Donate to e Foundation | Murena handsets with /e/OS | Own a part of Murena! Learn more

Commit 4c10b461 authored by Michael Butler's avatar Michael Butler Committed by android-build-merger
Browse files

Move neuralnetworks HAL to hardware/interfaces am: 376ec0c0 

am: ebd55659

Change-Id: I757deab84272e826589903a60d9a4f193099d316
parents e3ec3503 ebd55659

neuralnetworks/1.0/Android.bp

0 → 100644
+70 −0
Original line number Diff line number Diff line 
// This file is autogenerated by hidl-gen. Do not edit manually.



filegroup {

    name: "android.hardware.neuralnetworks@1.0_hal",

    srcs: [

        "types.hal",

        "IDevice.hal",

        "IPreparedModel.hal",

    ],

}



genrule {

    name: "android.hardware.neuralnetworks@1.0_genc++",

    tools: ["hidl-gen"],

    cmd: "$(location hidl-gen) -o $(genDir) -Lc++-sources -randroid.hardware:hardware/interfaces -randroid.hidl:system/libhidl/transport android.hardware.neuralnetworks@1.0",

    srcs: [

        ":android.hardware.neuralnetworks@1.0_hal",

    ],

    out: [

        "android/hardware/neuralnetworks/1.0/types.cpp",

        "android/hardware/neuralnetworks/1.0/DeviceAll.cpp",

        "android/hardware/neuralnetworks/1.0/PreparedModelAll.cpp",

    ],

}



genrule {

    name: "android.hardware.neuralnetworks@1.0_genc++_headers",

    tools: ["hidl-gen"],

    cmd: "$(location hidl-gen) -o $(genDir) -Lc++-headers -randroid.hardware:hardware/interfaces -randroid.hidl:system/libhidl/transport android.hardware.neuralnetworks@1.0",

    srcs: [

        ":android.hardware.neuralnetworks@1.0_hal",

    ],

    out: [

        "android/hardware/neuralnetworks/1.0/types.h",

        "android/hardware/neuralnetworks/1.0/hwtypes.h",

        "android/hardware/neuralnetworks/1.0/IDevice.h",

        "android/hardware/neuralnetworks/1.0/IHwDevice.h",

        "android/hardware/neuralnetworks/1.0/BnHwDevice.h",

        "android/hardware/neuralnetworks/1.0/BpHwDevice.h",

        "android/hardware/neuralnetworks/1.0/BsDevice.h",

        "android/hardware/neuralnetworks/1.0/IPreparedModel.h",

        "android/hardware/neuralnetworks/1.0/IHwPreparedModel.h",

        "android/hardware/neuralnetworks/1.0/BnHwPreparedModel.h",

        "android/hardware/neuralnetworks/1.0/BpHwPreparedModel.h",

        "android/hardware/neuralnetworks/1.0/BsPreparedModel.h",

    ],

}



cc_library_shared {

    name: "android.hardware.neuralnetworks@1.0",

    defaults: ["hidl-module-defaults"],

    generated_sources: ["android.hardware.neuralnetworks@1.0_genc++"],

    generated_headers: ["android.hardware.neuralnetworks@1.0_genc++_headers"],

    export_generated_headers: ["android.hardware.neuralnetworks@1.0_genc++_headers"],

    vendor_available: true,

    shared_libs: [

        "libhidlbase",

        "libhidltransport",

        "libhwbinder",

        "liblog",

        "libutils",

        "libcutils",

    ],

    export_shared_lib_headers: [

        "libhidlbase",

        "libhidltransport",

        "libhwbinder",

        "libutils",

    ],

}

neuralnetworks/1.0/IDevice.hal

0 → 100644
+31 −0
Original line number Diff line number Diff line 
/*

 * Copyright (C) 2017 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.

 */



/* This HAL is a work in progress */



package android.hardware.neuralnetworks@1.0;



import IPreparedModel;



interface IDevice {

    initialize() generates(Capabilities capabilities);



    getSupportedSubgraph(Model model) generates(vec<bool> supported);



    prepareModel(Model model) generates(IPreparedModel preparedModel);



    getStatus() generates(DeviceStatus status);

};

neuralnetworks/1.0/IPreparedModel.hal

0 → 100644
+25 −0
Original line number Diff line number Diff line 
/*

 * Copyright (C) 2017 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.

 */



/* This HAL is a work in progress */



package android.hardware.neuralnetworks@1.0;



interface IPreparedModel {

    // TODO: The execution is synchronous.  Change that to have a callback on completion.

    // Multiple threads can call this execute function concurrently.

    execute(Request request) generates(bool success);

};

neuralnetworks/1.0/types.hal

0 → 100644
+174 −0
Original line number Diff line number Diff line 
/*

 * Copyright (C) 2017 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.

 */



/* This HAL is a work in progress */



package android.hardware.neuralnetworks@1.0;



// The types an operand can have.

// These values are the same as found in the NeuralNetworks.h file.

// When modifying, be sure to update HAL_NUM_OPERAND_TYPES in HalIntefaces.h.

enum OperandType : uint32_t {

    FLOAT16                   = 0,

    FLOAT32                   = 1,

    INT8                      = 2,

    UINT8                     = 3,

    INT16                     = 4,

    UINT16                    = 5,

    INT32                     = 6,

    UINT32                    = 7,

    TENSOR_FLOAT16            = 8,

    TENSOR_FLOAT32            = 9,

    TENSOR_SYMMETRICAL_QUANT8 = 10,

};



// The type of operations.  Unlike the operation types found in

// NeuralNetworks.h file, these specify the data type they operate on.

// This is done to simplify the work of drivers.

// TODO: Currently they are the same.  Add a conversion when finalizing the model.

// When modifying, be sure to update HAL_NUM_OPERATION_TYPES in HalIntefaces.h.

enum OperationType : uint32_t {

    AVERAGE_POOL_FLOAT32                 = 0,

    CONCATENATION_FLOAT32                = 1,

    CONV_FLOAT32                         = 2,

    DEPTHWISE_CONV_FLOAT32               = 3,

    MAX_POOL_FLOAT32                     = 4,

    L2_POOL_FLOAT32                      = 5,

    DEPTH_TO_SPACE_FLOAT32               = 6,

    SPACE_TO_DEPTH_FLOAT32               = 7,

    LOCAL_RESPONSE_NORMALIZATION_FLOAT32 = 8,

    SOFTMAX_FLOAT32                      = 9,

    RESHAPE_FLOAT32                      = 10,

    SPLIT_FLOAT32                        = 11,

    FAKE_QUANT_FLOAT32                   = 12,

    ADD_FLOAT32                          = 13,

    FULLY_CONNECTED_FLOAT32              = 14,

    CAST_FLOAT32                         = 15,

    MUL_FLOAT32                          = 16,

    L2_NORMALIZATION_FLOAT32             = 17,

    LOGISTIC_FLOAT32                     = 18,

    RELU_FLOAT32                         = 19,

    RELU6_FLOAT32                        = 20,

    RELU1_FLOAT32                        = 21,

    TANH_FLOAT32                         = 22,

    DEQUANTIZE_FLOAT32                   = 23,

    FLOOR_FLOAT32                        = 24,

    GATHER_FLOAT32                       = 25,

    RESIZE_BILINEAR_FLOAT32              = 26,

    LSH_PROJECTION_FLOAT32               = 27,

    LSTM_FLOAT32                         = 28,

    SVDF_FLOAT32                         = 29,

    RNN_FLOAT32                          = 30,

    N_GRAM_FLOAT32                       = 31,

    LOOKUP_FLOAT32                       = 32,

};



// Two special values that can be used instead of a regular poolIndex.

enum LocationValues : uint32_t {

    // The location will be specified at runtime. It's either a temporary

    // variable, an input, or an output.

    LOCATION_AT_RUN_TIME = 0xFFFFFFFF,

    // The operand's value is stored in the

    // TODO: Only for old

    LOCATION_SAME_BLOCK = 0xFFFFFFFE

};



// Status of a device.

enum DeviceStatus : uint32_t {

    AVAILABLE,

    BUSY,

    OFFLINE,

    UNKNOWN  // Do we need this?

};



// For the reference workload

// Used by a driver to report its performance characteristics.

// TODO revisit the data types and scales.

struct PerformanceInfo {

    float execTime;    // in nanoseconds

    float powerUsage;  // in picoJoules

};



// The capabilities of a driver.

struct Capabilities {

    vec<OperationType> supportedOperationTypes;

    // TODO Do the same for baseline model IDs

    bool cachesCompilation;

    // TODO revisit the data types and scales.

    float bootupTime;  // in nanoseconds

    PerformanceInfo float16Performance;

    PerformanceInfo float32Performance;

    PerformanceInfo quantized8Performance;

};



// Describes the location of a data object.

struct DataLocation {

    // The index of the memory pool where this location is found.

    // Two special values can also be used.  See the LOCATION_* constants above.

    uint32_t poolIndex;

    // Offset in bytes from the start of the pool.

    uint32_t offset;

    // The length of the data, in bytes.

    uint32_t length;

};



struct Operand {

    OperandType type;

    vec<uint32_t> dimensions;



    // The number of operations that uses this operand as input.

    // TODO It would be nice to track the actual consumers, e.g. vec<uint32_t> consumers;

    uint32_t numberOfConsumers;



    float scale;

    int32_t zeroPoint;



    // Where to find the data for this operand.

    DataLocation location;

};



// Describes one operation of the graph.

struct Operation {

    // The type of operation.

    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;

};



struct InputOutputInfo {

    DataLocation location;

    // If dimensions.size() > 0, we have updated dimensions.

    vec<uint32_t> dimensions;

};



struct Model {

    vec<Operand> operands;

    vec<Operation> operations;

    vec<uint32_t> inputIndexes;

    vec<uint32_t> outputIndexes;

    vec<uint8_t> operandValues;

    vec<memory> pools;

};



struct Request {

    vec<InputOutputInfo> inputs;

    vec<InputOutputInfo> outputs;

    vec<memory> pools;

};

neuralnetworks/Android.bp

0 → 100644
+4 −0
Original line number Diff line number Diff line 
// This is an autogenerated file, do not edit.

subdirs = [

    "1.0",

]