transformers/docs/source/en/quantization/fbgemm_fp8.md

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).

Tip

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 [~PreTrainedModel.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
)

[~PreTrainedModel.save_pretrained] and [~PreTrainedModel.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.