def test_get_pipeline_input_node():
# 1) No InputNode found
ray_dag = combine.bind(1, 2)
with _DAGNodeNameGenerator() as node_name_generator:
serve_dag = ray_dag.apply_recursive(
lambda node: transform_ray_dag_to_serve_dag(node, node_name_generator)
)
with pytest.raises(
AssertionError, match="There should be one and only one InputNode"
):
get_pipeline_input_node(serve_dag)
# 2) More than one InputNode found
with InputNode() as dag_input:
a = combine.bind(dag_input[0], dag_input[1])
with InputNode() as dag_input_2:
b = combine.bind(dag_input_2[0], dag_input_2[1])
ray_dag = combine.bind(a, b)
with pytest.raises(
AssertionError, match="Each DAG should only have one unique InputNode"
):
with _DAGNodeNameGenerator() as node_name_generator:
serve_dag = ray_dag.apply_recursive(
lambda node: transform_ray_dag_to_serve_dag(node, node_name_generator)
)
get_pipeline_input_node(serve_dag)
def _test_deployment_json_serde_helper(ray_dag: DAGNode,
input=None,
expected_num_deployments=None):
"""Helper function for DeploymentNode and DeploymentMethodNode calls, checks
the following:
1) Transform ray dag to serve dag, and ensure serve dag is JSON
serializable.
2) Serve dag JSON and be deserialized back to serve dag.
3) Deserialized serve dag can extract correct number and definition of
serve deployments.
"""
with _DAGNodeNameGenerator() as node_name_generator:
serve_root_dag = ray_dag.apply_recursive(
lambda node: transform_ray_dag_to_serve_dag(
node, node_name_generator))
deserialized_deployments = extract_deployments_from_serve_dag(
serve_root_dag)
serve_executor_root_dag = serve_root_dag.apply_recursive(
transform_serve_dag_to_serve_executor_dag)
json_serialized = json.dumps(serve_executor_root_dag, cls=DAGNodeEncoder)
deserialized_serve_executor_root_dag_node = json.loads(
json_serialized, object_hook=dagnode_from_json)
assert len(deserialized_deployments) == expected_num_deployments
# Deploy deserilized version to ensure JSON serde correctness
for model in deserialized_deployments:
model.deploy()
if input is None:
assert ray.get(ray_dag.execute()) == ray.get(
serve_executor_root_dag.execute())
else:
assert ray.get(ray_dag.execute(input)) == ray.get(
serve_executor_root_dag.execute(input))
return serve_executor_root_dag, deserialized_serve_executor_root_dag_node
def test_multi_instantiation_class_nested_deployment_arg(serve_instance):
"""
Test we can pass deployments with **nested** init_arg or init_kwarg,
instantiated multiple times for the same class, and we can still correctly
replace args with deployment handle and parse correct deployment instances.
"""
ray_dag, _ = get_multi_instantiation_class_nested_deployment_arg_dag()
with _DAGNodeNameGenerator() as node_name_generator:
serve_root_dag = ray_dag.apply_recursive(
lambda node: transform_ray_dag_to_serve_dag(
node, node_name_generator))
print(f"Serve DAG: \n{serve_root_dag}")
deployments = extract_deployments_from_serve_dag(serve_root_dag)
assert len(deployments) == 3
# Ensure Deployments with other deployment nodes in init arg are replaced
# with correct handle
combine_deployment = deployments[2]
init_arg_handle = combine_deployment.init_args[0]
assert isinstance(init_arg_handle, RayServeLazySyncHandle)
assert init_arg_handle.deployment_name == "Model"
init_kwarg_handle = combine_deployment.init_kwargs["m2"][NESTED_HANDLE_KEY]
assert isinstance(init_kwarg_handle, RayServeLazySyncHandle)
assert init_kwarg_handle.deployment_name == "Model_1"
for deployment in deployments:
deployment.deploy()
_validate_consistent_python_output(deployments[2],
ray_dag,
"Combine",
input=1,
output=5)
def test_unique_name_reset_upon_build(serve_instance):
ray_dag, _ = get_multi_instantiation_class_deployment_in_init_args_dag()
with _DAGNodeNameGenerator() as node_name_generator:
serve_root_dag = ray_dag.apply_recursive(
lambda node: transform_ray_dag_to_serve_dag(
node, node_name_generator))
deployments = extract_deployments_from_serve_dag(serve_root_dag)
assert deployments[0].name == "Model"
assert deployments[1].name == "Model_1"
with _DAGNodeNameGenerator() as node_name_generator:
serve_root_dag = ray_dag.apply_recursive(
lambda node: transform_ray_dag_to_serve_dag(
node, node_name_generator))
deployments = extract_deployments_from_serve_dag(serve_root_dag)
# Assert we don't keep increasing suffix id between build() calls
assert deployments[0].name == "Model"
assert deployments[1].name == "Model_1"
def test_simple_single_class(serve_instance):
ray_dag, _ = get_simple_class_with_class_method_dag()
with _DAGNodeNameGenerator() as node_name_generator:
serve_root_dag = ray_dag.apply_recursive(
lambda node: transform_ray_dag_to_serve_dag(node, node_name_generator)
)
deployments = extract_deployments_from_serve_dag(serve_root_dag)
assert len(deployments) == 1
deployments[0].deploy()
_validate_consistent_python_output(
deployments[0], ray_dag, "Model", input=1, output=0.6
)
def test_single_class_with_invalid_deployment_options(serve_instance):
with InputNode() as dag_input:
model = Model.options(name="my_deployment").bind(2, ratio=0.3)
ray_dag = model.forward.bind(dag_input)
with DAGNodeNameGenerator() as node_name_generator:
serve_root_dag = ray_dag.apply_recursive(
lambda node: transform_ray_dag_to_serve_dag(
node, node_name_generator))
deployments = extract_deployments_from_serve_dag(serve_root_dag)
assert len(deployments) == 1
with pytest.raises(
ValueError,
match="Specifying 'name' in ray_actor_options is not allowed"):
deployments[0].deploy()
def test_func_class_with_class_method_dag(serve_instance):
ray_dag, _ = get_func_class_with_class_method_dag()
with _DAGNodeNameGenerator() as node_name_generator:
serve_root_dag = ray_dag.apply_recursive(
lambda node: transform_ray_dag_to_serve_dag(
node, node_name_generator))
deployments = extract_deployments_from_serve_dag(serve_root_dag)
serve_executor_root_dag = serve_root_dag.apply_recursive(
transform_serve_dag_to_serve_executor_dag)
assert len(deployments) == 3
for deployment in deployments:
deployment.deploy()
assert ray.get(ray_dag.execute(1, 2, 3)) == 8
assert ray.get(serve_executor_root_dag.execute(1, 2, 3)) == 8
def test_build_simple_func_dag(serve_instance):
ray_dag, _ = get_simple_func_dag()
with _DAGNodeNameGenerator() as node_name_generator:
serve_root_dag = ray_dag.apply_recursive(
lambda node: transform_ray_dag_to_serve_dag(
node, node_name_generator))
serve_root_dag = ray_dag.apply_recursive(transform_ray_dag_to_serve_dag)
deployments = extract_deployments_from_serve_dag(serve_root_dag)
assert len(deployments) == 1
deployments[0].deploy()
deployment_handle = deployments[0].get_handle()
# Because the bound kwarg is stored in dag, so it has to be explicitly passed in.
assert ray.get(deployment_handle.remote(1, 2, kwargs_output=1)) == 4
assert ray.get(ray_dag.execute([1, 2])) == 4
def test_single_class_with_valid_ray_options(serve_instance):
with InputNode() as dag_input:
model = Model.options(num_cpus=1, memory=1000).bind(2, ratio=0.3)
ray_dag = model.forward.bind(dag_input)
with _DAGNodeNameGenerator() as node_name_generator:
serve_root_dag = ray_dag.apply_recursive(
lambda node: transform_ray_dag_to_serve_dag(node, node_name_generator)
)
deployments = extract_deployments_from_serve_dag(serve_root_dag)
assert len(deployments) == 1
deployments[0].deploy()
_validate_consistent_python_output(
deployments[0], ray_dag, deployments[0].name, input=1, output=0.6
)
deployment = serve.get_deployment(deployments[0].name)
assert deployment.ray_actor_options.get("num_cpus") == 1
assert deployment.ray_actor_options.get("memory") == 1000
assert deployment.ray_actor_options.get("runtime_env") == {}
def test_shared_deployment_handle(serve_instance):
"""
Test we can re-use the same deployment handle multiple times or in
multiple places, without incorrectly parsing duplicated deployments.
"""
ray_dag, _ = get_shared_deployment_handle_dag()
with _DAGNodeNameGenerator() as node_name_generator:
serve_root_dag = ray_dag.apply_recursive(
lambda node: transform_ray_dag_to_serve_dag(node, node_name_generator)
)
print(f"Serve DAG: \n{serve_root_dag}")
deployments = extract_deployments_from_serve_dag(serve_root_dag)
assert len(deployments) == 2
for deployment in deployments:
deployment.deploy()
_validate_consistent_python_output(
deployments[1], ray_dag, "Combine", input=1, output=4
)
def test_multi_instantiation_class_deployment_in_init_args(serve_instance):
"""
Test we can pass deployments as init_arg or init_kwarg, instantiated
multiple times for the same class, and we can still correctly replace
args with deployment handle and parse correct deployment instances.
"""
ray_dag, _ = get_multi_instantiation_class_deployment_in_init_args_dag()
with _DAGNodeNameGenerator() as node_name_generator:
serve_root_dag = ray_dag.apply_recursive(
lambda node: transform_ray_dag_to_serve_dag(node, node_name_generator)
)
print(f"Serve DAG: \n{serve_root_dag}")
deployments = extract_deployments_from_serve_dag(serve_root_dag)
assert len(deployments) == 3
for deployment in deployments:
deployment.deploy()
_validate_consistent_python_output(
deployments[2], ray_dag, "Combine", input=1, output=5
)
def ray_dag_to_serve_dag(dag: DAGNode):
with _DAGNodeNameGenerator() as deployment_name_generator:
serve_dag = dag.apply_recursive(
lambda node: transform_ray_dag_to_serve_dag(
node, deployment_name_generator))
return serve_dag
