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69 lines
2.9 KiB
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69 lines
2.9 KiB
Markdown
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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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# Optimum Quanto
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[Quanto](https://github.com/huggingface/optimum-quanto) is a PyTorch quantization backend for [Optimum](https://huggingface.co/docs/optimum/index). It features linear quantization for weights (float8, int8, int4, int2) with accuracy very similar to full-precision models. Quanto is compatible with any model modality and device, making it simple to use regardless of hardware.
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Quanto is also compatible with [torch.compile](https://pytorch.org/tutorials/intermediate/torch_compile_tutorial.html) for faster generation.
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Install Quanto with the following command.
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```bash
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pip install optimum-quanto accelerate transformers
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```
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Quantize a model by creating a [`QuantoConfig`] and specifying the `weights` parameter to quantize to. This works for any model in any modality as long as it contains [torch.nn.Linear](https://pytorch.org/docs/stable/generated/torch.nn.Linear.html) layers.
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> [!TIP]
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> The Transformers integration only supports weight quantization. Use the Quanto library directly if you need activation quantization, calibration, or QAT.
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```py
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from transformers import AutoModelForCausalLM, AutoTokenizer, QuantoConfig
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quant_config = QuantoConfig(weights="int8")
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model = transformers.AutoModelForCausalLM.from_pretrained(
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"meta-llama/Llama-3.1-8B",
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torch_dtype="auto",
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device_map="auto",
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quantization_config=quant_config
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)
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```
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## torch.compile
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Wrap a Quanto model with [torch.compile](https://pytorch.org/tutorials/intermediate/torch_compile_tutorial.html) for faster generation.
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```py
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import torch
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from transformers import AutoModelForSpeechSeq2Seq, QuantoConfig
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quant_config = QuantoConfig(weights="int8")
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model = AutoModelForSpeechSeq2Seq.from_pretrained(
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"openai/whisper-large-v2",
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torch_dtype="auto",
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device_map="auto",
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quantization_config=quant_config
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)
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model = torch.compile(model)
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```
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## Resources
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Read the [Quanto: a PyTorch quantization backend for Optimum](https://huggingface.co/blog/quanto-introduction) blog post to learn more about the library design and benchmarks.
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For more hands-on examples, take a look at the Quanto [notebook](https://colab.research.google.com/drive/16CXfVmtdQvciSh9BopZUDYcmXCDpvgrT?usp=sharing). |