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Thanks for the suggestion. We don't currently have plans to implement these ops. You can find a softmax implementation in the LLM use case. There is already a ReduceSum operator and a MaxPool operator, you could try to adapt them to obtain ReduceMax.
Thanks for the pointers. Do you have additional guides on how one implements a custom op in this context? I really would need to convert my existing (torch) model, I can't re-write and retrain a quantized version.
It does seem like the reduce_sum could be adapted easily (np.sum -> np.amax) although I don't know if that's FHE compliant. Also I notice that there's already an implementation for Softmax, although the docstring says it's not FHE compliant it doesn't elaborate as to why.
Feature request
Request the implementation of the following ONNX operators:
Motivation
These operators are common in neural networks of many types; softmax is common in classification, reducemax is common in CNNs.
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