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TensorFlow 1 version
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View source on GitHub
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Builds an operator that compiles and runs computation with XLA. (deprecated)
tf.xla.experimental.compile(
computation, inputs=None
)
Args | |
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computation
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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.
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Returns | |
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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 | |
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RuntimeError
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if called when eager execution is enabled. |
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.