Quantization using SmoothQuant#

This folder contains an example of quantizing a opt-125m model using the ONNX quantizer of Quark. It also shows how to use the Smooth Quant algorithm.

The example has the following parts:

Pip requirements
Prepare model
Quantization without Smooth_Quant
Quantization with Smooth_Quant
Evaluation

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 Without Smooth Quant#

The quantizer takes the float model and produces a quantized model without Smooth Quant.

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.

Quantization With Smooth Quant#

The quantizer takes the float model and produces a quantized model with Smooth Quant.

cp -r models smoothed_quantized_models && rm smoothed_quantized_models/model.onnx
python quantize_model.py --input_model_path models/model.onnx \
                         --output_model_path smoothed_quantized_models/smoothed_quantized_model.onnx \
                         --config INT8_TRANSFORMER_DEFAULT \
                         --include_sq

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

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 without Smooth Quant:

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

Test the PPL of the quantized model with Smooth Quant:

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

	Float Model	Quantized Model without Smooth Quant	Quantized Model with Smooth Quant
Model Size	480 MB	384 MB	385 MB
PPL	27.0317	28.6846	28.4315

Quantization using SmoothQuant

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