Dynamic Quantization for OPT-125M

Dynamic Quantization for OPT-125M#

This folder contains an example of quantizing an opt-125m model using the ONNX quantizer of Quark. The example has the following parts:

Pip requirements#

Install the necessary python packages:

python -m pip install -r requirements.txt

Prepare model#

Get opt-125m torch model:

wget -P opt-125m https://huggingface.co/facebook/opt-125m/resolve/main/pytorch_model.bin
wget -P opt-125m https://huggingface.co/facebook/opt-125m/resolve/main/config.json
wget -P opt-125m https://huggingface.co/facebook/opt-125m/resolve/main/tokenizer_config.json
wget -P opt-125m https://huggingface.co/facebook/opt-125m/resolve/main/vocab.json
wget -P opt-125m https://huggingface.co/facebook/opt-125m/resolve/main/merges.txt
wget -P opt-125m https://huggingface.co/facebook/opt-125m/resolve/main/generation_config.json
wget -P opt-125m https://huggingface.co/facebook/opt-125m/resolve/main/special_tokens_map.json

Export onnx model from opt-125m torch model:

mkdir models && optimum-cli export onnx --model ./opt-125m --task text-generation ./models/

Quantization#

The quantizer takes the float model and produces a dynamically quantized model:

cp -r models dynamic_quantized_models && rm dynamic_quantized_models/model.onnx
python quantize_model.py --input_model_path models/model.onnx \
                         --output_model_path dynamic_quantized_models/dynamic_quantized_model.onnx \
                         --config UINT8_DYNAMIC_QUANT

This command will generate a quantized model under the dynamic_quantized_models folder, using the UInt8 dynamic quantization configuration.

Evaluation#

Test the PPL of the float model on wikitext2.raw:

python onnx_validate.py --model_name_or_path models/ --per_gpu_eval_batch_size 1 --block_size 2048 --do_onnx_eval --no_cuda

Test the PPL of the dynamic quantized model:

python onnx_validate.py --model_name_or_path dynamic_quantized_models/ --per_gpu_eval_batch_size 1 --block_size 2048 --do_onnx_eval --no_cuda

Float Model

Dynamic Quantized Model

Model Size

480 MB

120 MB

PPL

27.0317

28.6006