QuIP#: QuIP with Lattice Codebooks

This repository contains the official code for QuIP#, a weights-only quantization method that is able to achieve near fp16 performance using only 2 bits per weight. QuIP# combines lattice codebooks with incoherence processing to create state-of-the-art quantized models. We provide a full suite of 2, 3, and 4 bit Llama models quantized using QuIP# as well as other Llama-architecture models (e.g. Mistral). We also provide a full codebase that allows users to quantize and deploy their own models as well as CUDA kernels that accelerate inference for QuIP# models.

Method	Precision	Wiki $\downarrow$	C4 $\downarrow$	ArcE $\uparrow$	PiQA $\uparrow$
Native	16 bit	3.120	5.533	0.597	0.809
OPTQ	3 bit	4.577	6.838	0.544	0.786
OPTQ	2 bit	109.820	62.692	0.253	0.505
QuIP	2 bit	5.574	8.268	0.544	0.751
QuIP#	2 bit	4.159	6.529	0.595	0.786

Quantization results on Llama 2 70B. QuIP# achieves near-native performance at 2 bits, outperforming all other presented baselines.

☞ Read more about QuIP# and how it works here!

News

• We've released new 3 and 4 bit models that use residual vector quantization (RVQ) to get the benefits of the $E_8$ lattice at higher bitrates. Learn more about these models in the blog post above and check out the models in our model zoo!
• We merged in a faster E8P kernel that (with CUDA graphs) is around twice as fast as before. Make sure to pull the latest code and models and recompile quiptools to get the faster kernel. As a reminder, hf.generate() does not work with CUDA graphs so the generation speed in interactive_gen.py is not representative of reality.
• We fixed a duplicated entry in the E8P codebook and updated the result tables.

Installation

• Clone the repo
• Install the requirements via pip install -r requirements.txt. You may want to use the official pytorch commands to get the CUDA versions.
• Build and install the matmul CUDA kernels. (cd quiptools && python setup.py install && cd ../)

Quantization

• To quantize a Llama architecture (q/k/v/o/up/gate/down) model: python quantize_llama.py --<FLAGS>. The primary flags are as follows. See the arg list for the remaining flags.
  • --save_path <output path>.
  • --base_model <Hugging Face (HF) model card or local path>. For Llama 1, we provide weights at relaxml/Llama-1-<7,13,30,65>b-hf. For other models, use model cards from HF.
  • --hessian_path <path to precomputed Hessians>. We provide precomputed Hessians at repo_id's relaxml/Hessians*-<n>. These Hessians were computed with n samples and the context length and attention mask used to train the original model. To download them, run python scripts/download_hf.py --folder_path <local path to save Hessians> --repo_id <repo_id> --read_token <huggingface read token>.
  • --codebook <codebook argument>. We recommend using the 2 bit E8P codebook with E8P12. This codebook gives the best quantization at 2 bits. Other options are the 2 bit D4 codebook and the 4 bit Half Integer grid HI4B1C. See our blog post for details on the codebooks.
  • --scale_override <quantization scale parameter>. We suggest the following scale parameters for each codebook: {E8P12: 0.9, D4: 1.1, HI4B1C: 2.7}, however you may want to play around with scales if quantizing your own models.
• To convert a quantized model to the HF format: CUDA_VISIBLE_DEVICES=0 python hfize_llama.py --quantized_path <output path of quantize_llama.py> --hf_output_path <path to save HF version>
• To generate your own Hessians for a Llama architecture model: python hessian_offline_llama --<FLAGS>. The primary flags are as follows. See the arg list for the remaining flags. Hessian calculation uses a fp64 accumulator for numerical accuracy. Running this script on a device with slow fp64 capabilities will take longer.
  • --batch_size Batch size per GPU. Tune so you don't run out of memory.
  • --devset_size Size of devset to use for Hessian generation.
  • --ctx_size Context size (sequence length) to use for Hessian generation.
  • --base_model Same as in quantize_llama.py.

I want to quantize a non-Llama architecture model, what do I do?

Currently, hessian_offline_llama.py, quantize_llama.py, and hfize_llama.py are written for the Llama architecture. However, the only "special" things they do are identify the relevant nn.Linear layers that need to be quantized (q/k/v/o/up/gate/down), inject Hessian hooks, and quantize them. If you want to quantize a non-Llama architecture model, you will need to find the relevant nn.Linear files and make your own hessian_offline/quantize/hfize files. This should be pretty straightforward and feel free to open a GitHub ticket if you run into any issues. You will also need copy modeling_<architecture>.py from the HF source into the models/ folder and replace the relevant nn.Linear layers with QuantizedLinear layers (see how models/llama.py does it). Our current quantize_llama.py implementation fuses the q/k/v layers and the up/gate layers for increased speed since they share the same Hessians. However, this is not a requirement and you can also quantize those layers individually.

Evaluation

See our blog post for a full set of results.

• Perplexity on Wikitext2 and C4: CUDA_VISIBLE_DEVICES=0 python eval_ppl.py --hf_path <HF version path>
• Zero shot tasks: CUDA_VISIBLE_DEVICES=0 python eval_zeroshot.py --tasks arc_challenge,arc_easy,boolq,piqa,winogrande --batch_size <batch size> --hf_path <HF version path>
• Timing test for forward pass of one token: CUDA_VISIBLE_DEVICES=0 python gen_speed.py --hf_path <HF version path> --batch_size <batch_size>.

The CUDA_VISIBLE_DEVICES environmental variable is only needed if you get CUDA errors from running on more GPUs than needed to fit the model. This is an artifact of HF accelerate.

Text Generation

To use our models as part of an interactive generation script, run CUDA_VISIBLE_DEVICES=0 python interactive_gen.py --hf_path <HF version path> --max_length <max generation length>. interactive_gen.py is very rudimentary and you may want to write your own. All it does is call HF's .generate() function.

Model Zoo

We provide quantized models available on HF. To use them, pass the given HF repo_id to --hf_path. See our blog post for details on the codebooks.

Lattice Codebook	Base Model	Weight Bits	HF repo_id
E8P 2 Bit	Llama 2 70b	2	relaxml/Llama-2-70b-E8P-2Bit
	Llama 2 70b chat	2	relaxml/Llama-2-70b-chat-E8P-2Bit
	Llama 2 13b	2	relaxml/Llama-2-13b-E8P-2Bit
	Llama 2 13b chat	2	relaxml/Llama-2-13b-chat-E8P-2Bit
	Llama 2 7b	2	relaxml/Llama-2-7b-E8P-2Bit
	Llama 2 7b chat	2	relaxml/Llama-2-7b-chat-E8P-2Bit
	Llama 1 65b	2	relaxml/Llama-1-65b-E8P-2Bit
	Llama 1 30b	2	relaxml/Llama-1-30b-E8P-2Bit
	Llama 1 13b	2	relaxml/Llama-1-13b-E8P-2Bit
	Llama 1 7b	2	relaxml/Llama-1-7b-E8P-2Bit
	Mistral 7b	2	relaxml/Mistral-7b-E8P-2Bit
	OpenHermes 2.5	2	relaxml/Openhermes-7b-E8P-2Bit
E8P RVQ 3 Bit	Llama 2 70b	3	relaxml/Llama-2-70b-E8PRVQ-3Bit
	Llama 2 70b chat	3	relaxml/Llama-2-70b-chat-E8PRVQ-3Bit
	Llama 2 13b	3	relaxml/Llama-2-13b-E8PRVQ-3Bit
	Llama 2 13b chat	3	relaxml/Llama-2-13b-chat-E8PRVQ-3Bit
	Llama 2 7b	3	relaxml/Llama-2-7b-E8PRVQ-3Bit
	Llama 2 7b chat	3	relaxml/Llama-2-7b-chat-E8PRVQ-3Bit
	Llama 1 65b	3	relaxml/Llama-1-65b-E8PRVQ-3Bit
	Llama 1 30b	3	relaxml/Llama-1-30b-E8PRVQ-3Bit
	Llama 1 13b	3	relaxml/Llama-1-13b-E8PRVQ-3Bit
	Llama 1 7b	3	relaxml/Llama-1-7b-E8PRVQ-3Bit
	Mistral 7b	3	relaxml/Mistral-7b-E8PRVQ-3Bit
	OpenHermes 2.5	3	relaxml/Openhermes-7b-E8PRVQ-3Bit
E8P RVQ 4 Bit	Llama 2 70b	4	relaxml/Llama-2-70b-E8PRVQ-4Bit
	Llama 2 70b chat	4	relaxml/Llama-2-70b-chat-E8PRVQ-4Bit
	Llama 2 13b	4	relaxml/Llama-2-13b-E8PRVQ-4Bit
	Llama 2 13b chat	4	relaxml/Llama-2-13b-chat-E8PRVQ-4Bit
	Llama 2 7b	4	relaxml/Llama-2-7b-E8PRVQ-4Bit
	Llama 2 7b chat	4	relaxml/Llama-2-7b-chat-E8PRVQ-4Bit
	Llama 1 65b	4	relaxml/Llama-1-65b-E8PRVQ-4Bit
	Llama 1 30b	4	relaxml/Llama-1-30b-E8PRVQ-4Bit
	Llama 1 13b	4	relaxml/Llama-1-13b-E8PRVQ-4Bit
	Llama 1 7b	4	relaxml/Llama-1-7b-E8PRVQ-4Bit
	Mistral 7b	4	relaxml/Mistral-7b-E8PRVQ-4Bit
	OpenHermes 2.5	4	relaxml/Openhermes-7b-E8PRVQ-4Bit

CUDA Graphs

We provide a wrapper class that integrates our models with CUDA graphs in model/graph_wrapper.py. Currently, the torch CUDA graph implementation does not work with HF's .generate() function, but model calls with static input and output sizes can utilize the CUDA graph wrapper for better performance. Most of our evaluation scripts use the graph wrapper by default unless the --no_use_cuda_graph flag is passed in.

Other

Use of Llama models is governed by the Meta license available here. Use of Mistral models is governed by the Apache 2.0 license. Use of this code is governed by the GNU GPL v3 license.