AutoGPTQ/examples

Examples

To run example scripts in this folder, one must first install auto_gptq as described in this

Quantization

Commands in this chapter should be run under quantization folder.

Basic Usage

To Execute basic_usage.py, using command like this:

python basic_usage.py

To Execute basic_usage_with_wikitext2.py, using command like this:

python basic_usage_with_wikitext2.py

Note: There is about 0.6 ppl degrade on opt-125m model using AutoGPTQ, compared to GPTQ-for-LLaMa.

Quantize with Alpaca

To Execute quant_with_alpaca.py, using command like this:

python quant_with_alpaca.py --pretrained_model_dir "facebook/opt-125m" --per_gpu_max_memory 4 --quant_batch_size 16

Use --help flag to see detailed descriptions for more command arguments.

The alpaca dataset used in here is a cleaned version provided by gururise in AlpacaDataCleaned

Evaluation

Commands in this chapter should be run under evaluation folder.

Language Modeling Task

run_language_modeling_task.py script gives an example of using LanguageModelingTask to evaluate model's performance on language modeling task before and after quantization using tatsu-lab/alpaca dataset.

To execute this script, using command like this:

CUDA_VISIBLE_DEVICES=0 python run_language_modeling_task.py --base_model_dir PATH/TO/BASE/MODEL/DIR --quantized_model_dir PATH/TO/QUANTIZED/MODEL/DIR

Use --help flag to see detailed descriptions for more command arguments.

Sequence Classification Task

run_sequence_classification_task.py script gives an example of using SequenceClassificationTask to evaluate model's performance on sequence classification task before and after quantization using cardiffnlp/tweet_sentiment_multilingual dataset.

To execute this script, using command like this:

CUDA_VISIBLE_DEVICES=0 python run_sequence_classification_task.py --base_model_dir PATH/TO/BASE/MODEL/DIR --quantized_model_dir PATH/TO/QUANTIZED/MODEL/DIR

Use --help flag to see detailed descriptions for more command arguments.

Text Summarization Task

run_text_summarization_task.py script gives an example of using TextSummarizationTask to evaluate model's performance on text summarization task before and after quantization using samsum dataset.

To execute this script, using command like this:

CUDA_VISIBLE_DEVICES=0 python run_text_summarization_task.py --base_model_dir PATH/TO/BASE/MODEL/DIR --quantized_model_dir PATH/TO/QUANTIZED/MODEL/DIR

Use --help flag to see detailed descriptions for more command arguments.

Push To Hub

Commands in this chapter should be run under push_to_hub folder.

You can upload and share your quantized model to Hugging Face Hub by using push_to_hub function.

push_quantized_model_to_hf_hub.py provide a simple example to upload quantized model, tokenizer and configs at once.

First, you need to login, run the following command in the virtual environment where Hugging Face Transformers is installed:

huggingface-cli login

Then run the script like this:

python push_quantized_model_to_hf_hub.py --quantized_model_dir PATH/TO/QUANTIZED/MODEL/DIR --tokenizer_dir PATH/TO/TOKENIZER/DIR --repo_id REPO/ID

Use --help flag to see detailed descriptions for more command arguments.

Benchmark

Commands in this chapter should be run under benchmark folder.

Generation Speed

generation_speed.py script gives an example of how to benchmark the generations speed of pretrained and quantized models that auto_gptq supports, this benchmarks model generation speed in tokens/s metric.

To execute this script, using command like this:

CUDA_VISIBLE_DEVICES=0 python generation_speed.py --model_name_pr_path PATH/TO/MODEL/DIR

Use --help flag to see detailed descriptions for more command arguments.

PEFT

Commands in this chapter should be run under peft folder.

Lora

peft_lora_clm.py script gives an example of instruction tuning gptq quantized model's lora adapter using tools in auto_gptq.utils.peft_utils and 🤗 peft on alpaca dataset.

To execute this script, using command like this:

CUDA_VISIBLE_DEVICES=0 python peft_lora_clm.py --model_name_or_path PATH/TO/MODEL/DIR

Use --help flag to see detailed descriptions for more command arguments.

AdaLora

peft_adalora_clm.py script gives an example of instruction tuning gptq quantized model's adalora adapter using tools in auto_gptq.utils.peft_utils and 🤗 peft on alpaca dataset.

To execute this script, using command like this:

CUDA_VISIBLE_DEVICES=0 python peft_adalora_clm.py --model_name_or_path PATH/TO/MODEL/DIR

Use --help flag to see detailed descriptions for more command arguments.

AdaptionPrompt

peft_adaption_prompt_clm.py script gives an example of instruction tuning gptq quantized model's adaption_prompt adapter(llama-adapter) using tools in auto_gptq.utils.peft_utils and 🤗 peft on alpaca dataset.

To execute this script, using command like this:

CUDA_VISIBLE_DEVICES=0 python peft_adaption_prompt_clm.py --model_name_or_path PATH/TO/MODEL/DIR

Use --help flag to see detailed descriptions for more command arguments.

If you want to try models other than llama, you can install peft from source using this branch, see here to check what other models are also supported, and with this branch installed, you can also use ADAPTION_PROMPT_V2 peft type (llama-adapter-v2) by simply replace AdaptionPromptConfig with AdaptionPromptV2Config in the script.