runtime/CompilationBuilder.cpp - 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.
  */

 #define LOG_TAG "CompilationBuilder"

 #include "CompilationBuilder.h"

 #include <LegacyUtils.h>
 #include <nnapi/IBurst.h>
 #include <nnapi/SharedMemory.h>
 #include <nnapi/Types.h>

 #include <algorithm>
 #include <limits>
 #include <memory>
 #include <string>
 #include <utility>
 #include <vector>

 #include "BurstBuilder.h"
 #include "ExecutionBuilder.h"
 #include "ExecutionPlan.h"
 #include "Manager.h"
 #include "ModelBuilder.h"
 #include "TypeManager.h"

 namespace android {
 namespace nn {

 CompilationBuilder::CompilationBuilder(const ModelBuilder* model,
                                        const std::vector<std::shared_ptr<Device>>& devices,
                                        bool explicitDeviceList)
     : mModel(model),
       mPartitioning(explicitDeviceList ? DeviceManager::kPartitioningWithoutFallback
                                        : DeviceManager::get()->getPartitioning()),
       mDevices(devices),
       mExplicitDeviceList(explicitDeviceList) {
     VLOG(COMPILATION) << "CompilationBuilder::CompilationBuilder";
 }

 int CompilationBuilder::finish() {
     if (mFinished) {
         LOG(ERROR) << "ANeuralNetworksCompilation_finish called more than once";
         return ANEURALNETWORKS_BAD_STATE;
     }
     // TODO validate the rest

     // Init telemetry info, start measuring compilation time
     mTelemetryInfo = TelemetryInfo{};
     const auto scopedTimeNanoMeasurer = TimeNanoMeasurer(&mTelemetryInfo->compilationTimeNanos);

     const auto deadline = makeDeadline(mTimeoutDuration);

     mFinished = true;
     if (mIsCacheInfoProvided) {
         mPlan.setCaching(&mCacheInfo, mToken);
     }
     if (mPartitioning) {
         int n = mModel->partitionTheWork(mDevices, mPreference, mPriority, deadline, &mPlan,
                                          mMetadata, mFailPartitioning);
         switch (n) {
             case ANEURALNETWORKS_NO_ERROR:
                 return n;
             case ANEURALNETWORKS_UNEXPECTED_NULL:
             case ANEURALNETWORKS_BAD_DATA:
                 // The two error codes above should only be used for errors in the user's
                 // request. In case of a user error, we won't try any fallback.
                 // TODO: Document this in NeuralNetworks.h and in the HAL. Make sure
                 // driver writers know which code they can return.
                 return n;
             default:
                 // The error might be recoverable. Return the error only if falling back
                 // is not allowed.
                 if (!DeviceManager::partitioningAllowsFallback(mPartitioning)) {
                     return n;
                 }
                 if (mModel->hasOEMOperation()) {
                     LOG(ERROR) << "Cannot fall back to CPU because of an OEM operation";
                     return n;
                 }
                 if (mModel->hasExtensionOperation()) {
                     LOG(ERROR) << "Cannot fall back to CPU because of an extension operation";
                     return n;
                 }
                 break;
         }
     }

     // Fallback to CPU
     mTelemetryInfo->fallbackToCpuFromError = true;
     VLOG(COMPILATION) << "CompilationBuilder::finish with CPU fallback";
     mPlan.reset();
     mPlan.becomeSingleStep(DeviceManager::getCpuDevice(), mModel);
     return mPlan.finish(mPreference, mPriority, deadline, mMetadata, ANEURALNETWORKS_NO_ERROR);
 }

 int CompilationBuilder::setPreference(int32_t preference) {
     if (mFinished) {
         LOG(ERROR) << "ANeuralNetworksCompilation_setPreference can't modify after compilation "
                       "finished";
         return ANEURALNETWORKS_BAD_STATE;
     }
     if (preference >= kNumberOfPreferences) {
         LOG(ERROR) << "ANeuralNetworksCompilation_setPreference invalid preference " << preference;
         return ANEURALNETWORKS_BAD_DATA;
     }

     mPreference = preference;
     return ANEURALNETWORKS_NO_ERROR;
 }

 int CompilationBuilder::setCaching(const std::string& cacheDir, const uint8_t* token) {
     if (mFinished) {
         LOG(ERROR)
                 << "ANeuralNetworksCompilation_setCaching can't modify after compilation finished";
         return ANEURALNETWORKS_BAD_STATE;
     }
     std::string path = cacheDir;
     // Make sure the cache dir can concat with the filename.
     if (!path.empty() && path.back() != '/') {
         path.push_back('/');
     }
     mCacheInfo.variant = std::move(path);
     std::copy(token, token + ANEURALNETWORKS_BYTE_SIZE_OF_CACHE_TOKEN, mToken);
     mIsCacheInfoProvided = true;
     return ANEURALNETWORKS_NO_ERROR;
 }

 static GeneralResult<SharedHandle> createCacheHandle(int fd) {
     base::unique_fd duplicatedFd = NN_TRY(dupFd(fd));
     return std::make_shared<const Handle>(std::move(duplicatedFd));
 }

 static GeneralResult<std::vector<SharedHandle>> createCacheHandleVec(const int* fds,
                                                                      uint32_t numFds) {
     std::vector<SharedHandle> handles;
     handles.reserve(numFds);
     for (uint32_t i = 0; i < numFds; i++) {
         handles.push_back(NN_TRY(createCacheHandle(fds[i])));
     }
     return handles;
 }

 int CompilationBuilder::setCachingFromFds(const int* modelCacheFds,
                                           const uint32_t numModelCacheFiles,
                                           const int* dataCacheFds, const uint32_t numDataCacheFiles,
                                           const uint8_t* token) {
     if (mFinished) {
         LOG(ERROR) << "SL_ANeuralNetworksCompilation_setCachingFromFds can't modify after "
                       "compilation finished";
         return ANEURALNETWORKS_BAD_STATE;
     }
     auto modelCache = createCacheHandleVec(modelCacheFds, numModelCacheFiles);
     if (!modelCache.has_value()) {
         LOG(ERROR) << "SL_ANeuralNetworksCompilation_setCachingFromFds can't duplicate model cache "
                       "fds: "
                    << modelCache.error().message;
         return ANEURALNETWORKS_BAD_DATA;
     }
     auto dataCache = createCacheHandleVec(dataCacheFds, numDataCacheFiles);
     if (!dataCache.has_value()) {
         LOG(ERROR) << "SL_ANeuralNetworksCompilation_setCachingFromFds can't duplicate data cache "
                       "fds: "
                    << dataCache.error().message;
         return ANEURALNETWORKS_BAD_DATA;
     }
     mCacheInfo.variant = CacheHandles{
             .modelCache = std::move(modelCache).value(),
             .dataCache = std::move(dataCache).value(),
     };
     std::copy(token, token + ANEURALNETWORKS_BYTE_SIZE_OF_CACHE_TOKEN, mToken);
     mIsCacheInfoProvided = true;
     return ANEURALNETWORKS_NO_ERROR;
 }

 int CompilationBuilder::setPriority(int32_t priority) {
     if (mFinished) {
         LOG(ERROR) << "ANeuralNetworksCompilation_setPriority can't modify after compilation "
                       "finished";
         return ANEURALNETWORKS_BAD_STATE;
     }
     if (priority != ANEURALNETWORKS_PRIORITY_LOW && priority != ANEURALNETWORKS_PRIORITY_MEDIUM &&
         priority != ANEURALNETWORKS_PRIORITY_HIGH) {
         LOG(ERROR) << "ANeuralNetworksCompilation_setPriority invalid priority " << priority;
         return ANEURALNETWORKS_BAD_DATA;
     }

     mPriority = priority;
     return ANEURALNETWORKS_NO_ERROR;
 }

 int CompilationBuilder::setTimeoutDuration(uint64_t duration) {
     if (mFinished) {
         LOG(ERROR) << "ANeuralNetworksCompilation_setTimeout can't modify after compilation "
                       "finished";
         return ANEURALNETWORKS_BAD_STATE;
     }
     if (!mExplicitDeviceList || (mDevices.size() != 1)) {
         LOG(ERROR) << "ANeuralNetworksCompilation_setTimeout called on an "
                       "ANeuralNetworksCompilation that was not created by "
                       "ANeuralNetworksCompilation_createForDevices with numDevices = 1";
         return ANEURALNETWORKS_BAD_DATA;
     }
     if (duration > 0) {
         mTimeoutDuration = duration;
     } else {
         mTimeoutDuration.reset();
     }
     return ANEURALNETWORKS_NO_ERROR;
 }

 int CompilationBuilder::addExtensionAttribute(const char* extensionName,
                                               uint16_t attributeCodeWithinExtension,
                                               const void* data, size_t length) {
     if (mFinished) {
         LOG(ERROR) << "ANeuralNetworksCompilation_addExtensionAttribute can't modify after "
                       "compilation finished";
         return ANEURALNETWORKS_BAD_STATE;
     }
     if (!mExplicitDeviceList || (mDevices.size() != 1)) {
         LOG(ERROR) << "ANeuralNetworksCompilation_addExtensionAttribute called on an "
                       "ANeuralNetworksCompilation that was not created by "
                       "ANeuralNetworksCompilation_createForDevices with numDevices = 1";
         return ANEURALNETWORKS_BAD_DATA;
     }
     int32_t attributeToken = 0;
     if (!TypeManager::get()->getExtensionType(extensionName, attributeCodeWithinExtension,
                                               &attributeToken)) {
         return ANEURALNETWORKS_BAD_DATA;
     }
     if (std::find_if(mMetadata.begin(), mMetadata.end(), [attributeToken](const auto& entry) {
             return attributeToken == entry.token;
         }) != mMetadata.end()) {
         LOG(ERROR) << "ANeuralNetworksCompilation_addExtensionAttribute called more than once for "
                       "the same attribute";
         return ANEURALNETWORKS_BAD_DATA;
     }
     const uint8_t* dataPtr = reinterpret_cast<const uint8_t*>(data);
     mMetadata.push_back({attributeToken, std::vector<uint8_t>(dataPtr, dataPtr + length)});
     return ANEURALNETWORKS_NO_ERROR;
 }

 int CompilationBuilder::forTest_setPartitioning(uint32_t partitioning) {
     if (mFinished) {
         LOG(ERROR) << "CompilationBuilder::forTest_setPartitioning can't modify after compilation "
                       "finished";
         return ANEURALNETWORKS_BAD_STATE;
     }

     mPartitioning = partitioning;
     return ANEURALNETWORKS_NO_ERROR;
 }

 int CompilationBuilder::forTest_failPartitioning(int fail) {
     if (mFinished) {
         LOG(ERROR) << "CompilationBuilder::forTest_failPartitioning can't modify after compilation "
                       "finished";
         return ANEURALNETWORKS_BAD_STATE;
     }

     mFailPartitioning = fail;
     return ANEURALNETWORKS_NO_ERROR;
 }

 int CompilationBuilder::getPreferredMemoryAlignmentForInput(uint32_t index,
                                                             uint32_t* alignment) const {
     CHECK(alignment != nullptr);
     if (!mFinished) {
         LOG(ERROR) << "ANeuralNetworksCompilation_getPreferredMemoryAlignmentForInput passed an "
                       "unfinished compilation";
         return ANEURALNETWORKS_BAD_STATE;
     }
     if (!mPlan.isValid()) {
         LOG(ERROR) << "ANeuralNetworksCompilation_getPreferredMemoryAlignmentForInput passed an "
                       "invalid compilation";
         return ANEURALNETWORKS_BAD_STATE;
     }
     if (index >= mModel->inputCount()) {
         LOG(ERROR) << "ANeuralNetworksCompilation_getPreferredMemoryAlignmentForInput passed an "
                       "invalid input index "
                    << index;
         return ANEURALNETWORKS_BAD_DATA;
     }
     *alignment = mPlan.getMemoryPreference(IOType::INPUT, index).alignment;
     return ANEURALNETWORKS_NO_ERROR;
 }

 int CompilationBuilder::getPreferredMemoryPaddingForInput(uint32_t index, uint32_t* padding) const {
     CHECK(padding != nullptr);
     if (!mFinished) {
         LOG(ERROR) << "ANeuralNetworksCompilation_getPreferredMemoryPaddingForInput passed an "
                       "unfinished compilation";
         return ANEURALNETWORKS_BAD_STATE;
     }
     if (!mPlan.isValid()) {
         LOG(ERROR) << "ANeuralNetworksCompilation_getPreferredMemoryPaddingForInput passed an "
                       "invalid compilation";
         return ANEURALNETWORKS_BAD_STATE;
     }
     if (index >= mModel->inputCount()) {
         LOG(ERROR) << "ANeuralNetworksCompilation_getPreferredMemoryPaddingForInput passed an "
                       "invalid input index "
                    << index;
         return ANEURALNETWORKS_BAD_DATA;
     }
     *padding = mPlan.getMemoryPreference(IOType::INPUT, index).padding;
     return ANEURALNETWORKS_NO_ERROR;
 }

 int CompilationBuilder::getPreferredMemoryAlignmentForOutput(uint32_t index,
                                                              uint32_t* alignment) const {
     CHECK(alignment != nullptr);
     if (!mFinished) {
         LOG(ERROR) << "ANeuralNetworksCompilation_getPreferredMemoryAlignmentForOutput passed an "
                       "unfinished compilation";
         return ANEURALNETWORKS_BAD_STATE;
     }
     if (!mPlan.isValid()) {
         LOG(ERROR) << "ANeuralNetworksCompilation_getPreferredMemoryAlignmentForOutput passed an "
                       "invalid compilation";
         return ANEURALNETWORKS_BAD_STATE;
     }
     if (index >= mModel->outputCount()) {
         LOG(ERROR) << "ANeuralNetworksCompilation_getPreferredMemoryAlignmentForOutput passed an "
                       "invalid output index "
                    << index;
         return ANEURALNETWORKS_BAD_DATA;
     }
     *alignment = mPlan.getMemoryPreference(IOType::OUTPUT, index).alignment;
     return ANEURALNETWORKS_NO_ERROR;
 }

 int CompilationBuilder::getPreferredMemoryPaddingForOutput(uint32_t index,
                                                            uint32_t* padding) const {
     CHECK(padding != nullptr);
     if (!mFinished) {
         LOG(ERROR) << "ANeuralNetworksCompilation_getPreferredMemoryPaddingForOutput passed an "
                       "unfinished compilation";
         return ANEURALNETWORKS_BAD_STATE;
     }
     if (!mPlan.isValid()) {
         LOG(ERROR) << "ANeuralNetworksCompilation_getPreferredMemoryPaddingForOutput passed an "
                       "invalid compilation";
         return ANEURALNETWORKS_BAD_STATE;
     }
     if (index >= mModel->outputCount()) {
         LOG(ERROR) << "ANeuralNetworksCompilation_getPreferredMemoryPaddingForOutput passed an "
                       "invalid output index "
                    << index;
         return ANEURALNETWORKS_BAD_DATA;
     }
     *padding = mPlan.getMemoryPreference(IOType::OUTPUT, index).padding;
     return ANEURALNETWORKS_NO_ERROR;
 }

 int CompilationBuilder::createExecution(ExecutionBuilder** execution) {
     if (!mFinished) {
         LOG(ERROR) << "ANeuralNetworksExecution_create passed an unfinished compilation";
         *execution = nullptr;
         return ANEURALNETWORKS_BAD_STATE;
     }
     if (!mPlan.isValid()) {
         LOG(ERROR) << "ANeuralNetworksExecution_create passed an invalid compilation";
         *execution = nullptr;
         return ANEURALNETWORKS_BAD_STATE;
     }
     if (mPlan.isSimple()) {
         *execution = new (std::nothrow) SimpleExecutionBuilder(this);
     } else {
         *execution = new (std::nothrow) CompoundExecutionBuilder(this);
     }
     return (*execution ? ANEURALNETWORKS_NO_ERROR : ANEURALNETWORKS_OUT_OF_MEMORY);
 }

 int CompilationBuilder::createBurst(BurstBuilder** burst) {
     if (!mFinished) {
         LOG(ERROR) << "ANeuralNetworksBurst_create passed an unfinished compilation";
         *burst = nullptr;
         return ANEURALNETWORKS_BAD_STATE;
     }
     if (!mPlan.isValid()) {
         LOG(ERROR) << "ANeuralNetworksBurst_create passed an invalid compilation";
         *burst = nullptr;
         return ANEURALNETWORKS_BAD_STATE;
     }
     std::vector<SharedBurst> burstControllers = mPlan.makeBursts();
     *burst = new (std::nothrow) BurstBuilder(this, std::move(burstControllers));
     return (*burst ? ANEURALNETWORKS_NO_ERROR : ANEURALNETWORKS_OUT_OF_MEMORY);
 }

 int CompilationBuilder::forEachStepRoleOfInput(uint32_t index,
                                                const StepRoleCallback& callback) const {
     if (!mFinished) {
         LOG(ERROR) << "ANeuralNetworksMemoryDesc_addInputRole passed an unfinished compilation";
         return ANEURALNETWORKS_BAD_STATE;
     }
     if (!mPlan.isValid()) {
         LOG(ERROR) << "ANeuralNetworksMemoryDesc_addInputRole passed an invalid compilation";
         return ANEURALNETWORKS_BAD_STATE;
     }
     if (index >= mModel->inputCount()) {
         LOG(ERROR) << "ANeuralNetworksMemoryDesc_addInputRole passed an invalid input index "
                    << index;
         return ANEURALNETWORKS_BAD_DATA;
     }
     mPlan.forEachStepRoleOfInput(index, callback);
     return ANEURALNETWORKS_NO_ERROR;
 }

 int CompilationBuilder::forEachStepRoleOfOutput(uint32_t index,
                                                 const StepRoleCallback& callback) const {
     if (!mFinished) {
         LOG(ERROR) << "ANeuralNetworksMemoryDesc_addOutputRole passed an unfinished compilation";
         return ANEURALNETWORKS_BAD_STATE;
     }
     if (!mPlan.isValid()) {
         LOG(ERROR) << "ANeuralNetworksMemoryDesc_addOutputRole passed an invalid compilation";
         return ANEURALNETWORKS_BAD_STATE;
     }
     if (index >= mModel->outputCount()) {
         LOG(ERROR) << "ANeuralNetworksMemoryDesc_addOutputRole passed an invalid output index "
                    << index;
         return ANEURALNETWORKS_BAD_DATA;
     }
     mPlan.forEachStepRoleOfOutput(index, callback);
     return ANEURALNETWORKS_NO_ERROR;
 }

 }  // namespace nn
 }  // namespace android
	/*
	* 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.
	*/

	#define LOG_TAG "CompilationBuilder"

	#include "CompilationBuilder.h"

	#include <LegacyUtils.h>
	#include <nnapi/IBurst.h>
	#include <nnapi/SharedMemory.h>
	#include <nnapi/Types.h>

	#include <algorithm>
	#include <limits>
	#include <memory>
	#include <string>
	#include <utility>
	#include <vector>

	#include "BurstBuilder.h"
	#include "ExecutionBuilder.h"
	#include "ExecutionPlan.h"
	#include "Manager.h"
	#include "ModelBuilder.h"
	#include "TypeManager.h"

	namespace android {
	namespace nn {

	CompilationBuilder::CompilationBuilder(const ModelBuilder* model,
	const std::vector<std::shared_ptr<Device>>& devices,
	bool explicitDeviceList)
	: mModel(model),
	mPartitioning(explicitDeviceList ? DeviceManager::kPartitioningWithoutFallback
	: DeviceManager::get()->getPartitioning()),
	mDevices(devices),
	mExplicitDeviceList(explicitDeviceList) {
	VLOG(COMPILATION) << "CompilationBuilder::CompilationBuilder";
	}

	int CompilationBuilder::finish() {
	if (mFinished) {
	LOG(ERROR) << "ANeuralNetworksCompilation_finish called more than once";
	return ANEURALNETWORKS_BAD_STATE;
	}
	// TODO validate the rest

	// Init telemetry info, start measuring compilation time
	mTelemetryInfo = TelemetryInfo{};
	const auto scopedTimeNanoMeasurer = TimeNanoMeasurer(&mTelemetryInfo->compilationTimeNanos);

	const auto deadline = makeDeadline(mTimeoutDuration);

	mFinished = true;
	if (mIsCacheInfoProvided) {
	mPlan.setCaching(&mCacheInfo, mToken);
	}
	if (mPartitioning) {
	int n = mModel->partitionTheWork(mDevices, mPreference, mPriority, deadline, &mPlan,
	mMetadata, mFailPartitioning);
	switch (n) {
	case ANEURALNETWORKS_NO_ERROR:
	return n;
	case ANEURALNETWORKS_UNEXPECTED_NULL:
	case ANEURALNETWORKS_BAD_DATA:
	// The two error codes above should only be used for errors in the user's
	// request. In case of a user error, we won't try any fallback.
	// TODO: Document this in NeuralNetworks.h and in the HAL. Make sure
	// driver writers know which code they can return.
	return n;
	default:
	// The error might be recoverable. Return the error only if falling back
	// is not allowed.
	if (!DeviceManager::partitioningAllowsFallback(mPartitioning)) {
	return n;
	}
	if (mModel->hasOEMOperation()) {
	LOG(ERROR) << "Cannot fall back to CPU because of an OEM operation";
	return n;
	}
	if (mModel->hasExtensionOperation()) {
	LOG(ERROR) << "Cannot fall back to CPU because of an extension operation";
	return n;
	}
	break;
	}
	}

	// Fallback to CPU
	mTelemetryInfo->fallbackToCpuFromError = true;
	VLOG(COMPILATION) << "CompilationBuilder::finish with CPU fallback";
	mPlan.reset();
	mPlan.becomeSingleStep(DeviceManager::getCpuDevice(), mModel);
	return mPlan.finish(mPreference, mPriority, deadline, mMetadata, ANEURALNETWORKS_NO_ERROR);
	}

	int CompilationBuilder::setPreference(int32_t preference) {
	if (mFinished) {
	LOG(ERROR) << "ANeuralNetworksCompilation_setPreference can't modify after compilation "
	"finished";
	return ANEURALNETWORKS_BAD_STATE;
	}
	if (preference >= kNumberOfPreferences) {
	LOG(ERROR) << "ANeuralNetworksCompilation_setPreference invalid preference " << preference;
	return ANEURALNETWORKS_BAD_DATA;
	}

	mPreference = preference;
	return ANEURALNETWORKS_NO_ERROR;
	}

	int CompilationBuilder::setCaching(const std::string& cacheDir, const uint8_t* token) {
	if (mFinished) {
	LOG(ERROR)
	<< "ANeuralNetworksCompilation_setCaching can't modify after compilation finished";
	return ANEURALNETWORKS_BAD_STATE;
	}
	std::string path = cacheDir;
	// Make sure the cache dir can concat with the filename.
	if (!path.empty() && path.back() != '/') {
	path.push_back('/');
	}
	mCacheInfo.variant = std::move(path);
	std::copy(token, token + ANEURALNETWORKS_BYTE_SIZE_OF_CACHE_TOKEN, mToken);
	mIsCacheInfoProvided = true;
	return ANEURALNETWORKS_NO_ERROR;
	}

	static GeneralResult<SharedHandle> createCacheHandle(int fd) {
	base::unique_fd duplicatedFd = NN_TRY(dupFd(fd));
	return std::make_shared<const Handle>(std::move(duplicatedFd));
	}

	static GeneralResult<std::vector<SharedHandle>> createCacheHandleVec(const int* fds,
	uint32_t numFds) {
	std::vector<SharedHandle> handles;
	handles.reserve(numFds);
	for (uint32_t i = 0; i < numFds; i++) {
	handles.push_back(NN_TRY(createCacheHandle(fds[i])));
	}
	return handles;
	}

	int CompilationBuilder::setCachingFromFds(const int* modelCacheFds,
	const uint32_t numModelCacheFiles,
	const int* dataCacheFds, const uint32_t numDataCacheFiles,
	const uint8_t* token) {
	if (mFinished) {
	LOG(ERROR) << "SL_ANeuralNetworksCompilation_setCachingFromFds can't modify after "
	"compilation finished";
	return ANEURALNETWORKS_BAD_STATE;
	}
	auto modelCache = createCacheHandleVec(modelCacheFds, numModelCacheFiles);
	if (!modelCache.has_value()) {
	LOG(ERROR) << "SL_ANeuralNetworksCompilation_setCachingFromFds can't duplicate model cache "
	"fds: "
	<< modelCache.error().message;
	return ANEURALNETWORKS_BAD_DATA;
	}
	auto dataCache = createCacheHandleVec(dataCacheFds, numDataCacheFiles);
	if (!dataCache.has_value()) {
	LOG(ERROR) << "SL_ANeuralNetworksCompilation_setCachingFromFds can't duplicate data cache "
	"fds: "
	<< dataCache.error().message;
	return ANEURALNETWORKS_BAD_DATA;
	}
	mCacheInfo.variant = CacheHandles{
	.modelCache = std::move(modelCache).value(),
	.dataCache = std::move(dataCache).value(),
	};
	std::copy(token, token + ANEURALNETWORKS_BYTE_SIZE_OF_CACHE_TOKEN, mToken);
	mIsCacheInfoProvided = true;
	return ANEURALNETWORKS_NO_ERROR;
	}

	int CompilationBuilder::setPriority(int32_t priority) {
	if (mFinished) {
	LOG(ERROR) << "ANeuralNetworksCompilation_setPriority can't modify after compilation "
	"finished";
	return ANEURALNETWORKS_BAD_STATE;
	}
	if (priority != ANEURALNETWORKS_PRIORITY_LOW && priority != ANEURALNETWORKS_PRIORITY_MEDIUM &&
	priority != ANEURALNETWORKS_PRIORITY_HIGH) {
	LOG(ERROR) << "ANeuralNetworksCompilation_setPriority invalid priority " << priority;
	return ANEURALNETWORKS_BAD_DATA;
	}

	mPriority = priority;
	return ANEURALNETWORKS_NO_ERROR;
	}

	int CompilationBuilder::setTimeoutDuration(uint64_t duration) {
	if (mFinished) {
	LOG(ERROR) << "ANeuralNetworksCompilation_setTimeout can't modify after compilation "
	"finished";
	return ANEURALNETWORKS_BAD_STATE;
	}
	if (!mExplicitDeviceList \|\| (mDevices.size() != 1)) {
	LOG(ERROR) << "ANeuralNetworksCompilation_setTimeout called on an "
	"ANeuralNetworksCompilation that was not created by "
	"ANeuralNetworksCompilation_createForDevices with numDevices = 1";
	return ANEURALNETWORKS_BAD_DATA;
	}
	if (duration > 0) {
	mTimeoutDuration = duration;
	} else {
	mTimeoutDuration.reset();
	}
	return ANEURALNETWORKS_NO_ERROR;
	}

	int CompilationBuilder::addExtensionAttribute(const char* extensionName,
	uint16_t attributeCodeWithinExtension,
	const void* data, size_t length) {
	if (mFinished) {
	LOG(ERROR) << "ANeuralNetworksCompilation_addExtensionAttribute can't modify after "
	"compilation finished";
	return ANEURALNETWORKS_BAD_STATE;
	}
	if (!mExplicitDeviceList \|\| (mDevices.size() != 1)) {
	LOG(ERROR) << "ANeuralNetworksCompilation_addExtensionAttribute called on an "
	"ANeuralNetworksCompilation that was not created by "
	"ANeuralNetworksCompilation_createForDevices with numDevices = 1";
	return ANEURALNETWORKS_BAD_DATA;
	}
	int32_t attributeToken = 0;
	if (!TypeManager::get()->getExtensionType(extensionName, attributeCodeWithinExtension,
	&attributeToken)) {
	return ANEURALNETWORKS_BAD_DATA;
	}
	if (std::find_if(mMetadata.begin(), mMetadata.end(), [attributeToken](const auto& entry) {
	return attributeToken == entry.token;
	}) != mMetadata.end()) {
	LOG(ERROR) << "ANeuralNetworksCompilation_addExtensionAttribute called more than once for "
	"the same attribute";
	return ANEURALNETWORKS_BAD_DATA;
	}
	const uint8_t* dataPtr = reinterpret_cast<const uint8_t*>(data);
	mMetadata.push_back({attributeToken, std::vector<uint8_t>(dataPtr, dataPtr + length)});
	return ANEURALNETWORKS_NO_ERROR;
	}

	int CompilationBuilder::forTest_setPartitioning(uint32_t partitioning) {
	if (mFinished) {
	LOG(ERROR) << "CompilationBuilder::forTest_setPartitioning can't modify after compilation "
	"finished";
	return ANEURALNETWORKS_BAD_STATE;
	}

	mPartitioning = partitioning;
	return ANEURALNETWORKS_NO_ERROR;
	}

	int CompilationBuilder::forTest_failPartitioning(int fail) {
	if (mFinished) {
	LOG(ERROR) << "CompilationBuilder::forTest_failPartitioning can't modify after compilation "
	"finished";
	return ANEURALNETWORKS_BAD_STATE;
	}

	mFailPartitioning = fail;
	return ANEURALNETWORKS_NO_ERROR;
	}

	int CompilationBuilder::getPreferredMemoryAlignmentForInput(uint32_t index,
	uint32_t* alignment) const {
	CHECK(alignment != nullptr);
	if (!mFinished) {
	LOG(ERROR) << "ANeuralNetworksCompilation_getPreferredMemoryAlignmentForInput passed an "
	"unfinished compilation";
	return ANEURALNETWORKS_BAD_STATE;
	}
	if (!mPlan.isValid()) {
	LOG(ERROR) << "ANeuralNetworksCompilation_getPreferredMemoryAlignmentForInput passed an "
	"invalid compilation";
	return ANEURALNETWORKS_BAD_STATE;
	}
	if (index >= mModel->inputCount()) {
	LOG(ERROR) << "ANeuralNetworksCompilation_getPreferredMemoryAlignmentForInput passed an "
	"invalid input index "
	<< index;
	return ANEURALNETWORKS_BAD_DATA;
	}
	*alignment = mPlan.getMemoryPreference(IOType::INPUT, index).alignment;
	return ANEURALNETWORKS_NO_ERROR;
	}

	int CompilationBuilder::getPreferredMemoryPaddingForInput(uint32_t index, uint32_t* padding) const {
	CHECK(padding != nullptr);
	if (!mFinished) {
	LOG(ERROR) << "ANeuralNetworksCompilation_getPreferredMemoryPaddingForInput passed an "
	"unfinished compilation";
	return ANEURALNETWORKS_BAD_STATE;
	}
	if (!mPlan.isValid()) {
	LOG(ERROR) << "ANeuralNetworksCompilation_getPreferredMemoryPaddingForInput passed an "
	"invalid compilation";
	return ANEURALNETWORKS_BAD_STATE;
	}
	if (index >= mModel->inputCount()) {
	LOG(ERROR) << "ANeuralNetworksCompilation_getPreferredMemoryPaddingForInput passed an "
	"invalid input index "
	<< index;
	return ANEURALNETWORKS_BAD_DATA;
	}
	*padding = mPlan.getMemoryPreference(IOType::INPUT, index).padding;
	return ANEURALNETWORKS_NO_ERROR;
	}

	int CompilationBuilder::getPreferredMemoryAlignmentForOutput(uint32_t index,
	uint32_t* alignment) const {
	CHECK(alignment != nullptr);
	if (!mFinished) {
	LOG(ERROR) << "ANeuralNetworksCompilation_getPreferredMemoryAlignmentForOutput passed an "
	"unfinished compilation";
	return ANEURALNETWORKS_BAD_STATE;
	}
	if (!mPlan.isValid()) {
	LOG(ERROR) << "ANeuralNetworksCompilation_getPreferredMemoryAlignmentForOutput passed an "
	"invalid compilation";
	return ANEURALNETWORKS_BAD_STATE;
	}
	if (index >= mModel->outputCount()) {
	LOG(ERROR) << "ANeuralNetworksCompilation_getPreferredMemoryAlignmentForOutput passed an "
	"invalid output index "
	<< index;
	return ANEURALNETWORKS_BAD_DATA;
	}
	*alignment = mPlan.getMemoryPreference(IOType::OUTPUT, index).alignment;
	return ANEURALNETWORKS_NO_ERROR;
	}

	int CompilationBuilder::getPreferredMemoryPaddingForOutput(uint32_t index,
	uint32_t* padding) const {
	CHECK(padding != nullptr);
	if (!mFinished) {
	LOG(ERROR) << "ANeuralNetworksCompilation_getPreferredMemoryPaddingForOutput passed an "
	"unfinished compilation";
	return ANEURALNETWORKS_BAD_STATE;
	}
	if (!mPlan.isValid()) {
	LOG(ERROR) << "ANeuralNetworksCompilation_getPreferredMemoryPaddingForOutput passed an "
	"invalid compilation";
	return ANEURALNETWORKS_BAD_STATE;
	}
	if (index >= mModel->outputCount()) {
	LOG(ERROR) << "ANeuralNetworksCompilation_getPreferredMemoryPaddingForOutput passed an "
	"invalid output index "
	<< index;
	return ANEURALNETWORKS_BAD_DATA;
	}
	*padding = mPlan.getMemoryPreference(IOType::OUTPUT, index).padding;
	return ANEURALNETWORKS_NO_ERROR;
	}

	int CompilationBuilder::createExecution(ExecutionBuilder** execution) {
	if (!mFinished) {
	LOG(ERROR) << "ANeuralNetworksExecution_create passed an unfinished compilation";
	*execution = nullptr;
	return ANEURALNETWORKS_BAD_STATE;
	}
	if (!mPlan.isValid()) {
	LOG(ERROR) << "ANeuralNetworksExecution_create passed an invalid compilation";
	*execution = nullptr;
	return ANEURALNETWORKS_BAD_STATE;
	}
	if (mPlan.isSimple()) {
	*execution = new (std::nothrow) SimpleExecutionBuilder(this);
	} else {
	*execution = new (std::nothrow) CompoundExecutionBuilder(this);
	}
	return (*execution ? ANEURALNETWORKS_NO_ERROR : ANEURALNETWORKS_OUT_OF_MEMORY);
	}

	int CompilationBuilder::createBurst(BurstBuilder** burst) {
	if (!mFinished) {
	LOG(ERROR) << "ANeuralNetworksBurst_create passed an unfinished compilation";
	*burst = nullptr;
	return ANEURALNETWORKS_BAD_STATE;
	}
	if (!mPlan.isValid()) {
	LOG(ERROR) << "ANeuralNetworksBurst_create passed an invalid compilation";
	*burst = nullptr;
	return ANEURALNETWORKS_BAD_STATE;
	}
	std::vector<SharedBurst> burstControllers = mPlan.makeBursts();
	*burst = new (std::nothrow) BurstBuilder(this, std::move(burstControllers));
	return (*burst ? ANEURALNETWORKS_NO_ERROR : ANEURALNETWORKS_OUT_OF_MEMORY);
	}

	int CompilationBuilder::forEachStepRoleOfInput(uint32_t index,
	const StepRoleCallback& callback) const {
	if (!mFinished) {
	LOG(ERROR) << "ANeuralNetworksMemoryDesc_addInputRole passed an unfinished compilation";
	return ANEURALNETWORKS_BAD_STATE;
	}
	if (!mPlan.isValid()) {
	LOG(ERROR) << "ANeuralNetworksMemoryDesc_addInputRole passed an invalid compilation";
	return ANEURALNETWORKS_BAD_STATE;
	}
	if (index >= mModel->inputCount()) {
	LOG(ERROR) << "ANeuralNetworksMemoryDesc_addInputRole passed an invalid input index "
	<< index;
	return ANEURALNETWORKS_BAD_DATA;
	}
	mPlan.forEachStepRoleOfInput(index, callback);
	return ANEURALNETWORKS_NO_ERROR;
	}

	int CompilationBuilder::forEachStepRoleOfOutput(uint32_t index,
	const StepRoleCallback& callback) const {
	if (!mFinished) {
	LOG(ERROR) << "ANeuralNetworksMemoryDesc_addOutputRole passed an unfinished compilation";
	return ANEURALNETWORKS_BAD_STATE;
	}
	if (!mPlan.isValid()) {
	LOG(ERROR) << "ANeuralNetworksMemoryDesc_addOutputRole passed an invalid compilation";
	return ANEURALNETWORKS_BAD_STATE;
	}
	if (index >= mModel->outputCount()) {
	LOG(ERROR) << "ANeuralNetworksMemoryDesc_addOutputRole passed an invalid output index "
	<< index;
	return ANEURALNETWORKS_BAD_DATA;
	}
	mPlan.forEachStepRoleOfOutput(index, callback);
	return ANEURALNETWORKS_NO_ERROR;
	}

	} // namespace nn
	} // namespace android