[Apache TVM Discuss] [Development/RFC] [RFC][Quantization] A new quantization framework in TVM: initial RFC (1/4)

Thanks, that makes sense. I was thinking that while calibration, you could use different attributes for `simulated_quantize` and `simulated_dequantize` ops. In the callback of calibrating an operator, one can simulate the affine space and argue about scales and zero points. But for capturing r

[Apache TVM Discuss] [Development/RFC] [RFC][Quantization] A new quantization framework in TVM: initial RFC (1/4)

I apologize for the long delay. Thanks @electriclilies and team for nicely written RFC. I support the idea. Reading through the comments, it seems that many of us are in agreement about the AutoQ and its reliance on QNN extension. The mentioned pain points mostly revolve around * The inconsi

[Apache TVM Discuss] [Development] Role of the LLVM autovectorizer in TVM

@kevinthesun Pinging in case you have wondered about this before --- [Visit Topic](https://discuss.tvm.apache.org/t/role-of-the-llvm-autovectorizer-in-tvm/8388/2) to respond. You are receiving this because you enabled mailing list mode. To unsubscribe from these emails, [click here](htt

[Apache TVM Discuss] [Development] Quantized models and legalization pass

Sorry for late reply. Can you try this? tinfo is nothing but just te placeholder. ~~~ diff --git a/python/tvm/relay/qnn/op/legalizations.py b/python/tvm/relay/qnn/op/legalizations.py index 50e5a02f8..8add434c1 100644 --- a/python/tvm/relay/qnn/op/legalizations.py +++ b/python/tvm/relay/qnn/op/