Merge "Add HAL entry to allow querying the device type" (b5eb0fc9) · Commits · e / os / android_hardware_interfaces

neuralnetworks/1.2/IDevice.hal

+20 −0

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		@@ -55,6 +55,26 @@ interface IDevice extends @1.1::IDevice {
		*/
		getVersionString() generates (ErrorStatus status, string version);

		/**
		* Get the type of a given device.
		*
		* The device type can be used to help application developers to distribute
		* Machine Learning workloads and other workloads such as graphical rendering.
		* E.g., for an app which renders AR scenes based on real time object detection
		* results, the developer could choose an ACCELERATOR type device for ML
		* workloads, and reserve GPU for graphical rendering.
		*
		* @param status Error status returned from querying the device type. Must be:
		* - NONE if the query was successful
		* - DEVICE_UNAVAILABLE if driver is offline or busy
		* - GENERAL_FAILURE if the query resulted in an
		* unspecified error
		* @param type The DeviceType of the device. Please note, this is not a
		* bitfield of DeviceTypes. Each device must only be of a
		* single DeviceType.
		*/
		getType() generates (ErrorStatus status, DeviceType type);

		/**
		* Gets the supported operations in a model.
		*

+20 −0

Original line number	Diff line number	Diff line
		@@ -179,6 +179,26 @@ enum OperationTypeRange : uint32_t {
		OPERATION_OEM_MAX = 10000,
		};

		/**
		* Device types.
		*
		* The type of NNAPI device.
		*/
		enum DeviceType : int32_t {
		// Leaving 0 unused as it means unknown type in NDK NNAPI. There is no
		// HAL equivalent of unknown type and a 1.2 HAL implementation must belong
		// to one of the categories below.
		/** The device does not fall into any category below. */
		OTHER = 1,
		/** The device runs NNAPI models on single or multi-core CPU. */
		CPU = 2,
		/** The device can run NNAPI models and also accelerate graphics APIs such
		* as OpenGL ES and Vulkan. */
		GPU = 3,
		/** Dedicated accelerator for Machine Learning workloads. */
		ACCELERATOR = 4,
		};

		/**
		* Describes one operation of the model's graph.
		*/

+10 −0

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		@@ -45,6 +45,16 @@ TEST_F(NeuralnetworksHidlTest, GetDeviceVersionStringTest) {
		});
		EXPECT_TRUE(ret.isOk());
		}

		// device type test
		TEST_F(NeuralnetworksHidlTest, GetDeviceTypeTest) {
		Return<void> ret = device->getType([](ErrorStatus status, DeviceType type) {
		EXPECT_EQ(ErrorStatus::NONE, status);
		EXPECT_TRUE(type == DeviceType::OTHER \|\| type == DeviceType::CPU \|\|
		type == DeviceType::GPU \|\| type == DeviceType::ACCELERATOR);
		});
		EXPECT_TRUE(ret.isOk());
		}
		} // namespace functional
		} // namespace vts
		} // namespace V1_2