#
FBGEMM

FBGEMM (Facebook GEneral Matrix Multiplication) is a low-precision matrix multiplication library for small batch sizes and support for accuracy-loss minimizing techniques such as row-wise quantization and outlier-aware quantization. With FBGEMM, quantize a models weights to 8-bits/channel and the activations to 8-bits/token (also known as fp8 or w8a8).

You need a GPU with compute capability 9+ like a H100.

Install the FBGEMM_GPU package with the command below to ensure you have the latest version.

    pip install --upgrade accelerate fbgemm-gpu torch

If you’re having installation issues, try installing the nightly release.

Create a FbgemmFp8Config and pass it to from_pretrained() to quantize a model to fp8.

    from transformers import FbgemmFp8Config, AutoModelForCausalLM

    quantization_config = FbgemmFp8Config()
    quantized_model = AutoModelForCausalLM.from_pretrained(
        "meta-llama/Meta-Llama-3-8B",
        torch_dtype="auto",
        device_map="auto",
        quantization_config=quantization_config
    )

save_pretrained() and from_pretrained() enable saving and loading a quantized model.

    quant_path = "/path/to/save/quantized/model"
    model.save_pretrained(quant_path)
    model = AutoModelForCausalLM.from_pretrained(quant_path, device_map="auto")

##
Resources

Read the Open-sourcing FBGEMM for state-of-the-art server-side inference blog post for more details on FBGEMM.

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