73 lines
4.5 KiB
Markdown
73 lines
4.5 KiB
Markdown
# Quantization
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TGI offers many quantization schemes to run LLMs effectively and fast based on your use-case. TGI supports GPTQ, AWQ, bits-and-bytes, EETQ, Marlin, EXL2 and fp8 quantization.
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To leverage GPTQ, AWQ, Marlin and EXL2 quants, you must provide pre-quantized weights. Whereas for bits-and-bytes, EETQ and fp8, weights are quantized by TGI on the fly.
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We recommend using the official quantization scripts for creating your quants:
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1. [AWQ](https://github.com/casper-hansen/AutoAWQ/blob/main/examples/quantize.py)
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2. [GPTQ/ Marlin](https://github.com/AutoGPTQ/AutoGPTQ/blob/main/examples/quantization/basic_usage.py)
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3. [EXL2](https://github.com/turboderp/exllamav2/blob/master/doc/convert.md)
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For on-the-fly quantization you simply need to pass one of the supported quantization types and TGI takes care of the rest.
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## Quantization with bitsandbytes, EETQ & fp8
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bitsandbytes is a library used to apply 8-bit and 4-bit quantization to models. Unlike GPTQ quantization, bitsandbytes doesn't require a calibration dataset or any post-processing – weights are automatically quantized on load. However, inference with bitsandbytes is slower than GPTQ or FP16 precision.
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8-bit quantization enables multi-billion parameter scale models to fit in smaller hardware without degrading performance too much.
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In TGI, you can use 8-bit quantization by adding `--quantize bitsandbytes` like below 👇
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```bash
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docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:2.3.1 --model-id $model --quantize bitsandbytes
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```
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4-bit quantization is also possible with bitsandbytes. You can choose one of the following 4-bit data types: 4-bit float (`fp4`), or 4-bit `NormalFloat` (`nf4`). These data types were introduced in the context of parameter-efficient fine-tuning, but you can apply them for inference by automatically converting the model weights on load.
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In TGI, you can use 4-bit quantization by adding `--quantize bitsandbytes-nf4` or `--quantize bitsandbytes-fp4` like below 👇
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```bash
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docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:2.3.1 --model-id $model --quantize bitsandbytes-nf4
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```
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You can get more information about 8-bit quantization by reading this [blog post](https://huggingface.co/blog/hf-bitsandbytes-integration), and 4-bit quantization by reading [this blog post](https://huggingface.co/blog/4bit-transformers-bitsandbytes).
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Similarly you can use pass you can pass `--quantize eetq` or `--quantize fp8` for respective quantization schemes.
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In addition to this, TGI allows creating GPTQ quants directly by passing the model weights and a calibration dataset.
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## Quantization with GPTQ
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GPTQ is a post-training quantization method to make the model smaller. It quantizes the layers by finding a compressed version of that weight, that will yield a minimum mean squared error like below 👇
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Given a layer \\(l\\) with weight matrix \\(W_{l}\\) and layer input \\(X_{l}\\), find quantized weight \\(\\hat{W}_{l}\\):
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$$({\hat{W}_{l}}^{*} = argmin_{\hat{W_{l}}} ||W_{l}X-\hat{W}_{l}X||^{2}_{2})$$
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TGI allows you to both run an already GPTQ quantized model (see available models [here](https://huggingface.co/models?search=gptq)) or quantize a model of your choice using quantization script. You can run a quantized model by simply passing --quantize like below 👇
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```bash
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docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:2.3.1 --model-id $model --quantize gptq
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```
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Note that TGI's GPTQ implementation doesn't use [AutoGPTQ](https://github.com/PanQiWei/AutoGPTQ) under the hood. However, models quantized using AutoGPTQ or Optimum can still be served by TGI.
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To quantize a given model using GPTQ with a calibration dataset, simply run
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```bash
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text-generation-server quantize tiiuae/falcon-40b /data/falcon-40b-gptq
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# Add --upload-to-model-id MYUSERNAME/falcon-40b to push the created model to the hub directly
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```
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This will create a new directory with the quantized files which you can use with,
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```bash
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text-generation-launcher --model-id /data/falcon-40b-gptq/ --sharded true --num-shard 2 --quantize gptq
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```
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You can learn more about the quantization options by running `text-generation-server quantize --help`.
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If you wish to do more with GPTQ models (e.g. train an adapter on top), you can read about transformers GPTQ integration [here](https://huggingface.co/blog/gptq-integration).
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You can learn more about GPTQ from the [paper](https://arxiv.org/pdf/2210.17323.pdf).
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