Add benchmark methodology to profiling skill - #276
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Add comprehensive benchmark dimensions covering: - 5 pipeline options (ORT CUDA, GenAI CUDA/CPU, HF PyTorch) - 5 dtype options (F16, BF16, F32, Q4_K_M, NF4) - 3 EP variants with attention kernel mapping - 4 input modalities (text, image+text, audio+text, all) - 4 core metrics (decode tok/s, TTFT, VRAM, model size) - 5 additional dimensions (cache type, prompt length, batch size, padding strategy, KV buffer sharing) - 6 best practices for reproducible benchmarks Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> Signed-off-by: Justin Chu <justinchu@microsoft.com>
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Lowering opset is not the right thing to do. We should update onnxruntime kernel registration |
Address reviewer feedback: lowering opset is not the right approach. The correct fix for opset version mismatches is to update ORT kernel registration for the missing opset versions. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> Signed-off-by: Justin Chu <justinchu@microsoft.com>
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Add a comprehensive benchmark methodology section to the profiling skill.
Benchmark dimensions covered