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ptq_dynamic

README.md

Step-by-Step

This example load a language translation model and confirm its accuracy and speed based on GLUE data.

Prerequisite

1. Environment

pip install neural-compressor
pip install -r requirements.txt

Note: Validated ONNX Runtime Version.

2. Prepare Model

Supported model identifier from huggingface.co:

Model Identifier
Intel/bert-base-uncased-mrpc
Intel/roberta-base-mrpc
Intel/xlm-roberta-base-mrpc
Intel/camembert-base-mrpc
distilbert-base-uncased-finetuned-sst-2-english
Alireza1044/albert-base-v2-sst2
Intel/MiniLM-L12-H384-uncased-mrpc
philschmid/MiniLM-L6-H384-uncased-sst2
bert-base-cased-finetuned-mrpc
Intel/electra-small-discriminator-mrpc
M-FAC/bert-mini-finetuned-mrpc
Intel/xlnet-base-cased-mrpc
Intel/bart-large-mrpc
Intel/deberta-v3-base-mrpc 
python prepare_model.py  --input_model=Intel/bert-base-uncased-mrpc  --output_model=bert-base-uncased-mrpc.onnx

3. Prepare Dataset

Download the GLUE data with prepare_data.sh script.

export GLUE_DIR=/path/to/glue_data
export TASK_NAME=MRPC # or SST

bash prepare_data.sh --data_dir=$GLUE_DIR --task_name=$TASK_NAME

Run

1. Quantization

Dynamic quantization:

bash run_quant.sh --input_model=path/to/model \ # model path as *.onnx
                   --output_model=path/to/model_tune \ # model path as *.onnx
                   --dataset_location=path/to/glue/data

2. Benchmark

bash run_benchmark.sh --input_model=path/to/model \ # model path as *.onnx
                      --dataset_location=path/to/glue/data \ 
                      --batch_size=batch_size \ 
                      --mode=performance # or accuracy