[GitHub] [incubator-mxnet] matteosal edited a comment on issue #20109: 2.0 migration guide?

[GitBox <gi...@apache.org>]

matteosal edited a comment on issue #20109:
URL: https://github.com/apache/incubator-mxnet/issues/20109#issuecomment-812614000


   Ok I went looking for how the python wrapper ([symbol.py](https://github.com/apache/incubator-mxnet/blob/master/python/mxnet/symbol.py) and [executor.py](https://github.com/apache/incubator-mxnet/blob/master/python/mxnet/executor.py)) got adapted to work with the cached op API and I'm starting to get a feeling of the current scenario. Some questions:
   
   * One major difference is that while the the old C executor required arrays at construction time, cached op requires them at evaluation time (`MXInvokeCachedOp`). Cached op also has a flag called `static_alloc`. This suggests that when such flag is activated the graph is actually "compiled" and shipped to the execution device by `MXInvokeCachedOp`, similarly to what `MXExecutorBind` used to do (except `MXInvokeCachedOp` also immediately runs the graph). Is this reasoning correct? 
   * Following the reasoning of the previous point, calling `MXInvokeCachedOp` twice in a row specifying CPU and GPU devices (with `static_alloc = true`) will allocate the graph on both system memory and GPU memory. Is there a way to selectively free CPU or GPU memory?
   * Again on the same reasoning, what happens if `MXInvokeCachedOp` is called multiple times with NDArrays of different shapes? Will the arrays share memory?
   * What is the purpose of flags `forward_bulk_size` and `backward_bulk_size`?


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