LCEInterpreter and converter design about compute-engine HOT 1 OPEN

1. Coincidentally I happened to be looking into this today and it seems to works as follows:

The CLI flag use_xnnpack of lce_benchmark_model basically decides which OpResolver to use: when use_xnnpack=false then it uses BuiltinOpResolverWithoutDefaultDelegates, otherwise it uses BuiltinOpResolver. The op resolver has a GetDelegates() method, and the BuiltinOpResolver will choose the XNNPACK delegate when it is enabled via bazel build options, and by default they enable XNNPACK.

So the simplest would be to add a use_xnnpack bool argument to the Python interpreter and then on this line choose the BuiltinOpResolverWithoutDefaultDelegates whenever use_xnnpack == false:

To have more control over the options passed to XNNPACK (for example in the TF master branch we can enable/disable int8 kernels through these options), it seems that the easiest way is to follow the instructions at "Enable XNNPACK via low level delegate API". I haven't tried it, but I would think the best way is to always use BuiltinOpResolverWithoutDefaultDelegates and then use that 'low level delegate API' to set the desired XNNPACK options whenever use_xnnpack == true.

2. I have never looked at the Select TF Ops mechanism of TFLite, so I'm afraid I can't give any hints for this.

from compute-engine.