Relocate ExecutionBurst* classes to NN util code am: f6b2d1ada3 am: 6c3ab4c2ad (95de1b2b) · Commits · e / os / android_hardware_interfaces

neuralnetworks/1.2/utils/Android.bp

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		@@ -18,6 +18,7 @@ cc_library_static {
		name: "neuralnetworks_utils_hal_1_2",
		defaults: ["neuralnetworks_utils_defaults"],
		srcs: ["src/*"],
		exclude_srcs: ["src/ExecutionBurst*"],
		local_include_dirs: ["include/nnapi/hal/1.2/"],
		export_include_dirs: ["include"],
		cflags: ["-Wthread-safety"],

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

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		/*
		* Copyright (C) 2019 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.
		*/

		#ifndef ANDROID_FRAMEWORKS_ML_NN_COMMON_EXECUTION_BURST_CONTROLLER_H
		#define ANDROID_FRAMEWORKS_ML_NN_COMMON_EXECUTION_BURST_CONTROLLER_H

		#include <android-base/macros.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/IBurstContext.h>
		#include <android/hardware/neuralnetworks/1.2/IPreparedModel.h>
		#include <android/hardware/neuralnetworks/1.2/types.h>
		#include <fmq/MessageQueue.h>
		#include <hidl/MQDescriptor.h>

		#include <atomic>
		#include <chrono>
		#include <map>
		#include <memory>
		#include <mutex>
		#include <stack>
		#include <tuple>
		#include <utility>
		#include <vector>

		namespace android::nn {

		/**
		* Number of elements in the FMQ.
		*/
		constexpr const size_t kExecutionBurstChannelLength = 1024;

		/**
		* Function to serialize a request.
		*
		* Prefer calling RequestChannelSender::send.
		*
		* @param request Request object without the pool information.
		* @param measure Whether to collect timing information for the execution.
		* @param memoryIds Slot identifiers corresponding to memory resources for the
		* request.
		* @return Serialized FMQ request data.
		*/
		std::vector<hardware::neuralnetworks::V1_2::FmqRequestDatum> serialize(
		const hardware::neuralnetworks::V1_0::Request& request,
		hardware::neuralnetworks::V1_2::MeasureTiming measure, const std::vector<int32_t>& slots);

		/**
		* Deserialize the FMQ result data.
		*
		* The three resulting fields are the status of the execution, the dynamic
		* shapes of the output tensors, and the timing information of the execution.
		*
		* @param data Serialized FMQ result data.
		* @return Result object if successfully deserialized, std::nullopt otherwise.
		*/
		std::optional<std::tuple<hardware::neuralnetworks::V1_0::ErrorStatus,
		std::vector<hardware::neuralnetworks::V1_2::OutputShape>,
		hardware::neuralnetworks::V1_2::Timing>>
		deserialize(const std::vector<hardware::neuralnetworks::V1_2::FmqResultDatum>& data);

		/**
		* Convert result code to error status.
		*
		* @param resultCode Result code to be converted.
		* @return ErrorStatus Resultant error status.
		*/
		hardware::neuralnetworks::V1_0::ErrorStatus legacyConvertResultCodeToErrorStatus(int resultCode);

		/**
		* ResultChannelReceiver is responsible for waiting on the channel until the
		* packet is available, extracting the packet from the channel, and
		* deserializing the packet.
		*
		* Because the receiver can wait on a packet that may never come (e.g., because
		* the sending side of the packet has been closed), this object can be
		* invalidated, unblocking the receiver.
		*/
		class ResultChannelReceiver {
		using FmqResultDescriptor =
		hardware::MQDescriptorSync<hardware::neuralnetworks::V1_2::FmqResultDatum>;
		using FmqResultChannel = hardware::MessageQueue<hardware::neuralnetworks::V1_2::FmqResultDatum,
		hardware::kSynchronizedReadWrite>;

		public:
		/**
		* Create the receiving end of a result channel.
		*
		* Prefer this call over the constructor.
		*
		* @param channelLength Number of elements in the FMQ.
		* @param pollingTimeWindow How much time (in microseconds) the
		* ResultChannelReceiver is allowed to poll the FMQ before waiting on
		* the blocking futex. Polling may result in lower latencies at the
		* potential cost of more power usage.
		* @return A pair of ResultChannelReceiver and the FMQ descriptor on
		* successful creation, both nullptr otherwise.
		*/
		static std::pair<std::unique_ptr<ResultChannelReceiver>, const FmqResultDescriptor*> create(
		size_t channelLength, std::chrono::microseconds pollingTimeWindow);

		/**
		* Get the result from the channel.
		*
		* This method will block until either:
		* 1) The packet has been retrieved, or
		* 2) The receiver has been invalidated
		*
		* @return Result object if successfully received, std::nullopt if error or
		* if the receiver object was invalidated.
		*/
		std::optional<std::tuple<hardware::neuralnetworks::V1_0::ErrorStatus,
		std::vector<hardware::neuralnetworks::V1_2::OutputShape>,
		hardware::neuralnetworks::V1_2::Timing>>
		getBlocking();

		/**
		* Method to mark the channel as invalid, unblocking any current or future
		* calls to ResultChannelReceiver::getBlocking.
		*/
		void invalidate();

		// prefer calling ResultChannelReceiver::getBlocking
		std::optional<std::vector<hardware::neuralnetworks::V1_2::FmqResultDatum>> getPacketBlocking();

		ResultChannelReceiver(std::unique_ptr<FmqResultChannel> fmqResultChannel,
		std::chrono::microseconds pollingTimeWindow);

		private:
		const std::unique_ptr<FmqResultChannel> mFmqResultChannel;
		std::atomic<bool> mValid{true};
		const std::chrono::microseconds kPollingTimeWindow;
		};

		/**
		* RequestChannelSender is responsible for serializing the result packet of
		* information, sending it on the result channel, and signaling that the data is
		* available.
		*/
		class RequestChannelSender {
		using FmqRequestDescriptor =
		hardware::MQDescriptorSync<hardware::neuralnetworks::V1_2::FmqRequestDatum>;
		using FmqRequestChannel =
		hardware::MessageQueue<hardware::neuralnetworks::V1_2::FmqRequestDatum,
		hardware::kSynchronizedReadWrite>;

		public:
		/**
		* Create the sending end of a request channel.
		*
		* Prefer this call over the constructor.
		*
		* @param channelLength Number of elements in the FMQ.
		* @return A pair of ResultChannelReceiver and the FMQ descriptor on
		* successful creation, both nullptr otherwise.
		*/
		static std::pair<std::unique_ptr<RequestChannelSender>, const FmqRequestDescriptor*> create(
		size_t channelLength);

		/**
		* Send the request to the channel.
		*
		* @param request Request object without the pool information.
		* @param measure Whether to collect timing information for the execution.
		* @param memoryIds Slot identifiers corresponding to memory resources for
		* the request.
		* @return 'true' on successful send, 'false' otherwise.
		*/
		bool send(const hardware::neuralnetworks::V1_0::Request& request,
		hardware::neuralnetworks::V1_2::MeasureTiming measure,
		const std::vector<int32_t>& slots);

		/**
		* Method to mark the channel as invalid, causing all future calls to
		* RequestChannelSender::send to immediately return false without attempting
		* to send a message across the FMQ.
		*/
		void invalidate();

		// prefer calling RequestChannelSender::send
		bool sendPacket(const std::vector<hardware::neuralnetworks::V1_2::FmqRequestDatum>& packet);

		RequestChannelSender(std::unique_ptr<FmqRequestChannel> fmqRequestChannel);

		private:
		const std::unique_ptr<FmqRequestChannel> mFmqRequestChannel;
		std::atomic<bool> mValid{true};
		};

		/**
		* The ExecutionBurstController class manages both the serialization and
		* deserialization of data across FMQ, making it appear to the runtime as a
		* regular synchronous inference. Additionally, this class manages the burst's
		* memory cache.
		*/
		class ExecutionBurstController {
		DISALLOW_IMPLICIT_CONSTRUCTORS(ExecutionBurstController);

		public:
		/**
		* NN runtime burst callback object and memory cache.
		*
		* ExecutionBurstCallback associates a hidl_memory object with a slot number
		* to be passed across FMQ. The ExecutionBurstServer can use this callback
		* to retrieve this hidl_memory corresponding to the slot via HIDL.
		*
		* Whenever a hidl_memory object is copied, it will duplicate the underlying
		* 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 {
		DISALLOW_COPY_AND_ASSIGN(ExecutionBurstCallback);

		public:
		ExecutionBurstCallback() = default;

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

		/**
		* This function performs one of two different actions:
		* 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.
		* @param keys Unique identifiers where each element corresponds to a
		* memory resource element in "memories".
		* @return Unique slot identifiers where each returned slot element
		* corresponds to a memory resource element in "memories".
		*/
		std::vector<int32_t> getSlots(const hardware::hidl_vec<hardware::hidl_memory>& memories,
		const std::vector<intptr_t>& keys);

		/*
		* This function performs two different actions:
		* 1) Removes an entry from the cache (if present), including the local
		* storage of the hidl_memory object. Note that this call does not
		* free any corresponding hidl_memory object in ExecutionBurstServer,
		* which is separately freed via IBurstContext::freeMemory.
		* 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
		* 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.
		*/
		std::pair<bool, int32_t> freeMemory(intptr_t key);

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

		std::mutex mMutex;
		std::stack<int32_t, std::vector<int32_t>> mFreeSlots;
		std::map<intptr_t, int32_t> mMemoryIdToSlot;
		std::vector<hardware::hidl_memory> mMemoryCache;
		};

		/**
		* Creates a burst controller on a prepared model.
		*
		* Prefer this over ExecutionBurstController's constructor.
		*
		* @param preparedModel Model prepared for execution to execute on.
		* @param pollingTimeWindow How much time (in microseconds) the
		* ExecutionBurstController is allowed to poll the FMQ before waiting on
		* the blocking futex. Polling may result in lower latencies at the
		* potential cost of more power usage.
		* @return ExecutionBurstController Execution burst controller object.
		*/
		static std::unique_ptr<ExecutionBurstController> create(
		const sp<hardware::neuralnetworks::V1_2::IPreparedModel>& preparedModel,
		std::chrono::microseconds pollingTimeWindow);

		// prefer calling ExecutionBurstController::create
		ExecutionBurstController(const std::shared_ptr<RequestChannelSender>& requestChannelSender,
		const std::shared_ptr<ResultChannelReceiver>& resultChannelReceiver,
		const sp<hardware::neuralnetworks::V1_2::IBurstContext>& burstContext,
		const sp<ExecutionBurstCallback>& callback,
		const sp<hardware::hidl_death_recipient>& deathHandler = nullptr);

		// explicit destructor to unregister the death recipient
		~ExecutionBurstController();

		/**
		* Execute a request on a model.
		*
		* @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:
		std::mutex mMutex;
		const std::shared_ptr<RequestChannelSender> mRequestChannelSender;
		const std::shared_ptr<ResultChannelReceiver> mResultChannelReceiver;
		const sp<hardware::neuralnetworks::V1_2::IBurstContext> mBurstContext;
		const sp<ExecutionBurstCallback> mMemoryCache;
		const sp<hardware::hidl_death_recipient> mDeathHandler;
		};

		} // namespace android::nn

		#endif // ANDROID_FRAMEWORKS_ML_NN_COMMON_EXECUTION_BURST_CONTROLLER_H

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

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neuralnetworks/1.2/utils/src/ExecutionBurstController.cpp

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neuralnetworks/1.2/utils/src/ExecutionBurstServer.cpp

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