif_coefficient) embeddings, token_mapping, weights = quantize_vocabulary( n_clusters=vocabulary_quantization, weights=weights, embeddings=embeddings ) embeddings = apply_pca(embeddings, pca_dims) else: # Post-process the embeddings. weights = compute_weights(len(embeddings), sif_coefficient=sif_coefficient) embeddings = apply_pca(embeddings, pca_dims) embeddings = embeddings * weights[:, None] weights = None token_mapping = None # Quantize the embeddings. embeddings = quantize_embeddings(embeddings, quantize_to) model_name = getattr(model, "name_or_path", "") config = { "model_type": "model2vec", "architectures": ["StaticModel"], "tokenizer_name": model_name, "apply_pca": pca_dims, "sif_coefficient": sif_coefficient, "hidden_dim": embeddings.shape[1], "seq_length": 1000000, # Set this to a high value since we don't have a sequence length limit. "normalize": True, "pooling": pooling, } if os.path.exists(model_name): # Using a local model. Get the model name from the path. model_name = os.path.basename(model_name) language = None else: # Get the language from the model card. try: info = model_info(model_name) language = info.cardData.get("language", None) if info.cardData is not None else None except Exception as e: # NOTE: bare except because there's many reasons this can fail. logger.warning(f"Couldn't get the model info from the Hugging Face Hub: {e}. Setting language to None.") language = None return StaticModel( vectors=embeddings, weights=weights, token_mapping=token_mapping, tokenizer=tokenizer_model.to_tokenizer(), config=config, base_model_name=model_name, language=language, normalize=True, ) def _validate_parameters( sif_coefficient: float | None, token_remove_pattern: str | None, ) -> tuple[float | None, re.Pattern[str] | None]: """Validate the parameters passed to the distillation function. :param sif_coefficient: The SIF coefficient to use. If this is None, no weighting is applied. Should be a value >= 0 and < 1.0. A value of 1e-4 is a good default. :param token_remove_pattern: If this is set to a string, we compile this into a regex. Any tokens that conform to this regex pattern will be removed from the vocabulary. :return: The SIF coefficient to use. :raises ValueError: if the regex can't be compiled. """ if sif_coefficient is not None: if not 0 < sif_coefficient < 1.0: raise ValueError("SIF coefficient must be a value > 0 and < 1.0.") token_remove_regex: re.Pattern[str] | None = None if token_remove_pattern is not None: try: token_remove_regex = re.compile(token_remove_pattern) except re.error as e: raise ValueError(f"Couldn't compile the regex pattern: {e}") from e return sif_coefficient, token_remove_regex def distill( model_name: str, vocabulary: list[str] | None = None, device: str | None = None, pca_dims: PCADimType = 256, sif_coefficient: float | None = 1e-4, token_remove_pattern: str | None = r"\[unused\d+\]", trust_remote_code: bool = False, quantize_to: DType | str = DType.Float16, vocabulary_quantization: int | None = None, pooling: PoolingMode | str = PoolingMode.MEAN, ) -> StaticModel: """Distill a staticmodel from a sentence transformer. This function creates a set of embeddings from a sentence transformer. It does this by doing either a forward pass for all subword tokens in the tokenizer, or by doing a forward pass for all tokens in a passed vocabulary. If you pass through a vocabulary, we create a custom word tokenizer for that vocabulary. If you don't pass a vocabulary, we use the model's tokenizer directly. :param model_name: The model name to use. Any sentencetransformer compatible model works. :param vocabulary: The vocabulary to use. If this is None, we use the model's vocabulary. :param device: The device to use. :param pca_dims: The number of components to use for PCA. If this is None, we don't apply PCA. If this is 'auto', we don't reduce dimenionality, but still apply PCA. :param sif_coefficient: The SIF coefficient to use. If this is None, no weighting is applied. Should be a value >= 0 and < 1.0. A value of 1e-4 is a good default. :param token_remove_pattern: If this is set to a string, we compile this into a regex. Any tokens that conform to this regex pattern will be removed from the vocabulary. :param trust_remote_code: Whether to trust the remote code. If this is False, we will only load components coming from `transformers`. If this is True, we will load all components. :param quantize_to: The data type to quantize to. Can be any of the DType enum members or their string equivalents. :param vocabulary_quantization: The number of clusters to use for vocabulary quantization. If this is None, no quantization is performed. :param pooling: The pooling mode to use for creating embeddings. Can be one of: 'mean' (default): mean over all tokens. Robust and works well in most cases. 'last': use the last token's hidden state (often the [EOS] token). Common for decoder-style models. 'first': use the first token's hidden state ([CLS] token in BERT-style models). 'pooler': use the pooler output (if available). This is often a non-linear projection of the [CLS] token. :return: A StaticModel """ model: PreTrainedModel = AutoModel.from_pretrained(model_name, trust_remote_code=trust_remote_code) tokenizer = cast( PreTrainedTokenizerFast, AutoTokenizer.from_pretrained(model_name, trust_remote_code=trust_remote_code, use_fast=True), ) return distill_from_model( model=model, tokenizer=tokenizer, vocabulary=vocabulary, device=device, pca_dims=pca_dims, token_remove_pattern=token_remove_pattern, sif_coefficient=sif_coefficient, quantize_to=quantize_to, vocabulary_quantization=vocabulary_quantization, pooling=pooling, )