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73 lines
3.9 KiB
Markdown
73 lines
3.9 KiB
Markdown
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# VPTQ
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[Vector Post-Training Quantization (VPTQ)](https://github.com/microsoft/VPTQ) is a Post-Training Quantization (PTQ) method that leverages vector quantization to quantize LLMs at an extremely low bit-width (<2-bit). VPTQ can compress a 70B, even a 405B model, to 1-2 bits without retraining and still maintain a high-degree of accuracy. It is a lightweight quantization algorithm that takes ~17 hours to quantize a 405B model. VPTQ features agile quantization inference with low decoding overhead and high throughput and Time To First Token (TTFT).
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Run the command below to install VPTQ which provides efficient kernels for inference on NVIDIA and AMD GPUs.
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```bash
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pip install vptq
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```
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The [VPTQ-community](https://huggingface.co/VPTQ-community) provides a collection of VPTQ-quantized models. The model name contains information about its bitwidth (excluding cookbook, parameter, and padding overhead). Consider the [Meta-Llama-3.1-70B-Instruct-v8-k65536-256-woft] model as an example.
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- The model name is Meta-Llama-3.1-70B-Instruct.
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- The number of centroids is given by 65536 (2^16).
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- The number of residual centroids is given by 256 (2^8).
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The equivalent bit-width calculation is given by the following.
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- index: log2(65536) = 16 / 8 = 2-bits
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- residual index: log2(256) = 8 / 8 = 1-bit
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- total bit-width: 2 + 1 = 3-bits
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From here, estimate the model size by multiplying 70B * 3-bits / 8-bits/byte for a total of 26.25GB.
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Load a VPTQ quantized model with [`~PreTrainedModel.from_pretrained`].
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```py
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from transformers import AutoTokenizer, AutoModelForCausalLM
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quantized_model = AutoModelForCausalLM.from_pretrained(
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"VPTQ-community/Meta-Llama-3.1-70B-Instruct-v16-k65536-65536-woft",
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dtype="auto",
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device_map="auto"
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)
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```
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To quantize your own model, refer to the [VPTQ Quantization Algorithm Tutorial](https://github.com/microsoft/VPTQ/blob/algorithm/algorithm.md) tutorial.
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## Benchmarks
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VPTQ achieves better accuracy and higher throughput with lower quantization overhead across models of different sizes. The following experimental results are for reference only; VPTQ can achieve better outcomes under reasonable parameters, especially in terms of model accuracy and inference speed.
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| Model | bitwidth | W2↓ | C4↓ | AvgQA↑ | tok/s↑ | mem(GB) | cost/h↓ |
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| ----------- | -------- | ---- | ---- | ------ | ------ | ------- | ------- |
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| LLaMA-2 7B | 2.02 | 6.13 | 8.07 | 58.2 | 39.9 | 2.28 | 2 |
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| | 2.26 | 5.95 | 7.87 | 59.4 | 35.7 | 2.48 | 3.1 |
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| LLaMA-2 13B | 2.02 | 5.32 | 7.15 | 62.4 | 26.9 | 4.03 | 3.2 |
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| | 2.18 | 5.28 | 7.04 | 63.1 | 18.5 | 4.31 | 3.6 |
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| LLaMA-2 70B | 2.07 | 3.93 | 5.72 | 68.6 | 9.7 | 19.54 | 19 |
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| | 2.11 | 3.92 | 5.71 | 68.7 | 9.7 | 20.01 | 19 |
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## Resources
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See an example demo of VPTQ on the VPTQ Online Demo [Space](https://huggingface.co/spaces/microsoft/VPTQ) or try running the VPTQ inference [notebook](https://colab.research.google.com/github/microsoft/VPTQ/blob/main/notebooks/vptq_example.ipynb).
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For more information, read the VPTQ [paper](https://huggingface.co/papers/2409.17066).
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