Import a ConcreteFunction and convert it to a textual MLIR module.
tf.mlir.experimental.convert_function(
concrete_function,
pass_pipeline='tf-standard-pipeline',
show_debug_info=False
)
This API is only intended for inspecting the internals of TensorFlow and the
string returned is at the moment intended for debugging purposes.
A tf.function can be
imported and converted from TensorFlow to TensorFlow MLIR with this API by
extracting its ConcreteFunction (eagerly-executing wrapper around a
tf.Graph).
For example:
@tf.function
def add(a, b):
return a + b
concrete_function = add.get_concrete_function(
tf.TensorSpec(None, tf.dtypes.float32),
tf.TensorSpec(None, tf.dtypes.float32))
tf.mlir.experimental.convert_function(concrete_function)
'...module attributes {...} {...}...'
Args |
concrete_function
|
An object of type ConcreteFunction.
|
pass_pipeline
|
A textual description of an MLIR Pass Pipeline to run on the
module, see MLIR documentation for the
textual pass pipeline syntax.
|
show_debug_info
|
Whether to include locations in the emitted textual form.
|
Returns |
A textual representation of the MLIR module corresponding to the
ConcreteFunction.
|
Raises |
InvalidArgumentError
|
if concrete_function is invalid or cannot be converted
to MLIR.
|