Microsoft Contrib Operator ToDO

[TedThemistokleous]

As noted in a few meetings now various quantization and optimizations when models are converted to ONNX format incorporate many of the Microsoft contrib operator set.

This is a superset of the ONNX specification: https://github.com/onnx/onnx/blob/main/docs/Operators.md

The full list of these Microsoft Contrib operators are found here: https://github.com/microsoft/onnxruntime/blob/main/docs/ContribOperators.md#com.microsoft.MatMulIntegerToFloat

Eventually the goal is to have similar support as we do with the Onnx spec with the Microsoft operators implimented as part of MIGraphX. Luckily many of these operators are a composite of a subset of MIGraphX ops, or a variant where we can leverage existing parsing, optimizations and functionality to the codebase.

Current operators that have recently surfaced from some of our models and various quantizations we want support for sooner than later.

Please add others as you see fit or you come across these in your runs. I've formatted this as follows to help with planning

Model - Toolchain (Quark/Olive/Onnxruntime) + Model Name - Status (linked to a PR in MIGraphx, or TBD status)

• MatMulIntegerToFloat - Bert Onnxrt Quant/ Quark int8 Quantized int8 Whisper - Parser changes to handle MatMulIntegerToFloat #3445
• QLinearConv - Quark Quantized int8 Whisper - TBD
• SparseAttention - Phi3 Small - (WIP sparse_attention #3805) (Add partial onnx support for com.microsoft.SparseAttention #3866)
• GroupNorm - Olive Optimized Stable Diffusion 1.5, Turbo & XL (encode/decode/Unet) -Add contrib groupnorm #3678
• MultiHeadAttention - Olive Optimized Stable Diffusion 1.5, Turbo and XL (Unet) - Add MultiHeadAttention #3650
• NhwcConv - Olive Optimized Stable Diffusion 1.5 - (Add onnx support for com.microsoft.NhwcConv #3796)
• GroupQuerryAttention - Onnxruntime LLama V2/Phi 3 small/mini/medium 4k/8k Context Length - Add GroupQueryAttention with KV-Cache #3425
• MatMulNBits - Onnxruntime Llama V2 - Add support for MatMulNBits #3496
• RotaryEmbedding - Olive Optimized Llama V2/DirectML Compiled Llama V2 - (RotaryEmbedding Contrib OP #3695 Add RotaryEmbedding contrib op #3812)
• Attention - Seen during Onnxruntime optimized versions of BERT - (Add Attention Microsoft Contrib Operator #3816)
• SkipLayerNormalization - Seen during Onnxruntime optimized versions of BERT -Adding skip-layer-normalization operator #3917

opreators we also want

• QAttention - Unsupported operator: com.microsoft.QAttention #2759

Aug 13th - Adding in Mixsture of Experts models - https://github.com/microsoft/onnxruntime/blob/main/docs/ContribOperators.md#com.microsoft.MoE

• MoE - Seen for Models like llama4, gpt-oss
• QMoE - Seen for the above as well ^

Seen with stable diffusion variants/optimizations (Using ORT toolset)

• EmbedLayerNormalization
• SkipGroupNorm

[TedThemistokleous]

Took a look at the SD 1_5 Olive set of Models Unet, VAE encoder/decoder - GroupNorm is everywhere. Consistently before multiple Convolutions

UNet has MultiHeadAttention operator

[TedThemistokleous]

We have GroupNormalization in MIGraphX and GroupNorm seems to be a slight variant on that. Seems like there can be small effort to port this over based on these changes : #2242

https://github.com/onnx/onnx/blob/main/docs/Operators.md#GroupNormalization
vs
https://github.com/microsoft/onnxruntime/blob/main/docs/ContribOperators.md#com.microsoft.GroupNorm

Similar to how we handle additional inputs with variants on standard ops (matmul,conv, matmulinteger, etc)

Onnxruntime changes to support both are trivial and posted: ROCm/onnxruntime#73

[TedThemistokleous]

Looking at SD 3 Medium model

Unet, TextEncoders 1/2/3 - Needs layernormalization - We have this in MIGraphX just needs support in Onnxruntime ROCm/onnxruntime#73

Tokenizer 1/2 - Seeing ClipTokenizer
Tokenizer 3 - SentencepieceTokenizer
VAE Encode/Decode Use InstanceNormalization (in MIGraphX)

[TedThemistokleous]

SD XL

Unet - Needs MultiHeadAttention and GroupNorm
Tokenizer - LayerNormalization all supported
VAE Encode/Decode - Needs GroupNorm

[TedThemistokleous]

SD Turbo

ControllerNet/Unet/VAE Encode/VAE Decode - Needs GroupNorm
ControllerNet/Unet - Needs MultiLayerAttention

[TedThemistokleous]

Flux1, we should be able to run this without any additional modifications of the code (MIGraphx, + MIGraphX EP)

[TedThemistokleous]

MatMulIntegerToFloat seems to be useful by Optimized Bert that's int8 quantized by Onnxruntime using DynamicQuantizeLinear + MatMulInteger + cast to do the dequantization.

[TedThemistokleous]

Llama V2 requies we also have RotaryEmbedding operator

Adding to this list

[amd-mwu10004]

#2759 - Highest priority
#2756
#2778