Tensorflow and onednn logs
Hi @akote123 , could you share the original verbose log, thanks! Hi @huiyan2021, I have uploaded here  [vit_intel_log.zip](https://github.com/tensorflow/tensorflow/files/12105129/vit_intel_log.zip)  
 I guess the reason is that _mklops are logged by <<, where the outputs are fully buffered if they are redirected to a file, while oneDNN verbose always flush stdout immediately: https://github.com/search?q=repo%3Aoneapi-src%2FoneDNN+fflush&type=code @huiyan2021 , one more observation what I found is some ops are common_runtime/eager/execute.cc and some are in common_runtime/executor.cc , here I am not able to understand why there is two path for execution  Hi @akote123,

from the log I can see you are using xla, so for ops can not be jitted, they come to common_runtime/eager/execute.cc, otherwise they come to common_runtime/executor.cc

also, suggest that you use [trace viewer](https://www.tensorflow.org/guide/profiler#trace_viewer) to trace executions.   @huiyan2021 , do we have file location tensorflow source code where checking happen whether to go common_runtime/eager/execute.cc or the other one.
 @akote123 , you can refer to this article: https://whatdhack.medium.com/tensorflow-graph-graphdef-grappler-xla-mlir-llvm-etc-615191e96ebc, see XLA Flow part and call stack @huiyan2021 , In tensorflow the single model can go in both XLA and oneDNN or is it like either it will use XLA or oneDNN only
 Both. There may be different scenarios: 
1. Some parts of the model go in XLA path, some parts go in oneDNN path.
2. Intel recently submitted a [pilot PR](https://github.com/tensorflow/tensorflow/pull/61237) to accelerate XLA’s Dot op with oneDNN.

You can refer to this RFC: https://docs.google.com/document/d/1ZzMcrjxITJeN2IjjgbzUjHh-4W1YgDUus3j25Dvn9ng/edit @huiyan2021 ,Thank you for the pointers I will got through it .
Actually for pretrained models how we can enable XLA  both for inference and transfer learning same as training, you can refer to https://www.tensorflow.org/xla#enable_xla_for_tensorflow_models
Hi, 
    I am trying to analyse the call flow of tensorflow and onednn. I am setting up environment vars as 
export ONEDNN_VERBOSE=1
export TF_CPP_MAX_VLOG_LEVEL=1  
export omp_num_threads=1

I am collecting the logs. I was trying to map _mklops with onednn primitive. But here the onednn and mkl calls are random in log that is after 10 mkl calls I am seeing 20 onednn calls. 

Is there any flags need to be set to get logs with correct mapping  or do we need to map manually
[filtered_intel_log.txt](https://github.com/tensorflow/tensorflow/files/12093285/filtered_intel_log.txt)

