tequilaOS/platform_hardware_interfaces
2
0
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity

Create NeuralNetworks HAL v1.1 for new OperationTypes

Browse source 
Test: mm
Change-Id: I08efaba79ec28a2f89e94a84ab88b0fa701b7d98
This commit is contained in:
Michael Butler 2018-01-19 18:48:13 -08:00 committed by Miao Wang
parent 075d928e0b
commit 5c6ee9ecef
3 changed files with 463 additions and 0 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

24
neuralnetworks/1.1/Android.bp Normal file
View file

@ -0,0 +1,24 @@
// 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,
}

106
neuralnetworks/1.1/IDevice.hal Normal file
View file

@ -0,0 +1,106 @@
/*
* 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);
};

333
neuralnetworks/1.1/types.hal Normal file
View file

@ -0,0 +1,333 @@
/*
* 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;
};