runtime/ExecutionBuilder.h - platform/packages/modules/NeuralNetworks - Git at Google

 /*
  * 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.
  */

 #ifndef ANDROID_FRAMEWORKS_ML_NN_RUNTIME_EXECUTION_BUILDER_H
 #define ANDROID_FRAMEWORKS_ML_NN_RUNTIME_EXECUTION_BUILDER_H

 #include <atomic>
 #include <memory>
 #include <vector>

 #include "Callbacks.h"
 #include "HalInterfaces.h"
 #include "Memory.h"
 #include "ModelArgumentInfo.h"
 #include "ModelBuilder.h"
 #include "NeuralNetworks.h"

 namespace android {
 namespace nn {

 class BurstBuilder;
 class CompilationBuilder;
 class Device;
 class ExecutionBurstController;
 class ExecutionPlan;
 class ExecutionStep;
 class Memory;
 class ModelBuilder;
 class PreparedModel;
 class StepExecutor;

 class ExecutionBuilder {
     friend class StepExecutor;

    public:
     ExecutionBuilder(const CompilationBuilder* compilation);

     int setInput(uint32_t index, const ANeuralNetworksOperandType* type, const void* buffer,
                  size_t length);
     int setInputFromMemory(uint32_t index, const ANeuralNetworksOperandType* type,
                            const Memory* memory, size_t offset, size_t length);
     int setOutput(uint32_t index, const ANeuralNetworksOperandType* type, void* buffer,
                   size_t length);
     int setOutputFromMemory(uint32_t index, const ANeuralNetworksOperandType* type,
                             const Memory* memory, size_t offset, size_t length);

     int setMeasureTiming(bool measure);

     int getDuration(int32_t durationCode, uint64_t* duration) const;

     int computeAsynchronously(sp<ExecutionCallback>* synchronizationCallback) {
         CHECK(synchronizationCallback != nullptr);
         return compute(synchronizationCallback);
     }
     int computeSynchronously() { return compute(nullptr); }
     int burstCompute(BurstBuilder* burst) { return compute(nullptr, burst); }

     // Initialize output dimensional information from ModelArgumentInfo.
     void initializeOutputShapes(std::vector<hal::OutputShape>* outputShapes) const;

     int getOutputOperandDimensions(uint32_t index, uint32_t* dimensions);
     int getOutputOperandRank(uint32_t index, uint32_t* rank);

     // Handshake with lower-level execution support
     bool measureTiming() const { return mMeasureTiming; }
     void reportTiming(hal::Timing timing) { mTiming = timing; }

     const CompilationBuilder* getCompilation() const { return mCompilation; }
     const ModelBuilder* getModel() const { return mModel; }

     hal::ErrorStatus finish(hal::ErrorStatus error,
                             const std::vector<hal::OutputShape>& outputShapes);

    private:
     // If a callback is provided, then this is asynchronous. If a callback is
     // not provided (i.e., is nullptr), then this is synchronous.
     //
     // If burst is provided, then the burst path will be used. If a burst is not
     // provided (i.e., is nullptr), then a synchronous execution will occur.
     //
     // Providing both synchronizationCallback and burstBuilder is an error.
     int compute(sp<ExecutionCallback>* synchronizationCallback,
                 BurstBuilder* burstBuilder = nullptr);

     const CompilationBuilder* mCompilation;

     // Update output dimensional information from OutputShape to ModelArgumentInfo.
     bool updateOutputShapes(const std::vector<hal::OutputShape>& outputShapes);

     const ModelBuilder* mModel;
     const ExecutionPlan* mPlan;

     // This is a DeviceManager::kPartitioning* value captured from
     // CompilationBuilder when the ExecutionBuilder is constructed.
     uint32_t mPartitioning;

     // The information we'll send to the driver about the inputs and outputs.
     // Note that we build this in two steps:
     // 1. As the arguments are specified, set the corresponding mInputs or mOutputs element.
     //    If set from a pointer, don't set the location in the RequestArgument but store it
     //    instead in mInputBuffers or mOutputBuffers.
     // 2. Once we have all the inputs and outputs, if needed, allocate shared memory for
     //    the m*Buffers entries.  Copy the input values into the shared memory.
     // We do this to avoid creating a lot of shared memory objects if we have a lot of
     // parameters specified via pointers.  We also avoid copying in the case where
     // some of the nodes will interpreted on the CPU anyway.
     std::vector<ModelArgumentInfo> mInputs;
     std::vector<ModelArgumentInfo> mOutputs;
     MemoryTracker mMemories;

     // Do we ask the driver to measure timing?
     bool mMeasureTiming = false;

     // Timing reported from the driver
     hal::Timing mTiming = {};

     // Properties cannot be set once the execution has started.
     std::atomic_bool mStarted = false;

     // Timing and output shapes can only be queried after the execution is
     // finished.
     std::atomic_bool mFinished = false;
 };

 // class StepExecutor is used to execute a single "step" in a
 // potentially multiple step execution process.  The graph associated
 // with that step is executed in its entirety on a single device (or
 // on the CPU).
 class StepExecutor {
    public:
     // executionBuilder
     //     Describes the full (possibly multiple-"step") execution.
     // model
     //     The model to be executed by the executor.  Possibly a
     //     submodel of the model from executionBuilder.
     // driver, preparedModel
     //     The device on which to execute the "step", and the prepared
     //     model to execute on that device.  (Both are nullptr in the
     //     case of CPU.)
     StepExecutor(ExecutionBuilder* executionBuilder, const ModelBuilder* model,
                  std::shared_ptr<Device> device, std::shared_ptr<PreparedModel> preparedModel);

     // Map inputs and outputs from ExecutionBuilder to StepExecutor,
     // in the case where we have a single-"step" execution (i.e., the executor
     // is executing the entire model from the ExecutionBuilder).
     void mapInputsAndOutputsTrivially();

     // Update output shapes returned from ExecutionCallback to ExecutionBuilder.
     bool updateOutputShapes(const std::vector<hal::OutputShape>& from,
                             std::vector<hal::OutputShape>* to);

     // Map inputs and outputs from ExecutionBuilder to StepExecutor,
     // one at a time.  Note that these are input/output indexes, not
     // operand indexes.
     void mapInput(uint32_t builderIndex, uint32_t executorIndex) {
         mapInputOrOutput(mExecutionBuilder->mInputs[builderIndex], &mInputs[executorIndex]);
     }
     void mapOutput(uint32_t builderIndex, uint32_t executorIndex) {
         mapInputOrOutput(mExecutionBuilder->mOutputs[builderIndex], &mOutputs[executorIndex]);
     }
     void mapOutputToInput(uint32_t builderIndex, uint32_t executorIndex) {
         mapInputOrOutput(mExecutionBuilder->mOutputs[builderIndex], &mInputs[executorIndex]);
     }

     // The input or output is assumed to have the size of the
     // corresponding operand.
     int setInputFromTemporaryMemory(uint32_t inputIndex, const Memory* memory, uint32_t offset) {
         return setInputOrOutputFromTemporaryMemory(mModel->getInputOperand(inputIndex), memory,
                                                    offset, &mInputs.at(inputIndex));
     }
     int setOutputFromTemporaryMemory(uint32_t outputIndex, const Memory* memory, uint32_t offset) {
         return setInputOrOutputFromTemporaryMemory(mModel->getOutputOperand(outputIndex), memory,
                                                    offset, &mOutputs.at(outputIndex));
     }

     // Executes using the (driver, preparedModel) specified at construction time.
     int startCompute(sp<ExecutionCallback>* synchronizationCallback,
                      const std::shared_ptr<ExecutionBurstController>& burstController = nullptr);

     // Re-compiles and executes using the CPU, regardless of the (driver,
     // preparedModel) specified at construction time.
     int startComputeOnCpuFallback(sp<ExecutionCallback>* synchronizationCallback);

     bool isCpu() const;

     // ExecutionStep has the index mapping between ExecutionBuilder and StepExecutor.
     void setExecutionStep(const std::shared_ptr<const ExecutionStep>& step) {
         mExecutionStep = step;
     }

    private:
     void mapInputOrOutput(const ModelArgumentInfo& builderInputOrOutput,
                           ModelArgumentInfo* executorInputOrOutput);

     int setInputOrOutputFromTemporaryMemory(const hal::Operand& inputOrOutputOperand,
                                             const Memory* memory, uint32_t offset,
                                             ModelArgumentInfo* inputOrOutputInfo);

     // describes the full (possibly multiple-"step") execution
     ExecutionBuilder* mExecutionBuilder;

     // describes the single execution step
     std::shared_ptr<const ExecutionStep> mExecutionStep = nullptr;

     // model to be executed on the executor, in both original and
     // compiled forms; and device on which to execute it
     const ModelBuilder* mModel;
     std::shared_ptr<Device> mDevice;
     std::shared_ptr<PreparedModel> mPreparedModel;

     // The information we'll send to the driver about the inputs and outputs.
     // Note that we build this in two steps:
     // 1. As the arguments are specified, set the corresponding mInputs or mOutputs element.
     //    If set from a pointer, don't set the location in the RequestArgument but store it
     //    instead in mInputBuffers or mOutputBuffers.
     // 2. Once we have all the inputs and outputs, if needed, allocate shared memory for
     //    the m*Buffers entries.  Copy the input values into the shared memory.
     // We do this to avoid creating a lot of shared memory objects if we have a lot of
     // parameters specified via pointers.  We also avoid copying in the case where
     // some of the nodes will interpreted on the CPU anyway.
     std::vector<ModelArgumentInfo> mInputs;
     std::vector<ModelArgumentInfo> mOutputs;
     MemoryTracker mMemories;
 };

 }  // namespace nn
 }  // namespace android

 #endif  // ANDROID_FRAMEWORKS_ML_NN_RUNTIME_EXECUTION_BUILDER_H
	/*
	* 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.
	*/

	#ifndef ANDROID_FRAMEWORKS_ML_NN_RUNTIME_EXECUTION_BUILDER_H
	#define ANDROID_FRAMEWORKS_ML_NN_RUNTIME_EXECUTION_BUILDER_H

	#include <atomic>
	#include <memory>
	#include <vector>

	#include "Callbacks.h"
	#include "HalInterfaces.h"
	#include "Memory.h"
	#include "ModelArgumentInfo.h"
	#include "ModelBuilder.h"
	#include "NeuralNetworks.h"

	namespace android {
	namespace nn {

	class BurstBuilder;
	class CompilationBuilder;
	class Device;
	class ExecutionBurstController;
	class ExecutionPlan;
	class ExecutionStep;
	class Memory;
	class ModelBuilder;
	class PreparedModel;
	class StepExecutor;

	class ExecutionBuilder {
	friend class StepExecutor;

	public:
	ExecutionBuilder(const CompilationBuilder* compilation);

	int setInput(uint32_t index, const ANeuralNetworksOperandType* type, const void* buffer,
	size_t length);
	int setInputFromMemory(uint32_t index, const ANeuralNetworksOperandType* type,
	const Memory* memory, size_t offset, size_t length);
	int setOutput(uint32_t index, const ANeuralNetworksOperandType* type, void* buffer,
	size_t length);
	int setOutputFromMemory(uint32_t index, const ANeuralNetworksOperandType* type,
	const Memory* memory, size_t offset, size_t length);

	int setMeasureTiming(bool measure);

	int getDuration(int32_t durationCode, uint64_t* duration) const;

	int computeAsynchronously(sp<ExecutionCallback>* synchronizationCallback) {
	CHECK(synchronizationCallback != nullptr);
	return compute(synchronizationCallback);
	}
	int computeSynchronously() { return compute(nullptr); }
	int burstCompute(BurstBuilder* burst) { return compute(nullptr, burst); }

	// Initialize output dimensional information from ModelArgumentInfo.
	void initializeOutputShapes(std::vector<hal::OutputShape>* outputShapes) const;

	int getOutputOperandDimensions(uint32_t index, uint32_t* dimensions);
	int getOutputOperandRank(uint32_t index, uint32_t* rank);

	// Handshake with lower-level execution support
	bool measureTiming() const { return mMeasureTiming; }
	void reportTiming(hal::Timing timing) { mTiming = timing; }

	const CompilationBuilder* getCompilation() const { return mCompilation; }
	const ModelBuilder* getModel() const { return mModel; }

	hal::ErrorStatus finish(hal::ErrorStatus error,
	const std::vector<hal::OutputShape>& outputShapes);

	private:
	// If a callback is provided, then this is asynchronous. If a callback is
	// not provided (i.e., is nullptr), then this is synchronous.
	//
	// If burst is provided, then the burst path will be used. If a burst is not
	// provided (i.e., is nullptr), then a synchronous execution will occur.
	//
	// Providing both synchronizationCallback and burstBuilder is an error.
	int compute(sp<ExecutionCallback>* synchronizationCallback,
	BurstBuilder* burstBuilder = nullptr);

	const CompilationBuilder* mCompilation;

	// Update output dimensional information from OutputShape to ModelArgumentInfo.
	bool updateOutputShapes(const std::vector<hal::OutputShape>& outputShapes);

	const ModelBuilder* mModel;
	const ExecutionPlan* mPlan;

	// This is a DeviceManager::kPartitioning* value captured from
	// CompilationBuilder when the ExecutionBuilder is constructed.
	uint32_t mPartitioning;

	// The information we'll send to the driver about the inputs and outputs.
	// Note that we build this in two steps:
	// 1. As the arguments are specified, set the corresponding mInputs or mOutputs element.
	// If set from a pointer, don't set the location in the RequestArgument but store it
	// instead in mInputBuffers or mOutputBuffers.
	// 2. Once we have all the inputs and outputs, if needed, allocate shared memory for
	// the m*Buffers entries. Copy the input values into the shared memory.
	// We do this to avoid creating a lot of shared memory objects if we have a lot of
	// parameters specified via pointers. We also avoid copying in the case where
	// some of the nodes will interpreted on the CPU anyway.
	std::vector<ModelArgumentInfo> mInputs;
	std::vector<ModelArgumentInfo> mOutputs;
	MemoryTracker mMemories;

	// Do we ask the driver to measure timing?
	bool mMeasureTiming = false;

	// Timing reported from the driver
	hal::Timing mTiming = {};

	// Properties cannot be set once the execution has started.
	std::atomic_bool mStarted = false;

	// Timing and output shapes can only be queried after the execution is
	// finished.
	std::atomic_bool mFinished = false;
	};

	// class StepExecutor is used to execute a single "step" in a
	// potentially multiple step execution process. The graph associated
	// with that step is executed in its entirety on a single device (or
	// on the CPU).
	class StepExecutor {
	public:
	// executionBuilder
	// Describes the full (possibly multiple-"step") execution.
	// model
	// The model to be executed by the executor. Possibly a
	// submodel of the model from executionBuilder.
	// driver, preparedModel
	// The device on which to execute the "step", and the prepared
	// model to execute on that device. (Both are nullptr in the
	// case of CPU.)
	StepExecutor(ExecutionBuilder* executionBuilder, const ModelBuilder* model,
	std::shared_ptr<Device> device, std::shared_ptr<PreparedModel> preparedModel);

	// Map inputs and outputs from ExecutionBuilder to StepExecutor,
	// in the case where we have a single-"step" execution (i.e., the executor
	// is executing the entire model from the ExecutionBuilder).
	void mapInputsAndOutputsTrivially();

	// Update output shapes returned from ExecutionCallback to ExecutionBuilder.
	bool updateOutputShapes(const std::vector<hal::OutputShape>& from,
	std::vector<hal::OutputShape>* to);

	// Map inputs and outputs from ExecutionBuilder to StepExecutor,
	// one at a time. Note that these are input/output indexes, not
	// operand indexes.
	void mapInput(uint32_t builderIndex, uint32_t executorIndex) {
	mapInputOrOutput(mExecutionBuilder->mInputs[builderIndex], &mInputs[executorIndex]);
	}
	void mapOutput(uint32_t builderIndex, uint32_t executorIndex) {
	mapInputOrOutput(mExecutionBuilder->mOutputs[builderIndex], &mOutputs[executorIndex]);
	}
	void mapOutputToInput(uint32_t builderIndex, uint32_t executorIndex) {
	mapInputOrOutput(mExecutionBuilder->mOutputs[builderIndex], &mInputs[executorIndex]);
	}

	// The input or output is assumed to have the size of the
	// corresponding operand.
	int setInputFromTemporaryMemory(uint32_t inputIndex, const Memory* memory, uint32_t offset) {
	return setInputOrOutputFromTemporaryMemory(mModel->getInputOperand(inputIndex), memory,
	offset, &mInputs.at(inputIndex));
	}
	int setOutputFromTemporaryMemory(uint32_t outputIndex, const Memory* memory, uint32_t offset) {
	return setInputOrOutputFromTemporaryMemory(mModel->getOutputOperand(outputIndex), memory,
	offset, &mOutputs.at(outputIndex));
	}

	// Executes using the (driver, preparedModel) specified at construction time.
	int startCompute(sp<ExecutionCallback>* synchronizationCallback,
	const std::shared_ptr<ExecutionBurstController>& burstController = nullptr);

	// Re-compiles and executes using the CPU, regardless of the (driver,
	// preparedModel) specified at construction time.
	int startComputeOnCpuFallback(sp<ExecutionCallback>* synchronizationCallback);

	bool isCpu() const;

	// ExecutionStep has the index mapping between ExecutionBuilder and StepExecutor.
	void setExecutionStep(const std::shared_ptr<const ExecutionStep>& step) {
	mExecutionStep = step;
	}

	private:
	void mapInputOrOutput(const ModelArgumentInfo& builderInputOrOutput,
	ModelArgumentInfo* executorInputOrOutput);

	int setInputOrOutputFromTemporaryMemory(const hal::Operand& inputOrOutputOperand,
	const Memory* memory, uint32_t offset,
	ModelArgumentInfo* inputOrOutputInfo);

	// describes the full (possibly multiple-"step") execution
	ExecutionBuilder* mExecutionBuilder;

	// describes the single execution step
	std::shared_ptr<const ExecutionStep> mExecutionStep = nullptr;

	// model to be executed on the executor, in both original and
	// compiled forms; and device on which to execute it
	const ModelBuilder* mModel;
	std::shared_ptr<Device> mDevice;
	std::shared_ptr<PreparedModel> mPreparedModel;

	// The information we'll send to the driver about the inputs and outputs.
	// Note that we build this in two steps:
	// 1. As the arguments are specified, set the corresponding mInputs or mOutputs element.
	// If set from a pointer, don't set the location in the RequestArgument but store it
	// instead in mInputBuffers or mOutputBuffers.
	// 2. Once we have all the inputs and outputs, if needed, allocate shared memory for
	// the m*Buffers entries. Copy the input values into the shared memory.
	// We do this to avoid creating a lot of shared memory objects if we have a lot of
	// parameters specified via pointers. We also avoid copying in the case where
	// some of the nodes will interpreted on the CPU anyway.
	std::vector<ModelArgumentInfo> mInputs;
	std::vector<ModelArgumentInfo> mOutputs;
	MemoryTracker mMemories;
	};

	} // namespace nn
	} // namespace android

	#endif // ANDROID_FRAMEWORKS_ML_NN_RUNTIME_EXECUTION_BUILDER_H