No, it is more complicated than that.
Currently our DNNL backends has two patterns, one for conv + relu and another for conv + bias + relu: https://github.com/apache/incubator-tvm/blob/master/python/tvm/relay/op/contrib/dnnl.py#L84-L86 Suppose we use optional to represent the two patterns. We would have one pattern named "fused_conv", let's say, that can match both conv + bias and conv + bias + relu. In the current infra, the DNNL backend would receive a composite function with attribute "fused_conv", and the subgraph is either conv + bias or conv + bias + relu, depending on whether bias is matched. To tell the difference, we need `IsOp(relu->args[0], "nn.bias_add")`. In my use case, I have four optionals to match "qconv + hswish", so I'd need four `IsOp(...)` calls. It would be really nice if the composite attribute tells me which optionals are matched ("conv_relu" or "conv_bias_relu", rather than "fused_conv"). A user can associate a list of optional op names for each pattern, and `MergeComposite` pass can compare the matched fragement with optional names to come up with a good attribute. --- [Visit Topic](https://discuss.tvm.ai/t/byoc-use-pattern-language-to-create-composite-functions/6727/7) to respond. You are receiving this because you enabled mailing list mode. To unsubscribe from these emails, [click here](https://discuss.tvm.ai/email/unsubscribe/ad4478b2e009fb3cafc65d6a30b61a6b6d72ca6427229705d1c3c3a1dc0f37af).
