# XLA: Optimizing Compiler for Machine Learning [OpenXLA](https://openxla.org) is a domain-specific compiler for linear algebra that can accelerate TensorFlow models with potentially no source code changes. ## Introduction 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 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): return tf.reduce_sum(x + y * z) ``` Run without XLA, the graph launches three kernels: one for the multiplication, one for the addition and one for the reduction. However, XLA can optimize the graph so that it computes the result in a single kernel launch. It does this by "fusing" the addition, multiplication and reduction into a single GPU kernel. Moreover, this fused operation does not write out the intermediate values produced by `y*z` and `x+y*z` to memory; instead it "streams" the results of these intermediate computations directly to their users while keeping them entirely in GPU registers. Fusion is XLA's single most important optimization. Memory bandwidth is typically the scarcest resource on hardware accelerators, so removing memory operations is one of the best ways to improve performance. ## Enable XLA for TensorFlow models ### Explicit compilation with `tf.function(jit_compile=True)` Explicit compilation API offers a fine-grained control for choosing which functions should be compiled. For example, the following TensorFlow function which performs the MNIST training is compiled with XLA: ``` @tf.function(jit_compile=True) def train_mnist(images, labels): images, labels = cast(images, labels) with tf.GradientTape() as tape: predicted_labels = layer(images) loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits( logits=predicted_labels, labels=labels )) layer_variables = layer.trainable_variables grads = tape.gradient(loss, layer_variables) optimizer.apply_gradients(zip(grads, layer_variables)) ``` The `jit_compile` API has _must-compile_ semantics: either the entire function is compiled with XLA, or an `errors.InvalidArgumentError` exception is thrown. XLA can not currently compile functions where dimensions are not _inferrable_: that is, if it's not possible to infer the dimensions of all tensors without running the entire computation. For example, the following function will not compile: ``` @tf.function def not_compilable(x): return tf.unique(x) ``` Shapes can vary across the runs though: ``` @tf.function(jit_compile=True) def recompiled_on_launch(a, b): return a + b recompiled_on_launch(tf.ones([1, 10]), tf.ones([1, 10])) recompiled_on_launch(tf.ones([1, 100]), tf.ones([1, 100])) ``` Note: Nesting behavior: the function will be compiled if at least one function in its call stack has `jit_compile=True`. See the [tutorial colab](./tutorials/jit_compile.ipynb) for a more detailed usage example, and a [tutorial video](https://www.youtube.com/watch?v=cPAD9vLKE0c) on `jit_compile=True` usage. ### Usage with Keras For Keras models, `jit_compile=True` can be set as an argument to [`model.compile`](https://www.tensorflow.org/api_docs/python/tf/keras/Model#compile): ``` model.compile(optimizer="adam", jit_compile=True) ``` ### Usage with distributed strategy XLA:GPU can be used with TF distributed strategy ([`MirroredStrategy`](https://www.tensorflow.org/api_docs/python/tf/distribute/MirroredStrategy) or [`MultiWorkerMirroredStrategy`](https://www.tensorflow.org/api_docs/python/tf/distribute/experimental/MultiWorkerMirroredStrategy)) by annotating step function with `jit_compile=True`: ``` @tf.function(jit_compile=True) def step_fn(): t = tf.ones(shape=[100], dtype=tf.float32) ctx = tf.distribute.get_replica_context() return ctx.all_reduce(tf.distribute.ReduceOp.SUM, t) @tf.function def run_fn(): return strategy.run(step_fn) ``` ### Auto-clustering A simple way to start using XLA in TensorFlow models without any changes is to enable _auto-clustering_, which automatically finds _clusters_ (connected subgraphs) within the TensorFlow functions which can be compiled and executed using XLA. Auto-clustering on GPU can be enabled by setting the `TF_XLA_FLAGS` environment variable: Note: In TF2, only the code inside `tf.function` will be clustered. ``` $ TF_XLA_FLAGS=--tf_xla_auto_jit=2 path/to/your/tf/program ``` Auto-clustering is currently optimized for GPU workloads, but it can also be enabled on CPU by additionally using the flag `--tf_xla_cpu_global_jit`: ``` $ TF_XLA_FLAGS="--tf_xla_auto_jit=2 --tf_xla_cpu_global_jit" path/to/your/program ``` Note: Auto-clustering support on CPU and on multi-GPU environments is experimental. For a detailed usage example see the [auto-clustering tutorial colab](./tutorials/autoclustering_xla.ipynb). ## Inspect compiled programs XLA provides introspection facilities which let you inspect the generated programs. To dump the generated programs, use the environment variable `XLA_FLAGS`: ``` $ XLA_FLAGS="--xla_dump_to=/tmp/generated" TF_XLA_FLAGS="--tf_xla_auto_jit=2" my/tensorflow/program ``` After the dumping is performed, you can find the following files in `/tmp/generated`: - `module_XXXX.*_optimizations.txt` Generated [XLA programs](./operation_semantics.md), one per each compiled cluster. Attaching those when submitting XLA bug reports is extremely helpful! - `module_XXXX.ir-*.ll` Generated files in [LLVM](https://llvm.org/docs/LangRef.html) intermediate representation, with [NVPTX](https://llvm.org/docs/NVPTXUsage.html) intrinsics. - `module_XXXX.ptx` Generated [PTX](https://docs.nvidia.com/cuda/parallel-thread-execution/index.html) files. You can also dump the graph visualizing the embedding of XLA clusters inside of the TensorFlow graph with: ``` $ TF_DUMP_GRAPH_PREFIX=/tmp/generated TF_XLA_FLAGS="--tf_xla_clustering_debug" ``` ## Reproducible bug reports A bug report is much easier to reproduce if it includes dumps for the generated XLA programs and the used auto-clustering embedding. To generate them for a TensorFlow program running with auto-clustering, launch: ``` $ TF_DUMP_GRAPH_PREFIX=/tmp/generated \ TF_XLA_FLAGS="--tf_xla_clustering_debug --tf_xla_auto_jit=2" \ XLA_FLAGS="--xla_dump_hlo_as_text --xla_dump_to=/tmp/generated" \ my/tensorflow/program" ``` When filing bugs, attach the contents of the `/tmp/generated` directory (referenced above). If possible, try to isolate a bug to a single XLA program by using the [`run_hlo_module`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/compiler/xla/tools/run_hlo_module_main.cc) and iteratively running it on generated programs. ## Further reading - [OpenXLA Documentation](https://openxla.org) OpenXLA Documentation - [Known Issues](./known_issues.md) List of known issues with XLA+TF - [XLA - TensorFlow, Compiled](https://developers.googleblog.com/2017/03/xla-tensorflow-compiled.html): Read on Google Developers Blog - Check out the [XLA source](https://github.com/openxla/xla) on Github! ## 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 - [Nx](https://github.com/elixir-nx/nx): Numerical computing library for the Elixir programming language ## Talks ### Using XLA from TF using `jit_compile=True` ### XLA Overview