Thanks @masahi.
Okay so, I tried with this sequence of passes: >seq1 = tvm.transform.Sequential( > > [relay.transform.InferType(), > relay.transform.SimplifyInference(), > relay.transform.FoldConstant(), > relay.transform.FoldScaleAxis(), > relay.transform.SimplifyInference(), > relay.transform.FoldConstant() > ]) I get "add" ops as it is; they are not getting folded to the preceding conv2d's bias. Also, if suppose there is no bias_add corresponding to a conv2d but batchnorm is there so after folding the batchnorm will a new bias_add op be created eventually to adjust the shift or the shift will remain as an add op in that case? --- [Visit Topic](https://discuss.tvm.apache.org/t/batchnorm-op-fusion-in-tvm/12391/7) to respond. You are receiving this because you enabled mailing list mode. To unsubscribe from these emails, [click here](https://discuss.tvm.apache.org/email/unsubscribe/6fbd906289a1e5b6a84436c58756c459409e123ddf0ed3f1f09c650e060223c1).
