def CreateValue(self, request, context):
"""Creates a value embedded in the executor.
Args:
request: An instance of `executor_pb2.CreateValueRequest`.
context: An instance of `grpc.ServicerContext`.
Returns:
An instance of `executor_pb2.CreateValueResponse`.
"""
py_typecheck.check_type(request, executor_pb2.CreateValueRequest)
try:
value, value_type = (executor_service_utils.deserialize_value(
request.value))
value_id = str(uuid.uuid4())
future_val = asyncio.run_coroutine_threadsafe(
self._executor.create_value(value, value_type),
self._event_loop)
with self._lock:
self._values[value_id] = future_val
return executor_pb2.CreateValueResponse(
value_ref=executor_pb2.ValueRef(id=value_id))
except (ValueError, TypeError) as err:
logging.error(traceback.format_exc())
context.set_code(grpc.StatusCode.INVALID_ARGUMENT)
context.set_details(str(err))
return executor_pb2.CreateValueResponse()
def get_value(self, value_id):
response = self._stub.Compute(
executor_pb2.ComputeRequest(value_ref=executor_pb2.ValueRef(
id=value_id)))
py_typecheck.check_type(response, executor_pb2.ComputeResponse)
value, _ = executor_service_utils.deserialize_value(response.value)
return value
async def _compute(self, value_ref):
py_typecheck.check_type(value_ref, executor_pb2.ValueRef)
request = executor_pb2.ComputeRequest(value_ref=value_ref)
response = self._stub.Compute(request)
py_typecheck.check_type(response, executor_pb2.ComputeResponse)
value, _ = executor_service_utils.deserialize_value(response.value)
return value
def test_serialize_deserialize_sequence_of_namedtuples(self):
test_tuple_type = collections.namedtuple('TestTuple', ['a', 'b', 'c'])
def make_test_tuple(x):
return test_tuple_type(a=x * 2,
b=tf.cast(x, tf.int32),
c=tf.cast(x - 1, tf.float32))
ds = tf.data.Dataset.range(5).map(make_test_tuple)
element_type = computation_types.NamedTupleType([
('a', tf.int64),
('b', tf.int32),
('c', tf.float32),
])
sequence_type = computation_types.SequenceType(element=element_type)
value_proto, value_type = executor_service_utils.serialize_value(
ds, sequence_type)
self.assertIsInstance(value_proto, executor_pb2.Value)
self.assertEqual(value_type, sequence_type)
y, type_spec = executor_service_utils.deserialize_value(value_proto)
self.assertEqual(type_spec, sequence_type)
actual_values = self.evaluate([y_val for y_val in y])
expected_values = [
test_tuple_type(a=x * 2, b=x, c=x - 1.) for x in range(5)
]
for actual, expected in zip(actual_values, expected_values):
self.assertAllClose(actual, expected)
def get_value(self, value_id: str):
"""Retrieves a value using the `Compute` endpoint."""
response = self._stub.Compute(
executor_pb2.ComputeRequest(value_ref=executor_pb2.ValueRef(
id=value_id)))
py_typecheck.check_type(response, executor_pb2.ComputeResponse)
value, _ = executor_service_utils.deserialize_value(response.value)
return value
def test_serialize_deserialize_nested_tuple_value_without_names(self): x = tuple([10, 20]) x_type = computation_types.to_type(tuple([tf.int32, tf.int32])) value_proto, value_type = executor_service_utils.serialize_value(x, x_type) self.assertIsInstance(value_proto, executor_pb2.Value) self.assertEqual(str(value_type), '<int32,int32>') y, type_spec = executor_service_utils.deserialize_value(value_proto) self.assertEqual(str(type_spec), str(x_type)) self.assertCountEqual(y, (10, 20))
def test_serialize_deserialize_tensor_value_with_nontrivial_shape(self):
x = tf.constant([10, 20, 30]).numpy()
value_proto, value_type = executor_service_utils.serialize_value(
x, computation_types.TensorType(tf.int32, [3]))
self.assertIsInstance(value_proto, executor_pb2.Value)
self.assertEqual(str(value_type), 'int32[3]')
y, type_spec = executor_service_utils.deserialize_value(value_proto)
self.assertEqual(str(type_spec), 'int32[3]')
self.assertTrue(np.array_equal(x, y))
def test_serialize_deserialize_tensor_value_with_different_dtype(self):
x = tf.constant(10.0).numpy()
value_proto, value_type = (executor_service_utils.serialize_value(
x, computation_types.TensorType(tf.int32)))
self.assertIsInstance(value_proto, executor_pb2.Value)
self.assertEqual(str(value_type), 'int32')
y, type_spec = executor_service_utils.deserialize_value(value_proto)
self.assertEqual(str(type_spec), 'int32')
self.assertEqual(y, 10)
def test_serialize_deserialize_sequence_of_scalars(self):
ds = tf.data.Dataset.range(5).map(lambda x: x * 2)
value_proto, value_type = executor_service_utils.serialize_value(
ds, computation_types.SequenceType(tf.int64))
self.assertIsInstance(value_proto, executor_pb2.Value)
self.assertEqual(str(value_type), 'int64*')
y, type_spec = executor_service_utils.deserialize_value(value_proto)
self.assertEqual(str(type_spec), 'int64*')
self.assertAllEqual([y_val for y_val in y], [x * 2 for x in range(5)])
def test_serialize_deserialize_federated_at_server(self):
x = 10
x_type = type_factory.at_server(tf.int32)
value_proto, value_type = executor_service_utils.serialize_value(
x, x_type)
self.assertIsInstance(value_proto, executor_pb2.Value)
self.assertEqual(str(value_type), 'int32@SERVER')
y, type_spec = executor_service_utils.deserialize_value(value_proto)
self.assertEqual(str(type_spec), str(x_type))
self.assertEqual(y, 10)
def test_serialize_deserialize_federated_at_clients(self):
x = [10, 20]
x_type = type_factory.at_clients(tf.int32)
value_proto, value_type = executor_service_utils.serialize_value(
x, x_type)
self.assertIsInstance(value_proto, executor_pb2.Value)
self.assertEqual(str(value_type), '{int32}@CLIENTS')
y, type_spec = executor_service_utils.deserialize_value(value_proto)
self.assertEqual(str(type_spec), str(x_type))
self.assertEqual(y, [10, 20])
def test_serialize_deserialize_tensor_value(self):
x = tf.constant(10.0).numpy()
type_spec = computation_types.TensorType(tf.as_dtype(x.dtype), x.shape)
value_proto, value_type = executor_service_utils.serialize_value(
x, type_spec)
self.assertIsInstance(value_proto, executor_pb2.Value)
self.assertEqual(str(value_type), 'float32')
y, type_spec = executor_service_utils.deserialize_value(value_proto)
self.assertEqual(str(type_spec), 'float32')
self.assertTrue(np.array_equal(x, y))
async def _compute(self, value_ref):
py_typecheck.check_type(value_ref, executor_pb2.ValueRef)
request = executor_pb2.ComputeRequest(value_ref=value_ref)
if not self._bidi_stream:
response = self._stub.Compute(request)
else:
response = (await self._bidi_stream.send_request(
executor_pb2.ExecuteRequest(compute=request))).compute
py_typecheck.check_type(response, executor_pb2.ComputeResponse)
value, _ = executor_service_utils.deserialize_value(response.value)
return value
def test_serialize_deserialize_computation_value(self):
@computations.tf_computation
def comp():
return tf.constant(10)
value_proto, value_type = executor_service_utils.serialize_value(comp)
self.assertEqual(value_proto.WhichOneof('value'), 'computation')
self.assertEqual(str(value_type), '( -> int32)')
comp, type_spec = executor_service_utils.deserialize_value(value_proto)
self.assertIsInstance(comp, computation_pb2.Computation)
self.assertEqual(str(type_spec), '( -> int32)')
def test_serialize_deserialize_sequence_of_nested_structures(self):
test_tuple_type = collections.namedtuple('TestTuple', ['u', 'v'])
def _make_nested_tf_structure(x):
return collections.OrderedDict([
('b', tf.cast(x, tf.int32)),
('a',
tuple([
x,
test_tuple_type(x * 2, x * 3),
collections.OrderedDict([('x', x**2), ('y', x**3)])
])),
])
ds = tf.data.Dataset.range(5).map(_make_nested_tf_structure)
element_type = computation_types.NamedTupleType([
('b', tf.int32),
('a',
computation_types.NamedTupleType([
(None, tf.int64),
(None, test_tuple_type(tf.int64, tf.int64)),
(None,
computation_types.NamedTupleType([('x', tf.int64),
('y', tf.int64)])),
])),
])
sequence_type = computation_types.SequenceType(element=element_type)
value_proto, value_type = executor_service_utils.serialize_value(
ds, sequence_type)
self.assertIsInstance(value_proto, executor_pb2.Value)
self.assertEqual(value_type, sequence_type)
y, type_spec = executor_service_utils.deserialize_value(value_proto)
# These aren't the same because ser/de destroys the PyContainer
type_spec.check_equivalent_to(sequence_type)
def _build_expected_structure(x):
return collections.OrderedDict([
('b', x),
('a',
tuple([
x,
test_tuple_type(x * 2, x * 3),
collections.OrderedDict([('x', x**2), ('y', x**3)])
])),
])
actual_values = self.evaluate([y_val for y_val in y])
expected_values = [_build_expected_structure(x) for x in range(5)]
for actual, expected in zip(actual_values, expected_values):
self.assertEqual(type(actual), type(expected))
self.assertAllClose(actual, expected)
def test_serialize_deserialize_nested_tuple_value_with_names(self):
x = collections.OrderedDict([('a', 10), ('b', [20, 30]),
('c', collections.OrderedDict([('d', 40)]))])
x_type = computation_types.to_type(
collections.OrderedDict([('a', tf.int32), ('b', [tf.int32, tf.int32]),
('c', collections.OrderedDict([('d', tf.int32)
]))]))
value_proto, value_type = executor_service_utils.serialize_value(x, x_type)
self.assertIsInstance(value_proto, executor_pb2.Value)
self.assertEqual(str(value_type), '<a=int32,b=<int32,int32>,c=<d=int32>>')
y, type_spec = executor_service_utils.deserialize_value(value_proto)
self.assertEqual(str(type_spec), str(x_type))
self.assertTrue(str(y), '<a=10,b=<20,30>,c=<d=40>>')
def test_serialize_deserialize_sequence_of_tuples(self):
ds = tf.data.Dataset.range(5).map(lambda x: (x * 2, tf.cast(
x, tf.int32), tf.cast(x - 1, tf.float32)))
value_proto, value_type = executor_service_utils.serialize_value(
ds,
computation_types.SequenceType(element=(tf.int64, tf.int32,
tf.float32)))
self.assertIsInstance(value_proto, executor_pb2.Value)
self.assertEqual(str(value_type), '<int64,int32,float32>*')
y, type_spec = executor_service_utils.deserialize_value(value_proto)
self.assertEqual(str(type_spec), '<int64,int32,float32>*')
self.assertAllEqual(self.evaluate([y_val for y_val in y]),
[(x * 2, x, x - 1.) for x in range(5)])
def CreateValue(
self,
request: executor_pb2.CreateValueRequest,
context: grpc.ServicerContext,
) -> executor_pb2.CreateValueResponse:
"""Creates a value embedded in the executor."""
py_typecheck.check_type(request, executor_pb2.CreateValueRequest)
try:
value, value_type = (
executor_service_utils.deserialize_value(request.value))
value_id = str(uuid.uuid4())
future_val = asyncio.run_coroutine_threadsafe(
self._executor.create_value(value, value_type), self._event_loop)
with self._lock:
self._values[value_id] = future_val
return executor_pb2.CreateValueResponse(
value_ref=executor_pb2.ValueRef(id=value_id))
except (ValueError, TypeError) as err:
_set_invalid_arg_err(context, err)
return executor_pb2.CreateValueResponse()
def CreateValue(
self,
request: executor_pb2.CreateValueRequest,
context: grpc.ServicerContext,
) -> executor_pb2.CreateValueResponse:
"""Creates a value embedded in the executor."""
py_typecheck.check_type(request, executor_pb2.CreateValueRequest)
try:
with tracing.span('ExecutorService.CreateValue', 'deserialize_value'):
value, value_type = (
executor_service_utils.deserialize_value(request.value))
value_id = str(uuid.uuid4())
coro = self._executor.create_value(value, value_type)
future_val = self._run_coro_threadsafe_with_tracing(coro)
with self._lock:
self._values[value_id] = future_val
return executor_pb2.CreateValueResponse(
value_ref=executor_pb2.ValueRef(id=value_id))
except (ValueError, TypeError) as err:
_set_invalid_arg_err(context, err)
return executor_pb2.CreateValueResponse()
def CreateValue(self, request, context):
"""Creates a value embedded in the executor.
Args:
request: An instance of `executor_pb2.CreateValueRequest`.
context: An instance of `grpc.ServicerContext`.
Returns:
An instance of `executor_pb2.CreateValueResponse`.
"""
py_typecheck.check_type(request, executor_pb2.CreateValueRequest)
try:
value, value_type = (executor_service_utils.deserialize_value(
request.value))
value_id = str(uuid.uuid4())
future_val = self._executor.create_value(value, value_type)
with self._lock:
self._values[value_id] = future_val
return executor_pb2.CreateValueResponse(
value_ref=executor_pb2.ValueRef(id=value_id))
except (ValueError, TypeError):
context.set_code(grpc.StatusCode.INVALID_ARGUMENT)
return executor_pb2.CreateValueResponse()
