Turbine Camp - Githubissues

Introduction

This page provides information on implementing complete support of ONNX operators in Shark/IREE front-end. This effort is a part of overall ONNX quality improvement tracked by #812

Pre-requisites

Must read full ONNX
Read torch-MLIR architecture
Read add new torch op

Development Workspace

Create a fork of https://github.com/llvm/torch-mlir and follow build instructions to build a clone of your fork and push changes using Pull Requests

Guidelines

Assign an ONNX Ops from the list below to your GitHub ID by hovering your mouse on it and clicking on the bullseye (that will show to the right) to create a new issue, assign that issue to yourself and use that to track your work
Study (thoroughly) the specification of the operator you picked on onnx-ops
Use alt_e2eshark test to create multiple tests to comprehensively test all features and attributes of the operator -- this also help you understand how the operator is expected to behave
Test torch-mlir/iree using the tests created and make code changes in torch-mlir to have correct inference numeric for llvm-cpu target working, if an iree codgen issue, file an issue on iree and note that in issue you created and assigned to self for the op

Contact:

@vivekkhandelwal1 (Indian Time Zone)
@AmosLewis (US Pacific Time Zone)
@zjgarvey (US Central Time Zone)
@marbre (Central EU Time Zone)

Onnx Ops( Known missing features)

[ ] #835
[ ] #840
[ ] #836
[ ] #839
[ ] GridSample (mode - Cubic, paddding_mode only zero is supported)
[ ] #843
[ ] #838
[ ] #858
[ ] #844
[ ] ScatterElements (reduction : max, min, add) https://github.com/nod-ai/SHARK-Turbine/issues/823
[ ] #842
[ ] RoiAlign (roi max pooling without default, coordTfMode and sampling_ratio)
[ ] #834
[ ] #837
[ ] #833
[ ] #841
[ ] Col2Im (5D input tensor)

Onnx Ops( Known Quality Issues)

[ ] AffineGrid
[ ] Bernoulli
[ ] Cast
[ ] DequantizeLinear
[x] #856
[ ] MelWeightMatrix
[ ] QuantizeLinear
[ ] RandomUniformLike

nod-ai / SHARK-ModelDev

Turbine Camp #828