message: [XLA Docs] Mention other frontends for XLA
diff: -# XLA: Optimizing Compiler for TensorFlow
  +# XLA: Optimizing Compiler for Machine Learning
  XLA (Accelerated Linear Algebra) is a domain-specific compiler for linear
  -algebra that accelerates TensorFlow models with potentially no source code
  +algebra that can accelerate TensorFlow models with potentially no source code
  changes.
  The results are improvements in speed and memory usage: most internal benchmarks
  @@ -18,11 +18,11 @@ When a TensorFlow program is run, all of the operations are executed
  individually by the TensorFlow executor. Each TensorFlow operation has a
  precompiled GPU kernel implementation that the executor dispatches to.
  -XLA provides an alternative mode of running TF models: it compiles the
  -TensorFlow graph into a sequence of computation kernels generated specifically
  -for the given model. Because these kernels are unique to the model, they can
  -exploit model-specific information for optimization. For example, let's look at
  -an optimization XLA does in the context of a simple TensorFlow computation:
  +XLA provides an alternative mode of running models: it compiles the TensorFlow
  +graph into a sequence of computation kernels generated specifically for the
  +given model. Because these kernels are unique to the model, they can exploit
  +model-specific information for optimization. For example, let's look at an
  +optimization XLA does in the context of a simple TensorFlow computation:
  ```
  def model_fn(x, y, z):
  @@ -154,6 +154,16 @@ 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)
  and iteratively running it on generated programs.
  +## XLA Frontends
  +
  +Apart from TensorFlow, XLA programs can be generated by:
  +
  +- [JAX](https://github.com/google/jax): Composable transformations of
  + Python+NumPy programs
  +- [Julia](https://github.com/JuliaTPU/XLA.jl): The Julia language for
  + scientific computing
  +- [PyTorch](https://github.com/pytorch/xla): PyTorch framework
  +
  ## Further reading
  - [XLA Architecture](./architecture.md): Overview of the XLA architecture
  