tf.autograph.experimental.set_loop_options | TensorFlow v2.16.1 (original) (raw)

tf.autograph.experimental.set_loop_options

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Specifies additional arguments to be passed to the enclosing while_loop.

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Compat aliases for migration

tf.compat.v1.autograph.experimental.set_loop_options

tf.autograph.experimental.set_loop_options(
    parallel_iterations=UNSPECIFIED,
    swap_memory=UNSPECIFIED,
    maximum_iterations=UNSPECIFIED,
    shape_invariants=UNSPECIFIED
)

The parameters apply to and only to the immediately enclosing loop. It only has effect if the loop is staged as a TF while_loop; otherwise the parameters have no effect.

Usage
>>> @tf.function(autograph=True) ... def f(): ... n = 0 ... for i in tf.range(10): ... tf.autograph.experimental.set_loop_options(maximum_iterations=3) ... n += 1 ... return n @tf.function(autograph=True) def f(): v = tf.constant((0,)) for i in tf.range(3): tf.autograph.experimental.set_loop_options( shape_invariants=[(v, tf.TensorShape([None]))] ) v = tf.concat((v, [i]), 0) return v

Also see tf.while_loop.

Args
parallel_iterations	The maximum number of iterations allowed to run in parallel at any given time. Note that this does not guarantee parallel execution.
swap_memory	Whether to store intermediate values needed for gradients on the CPU instead of GPU.
maximum_iterations	Allows limiting the total number of iterations executed by the loop.
shape_invariants	Allows controlling the argument with the same name passed to tf.while_loop. Unlike tf.while_loop, this is a list of(tensor, shape) pairs.

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Last updated 2024-04-26 UTC.