def test_runtime_error_rewriting(self):
def g(x, s):
while tf.reduce_sum(x) > s:
x //= 0
return x
def test_fn(x):
return g(x, 10)
compiled_fn = ag.to_graph(test_fn)
with self.assertRaises(ag.TfRuntimeError) as error:
with self.cached_session() as sess:
x = compiled_fn(tf.constant([4, 8]))
with ag.improved_errors(compiled_fn):
sess.run(x)
expected = error.exception
custom_traceback = expected.custom_traceback
found_correct_filename = False
num_test_fn_frames = 0
num_g_frames = 0
for frame in custom_traceback:
filename, _, fn_name, source_code = frame
self.assertFalse('/tmp/' in filename)
self.assertFalse('control_flow.py' in filename)
self.assertFalse('ag__.' in fn_name)
found_correct_filename |= __file__ in filename
num_test_fn_frames += int('test_fn' == fn_name and
'return g(x, 10)' in source_code)
num_g_frames += int('g' == fn_name and 'x //= 0' in source_code)
self.assertTrue(found_correct_filename)
self.assertEqual(num_test_fn_frames, 1)
self.assertEqual(num_g_frames, 1)
def test_graph_construction_error_rewriting_call_tree(self):
def test_fn():
return tf.random_normal((2, 3), mean=0.0, dtype=tf.int32)
def inner_caller():
return test_fn()
def caller():
return inner_caller()
with self.assertRaises(ag.GraphConstructionError) as error:
graph = ag.to_graph(caller)
graph()
expected = error.exception
custom_traceback = expected.custom_traceback
found_correct_filename = False
num_test_fn_names = 0
num_inner_caller_names = 0
num_caller_names = 0
for frame in custom_traceback:
filename, _, fn_name, _ = frame
self.assertFalse('/tmp/' in filename)
found_correct_filename |= __file__ in filename
self.assertNotEqual('tf__test_fn', fn_name)
num_test_fn_names += int('test_fn' == fn_name)
self.assertNotEqual('tf__inner_caller', fn_name)
num_inner_caller_names += int('inner_caller' == fn_name)
self.assertNotEqual('tf__caller', fn_name)
num_caller_names += int('caller' == fn_name)
self.assertTrue(found_correct_filename)
self.assertEqual(num_test_fn_names, 1)
self.assertEqual(num_inner_caller_names, 1)
self.assertEqual(num_caller_names, 1)
def test_runtime_error_rewriting(self):
def g(x, s):
while tf.reduce_sum(x) > s:
x //= 0
return x
def test_fn(x):
return g(x, 10)
compiled_fn = ag.to_graph(test_fn)
with self.assertRaises(ag.TfRuntimeError) as error:
with self.cached_session() as sess:
x = compiled_fn(tf.constant([4, 8]))
with ag.improved_errors(compiled_fn):
sess.run(x)
expected = error.exception
custom_traceback = expected.custom_traceback
found_correct_filename = False
num_test_fn_frames = 0
num_g_frames = 0
for frame in custom_traceback:
filename, _, fn_name, source_code = frame
self.assertFalse('/tmp/' in filename)
self.assertFalse('control_flow.py' in filename)
self.assertFalse('ag__.' in fn_name)
found_correct_filename |= __file__ in filename
num_test_fn_frames += int('test_fn' == fn_name
and 'return g(x, 10)' in source_code)
num_g_frames += int('g' == fn_name and 'x //= 0' in source_code)
self.assertTrue(found_correct_filename)
self.assertEqual(num_test_fn_frames, 1)
self.assertEqual(num_g_frames, 1)
def test_runtime_error_rewriting_nested(self):
def test_fn(x):
def g(y):
return y**2 // 0
s = 0
for xi in x:
s += g(xi)
return s
compiled_fn = ag.to_graph(test_fn)
# TODO(b/111408261): Nested functions currently do not rewrite correctly,
# when they do we should change this test to check for the same traceback
# properties as the other tests. This should throw a runtime error with a
# frame with "g" as the function name but because we don't yet add
# try/except blocks to inner functions the name is "tf__g".
with self.assertRaises(ag.TfRuntimeError) as error:
with self.cached_session() as sess:
x = compiled_fn(tf.constant([4, 8]))
with ag.improved_errors(compiled_fn):
sess.run(x)
expected = error.exception
custom_traceback = expected.custom_traceback
num_tf_g_frames = 0
for frame in custom_traceback:
_, _, fn_name, _ = frame
self.assertNotEqual('g', fn_name)
num_tf_g_frames += int('tf__g' == fn_name)
self.assertEqual(num_tf_g_frames, 1)
def test_graph_construction_error_rewriting_call_tree(self):
def test_fn():
return tf.random_normal((2, 3), mean=0.0, dtype=tf.int32)
def inner_caller():
return test_fn()
def caller():
return inner_caller()
with self.assertRaises(ag.GraphConstructionError) as error:
graph = ag.to_graph(caller)
graph()
expected = error.exception
custom_traceback = expected.custom_traceback
found_correct_filename = False
num_test_fn_names = 0
num_inner_caller_names = 0
num_caller_names = 0
for frame in custom_traceback:
filename, _, fn_name, _ = frame
self.assertFalse('/tmp/' in filename)
found_correct_filename |= __file__ in filename
self.assertNotEqual('tf__test_fn', fn_name)
num_test_fn_names += int('test_fn' == fn_name)
self.assertNotEqual('tf__inner_caller', fn_name)
num_inner_caller_names += int('inner_caller' == fn_name)
self.assertNotEqual('tf__caller', fn_name)
num_caller_names += int('caller' == fn_name)
self.assertTrue(found_correct_filename)
self.assertEqual(num_test_fn_names, 1)
self.assertEqual(num_inner_caller_names, 1)
self.assertEqual(num_caller_names, 1)
def test_runtime_error_rewriting_nested(self):
def test_fn(x):
def g(y):
return y**2 // 0
s = 0
for xi in x:
s += g(xi)
return s
compiled_fn = ag.to_graph(test_fn)
# TODO(b/111408261): Nested functions currently do not rewrite correctly,
# when they do we should change this test to check for the same traceback
# properties as the other tests. This should throw a runtime error with a
# frame with "g" as the function name but because we don't yet add
# try/except blocks to inner functions the name is "tf__g".
with self.assertRaises(ag.TfRuntimeError) as error:
with self.cached_session() as sess:
x = compiled_fn(tf.constant([4, 8]))
with ag.improved_errors(compiled_fn):
sess.run(x)
expected = error.exception
custom_traceback = expected.custom_traceback
num_tf_g_frames = 0
for frame in custom_traceback:
_, _, fn_name, _ = frame
self.assertNotEqual('g', fn_name)
num_tf_g_frames += int('tf__g' == fn_name)
self.assertEqual(num_tf_g_frames, 1)
def test_graph_construction_error_rewriting_class(self):
class TestClass(object):
def test_fn(self):
return tf.random_normal((2, 3), mean=0.0, dtype=tf.int32)
def inner_caller(self):
return self.test_fn()
def caller(self):
return self.inner_caller()
# Note we expect a TypeError here because the traceback will not be
# rewritten for classes.
with self.assertRaises(TypeError):
graph = ag.to_graph(TestClass)
graph().caller()
def test_graph_construction_error_rewriting_class(self):
class TestClass(object):
def test_fn(self):
return tf.random_normal((2, 3), mean=0.0, dtype=tf.int32)
def inner_caller(self):
return self.test_fn()
def caller(self):
return self.inner_caller()
# Note we expect a TypeError here because the traceback will not be
# rewritten for classes.
with self.assertRaises(TypeError):
graph = ag.to_graph(TestClass)
graph().caller()
def test_basic(self):
converted = ag.to_graph(list_used_as_tuple)
result = converted()
with self.cached_session() as sess:
self.assertAllEqual(self.evaluate(result), [1, 2, 3])
def _createEqnModel(self, inputVar, outputVar, mdlName, eqMdl):
"""
Build a K-CHAIN model using input and output variables from the KG and
the physics equation.
Arguments:
inputVar (JSON array):
array of JSON variable objects with name, type, value, and unit fields
outputVar (JSON array):
array of JSON variable objects with name, type, value, and unit fields
mdlName (string):
Name to assign to the final model (E.g.: 'Newtons2ndLaw')
eqMdl (string):
Equation relating inputs to output (E.g.: "c = a * b")
Returns:
(TensorFlow Graph, string):
* TensorFlow Graph: Computational graph of the physics equation
* metagraphLoc: string of location on disk where computational model was stored
"""
in_dims = len(inputVar)
inStr = inputVar[0]['name'] + ' = inArg[0]'
for ii in range(1, in_dims):
inStr = inStr + '\n ' + inputVar[ii][
'name'] + ' = inArg[' + str(ii) + ']'
#4 spaces is ideal for indentation
#construct the python function around the python snippet
stringfun = 'import tensorflow as tf'\
+'\ndef '+mdlName+'(inArg):'\
+'\n '+ inStr\
+'\n '+ eqMdl\
+'\n return '+ outputVar[0]['name'] + '\n\n'
print(stringfun)
#write the python code into a file where AutoGraph can read it
self._makePyFile(stringfun)
#reload the eqnModels package as a method was newly added
imp.reload(eqnModels)
#get the method created by the code
tmp_method = getattr(eqnModels, mdlName)
if self.debug:
print(tmp_method)
metagraphLoc = "../models/" + mdlName
tf.reset_default_graph()
mdl = tf.Graph()
with mdl.as_default():
invars = []
for ii in range(in_dims):
#create list on input variables
tfType = self._getVarType(inputVar[ii]['type'])
invars.append(tf.placeholder(tfType,
name=inputVar[ii]['name']))
tfType = self._getVarType(outputVar[0]['type'])
#create TensorFlow graph from python code
tf_model = ag.to_graph(tmp_method)
#obtain TensorFlow model of input and outputs
output = tf_model(invars)
tf.add_to_collection("output", output)
for node in invars:
tf.add_to_collection("input", node)
#save model locally as a MetaGraph
tf.train.export_meta_graph(filename=metagraphLoc + '.meta',
graph=mdl)
return mdl, metagraphLoc
def test_basic(self):
converted = ag.to_graph(list_used_as_tuple)
result = converted()
with self.cached_session() as sess:
self.assertAllEqual(self.evaluate(result), [1, 2, 3])
