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XLA: Optimizing Compiler for Machine Learning
XLA (Accelerated Linear Algebra) is a domain-specific compiler for linear algebra that can accelerate TensorFlow models with potentially no source code changes.
The results are improvements in speed and memory usage: e.g. in BERT MLPerf submission using 8 Volta V100 GPUs using XLA has achieved a ~7x performance improvement and ~5x batch size improvement:
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 for a more detailed usage example.
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.
AOT (Ahead-of-time) compilation for CPU with tfcompile
You can also use a standalone tfcompile tool, which converts
TensorFlow graph into executable code (for x86-64 CPU only).
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.txtGenerated XLA programs, one per each compiled cluster. Attaching those when submitting XLA bug reports is extremely helpful! -
module_XXXX.ir-*.llGenerated files in LLVM intermediate representation, with NVPTX intrinsics. -
module_XXXX.ptxGenerated PTX 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
replay_computation
and iteratively running it on generated programs.
Further reading
- Known Issues List of known issues with XLA
- XLA Architecture: Overview of the XLA architecture
- XLA - TensorFlow, Compiled: Read on Google Developers Blog
- Check out the XLA source on Github!
XLA Frontends
Apart from TensorFlow, XLA programs can be generated by: