251 lines
9.8 KiB
Python
251 lines
9.8 KiB
Python
# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
# ==============================================================================
|
|
|
|
from __future__ import absolute_import
|
|
from __future__ import division
|
|
from __future__ import print_function
|
|
|
|
from absl.testing import parameterized
|
|
|
|
from tensorflow.core.protobuf import config_pb2
|
|
from tensorflow.python.eager import def_function
|
|
from tensorflow.python.eager import function
|
|
from tensorflow.python.framework import constant_op
|
|
from tensorflow.python.framework import dtypes
|
|
from tensorflow.python.framework import ops
|
|
from tensorflow.python.framework import tensor_spec
|
|
from tensorflow.python.ops import array_ops
|
|
from tensorflow.python.ops import math_ops
|
|
from tensorflow.python.ops import variables
|
|
from tensorflow.python.platform import test
|
|
|
|
|
|
@parameterized.named_parameters(
|
|
dict(testcase_name='Defun', function_decorator=function.defun),
|
|
dict(testcase_name='DefFunction', function_decorator=def_function.function))
|
|
class ArgumentNamingTests(test.TestCase, parameterized.TestCase):
|
|
"""Tests for recognizable export signatures from concrete functions."""
|
|
|
|
def testBasic(self, function_decorator):
|
|
@function_decorator
|
|
def fn(a, b):
|
|
return a + b, a * b
|
|
# Call the function to make def_function happy
|
|
fn(array_ops.ones([]), array_ops.ones([]))
|
|
|
|
fn_op = fn.get_concrete_function(
|
|
tensor_spec.TensorSpec(shape=(None,), dtype=dtypes.float32),
|
|
tensor_spec.TensorSpec(shape=(), dtype=dtypes.float32))
|
|
self.assertEqual(
|
|
['a', 'b'],
|
|
[inp.op.name for inp in fn_op.inputs])
|
|
self.assertEqual(
|
|
[b'a', b'b'],
|
|
[inp.op.get_attr('_user_specified_name') for inp in fn_op.inputs])
|
|
self.assertEqual(2, len(fn_op.graph.structured_outputs))
|
|
self.assertAllClose(
|
|
[3., 2.],
|
|
fn_op(constant_op.constant(1.), constant_op.constant(2.)))
|
|
self.assertAllClose(
|
|
[3., 2.],
|
|
fn_op(a=constant_op.constant(1.), b=constant_op.constant(2.)))
|
|
|
|
def testVariable(self, function_decorator):
|
|
@function_decorator
|
|
def fn(a, b):
|
|
return a + b, a * b
|
|
# Call the function to make def_function happy
|
|
fn(array_ops.ones([]), array_ops.ones([]))
|
|
|
|
fn_op = fn.get_concrete_function(
|
|
tensor_spec.TensorSpec(shape=(None,), dtype=dtypes.float32),
|
|
variables.Variable(1.))
|
|
self.assertEqual(
|
|
['a', 'b'],
|
|
[inp.op.name for inp in fn_op.inputs])
|
|
self.assertEqual(
|
|
[b'a', b'b'],
|
|
[inp.op.get_attr('_user_specified_name') for inp in fn_op.inputs])
|
|
self.assertEqual(2, len(fn_op.graph.structured_outputs))
|
|
|
|
def testDictReturned(self, function_decorator):
|
|
@function_decorator
|
|
def fn(x, z=(1., 2.), y=3.):
|
|
z1, z2 = z
|
|
return {'alpha': x + y + z1, 'beta': x * y + z2}
|
|
# Call the function to make def_function happy
|
|
fn(array_ops.ones([]))
|
|
|
|
fn_op = fn.get_concrete_function(
|
|
x=tensor_spec.TensorSpec(shape=(None,), dtype=dtypes.float32),
|
|
y=tensor_spec.TensorSpec(shape=(), dtype=dtypes.float32))
|
|
self.assertEqual(
|
|
['x', 'y'],
|
|
[inp.op.name for inp in fn_op.inputs])
|
|
self.assertEqual(
|
|
[b'x', b'y'],
|
|
[inp.op.get_attr('_user_specified_name') for inp in fn_op.inputs])
|
|
self.assertEqual({'alpha', 'beta'},
|
|
set(fn_op.graph.structured_outputs.keys()))
|
|
|
|
fn_op2 = fn.get_concrete_function(
|
|
z=(tensor_spec.TensorSpec(shape=(None,), dtype=dtypes.float32,
|
|
name='z_first'),
|
|
tensor_spec.TensorSpec(shape=(), dtype=dtypes.float32,
|
|
name='z_second')),
|
|
y=tensor_spec.TensorSpec(shape=(), dtype=dtypes.float32, name='custom'),
|
|
x=4.)
|
|
self.assertEqual(
|
|
['z_first', 'z_second', 'custom'],
|
|
[inp.op.name for inp in fn_op2.inputs])
|
|
self.assertEqual(
|
|
[b'z_first', b'z_second', b'custom'],
|
|
[inp.op.get_attr('_user_specified_name') for inp in fn_op2.inputs])
|
|
|
|
fn_op3 = fn.get_concrete_function(
|
|
tensor_spec.TensorSpec(shape=(), dtype=dtypes.float32, name='custom'),
|
|
z=(tensor_spec.TensorSpec(shape=(None,), dtype=dtypes.float32,
|
|
name='z1'),
|
|
tensor_spec.TensorSpec(shape=(), dtype=dtypes.float32, name='z2')),
|
|
y=tensor_spec.TensorSpec(shape=(), dtype=dtypes.float32))
|
|
self.assertEqual(
|
|
['custom', 'z1', 'z2', 'y'],
|
|
[inp.op.name for inp in fn_op3.inputs])
|
|
self.assertEqual(
|
|
[b'custom', b'z1', b'z2', b'y'],
|
|
[inp.op.get_attr('_user_specified_name') for inp in fn_op3.inputs])
|
|
|
|
def testMethod(self, function_decorator):
|
|
class HasMethod(object):
|
|
|
|
@function_decorator
|
|
def method(self, x):
|
|
return x
|
|
|
|
has_method = HasMethod()
|
|
# Call the function to make def_function happy
|
|
HasMethod.method(has_method, array_ops.ones([]))
|
|
class_op = HasMethod.method.get_concrete_function(
|
|
has_method, tensor_spec.TensorSpec(shape=(), dtype=dtypes.float32))
|
|
self.assertEqual(
|
|
['x'],
|
|
[inp.op.name for inp in class_op.inputs])
|
|
self.assertEqual(
|
|
[b'x'],
|
|
[inp.op.get_attr('_user_specified_name') for inp in class_op.inputs])
|
|
# Call the function to make def_function happy
|
|
has_method.method(array_ops.ones([]))
|
|
method_op = has_method.method.get_concrete_function(
|
|
tensor_spec.TensorSpec(shape=(), dtype=dtypes.float32))
|
|
self.assertEqual(
|
|
['x'],
|
|
[inp.op.name for inp in method_op.inputs])
|
|
self.assertEqual(
|
|
[b'x'],
|
|
[inp.op.get_attr('_user_specified_name') for inp in method_op.inputs])
|
|
# TODO(allenl): It should be possible to override names when exporting. Do
|
|
# TensorSpec names need to go in cache keys? Or maybe get_concrete_function
|
|
# should always retrace?
|
|
self.skipTest('Not working')
|
|
method_op = has_method.method.get_concrete_function(
|
|
tensor_spec.TensorSpec(shape=(), dtype=dtypes.float32, name='y'))
|
|
self.assertEqual(
|
|
['y'],
|
|
[inp.op.name for inp in method_op.inputs])
|
|
self.assertEqual(
|
|
[b'y'],
|
|
[inp.op.get_attr('_user_specified_name') for inp in method_op.inputs])
|
|
|
|
def testMethodSignature(self, function_decorator):
|
|
|
|
class HasMethod(object):
|
|
|
|
@function_decorator(
|
|
input_signature=(tensor_spec.TensorSpec(
|
|
shape=None, dtype=dtypes.float64, name='y'),))
|
|
def method(self, x):
|
|
hash(self) # No weak proxies passed as `self`
|
|
return x
|
|
|
|
has_method = HasMethod()
|
|
# Call the function to make def_function happy
|
|
has_method.method(array_ops.ones([], dtype=dtypes.float64))
|
|
method_op = has_method.method.get_concrete_function()
|
|
self.assertEqual(
|
|
['y'],
|
|
[inp.op.name for inp in method_op.inputs])
|
|
self.assertEqual(
|
|
[b'y'],
|
|
[inp.op.get_attr('_user_specified_name') for inp in method_op.inputs])
|
|
method_op2 = has_method.method.get_concrete_function()
|
|
self.assertEqual(
|
|
['y'],
|
|
[inp.op.name for inp in method_op2.inputs])
|
|
self.assertEqual(
|
|
[b'y'],
|
|
[inp.op.get_attr('_user_specified_name') for inp in method_op2.inputs])
|
|
|
|
def testVariadic(self, function_decorator):
|
|
@function_decorator
|
|
def variadic_fn(x, *args, **kwargs):
|
|
return x + math_ops.add_n(list(args) + list(kwargs.values()))
|
|
|
|
# Call the function to make def_function happy
|
|
variadic_fn(array_ops.ones([]), array_ops.ones([]))
|
|
variadic_op = variadic_fn.get_concrete_function(
|
|
tensor_spec.TensorSpec(shape=(), dtype=dtypes.float32),
|
|
tensor_spec.TensorSpec(shape=None, dtype=dtypes.float32, name='y'),
|
|
tensor_spec.TensorSpec(shape=(), dtype=dtypes.float32),
|
|
tensor_spec.TensorSpec(shape=(), dtype=dtypes.float32,
|
|
name='second_variadic'),
|
|
z=tensor_spec.TensorSpec(shape=(), dtype=dtypes.float32),
|
|
zz=tensor_spec.TensorSpec(shape=(), dtype=dtypes.float32, name='cust'))
|
|
self.assertEqual(
|
|
['x', 'y', 'args_1', 'second_variadic', 'z', 'cust'],
|
|
[inp.op.name for inp in variadic_op.inputs])
|
|
self.assertEqual(
|
|
[b'x', b'y', b'args_1', b'second_variadic', b'z', b'cust'],
|
|
[inp.op.get_attr('_user_specified_name') for inp in variadic_op.inputs])
|
|
|
|
def testVariadicInputSignature(self, function_decorator):
|
|
@function_decorator(
|
|
input_signature=(
|
|
tensor_spec.TensorSpec(shape=None, dtype=dtypes.float32),
|
|
tensor_spec.TensorSpec(shape=None, dtype=dtypes.float32, name='y'),
|
|
tensor_spec.TensorSpec(shape=(), dtype=dtypes.float32),
|
|
tensor_spec.TensorSpec(shape=(), dtype=dtypes.float32, name='z'),
|
|
))
|
|
def variadic_fn(x, *args):
|
|
return x + math_ops.add_n(list(args))
|
|
|
|
# Call the function to make def_function happy
|
|
variadic_fn(array_ops.ones([]), array_ops.ones([]),
|
|
array_ops.ones([]), array_ops.ones([]))
|
|
variadic_op = variadic_fn.get_concrete_function()
|
|
self.assertIn(b'variadic_fn', variadic_op.name)
|
|
self.assertEqual(
|
|
['x', 'y', 'args_1', 'z'],
|
|
[inp.op.name for inp in variadic_op.inputs])
|
|
self.assertEqual(
|
|
[b'x', b'y', b'args_1', b'z'],
|
|
[inp.op.get_attr('_user_specified_name')
|
|
for inp in variadic_op.inputs])
|
|
|
|
|
|
if __name__ == '__main__':
|
|
ops.enable_eager_execution(
|
|
config=config_pb2.ConfigProto(device_count={'CPU': 4}))
|
|
test.main()
|