444 lines
18 KiB
C++
444 lines
18 KiB
C++
/*
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* Copyright (C) 2017 The Android Open Source Project
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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#define LOG_TAG "CompilationBuilder"
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#include "CompilationBuilder.h"
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#include <LegacyUtils.h>
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#include <nnapi/IBurst.h>
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#include <nnapi/SharedMemory.h>
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#include <nnapi/Types.h>
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#include <algorithm>
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#include <limits>
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#include <memory>
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#include <string>
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#include <utility>
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#include <vector>
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#include "BurstBuilder.h"
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#include "ExecutionBuilder.h"
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#include "ExecutionPlan.h"
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#include "Manager.h"
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#include "ModelBuilder.h"
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#include "TypeManager.h"
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namespace android {
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namespace nn {
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CompilationBuilder::CompilationBuilder(const ModelBuilder* model,
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const std::vector<std::shared_ptr<Device>>& devices,
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bool explicitDeviceList)
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: mModel(model),
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mPartitioning(explicitDeviceList ? DeviceManager::kPartitioningWithoutFallback
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: DeviceManager::get()->getPartitioning()),
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mDevices(devices),
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mExplicitDeviceList(explicitDeviceList) {
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VLOG(COMPILATION) << "CompilationBuilder::CompilationBuilder";
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}
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int CompilationBuilder::finish() {
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if (mFinished) {
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LOG(ERROR) << "ANeuralNetworksCompilation_finish called more than once";
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return ANEURALNETWORKS_BAD_STATE;
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}
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// TODO validate the rest
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// Init telemetry info, start measuring compilation time
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mTelemetryInfo = TelemetryInfo{};
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const auto scopedTimeNanoMeasurer = TimeNanoMeasurer(&mTelemetryInfo->compilationTimeNanos);
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const auto deadline = makeDeadline(mTimeoutDuration);
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mFinished = true;
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if (mIsCacheInfoProvided) {
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mPlan.setCaching(&mCacheInfo, mToken);
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}
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if (mPartitioning) {
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int n = mModel->partitionTheWork(mDevices, mPreference, mPriority, deadline, &mPlan,
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mMetadata, mFailPartitioning);
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switch (n) {
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case ANEURALNETWORKS_NO_ERROR:
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return n;
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case ANEURALNETWORKS_UNEXPECTED_NULL:
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case ANEURALNETWORKS_BAD_DATA:
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// The two error codes above should only be used for errors in the user's
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// request. In case of a user error, we won't try any fallback.
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// TODO: Document this in NeuralNetworks.h and in the HAL. Make sure
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// driver writers know which code they can return.
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return n;
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default:
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// The error might be recoverable. Return the error only if falling back
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// is not allowed.
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if (!DeviceManager::partitioningAllowsFallback(mPartitioning)) {
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return n;
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}
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if (mModel->hasOEMOperation()) {
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LOG(ERROR) << "Cannot fall back to CPU because of an OEM operation";
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return n;
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}
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if (mModel->hasExtensionOperation()) {
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LOG(ERROR) << "Cannot fall back to CPU because of an extension operation";
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return n;
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}
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break;
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}
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}
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// Fallback to CPU
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mTelemetryInfo->fallbackToCpuFromError = true;
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VLOG(COMPILATION) << "CompilationBuilder::finish with CPU fallback";
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mPlan.reset();
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mPlan.becomeSingleStep(DeviceManager::getCpuDevice(), mModel);
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return mPlan.finish(mPreference, mPriority, deadline, mMetadata, ANEURALNETWORKS_NO_ERROR);
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}
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int CompilationBuilder::setPreference(int32_t preference) {
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if (mFinished) {
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LOG(ERROR) << "ANeuralNetworksCompilation_setPreference can't modify after compilation "
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"finished";
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return ANEURALNETWORKS_BAD_STATE;
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}
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if (preference >= kNumberOfPreferences) {
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LOG(ERROR) << "ANeuralNetworksCompilation_setPreference invalid preference " << preference;
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return ANEURALNETWORKS_BAD_DATA;
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}
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mPreference = preference;
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return ANEURALNETWORKS_NO_ERROR;
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}
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int CompilationBuilder::setCaching(const std::string& cacheDir, const uint8_t* token) {
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if (mFinished) {
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LOG(ERROR)
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<< "ANeuralNetworksCompilation_setCaching can't modify after compilation finished";
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return ANEURALNETWORKS_BAD_STATE;
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}
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std::string path = cacheDir;
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// Make sure the cache dir can concat with the filename.
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if (!path.empty() && path.back() != '/') {
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path.push_back('/');
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}
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mCacheInfo.variant = std::move(path);
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std::copy(token, token + ANEURALNETWORKS_BYTE_SIZE_OF_CACHE_TOKEN, mToken);
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mIsCacheInfoProvided = true;
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return ANEURALNETWORKS_NO_ERROR;
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}
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static GeneralResult<SharedHandle> createCacheHandle(int fd) {
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base::unique_fd duplicatedFd = NN_TRY(dupFd(fd));
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return std::make_shared<const Handle>(std::move(duplicatedFd));
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}
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static GeneralResult<std::vector<SharedHandle>> createCacheHandleVec(const int* fds,
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uint32_t numFds) {
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std::vector<SharedHandle> handles;
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handles.reserve(numFds);
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for (uint32_t i = 0; i < numFds; i++) {
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handles.push_back(NN_TRY(createCacheHandle(fds[i])));
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}
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return handles;
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}
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int CompilationBuilder::setCachingFromFds(const int* modelCacheFds,
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const uint32_t numModelCacheFiles,
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const int* dataCacheFds, const uint32_t numDataCacheFiles,
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const uint8_t* token) {
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if (mFinished) {
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LOG(ERROR) << "SL_ANeuralNetworksCompilation_setCachingFromFds can't modify after "
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"compilation finished";
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return ANEURALNETWORKS_BAD_STATE;
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}
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auto modelCache = createCacheHandleVec(modelCacheFds, numModelCacheFiles);
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if (!modelCache.has_value()) {
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LOG(ERROR) << "SL_ANeuralNetworksCompilation_setCachingFromFds can't duplicate model cache "
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"fds: "
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<< modelCache.error().message;
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return ANEURALNETWORKS_BAD_DATA;
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}
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auto dataCache = createCacheHandleVec(dataCacheFds, numDataCacheFiles);
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if (!dataCache.has_value()) {
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LOG(ERROR) << "SL_ANeuralNetworksCompilation_setCachingFromFds can't duplicate data cache "
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"fds: "
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<< dataCache.error().message;
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return ANEURALNETWORKS_BAD_DATA;
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}
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mCacheInfo.variant = CacheHandles{
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.modelCache = std::move(modelCache).value(),
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.dataCache = std::move(dataCache).value(),
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};
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std::copy(token, token + ANEURALNETWORKS_BYTE_SIZE_OF_CACHE_TOKEN, mToken);
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mIsCacheInfoProvided = true;
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return ANEURALNETWORKS_NO_ERROR;
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}
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int CompilationBuilder::setPriority(int32_t priority) {
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if (mFinished) {
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LOG(ERROR) << "ANeuralNetworksCompilation_setPriority can't modify after compilation "
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"finished";
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return ANEURALNETWORKS_BAD_STATE;
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}
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if (priority != ANEURALNETWORKS_PRIORITY_LOW && priority != ANEURALNETWORKS_PRIORITY_MEDIUM &&
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priority != ANEURALNETWORKS_PRIORITY_HIGH) {
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LOG(ERROR) << "ANeuralNetworksCompilation_setPriority invalid priority " << priority;
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return ANEURALNETWORKS_BAD_DATA;
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}
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mPriority = priority;
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return ANEURALNETWORKS_NO_ERROR;
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}
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int CompilationBuilder::setTimeoutDuration(uint64_t duration) {
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if (mFinished) {
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LOG(ERROR) << "ANeuralNetworksCompilation_setTimeout can't modify after compilation "
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"finished";
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return ANEURALNETWORKS_BAD_STATE;
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}
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if (!mExplicitDeviceList || (mDevices.size() != 1)) {
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LOG(ERROR) << "ANeuralNetworksCompilation_setTimeout called on an "
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"ANeuralNetworksCompilation that was not created by "
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"ANeuralNetworksCompilation_createForDevices with numDevices = 1";
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return ANEURALNETWORKS_BAD_DATA;
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}
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if (duration > 0) {
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mTimeoutDuration = duration;
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} else {
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mTimeoutDuration.reset();
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}
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return ANEURALNETWORKS_NO_ERROR;
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}
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int CompilationBuilder::addExtensionAttribute(const char* extensionName,
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uint16_t attributeCodeWithinExtension,
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const void* data, size_t length) {
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if (mFinished) {
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LOG(ERROR) << "ANeuralNetworksCompilation_addExtensionAttribute can't modify after "
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"compilation finished";
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return ANEURALNETWORKS_BAD_STATE;
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}
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if (!mExplicitDeviceList || (mDevices.size() != 1)) {
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LOG(ERROR) << "ANeuralNetworksCompilation_addExtensionAttribute called on an "
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"ANeuralNetworksCompilation that was not created by "
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"ANeuralNetworksCompilation_createForDevices with numDevices = 1";
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return ANEURALNETWORKS_BAD_DATA;
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}
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int32_t attributeToken = 0;
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if (!TypeManager::get()->getExtensionType(extensionName, attributeCodeWithinExtension,
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&attributeToken)) {
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return ANEURALNETWORKS_BAD_DATA;
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}
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if (std::find_if(mMetadata.begin(), mMetadata.end(), [attributeToken](const auto& entry) {
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return attributeToken == entry.token;
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}) != mMetadata.end()) {
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LOG(ERROR) << "ANeuralNetworksCompilation_addExtensionAttribute called more than once for "
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"the same attribute";
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return ANEURALNETWORKS_BAD_DATA;
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}
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const uint8_t* dataPtr = reinterpret_cast<const uint8_t*>(data);
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mMetadata.push_back({attributeToken, std::vector<uint8_t>(dataPtr, dataPtr + length)});
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return ANEURALNETWORKS_NO_ERROR;
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}
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int CompilationBuilder::forTest_setPartitioning(uint32_t partitioning) {
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if (mFinished) {
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LOG(ERROR) << "CompilationBuilder::forTest_setPartitioning can't modify after compilation "
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"finished";
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return ANEURALNETWORKS_BAD_STATE;
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}
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mPartitioning = partitioning;
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return ANEURALNETWORKS_NO_ERROR;
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}
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int CompilationBuilder::forTest_failPartitioning(int fail) {
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if (mFinished) {
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LOG(ERROR) << "CompilationBuilder::forTest_failPartitioning can't modify after compilation "
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"finished";
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return ANEURALNETWORKS_BAD_STATE;
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}
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mFailPartitioning = fail;
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return ANEURALNETWORKS_NO_ERROR;
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}
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int CompilationBuilder::getPreferredMemoryAlignmentForInput(uint32_t index,
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uint32_t* alignment) const {
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CHECK(alignment != nullptr);
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if (!mFinished) {
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LOG(ERROR) << "ANeuralNetworksCompilation_getPreferredMemoryAlignmentForInput passed an "
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"unfinished compilation";
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return ANEURALNETWORKS_BAD_STATE;
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}
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if (!mPlan.isValid()) {
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LOG(ERROR) << "ANeuralNetworksCompilation_getPreferredMemoryAlignmentForInput passed an "
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"invalid compilation";
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return ANEURALNETWORKS_BAD_STATE;
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}
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if (index >= mModel->inputCount()) {
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LOG(ERROR) << "ANeuralNetworksCompilation_getPreferredMemoryAlignmentForInput passed an "
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"invalid input index "
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<< index;
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return ANEURALNETWORKS_BAD_DATA;
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}
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*alignment = mPlan.getMemoryPreference(IOType::INPUT, index).alignment;
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return ANEURALNETWORKS_NO_ERROR;
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}
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int CompilationBuilder::getPreferredMemoryPaddingForInput(uint32_t index, uint32_t* padding) const {
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CHECK(padding != nullptr);
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if (!mFinished) {
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LOG(ERROR) << "ANeuralNetworksCompilation_getPreferredMemoryPaddingForInput passed an "
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"unfinished compilation";
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return ANEURALNETWORKS_BAD_STATE;
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}
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if (!mPlan.isValid()) {
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LOG(ERROR) << "ANeuralNetworksCompilation_getPreferredMemoryPaddingForInput passed an "
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"invalid compilation";
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return ANEURALNETWORKS_BAD_STATE;
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}
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if (index >= mModel->inputCount()) {
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LOG(ERROR) << "ANeuralNetworksCompilation_getPreferredMemoryPaddingForInput passed an "
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"invalid input index "
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<< index;
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return ANEURALNETWORKS_BAD_DATA;
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}
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*padding = mPlan.getMemoryPreference(IOType::INPUT, index).padding;
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return ANEURALNETWORKS_NO_ERROR;
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}
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int CompilationBuilder::getPreferredMemoryAlignmentForOutput(uint32_t index,
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uint32_t* alignment) const {
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CHECK(alignment != nullptr);
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if (!mFinished) {
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LOG(ERROR) << "ANeuralNetworksCompilation_getPreferredMemoryAlignmentForOutput passed an "
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"unfinished compilation";
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return ANEURALNETWORKS_BAD_STATE;
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}
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if (!mPlan.isValid()) {
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LOG(ERROR) << "ANeuralNetworksCompilation_getPreferredMemoryAlignmentForOutput passed an "
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"invalid compilation";
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return ANEURALNETWORKS_BAD_STATE;
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}
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if (index >= mModel->outputCount()) {
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LOG(ERROR) << "ANeuralNetworksCompilation_getPreferredMemoryAlignmentForOutput passed an "
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"invalid output index "
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<< index;
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return ANEURALNETWORKS_BAD_DATA;
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}
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*alignment = mPlan.getMemoryPreference(IOType::OUTPUT, index).alignment;
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return ANEURALNETWORKS_NO_ERROR;
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}
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int CompilationBuilder::getPreferredMemoryPaddingForOutput(uint32_t index,
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uint32_t* padding) const {
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CHECK(padding != nullptr);
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if (!mFinished) {
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LOG(ERROR) << "ANeuralNetworksCompilation_getPreferredMemoryPaddingForOutput passed an "
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"unfinished compilation";
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return ANEURALNETWORKS_BAD_STATE;
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}
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if (!mPlan.isValid()) {
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LOG(ERROR) << "ANeuralNetworksCompilation_getPreferredMemoryPaddingForOutput passed an "
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"invalid compilation";
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return ANEURALNETWORKS_BAD_STATE;
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}
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if (index >= mModel->outputCount()) {
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LOG(ERROR) << "ANeuralNetworksCompilation_getPreferredMemoryPaddingForOutput passed an "
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"invalid output index "
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<< index;
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return ANEURALNETWORKS_BAD_DATA;
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}
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*padding = mPlan.getMemoryPreference(IOType::OUTPUT, index).padding;
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return ANEURALNETWORKS_NO_ERROR;
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}
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int CompilationBuilder::createExecution(ExecutionBuilder** execution) {
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if (!mFinished) {
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LOG(ERROR) << "ANeuralNetworksExecution_create passed an unfinished compilation";
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*execution = nullptr;
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return ANEURALNETWORKS_BAD_STATE;
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}
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if (!mPlan.isValid()) {
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LOG(ERROR) << "ANeuralNetworksExecution_create passed an invalid compilation";
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*execution = nullptr;
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return ANEURALNETWORKS_BAD_STATE;
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}
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if (mPlan.isSimple()) {
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*execution = new (std::nothrow) SimpleExecutionBuilder(this);
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} else {
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*execution = new (std::nothrow) CompoundExecutionBuilder(this);
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}
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return (*execution ? ANEURALNETWORKS_NO_ERROR : ANEURALNETWORKS_OUT_OF_MEMORY);
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}
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int CompilationBuilder::createBurst(BurstBuilder** burst) {
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if (!mFinished) {
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LOG(ERROR) << "ANeuralNetworksBurst_create passed an unfinished compilation";
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*burst = nullptr;
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return ANEURALNETWORKS_BAD_STATE;
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}
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if (!mPlan.isValid()) {
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LOG(ERROR) << "ANeuralNetworksBurst_create passed an invalid compilation";
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*burst = nullptr;
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return ANEURALNETWORKS_BAD_STATE;
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}
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std::vector<SharedBurst> burstControllers = mPlan.makeBursts();
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*burst = new (std::nothrow) BurstBuilder(this, std::move(burstControllers));
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return (*burst ? ANEURALNETWORKS_NO_ERROR : ANEURALNETWORKS_OUT_OF_MEMORY);
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}
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int CompilationBuilder::forEachStepRoleOfInput(uint32_t index,
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const StepRoleCallback& callback) const {
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if (!mFinished) {
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LOG(ERROR) << "ANeuralNetworksMemoryDesc_addInputRole passed an unfinished compilation";
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return ANEURALNETWORKS_BAD_STATE;
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}
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if (!mPlan.isValid()) {
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LOG(ERROR) << "ANeuralNetworksMemoryDesc_addInputRole passed an invalid compilation";
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return ANEURALNETWORKS_BAD_STATE;
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}
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if (index >= mModel->inputCount()) {
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LOG(ERROR) << "ANeuralNetworksMemoryDesc_addInputRole passed an invalid input index "
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<< index;
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return ANEURALNETWORKS_BAD_DATA;
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}
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mPlan.forEachStepRoleOfInput(index, callback);
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return ANEURALNETWORKS_NO_ERROR;
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}
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int CompilationBuilder::forEachStepRoleOfOutput(uint32_t index,
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const StepRoleCallback& callback) const {
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if (!mFinished) {
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LOG(ERROR) << "ANeuralNetworksMemoryDesc_addOutputRole passed an unfinished compilation";
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return ANEURALNETWORKS_BAD_STATE;
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}
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if (!mPlan.isValid()) {
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LOG(ERROR) << "ANeuralNetworksMemoryDesc_addOutputRole passed an invalid compilation";
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return ANEURALNETWORKS_BAD_STATE;
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}
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if (index >= mModel->outputCount()) {
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LOG(ERROR) << "ANeuralNetworksMemoryDesc_addOutputRole passed an invalid output index "
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<< index;
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return ANEURALNETWORKS_BAD_DATA;
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}
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mPlan.forEachStepRoleOfOutput(index, callback);
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return ANEURALNETWORKS_NO_ERROR;
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}
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} // namespace nn
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} // namespace android
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