58 lines
3.2 KiB
Markdown
58 lines
3.2 KiB
Markdown
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# AQLM
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> [!TIP]
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> Try AQLM on [Google Colab](https://colab.research.google.com/drive/1-xZmBRXT5Fm3Ghn4Mwa2KRypORXb855X?usp=sharing)!
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Additive Quantization of Language Models ([AQLM](https://arxiv.org/abs/2401.06118)) is a Large Language Models compression method. It quantizes multiple weights together and take advantage of interdependencies between them. AQLM represents groups of 8-16 weights as a sum of multiple vector codes.
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Inference support for AQLM is realised in the `aqlm` library. Make sure to install it to run the models (note aqlm works only with python>=3.10):
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```bash
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pip install aqlm[gpu,cpu]
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```
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The library provides efficient kernels for both GPU and CPU inference and training.
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The instructions on how to quantize models yourself, as well as all the relevant code can be found in the corresponding GitHub [repository](https://github.com/Vahe1994/AQLM). To run AQLM models simply load a model that has been quantized with AQLM:
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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quantized_model = AutoModelForCausalLM.from_pretrained(
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"ISTA-DASLab/Mixtral-8x7b-AQLM-2Bit-1x16-hf",
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torch_dtype="auto",
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device_map="auto"
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)
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tokenizer = AutoTokenizer.from_pretrained("ISTA-DASLab/Mixtral-8x7b-AQLM-2Bit-1x16-hf")
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```
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## PEFT
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Starting with version `aqlm 1.0.2`, AQLM supports Parameter-Efficient Fine-Tuning in a form of [LoRA](https://huggingface.co/docs/peft/package_reference/lora) integrated into the [PEFT](https://huggingface.co/blog/peft) library.
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## AQLM configurations
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AQLM quantization setups vary mainly on the number of codebooks used as well as codebook sizes in bits. The most popular setups, as well as inference kernels they support are:
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| Kernel | Number of codebooks | Codebook size, bits | Notation | Accuracy | Speedup | Fast GPU inference | Fast CPU inference |
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|---|---------------------|---------------------|----------|-------------|-------------|--------------------|--------------------|
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| Triton | K | N | KxN | - | Up to ~0.7x | ✅ | ❌ |
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| CUDA | 1 | 16 | 1x16 | Best | Up to ~1.3x | ✅ | ❌ |
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| CUDA | 2 | 8 | 2x8 | OK | Up to ~3.0x | ✅ | ❌ |
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| Numba | K | 8 | Kx8 | Good | Up to ~4.0x | ❌ | ✅ |
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