| commit | 4a7687deafc1c785b8e23b1ab045e2582c0a9dbb | [log] [tgz] |
|---|---|---|
| author | Reed Wanderman-Milne <reedwm@google.com> | Wed Sep 25 16:55:37 2019 -0700 |
| committer | TensorFlower Gardener <gardener@tensorflow.org> | Wed Sep 25 20:11:54 2019 -0700 |
| tree | 35d3bbddf9aa96c17f8caf095a673fef4b11b7d2 | |
| parent | 54d651376c4946612edb2df785c2b5306f7db17e [diff] |
Support TPUStrategy in the mixed precision API. This is done by creating a dynamic subclass of AutoCastVariable when it wraps a DistributedVariable subclass. Now, when wrapping a variable that subclasses from DistributedVariable, a dynamic class subclassing both AutoCastVariable and variable.__class__ is created. That way, the AutoCastVariable will still pass isinstance(auto_cast_variable, variable.__class__) checks. This allows AutoCastVariables to work with TPUStrategy, which extensively uses isinstance. Alternatives considered: 1. Remove all isinstance checks of distributed values from distribution strategy. This is difficult, and people could add isinstance checks in the future. 2. Adding a metaclass to DistributedValues, and overriding __instancecheck__. Or using an ABCMeta metaclass to register a custom subclass. The issue is using metaclasses is complicated and I'd rather avoid it. 3. Using a variable_creator_scope with a lowered priority to have DistributedVariable wrap AutoCastVariables, instead of having AutoCastVariables wrap DistributedVariables. This requires modifications to DistributedVariables and requires the user a private TF API in Keras, so I did not go with this approach. PiperOrigin-RevId: 271237386
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