message: [XLA/Docs] Update known issues list
diff: @@ -5,7 +5,7 @@ the TensorFlow interop has a number of known sharp corners.
  ## TensorArray TF/XLA interconversion
  -The problem manifests itself as an error message
  +*Error message*:
  `Support for TensorList crossing the XLA/TF boundary is not implemented`.
  XLA supports `tf.TensorArray`. However, the _interconversion_ between TF and
  @@ -13,15 +13,31 @@ 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.
  +*Workaround*: compile the outermost scope which is taking the derivative.
  -## Dynamic `tf.TensorArray` is not supported
  +## TensorFlow while loops need to be bounded (or have backprop disabled)
  +
  +*Error message*: `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`.
  +
  +TF while [loops](https://www.tensorflow.org/api_docs/python/tf/while_loop)
  +created using `tf.while_loop` support backpropagation by accumulating all
  +intermediate results in a `TensorArray`, but XLA only supports bounded
  +`TensorArray`s.
  +
  +*Workaround*: all compiled while loops need to either have `maximum_iterations`
  +parameter set to a constant value known at compile time, or backpropagation
  +disabled using `back_prop=False`.
  +
  +## Dynamic `tf.TensorArray`
  Writes into `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.
  +*Workaround*: provide a statically known bound to your arrays.
  ## Random number generation
  @@ -29,13 +45,3 @@ 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.
  -
  -## TensorFlow while loops need to be bounded (or have backprop disabled)
  -
  -TF while [loops](https://www.tensorflow.org/api_docs/python/tf/while_loop)
  -created using `tf.while_loop` support backpropagation by accumulating all
  -intermediate results in a `TensorArray`.
  -
  -Since XLA only supports bounded `TensorArray`s, all compiled while loops need to
  -either have `maximum_iterations` parameter set to a constant value known at
  -compile time, or backpropagation disabled using `back_prop=False`.
  