Create NeuralNetworks HAL v1.1 for new OperationTypes
Test: mm
Change-Id: I08efaba79ec28a2f89e94a84ab88b0fa701b7d98
(cherry picked from commit 5c6ee9ecefa53efe5f5ac2525196ed5e0ace7170)
diff --git a/neuralnetworks/1.1/Android.bp b/neuralnetworks/1.1/Android.bp
new file mode 100644
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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,
+}
+
diff --git a/neuralnetworks/1.1/IDevice.hal b/neuralnetworks/1.1/IDevice.hal
new file mode 100644
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+++ b/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);
+};
diff --git a/neuralnetworks/1.1/types.hal b/neuralnetworks/1.1/types.hal
new file mode 100644
index 0000000..18863d3
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+++ b/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;
+};