tf.types.experimental.GenericFunction

Base class for polymorphic graph functions.

Inherits From: Callable

Graph functions are Python callable objects that dispatch calls to a TensorFlow graph. Polymorphic graph functions can be backed by multiple TF graphs, and automatically select the appropriate specialization based on the type of input they were called with. They may also create specializations on the fly if necessary, for example by tracing.

Also see tf.function.

Methods

experimental_get_compiler_ir

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experimental_get_compiler_ir(
    *args, **kwargs
)

Returns compiler IR for the compiled function.

This API is intended only for debugging as there are no guarantees on backwards compatibility of returned IR or the allowed values of stage.

@tf.function(jit_compile=True)
def f(x):
  return x + 1

f.experimental_get_compiler_ir(tf.random.normal([10, 10])(stage='hlo')

HloModule a_inference_f_13__.9

ENTRY %a_inference_f_13__.9 (arg0.1: f32[10,10]) -> f32[10,10] {
  %arg0.1 = f32[10,10]{1,0} parameter(0), parameter_replication={false}
  %reshape.2 = f32[10,10]{1,0} reshape(f32[10,10]{1,0} %arg0.1)
  %constant.3 = f32[] constant(1)
  %broadcast.4 = f32[10,10]{1,0} broadcast(f32[] %constant.3)
  %add.5 = f32[10,10]{1,0} add(f32[10,10]{1,0} %reshape.2,
                               f32[10,10]{1,0} %broadcast.4)
  %reshape.6 = f32[10,10]{1,0} reshape(f32[10,10]{1,0} %add.5)
  %tuple.7 = (f32[10,10]{1,0}) tuple(f32[10,10]{1,0} %reshape.6)
  ROOT %get-tuple-element.8 = f32[10,10]{1,0}
    get-tuple-element((f32[10,10]{1,0}) %tuple.7), index=0
}

get_concrete_function

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get_concrete_function(
    *args, **kwargs
) -> tf.types.experimental.ConcreteFunction

Returns a ConcreteFunction specialized to input types.

The arguments specified by args and kwargs follow normal function call rules. The returned ConcreteFunction has the same set of positional and keyword arguments as self, but their types are compatible to the types specified by args and kwargs (though not neccessarily equal).

@tf.function
def f(x):
  return x
f_concrete = f.get_concrete_function(tf.constant(1.0))
f_concrete = f.get_concrete_function(x=tf.constant(1.0))

Unlike normal calls, get_concrete_function allow type specifiers instead of TensorFlow objects, so for example tf.Tensors may be replaced with tf.TensorSpecs.

@tf.function
def f(x):
  return x
f_concrete = f.get_concrete_function(tf.TensorSpec([], tf.float64))

If the function definition allows only one specialization, args and kwargs may be omitted altogether.

@tf.function(input_signature=[tf.TensorSpec(None, tf.float32)])
def f(x):
  return x
f_concrete = f.get_concrete_function()

The returned ConcreteFunction can be called normally:

f_concrete(tf.constant(1.0))
<tf.Tensor: shape=(), dtype=float32, numpy=1.0>
f_concrete(x=tf.constant(1.0))
<tf.Tensor: shape=(), dtype=float32, numpy=1.0>

__call__

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__call__(
    *args, **kwargs
)

Executes this callable.

This behaves like a regular op - in eager mode, it immediately starts execution, returning results. In graph mode, it creates ops which return symbolic TensorFlow values (like tf.Tensor, tf.data.Dataset, etc.). For example, tf.function callables typically generate a tf.raw_ops.PartitionedCall op, but not always - the exact operations being generated are an internal implementation detail.