diff options
Diffstat (limited to 'neuralnetworks/utils/adapter/hidl/src')
| -rw-r--r-- | neuralnetworks/utils/adapter/hidl/src/Adapter.cpp | 45 | ||||
| -rw-r--r-- | neuralnetworks/utils/adapter/hidl/src/Buffer.cpp | 83 | ||||
| -rw-r--r-- | neuralnetworks/utils/adapter/hidl/src/Burst.cpp | 259 | ||||
| -rw-r--r-- | neuralnetworks/utils/adapter/hidl/src/Device.cpp | 546 | ||||
| -rw-r--r-- | neuralnetworks/utils/adapter/hidl/src/PreparedModel.cpp | 433 |
5 files changed, 1366 insertions, 0 deletions
diff --git a/neuralnetworks/utils/adapter/hidl/src/Adapter.cpp b/neuralnetworks/utils/adapter/hidl/src/Adapter.cpp new file mode 100644 index 0000000000..782e815a83 --- /dev/null +++ b/neuralnetworks/utils/adapter/hidl/src/Adapter.cpp @@ -0,0 +1,45 @@ +/* + * Copyright (C) 2020 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. + */ + +#include "Adapter.h" + +#include "Device.h" + +#include <android/hardware/neuralnetworks/1.3/IDevice.h> +#include <nnapi/IDevice.h> +#include <nnapi/Types.h> + +#include <functional> +#include <memory> +#include <thread> + +// See hardware/interfaces/neuralnetworks/utils/README.md for more information on HIDL interface +// lifetimes across processes and for protecting asynchronous calls across HIDL. + +namespace android::hardware::neuralnetworks::adapter { + +sp<V1_3::IDevice> adapt(nn::SharedDevice device, Executor executor) { + return sp<Device>::make(std::move(device), std::move(executor)); +} + +sp<V1_3::IDevice> adapt(nn::SharedDevice device) { + Executor defaultExecutor = [](Task task, nn::OptionalTimePoint /*deadline*/) { + std::thread(std::move(task)).detach(); + }; + return adapt(std::move(device), std::move(defaultExecutor)); +} + +} // namespace android::hardware::neuralnetworks::adapter diff --git a/neuralnetworks/utils/adapter/hidl/src/Buffer.cpp b/neuralnetworks/utils/adapter/hidl/src/Buffer.cpp new file mode 100644 index 0000000000..3a04bf6b79 --- /dev/null +++ b/neuralnetworks/utils/adapter/hidl/src/Buffer.cpp @@ -0,0 +1,83 @@ +/* + * Copyright (C) 2020 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. + */ + +#include "Buffer.h" + +#include <android-base/logging.h> +#include <android/hardware/neuralnetworks/1.3/IBuffer.h> +#include <android/hardware/neuralnetworks/1.3/types.h> +#include <nnapi/IBuffer.h> +#include <nnapi/TypeUtils.h> +#include <nnapi/Types.h> +#include <nnapi/hal/1.3/Utils.h> +#include <memory> + +// See hardware/interfaces/neuralnetworks/utils/README.md for more information on HIDL interface +// lifetimes across processes and for protecting asynchronous calls across HIDL. + +namespace android::hardware::neuralnetworks::adapter { +namespace { + +template <typename Type> +auto convertInput(const Type& object) -> decltype(nn::convert(std::declval<Type>())) { + auto result = nn::convert(object); + if (!result.has_value()) { + result.error().code = nn::ErrorStatus::INVALID_ARGUMENT; + } + return result; +} + +nn::GeneralResult<void> copyTo(const nn::SharedBuffer& buffer, const hidl_memory& dst) { + const auto memory = NN_TRY(convertInput(dst)); + NN_TRY(buffer->copyTo(memory)); + return {}; +} + +nn::GeneralResult<void> copyFrom(const nn::SharedBuffer& buffer, const hidl_memory& src, + const hidl_vec<uint32_t>& dimensions) { + const auto memory = NN_TRY(convertInput(src)); + NN_TRY(buffer->copyFrom(memory, dimensions)); + return {}; +} + +} // namespace + +Buffer::Buffer(nn::SharedBuffer buffer) : kBuffer(std::move(buffer)) { + CHECK(kBuffer != nullptr); +} + +Return<V1_3::ErrorStatus> Buffer::copyTo(const hidl_memory& dst) { + auto result = adapter::copyTo(kBuffer, dst); + if (!result.has_value()) { + const auto [message, code] = std::move(result).error(); + LOG(ERROR) << "adapter::Buffer::copyTo failed with " << code << ": " << message; + return V1_3::utils::convert(code).value(); + } + return V1_3::ErrorStatus::NONE; +} + +Return<V1_3::ErrorStatus> Buffer::copyFrom(const hidl_memory& src, + const hidl_vec<uint32_t>& dimensions) { + auto result = adapter::copyFrom(kBuffer, src, dimensions); + if (!result.has_value()) { + const auto [message, code] = std::move(result).error(); + LOG(ERROR) << "adapter::Buffer::copyFrom failed with " << code << ": " << message; + return V1_3::utils::convert(code).value(); + } + return V1_3::ErrorStatus::NONE; +} + +} // namespace android::hardware::neuralnetworks::adapter diff --git a/neuralnetworks/utils/adapter/hidl/src/Burst.cpp b/neuralnetworks/utils/adapter/hidl/src/Burst.cpp new file mode 100644 index 0000000000..8b2e1dd465 --- /dev/null +++ b/neuralnetworks/utils/adapter/hidl/src/Burst.cpp @@ -0,0 +1,259 @@ +/* + * 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. + */ + +#include "Burst.h" + +#include <android-base/logging.h> +#include <nnapi/IBurst.h> +#include <nnapi/Result.h> +#include <nnapi/TypeUtils.h> +#include <nnapi/Types.h> +#include <nnapi/Validation.h> +#include <nnapi/hal/1.0/Conversions.h> +#include <nnapi/hal/1.0/HandleError.h> +#include <nnapi/hal/1.0/ProtectCallback.h> +#include <nnapi/hal/1.2/BurstUtils.h> +#include <nnapi/hal/1.2/Conversions.h> +#include <nnapi/hal/TransferValue.h> + +#include <algorithm> +#include <cstring> +#include <limits> +#include <map> +#include <memory> +#include <tuple> +#include <utility> +#include <vector> + +#include "Tracing.h" + +namespace android::hardware::neuralnetworks::adapter { +namespace { + +constexpr V1_2::Timing kTiming = {std::numeric_limits<uint64_t>::max(), + std::numeric_limits<uint64_t>::max()}; + +nn::GeneralResult<std::vector<nn::SharedMemory>> getMemoriesCallback( + V1_0::ErrorStatus status, const hidl_vec<hidl_memory>& memories) { + HANDLE_STATUS_HIDL(status) << "getting burst memories failed with " << toString(status); + std::vector<nn::SharedMemory> canonicalMemories; + canonicalMemories.reserve(memories.size()); + for (const auto& memory : memories) { + canonicalMemories.push_back(NN_TRY(nn::convert(memory))); + } + return canonicalMemories; +} + +} // anonymous namespace + +Burst::MemoryCache::MemoryCache(nn::SharedBurst burstExecutor, + sp<V1_2::IBurstCallback> burstCallback) + : kBurstExecutor(std::move(burstExecutor)), kBurstCallback(std::move(burstCallback)) { + CHECK(kBurstExecutor != nullptr); + CHECK(kBurstCallback != nullptr); +} + +nn::GeneralResult<std::vector<std::pair<nn::SharedMemory, nn::IBurst::OptionalCacheHold>>> +Burst::MemoryCache::getCacheEntries(const std::vector<int32_t>& slots) { + std::lock_guard guard(mMutex); + NN_TRY(ensureCacheEntriesArePresentLocked(slots)); + + std::vector<std::pair<nn::SharedMemory, nn::IBurst::OptionalCacheHold>> results; + results.reserve(slots.size()); + for (int32_t slot : slots) { + results.push_back(NN_TRY(getCacheEntryLocked(slot))); + } + + return results; +} + +nn::GeneralResult<void> Burst::MemoryCache::ensureCacheEntriesArePresentLocked( + const std::vector<int32_t>& slots) { + const auto slotIsKnown = [this](int32_t slot) + REQUIRES(mMutex) { return mCache.count(slot) > 0; }; + + // find unique unknown slots + std::vector<int32_t> unknownSlots = slots; + std::sort(unknownSlots.begin(), unknownSlots.end()); + auto unknownSlotsEnd = std::unique(unknownSlots.begin(), unknownSlots.end()); + unknownSlotsEnd = std::remove_if(unknownSlots.begin(), unknownSlotsEnd, slotIsKnown); + unknownSlots.erase(unknownSlotsEnd, unknownSlots.end()); + + // quick-exit if all slots are known + if (unknownSlots.empty()) { + return {}; + } + + auto cb = neuralnetworks::utils::CallbackValue(getMemoriesCallback); + + const auto ret = kBurstCallback->getMemories(unknownSlots, cb); + HANDLE_TRANSPORT_FAILURE(ret); + + auto returnedMemories = NN_TRY(cb.take()); + + if (returnedMemories.size() != unknownSlots.size()) { + return NN_ERROR() << "Burst::MemoryCache::ensureCacheEntriesArePresentLocked: Error " + "retrieving memories -- count mismatch between requested memories (" + << unknownSlots.size() << ") and returned memories (" + << returnedMemories.size() << ")"; + } + + // add memories to unknown slots + for (size_t i = 0; i < unknownSlots.size(); ++i) { + addCacheEntryLocked(unknownSlots[i], std::move(returnedMemories[i])); + } + + return {}; +} + +nn::GeneralResult<std::pair<nn::SharedMemory, nn::IBurst::OptionalCacheHold>> +Burst::MemoryCache::getCacheEntryLocked(int32_t slot) { + if (const auto iter = mCache.find(slot); iter != mCache.end()) { + return iter->second; + } + return NN_ERROR() << "Burst::MemoryCache::getCacheEntryLocked failed because slot " << slot + << " is not present in the cache"; +} + +void Burst::MemoryCache::addCacheEntryLocked(int32_t slot, nn::SharedMemory memory) { + auto hold = kBurstExecutor->cacheMemory(memory); + mCache.emplace(slot, std::make_pair(std::move(memory), std::move(hold))); +} + +void Burst::MemoryCache::removeCacheEntry(int32_t slot) { + std::lock_guard guard(mMutex); + mCache.erase(slot); +} + +// Burst methods + +nn::GeneralResult<sp<Burst>> Burst::create( + const sp<V1_2::IBurstCallback>& callback, + const MQDescriptorSync<V1_2::FmqRequestDatum>& requestChannel, + const MQDescriptorSync<V1_2::FmqResultDatum>& resultChannel, nn::SharedBurst burstExecutor, + std::chrono::microseconds pollingTimeWindow) { + // check inputs + if (callback == nullptr || burstExecutor == nullptr) { + return NN_ERROR() << "Burst::create passed a nullptr"; + } + + // create FMQ objects + auto requestChannelReceiver = + NN_TRY(V1_2::utils::RequestChannelReceiver::create(requestChannel, pollingTimeWindow)); + auto resultChannelSender = NN_TRY(V1_2::utils::ResultChannelSender::create(resultChannel)); + + // check FMQ objects + CHECK(requestChannelReceiver != nullptr); + CHECK(resultChannelSender != nullptr); + + // make and return context + return sp<Burst>::make(PrivateConstructorTag{}, callback, std::move(requestChannelReceiver), + std::move(resultChannelSender), std::move(burstExecutor)); +} + +Burst::Burst(PrivateConstructorTag /*tag*/, const sp<V1_2::IBurstCallback>& callback, + std::unique_ptr<V1_2::utils::RequestChannelReceiver> requestChannel, + std::unique_ptr<V1_2::utils::ResultChannelSender> resultChannel, + nn::SharedBurst burstExecutor) + : mCallback(callback), + mRequestChannelReceiver(std::move(requestChannel)), + mResultChannelSender(std::move(resultChannel)), + mBurstExecutor(std::move(burstExecutor)), + mMemoryCache(mBurstExecutor, mCallback) { + // TODO: highly document the threading behavior of this class + mWorker = std::thread([this] { task(); }); +} + +Burst::~Burst() { + // set teardown flag + mTeardown = true; + mRequestChannelReceiver->invalidate(); + + // wait for task thread to end + mWorker.join(); +} + +Return<void> Burst::freeMemory(int32_t slot) { + mMemoryCache.removeCacheEntry(slot); + return Void(); +} + +void Burst::task() { + // loop until the burst object is being destroyed + while (!mTeardown) { + // receive request + auto arguments = mRequestChannelReceiver->getBlocking(); + + // if the request packet was not properly received, return a generic error and skip the + // execution + // + // if the burst is being torn down, skip the execution so the "task" function can end + if (!arguments.has_value()) { + if (!mTeardown) { + mResultChannelSender->send(V1_0::ErrorStatus::GENERAL_FAILURE, {}, kTiming); + } + continue; + } + + // unpack the arguments; types are Request, std::vector<int32_t>, and V1_2::MeasureTiming, + // respectively + const auto [requestWithoutPools, slotsOfPools, measure] = std::move(arguments).value(); + + auto result = execute(requestWithoutPools, slotsOfPools, measure); + + // return result + if (result.has_value()) { + const auto& [outputShapes, timing] = result.value(); + mResultChannelSender->send(V1_0::ErrorStatus::NONE, outputShapes, timing); + } else { + const auto& [message, code, outputShapes] = result.error(); + LOG(ERROR) << "IBurst::execute failed with " << code << ": " << message; + mResultChannelSender->send(V1_2::utils::convert(code).value(), + V1_2::utils::convert(outputShapes).value(), kTiming); + } + } +} + +nn::ExecutionResult<std::pair<hidl_vec<V1_2::OutputShape>, V1_2::Timing>> Burst::execute( + const V1_0::Request& requestWithoutPools, const std::vector<int32_t>& slotsOfPools, + V1_2::MeasureTiming measure) { + NNTRACE_FULL(NNTRACE_LAYER_IPC, NNTRACE_PHASE_EXECUTION, + "Burst getting memory, executing, and returning results"); + + // ensure executor with cache has required memory + const auto cacheEntries = NN_TRY(mMemoryCache.getCacheEntries(slotsOfPools)); + + // convert request, populating its pools + // This code performs an unvalidated convert because the request object without its pools is + // invalid because it is incomplete. Instead, the validation is performed after the memory pools + // have been added to the request. + auto canonicalRequest = NN_TRY(nn::unvalidatedConvert(requestWithoutPools)); + CHECK(canonicalRequest.pools.empty()); + std::transform(cacheEntries.begin(), cacheEntries.end(), + std::back_inserter(canonicalRequest.pools), + [](const auto& cacheEntry) { return cacheEntry.first; }); + NN_TRY(validate(canonicalRequest)); + + nn::MeasureTiming canonicalMeasure = NN_TRY(nn::convert(measure)); + + const auto [outputShapes, timing] = + NN_TRY(mBurstExecutor->execute(canonicalRequest, canonicalMeasure, {}, {})); + + return std::make_pair(NN_TRY(V1_2::utils::convert(outputShapes)), + NN_TRY(V1_2::utils::convert(timing))); +} + +} // namespace android::hardware::neuralnetworks::adapter diff --git a/neuralnetworks/utils/adapter/hidl/src/Device.cpp b/neuralnetworks/utils/adapter/hidl/src/Device.cpp new file mode 100644 index 0000000000..4993a80a93 --- /dev/null +++ b/neuralnetworks/utils/adapter/hidl/src/Device.cpp @@ -0,0 +1,546 @@ +/* + * Copyright (C) 2020 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. + */ + +#include "Device.h" + +#include "Buffer.h" +#include "PreparedModel.h" + +#include <android-base/logging.h> +#include <android/hardware/neuralnetworks/1.0/IPreparedModelCallback.h> +#include <android/hardware/neuralnetworks/1.0/types.h> +#include <android/hardware/neuralnetworks/1.1/types.h> +#include <android/hardware/neuralnetworks/1.2/IPreparedModelCallback.h> +#include <android/hardware/neuralnetworks/1.2/types.h> +#include <android/hardware/neuralnetworks/1.3/IDevice.h> +#include <android/hardware/neuralnetworks/1.3/IPreparedModelCallback.h> +#include <android/hardware/neuralnetworks/1.3/types.h> +#include <nnapi/IBuffer.h> +#include <nnapi/IDevice.h> +#include <nnapi/IPreparedModel.h> +#include <nnapi/Result.h> +#include <nnapi/TypeUtils.h> +#include <nnapi/Types.h> +#include <nnapi/hal/1.0/Conversions.h> +#include <nnapi/hal/1.0/Utils.h> +#include <nnapi/hal/1.1/Conversions.h> +#include <nnapi/hal/1.1/Utils.h> +#include <nnapi/hal/1.2/Conversions.h> +#include <nnapi/hal/1.2/Utils.h> +#include <nnapi/hal/1.3/Conversions.h> +#include <nnapi/hal/1.3/Utils.h> + +#include <memory> + +// See hardware/interfaces/neuralnetworks/utils/README.md for more information on HIDL interface +// lifetimes across processes and for protecting asynchronous calls across HIDL. + +namespace android::hardware::neuralnetworks::adapter { +namespace { + +template <typename Type> +auto convertInput(const Type& object) -> decltype(nn::convert(std::declval<Type>())) { + auto result = nn::convert(object); + if (!result.has_value()) { + result.error().code = nn::ErrorStatus::INVALID_ARGUMENT; + } + return result; +} + +using PrepareModelResult = nn::GeneralResult<nn::SharedPreparedModel>; + +sp<PreparedModel> adaptPreparedModel(nn::SharedPreparedModel preparedModel, Executor executor) { + if (preparedModel == nullptr) { + return nullptr; + } + return sp<PreparedModel>::make(std::move(preparedModel), std::move(executor)); +} + +void notify(V1_0::IPreparedModelCallback* callback, nn::ErrorStatus status, + const sp<PreparedModel>& hidlPreparedModel) { + if (callback != nullptr) { + const auto hidlStatus = V1_0::utils::convert(status).value(); + const auto ret = callback->notify(hidlStatus, hidlPreparedModel); + if (!ret.isOk()) { + LOG(ERROR) << "V1_0::IPreparedModelCallback::notify failed with " << ret.description(); + } + } +} + +void notify(V1_2::IPreparedModelCallback* callback, nn::ErrorStatus status, + const sp<PreparedModel>& hidlPreparedModel) { + if (callback != nullptr) { + const auto hidlStatus = V1_2::utils::convert(status).value(); + const auto ret = callback->notify_1_2(hidlStatus, hidlPreparedModel); + if (!ret.isOk()) { + LOG(ERROR) << "V1_2::IPreparedModelCallback::notify_1_2 failed with " + << ret.description(); + } + } +} + +void notify(V1_3::IPreparedModelCallback* callback, nn::ErrorStatus status, + const sp<PreparedModel>& hidlPreparedModel) { + if (callback != nullptr) { + const auto hidlStatus = V1_3::utils::convert(status).value(); + const auto ret = callback->notify_1_3(hidlStatus, hidlPreparedModel); + if (!ret.isOk()) { + LOG(ERROR) << "V1_3::IPreparedModelCallback::notify_1_3 failed with " + << ret.description(); + } + } +} + +template <typename CallbackType> +void notify(CallbackType* callback, PrepareModelResult result, Executor executor) { + if (!result.has_value()) { + const auto [message, status] = std::move(result).error(); + LOG(ERROR) << message; + notify(callback, status, nullptr); + } else { + auto preparedModel = std::move(result).value(); + auto hidlPreparedModel = adaptPreparedModel(std::move(preparedModel), std::move(executor)); + notify(callback, nn::ErrorStatus::NONE, std::move(hidlPreparedModel)); + } +} + +template <typename ModelType> +nn::GeneralResult<hidl_vec<bool>> getSupportedOperations(const nn::SharedDevice& device, + const ModelType& model) { + const auto nnModel = NN_TRY(convertInput(model)); + return NN_TRY(device->getSupportedOperations(nnModel)); +} + +nn::GeneralResult<void> prepareModel(const nn::SharedDevice& device, const Executor& executor, + const V1_0::Model& model, + const sp<V1_0::IPreparedModelCallback>& callback) { + if (callback.get() == nullptr) { + return NN_ERROR(nn::ErrorStatus::INVALID_ARGUMENT) << "Invalid callback"; + } + + auto nnModel = NN_TRY(convertInput(model)); + + Task task = [device, nnModel = std::move(nnModel), executor, callback] { + auto result = device->prepareModel(nnModel, nn::ExecutionPreference::DEFAULT, + nn::Priority::DEFAULT, {}, {}, {}, {}); + notify(callback.get(), std::move(result), executor); + }; + executor(std::move(task), {}); + + return {}; +} + +nn::GeneralResult<void> prepareModel_1_1(const nn::SharedDevice& device, const Executor& executor, + const V1_1::Model& model, + V1_1::ExecutionPreference preference, + const sp<V1_0::IPreparedModelCallback>& callback) { + if (callback.get() == nullptr) { + return NN_ERROR(nn::ErrorStatus::INVALID_ARGUMENT) << "Invalid callback"; + } + + auto nnModel = NN_TRY(convertInput(model)); + const auto nnPreference = NN_TRY(convertInput(preference)); + + Task task = [device, nnModel = std::move(nnModel), nnPreference, executor, callback] { + auto result = + device->prepareModel(nnModel, nnPreference, nn::Priority::DEFAULT, {}, {}, {}, {}); + notify(callback.get(), std::move(result), executor); + }; + executor(std::move(task), {}); + + return {}; +} + +nn::GeneralResult<void> prepareModel_1_2(const nn::SharedDevice& device, const Executor& executor, + const V1_2::Model& model, + V1_1::ExecutionPreference preference, + const hidl_vec<hidl_handle>& modelCache, + const hidl_vec<hidl_handle>& dataCache, + const CacheToken& token, + const sp<V1_2::IPreparedModelCallback>& callback) { + if (callback.get() == nullptr) { + return NN_ERROR(nn::ErrorStatus::INVALID_ARGUMENT) << "Invalid callback"; + } + + auto nnModel = NN_TRY(convertInput(model)); + const auto nnPreference = NN_TRY(convertInput(preference)); + auto nnModelCache = NN_TRY(convertInput(modelCache)); + auto nnDataCache = NN_TRY(convertInput(dataCache)); + const auto nnToken = nn::CacheToken(token); + + Task task = [device, nnModel = std::move(nnModel), nnPreference, + nnModelCache = std::move(nnModelCache), nnDataCache = std::move(nnDataCache), + nnToken, executor, callback] { + auto result = device->prepareModel(nnModel, nnPreference, nn::Priority::DEFAULT, {}, + nnModelCache, nnDataCache, nnToken); + notify(callback.get(), std::move(result), executor); + }; + executor(std::move(task), {}); + + return {}; +} + +nn::GeneralResult<void> prepareModel_1_3( + const nn::SharedDevice& device, const Executor& executor, const V1_3::Model& model, + V1_1::ExecutionPreference preference, V1_3::Priority priority, + const V1_3::OptionalTimePoint& deadline, const hidl_vec<hidl_handle>& modelCache, + const hidl_vec<hidl_handle>& dataCache, const CacheToken& token, + const sp<V1_3::IPreparedModelCallback>& callback) { + if (callback.get() == nullptr) { + return NN_ERROR(nn::ErrorStatus::INVALID_ARGUMENT) << "Invalid callback"; + } + + auto nnModel = NN_TRY(convertInput(model)); + const auto nnPreference = NN_TRY(convertInput(preference)); + const auto nnPriority = NN_TRY(convertInput(priority)); + const auto nnDeadline = NN_TRY(convertInput(deadline)); + auto nnModelCache = NN_TRY(convertInput(modelCache)); + auto nnDataCache = NN_TRY(convertInput(dataCache)); + const auto nnToken = nn::CacheToken(token); + + Task task = [device, nnModel = std::move(nnModel), nnPreference, nnPriority, nnDeadline, + nnModelCache = std::move(nnModelCache), nnDataCache = std::move(nnDataCache), + nnToken, executor, callback] { + auto result = device->prepareModel(nnModel, nnPreference, nnPriority, nnDeadline, + nnModelCache, nnDataCache, nnToken); + notify(callback.get(), std::move(result), executor); + }; + executor(std::move(task), nnDeadline); + + return {}; +} + +nn::GeneralResult<void> prepareModelFromCache(const nn::SharedDevice& device, + const Executor& executor, + const hidl_vec<hidl_handle>& modelCache, + const hidl_vec<hidl_handle>& dataCache, + const CacheToken& token, + const sp<V1_2::IPreparedModelCallback>& callback) { + if (callback.get() == nullptr) { + return NN_ERROR(nn::ErrorStatus::INVALID_ARGUMENT) << "Invalid callback"; + } + + auto nnModelCache = NN_TRY(convertInput(modelCache)); + auto nnDataCache = NN_TRY(convertInput(dataCache)); + const auto nnToken = nn::CacheToken(token); + + Task task = [device, nnModelCache = std::move(nnModelCache), + nnDataCache = std::move(nnDataCache), nnToken, executor, callback] { + auto result = device->prepareModelFromCache({}, nnModelCache, nnDataCache, nnToken); + notify(callback.get(), std::move(result), executor); + }; + executor(std::move(task), {}); + + return {}; +} + +nn::GeneralResult<void> prepareModelFromCache_1_3( + const nn::SharedDevice& device, const Executor& executor, + const V1_3::OptionalTimePoint& deadline, const hidl_vec<hidl_handle>& modelCache, + const hidl_vec<hidl_handle>& dataCache, const CacheToken& token, + const sp<V1_3::IPreparedModelCallback>& callback) { + if (callback.get() == nullptr) { + return NN_ERROR(nn::ErrorStatus::INVALID_ARGUMENT) << "Invalid callback"; + } + + const auto nnDeadline = NN_TRY(convertInput(deadline)); + auto nnModelCache = NN_TRY(convertInput(modelCache)); + auto nnDataCache = NN_TRY(convertInput(dataCache)); + const auto nnToken = nn::CacheToken(token); + + auto task = [device, nnDeadline, nnModelCache = std::move(nnModelCache), + nnDataCache = std::move(nnDataCache), nnToken, executor, callback] { + auto result = device->prepareModelFromCache(nnDeadline, nnModelCache, nnDataCache, nnToken); + notify(callback.get(), std::move(result), executor); + }; + executor(std::move(task), nnDeadline); + + return {}; +} + +nn::GeneralResult<nn::SharedPreparedModel> downcast(const sp<V1_3::IPreparedModel>& preparedModel) { + if (preparedModel == nullptr) { + return NN_ERROR(nn::ErrorStatus::INVALID_ARGUMENT) << "preparedModel is nullptr"; + } + if (preparedModel->isRemote()) { + return NN_ERROR(nn::ErrorStatus::INVALID_ARGUMENT) << "Cannot convert remote models"; + } + + // This static_cast is safe because adapter::PreparedModel is the only class that implements + // the IPreparedModel interface in the adapter service code. + const auto* casted = static_cast<const PreparedModel*>(preparedModel.get()); + return casted->getUnderlyingPreparedModel(); +} + +nn::GeneralResult<std::vector<nn::SharedPreparedModel>> downcastAll( + const hidl_vec<sp<V1_3::IPreparedModel>>& preparedModels) { + std::vector<nn::SharedPreparedModel> canonical; + canonical.reserve(preparedModels.size()); + for (const auto& preparedModel : preparedModels) { + canonical.push_back(NN_TRY(downcast(preparedModel))); + } + return canonical; +} + +nn::GeneralResult<std::pair<sp<V1_3::IBuffer>, uint32_t>> allocate( + const nn::SharedDevice& device, const V1_3::BufferDesc& desc, + const hidl_vec<sp<V1_3::IPreparedModel>>& preparedModels, + const hidl_vec<V1_3::BufferRole>& inputRoles, + const hidl_vec<V1_3::BufferRole>& outputRoles) { + auto nnDesc = NN_TRY(convertInput(desc)); + auto nnPreparedModels = NN_TRY(downcastAll(preparedModels)); + auto nnInputRoles = NN_TRY(convertInput(inputRoles)); + auto nnOutputRoles = NN_TRY(convertInput(outputRoles)); + + auto buffer = NN_TRY(device->allocate(nnDesc, nnPreparedModels, nnInputRoles, nnOutputRoles)); + + const nn::Request::MemoryDomainToken token = buffer->getToken(); + auto hidlBuffer = sp<Buffer>::make(std::move(buffer)); + return std::make_pair(std::move(hidlBuffer), static_cast<uint32_t>(token)); +} + +} // namespace + +Device::Device(nn::SharedDevice device, Executor executor) + : kDevice(std::move(device)), kExecutor(std::move(executor)) { + CHECK(kDevice != nullptr); + CHECK(kExecutor != nullptr); +} + +Return<void> Device::getCapabilities(getCapabilities_cb cb) { + const auto capabilities = V1_0::utils::convert(kDevice->getCapabilities()).value(); + cb(V1_0::ErrorStatus::NONE, capabilities); + return Void(); +} + +Return<void> Device::getCapabilities_1_1(getCapabilities_1_1_cb cb) { + const auto capabilities = V1_1::utils::convert(kDevice->getCapabilities()).value(); + cb(V1_0::ErrorStatus::NONE, capabilities); + return Void(); +} + +Return<void> Device::getCapabilities_1_2(getCapabilities_1_2_cb cb) { + const auto capabilities = V1_2::utils::convert(kDevice->getCapabilities()).value(); + cb(V1_0::ErrorStatus::NONE, capabilities); + return Void(); +} + +Return<void> Device::getCapabilities_1_3(getCapabilities_1_3_cb cb) { + const auto capabilities = V1_3::utils::convert(kDevice->getCapabilities()).value(); + cb(V1_3::ErrorStatus::NONE, capabilities); + return Void(); +} + +Return<void> Device::getVersionString(getVersionString_cb cb) { + cb(V1_0::ErrorStatus::NONE, kDevice->getVersionString()); + return Void(); +} + +Return<void> Device::getType(getType_cb cb) { + const auto maybeDeviceType = V1_2::utils::convert(kDevice->getType()); + if (!maybeDeviceType.has_value()) { + const auto& [message, code] = maybeDeviceType.error(); + LOG(ERROR) << "adapter::Device::getType failed with " << code << ": " << message; + cb(V1_2::utils::convert(code).value(), {}); + } else { + cb(V1_0::ErrorStatus::NONE, maybeDeviceType.value()); + } + return Void(); +} + +Return<void> Device::getSupportedExtensions(getSupportedExtensions_cb cb) { + const auto maybeSupportedExtensions = V1_2::utils::convert(kDevice->getSupportedExtensions()); + if (!maybeSupportedExtensions.has_value()) { + const auto& [message, code] = maybeSupportedExtensions.error(); + LOG(ERROR) << "adapter::Device::getSupportedExtensions failed with " << code << ": " + << message; + cb(V1_2::utils::convert(code).value(), {}); + } else { + cb(V1_0::ErrorStatus::NONE, maybeSupportedExtensions.value()); + } + return Void(); +} + +Return<void> Device::getSupportedOperations(const V1_0::Model& model, + getSupportedOperations_cb cb) { + const auto result = adapter::getSupportedOperations(kDevice, model); + if (!result.has_value()) { + const auto& [message, code] = result.error(); + LOG(ERROR) << "adapter::Device::getSupportedOperations_1_0 failed with " << code << ": " + << message; + cb(V1_0::utils::convert(code).value(), {}); + } else { + cb(V1_0::ErrorStatus::NONE, result.value()); + } + return Void(); +} + +Return<void> Device::getSupportedOperations_1_1(const V1_1::Model& model, + getSupportedOperations_1_1_cb cb) { + const auto result = adapter::getSupportedOperations(kDevice, model); + if (!result.has_value()) { + const auto& [message, code] = result.error(); + LOG(ERROR) << "adapter::Device::getSupportedOperations_1_1 failed with " << code << ": " + << message; + cb(V1_1::utils::convert(code).value(), {}); + } else { + cb(V1_0::ErrorStatus::NONE, result.value()); + } + return Void(); +} + +Return<void> Device::getSupportedOperations_1_2(const V1_2::Model& model, + getSupportedOperations_1_2_cb cb) { + const auto result = adapter::getSupportedOperations(kDevice, model); + if (!result.has_value()) { + const auto& [message, code] = result.error(); + LOG(ERROR) << "adapter::Device::getSupportedOperations_1_2 failed with " << code << ": " + << message; + cb(V1_2::utils::convert(code).value(), {}); + } else { + cb(V1_0::ErrorStatus::NONE, result.value()); + } + return Void(); +} + +Return<void> Device::getSupportedOperations_1_3(const V1_3::Model& model, + getSupportedOperations_1_3_cb cb) { + const auto result = adapter::getSupportedOperations(kDevice, model); + if (!result.has_value()) { + const auto& [message, code] = result.error(); + LOG(ERROR) << "adapter::Device::getSupportedOperations_1_3 failed with " << code << ": " + << message; + cb(V1_3::utils::convert(code).value(), {}); + } else { + cb(V1_3::ErrorStatus::NONE, result.value()); + } + return Void(); +} + +Return<void> Device::getNumberOfCacheFilesNeeded(getNumberOfCacheFilesNeeded_cb cb) { + const auto [numModelCache, numDataCache] = kDevice->getNumberOfCacheFilesNeeded(); + cb(V1_0::ErrorStatus::NONE, numModelCache, numDataCache); + return Void(); +} + +Return<V1_0::ErrorStatus> Device::prepareModel(const V1_0::Model& model, + const sp<V1_0::IPreparedModelCallback>& callback) { + auto result = adapter::prepareModel(kDevice, kExecutor, model, callback); + if (!result.has_value()) { + auto [message, code] = std::move(result).error(); + LOG(ERROR) << "adapter::Device::prepareModel failed with " << code << ": " << message; + notify(callback.get(), code, nullptr); + return V1_0::utils::convert(code).value(); + } + return V1_0::ErrorStatus::NONE; +} + +Return<V1_0::ErrorStatus> Device::prepareModel_1_1( + const V1_1::Model& model, V1_1::ExecutionPreference preference, + const sp<V1_0::IPreparedModelCallback>& callback) { + auto result = adapter::prepareModel_1_1(kDevice, kExecutor, model, preference, callback); + if (!result.has_value()) { + auto [message, code] = std::move(result).error(); + LOG(ERROR) << "adapter::Device::prepareModel_1_1 failed with " << code << ": " << message; + notify(callback.get(), code, nullptr); + return V1_1::utils::convert(code).value(); + } + return V1_0::ErrorStatus::NONE; +} + +Return<V1_0::ErrorStatus> Device::prepareModel_1_2( + const V1_2::Model& model, V1_1::ExecutionPreference preference, + const hidl_vec<hidl_handle>& modelCache, const hidl_vec<hidl_handle>& dataCache, + const CacheToken& token, const sp<V1_2::IPreparedModelCallback>& callback) { + auto result = adapter::prepareModel_1_2(kDevice, kExecutor, model, preference, modelCache, + dataCache, token, callback); + if (!result.has_value()) { + auto [message, code] = std::move(result).error(); + LOG(ERROR) << "adapter::Device::prepareModel_1_2 failed with " << code << ": " << message; + notify(callback.get(), code, nullptr); + return V1_2::utils::convert(code).value(); + } + return V1_0::ErrorStatus::NONE; +} + +Return<V1_3::ErrorStatus> Device::prepareModel_1_3( + const V1_3::Model& model, V1_1::ExecutionPreference preference, V1_3::Priority priority, + const V1_3::OptionalTimePoint& deadline, const hidl_vec<hidl_handle>& modelCache, + const hidl_vec<hidl_handle>& dataCache, const CacheToken& token, + const sp<V1_3::IPreparedModelCallback>& callback) { + auto result = adapter::prepareModel_1_3(kDevice, kExecutor, model, preference, priority, + deadline, modelCache, dataCache, token, callback); + if (!result.has_value()) { + auto [message, code] = std::move(result).error(); + LOG(ERROR) << "adapter::Device::prepareModel_1_3 failed with " << code << ": " << message; + notify(callback.get(), code, nullptr); + return V1_3::utils::convert(code).value(); + } + return V1_3::ErrorStatus::NONE; +} + +Return<V1_0::ErrorStatus> Device::prepareModelFromCache( + const hidl_vec<hidl_handle>& modelCache, const hidl_vec<hidl_handle>& dataCache, + const CacheToken& token, const sp<V1_2::IPreparedModelCallback>& callback) { + auto result = adapter::prepareModelFromCache(kDevice, kExecutor, modelCache, dataCache, token, + callback); + if (!result.has_value()) { + auto [message, code] = std::move(result).error(); + LOG(ERROR) << "adapter::Device::prepareModelFromCache failed with " << code << ": " + << message; + notify(callback.get(), code, nullptr); + return V1_2::utils::convert(code).value(); + } + return V1_0::ErrorStatus::NONE; +} + +Return<V1_3::ErrorStatus> Device::prepareModelFromCache_1_3( + const V1_3::OptionalTimePoint& deadline, const hidl_vec<hidl_handle>& modelCache, + const hidl_vec<hidl_handle>& dataCache, const CacheToken& token, + const sp<V1_3::IPreparedModelCallback>& callback) { + auto result = adapter::prepareModelFromCache_1_3(kDevice, kExecutor, deadline, modelCache, + dataCache, token, callback); + if (!result.has_value()) { + auto [message, code] = std::move(result).error(); + LOG(ERROR) << "adapter::Device::prepareModelFromCache_1_3 failed with " << code << ": " + << message; + notify(callback.get(), code, nullptr); + return V1_3::utils::convert(code).value(); + } + return V1_3::ErrorStatus::NONE; +} + +Return<V1_0::DeviceStatus> Device::getStatus() { + return V1_0::DeviceStatus::AVAILABLE; +} + +Return<void> Device::allocate(const V1_3::BufferDesc& desc, + const hidl_vec<sp<V1_3::IPreparedModel>>& preparedModels, + const hidl_vec<V1_3::BufferRole>& inputRoles, + const hidl_vec<V1_3::BufferRole>& outputRoles, allocate_cb cb) { + auto result = adapter::allocate(kDevice, desc, preparedModels, inputRoles, outputRoles); + if (!result.has_value()) { + const auto [message, code] = std::move(result).error(); + LOG(ERROR) << "adapter::Device::allocate failed with " << code << ": " << message; + cb(V1_3::utils::convert(code).value(), nullptr, /*token=*/0); + return Void(); + } + auto [buffer, token] = std::move(result).value(); + cb(V1_3::ErrorStatus::NONE, buffer, token); + return Void(); +} + +} // namespace android::hardware::neuralnetworks::adapter diff --git a/neuralnetworks/utils/adapter/hidl/src/PreparedModel.cpp b/neuralnetworks/utils/adapter/hidl/src/PreparedModel.cpp new file mode 100644 index 0000000000..71060d5ca7 --- /dev/null +++ b/neuralnetworks/utils/adapter/hidl/src/PreparedModel.cpp @@ -0,0 +1,433 @@ +/* + * Copyright (C) 2020 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. + */ + +#include "PreparedModel.h" + +#include "Burst.h" + +#include <android-base/logging.h> +#include <android/hardware/neuralnetworks/1.0/IExecutionCallback.h> +#include <android/hardware/neuralnetworks/1.0/types.h> +#include <android/hardware/neuralnetworks/1.2/IBurstCallback.h> +#include <android/hardware/neuralnetworks/1.2/IExecutionCallback.h> +#include <android/hardware/neuralnetworks/1.2/types.h> +#include <android/hardware/neuralnetworks/1.3/IExecutionCallback.h> +#include <android/hardware/neuralnetworks/1.3/IFencedExecutionCallback.h> +#include <android/hardware/neuralnetworks/1.3/IPreparedModel.h> +#include <android/hardware/neuralnetworks/1.3/types.h> +#include <nnapi/IPreparedModel.h> +#include <nnapi/TypeUtils.h> +#include <nnapi/Types.h> +#include <nnapi/Validation.h> +#include <nnapi/hal/1.0/Utils.h> +#include <nnapi/hal/1.2/Utils.h> +#include <nnapi/hal/1.3/Conversions.h> +#include <nnapi/hal/1.3/Utils.h> + +#include <memory> +#include <thread> + +// See hardware/interfaces/neuralnetworks/utils/README.md for more information on HIDL interface +// lifetimes across processes and for protecting asynchronous calls across HIDL. + +namespace android::hardware::neuralnetworks::adapter { +namespace { + +template <typename Type> +auto convertInput(const Type& object) -> decltype(nn::convert(std::declval<Type>())) { + auto result = nn::convert(object); + if (!result.has_value()) { + result.error().code = nn::ErrorStatus::INVALID_ARGUMENT; + } + return result; +} + +nn::GeneralResult<nn::Version> validateRequestForModel(const nn::Request& request, + const nn::Model& model) { + nn::GeneralResult<nn::Version> version = nn::validateRequestForModel(request, model); + if (!version.ok()) { + version.error().code = nn::ErrorStatus::INVALID_ARGUMENT; + } + return version; +} + +class FencedExecutionCallback final : public V1_3::IFencedExecutionCallback { + public: + explicit FencedExecutionCallback(const nn::ExecuteFencedInfoCallback& callback) + : kCallback(callback) { + CHECK(callback != nullptr); + } + + Return<void> getExecutionInfo(getExecutionInfo_cb cb) override { + const auto result = kCallback(); + if (!result.has_value()) { + const auto& [message, code] = result.error(); + const auto status = + V1_3::utils::convert(code).value_or(V1_3::ErrorStatus::GENERAL_FAILURE); + LOG(ERROR) << message; + cb(status, V1_2::utils::kNoTiming, V1_2::utils::kNoTiming); + return Void(); + } + const auto [timingLaunched, timingFenced] = result.value(); + const auto hidlTimingLaunched = V1_3::utils::convert(timingLaunched).value(); + const auto hidlTimingFenced = V1_3::utils::convert(timingFenced).value(); + cb(V1_3::ErrorStatus::NONE, hidlTimingLaunched, hidlTimingFenced); + return Void(); + } + + private: + const nn::ExecuteFencedInfoCallback kCallback; +}; + +using ExecutionResult = nn::ExecutionResult<std::pair<std::vector<nn::OutputShape>, nn::Timing>>; + +void notify(V1_0::IExecutionCallback* callback, nn::ErrorStatus status, + const std::vector<nn::OutputShape>& /*outputShapes*/, const nn::Timing& /*timing*/) { + if (callback != nullptr) { + const auto hidlStatus = V1_0::utils::convert(status).value(); + const auto ret = callback->notify(hidlStatus); + if (!ret.isOk()) { + LOG(ERROR) << "V1_0::IExecutionCallback::notify failed with " << ret.description(); + } + } +} + +void notify(V1_2::IExecutionCallback* callback, nn::ErrorStatus status, + const std::vector<nn::OutputShape>& outputShapes, const nn::Timing& timing) { + if (callback != nullptr) { + const auto hidlStatus = V1_2::utils::convert(status).value(); + const auto hidlOutputShapes = V1_2::utils::convert(outputShapes).value(); + const auto hidlTiming = V1_2::utils::convert(timing).value(); + const auto ret = callback->notify_1_2(hidlStatus, hidlOutputShapes, hidlTiming); + if (!ret.isOk()) { + LOG(ERROR) << "V1_2::IExecutionCallback::notify_1_2 failed with " << ret.description(); + } + } +} + +void notify(V1_3::IExecutionCallback* callback, nn::ErrorStatus status, + const std::vector<nn::OutputShape>& outputShapes, const nn::Timing& timing) { + if (callback != nullptr) { + const auto hidlStatus = V1_3::utils::convert(status).value(); + const auto hidlOutputShapes = V1_3::utils::convert(outputShapes).value(); + const auto hidlTiming = V1_3::utils::convert(timing).value(); + const auto ret = callback->notify_1_3(hidlStatus, hidlOutputShapes, hidlTiming); + if (!ret.isOk()) { + LOG(ERROR) << "V1_3::IExecutionCallback::notify_1_3 failed with " << ret.description(); + } + } +} + +template <typename CallbackType> +void notify(CallbackType* callback, ExecutionResult result) { + if (!result.has_value()) { + const auto [message, status, outputShapes] = std::move(result).error(); + LOG(ERROR) << message; + notify(callback, status, outputShapes, {}); + } else { + const auto [outputShapes, timing] = std::move(result).value(); + notify(callback, nn::ErrorStatus::NONE, outputShapes, timing); + } +} + +nn::GeneralResult<void> execute(const nn::SharedPreparedModel& preparedModel, + const Executor& executor, const V1_0::Request& request, + const sp<V1_0::IExecutionCallback>& callback) { + if (callback.get() == nullptr) { + return NN_ERROR(nn::ErrorStatus::INVALID_ARGUMENT) << "Invalid callback"; + } + + auto nnRequest = NN_TRY(convertInput(request)); + + const std::any resource = preparedModel->getUnderlyingResource(); + if (const auto* model = std::any_cast<const nn::Model*>(&resource)) { + CHECK(*model != nullptr); + NN_TRY(adapter::validateRequestForModel(nnRequest, **model)); + } + + Task task = [preparedModel, nnRequest = std::move(nnRequest), callback] { + auto result = preparedModel->execute(nnRequest, nn::MeasureTiming::NO, {}, {}); + notify(callback.get(), std::move(result)); + }; + executor(std::move(task), {}); + + return {}; +} + +nn::GeneralResult<void> execute_1_2(const nn::SharedPreparedModel& preparedModel, + const Executor& executor, const V1_0::Request& request, + V1_2::MeasureTiming measure, + const sp<V1_2::IExecutionCallback>& callback) { + if (callback.get() == nullptr) { + return NN_ERROR(nn::ErrorStatus::INVALID_ARGUMENT) << "Invalid callback"; + } + + auto nnRequest = NN_TRY(convertInput(request)); + const auto nnMeasure = NN_TRY(convertInput(measure)); + + const std::any resource = preparedModel->getUnderlyingResource(); + if (const auto* model = std::any_cast<const nn::Model*>(&resource)) { + CHECK(*model != nullptr); + NN_TRY(adapter::validateRequestForModel(nnRequest, **model)); + } + + Task task = [preparedModel, nnRequest = std::move(nnRequest), nnMeasure, callback] { + auto result = preparedModel->execute(nnRequest, nnMeasure, {}, {}); + notify(callback.get(), std::move(result)); + }; + executor(std::move(task), {}); + + return {}; +} + +nn::GeneralResult<void> execute_1_3(const nn::SharedPreparedModel& preparedModel, + const Executor& executor, const V1_3::Request& request, + V1_2::MeasureTiming measure, + const V1_3::OptionalTimePoint& deadline, + const V1_3::OptionalTimeoutDuration& loopTimeoutDuration, + const sp<V1_3::IExecutionCallback>& callback) { + if (callback.get() == nullptr) { + return NN_ERROR(nn::ErrorStatus::INVALID_ARGUMENT) << "Invalid callback"; + } + + auto nnRequest = NN_TRY(convertInput(request)); + const auto nnMeasure = NN_TRY(convertInput(measure)); + const auto nnDeadline = NN_TRY(convertInput(deadline)); + const auto nnLoopTimeoutDuration = NN_TRY(convertInput(loopTimeoutDuration)); + + const std::any resource = preparedModel->getUnderlyingResource(); + if (const auto* model = std::any_cast<const nn::Model*>(&resource)) { + CHECK(*model != nullptr); + NN_TRY(adapter::validateRequestForModel(nnRequest, **model)); + } + + Task task = [preparedModel, nnRequest = std::move(nnRequest), nnMeasure, nnDeadline, + nnLoopTimeoutDuration, callback] { + auto result = + preparedModel->execute(nnRequest, nnMeasure, nnDeadline, nnLoopTimeoutDuration); + notify(callback.get(), std::move(result)); + }; + executor(std::move(task), nnDeadline); + + return {}; +} + +nn::ExecutionResult<std::pair<hidl_vec<V1_2::OutputShape>, V1_2::Timing>> executeSynchronously( + const nn::SharedPreparedModel& preparedModel, const V1_0::Request& request, + V1_2::MeasureTiming measure) { + const auto nnRequest = NN_TRY(convertInput(request)); + const auto nnMeasure = NN_TRY(convertInput(measure)); + + const auto [outputShapes, timing] = + NN_TRY(preparedModel->execute(nnRequest, nnMeasure, {}, {})); + + auto hidlOutputShapes = NN_TRY(V1_2::utils::convert(outputShapes)); + const auto hidlTiming = NN_TRY(V1_2::utils::convert(timing)); + return std::make_pair(std::move(hidlOutputShapes), hidlTiming); +} + +nn::ExecutionResult<std::pair<hidl_vec<V1_2::OutputShape>, V1_2::Timing>> executeSynchronously_1_3( + const nn::SharedPreparedModel& preparedModel, const V1_3::Request& request, + V1_2::MeasureTiming measure, const V1_3::OptionalTimePoint& deadline, + const V1_3::OptionalTimeoutDuration& loopTimeoutDuration) { + const auto nnRequest = NN_TRY(convertInput(request)); + const auto nnMeasure = NN_TRY(convertInput(measure)); + const auto nnDeadline = NN_TRY(convertInput(deadline)); + const auto nnLoopTimeoutDuration = NN_TRY(convertInput(loopTimeoutDuration)); + + const auto [outputShapes, timing] = + NN_TRY(preparedModel->execute(nnRequest, nnMeasure, nnDeadline, nnLoopTimeoutDuration)); + + auto hidlOutputShapes = NN_TRY(V1_3::utils::convert(outputShapes)); + const auto hidlTiming = NN_TRY(V1_3::utils::convert(timing)); + return std::make_pair(std::move(hidlOutputShapes), hidlTiming); +} + +nn::GeneralResult<std::vector<nn::SyncFence>> convertSyncFences( + const hidl_vec<hidl_handle>& handles) { + auto nnHandles = NN_TRY(convertInput(handles)); + std::vector<nn::SyncFence> syncFences; + syncFences.reserve(handles.size()); + for (auto&& handle : nnHandles) { + if (auto syncFence = nn::SyncFence::create(std::move(handle)); !syncFence.ok()) { + return nn::error(nn::ErrorStatus::INVALID_ARGUMENT) << std::move(syncFence).error(); + } else { + syncFences.push_back(std::move(syncFence).value()); + } + } + return syncFences; +} + +nn::GeneralResult<sp<V1_2::IBurstContext>> configureExecutionBurst( + const nn::SharedPreparedModel& preparedModel, const sp<V1_2::IBurstCallback>& callback, + const MQDescriptorSync<V1_2::FmqRequestDatum>& requestChannel, + const MQDescriptorSync<V1_2::FmqResultDatum>& resultChannel) { + auto burstExecutor = NN_TRY(preparedModel->configureExecutionBurst()); + return Burst::create(callback, requestChannel, resultChannel, std::move(burstExecutor), + V1_2::utils::getBurstServerPollingTimeWindow()); +} + +nn::GeneralResult<std::pair<hidl_handle, sp<V1_3::IFencedExecutionCallback>>> executeFenced( + const nn::SharedPreparedModel& preparedModel, const V1_3::Request& request, + const hidl_vec<hidl_handle>& waitFor, V1_2::MeasureTiming measure, + const V1_3::OptionalTimePoint& deadline, + const V1_3::OptionalTimeoutDuration& loopTimeoutDuration, + const V1_3::OptionalTimeoutDuration& duration) { + const auto nnRequest = NN_TRY(convertInput(request)); + const auto nnWaitFor = NN_TRY(convertSyncFences(waitFor)); + const auto nnMeasure = NN_TRY(convertInput(measure)); + const auto nnDeadline = NN_TRY(convertInput(deadline)); + const auto nnLoopTimeoutDuration = NN_TRY(convertInput(loopTimeoutDuration)); + const auto nnDuration = NN_TRY(convertInput(duration)); + + auto [syncFence, executeFencedCallback] = NN_TRY(preparedModel->executeFenced( + nnRequest, nnWaitFor, nnMeasure, nnDeadline, nnLoopTimeoutDuration, nnDuration)); + + auto hidlSyncFence = NN_TRY(V1_3::utils::convert(syncFence.getSharedHandle())); + auto hidlExecuteFencedCallback = sp<FencedExecutionCallback>::make(executeFencedCallback); + return std::make_pair(std::move(hidlSyncFence), std::move(hidlExecuteFencedCallback)); +} + +} // namespace + +PreparedModel::PreparedModel(nn::SharedPreparedModel preparedModel, Executor executor) + : kPreparedModel(std::move(preparedModel)), kExecutor(std::move(executor)) { + CHECK(kPreparedModel != nullptr); + CHECK(kExecutor != nullptr); +} + +nn::SharedPreparedModel PreparedModel::getUnderlyingPreparedModel() const { + return kPreparedModel; +} + +Return<V1_0::ErrorStatus> PreparedModel::execute(const V1_0::Request& request, + const sp<V1_0::IExecutionCallback>& callback) { + auto result = adapter::execute(kPreparedModel, kExecutor, request, callback); + if (!result.has_value()) { + auto [message, code] = std::move(result).error(); + LOG(ERROR) << "adapter::PreparedModel::execute failed with " << code << ": " << message; + notify(callback.get(), code, {}, {}); + return V1_0::utils::convert(code).value(); + } + return V1_0::ErrorStatus::NONE; +} + +Return<V1_0::ErrorStatus> PreparedModel::execute_1_2(const V1_0::Request& request, + V1_2::MeasureTiming measure, + const sp<V1_2::IExecutionCallback>& callback) { + auto result = adapter::execute_1_2(kPreparedModel, kExecutor, request, measure, callback); + if (!result.has_value()) { + auto [message, code] = std::move(result).error(); + LOG(ERROR) << "adapter::PreparedModel::execute_1_2 failed with " << code << ": " << message; + notify(callback.get(), code, {}, {}); + return V1_2::utils::convert(code).value(); + } + return V1_0::ErrorStatus::NONE; +} + +Return<V1_3::ErrorStatus> PreparedModel::execute_1_3( + const V1_3::Request& request, V1_2::MeasureTiming measure, + const V1_3::OptionalTimePoint& deadline, + const V1_3::OptionalTimeoutDuration& loopTimeoutDuration, + const sp<V1_3::IExecutionCallback>& callback) { + auto result = adapter::execute_1_3(kPreparedModel, kExecutor, request, measure, deadline, + loopTimeoutDuration, callback); + if (!result.has_value()) { + auto [message, code] = std::move(result).error(); + LOG(ERROR) << "adapter::PreparedModel::execute_1_3 failed with " << code << ": " << message; + notify(callback.get(), code, {}, {}); + return V1_3::utils::convert(code).value(); + } + return V1_3::ErrorStatus::NONE; +} + +Return<void> PreparedModel::executeSynchronously(const V1_0::Request& request, + V1_2::MeasureTiming measure, + executeSynchronously_cb cb) { + auto result = adapter::executeSynchronously(kPreparedModel, request, measure); + if (!result.has_value()) { + auto [message, code, outputShapes] = std::move(result).error(); + LOG(ERROR) << "adapter::PreparedModel::executeSynchronously failed with " << code << ": " + << message; + cb(V1_2::utils::convert(code).value(), V1_2::utils::convert(outputShapes).value(), + V1_2::utils::kNoTiming); + return Void(); + } + auto [outputShapes, timing] = std::move(result).value(); + cb(V1_0::ErrorStatus::NONE, outputShapes, timing); + return Void(); +} + +Return<void> PreparedModel::executeSynchronously_1_3( + const V1_3::Request& request, V1_2::MeasureTiming measure, + const V1_3::OptionalTimePoint& deadline, + const V1_3::OptionalTimeoutDuration& loopTimeoutDuration, executeSynchronously_1_3_cb cb) { + auto result = adapter::executeSynchronously_1_3(kPreparedModel, request, measure, deadline, + loopTimeoutDuration); + if (!result.has_value()) { + auto [message, code, outputShapes] = std::move(result).error(); + LOG(ERROR) << "adapter::PreparedModel::executeSynchronously_1_3 failed with " << code + << ": " << message; + cb(V1_3::utils::convert(code).value(), V1_3::utils::convert(outputShapes).value(), + V1_2::utils::kNoTiming); + return Void(); + } + auto [outputShapes, timing] = std::move(result).value(); + cb(V1_3::ErrorStatus::NONE, outputShapes, timing); + return Void(); +} + +Return<void> PreparedModel::configureExecutionBurst( + const sp<V1_2::IBurstCallback>& callback, + const MQDescriptorSync<V1_2::FmqRequestDatum>& requestChannel, + const MQDescriptorSync<V1_2::FmqResultDatum>& resultChannel, + configureExecutionBurst_cb cb) { + auto result = adapter::configureExecutionBurst(kPreparedModel, callback, requestChannel, + resultChannel); + if (!result.has_value()) { + auto [message, code] = std::move(result).error(); + LOG(ERROR) << "adapter::PreparedModel::configureExecutionBurst failed with " << code << ": " + << message; + cb(V1_2::utils::convert(code).value(), nullptr); + return Void(); + } + auto burstContext = std::move(result).value(); + cb(V1_0::ErrorStatus::NONE, std::move(burstContext)); + return Void(); +} + +Return<void> PreparedModel::executeFenced(const V1_3::Request& request, + const hidl_vec<hidl_handle>& waitFor, + V1_2::MeasureTiming measure, + const V1_3::OptionalTimePoint& deadline, + const V1_3::OptionalTimeoutDuration& loopTimeoutDuration, + const V1_3::OptionalTimeoutDuration& duration, + executeFenced_cb callback) { + auto result = adapter::executeFenced(kPreparedModel, request, waitFor, measure, deadline, + loopTimeoutDuration, duration); + if (!result.has_value()) { + auto [message, code] = std::move(result).error(); + LOG(ERROR) << "adapter::PreparedModel::executeFenced failed with " << code << ": " + << message; + callback(V1_3::utils::convert(code).value(), {}, nullptr); + return Void(); + } + auto [syncFence, executeFencedCallback] = std::move(result).value(); + callback(V1_3::ErrorStatus::NONE, syncFence, executeFencedCallback); + return Void(); +} + +} // namespace android::hardware::neuralnetworks::adapter |