There is a notebook version of that tutorial here.
This guide explains how to compile, load, and use Sentence Transformers (SBERT) models on AWS Inferentia2 with Optimum Neuron, enabling efficient calculation of embeddings. Sentence Transformers are powerful models for generating sentence embeddings. You can use this Sentence Transformers to compute sentence / text embeddings for more than 100 languages. These embeddings can then be compared e.g. with cosine-similarity to find sentences with a similar meaning. This can be useful for semantic textual similarity, semantic search, or paraphrase mining.
Note: Currently only text models are supported, we are working on vision support for CLIP.
First, you need to convert your Sentence Transformers model to a format compatible with AWS Inferentia2. You can compile Sentence Transformers models with Optimum Neuron using the optimum-cli
or NeuronModelForSentenceTransformers
class. Below you will find an example for both approaches. We have to make sure sentence-transformers
is installed. Thats only needed for exporting the model.
pip install sentence-transformers
Here we will use the NeuronModelForSentenceTransformers
, which can be used to convert any Sntence Transformers model to a format compatible with AWS Inferentia2 or load already converted models. When exporting models with the NeuronModelForSentenceTransformers
you need to set export=True
and define the input shape and batch size. The input shape is defined by the sequence_length
and the batch size by batch_size
.
from optimum.neuron import NeuronModelForSentenceTransformers
# Sentence Transformers model from HuggingFace
model_id = "BAAI/bge-small-en-v1.5"
input_shapes = {"batch_size": 1, "sequence_length": 384} # mandatory shapes
# Load Transformers model and export it to AWS Inferentia2
model = NeuronModelForSentenceTransformers.from_pretrained(model_id, export=True, **input_shapes)
# Save model to disk
model.save_pretrained("bge_emb_inf2/")
Here we will use the optimum-cli
to convert the model. Similar to the NeuronModelForSentenceTransformers
we need to define our input shape and batch size. The input shape is defined by the sequence_length
and the batch size by batch_size
. The optimum-cli
will automatically convert the model to a format compatible with AWS Inferentia2 and save it to the specified output directory.
optimum-cli export neuron -m BAAI/bge-small-en-v1.5 --library-name sentence_transformers --sequence_length 384 --batch_size 1 --task feature-extraction bge_emb_inf2/
Once we have a compiled Sentence Transformers model, which we either exported ourselves or is available on the Hugging Face Hub, we can load it and run inference. For loading the model we can use the NeuronModelForSentenceTransformers
class, which is an abstraction layer for the SentenceTransformer
class. The NeuronModelForSentenceTransformers
class will automatically pad the input to the specified sequence_length
and run inference on AWS Inferentia2.
from optimum.neuron import NeuronModelForSentenceTransformers
from transformers import AutoTokenizer
model_id_or_path = "bge_emb_inf2/"
tokenizer_id = "BAAI/bge-small-en-v1.5"
# Load model and tokenizer
model = NeuronModelForSentenceTransformers.from_pretrained(model_id_or_path)
tokenizer = AutoTokenizer.from_pretrained(tokenizer_id)
# Run inference
prompt = "I like to eat apples"
encoded_input = tokenizer(prompt, return_tensors='pt')
outputs = model(**encoded_input)
token_embeddings = outputs.token_embeddings
sentence_embedding = outputs.sentence_embedding
print(f"token embeddings: {token_embeddings.shape}") # torch.Size([1, 7, 384])
print(f"sentence_embedding: {sentence_embedding.shape}") # torch.Size([1, 384])
For deploying these models in a production environment, refer to the Amazon SageMaker Blog.