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Sentence Transformers on AWS Inferentia with Optimum Neuron

##
Text Models

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.

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Convert Sentence Transformers model to AWS Inferentia2

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. That’s only needed for exporting the model.

    pip install sentence-transformers

Here we will use the `NeuronModelForSentenceTransformers`, which can be used to convert any Sentence 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 --sequence_length 384 --batch_size 1 --task feature-extraction bge_emb_inf2/

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Load compiled Sentence Transformers model and run inference

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])

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Production Usage

For deploying these models in a production environment, refer to the Amazon SageMaker Blog.

##
CLIP

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Compile CLIP for AWS Inferentia2

You can compile CLIP models with Optimum Neuron either by using the `optimum-cli` or `NeuronModelForSentenceTransformers` class. Adopt one approach that you prefer:

- With the Optimum CLI

    optimum-cli export neuron -m sentence-transformers/clip-ViT-B-32 --sequence_length 64 --text_batch_size 3 --image_batch_size 1 --num_channels 3 --height 224 --width 224 --task feature-extraction --subfolder 0_CLIPModel clip_emb/

- With the `NeuronModelForSentenceTransformers` class

    from optimum.neuron import NeuronModelForSentenceTransformers

    model_id = "sentence-transformers/clip-ViT-B-32"

    # configs for compiling model
    input_shapes = {
        "num_channels": 3,
        "height": 224,
        "width": 224,
        "text_batch_size": 3,
        "image_batch_size": 1,
        "sequence_length": 64,
    }

    emb_model = NeuronModelForSentenceTransformers.from_pretrained(
        model_id, subfolder="0_CLIPModel", export=True, library_name="sentence_transformers", dynamic_batch_size=False, **input_shapes
    )

    # Save locally or upload to the HuggingFace Hub
    save_directory = "clip_emb/"
    emb_model.save_pretrained(save_directory)

###
Load compiled Sentence Transformers model and run inference

    from PIL import Image
    from sentence_transformers import util
    from transformers import CLIPProcessor

    from optimum.neuron import NeuronModelForSentenceTransformers

    save_directory = "clip_emb"
    emb_model = NeuronModelForSentenceTransformers.from_pretrained(save_directory)

    processor = CLIPProcessor.from_pretrained(save_directory)
    inputs = processor(
        text=["Two dogs in the snow", 'A cat on a table', 'A picture of London at night'], images=Image.open("two_dogs_in_snow.jpg"), return_tensors="pt", padding=True
    )

    outputs = emb_model(**inputs)

    # Compute cosine similarities
    cos_scores = util.cos_sim(outputs.image_embeds, outputs.text_embeds)
    print(cos_scores)

    # tensor([[0.3072, 0.1016, 0.1095]])

> Caveat

> Since compiled models with dynamic batching enabled only accept input tensors with the same batch size, we cannot set `dynamic_batch_size=True` if the input texts and images have different batch sizes. And as `NeuronModelForSentenceTransformers` class pads the inputs to the batch sizes (`text_batch_size` and `image_batch_size`) used during the compilation, you could use relatively larger batch sizes during the compilation for flexibility with the trade-off of compute.

> eg. if you want to encode 3 or 4 or 5 texts and 1 image, you could set `text_batch_size = 5 = max(3, 4, 5)` and `image_batch_size = 1` during the compilation.