Create NeuralNetworks HAL v1.1 for new OperationTypes (162aa583) · Commits · e / os / android_hardware_interfaces

neuralnetworks/1.1/Android.bp

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

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

neuralnetworks/1.1/IDevice.hal

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		/*
		* 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@1.1;

		import @1.0::ErrorStatus;
		import @1.0::IDevice;
		import @1.0::IPreparedModelCallback;

		/**
		* This interface represents a device driver.
		*/
		interface IDevice extends @1.0::IDevice {
		/**
		* Gets the supported operations in a model.
		*
		* getSupportedSubgraph 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_1(Model model)
		generates (ErrorStatus status, vec<bool> supportedOperations);

		/**
		* Creates a prepared model for execution.
		*
		* prepareModel is used to make any necessary transformations or alternative
		* representations to a model for execution, possiblly 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.
		*
		* The model is prepared asynchronously with respect to the caller. The
		* prepareModel function must verify the inputs to the prepareModel function
		* 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 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.
		*
		* 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 can only be finished when it is paired with a set of
		* inputs to the model. Note that the same prepared model object can be
		* used with different shapes of inputs on different (possibly concurrent)
		* executions.
		*
		* Multiple threads can call prepareModel on the same model concurrently.
		*
		* @param model The model to be prepared for execution.
		* @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 is
		* invalid
		*/
		prepareModel_1_1(Model model, IPreparedModelCallback callback)
		generates (ErrorStatus status);
		};

neuralnetworks/1.1/types.hal

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		/*
		* 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@1.1;

		import @1.0::Operand;
		import @1.0::OperationType;

		/**
		* Operation types.
		*
		* The type of an operation in a model.
		*/
		enum OperationType : @1.0::OperationType {
		/**
		* BatchToSpace for N-D tensors.
		*
		* This operation reshapes the "batch" dimension 0 into M + 1 dimensions of shape
		* block_shape + [batch], interleaves these blocks back into the grid defined by the
		* spatial dimensions [1, ..., M], to obtain a result with the same rank as the input.
		* The spatial dimensions of this intermediate result are then optionally cropped
		* according to the amount to crop to produce the output.
		* This is the reverse of SpaceToBatch.
		*
		* Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
		* {@link OperandType::TENSOR_QUANT8_ASYMM}
		* Supported tensor rank: up to 4
		*
		* Inputs:
		* 0: An n-D tensor, specifying the input.
		* 1: A 1-D Tensor of type TENSOR_INT32, the block sizes for each spatial dimension of the
		* input tensor. All values must be >= 1.
		* 2: A 1-D Tensor of type TENSOR_INT32, the amount to crop for each spatial diemension of the
		* input tensor. All values must be >= 0.
		*
		* Outputs:
		* 0: A tensor of the same type as input0.
		*/
		BATCH_TO_SPACE_ND = 29,

		/**
		* Divides the second tensor from the first tensor, element-wise.
		*
		* Takes two input tensors of identical OperandType and compatible dimensions. The output
		* is the result of dividing the first input tensor by the second, optionally
		* modified by an activation function.
		*
		* Two dimensions are compatible when:
		* 1. they are equal, or
		* 2. one of them is 1
		*
		* The size of the output is the maximum size along each dimension of the input operands.
		* It starts with the trailing dimensions, and works its way forward.
		*
		* Example:
		* input1.dimension = {4, 1, 2}
		* input2.dimension = {5, 4, 3, 1}
		* output.dimension = {5, 4, 3, 2}
		*
		* Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
		* Supported tensor rank: up to 4
		*
		* Inputs:
		* 0: An n-D tensor, specifying the first input.
		* 1: A tensor of the same type, and compatible dimensions as input0.
		* 2: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
		* Specifies the activation to invoke on the result of each addition.
		*
		* Outputs:
		* 0: A tensor of the same type as input0.
		*/
		DIV = 30,

		/**
		* Computes the mean of elements across dimensions of a tensor.
		*
		* Reduces input tensor along the dimensions given in axis. Unless keep_dims is true,
		* the rank of the tensor is reduced by 1 for each entry in axis. If keep_dims is
		* true, the reduced dimensions are retained with length 1.
		*
		* If axis has no entries, all dimensions are reduced, and a tensor with a single
		* element is returned.
		*
		* Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
		* {@link OperandType::TENSOR_QUANT8_ASYMM}
		* Supported tensor rank: up to 4
		*
		* Inputs:
		* 0: A tensor, specifying the input.
		* 1: A 1-D Tensor of type TENSOR_INT32. The dimensions to reduce. If None (the default),
		* reduces all dimensions. Must be in the range [-rank(input_tensor), rank(input_tensor)).
		* 2: An INT32 value, keep_dims. If positive, retains reduced dimensions with length 1.
		*
		* Outputs:
		* 0: A tensor of the same type as input0.
		*/
		MEAN = 31,

		/**
		* Pads a tensor.
		*
		* This operation pads a tensor according to the specified paddings.
		*
		* Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
		* {@link OperandType::TENSOR_QUANT8_ASYMM}
		* Supported tensor rank: up to 4
		*
		* Inputs:
		* 0: An n-D tensor, specifying the input.
		* 1: A 2-D Tensor of type TENSOR_INT32. The paddings, before and after for each spatial dimension
		* of the input tensor.
		*
		* Outputs:
		* 0: A tensor of the same type as input0.
		*/
		PAD = 32,

		/**
		* SpaceToBatch for N-D tensors.
		*
		* This operation divides "spatial" dimensions [1, ..., M] of the input into a grid of blocks
		* of shape block_shape, and interleaves these blocks with the "batch" dimension (0) such that
		* in the output, the spatial dimensions [1, ..., M] correspond to the position within the grid,
		* and the batch dimension combines both the position within a spatial block and the original
		* batch position. Prior to division into blocks, the spatial dimensions of the input are
		* optionally zero padded according to paddings.
		*
		* Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
		* {@link OperandType::TENSOR_QUANT8_ASYMM}
		* Supported tensor rank: up to 4
		*
		* Inputs:
		* 0: An n-D tensor, specifying the input.
		* 1: A 1-D Tensor of type TENSOR_INT32, the block sizes for each spatial dimension of the
		* input tensor. All values must be >= 1.
		* 2: A 2-D Tensor of type TENSOR_INT32, the paddings for each spatial diemension of the
		* input tensor. All values must be >= 0.
		*
		* Outputs:
		* 0: A tensor of the same type as input0.
		*/
		SPACE_TO_BATCH_ND = 33,

		/**
		* Removes dimensions of size 1 from the shape of a tensor.
		*
		* Given a tensor input, this operation returns a tensor of the same type with all
		* dimensions of size 1 removed. If you don't want to remove all size 1 dimensions,
		* you can remove specific size 1 dimensions by specifying axis.
		*
		* Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
		* {@link OperandType::TENSOR_QUANT8_ASYMM}
		* Supported tensor rank: up to 4
		*
		* Inputs:
		* 0: An n-D tensor, specifying the input.
		* 1: An 1-D Tensor of type TENSOR_INT32. The dimensions to squeeze. If None (the default),
		* squeezes all dimensions. If specified, only squeezes the dimensions listed. The dimension
		* index starts at 0. It is an error to squeeze a dimension that is not 1.
		*
		* Outputs:
		* 0: A tensor of the same type as input0. Contains the same data as input, but has one or more
		* dimensions of size 1 removed.
		*/
		SQUEEZE = 34,

		/**
		* Extracts a strided slice of a tensor.
		*
		* This op extracts a slice of size (end-begin)/stride from the given input tensor.
		* Starting at the location specified by begin the slice continues by adding
		* stride to the index until all dimensions are not less than end. Note that a stride can
		* be negative, which causes a reverse slice.
		*
		* Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
		* {@link OperandType::TENSOR_QUANT8_ASYMM}
		* Supported tensor rank: up to 4
		*
		* Inputs:
		* 0: An n-D tensor, specifying the input.
		* 1: A 1-D Tensor of type TENSOR_INT32, the starts of the dimensions of the input
		* tensor to be sliced.
		* 2: A 1-D Tensor of type TENSOR_INT32, the ends of the dimensions of the input
		* tensor to be sliced.
		* 3: A 1-D Tensor of type TENSOR_INT32, the strides of the dimensions of the input
		* tensor to be sliced.
		*
		* Outputs:
		* 0: A tensor of the same type as input0.
		*/
		STRIDED_SLICE = 35,

		/**
		* Subtracts the second tensor from the first tensor, element-wise.
		*
		* Takes two input tensors of identical type and compatible dimensions. The output
		* is the result of subtracting the second input tensor from the first one, optionally
		* modified by an activation function.
		*
		* Two dimensions are compatible when:
		* 1. they are equal, or
		* 2. one of them is 1
		*
		* The size of the output is the maximum size along each dimension of the input operands.
		* It starts with the trailing dimensions, and works its way forward.
		*
		* Example:
		* input1.dimension = {4, 1, 2}
		* input2.dimension = {5, 4, 3, 1}
		* output.dimension = {5, 4, 3, 2}
		*
		* Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
		* Supported tensor rank: up to 4
		*
		* Inputs:
		* 0: An n-D tensor, specifying the first input.
		* 1: A tensor of the same type, and compatible dimensions as input0.
		* 2: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
		* Specifies the activation to invoke on the result of each addition.
		*
		* Outputs:
		* 0: A tensor of the same type as input0.
		*/
		SUB = 36,

		/**
		* Transposes the input tensor, permuting the dimensions according to the perm tensor.
		*
		* The returned tensor's dimension i must correspond to the input dimension perm[i].
		* If perm is not given, it is set to (n-1...0), where n is the rank of the input tensor.
		* Hence by default, this operation performs a regular matrix transpose on 2-D input Tensors.
		*
		* Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
		* {@link OperandType::TENSOR_QUANT8_ASYMM}
		* Supported tensor rank: up to 4
		*
		* Inputs:
		* 0: An n-D tensor, specifying the input.
		* 1: A 1-D Tensor of type TENSOR_INT32, the permutation of the dimensions of the input
		* tensor.
		*
		* Outputs:
		* 0: A tensor of the same type as input0.
		*/
		TRANSPOSE = 37,
		};

		/**
		* 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.
		*/
		vec<Operation> operations;

		/**
		* Input indexes of the model.
		*
		* Each value corresponds to the index of the operand in "operands".
		*/
		vec<uint32_t> inputIndexes;

		/**
		* Output indexes of the model.
		*
		* 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 data that were
		* registered by the model.
		*
		* An operand's value must be located here if and only if Operand::lifetime
		* equals OperandLifeTime::CONSTANT_REFERENCE.
		*/
		vec<memory> pools;
		};