Builds an operator that compiles and runs computation
with XLA. (deprecated)
View aliases
Compat aliases for migration
See Migration guide for more details.
tf.xla.experimental.compile(
computation, inputs=None
)
Args | |
---|---|
computation
|
A Python function that builds a computation to apply to the
input. If the function takes n inputs, 'inputs' should be a list of n
Tensor s.
All |
inputs
|
A list of inputs or None (equivalent to an empty list). Each input
can be a nested structure containing values that can be converted to
Tensor s. Note that passing an N-dimension list of compatible values will
result in an N-dimension list of scalar Tensor s rather than a single
Rank-N Tensor . If you need a different behavior, convert parts of
inputs to Tensor s with tf.convert_to_tensor .
|
Returns | |
---|---|
List of Tensor s corresponding to the Tensor s from
the output of computation i.e. the same return value as if
computation(*inputs) is called directly, with the following exceptions:
|
Known issues | |
---|---|
When a tf.random operation is built with XLA, the implementation doesn't pass the user provided seed to the XLA compiler. As such, the XLA compiler generates a random number and uses it as a seed when compiling the operation. This implementation causes a violation of the Tensorflow defined semantics in two aspects. First, changing the value of the user defined seed doesn't change the numbers generated by the operation. Second, when a seed is not specified, running the program multiple times will generate the same numbers. |