neuralnetworks/1.2/types.hal - platform/hardware/interfaces - Git at Google

 /*
  * Copyright (C) 2018 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@1.2;

 import @1.0::DataLocation;
 import @1.0::OperandLifeTime;
 import @1.0::OperandType;
 import @1.0::PerformanceInfo;
 import @1.1::OperationType;

 enum OperandType : @1.0::OperandType {
     /**
      * An 8 bit boolean scalar value.
      *
      * Values of this operand type are either true or false. A zero value
      * represents false; any other value represents true.
      */
     BOOL = 6,
     /**
      * A tensor of 16 bit signed integers that represent real numbers.
      *
      * Attached to this tensor is a number representing real value scale that is
      * used to convert the 16 bit number to a real value in the following way:
      * realValue = integerValue * scale.
      *
      * scale is a 32 bit floating point with value greater then zero.
      */
     TENSOR_QUANT16_SYMM = 7,
     /** A tensor of 16 bit floating point values. */
     TENSOR_FLOAT16 = 8,
     /**
      * A tensor of 8 bit boolean values.
      *
      * Values of this operand type are either true or false. A zero value
      * represents false; any other value represents true.
      */
     TENSOR_BOOL8 = 9,
 };

 /**
  * The range of values in the OperandType enum.
  *
  * THE MAX VALUES MUST BE UPDATED WHEN ADDING NEW TYPES to the OperandType enum.
  */
 enum OperandTypeRange : uint32_t {
     OPERAND_FUNDAMENTAL_MIN = 0,
     OPERAND_FUNDAMENTAL_MAX = 9,
     OPERAND_OEM_MIN     = 10000,
     OPERAND_OEM_MAX     = 10001,
 };

 /**
  * Operation types.
  *
  * The type of an operation in a model.
  */
 enum OperationType : @1.1::OperationType {
     // TODO(b/116445845): Sync docs when all ops are implemented.
     ARGMAX = 38,
     ARGMIN = 39,
     PAD_V2 = 40,
     AXIS_ALIGNED_BBOX_TRANSFORM = 41,
     BIDIRECTIONAL_SEQUENCE_LSTM = 42,
     BIDIRECTIONAL_SEQUENCE_RNN = 43,
     BOX_WITH_NMS_LIMIT = 44,
     CAST = 45,
     CHANNEL_SHUFFLE = 46,
     DETECTION_OUTPUT = 47,
     EMBEDDING_LOOKUP_SPARSE = 48,
     EXP = 49,
     EXPAND_DIMS = 50,
     GATHER = 51,
     GENERATE_PROPOSALS = 52,
     GREATER = 53,
     GREATER_EQUAL = 54,
     GROUPED_CONV_2D = 55,
     HEATMAP_MAX_KEYPOINT = 56,
     LESS = 57,
     LESS_EQUAL = 58,
     LOG = 59,
     LOGICAL_AND = 60,
     LOGICAL_NOT = 61,
     LOGICAL_OR = 62,
     LOG_SOFTMAX = 63,
     MAXIMUM = 64,
     MINIMUM = 65,
     NEG = 66,
     POW = 67,
     PRELU = 68,
     PRIOR_BOX = 69,
     QUANTIZE = 70,
     QUANTIZED_16BIT_LSTM = 71,
     RANDOM_MULTINOMIAL = 72,
     REDUCE = 73,
     ROI_ALIGN = 74,
     RSQRT = 75,
     SELECT = 76,
     SIN = 77,
     SLICE = 78,
     SPARSE_TO_DENSE = 79,
     SPLIT = 80,
     SQRT = 81,
     TILE = 82,
     TOPK_V2 = 83,
     TRANSPOSE_CONV_2D = 84,
     UNIDIRECTIONAL_SEQUENCE_LSTM = 85,
     UNIDIRECTIONAL_SEQUENCE_RNN = 86,
     ROTATED_BBOX_TRANSFORM = 87,
     ABS = 88,
     ROI_POOLING = 89,
 };

 /**
  * The range of values in the OperationType enum.
  *
  * THE MAX VALUES MUST BE UPDATED WHEN ADDING NEW TYPES to the OperationType enum.
  */
 enum OperationTypeRange : uint32_t {
     OPERATION_FUNDAMENTAL_MIN = 0,
     OPERATION_FUNDAMENTAL_MAX = 87,
     OPERATION_OEM_MIN = 10000,
     OPERATION_OEM_MAX = 10000,
 };

 /**
  * 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;
 };

 /**
  * Describes one operand of the model's graph.
  */
 struct Operand {
     /**
      * Data type of the operand.
      */
     OperandType type;

     /**
      * Dimensions of the operand.
      *
      * For a scalar operand, dimensions.size() must be 0.
      *
      * For a tensor operand, dimensions.size() must be at least 1;
      * however, any of the dimensions may be unspecified.
      *
      * 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 or MODEL_OUTPUT. Fully
      *       specified dimensions must either be present in the
      *       Operand or they must be provided in the corresponding
      *       RequestArgument.
      *       EXCEPTION: If the input or output 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.
      */
     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;
 };

 /**
  * 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;
 };
	/*
	* Copyright (C) 2018 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@1.2;

	import @1.0::DataLocation;
	import @1.0::OperandLifeTime;
	import @1.0::OperandType;
	import @1.0::PerformanceInfo;
	import @1.1::OperationType;

	enum OperandType : @1.0::OperandType {
	/**
	* An 8 bit boolean scalar value.
	*
	* Values of this operand type are either true or false. A zero value
	* represents false; any other value represents true.
	*/
	BOOL = 6,
	/**
	* A tensor of 16 bit signed integers that represent real numbers.
	*
	* Attached to this tensor is a number representing real value scale that is
	* used to convert the 16 bit number to a real value in the following way:
	* realValue = integerValue * scale.
	*
	* scale is a 32 bit floating point with value greater then zero.
	*/
	TENSOR_QUANT16_SYMM = 7,
	/** A tensor of 16 bit floating point values. */
	TENSOR_FLOAT16 = 8,
	/**
	* A tensor of 8 bit boolean values.
	*
	* Values of this operand type are either true or false. A zero value
	* represents false; any other value represents true.
	*/
	TENSOR_BOOL8 = 9,
	};

	/**
	* The range of values in the OperandType enum.
	*
	* THE MAX VALUES MUST BE UPDATED WHEN ADDING NEW TYPES to the OperandType enum.
	*/
	enum OperandTypeRange : uint32_t {
	OPERAND_FUNDAMENTAL_MIN = 0,
	OPERAND_FUNDAMENTAL_MAX = 9,
	OPERAND_OEM_MIN = 10000,
	OPERAND_OEM_MAX = 10001,
	};

	/**
	* Operation types.
	*
	* The type of an operation in a model.
	*/
	enum OperationType : @1.1::OperationType {
	// TODO(b/116445845): Sync docs when all ops are implemented.
	ARGMAX = 38,
	ARGMIN = 39,
	PAD_V2 = 40,
	AXIS_ALIGNED_BBOX_TRANSFORM = 41,
	BIDIRECTIONAL_SEQUENCE_LSTM = 42,
	BIDIRECTIONAL_SEQUENCE_RNN = 43,
	BOX_WITH_NMS_LIMIT = 44,
	CAST = 45,
	CHANNEL_SHUFFLE = 46,
	DETECTION_OUTPUT = 47,
	EMBEDDING_LOOKUP_SPARSE = 48,
	EXP = 49,
	EXPAND_DIMS = 50,
	GATHER = 51,
	GENERATE_PROPOSALS = 52,
	GREATER = 53,
	GREATER_EQUAL = 54,
	GROUPED_CONV_2D = 55,
	HEATMAP_MAX_KEYPOINT = 56,
	LESS = 57,
	LESS_EQUAL = 58,
	LOG = 59,
	LOGICAL_AND = 60,
	LOGICAL_NOT = 61,
	LOGICAL_OR = 62,
	LOG_SOFTMAX = 63,
	MAXIMUM = 64,
	MINIMUM = 65,
	NEG = 66,
	POW = 67,
	PRELU = 68,
	PRIOR_BOX = 69,
	QUANTIZE = 70,
	QUANTIZED_16BIT_LSTM = 71,
	RANDOM_MULTINOMIAL = 72,
	REDUCE = 73,
	ROI_ALIGN = 74,
	RSQRT = 75,
	SELECT = 76,
	SIN = 77,
	SLICE = 78,
	SPARSE_TO_DENSE = 79,
	SPLIT = 80,
	SQRT = 81,
	TILE = 82,
	TOPK_V2 = 83,
	TRANSPOSE_CONV_2D = 84,
	UNIDIRECTIONAL_SEQUENCE_LSTM = 85,
	UNIDIRECTIONAL_SEQUENCE_RNN = 86,
	ROTATED_BBOX_TRANSFORM = 87,
	ABS = 88,
	ROI_POOLING = 89,
	};

	/**
	* The range of values in the OperationType enum.
	*
	* THE MAX VALUES MUST BE UPDATED WHEN ADDING NEW TYPES to the OperationType enum.
	*/
	enum OperationTypeRange : uint32_t {
	OPERATION_FUNDAMENTAL_MIN = 0,
	OPERATION_FUNDAMENTAL_MAX = 87,
	OPERATION_OEM_MIN = 10000,
	OPERATION_OEM_MAX = 10000,
	};

	/**
	* 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;
	};

	/**
	* Describes one operand of the model's graph.
	*/
	struct Operand {
	/**
	* Data type of the operand.
	*/
	OperandType type;

	/**
	* Dimensions of the operand.
	*
	* For a scalar operand, dimensions.size() must be 0.
	*
	* For a tensor operand, dimensions.size() must be at least 1;
	* however, any of the dimensions may be unspecified.
	*
	* 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 or MODEL_OUTPUT. Fully
	* specified dimensions must either be present in the
	* Operand or they must be provided in the corresponding
	* RequestArgument.
	* EXCEPTION: If the input or output 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.
	*/
	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;
	};

	/**
	* 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;
	};