def test_num_rand_ops_operation_seed(self):
"""Test random tensor generation consistancy in num_random_ops mode.
validate if random number generation match across two different program
orders.
"""
random_tool = migration_utils.DeterministicRandomTestTool(
mode="num_random_ops")
with random_tool.scope():
# operation seed = 0
a = tf.random.uniform(shape=(3, 1))
a = a * 3
# operation seed = 1
b = tf.random.uniform(shape=(3, 3))
b = b * 3
random_tool = migration_utils.DeterministicRandomTestTool(
mode="num_random_ops")
with random_tool.scope():
random_tool.operation_seed = 1
b_prime = tf.random.uniform(shape=(3, 3))
b_prime = b_prime * 3
random_tool.operation_seed = 0
a_prime = tf.random.uniform(shape=(3, 1))
a_prime = a_prime * 3
self.assertAllClose(a, a_prime)
self.assertAllClose(b, b_prime)
def test_num_rand_ops_program_order(self):
"""Test random tensor generation consistancy in num_random_ops mode.
validate that in this mode random number generation is sensitive to program
order, so the generated random tesnors should not match.
"""
random_tool = migration_utils.DeterministicRandomTestTool(
mode="num_random_ops")
with random_tool.scope():
a = tf.random.uniform(shape=(3, 1))
# adding additional computation/ops to the graph and ensuring consistant
# random number generation
a = a * 3
b = tf.random.uniform(shape=(3, 3))
b = b * 3
random_tool = migration_utils.DeterministicRandomTestTool(
mode="num_random_ops")
with random_tool.scope():
b_prime = tf.random.uniform(shape=(3, 3))
# adding additional computation/ops to the graph and ensuring consistant
# random number generation
b_prime = b_prime * 3
a_prime = tf.random.uniform(shape=(3, 1))
a_prime = a_prime * 3
# validate that the tensors are different
self.assertNotAllClose(a, a_prime)
self.assertNotAllClose(b, b_prime)
def test_constant_mode_seed_argument(self):
"""Test random tensor generation consistancy in constant mode.
Verify that the random tensor generated by setting the global seeed
in the args is consistant between graph and eager mode.
"""
random_tool = migration_utils.DeterministicRandomTestTool()
with random_tool.scope():
graph = tf.Graph()
with graph.as_default(), tf.compat.v1.Session(graph=graph) as sess:
# adding additional computation/ops to the graph and ensuring consistant
# random number generation
a = tf.compat.v1.random.uniform(shape=(3, 1), seed=1234)
a = a * 3
b = tf.compat.v1.random.uniform(shape=(3, 3), seed=1234)
b = b * 3
c = tf.compat.v1.glorot_uniform_initializer(seed=1234)(
shape=(6, 6), dtype=tf.float32)
graph_a, graph_b, graph_c = sess.run([a, b, c])
a = tf.compat.v2.random.uniform(shape=(3, 1), seed=1234)
a = a * 3
b = tf.compat.v2.random.uniform(shape=(3, 3), seed=1234)
b = b * 3
c = V2GlorotUniform(seed=1234)(shape=(6, 6), dtype=tf.float32)
# validate that the generated random tensors match
self.assertAllClose(graph_a, a)
self.assertAllClose(graph_b, b)
self.assertAllClose(graph_c, c)
def test_num_rand_ops_disallow_repeated_ops_seed(self):
"""Test random tensor generation consistancy in num_random_ops mode.
validate if DeterministicRandomTestTool disallows reusing already-used
operation seeds.
"""
random_tool = migration_utils.DeterministicRandomTestTool(
mode="num_random_ops")
with random_tool.scope():
random_tool.operation_seed = 1
b_prime = tf.random.uniform(shape=(3, 3))
b_prime = b_prime * 3
random_tool.operation_seed = 0
a_prime = tf.random.uniform(shape=(3, 1))
a_prime = a_prime * 3
error_string = "An exception should have been raised before this"
error_raised = "An exception should have been raised before this"
try:
c = tf.random.uniform(shape=(3, 1))
raise RuntimeError(error_string)
except ValueError as err:
err_raised = err
self.assertNotEqual(err_raised, error_string)
def test_num_rand_ops(self):
"""Test random tensor generation consistancy in num_random_ops mode.
Verify that the random tensor generated without using the seed is
consistant between graph and eager mode.
Random tensor generated should be different based on random ops ordering
"""
random_tool = migration_utils.DeterministicRandomTestTool(
mode="num_random_ops")
with random_tool.scope():
graph = tf.Graph()
with graph.as_default(), tf.compat.v1.Session(graph=graph) as sess:
# adding additional computation/ops to the graph and ensuring consistant
# random number generation
a = tf.compat.v1.random.uniform(shape=(3, 1))
a = a * 3
b = tf.compat.v1.random.uniform(shape=(3, 3))
b = b * 3
c = tf.compat.v1.random.uniform(shape=(3, 3))
c = c * 3
d = tf.compat.v1.glorot_uniform_initializer()(shape=(6, 6),
dtype=tf.float32)
graph_a, graph_b, graph_c, graph_d = sess.run([a, b, c, d])
random_tool = migration_utils.DeterministicRandomTestTool(
mode="num_random_ops")
with random_tool.scope():
a = tf.compat.v2.random.uniform(shape=(3, 1))
a = a * 3
b = tf.compat.v2.random.uniform(shape=(3, 3))
b = b * 3
c = tf.compat.v2.random.uniform(shape=(3, 3))
c = c * 3
d = V2GlorotUniform()(shape=(6, 6), dtype=tf.float32)
# validate that the generated random tensors match
self.assertAllClose(graph_a, a)
self.assertAllClose(graph_b, b)
self.assertAllClose(graph_c, c)
self.assertAllClose(graph_d, d)
# validate that the tensors differ based on ops ordering
self.assertNotAllClose(b, c)
self.assertNotAllClose(graph_b, graph_c)
def test_constant_mode_no_seed(self):
"""Test random tensor generation consistancy in constant mode.
Verify that the random tensor generated without using the seed is
consistant between graph and eager mode
"""
# Generate three random tensors to show how the stateful random number
# generation and glorot_uniform_initializer match between sessions and
# eager execution.
random_tool = migration_utils.DeterministicRandomTestTool()
with random_tool.scope():
graph = tf.Graph()
with graph.as_default(), tf.compat.v1.Session(graph=graph) as sess:
a = tf.compat.v1.random.uniform(shape=(3, 1))
# adding additional computation/ops to the graph and ensuring consistant
# random number generation
a = a * 3
b = tf.compat.v1.random.uniform(shape=(3, 3))
b = b * 3
c = tf.compat.v1.random.uniform(shape=(3, 3))
c = c * 3
d = tf.compat.v1.glorot_uniform_initializer()(shape=(6, 6),
dtype=tf.float32)
graph_a, graph_b, graph_c, graph_d = sess.run([a, b, c, d])
a = tf.compat.v2.random.uniform(shape=(3, 1))
a = a * 3
b = tf.compat.v2.random.uniform(shape=(3, 3))
b = b * 3
c = tf.compat.v2.random.uniform(shape=(3, 3))
c = c * 3
d = V2GlorotUniform()(shape=(6, 6), dtype=tf.float32)
# validate that the generated random tensors match
self.assertAllClose(graph_a, a)
self.assertAllClose(graph_b, b)
self.assertAllClose(graph_c, c)
self.assertAllClose(graph_d, d)
# In constant mode, because b and c were generated with the same seed within
# the same scope and have the same shape, they will have exactly the same
# values.
# validate that b and c are the same, also graph_b and graph_c
self.assertAllClose(b, c)
self.assertAllClose(graph_b, graph_c)