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Commit 593f0eca authored by Treehugger Robot's avatar Treehugger Robot Committed by Gerrit Code Review
Browse files

Merge changes from topic "aosp-nnapi-burst-compat-lib" 

* changes:
  Implement full canonical Burst in NN util code
  Add canonical types adapters for NNAPI AIDL interface
  Add missing NNAPI HIDL interface mock tests
parents cfab4c08 76e491fa

neuralnetworks/1.2/utils/Android.bp

+6 −1
Original line number Original line Diff line number Diff line
@@ -27,7 +27,6 @@ cc_library_static { 
    name: "neuralnetworks_utils_hal_1_2",

    name: "neuralnetworks_utils_hal_1_2",

    defaults: ["neuralnetworks_utils_defaults"],

    defaults: ["neuralnetworks_utils_defaults"],

    srcs: ["src/*"],

    srcs: ["src/*"],

    exclude_srcs: ["src/ExecutionBurst*"],

    local_include_dirs: ["include/nnapi/hal/1.2/"],

    local_include_dirs: ["include/nnapi/hal/1.2/"],

    export_include_dirs: ["include"],

    export_include_dirs: ["include"],

    cflags: ["-Wthread-safety"],

    cflags: ["-Wthread-safety"],
@@ -41,10 +40,16 @@ cc_library_static { 
        "android.hardware.neuralnetworks@1.0",

        "android.hardware.neuralnetworks@1.0",

        "android.hardware.neuralnetworks@1.1",

        "android.hardware.neuralnetworks@1.1",

        "android.hardware.neuralnetworks@1.2",

        "android.hardware.neuralnetworks@1.2",

        "libfmq",

    ],

    ],

    export_static_lib_headers: [

    export_static_lib_headers: [

        "neuralnetworks_utils_hal_common",

        "neuralnetworks_utils_hal_common",

    ],

    ],

    product_variables: {

        debuggable: { // eng and userdebug builds

            cflags: ["-DNN_DEBUGGABLE"],

        },

    },

}

}





cc_test {

cc_test {

neuralnetworks/1.2/utils/include/nnapi/hal/1.2/Conversions.h

+1 −0
Original line number Original line Diff line number Diff line
@@ -52,6 +52,7 @@ GeneralResult<Capabilities> convert(const hal::V1_2::Capabilities& capabilities) 
GeneralResult<Model> convert(const hal::V1_2::Model& model);

GeneralResult<Model> convert(const hal::V1_2::Model& model);

GeneralResult<MeasureTiming> convert(const hal::V1_2::MeasureTiming& measureTiming);

GeneralResult<MeasureTiming> convert(const hal::V1_2::MeasureTiming& measureTiming);

GeneralResult<Timing> convert(const hal::V1_2::Timing& timing);

GeneralResult<Timing> convert(const hal::V1_2::Timing& timing);

GeneralResult<SharedMemory> convert(const hardware::hidl_memory& memory);





GeneralResult<std::vector<Extension>> convert(

GeneralResult<std::vector<Extension>> convert(

        const hardware::hidl_vec<hal::V1_2::Extension>& extensions);

        const hardware::hidl_vec<hal::V1_2::Extension>& extensions);

neuralnetworks/1.2/utils/include/nnapi/hal/1.2/ExecutionBurstController.h

+113 −110
Original line number Original line Diff line number Diff line
@@ -14,23 +14,28 @@ 
 * limitations under the License.

 * limitations under the License.

 */

 */





#ifndef ANDROID_FRAMEWORKS_ML_NN_COMMON_EXECUTION_BURST_CONTROLLER_H

#ifndef ANDROID_HARDWARE_INTERFACES_NEURALNETWORKS_1_2_UTILS_EXECUTION_BURST_CONTROLLER_H

#define ANDROID_FRAMEWORKS_ML_NN_COMMON_EXECUTION_BURST_CONTROLLER_H

#define ANDROID_HARDWARE_INTERFACES_NEURALNETWORKS_1_2_UTILS_EXECUTION_BURST_CONTROLLER_H





#include "ExecutionBurstUtils.h"

#include "ExecutionBurstUtils.h"





#include <android-base/macros.h>

#include <android-base/thread_annotations.h>

#include <android/hardware/neuralnetworks/1.0/types.h>

#include <android/hardware/neuralnetworks/1.0/types.h>

#include <android/hardware/neuralnetworks/1.1/types.h>

#include <android/hardware/neuralnetworks/1.2/IBurstCallback.h>

#include <android/hardware/neuralnetworks/1.2/IBurstCallback.h>

#include <android/hardware/neuralnetworks/1.2/IBurstContext.h>

#include <android/hardware/neuralnetworks/1.2/IBurstContext.h>

#include <android/hardware/neuralnetworks/1.2/IPreparedModel.h>

#include <android/hardware/neuralnetworks/1.2/IPreparedModel.h>

#include <android/hardware/neuralnetworks/1.2/types.h>

#include <android/hardware/neuralnetworks/1.2/types.h>

#include <fmq/MessageQueue.h>

#include <fmq/MessageQueue.h>

#include <hidl/MQDescriptor.h>

#include <hidl/MQDescriptor.h>

#include <nnapi/IBurst.h>

#include <nnapi/IPreparedModel.h>

#include <nnapi/Result.h>

#include <nnapi/Types.h>

#include <nnapi/hal/ProtectCallback.h>





#include <atomic>

#include <atomic>

#include <chrono>

#include <chrono>

#include <functional>

#include <map>

#include <map>

#include <memory>

#include <memory>

#include <mutex>

#include <mutex>
@@ -39,147 +44,145 @@ 
#include <utility>

#include <utility>

#include <vector>

#include <vector>





namespace android::nn {

namespace android::hardware::neuralnetworks::V1_2::utils {





/**

/**

 * The ExecutionBurstController class manages both the serialization and

 * The ExecutionBurstController class manages both the serialization and deserialization of data

 * deserialization of data across FMQ, making it appear to the runtime as a

 * across FMQ, making it appear to the runtime as a regular synchronous inference. Additionally,

 * regular synchronous inference. Additionally, this class manages the burst's

 * this class manages the burst's memory cache.

 * memory cache.

 */

 */

class ExecutionBurstController {

class ExecutionBurstController final : public nn::IBurst {

    DISALLOW_IMPLICIT_CONSTRUCTORS(ExecutionBurstController);

    struct PrivateConstructorTag {};





  public:

  public:

    using FallbackFunction =

            std::function<nn::ExecutionResult<std::pair<std::vector<nn::OutputShape>, nn::Timing>>(

                    const nn::Request&, nn::MeasureTiming)>;



    /**

    /**

     * NN runtime burst callback object and memory cache.

     * NN runtime memory cache.

     *

     * MemoryCache associates a Memory object with a slot number to be passed across FMQ. The

     * ExecutionBurstServer can use this callback to retrieve a hidl_memory corresponding to the

     * slot via HIDL.

     *

     *

     * ExecutionBurstCallback associates a hidl_memory object with a slot number

     * Whenever a hidl_memory object is copied, it will duplicate the underlying file descriptor.

     * to be passed across FMQ. The ExecutionBurstServer can use this callback

     * Because the NN runtime currently copies the hidl_memory on each execution, it is difficult to

     * to retrieve this hidl_memory corresponding to the slot via HIDL.

     * associate hidl_memory objects with previously cached hidl_memory objects. For this reason,

     * callers of this class must pair each hidl_memory object with an associated key. For

     * efficiency, if two hidl_memory objects represent the same underlying buffer, they must use

     * the same key.

     *

     *

     * Whenever a hidl_memory object is copied, it will duplicate the underlying

     * This class is thread-safe.

     * file descriptor. Because the NN runtime currently copies the hidl_memory

     * on each execution, it is difficult to associate hidl_memory objects with

     * previously cached hidl_memory objects. For this reason, callers of this

     * class must pair each hidl_memory object with an associated key. For

     * efficiency, if two hidl_memory objects represent the same underlying

     * buffer, they must use the same key.

     */

     */

    class ExecutionBurstCallback : public hardware::neuralnetworks::V1_2::IBurstCallback {

    class MemoryCache : public std::enable_shared_from_this<MemoryCache> {

        DISALLOW_COPY_AND_ASSIGN(ExecutionBurstCallback);

        struct PrivateConstructorTag {};





      public:

      public:

        ExecutionBurstCallback() = default;

        using Task = std::function<void()>;

        using Cleanup = base::ScopeGuard<Task>;

        using SharedCleanup = std::shared_ptr<const Cleanup>;

        using WeakCleanup = std::weak_ptr<const Cleanup>;





        hardware::Return<void> getMemories(const hardware::hidl_vec<int32_t>& slots,

        // Custom constructor to pre-allocate cache sizes.

                                           getMemories_cb cb) override;

        MemoryCache();





        /**

        /**

         * This function performs one of two different actions:

         * Add a burst context to the MemoryCache object.

         * 1) If a key corresponding to a memory resource is unrecognized by the

         *    ExecutionBurstCallback object, the ExecutionBurstCallback object

         *    will allocate a slot, bind the memory to the slot, and return the

         *    slot identifier.

         * 2) If a key corresponding to a memory resource is recognized by the

         *    ExecutionBurstCallback object, the ExecutionBurstCallback object

         *    will return the existing slot identifier.

         *

         *

         * @param memories Memory resources used in an inference.

         * If this method is called, it must be called before the MemoryCache::cacheMemory or

         * @param keys Unique identifiers where each element corresponds to a

         * MemoryCache::getMemory is used.

         *     memory resource element in "memories".

         *

         * @return Unique slot identifiers where each returned slot element

         * @param burstContext Burst context to be added to the MemoryCache object.

         *     corresponds to a memory resource element in "memories".

         */

         */

        std::vector<int32_t> getSlots(const hardware::hidl_vec<hardware::hidl_memory>& memories,

        void setBurstContext(sp<IBurstContext> burstContext);

                                      const std::vector<intptr_t>& keys);





        /*

        /**

         * This function performs two different actions:

         * Cache a memory object in the MemoryCache object.

         * 1) Removes an entry from the cache (if present), including the local

         *

         *    storage of the hidl_memory object. Note that this call does not

         * @param memory Memory object to be cached while the returned `SharedCleanup` is alive.

         *    free any corresponding hidl_memory object in ExecutionBurstServer,

         * @return A pair of (1) a unique identifier for the cache entry and (2) a ref-counted

         *    which is separately freed via IBurstContext::freeMemory.

         *     "hold" object which preserves the cache as long as the hold object is alive.

         * 2) Return whether a cache entry was removed and which slot was removed if

         */

         *    found. If the key did not to correspond to any entry in the cache, a

        std::pair<int32_t, SharedCleanup> cacheMemory(const nn::SharedMemory& memory);

         *    slot number of 0 is returned. The slot number and whether the entry



         *    existed is useful so the same slot can be freed in the

        /**

         *    ExecutionBurstServer's cache via IBurstContext::freeMemory.

         * Get the memory object corresponding to a slot identifier.

         *

         * @param slot Slot which identifies the memory object to retrieve.

         * @return The memory object corresponding to slot, otherwise GeneralError.

         */

         */

        std::pair<bool, int32_t> freeMemory(intptr_t key);

        nn::GeneralResult<nn::SharedMemory> getMemory(int32_t slot);





      private:

      private:

        int32_t getSlotLocked(const hardware::hidl_memory& memory, intptr_t key);

        void freeMemory(const nn::SharedMemory& memory);

        int32_t allocateSlotLocked();

        int32_t allocateSlotLocked() REQUIRES(mMutex);





        std::mutex mMutex;

        std::mutex mMutex;

        std::stack<int32_t, std::vector<int32_t>> mFreeSlots;

        std::condition_variable mCond;

        std::map<intptr_t, int32_t> mMemoryIdToSlot;

        sp<IBurstContext> mBurstContext GUARDED_BY(mMutex);

        std::vector<hardware::hidl_memory> mMemoryCache;

        std::stack<int32_t, std::vector<int32_t>> mFreeSlots GUARDED_BY(mMutex);

        std::map<nn::SharedMemory, int32_t> mMemoryIdToSlot GUARDED_BY(mMutex);

        std::vector<nn::SharedMemory> mMemoryCache GUARDED_BY(mMutex);

        std::vector<WeakCleanup> mCacheCleaner GUARDED_BY(mMutex);

    };



    /**

     * HIDL Callback class to pass memory objects to the Burst server when given corresponding

     * slots.

     */

    class ExecutionBurstCallback : public IBurstCallback {

      public:

        // Precondition: memoryCache must be non-null.

        explicit ExecutionBurstCallback(const std::shared_ptr<MemoryCache>& memoryCache);



        // See IBurstCallback::getMemories for information on this method.

        Return<void> getMemories(const hidl_vec<int32_t>& slots, getMemories_cb cb) override;



      private:

        const std::weak_ptr<MemoryCache> kMemoryCache;

    };

    };





    /**

    /**

     * Creates a burst controller on a prepared model.

     * Creates a burst controller on a prepared model.

     *

     *

     * Prefer this over ExecutionBurstController's constructor.

     *

     * @param preparedModel Model prepared for execution to execute on.

     * @param preparedModel Model prepared for execution to execute on.

     * @param pollingTimeWindow How much time (in microseconds) the

     * @param pollingTimeWindow How much time (in microseconds) the ExecutionBurstController is

     *     ExecutionBurstController is allowed to poll the FMQ before waiting on

     *     allowed to poll the FMQ before waiting on the blocking futex. Polling may result in lower

     *     the blocking futex. Polling may result in lower latencies at the

     *     latencies at the potential cost of more power usage.

     *     potential cost of more power usage.

     * @return ExecutionBurstController Execution burst controller object.

     * @return ExecutionBurstController Execution burst controller object.

     */

     */

    static std::unique_ptr<ExecutionBurstController> create(

    static nn::GeneralResult<std::shared_ptr<const ExecutionBurstController>> create(

            const sp<hardware::neuralnetworks::V1_2::IPreparedModel>& preparedModel,

            const sp<IPreparedModel>& preparedModel, FallbackFunction fallback,

            std::chrono::microseconds pollingTimeWindow);

            std::chrono::microseconds pollingTimeWindow);





    // prefer calling ExecutionBurstController::create

    ExecutionBurstController(PrivateConstructorTag tag, FallbackFunction fallback,

    ExecutionBurstController(const std::shared_ptr<RequestChannelSender>& requestChannelSender,

                             std::unique_ptr<RequestChannelSender> requestChannelSender,

                             const std::shared_ptr<ResultChannelReceiver>& resultChannelReceiver,

                             std::unique_ptr<ResultChannelReceiver> resultChannelReceiver,

                             const sp<hardware::neuralnetworks::V1_2::IBurstContext>& burstContext,

                             sp<ExecutionBurstCallback> callback, sp<IBurstContext> burstContext,

                             const sp<ExecutionBurstCallback>& callback,

                             std::shared_ptr<MemoryCache> memoryCache,

                             const sp<hardware::hidl_death_recipient>& deathHandler = nullptr);

                             neuralnetworks::utils::DeathHandler deathHandler);





    // explicit destructor to unregister the death recipient

    // See IBurst::cacheMemory for information on this method.

    ~ExecutionBurstController();

    OptionalCacheHold cacheMemory(const nn::SharedMemory& memory) const override;





    /**

    // See IBurst::execute for information on this method.

     * Execute a request on a model.

    nn::ExecutionResult<std::pair<std::vector<nn::OutputShape>, nn::Timing>> execute(

     *

            const nn::Request& request, nn::MeasureTiming measure) const override;

     * @param request Arguments to be executed on a model.

     * @param measure Whether to collect timing measurements, either YES or NO

     * @param memoryIds Identifiers corresponding to each memory object in the

     *     request's pools.

     * @return A tuple of:

     *     - result code of the execution

     *     - dynamic output shapes from the execution

     *     - any execution time measurements of the execution

     *     - whether or not a failed burst execution should be re-run using a

     *       different path (e.g., IPreparedModel::executeSynchronously)

     */

    std::tuple<int, std::vector<hardware::neuralnetworks::V1_2::OutputShape>,

               hardware::neuralnetworks::V1_2::Timing, bool>

    compute(const hardware::neuralnetworks::V1_0::Request& request,

            hardware::neuralnetworks::V1_2::MeasureTiming measure,

            const std::vector<intptr_t>& memoryIds);



    /**

     * Propagate a user's freeing of memory to the service.

     *

     * @param key Key corresponding to the memory object.

     */

    void freeMemory(intptr_t key);





  private:

  private:

    std::mutex mMutex;

    mutable std::atomic_flag mExecutionInFlight = ATOMIC_FLAG_INIT;

    const std::shared_ptr<RequestChannelSender> mRequestChannelSender;

    const FallbackFunction kFallback;

    const std::shared_ptr<ResultChannelReceiver> mResultChannelReceiver;

    const std::unique_ptr<RequestChannelSender> mRequestChannelSender;

    const sp<hardware::neuralnetworks::V1_2::IBurstContext> mBurstContext;

    const std::unique_ptr<ResultChannelReceiver> mResultChannelReceiver;

    const sp<ExecutionBurstCallback> mMemoryCache;

    const sp<ExecutionBurstCallback> mBurstCallback;

    const sp<hardware::hidl_death_recipient> mDeathHandler;

    const sp<IBurstContext> mBurstContext;

    const std::shared_ptr<MemoryCache> mMemoryCache;

    // `kDeathHandler` must come after `mRequestChannelSender` and `mResultChannelReceiver` because

    // it holds references to both objects.

    const neuralnetworks::utils::DeathHandler kDeathHandler;

};

};





}  // namespace android::nn

}  // namespace android::hardware::neuralnetworks::V1_2::utils





#endif  // ANDROID_FRAMEWORKS_ML_NN_COMMON_EXECUTION_BURST_CONTROLLER_H
 
#endif  // ANDROID_HARDWARE_INTERFACES_NEURALNETWORKS_1_2_UTILS_EXECUTION_BURST_CONTROLLER_H

neuralnetworks/1.2/utils/include/nnapi/hal/1.2/ExecutionBurstServer.h

+74 −127

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neuralnetworks/1.2/utils/include/nnapi/hal/1.2/ExecutionBurstUtils.h

+109 −143

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