def test_deletion_while_in_blocking_call(self, dim, batched):
address = self.get_unix_address()
server = ops.Server([address])
q = tf.queue.FIFOQueue(1, [tf.int32])
@tf.function(input_signature=[tf.TensorSpec(dim, tf.int32)])
def foo(x):
q.enqueue(x)
q.enqueue(x)
q.enqueue(x)
return x
server.bind(foo, batched=batched)
server.start()
client = ops.Client(address)
with futures.ThreadPoolExecutor(max_workers=1) as executor:
f = executor.submit(client.foo, 42)
q.dequeue() # Wait for function to be called.
del server
try:
f.result()
# Non-deterministic if server manage to send CancelledError before
# shutting down or not.
except tf.errors.CancelledError:
pass
except tf.errors.UnavailableError:
pass
def test_nests(self, dim, batched):
address = self.get_unix_address()
server = ops.Server([address])
signature = (tf.TensorSpec(dim, tf.int32, name='arg1'),
Some(tf.TensorSpec(dim, tf.int32, name='arg2'), [
tf.TensorSpec(dim, tf.int32, name='arg3'),
tf.TensorSpec(dim, tf.int32, name='arg4')
]))
@tf.function(input_signature=signature)
def foo(*args):
return tf.nest.map_structure(lambda t: t + 1, args)
server.bind(foo, batched=batched)
server.start()
client = ops.Client(address)
inputs = (1, Some(2, [3, 4]))
expected_outputs = (2, Some(3, [4, 5]))
outputs = client.foo(inputs)
outputs = tf.nest.map_structure(lambda t: t.numpy(), outputs)
tf.nest.assert_same_structure(expected_outputs, outputs)
self.assertAllEqual(tf.nest.flatten(expected_outputs),
tf.nest.flatten(outputs))
server.shutdown()
def test_stress_test(self):
address = self.get_unix_address()
server = ops.Server([address])
@tf.function(input_signature=[tf.TensorSpec([5], tf.int32)])
def foo(x):
return x + 1
server.bind(foo, batched=True)
server.start()
num_clients = 10
num_calls = 100
clients = [ops.Client(address) for _ in range(num_clients)]
def do_calls(client):
for i in range(num_calls):
self.assertAllEqual(i + 1, client.foo(i))
with futures.ThreadPoolExecutor(max_workers=num_clients) as executor:
fs = [executor.submit(do_calls, client) for client in clients]
for i, f in enumerate(futures.as_completed(fs), 0):
f.result()
if i == num_clients // 2:
# Shutdown after at least half the clients have completed. Not
# possible to wait on all because the last batch may not be filled up
# so it can't complete.
try:
server.shutdown()
except tf.errors.UnavailableError:
pass
break
def test_not_fully_specified_outputs(self):
address = self.get_unix_address()
server = ops.Server([address])
@tf.function(input_signature=[tf.TensorSpec([1], tf.int32)])
def foo(x):
if tf.equal(x[0], 0):
return tf.zeros([])
elif tf.equal(x[0], 1):
return tf.zeros([2])
else:
return tf.zeros([1])
server.bind(foo, batched=True)
server.start()
client = ops.Client(address)
self.assertAllEqual(0, client.foo(42))
with self.assertRaisesRegex(
tf.errors.InvalidArgumentError,
'Output must be at least rank 1 when batched=True'):
client.foo(0)
with self.assertRaisesRegex(
tf.errors.InvalidArgumentError,
'All outputs must have the same batch size as '
'the inputs when batched=True, expected: 1 was: 2'):
client.foo(1)
server.shutdown()
def test_tpu_tf_function_same_device(self, dim, batched):
address = self.get_unix_address()
server = ops.Server([address])
with tf.device('/device:CPU:0'):
a = tf.Variable(1)
with tf.device('/device:CPU:0'):
@tf.function
def get_a_plus_one():
return a + 1
@tf.function(input_signature=[tf.TensorSpec(dim, tf.int32)])
def foo(x):
with tf.device('/device:CPU:0'):
b = x + get_a_plus_one()
return b + 1
server.bind(foo, batched=batched)
server.start()
client = ops.Client(address)
a = client.foo(1)
self.assertAllEqual(4, a)
server.shutdown()
def test_not_fully_specified_outputs(self):
address = self.get_unix_address()
server = ops.Server([address])
@tf.function(input_signature=[tf.TensorSpec([2], tf.int32)])
def foo(x):
if tf.equal(x[0], 0):
return tf.zeros([])
elif tf.equal(x[0], 1):
return tf.zeros([2])
else:
return tf.zeros([1])
server.bind(foo)
server.start()
def client():
client = ops.Client(address)
with self.assertRaisesRegex(
tf.errors.InvalidArgumentError,
'Output must be at least rank 1 when batching is enabled'):
client.foo(0)
with futures.ThreadPoolExecutor(max_workers=2) as executor:
f1 = executor.submit(client)
f2 = executor.submit(client)
f1.result()
f2.result()
server.shutdown()
def test_binding_function_twice(self):
address = self.get_unix_address()
server = ops.Server([address])
@tf.function(input_signature=[])
def foo():
return 42
server.bind(foo)
with self.assertRaisesRegex(tf.errors.InvalidArgumentError,
'Function \'foo\' was bound twice.'):
server.bind(foo)
def test_batched_at_least_one_input(self):
address = self.get_unix_address()
server = ops.Server([address])
@tf.function(input_signature=[])
def foo():
return 1
with self.assertRaisesRegex(
tf.errors.InvalidArgumentError,
'Function must have at least one input when batched=True'):
server.bind(foo, batched=True)
def test_batched_output_is_batched(self):
address = self.get_unix_address()
server = ops.Server([address])
@tf.function(input_signature=[tf.TensorSpec([1], tf.int32)])
def foo(unused_x):
return tf.zeros([3])
with self.assertRaisesRegex(
tf.errors.InvalidArgumentError,
'All outputs must have the same batch size as the inputs.'):
server.bind(foo, batched=True)
def test_starting_twice(self):
address = self.get_unix_address()
server = ops.Server([address])
@tf.function(input_signature=[tf.TensorSpec([], tf.int32)])
def foo(x):
return x + 1
server.bind(foo)
server.start()
with self.assertRaisesRegex(tf.errors.InvalidArgumentError,
'Server is already started'):
server.start()
def test_string(self, dim, batched):
address = self.get_unix_address()
server = ops.Server([address])
@tf.function(input_signature=[tf.TensorSpec(dim, tf.string)])
def hello(x):
return tf.strings.join([x, ' world'])
server.bind(hello, batched=batched)
server.start()
client = ops.Client(address)
self.assertAllEqual(b'hello world', client.hello('hello'))
server.shutdown()
def test_no_output(self):
address = self.get_unix_address()
server = ops.Server([address])
@tf.function(input_signature=[tf.TensorSpec([], tf.int32)])
def foo(x):
pass
server.bind(foo)
server.start()
client = ops.Client(address)
self.assertIsNone(client.foo(42))
server.shutdown()
def test_empty_input(self):
address = self.get_unix_address()
server = ops.Server([address])
@tf.function(input_signature=[])
def foo():
return 42
server.bind(foo)
server.start()
client = ops.Client(address)
self.assertAllEqual(42, client.foo())
server.shutdown()
def test_empty_output(self, dim, batched):
address = self.get_unix_address()
server = ops.Server([address])
@tf.function(input_signature=[tf.TensorSpec(dim, tf.int32)])
def foo(x):
return []
server.bind(foo, batched=batched)
server.start()
client = ops.Client(address)
self.assertAllEqual([], client.foo(42))
server.shutdown()
def test_simple(self):
address = self.get_unix_address()
server = ops.Server([address])
@tf.function(input_signature=[tf.TensorSpec([], tf.int32)])
def foo(x):
return x + 1
server.bind(foo)
server.start()
client = ops.Client(address)
self.assertAllEqual(43, client.foo(42))
server.shutdown()
def test_batch_auto_detection(self):
address = self.get_unix_address()
server = ops.Server([address])
@tf.function(input_signature=[tf.TensorSpec(2, tf.int32)])
def foo(x):
return x + 1
server.bind(foo)
server.start()
client = ops.Client(address)
t = tf.constant([1, 2])
self.assertAllEqual(t + 1, client.foo(t))
server.shutdown()
def test_call_after_shutdown(self, dim, batched):
address = self.get_unix_address()
server = ops.Server([address])
@tf.function(input_signature=[tf.TensorSpec(dim, tf.int32)])
def foo(x):
return x + 1
server.bind(foo, batched=batched)
server.start()
client = ops.Client(address)
server.shutdown()
with self.assertRaisesRegexp(tf.errors.UnavailableError, 'server closed'):
client.foo(42)
def test_empty_input(self):
address = self.get_unix_address()
server = ops.Server([address])
@tf.function(input_signature=[])
def foo():
return 42
# Empty input is only allowed when batched=False.
server.bind(foo, batched=False)
server.start()
client = ops.Client(address)
self.assertAllEqual(42, client.foo())
server.shutdown()
def test_batched_first_dimension_must_match(self):
address = self.get_unix_address()
server = ops.Server([address])
@tf.function(input_signature=[
tf.TensorSpec([1], tf.int32),
tf.TensorSpec([2], tf.int32)
])
def foo(x, y):
return x, y
with self.assertRaisesRegexp(
tf.errors.InvalidArgumentError,
'All inputs must have the same first dimension when batched=True'):
server.bind(foo, batched=True)
def test_batched_outputs_at_least_rank1(self):
address = self.get_unix_address()
server = ops.Server([address])
@tf.function(input_signature=[
tf.TensorSpec([1], tf.int32),
tf.TensorSpec([1], tf.int32)
])
def foo(unused_x, unused_y):
return 1
with self.assertRaisesRegex(
tf.errors.InvalidArgumentError,
'All outputs must at least be rank 1 when batched=True'):
server.bind(foo, batched=True)
def test_large_tensor(self, dim, batched):
address = self.get_unix_address()
server = ops.Server([address])
t = tf.fill([10, 1024, 1024], 1) # 40MB
@tf.function(input_signature=[tf.TensorSpec(dim + list(t.shape), tf.int32)])
def foo(x):
return x + 1
server.bind(foo, batched=batched)
server.start()
client = ops.Client(address)
self.assertAllEqual(t + 1, client.foo(t))
server.shutdown()
def test_not_fully_specified_outputs2(self):
address = self.get_unix_address()
server = ops.Server([address])
@tf.function(input_signature=[tf.TensorSpec([1], tf.int32)])
def foo(x):
result, = tf.py_function(lambda x: x, [x], [tf.int32])
result.set_shape([None])
return result
server.bind(foo, batched=True)
server.start()
client = ops.Client(address)
self.assertAllEqual(42, client.foo(42))
server.shutdown()
def test_upvalue(self, dim, batched):
address = self.get_unix_address()
server = ops.Server([address])
a = tf.constant(2)
@tf.function(input_signature=[tf.TensorSpec(dim, tf.int32)])
def foo(x):
return x / a
server.bind(foo, batched=batched)
server.start()
client = ops.Client(address)
self.assertAllEqual(21, client.foo(42))
server.shutdown()
def test_invalid_number_of_arguments(self, dim, batched):
address = self.get_unix_address()
server = ops.Server([address])
@tf.function(input_signature=[tf.TensorSpec(dim, tf.int32)])
def foo(x):
return x + 1
server.bind(foo, batched=batched)
server.start()
client = ops.Client(address)
with self.assertRaisesRegex(tf.errors.InvalidArgumentError,
'Expects 1 arguments, but 2 is provided'):
client.foo([42, 43])
server.shutdown()
def test_invalid_type(self, dim, batched):
address = self.get_unix_address()
server = ops.Server([address])
@tf.function(input_signature=[tf.TensorSpec(dim, tf.int32)])
def foo(x):
return x + 1
server.bind(foo, batched=batched)
server.start()
client = ops.Client(address)
with self.assertRaisesRegex(
tf.errors.InvalidArgumentError,
r'Expects arg\[0\] to be int32 but string is provided'):
client.foo('foo')
server.shutdown()
def test_failing_function(self, dim, batched):
address = self.get_unix_address()
server = ops.Server([address])
@tf.function(input_signature=[tf.TensorSpec(dim, tf.int32)])
def foo(x):
tf.assert_equal(1, x) # Will fail.
return x
server.bind(foo, batched=batched)
server.start()
client = ops.Client(address)
with self.assertRaisesRegex(tf.errors.InvalidArgumentError,
'assertion failed'):
client.foo(42)
server.shutdown()
def test_shutdown_waiting_for_full_batch(self):
address = self.get_unix_address()
server = ops.Server([address])
@tf.function(input_signature=[tf.TensorSpec([2], tf.int32)])
def foo(x):
return x + 1
server.bind(foo, batched=True)
server.start()
client = ops.Client(address)
with futures.ThreadPoolExecutor(max_workers=1) as executor:
f = executor.submit(client.foo, 42)
time.sleep(1)
server.shutdown()
with self.assertRaisesRegexp(tf.errors.UnavailableError, 'server closed'):
f.result()
def test_invalid_shape(self, dim, batched):
address = self.get_unix_address()
server = ops.Server([address])
@tf.function(input_signature=[tf.TensorSpec(dim + [4, 3], tf.int32)])
def foo(x):
return x
server.bind(foo, batched=batched)
server.start()
client = ops.Client(address)
with self.assertRaisesRegexp(
tf.errors.InvalidArgumentError,
r'Expects arg\[0\] to have shape \[4,3\] but had shape \[3,4\]'):
client.foo(tf.zeros([3, 4], tf.int32)) # Shape [3, 4], not [4, 3]
server.shutdown()
def test_call_after_shutdown_and_start(self, dim, batched):
address = self.get_unix_address()
server = ops.Server([address])
q = tf.queue.FIFOQueue(1, [tf.int32]) # To test cancellation is reset.
@tf.function(input_signature=[tf.TensorSpec(dim, tf.int32)])
def foo(x):
q.enqueue(x)
return x + 1
server.bind(foo, batched=batched)
server.start()
server.shutdown()
server.start()
client = ops.Client(address)
self.assertAllEqual(43, client.foo(42))
server.shutdown()
def test_two_clients(self, dim, batched):
address = self.get_unix_address()
server = ops.Server([address])
@tf.function(input_signature=[tf.TensorSpec(dim, tf.int32)])
def foo(x):
return x + 1
server.bind(foo, batched=batched)
server.start()
client = ops.Client(address)
client2 = ops.Client(address)
with futures.ThreadPoolExecutor(max_workers=2) as executor:
f0 = executor.submit(client.foo, 42)
f1 = executor.submit(client2.foo, 44)
self.assertAllEqual(43, f0.result())
self.assertAllEqual(45, f1.result())
server.shutdown()
