samskalicky commented on a change in pull request #17623: Dynamic subgraph
compile support
URL: https://github.com/apache/incubator-mxnet/pull/17623#discussion_r388635890
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File path: python/mxnet/gluon/block.py
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@@ -1026,6 +1030,53 @@ def _call_cached_op(self, *args):
out = [out]
return _regroup(out, self._out_format)
+ def optimize_for(self, x, *args, backend=None, backend_opts=None,
**kwargs):
+ """Activates or deactivates :py:class:`HybridBlock` s recursively. Has
no effect on
+ non-hybrid children.
+
+ Parameters
+ ----------
+ x : NDArray
+ first input to model
+ *args : NDArray
+ other inputs to model
+ backend : str
+ The name of backend, as registered in `SubgraphBackendRegistry`,
default None
+ backend_opts : dict of user-specified options to pass to the backend
for partitioning, optional
+ Passed on to `PrePartition` and `PostPartition` functions of
`SubgraphProperty`
+ static_alloc : bool, default False
+ Statically allocate memory to improve speed. Memory usage may
increase.
+ static_shape : bool, default False
+ Optimize for invariant input shapes between iterations. Must also
+ set static_alloc to True. Change of input shapes is still allowed
+ but slower.
+ """
+
+ # do hybrize API call
+ self.hybridize(True, backend, backend_opts, **kwargs)
Review comment:
calling `hybridize` and then `optimize_for` is fine. Since `optimize_for`
will just call `hybridize` again internally anyway.
Calling `optimize_for` and then `hybridize` though will reset the cachedOp
since `hybridize` calls `_clear_cached_op`. So not a problem per-se. User just
needs to realize what they are doing.
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