tf.xla.experimental.compile

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 n `Tensor`s. `computation` may return a list of `Tensor`s and `Operation`s. `Tensor`s must come before `Operation`s in the returned list. All `Operation`s 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 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`.

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.

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 to Tensors. 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 of inputs to Tensors 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: None output: a NoOp would be returned with a control dependency on `computation`. 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 on computation.
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.