Bert model optimization on Qualcomm NPU with SNPE SDK

This folder contains a sample use case of Olive to convert an bert model Onnx model, then to SNPE DLC and to evaluate the accuracy of the DLC model.

Performs optimization pipeline:

Pytorch Model -> Onnx Model with Dynamic Shape -> Onnx Model with Fixed Shape -> SNPE Model

Prerequisites

Download the SNPE SDK zip following instructions from Qualcomm

We test it with SNPE v2.18.0.240101.

Unzip the file and set the unzipped directory path as environment variable SNPE_ROOT.

olive configure-qualcomm-sdk --py_version 3.8 --sdk snpe

Run the conversion and quantization locally.

olive run --config bert_snpe.json

"Module 'qti.aisw.converters' has no attribute 'onnx': Refer to this: https://developer.qualcomm.com/comment/21810#comment-21810, change the import statement in {SNPE_ROOT}/lib/python/qti/aisw/converters/onnx/onnx_to_ir.py:L30 to:
```
from qti.aisw.converters.onnx import composable_custom_op_utils as ComposableCustomOp
```