[Core] Accelerator type API (#10561)

doc/source/advanced.rst

@@ -220,6 +220,21 @@ load balancing, gang scheduling, and priority-based scheduling.
     :noindex:
 
 
+Accelerator Types
+------------------
+
+Ray supports resource specific accelerator types. The `accelerator_type` field can be used to force to a task to run on a node with a specific type of accelerator. Under the hood, the accelerator type option is implemented as a custom resource demand of ``"accelerator_type:<type>": 0.001``. This forces the task to be placed on a node with that particular accelerator type available. This also lets the multi-node-type autoscaler know that there is demand for that type of resource, potentially triggering the launch of new nodes providing that accelerator.
+
+.. code-block:: python
+
+    from ray.accelerators import NVIDIA_TESLA_V100
+    
+    @ray.remote(num_gpus=1, accelerator_type=NVIDIA_TESLA_V100)
+    def train(data):
+        return "This function was run on a node with a Tesla V100 GPU"
+
+See `ray.util.accelerators` to see available accelerator types. Current automatically detected accelerator types include Nvidia GPUs.
+
 
 Nested Remote Functions
 -----------------------

python/ray/actor.py

@@ -264,7 +264,7 @@ class ActorClassMetadata:
     def __init__(self, language, modified_class,
                  actor_creation_function_descriptor, class_id, max_restarts,
                  max_task_retries, num_cpus, num_gpus, memory,
-                 object_store_memory, resources):
+                 object_store_memory, resources, accelerator_type):
         self.language = language
         self.modified_class = modified_class
         self.actor_creation_function_descriptor = \
@@ -279,6 +279,7 @@ class ActorClassMetadata:
         self.memory = memory
         self.object_store_memory = object_store_memory
         self.resources = resources
+        self.accelerator_type = accelerator_type
         self.last_export_session_and_job = None
         self.method_meta = ActorClassMethodMetadata.create(
             modified_class, actor_creation_function_descriptor)
@@ -336,7 +337,8 @@ class ActorClass:
     @classmethod
     def _ray_from_modified_class(cls, modified_class, class_id, max_restarts,
                                  max_task_retries, num_cpus, num_gpus, memory,
-                                 object_store_memory, resources):
+                                 object_store_memory, resources,
+                                 accelerator_type):
         for attribute in [
                 "remote",
                 "_remote",
@@ -368,7 +370,7 @@ class ActorClass:
             Language.PYTHON, modified_class,
             actor_creation_function_descriptor, class_id, max_restarts,
             max_task_retries, num_cpus, num_gpus, memory, object_store_memory,
-            resources)
+            resources, accelerator_type)
 
         return self
 
@@ -376,13 +378,13 @@ class ActorClass:
     def _ray_from_function_descriptor(
             cls, language, actor_creation_function_descriptor, max_restarts,
             max_task_retries, num_cpus, num_gpus, memory, object_store_memory,
-            resources):
+            resources, accelerator_type):
         self = ActorClass.__new__(ActorClass)
 
         self.__ray_metadata__ = ActorClassMetadata(
             language, None, actor_creation_function_descriptor, None,
             max_restarts, max_task_retries, num_cpus, num_gpus, memory,
-            object_store_memory, resources)
+            object_store_memory, resources, accelerator_type)
 
         return self
 
@@ -428,6 +430,7 @@ class ActorClass:
                 memory=None,
                 object_store_memory=None,
                 resources=None,
+                accelerator_type=None,
                 max_concurrency=None,
                 max_restarts=None,
                 max_task_retries=None,
@@ -536,8 +539,9 @@ class ActorClass:
         # decorator. Last three conditions are to check that no resources were
         # specified when _remote() was called.
         if (meta.num_cpus is None and meta.num_gpus is None
-                and meta.resources is None and num_cpus is None
-                and num_gpus is None and resources is None):
+                and meta.resources is None and meta.accelerator_type is None
+                and num_cpus is None and num_gpus is None and resources is None
+                and accelerator_type is None):
             # In the default case, actors acquire no resources for
             # their lifetime, and actor methods will require 1 CPU.
             cpus_to_use = ray_constants.DEFAULT_ACTOR_CREATION_CPU_SIMPLE
@@ -572,8 +576,8 @@ class ActorClass:
 
         resources = ray.utils.resources_from_resource_arguments(
             cpus_to_use, meta.num_gpus, meta.memory, meta.object_store_memory,
-            meta.resources, num_cpus, num_gpus, memory, object_store_memory,
-            resources)
+            meta.resources, meta.accelerator_type, num_cpus, num_gpus, memory,
+            object_store_memory, resources, accelerator_type)
 
         # If the actor methods require CPU resources, then set the required
         # placement resources. If actor_placement_resources is empty, then
@@ -904,7 +908,7 @@ def modify_class(cls):
 
 
 def make_actor(cls, num_cpus, num_gpus, memory, object_store_memory, resources,
-               max_restarts, max_task_retries):
+               accelerator_type, max_restarts, max_task_retries):
     Class = modify_class(cls)
 
     if max_restarts is None:
@@ -928,7 +932,8 @@ def make_actor(cls, num_cpus, num_gpus, memory, object_store_memory, resources,
 
     return ActorClass._ray_from_modified_class(
         Class, ActorClassID.from_random(), max_restarts, max_task_retries,
-        num_cpus, num_gpus, memory, object_store_memory, resources)
+        num_cpus, num_gpus, memory, object_store_memory, resources,
+        accelerator_type)
 
 
 def exit_actor():

python/ray/cross_language.py

@@ -79,10 +79,12 @@ def java_actor_class(class_name):
     return ActorClass._ray_from_function_descriptor(
         Language.JAVA,
         JavaFunctionDescriptor(class_name, "<init>", ""),
-        0,  # max_restarts,
-        0,  # max_task_retries,
-        None,  # num_cpus,
-        None,  # num_gpus,
-        None,  # memory,
-        None,  # object_store_memory,
-        None)  # resources,
+        max_restarts=0,
+        max_task_retries=0,
+        num_cpus=None,
+        num_gpus=None,
+        memory=None,
+        object_store_memory=None,
+        resources=None,
+        accelerator_type=None,
+    )

python/ray/ray_constants.py

@@ -130,7 +130,7 @@ DASHBOARD_DIED_ERROR = "dashboard_died"
 RAYLET_CONNECTION_ERROR = "raylet_connection_error"
 
 # Used in gpu detection
-RESOURCE_CONSTRAINT_PREFIX = "gpu_type:"
+RESOURCE_CONSTRAINT_PREFIX = "accelerator_type:"
 
 RESOURCES_ENVIRONMENT_VARIABLE = "RAY_OVERRIDE_RESOURCES"
 

python/ray/remote_function.py

@@ -61,9 +61,9 @@ class RemoteFunction:
     """
 
     def __init__(self, language, function, function_descriptor, num_cpus,
-                 num_gpus, memory, object_store_memory, resources, num_returns,
-                 max_calls, max_retries, placement_group,
-                 placement_group_bundle_index):
+                 num_gpus, memory, object_store_memory, resources,
+                 accelerator_type, num_returns, max_calls, max_retries,
+                 placement_group, placement_group_bundle_index):
         self._language = language
         self._function = function
         self._function_name = (
@@ -79,6 +79,7 @@ class RemoteFunction:
                 "setting object_store_memory is not implemented for tasks")
         self._object_store_memory = None
         self._resources = resources
+        self._accelerator_type = accelerator_type
         self._num_returns = (DEFAULT_REMOTE_FUNCTION_NUM_RETURN_VALS
                              if num_returns is None else num_returns)
         self._max_calls = (DEFAULT_REMOTE_FUNCTION_MAX_CALLS
@@ -149,6 +150,7 @@ class RemoteFunction:
                 num_gpus=None,
                 memory=None,
                 object_store_memory=None,
+                accelerator_type=None,
                 resources=None,
                 max_retries=None,
                 placement_group=None,
@@ -196,8 +198,9 @@ class RemoteFunction:
 
         resources = ray.utils.resources_from_resource_arguments(
             self._num_cpus, self._num_gpus, self._memory,
-            self._object_store_memory, self._resources, num_cpus, num_gpus,
-            memory, object_store_memory, resources)
+            self._object_store_memory, self._resources, self._accelerator_type,
+            num_cpus, num_gpus, memory, object_store_memory, resources,
+            accelerator_type)
 
         def invocation(args, kwargs):
             if self._is_cross_language:

python/ray/tests/test_advanced_3.py

@@ -12,6 +12,7 @@ import pytest
 
 import ray
 import ray.ray_constants as ray_constants
+import ray.util.accelerators
 import ray.cluster_utils
 import ray.test_utils
 from ray import resource_spec
@@ -687,6 +688,54 @@ Blacklisted:     No
     assert resource_spec._constraints_from_gpu_info(None) == {}
 
 
+def test_accelerator_type_api(shutdown_only):
+    v100 = ray.util.accelerators.NVIDIA_TESLA_V100
+    resource_name = f"{ray_constants.RESOURCE_CONSTRAINT_PREFIX}{v100}"
+    ray.init(num_cpus=4, resources={resource_name: 1})
+
+    quantity = 1
+
+    @ray.remote(accelerator_type=v100)
+    def decorated_func(quantity):
+        return ray.available_resources()[resource_name] < quantity
+
+    assert ray.get(decorated_func.remote(quantity))
+
+    def via_options_func(quantity):
+        return ray.available_resources()[resource_name] < quantity
+
+    assert ray.get(
+        ray.remote(via_options_func).options(
+            accelerator_type=v100).remote(quantity))
+
+    @ray.remote(accelerator_type=v100)
+    class DecoratedActor:
+        def __init__(self):
+            pass
+
+        def initialized(self):
+            pass
+
+    class ActorWithOptions:
+        def __init__(self):
+            pass
+
+        def initialized(self):
+            pass
+
+    decorated_actor = DecoratedActor.remote()
+    # Avoid a race condition where the actor hasn't been initialized and
+    # claimed the resources yet.
+    ray.get(decorated_actor.initialized.remote())
+    assert ray.available_resources()[resource_name] < quantity
+
+    quantity = ray.available_resources()[resource_name]
+    with_options = ray.remote(ActorWithOptions).options(
+        accelerator_type=v100).remote()
+    ray.get(with_options.initialized.remote())
+    assert ray.available_resources()[resource_name] < quantity
+
+
 if __name__ == "__main__":
     import pytest
     sys.exit(pytest.main(["-v", __file__]))

python/ray/util/accelerators/__init__.py

@@ -0,0 +1,6 @@
+from ray.util.accelerators.accelerators import NVIDIA_TESLA_V100
+
+__all__ = [
+    "NVIDIA_TESLA_V100", "NVIDIA_TESLA_P100", "NVIDIA_TESLA_T4",
+    "NVIDIA_TESLA_P4", "NVIDIA_TESLA_K80"
+]

python/ray/util/accelerators/accelerators.py

@@ -0,0 +1,5 @@
+NVIDIA_TESLA_V100 = "V100"
+NVIDIA_TESLA_P100 = "P100"
+NVIDIA_TESLA_T4 = "T4"
+NVIDIA_TESLA_P4 = "P4"
+NVIDIA_TESLA_K80 = "K80"

python/ray/utils.py

@@ -313,9 +313,10 @@ def set_cuda_visible_devices(gpu_ids):
 
 def resources_from_resource_arguments(
         default_num_cpus, default_num_gpus, default_memory,
-        default_object_store_memory, default_resources, runtime_num_cpus,
-        runtime_num_gpus, runtime_memory, runtime_object_store_memory,
-        runtime_resources):
+        default_object_store_memory, default_resources,
+        default_accelerator_type, runtime_num_cpus, runtime_num_gpus,
+        runtime_memory, runtime_object_store_memory, runtime_resources,
+        runtime_accelerator_type):
     """Determine a task's resource requirements.
 
     Args:
@@ -376,6 +377,13 @@ def resources_from_resource_arguments(
         resources["object_store_memory"] = ray_constants.to_memory_units(
             object_store_memory, round_up=True)
 
+    if runtime_accelerator_type is not None:
+        resources[f"{ray_constants.RESOURCE_CONSTRAINT_PREFIX}"
+                  f"{runtime_accelerator_type}"] = 0.001
+    elif default_accelerator_type is not None:
+        resources[f"{ray_constants.RESOURCE_CONSTRAINT_PREFIX}"
+                  f"{default_accelerator_type}"] = 0.001
+
     return resources
 
 

python/ray/worker.py

@@ -1653,6 +1653,7 @@ def make_decorator(num_returns=None,
                    memory=None,
                    object_store_memory=None,
                    resources=None,
+                   accelerator_type=None,
                    max_calls=None,
                    max_retries=None,
                    max_restarts=None,
@@ -1687,8 +1688,9 @@ def make_decorator(num_returns=None,
                     " integer")
             return ray.remote_function.RemoteFunction(
                 Language.PYTHON, function_or_class, None, num_cpus, num_gpus,
-                memory, object_store_memory, resources, num_returns, max_calls,
-                max_retries, placement_group, placement_group_bundle_index)
+                memory, object_store_memory, resources, accelerator_type,
+                num_returns, max_calls, max_retries, placement_group,
+                placement_group_bundle_index)
 
         if inspect.isclass(function_or_class):
             if num_returns is not None:
@@ -1709,7 +1711,8 @@ def make_decorator(num_returns=None,
                     " positive integer")
             return ray.actor.make_actor(function_or_class, num_cpus, num_gpus,
                                         memory, object_store_memory, resources,
-                                        max_restarts, max_task_retries)
+                                        accelerator_type, max_restarts,
+                                        max_task_retries)
 
         raise TypeError("The @ray.remote decorator must be applied to "
                         "either a function or to a class.")
@@ -1745,6 +1748,9 @@ def remote(*args, **kwargs):
     * **resources:** The quantity of various custom resources to reserve for
       this task or for the lifetime of the actor. This is a dictionary mapping
       strings (resource names) to numbers.
+    * **accelerator_type:** If specified, requires that the task or actor run
+      on a node with the specified type of accelerator. See `ray.accelerators`
+      for accelerator types.
     * **max_calls:** Only for *remote functions*. This specifies the maximum
       number of times that a given worker can execute the given remote function
       before it must exit (this can be used to address memory leaks in
@@ -1833,6 +1839,7 @@ def remote(*args, **kwargs):
             "memory",
             "object_store_memory",
             "resources",
+            "accelerator_type",
             "max_calls",
             "max_restarts",
             "max_task_retries",
@@ -1851,6 +1858,8 @@ def remote(*args, **kwargs):
         assert "CPU" not in resources, "Use the 'num_cpus' argument."
         assert "GPU" not in resources, "Use the 'num_gpus' argument."
 
+    accelerator_type = kwargs.get("accelerator_type")
+
     # Handle other arguments.
     num_returns = kwargs.get("num_returns")
     max_calls = kwargs.get("max_calls")
@@ -1867,6 +1876,7 @@ def remote(*args, **kwargs):
         memory=memory,
         object_store_memory=object_store_memory,
         resources=resources,
+        accelerator_type=accelerator_type,
         max_calls=max_calls,
         max_restarts=max_restarts,
         max_task_retries=max_task_retries,