Quantizing an OPT-125M Model

Quantizing an OPT-125M Model#

Note

For information on accessing Quark ONNX examples, refer to Accessing ONNX Examples. This example and the relevant files are available at onnx/weights_only_quantization/int8_qdq/llama2

This example describes how to quantize an opt-125m model using the ONNX quantizer of Quark.

Pip requirements#

Install the necessary Python packages:

python -m pip install -r requirements.txt

Prepare model#

Get opt-125m torch model:

mkdir opt-125m
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 quantized model.

cp -r models quantized_models && rm quantized_models/model.onnx
python quantize_model.py --input_model_path models/model.onnx \
                         --output_model_path quantized_models/quantized_model.onnx \
                         --config INT8_TRANSFORMER_DEFAULT

This command will generate a quantized model under the quantized_models folder, which was quantized by Int8 configuration for transformer-based models.

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 --onnx_model models/ --do_onnx_eval --no_cuda

Test the PPL of the quantized model:

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

Float Model

Quantized Model

Model Size

480 MB

384 MB

PPL

27.0317

28.6846

License#

Copyright (C) 2024, Advanced Micro Devices, Inc. All rights reserved. SPDX-License-Identifier: MIT