def test_to_graph_with_globals(self):
def test_fn(x):
global testing_global_numeric
testing_global_numeric = x + testing_global_numeric
return testing_global_numeric
# TODO(b/122368197)
with self.assertRaisesRegex(
errors.AutoGraphError, 'global keyword is not yet supported'):
api.to_graph(test_fn)
def test_to_graph_basic(self):
def test_fn(x, s):
while tf.reduce_sum(x) > s:
x //= 2
return x
compiled_fn = api.to_graph(test_fn)
with self.cached_session() as sess:
x = compiled_fn(constant_op.constant([4, 8]), 4)
self.assertListEqual([1, 2], sess.run(x).tolist())
def test_to_graph_caching_different_options(self):
def called_fn():
pass
def test_fn():
return called_fn()
converted_recursive = api.to_graph(test_fn, recursive=True)
converted_non_recursive = api.to_graph(test_fn, recursive=False)
self.assertNotEqual(converted_recursive.ag_module,
converted_non_recursive.ag_module)
self.assertIn('internal_convert_user_code=True',
tf_inspect.getsource(converted_recursive))
self.assertNotIn('internal_convert_user_code=False',
tf_inspect.getsource(converted_recursive))
self.assertIn('internal_convert_user_code=False',
tf_inspect.getsource(converted_non_recursive))
self.assertNotIn('internal_convert_user_code=True',
tf_inspect.getsource(converted_non_recursive))
def test_to_graph_with_kwargs_clashing_converted_call(self):
def called_fn(**kwargs):
return kwargs['f'] + kwargs['owner']
def test_fn():
# These arg names intentionally match converted_call's
return called_fn(f=1, owner=2)
compiled_fn = api.to_graph(test_fn)
self.assertEqual(compiled_fn(), 3)
def test_to_graph_basic(self):
def test_fn(x, s):
while tf.reduce_sum(x) > s:
x //= 2
return x
compiled_fn = api.to_graph(test_fn)
with tf.Graph().as_default():
x = compiled_fn(constant_op.constant((4, 8)), 4)
self.assertAllEqual(self.evaluate(x), (1, 2))
def test_to_graph_preserves_bindings(self):
y = 3
def test_fn():
return y
converted = api.to_graph(test_fn)
self.assertEqual(converted(), 3)
y = 7
self.assertEqual(converted(), 7)
def test_converted_call_already_converted(self):
def f(x):
return x == 0
x = api.converted_call(f, None, converter.ConversionOptions(),
(constant_op.constant(0),), {})
self.assertTrue(self.evaluate(x))
converted_f = api.to_graph(f)
x = api.converted_call(converted_f, None, converter.ConversionOptions(),
(constant_op.constant(0),), {})
self.assertTrue(self.evaluate(x))
def test_converted_call_already_converted(self):
def f(x):
return x == 0
with self.cached_session() as sess:
x = api.converted_call(f, None, converter.ConversionOptions(),
constant_op.constant(0))
self.assertTrue(sess.run(x))
converted_f = api.to_graph(f)
x = api.converted_call(converted_f, None, converter.ConversionOptions(),
constant_op.constant(0))
self.assertTrue(sess.run(x))
def test_to_graph_with_defaults(self):
foo = 4
def test_fn(x, s=foo):
while tf.reduce_sum(x) > s:
x //= 2
return x
compiled_fn = api.to_graph(test_fn)
with self.cached_session() as sess:
x = compiled_fn(constant_op.constant([4, 8]))
self.assertListEqual([1, 2], self.evaluate(x).tolist())
def test_converted_call_already_converted(self):
def f(x):
return x == 0
with self.test_session() as sess:
x = api.converted_call(f, False, False, False, {},
constant_op.constant(0))
self.assertTrue(sess.run(x))
converted_f = api.to_graph(f)
x = api.converted_call(converted_f, False, False, False, {},
constant_op.constant(0))
self.assertTrue(sess.run(x))
def test_converted_call_already_converted(self):
def f(x):
return x == 0
x = api.converted_call(f, None, converter.ConversionOptions(recursive=True),
(constant_op.constant(0),), {})
self.assertTrue(self.evaluate(x))
converted_f = api.to_graph(
f, experimental_optional_features=converter.Feature.ALL)
x = api.converted_call(converted_f, None,
converter.ConversionOptions(recursive=True),
(constant_op.constant(0),), {})
self.assertTrue(self.evaluate(x))
def test_to_graph_caching(self):
def test_fn(x):
if x > 0:
return x
else:
return -x
converted_functions = tuple(api.to_graph(test_fn) for _ in (-1, 0, 1))
# All outputs are from the same module. We can't use __module__ because
# that's reset when we instantiate the function (see conversion.py).
# TODO(mdan): Can and should we overwrite __module__ instead?
module_names = frozenset(f.ag_module for f in converted_functions)
self.assertEqual(len(module_names), 1)
self.assertNotIn('__main__', module_names)
self.assertEqual(len(frozenset(id(f) for f in converted_functions)), 3)
def f():
converted_g = api.to_graph(g)
converted_g()
def test_source_map_attribute_present(self):
def test_fn(y):
return y**2
self.assertTrue(hasattr(api.to_graph(test_fn), 'ag_source_map'))
"""Scales the loss, computes the gradients, and unscales the gradients."""
loss_scale_val = loss_scale()
with gradient_tape: # re-enter gradient tape so it sees the loss scaling
scaled_target = nest.map_structure(
lambda t: t * loss_scale_val, target)
old_grads = super(LossScaleGradientTape,
gradient_tape).gradient(scaled_target, sources,
output_gradients,
unconnected_gradients)
inv_loss_scale = 1.0 / loss_scale_val
grads = nest.map_structure(lambda g: inv_loss_scale * g, old_grads)
return grads
# Switch to a replica-context to compute gradients once per replica.
grads = distribution.experimental_run_v2(
replica_fn,
args=(loss_scale_gradient_tapes, target, sources,
output_gradients))
# Check for non-finite gradients possibly resulting from scaling
_, ready_to_update = loss_scale.update(grads)
return grads
# For some reason, AutoGraph does not convert _compute_gradients_until_finite
# automatically inside a tf.function, so we convert it manually.
# TODO(b/143572314): Determine why AutoGraph does not do the conversion
# automatically
_compute_gradients_until_finite_autograph = api.to_graph(
_compute_gradients_until_finite)
def test_source_map_attribute_present(self):
def test_fn(y):
return y**2
self.assertTrue(hasattr(api.to_graph(test_fn), 'ag_source_map'))
def test_to_graph_source_map(self):
def test_fn(y):
return y**2
self.assertTrue(hasattr(api.to_graph(test_fn), 'ag_source_map'))
def test_to_graph_source_map(self):
def test_fn(y):
return y**2
self.assertTrue(hasattr(api.to_graph(test_fn), 'ag_source_map'))
