Note: This repository contains quantization algorithm and the model evaluation code for SpQR method for LLM compression; The efficient inference code will be added soon.
It accompanies the research paper "SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression" .
To run SpQR with falcon make sure that you have torch>=2.0.0 with CUDA support.
Install packages from requirements.txt:
pip install -r requirements.txtNote: the results reported in the ArXiv paper where obtained using 4.28.dev0 version of transformers, commit id 464d420775.
The script will require downloading and caching locally the relevant tokenizer and the datasets. They will be saved in default Huggingface Datasets directory unless alternative location is provided by env variables. See relevant Datasets documentation section
This repository is expected to work with models of LLaMA, Falcon and OPT families so far.
For quantization with SpQR its is recommended to use the subset of the data model
was trained on. I.e. for quantization of LLaMA models we recommend to use the subset
of RedPajama and for Falcon quantization - RefinedWeb.Both subsets are stored in data directory:
data/red_pajama_n=1024.pthdata/refined_web_n=128.pth
Note These subsets are already processed with the corresponding model tokenizer. Use for different model will lead to unexpected behavior.
For OPT following GPTQ paper we recommend to use c4.
For the sake of convenience one can optionally log the data to Weights and Biases service (wandb).
Run pip install wandb for W&B logging.
Specify $WANDB_ENTITY, $WANDB_PROJECT, $WANDB_NAME environment variables prior to running experiments. use --wandb argument to enable logging
This code was developed and tested using a single A100 GPU with 80GB GPU RAM. It may successfully run on GPUs with 32GB+ VRAM for perplexity evaluation of up to LLaMA-65B and Falcon-40B models.
With --offload activations option, the model perplexity may be evaluated on machines with less VRAM: 24GB+ for Llama 65B and 6GB+ for Llama 7B.
The perplexity testing code also requires RAM amount sufficient to hold uncompressed model weights (e.g. ~130GB for Llama65B) and testing datasets.
For Language Model Evaluation Harness evaluation one needs to have enough memory to load whole model
on one or several devices + activation tensors.
The code requires the LLaMA model to be downloaded in Huggingface format and saved locally. The scripts below assume that $TRANSFORMERS_CACHE variable points to the Huggingface Transformers cache folder.
This script compresses the model and then tests its performance in terms of perplexity using WikiText2, C4, and Penn Treebank datasets.
The command to launch the script should look like this:
export MODEL_PATH=<PATH_TO_MODEL_DIR>
export DATASET=<INSERT DATASET NAME OR PATH TO CUSTOM DATA>
python main.py $MODEL_PATH $DATASET \
--wbits 4 \
--groupsize 16 \
--perchannel \
--qq_scale_bits 3 \
--qq_zero_bits 3 \
--qq_groupsize 16 \
--outlier_threshold=0.2 \
--permutation_order act_order \
--percdamp 1e0 \
--nsamples 128
The command above runs near-lossless compression as described in the article. Adjusting the above parameters allows for tighter compression with a slightly greater loss.
Note the launch arguments:
<PATH_TO_MODEL_DIR>- path to model folder, which containsconfig.jsonone of [c4, ptb, wikitext2, pajama, refinedweb, none]-- name of dataset to use for compression, or path to an alternative preprocessed and tokenized dataset.--wbits 3-- number of bits for quantized weights representation--groupsize 16-- size of first-order groups for compression--qq_groupsize 16-- size of second-order (quantized) groups for compression--qq_scale_bits 3 --qq_zero_bits 3-- bit sizes for quantizing first order weights' scale and zeros.--offload activations-- moves activations to RAM when not used. Reduces VRAM usage while slowing work by ~10%. runpython main.py --helpfor more details on command line arguments, including compression parameters.--save --load-- path to save/load quantized model.
To perform zero-shot evaluation, we use Language Model Evaluation Harness framework with slight modifications. This repository contains a copy of LM Evaluation Harness repo from early 2023 in lm-eval-harness folder.
Before running the code make sure that you have all the requirements and dependencies of lm-eval-harness installed. To install them run:
pip install -r lm-evaluation-harness/requirements.txt
The main script launching the evaluation procedure is lmeval.py .
Note. Current version of the script support only LLaMA/Falcon quantization. Therefore, set:
--model=hf-causal--model_args pretrained=$MODEL_PATHwhere$MODEL_PATHhas to be one of the LLaMA models
--quantization_args - list of comma separated arguments for quantizer. For details and options
refer to spqr_config.py.
Below is presented an example of benchmark launch.
export MODEL_PATH=<INSERT PATH_TO_MODEL_DIR>
export DATASET=<INSERT DATASET NAME OR PATH TO CUSTOM DATA>
python lmeval.py \
--model hf-causal \
--model_args pretrained=$MODEL_PATH,dtype=float16,use_accelerate=True \
--quantization_args dataset=$DATASET,wbits=4,groupsize=16,perchannel=True,qq_scale_bits=3,qq_zero_bits=3,qq_groupsize=16,percdamp=1.0,outlier_threshold=0.2,simplified_outliers=False,nsamples=128,offload_activations=True \
--tasks winogrande,piqa,hellaswag,arc_easy,arc_challenge \
--batch_size 1
Performance and runtime notes:
- For large models (LLaMA-30B, LLaMA-65B) specify
max_memory_per_gpu={value}GIBso that there are free 15-20GIB of GPU memory for each GPU to store activations for calibration. offload_activations=Trueslightly reduces peak memory consumption- Typically
LlaMA-30Brequires 1-2 A100 GPUs with 80Gb of memory andLlaMA-65Brequires 3 A100 with 80Gb each. - With enough spare GPU memory, one can raise batch size to accelerate evaluation process.
This repository also contains an efficient CUDA kernel implementation of the
SpQR matvec. The file inference_demo.py h orcontains a demo of this functionality
by running end-to-end model inference. Below is an example of how to launch it.
usage: inference_demo.py [-h] [--pretrained_model_path PRETRAINED_MODEL_PATH] [--compressed_model_path COMPRESSED_MODEL_PATH] --execution_mode {0,1}
options:
-h, --help show this help message and exit
--pretrained_model_path PRETRAINED_MODEL_PATH
Path to the model to the pretrained model
--compressed_model_path COMPRESSED_MODEL_PATH
Path to the compressed .pt model
--execution_mode {0,1}
If set to 0, will evaluate the dense pretrained model. If set to 1, will evaluate the spqr-quantized modelThis script also reports the mean and median time of the forward() passes and the total inference execution time.
In order to run the benchmark and test suite you need to build the sources used by these scripts. You can do so by running the following command:
/bin/bash scripts/build.sh which simply runs the setup.py script.
After running SpQR which produces the tensors stored in int8, in order to run the efficient inference kernels, one must convert the tensors produces by SpQR (legacy tensors) into the optimized storage format used by the cuda kernel. In order to do so, run the following script:
usage: convert_legacy_model_format.py [-h] --base_model BASE_MODEL --legacy_model_path LEGACY_MODEL_PATH [--sparse_strategy {csr,ptcsr,optimize_latency}] [--save_pt SAVE_PT] [--save_per_layer SAVE_PER_LAYER]
options:
-h, --help show this help message and exit
--base_model BASE_MODEL
path or name of the unquantized model
--legacy_model_path LEGACY_MODEL_PATH
path to legacy model
--sparse_strategy {csr,ptcsr,optimize_latency}
Sparse strategy storage. Options: csr, ptcsr, auto. CSR - Compressed Sparse Rows PTCSR - Alternative storage format optimize_latency - Use the current GPU to determine the optimal storage format to reduce
kernel latency
--save_pt SAVE_PT Save the converted quantized .pt model here
--save_per_layer SAVE_PER_LAYER
Save the converted quantized mTo convert a model into a Hugging Face compatible format, use convert_to_hf.py script:
usage: convert_to_hf.py [-h] [--model MODEL] [--config_path CONFIG_PATH] [--in_path_pt IN_PATH_PT] [--out_path OUT_PATH] [--save_safetensors] [--trust_remote_code] [--load_model] [--save_tokenizer]
options:
-h, --help show this help message and exit
--model MODEL Path to the model to base config on, as in AutoConfig.from_pretrained()
--config_path CONFIG_PATH
Path to the model to base config on, as in AutoConfig.from_pretrained()
--in_path_pt IN_PATH_PT
Path of the checkpoint to convert
--out_path OUT_PATH Path to save HF compatible checkpoint to
--save_safetensors Whether to save in safetensors format
--trust_remote_code Whether to trust remote code
--load_model Whether to load model
--save_tokenizer Whether to save tokenizerIn order to run the matvec benchmark suite, one should run:
bench_spqr.py [-h] --tensor_path TENSOR_PATH [--ptcsr_path PTCSR_PATH] [--output_path OUTPUT_PATH]
options:
-h, --help show this help message and exit
--tensor_path TENSOR_PATH
Path to folder containing the tensors of the formmodel_path/ 0/ tensor0 tensor1
--ptcsr_path PTCSR_PATH
Path to folder containing the tensors of the formmodel_path/ 0/ tensor0 tensor1
--output_path OUTPUT_PATH
Path to results *.csv file.
Make sure that the <tensor_path> and the optional <ptcsr_path. point to a folder containing quantized matrices produced by the convert_legacy_model_format.py script.
Use <cuda_device_id> to set the cuda device during benchmark. The script outputs the results in <results_output>.
In order to run the unittest, simply execute:
python3 tests/test.py@misc{dettmers2023spqr,
title={SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression},
author={Tim Dettmers and Ruslan Svirschevski and Vage Egiazarian and Denis Kuznedelev and Elias Frantar and Saleh Ashkboos and Alexander Borzunov and Torsten Hoefler and Dan Alistarh},
year={2023},
eprint={2306.03078},
archivePrefix={arXiv},
primaryClass={cs.CL}
}