tf.xla.experimental.compile | TensorFlow v2.16.1 (original) (raw)

tf.xla.experimental.compile

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Builds an operator that compiles and runs computation with XLA. (deprecated)

View aliases

Compat aliases for migration

tf.compat.v1.xla.experimental.compile

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 nTensors.computation may return a list of Tensors and Operations.Tensors must come before Operations in the returned list. All Operations returned from computation will be executed when evaluating any of the returned output tensors.
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 toTensors. Note that passing an N-dimension list of compatible values will result in an N-dimension list of scalar Tensors rather than a single Rank-N Tensor. If you need a different behavior, convert parts ofinputs to Tensors with tf.convert_to_tensor.

Returns
List of Tensors corresponding to the Tensors from the output of computation i.e. the same return value as if computation(*inputs) is called directly, with the following exceptions: None output: a NoOp would be returned with a control dependency oncomputation. Single value output: a tuple containing the value would be returned. Operation-only outputs: a NoOp would be returned with a control dependency on computation.

Returns

List of Tensors corresponding to the Tensors from the output of computation i.e. the same return value as if computation(*inputs) is called directly, with the following exceptions: None output: a NoOp would be returned with a control dependency oncomputation. Single value output: a tuple containing the value would be returned. Operation-only outputs: a NoOp would be returned with a control dependency on computation.

Raises
RuntimeError	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.

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.

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Last updated 2024-04-26 UTC.