Compilation with XLA can greatly improve the performance of your programs, but the TensorFlow interop has a number of known sharp corners.
tf.Variable on a different device
INVALID_ARGUMENT: Trying to access resource <Variable>
(defined @ <Loc>) located in device CPU:0 from device GPU:0
XLA cluster runs on exactly one device, and it can not read or write to
tf.Variable located on a different device. Usually this error message
indicates that the variable was not placed on the right device to begin with.
The error message should precisely specify the location of the offending
TensorArray TF/XLA interconversion is not supported
Support for TensorList crossing the XLA/TF boundary is not
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
Workaround: compile the outermost scope which is taking the derivative.
TensorFlow while loops need to be bounded (or have backprop disabled)
XLA compilation requires a fixed tensor list size. Set the max
number of elements. This could also happen if you're using a TensorArray in a
while loop that does not have its maximum_iteration set, you can fix this by
setting maximum_iteration to a suitable value.
Workaround: all compiled while loops need to either have
parameter set to a constant value known at compile time, or backpropagation
tf.TensorArray is not supported
tf.TensorArray(..., dynamic_size=True) are not compilable with
XLA, as such writes require an unknown number of reallocations when the array
exceeds the original bound.
Workaround: provide a statically known bound to your arrays.
Random number generation ignores TF seed
XLA currently ignores TF seeds to random operations. This affects stateful TF
random operations, such as
tf.nn.dropout. XLA will
behave as if the compilation was seeded with a new unique seed at each run
within the same process (the first run of the process will always yield the same