View source on GitHub |
Builds an operator that compiles and runs computation
with XLA.
tf.xla.experimental.compile( computation, inputs=None )
Note: In eager mode,computation
will have@tf.function
semantics.
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 tensors.
All |
inputs | A list of inputs or None (equivalent to an empty list). Each input can be a nested structure containing values that are convertible to tensors. Note that passing an N-dimension list of compatible values will result in a N-dimension list of scalar tensors rather than a single Rank-N tensors. If you need different behavior, convert part of inputs to tensors with tf.convert_to_tensor . |
Returns | |
---|---|
Same data structure as if computation(*inputs) is called directly with some exceptions for correctness. Exceptions include: 1) None output: a NoOp would be returned which control-depends on computation. 2) Single value output: A tuple containing the value would be returned. 3) Operation-only outputs: a NoOp would be returned which control-depends on computation. |
Raises | |
---|---|
RuntimeError | if called when eager execution is enabled. |
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.
© 2020 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/versions/r2.3/api_docs/python/tf/xla/experimental/compile