57 lines
2.5 KiB
Markdown
57 lines
2.5 KiB
Markdown
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<!--Copyright 2024 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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-->
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# FBGEMM
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[FBGEMM (Facebook GEneral Matrix Multiplication)](https://github.com/pytorch/FBGEMM) 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).
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> [!TIP]
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> You need a GPU with [compute capability 9+](https://developer.nvidia.com/cuda-gpus#collapseOne) like a H100.
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Install the FBGEMM_GPU package with the command below to ensure you have the latest version.
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```bash
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pip install --upgrade accelerate fbgemm-gpu torch
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```
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If you're having installation issues, try installing the [nightly release](https://pytorch.org/FBGEMM/fbgemm_gpu-development/InstallationInstructions.html#fbgemm-gpu-install-libraries:~:text=found%20here.-,Install%20the%20FBGEMM_GPU%20Package,-Install%20through%20PyTorch).
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Create a [`FbgemmFp8Config`] and pass it to [`~PreTrainedModel.from_pretrained`] to quantize a model to fp8.
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```py
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from transformers import FbgemmFp8Config, AutoModelForCausalLM
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quantization_config = FbgemmFp8Config()
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quantized_model = AutoModelForCausalLM.from_pretrained(
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"meta-llama/Meta-Llama-3-8B",
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dtype="auto",
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device_map="auto",
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quantization_config=quantization_config
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)
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```
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[`~PreTrainedModel.save_pretrained`] and [`~PreTrainedModel.from_pretrained`] enable saving and loading a quantized model.
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```py
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quant_path = "/path/to/save/quantized/model"
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model.save_pretrained(quant_path)
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model = AutoModelForCausalLM.from_pretrained(quant_path, device_map="auto")
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```
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## Resources
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Read the [Open-sourcing FBGEMM for state-of-the-art server-side inference](https://engineering.fb.com/2018/11/07/ml-applications/fbgemm/) blog post for more details on FBGEMM.
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