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tf.experimental.dispatch_for_api

Decorator that overrides the default implementation for a TensorFlow API.

Used in the notebooks

Used in the guide

The decorated function (known as the "dispatch target") will override the default implementation for the API when the API is called with parameters that match a specified type signature. Signatures are specified using dictionaries that map parameter names to type annotations. E.g., in the following example, masked_add will be called for tf.add if both x and y are MaskedTensors:

class MaskedTensor(extension_type.ExtensionType):
  values: tf.Tensor
  mask: tf.Tensor
@dispatch_for_api(tf.math.add, {'x': MaskedTensor, 'y': MaskedTensor})
def masked_add(x, y, name=None):
  return MaskedTensor(x.values + y.values, x.mask & y.mask)
mt = tf.add(MaskedTensor([1, 2], [True, False]), MaskedTensor(10, True))
print(f"values={mt.values.numpy()}, mask={mt.mask.numpy()}")
values=[11 12], mask=[ True False]

If multiple type signatures are specified, then the dispatch target will be called if any of the signatures match. For example, the following code registers masked_add to be called if x is a MaskedTensor or y is a MaskedTensor.

@dispatch_for_api(tf.math.add, {'x': MaskedTensor}, {'y':MaskedTensor})
def masked_add(x, y):
  x_values = x.values if isinstance(x, MaskedTensor) else x
  x_mask = x.mask if isinstance(x, MaskedTensor) else True
  y_values = y.values if isinstance(y, MaskedTensor) else y
  y_mask = y.mask if isinstance(y, MaskedTensor) else True
  return MaskedTensor(x_values + y_values, x_mask & y_mask)

The type annotations in type signatures may be type objects (e.g., MaskedTensor), typing.List values, or typing.Union values. For example, the following will register masked_concat to be called if values is a list of MaskedTensor values:

@dispatch_for_api(tf.concat, {'values': typing.List[MaskedTensor]})
def masked_concat(values, axis):
  return MaskedTensor(tf.concat([v.values for v in values], axis),
                      tf.concat([v.mask for v in values], axis))

Each type signature must contain at least one subclass of tf.CompositeTensor (which includes subclasses of tf.ExtensionType), and dispatch will only be triggered if at least one type-annotated parameter contains a CompositeTensor value. This rule avoids invoking dispatch in degenerate cases, such as the following examples:

  • @dispatch_for_api(tf.concat, {'values': List[MaskedTensor]}): Will not dispatch to the decorated dispatch target when the user calls tf.concat([]).

  • @dispatch_for_api(tf.add, {'x': Union[MaskedTensor, Tensor], 'y': Union[MaskedTensor, Tensor]}): Will not dispatch to the decorated dispatch target when the user calls tf.add(tf.constant(1), tf.constant(2)).

The dispatch target's signature must match the signature of the API that is being overridden. In particular, parameters must have the same names, and must occur in the same order. The dispatch target may optionally elide the "name" parameter, in which case it will be wrapped with a call to tf.name_scope when appropraite.

api The TensorFlow API to override.
*signatures Dictionaries mapping parameter names or indices to type annotations, specifying when the dispatch target should be called. In particular, the dispatch target will be called if any signature matches; and a signature matches if all of the specified parameters have types that match with the indicated type annotations. If no signatures are specified, then a signature will be read from the dispatch target function's type annotations.

A decorator that overrides the default implementation for api.

Registered APIs

The TensorFlow APIs that may be overridden by @dispatch_for_api are: