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[Docs] Document some known TF/XLA limitations.

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George Karpenkov 2020-05-27 10:52:00 -07:00 committed by TensorFlower Gardener
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tensorflow/compiler/xla/g3doc/index.md
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@ -174,9 +174,33 @@ When filing bugs, attach the contents of the `/tmp/generated` directory
If possible, try to isolate
a bug to a single XLA program by using the
[`replay_computation`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/compiler/xla/tools/replay_computation.cc)
[`replay_computation`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/compiler/xla/tools/run_hlo_module_main.cc)
and iteratively running it on generated programs.
## Known Issues
Compilation with XLA can greatly improve the performance of your programs, but
the TensorFlow interop has a number of known sharp corners.
### TensorArray TF/XLA Interconversion
The problem manifests itself as an error message
`Support for TensorList crossing the XLA/TF boundary is not implemented`.
XLA supports `tf.TensorArray`. However, the _interconversion_ between TF and
XLA representations is not implemented yet.
This error often arises when the `TensorArray` is used inside the compiled
block, but the derivative is taken outside.
Workaround: compile the outermost scope which is taking the derivative.
### Random Number Generation
XLA currently ignores TF seeds to random operations. This affects stateful TF
random operations, such as `tf.random.normal`, or `tf.nn.dropout`. XLA will
behave as if the compilation was seeded with a new unique seed at each run. This
limitation does not apply to stateless random ops.
## XLA Frontends
Apart from TensorFlow, XLA programs can be generated by: