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Add SimpLayerNorm, GQA to supported_ops #625
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@ankitm3k Please review |
@wine99 Can you please confirm the changes are functional with both benchmark_app & onnxruntime_perf_test app i.e. the model is fully supported without causing any subgraph partitions. May I know these Ops are enable individually for which OV devices viz. CPU, GPU , NPU or NPUW? Kindly also state the OV toolkit version since which this support was introduced of course you have mentioned 2025.1 here. |
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@MayureshV1 can you please take a look at this. With this merge, Phi Silica model will fail. |
The models mentioned in the comments (phi3 and llama3) are fully supported. The whole graph is run on OV EP (CPU and iGPU).
The ops are enabled for CPU and iGPU. Support for NPU/NPUW is WIP. The current OV version of the master branch is 2025.1.0. We built the master branch and the models are fully supported with the changes in this PR. |
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@sfatimar , I think you are right. Once we merge this PR ORT will not split subgraphs between MLAS and OV NPU. Entire model would try to execute on NPU. NPU compilation will fail due to lack of GQA support and entire model would run on OV CPU. @ankitm3k , @preetha-intel Do you have a suggestion ton how we can test graph split between OV NPU and OV CPU (GQA) and later run entire graph on OV NPU when GQA is supported. |
Description
Added GroupQueryAttention and SimplifiedLayerNormalization to supported ops.
Motivation and Context
With these two PR openvinotoolkit/openvino#28963 openvinotoolkit/openvino#28163 merged into OpenVINO, ONNX models generated by onnxruntime-genai containing these two ops will work.
Example models: Phi3 and Llama3 generated by the following command: