Merge changes from topic "NNAPI v1.3" (5af04da4) · Commits · e / os / android_hardware_interfaces

current.txt

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		@@ -586,5 +586,3 @@ fd65298e1e09e0e3c781ab18305920d757dbe55a3b459ce17814ec5cf6dfee99 android.hardwar
		# HALs released in Android R
		07d0a252b2d8fa35887908a996ba395cf392968395fc30afab791f46e0c22a52 android.hardware.boot@1.1::IBootControl
		74049a402be913963edfdd80828a53736570e9d8124a1bf18166b6ed46a6b0ab android.hardware.boot@1.1::types
		34515afa2bb792d3c6d8495a5f5d907d179c8507ca5e55c10050d02ae1d516ef android.hardware.neuralnetworks@1.3::IDevice
		e2d20d4eb24f40b44a3766d05f77052581cb3f4df35fb48c0cc5d9cdcf5c872e android.hardware.neuralnetworks@1.3::types

neuralnetworks/1.2/vts/functional/Android.bp

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		@@ -37,7 +37,6 @@ cc_test {
		"android.hardware.neuralnetworks@1.0",
		"android.hardware.neuralnetworks@1.1",
		"android.hardware.neuralnetworks@1.2",
		"android.hardware.neuralnetworks@1.3",
		"android.hidl.allocator@1.0",
		"android.hidl.memory@1.0",
		"libgmock",

neuralnetworks/1.3/Android.bp

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		// This file is autogenerated by hidl-gen -Landroidbp.

		hidl_interface {
		name: "android.hardware.neuralnetworks@1.3",
		root: "android.hardware",
		vndk: {
		enabled: true,
		},
		srcs: [
		"types.hal",
		"IDevice.hal",
		],
		interfaces: [
		"android.hardware.neuralnetworks@1.0",
		"android.hardware.neuralnetworks@1.1",
		"android.hardware.neuralnetworks@1.2",
		"android.hidl.base@1.0",
		"android.hidl.safe_union@1.0",
		],
		gen_java: false,
		}

neuralnetworks/1.3/IDevice.hal

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		/*
		* Copyright (C) 2019 The Android Open Source Project
		*
		* Licensed under the Apache License, Version 2.0 (the "License");
		* you may not use this file except in compliance with the License.
		* You may obtain a copy of the License at
		*
		* http://www.apache.org/licenses/LICENSE-2.0
		*
		* Unless required by applicable law or agreed to in writing, software
		* distributed under the License is distributed on an "AS IS" BASIS,
		* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
		* See the License for the specific language governing permissions and
		* limitations under the License.
		*/

		package android.hardware.neuralnetworks@1.3;

		import @1.0::ErrorStatus;
		import @1.1::ExecutionPreference;
		import @1.2::Constant;
		import @1.2::DeviceType;
		import @1.2::Extension;
		import @1.2::IDevice;
		import @1.2::IPreparedModelCallback;

		/**
		* This interface represents a device driver.
		*/
		interface IDevice extends @1.2::IDevice {
		/**
		* Gets the capabilities of a driver.
		*
		* @return status Error status of the call, must be:
		* - NONE if successful
		* - DEVICE_UNAVAILABLE if driver is offline or busy
		* - GENERAL_FAILURE if there is an unspecified error
		* @return capabilities Capabilities of the driver.
		*/
		getCapabilities_1_3() generates (ErrorStatus status, Capabilities capabilities);

		/**
		* Gets the supported operations in a model.
		*
		* getSupportedOperations indicates which operations of a model are fully
		* supported by the vendor driver. If an operation may not be supported for
		* any reason, getSupportedOperations must return false for that operation.
		*
		* @param model A model whose operations--and their corresponding operands--
		* are to be verified by the driver.
		* @return status Error status of the call, must be:
		* - NONE if successful
		* - DEVICE_UNAVAILABLE if driver is offline or busy
		* - GENERAL_FAILURE if there is an unspecified error
		* - INVALID_ARGUMENT if provided model is invalid
		* @return supportedOperations A list of supported operations, where true
		* indicates the operation is supported and false indicates the
		* operation is not supported. The index of "supported" corresponds with
		* the index of the operation it is describing.
		*/
		getSupportedOperations_1_3(Model model)
		generates (ErrorStatus status, vec<bool> supportedOperations);

		/**
		* Asynchronously creates a prepared model for execution and optionally
		* saves it into cache files.
		*
		* prepareModel is used to make any necessary transformations to or
		* alternative representations to a model for execution, possibly including
		* transformations on the constant data, optimization on the model's graph,
		* or compilation into the device's native binary format. The model itself
		* is not changed.
		*
		* Optionally, caching information may be provided for the driver to save
		* the prepared model to cache files for faster model compilation time when
		* the same model preparation is requested in the future. There are two
		* types of cache file handles provided to the driver: model cache and data
		* cache. For more information on the two types of cache handles, refer to
		* getNumberOfCacheFilesNeeded.
		*
		* The file descriptors must be opened with read and write permission. A
		* file may have any size, and the corresponding file descriptor may have
		* any offset. The driver must truncate a file to zero size before writing
		* to that file. The file descriptors may be closed by the client once the
		* asynchronous preparation has finished. The driver must dup a file
		* descriptor if it wants to get access to the cache file later.
		*
		* The model is prepared asynchronously with respect to the caller. The
		* prepareModel function must verify the inputs to the preparedModel
		* function related to preparing the model (as opposed to saving the
		* prepared model to cache) are correct. If there is an error, prepareModel
		* must immediately invoke the callback with the appropriate ErrorStatus
		* value and nullptr for the IPreparedModel, then return with the same
		* ErrorStatus. If the inputs to the prepareModel function that are related
		* to preparing the model are valid and there is no error, prepareModel must
		* launch an asynchronous task to prepare the model in the background, and
		* immediately return from prepareModel with ErrorStatus::NONE. If the
		* asynchronous task fails to launch, prepareModel must immediately invoke
		* the callback with ErrorStatus::GENERAL_FAILURE and nullptr for the
		* IPreparedModel, then return with ErrorStatus::GENERAL_FAILURE.
		*
		* When the asynchronous task has finished preparing the model, it must
		* immediately invoke the callback function provided as an input to
		* prepareModel. If the model was prepared successfully, the callback object
		* must be invoked with an error status of ErrorStatus::NONE and the
		* produced IPreparedModel object. If an error occurred preparing the model,
		* the callback object must be invoked with the appropriate ErrorStatus
		* value and nullptr for the IPreparedModel.
		*
		* Optionally, the driver may save the prepared model to cache during the
		* asynchronous preparation. Any error that occurs when saving to cache must
		* not affect the status of preparing the model. Even if the input arguments
		* related to the cache may be invalid, or the driver may fail to save to
		* cache, the prepareModel function must finish preparing the model. The
		* driver may choose not to save to cache even if the caching information is
		* provided and valid.
		*
		* The only information that may be unknown to the model at this stage is
		* the shape of the tensors, which may only be known at execution time. As
		* such, some driver services may return partially prepared models, where
		* the prepared model may only be finished when it is paired with a set of
		* inputs to the model. Note that the same prepared model object may be used
		* with different shapes of inputs on different (possibly concurrent)
		* executions.
		*
		* Multiple threads may call prepareModel on the same model concurrently.
		*
		* @param model The model to be prepared for execution.
		* @param preference Indicates the intended execution behavior of a prepared
		* model.
		* @param modelCache A vector of handles with each entry holding exactly one
		* cache file descriptor for the security-sensitive cache. The length of
		* the vector must either be 0 indicating that caching information is
		* not provided, or match the numModelCache returned from
		* getNumberOfCacheFilesNeeded. The cache handles will be provided in
		* the same order when retrieving the preparedModel from cache files
		* with prepareModelFromCache.
		* @param dataCache A vector of handles with each entry holding exactly one
		* cache file descriptor for the constants' cache. The length of the
		* vector must either be 0 indicating that caching information is not
		* provided, or match the numDataCache returned from
		* getNumberOfCacheFilesNeeded. The cache handles will be provided in
		* the same order when retrieving the preparedModel from cache files
		* with prepareModelFromCache.
		* @param token A caching token of length Constant::BYTE_SIZE_OF_CACHE_TOKEN
		* identifying the prepared model. The same token will be provided when
		* retrieving the prepared model from the cache files with
		* prepareModelFromCache. Tokens should be chosen to have a low rate of
		* collision for a particular application. The driver cannot detect a
		* collision; a collision will result in a failed execution or in a
		* successful execution that produces incorrect output values. If both
		* modelCache and dataCache are empty indicating that caching
		* information is not provided, this token must be ignored.
		* @param callback A callback object used to return the error status of
		* preparing the model for execution and the prepared model if
		* successful, nullptr otherwise. The callback object's notify function
		* must be called exactly once, even if the model could not be prepared.
		* @return status Error status of launching a task which prepares the model
		* in the background; must be:
		* - NONE if preparation task is successfully launched
		* - DEVICE_UNAVAILABLE if driver is offline or busy
		* - GENERAL_FAILURE if there is an unspecified error
		* - INVALID_ARGUMENT if one of the input arguments related to preparing
		* the model is invalid
		*/
		prepareModel_1_3(Model model, ExecutionPreference preference,
		vec<handle> modelCache, vec<handle> dataCache,
		uint8_t[Constant:BYTE_SIZE_OF_CACHE_TOKEN] token,
		IPreparedModelCallback callback)
		generates (ErrorStatus status);
		};

neuralnetworks/1.3/types.hal

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		/*
		* Copyright (C) 2019 The Android Open Source Project
		*
		* Licensed under the Apache License, Version 2.0 (the "License");
		* you may not use this file except in compliance with the License.
		* You may obtain a copy of the License at
		*
		* http://www.apache.org/licenses/LICENSE-2.0
		*
		* Unless required by applicable law or agreed to in writing, software
		* distributed under the License is distributed on an "AS IS" BASIS,
		* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
		* See the License for the specific language governing permissions and
		* limitations under the License.
		*/

		package android.hardware.neuralnetworks@1.3;

		import @1.0::DataLocation;
		import @1.0::OperandLifeTime;
		import @1.0::PerformanceInfo;
		import @1.2::OperandType;
		import @1.2::OperationType;
		import @1.2::SymmPerChannelQuantParams;

		import android.hidl.safe_union@1.0::Monostate;

		/**
		* NOTE: Since NNAPI 1.2, OEM operation and data type are deprecated. Extensions
		* are the preferred alternative.
		*
		* NOTE: Adding a new fundamental type requires updating the value of
		* OperandTypeRange::FUNDAMENTAL_MAX.
		*/
		enum OperandType : @1.2::OperandType {
		/**
		* A tensor of 8 bit signed integers that represent real numbers.
		*
		* Attached to this tensor are two numbers that can be used to convert the
		* 8 bit integer to the real value and vice versa. These two numbers are:
		* - scale: a 32 bit floating point value greater than zero.
		* - zeroPoint: a 32 bit integer, in range [-128, 127].
		*
		* The formula is:
		* real_value = (integer_value - zeroPoint) * scale.
		*
		* Available since API level 30.
		*/
		TENSOR_QUANT8_ASYMM_SIGNED = 14,
		};

		/**
		* The range of operand values in the OperandType enum.
		*/
		enum OperandTypeRange : uint32_t {
		BASE_MIN = 0,
		FUNDAMENTAL_MIN = 0,
		FUNDAMENTAL_MAX = 14,
		OEM_MIN = 10000,
		OEM_MAX = 10001,
		BASE_MAX = 0xFFFF,
		};


		/**
		* The capabilities of a driver.
		*
		* Performance of an operation comes from the type of its first operand.
		* This represents performance for non extension operand types.
		*/
		struct Capabilities {
		/**
		* Driver performance when operating on float32 data but performing
		* calculations with range and/or precision as low as that of the IEEE
		* 754 16-bit floating-point format.
		*/
		PerformanceInfo relaxedFloat32toFloat16PerformanceScalar;
		PerformanceInfo relaxedFloat32toFloat16PerformanceTensor;

		/**
		* Driver performance when operating on a particular data type.
		* In the case of float32 data, this is used when the calculations
		* are not relaxed.
		*/
		struct OperandPerformance {
		OperandType type;
		PerformanceInfo info;
		};

		/**
		* Performance by operand type. Must be sorted by OperandType.
		* If a particular OperandType is not present in operandPerformance,
		* its performance is treated as
		* { .execTime = FLT_MAX, .powerUsage = FLT_MAX }.
		*/
		vec<OperandPerformance> operandPerformance;
		};

		/**
		* Describes one operand of the model's graph.
		*/
		struct Operand {
		/**
		* The data type.
		*
		* Besides the values listed in {@link OperandType}, any value above
		* {@link OperandTypeRange::BASE_MAX} is possible and should be interpreted
		* as an extension type according to {@link Model::extensionNameToPrefix}.
		*/
		OperandType type;

		/**
		* Dimensions of the operand.
		*
		* For a scalar operand, dimensions.size() must be 0.
		*
		* A tensor operand with all dimensions specified has "fully
		* specified" dimensions. Whenever possible (i.e., whenever the
		* dimensions are known at model construction time), a tensor
		* operand should have (but is not required to have) fully
		* specified dimensions, in order to enable the best possible
		* performance.
		*
		* If a tensor operand's dimensions are not fully specified, the
		* dimensions of the operand are deduced from the operand
		* dimensions and values of the operation for which that operand
		* is an output.
		*
		* In the following situations, a tensor operand's dimensions must
		* be fully specified:
		*
		* . The operand has lifetime CONSTANT_COPY or
		* CONSTANT_REFERENCE.
		*
		* . The operand has lifetime MODEL_INPUT. Fully
		* specified dimensions must either be present in the
		* Operand or they must be provided in the corresponding
		* RequestArgument.
		* EXCEPTION: If the input is optional and omitted
		* (by setting the hasNoValue field of the corresponding
		* RequestArgument to true) then it need not have fully
		* specified dimensions.
		*
		* A tensor operand with some number of unspecified dimensions is
		* represented by setting each unspecified dimension to 0.
		*
		* A tensor operand with unspecified rank is represented by providing
		* an empty dimensions vector.
		*/
		vec<uint32_t> dimensions;

		/**
		* The number of times this operand appears as an operation input.
		*
		* (For example, if this operand appears once in one operation's
		* input list, and three times in another operation's input list,
		* then numberOfConsumers = 4.)
		*/
		uint32_t numberOfConsumers;

		/**
		* Quantized scale of the operand.
		*
		* Only applicable if the operand is of type TENSOR_QUANT8_ASYMM or
		* TENSOR_INT32.
		*/
		float scale;

		/**
		* Quantized zero-point offset of the operand.
		*
		* Only applicable if the operand is of type TENSOR_QUANT8_ASYMM.
		*/
		int32_t zeroPoint;

		/**
		* How the operand is used.
		*/
		OperandLifeTime lifetime;

		/**
		* Where to find the data for this operand.
		* If the lifetime is TEMPORARY_VARIABLE, MODEL_INPUT, MODEL_OUTPUT, or
		* NO_VALUE:
		* - All the fields must be 0.
		* If the lifetime is CONSTANT_COPY:
		* - location.poolIndex is 0.
		* - location.offset is the offset in bytes into Model.operandValues.
		* - location.length is set.
		* If the lifetime is CONSTANT_REFERENCE:
		* - location.poolIndex is set.
		* - location.offset is the offset in bytes into the specified pool.
		* - location.length is set.
		*/
		DataLocation location;

		/**
		* Additional parameters specific to a particular operand type.
		*/
		safe_union ExtraParams {
		/**
		* No additional parameters.
		*/
		Monostate none;

		/**
		* Symmetric per-channel quantization parameters.
		*
		* Only applicable to operands of type TENSOR_QUANT8_SYMM_PER_CHANNEL.
		*/
		SymmPerChannelQuantParams channelQuant;

		/**
		* Extension operand parameters.
		*
		* The framework treats this as an opaque data blob.
		* The format is up to individual extensions.
		*/
		vec<uint8_t> extension;
		} extraParams;
		};

		/**
		* Describes one operation of the model's graph.
		*/
		struct Operation {
		/**
		* The operation type.
		*/
		OperationType type;

		/**
		* Describes the table that contains the indexes of the inputs of the
		* operation. The offset is the index in the operandIndexes table.
		*/
		vec<uint32_t> inputs;

		/**
		* Describes the table that contains the indexes of the outputs of the
		* operation. The offset is the index in the operandIndexes table.
		*/
		vec<uint32_t> outputs;
		};

		/**
		* A Neural Network Model.
		*
		* This includes not only the execution graph, but also constant data such as
		* weights or scalars added at construction time. The only information that
		* may not be known is the shape of the input tensors.
		*/
		struct Model {
		/**
		* All operands included in the model.
		*/
		vec<Operand> operands;

		/**
		* All operations included in the model.
		*
		* The operations are sorted into execution order. Every operand
		* with lifetime MODEL_OUTPUT or TEMPORARY_VARIABLE must be
		* written before it is read.
		*/
		vec<Operation> operations;

		/**
		* Input indexes of the model. There must be at least one.
		*
		* Each value corresponds to the index of the operand in "operands".
		*/
		vec<uint32_t> inputIndexes;

		/**
		* Output indexes of the model. There must be at least one.
		*
		* Each value corresponds to the index of the operand in "operands".
		*/
		vec<uint32_t> outputIndexes;

		/**
		* A byte buffer containing operand data that were copied into the model.
		*
		* An operand's value must be located here if and only if Operand::lifetime
		* equals OperandLifeTime::CONSTANT_COPY.
		*/
		vec<uint8_t> operandValues;

		/**
		* A collection of shared memory pools containing operand values.
		*
		* An operand's value must be located here if and only if Operand::lifetime
		* equals OperandLifeTime::CONSTANT_REFERENCE.
		*/
		vec<memory> pools;

		/**
		* 'true' indicates TENSOR_FLOAT32 may be calculated with range and/or
		* precision as low as that of the IEEE 754 16-bit floating-point format.
		* 'false' indicates TENSOR_FLOAT32 must be calculated using at least the
		* range and precision of the IEEE 754 32-bit floating-point format.
		*/
		bool relaxComputationFloat32toFloat16;

		/**
		* The mapping between extension names and prefixes of operand and
		* operation type values.
		*
		* An operand or operation whose numeric type value is above
		* {@link OperandTypeRange::BASE_MAX} or
		* {@link OperationTypeRange::BASE_MAX} respectively should be interpreted
		* as an extension operand. The low
		* {@link Model::ExtensionTypeEncoding::LOW_BITS_TYPE} bits of the value
		* correspond to the type ID within the extension and the high
		* {@link Model::ExtensionTypeEncoding::HIGH_BITS_PREFIX} bits encode
		* the "prefix", which maps uniquely to the extension name.
		*
		* For example, if a model contains an operation whose value is
		* 0xAAAABBBB and extensionNameToPrefix contains an entry with
		* prefix=0xAAAA and name="vendor.test.test_extension", then
		* the operation should be interpreted as the operation 0xBBBB
		* of the extension named vendor.test.test_extension.
		*
		* This is a one-to-one correspondence. That is, there must be at most one
		* prefix corresponding to each extension name and at most one extension
		* name corresponding to each prefix.
		*/
		vec<ExtensionNameAndPrefix> extensionNameToPrefix;

		/**
		* A correspondence between an extension name and a prefix of operand and
		* operation type values.
		*/
		struct ExtensionNameAndPrefix {
		/**
		* The extension name.
		*
		* See {@link Extension::name} for the format specification.
		*/
		string name;

		/**
		* The unique extension identifier within the model.
		*
		* See {@link Model::extensionNameToPrefix}.
		*/
		uint16_t prefix;
		};

		/**
		* Numeric values of extension operand and operation types have the
		* following structure:
		* - 16 high bits represent the "prefix", which corresponds uniquely to the
		* extension name.
		* - 16 low bits represent the type ID within the extension.
		*/
		enum ExtensionTypeEncoding : uint8_t {
		HIGH_BITS_PREFIX = 16,
		LOW_BITS_TYPE = 16,
		};
		};