| /* |
| * 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 "ExecutionBuilder" |
| |
| #include "ExecutionBuilder.h" |
| |
| #include "CompilationBuilder.h" |
| #include "CpuExecutor.h" |
| #include "ExecutionBurstController.h" |
| #include "HalInterfaces.h" |
| #include "Manager.h" |
| #include "ModelBuilder.h" |
| #include "Tracing.h" |
| #include "TypeManager.h" |
| #include "Utils.h" |
| |
| #include <mutex> |
| #include <optional> |
| #include <thread> |
| #include <vector> |
| |
| namespace android { |
| namespace nn { |
| |
| using namespace hal; |
| |
| using HidlToken = hidl_array<uint8_t, ANEURALNETWORKS_BYTE_SIZE_OF_CACHE_TOKEN>; |
| |
| const Timing kNoTiming = {.timeOnDevice = UINT64_MAX, .timeInDriver = UINT64_MAX}; |
| |
| static MeasureTiming measureTiming(const ExecutionBuilder* execution) { |
| return execution->measureTiming() ? MeasureTiming::YES : MeasureTiming::NO; |
| } |
| |
| static bool checkDimensionInfo(const Operand& operand, const ANeuralNetworksOperandType* newType, |
| const char* tag, bool allowUnspecified) { |
| if (newType != nullptr) { |
| const Extension::OperandTypeInformation* info = nullptr; |
| if (isExtensionOperandType(operand.type)) { |
| NN_RET_CHECK(TypeManager::get()->getExtensionOperandTypeInfo(operand.type, &info)); |
| } |
| if (validateOperandType(*newType, info, tag, allowUnspecified) != |
| ANEURALNETWORKS_NO_ERROR) { |
| LOG(ERROR) << tag << ": Invalid newType"; |
| return false; |
| } |
| if (operand.dimensions.size() == 0) { |
| return true; |
| } |
| if (operand.dimensions.size() != newType->dimensionCount) { |
| LOG(ERROR) << tag << ": Setting with incompatible dimension count"; |
| return false; |
| } |
| for (uint32_t i = 0; i < newType->dimensionCount; i++) { |
| if (operand.dimensions[i] != newType->dimensions[i] && operand.dimensions[i] != 0) { |
| LOG(ERROR) << tag << ": Overriding a fully specified dimension is disallowed"; |
| return false; |
| } |
| } |
| } else { |
| if (!allowUnspecified && TypeManager::get()->isTensorType(operand.type) && |
| tensorHasUnspecifiedDimensions(operand)) { |
| LOG(ERROR) << tag << ": Setting with operand type that is not fully specified"; |
| return false; |
| } |
| } |
| return true; |
| } |
| |
| int ModelArgumentInfo::setFromPointer(const Operand& operand, |
| const ANeuralNetworksOperandType* type, void* data, |
| uint32_t length) { |
| if ((data == nullptr) != (length == 0)) { |
| const char* dataPtrMsg = data ? "NOT_NULLPTR" : "NULLPTR"; |
| LOG(ERROR) << "Data pointer must be nullptr if and only if length is zero (data = " |
| << dataPtrMsg << ", length = " << length << ")"; |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| if (data == nullptr) { |
| state = ModelArgumentInfo::HAS_NO_VALUE; |
| } else { |
| NN_RETURN_IF_ERROR(updateDimensionInfo(operand, type)); |
| if (operand.type != OperandType::OEM) { |
| uint32_t neededLength = TypeManager::get()->getSizeOfData(operand.type, dimensions); |
| if (neededLength != length && neededLength != 0) { |
| LOG(ERROR) << "Setting argument with invalid length: " << length |
| << ", expected length: " << neededLength; |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| } |
| state = ModelArgumentInfo::POINTER; |
| } |
| buffer = data; |
| locationAndLength = {.poolIndex = 0, .offset = 0, .length = length}; |
| return ANEURALNETWORKS_NO_ERROR; |
| } |
| |
| int ModelArgumentInfo::setFromMemory(const Operand& operand, const ANeuralNetworksOperandType* type, |
| uint32_t poolIndex, uint32_t offset, uint32_t length) { |
| NN_RETURN_IF_ERROR(updateDimensionInfo(operand, type)); |
| if (operand.type != OperandType::OEM) { |
| uint32_t neededLength = TypeManager::get()->getSizeOfData(operand.type, dimensions); |
| if (neededLength != length && neededLength != 0) { |
| LOG(ERROR) << "Setting argument with invalid length: " << length |
| << ", expected length: " << neededLength; |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| } |
| |
| state = ModelArgumentInfo::MEMORY; |
| locationAndLength = {.poolIndex = poolIndex, .offset = offset, .length = length}; |
| buffer = nullptr; |
| return ANEURALNETWORKS_NO_ERROR; |
| } |
| |
| int ModelArgumentInfo::setFromTemporaryMemory(const Operand& operand, uint32_t poolIndex, |
| uint32_t offset, uint32_t length) { |
| NN_RETURN_IF_ERROR(updateDimensionInfo(operand, nullptr)); |
| if (operand.type != OperandType::OEM) { |
| uint32_t neededLength = TypeManager::get()->getSizeOfData(operand.type, dimensions); |
| if (neededLength != length) { |
| LOG(ERROR) << "Setting argument with invalid length: " << length |
| << ", expected length: " << neededLength; |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| } |
| |
| state = ModelArgumentInfo::MEMORY; |
| locationAndLength = { |
| .poolIndex = poolIndex, |
| .offset = offset, |
| .length = length, |
| }; |
| buffer = nullptr; |
| return ANEURALNETWORKS_NO_ERROR; |
| } |
| |
| int ModelArgumentInfo::updateDimensionInfo(const Operand& operand, |
| const ANeuralNetworksOperandType* newType) { |
| if (newType == nullptr) { |
| dimensions = operand.dimensions; |
| } else { |
| const uint32_t count = newType->dimensionCount; |
| dimensions = hidl_vec<uint32_t>(count); |
| std::copy(&newType->dimensions[0], &newType->dimensions[count], dimensions.begin()); |
| } |
| return ANEURALNETWORKS_NO_ERROR; |
| } |
| |
| ExecutionBuilder::ExecutionBuilder(const CompilationBuilder* compilation) |
| : mCompilation(compilation), |
| mModel(compilation->mModel), |
| mPlan(&compilation->mPlan), |
| mPartitioning(compilation->mPartitioning), |
| mInputs(mModel->inputCount()), |
| mOutputs(mModel->outputCount()) { |
| VLOG(EXECUTION) << "ExecutionBuilder::ExecutionBuilder"; |
| } |
| |
| int ExecutionBuilder::setInput(uint32_t index, const ANeuralNetworksOperandType* type, |
| const void* buffer, size_t length) { |
| if (mStarted) { |
| LOG(ERROR) << "ANeuralNetworksExecution_setInput called after the " |
| "execution has started."; |
| return ANEURALNETWORKS_BAD_STATE; |
| } |
| uint32_t count = static_cast<uint32_t>(mInputs.size()); |
| if (index >= count) { |
| LOG(ERROR) << "ANeuralNetworksExecution_setInput bad index " << index << " " << count; |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| if (!checkDimensionInfo(mModel->getInputOperand(index), type, |
| "ANeuralNetworksExecution_setInput", buffer == nullptr)) { |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| if (length > 0xFFFFFFFF) { |
| LOG(ERROR) << "ANeuralNetworksExecution_setInput input exceeds max length " << length; |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| uint32_t l = static_cast<uint32_t>(length); |
| return mInputs[index].setFromPointer(mModel->getInputOperand(index), type, |
| const_cast<void*>(buffer), l); |
| } |
| |
| int ExecutionBuilder::setInputFromMemory(uint32_t index, const ANeuralNetworksOperandType* type, |
| const Memory* memory, size_t offset, size_t length) { |
| // Should be similar to StepExecutor::setInputOrOutputFromTemporaryMemory() |
| |
| if (mStarted) { |
| LOG(ERROR) << "ANeuralNetworksExecution_setInputFromMemory called after the " |
| "execution has started."; |
| return ANEURALNETWORKS_BAD_STATE; |
| } |
| uint32_t count = static_cast<uint32_t>(mInputs.size()); |
| if (index >= count) { |
| LOG(ERROR) << "ANeuralNetworksExecution_setInputFromMemory bad index " << index << " " |
| << count; |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| if (!checkDimensionInfo(mModel->getInputOperand(index), type, |
| "ANeuralNetworksExecution_setInputFromMemory", false)) { |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| // Both offset & length must be zero for Non-BLOB format AHardwareBuffer. |
| if (memory->getHidlMemory().name() == "hardware_buffer" && (offset != 0 || length != 0)) { |
| LOG(ERROR) << "ANeuralNetworksExecution_setInputFromMemory has non-zero offset and length" |
| << " for Non-BLOB format AHardwareBuffer."; |
| return ANEURALNETWORKS_BAD_DATA; |
| } else if (!memory->validateSize(offset, length)) { |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| // TODO validate the rest |
| uint32_t poolIndex = mMemories.add(memory); |
| return mInputs[index].setFromMemory(mModel->getInputOperand(index), type, poolIndex, offset, |
| length); |
| } |
| |
| int ExecutionBuilder::setOutput(uint32_t index, const ANeuralNetworksOperandType* type, |
| void* buffer, size_t length) { |
| if (mStarted) { |
| LOG(ERROR) << "ANeuralNetworksExecution_setOutput called after the " |
| "execution has started."; |
| return ANEURALNETWORKS_BAD_STATE; |
| } |
| uint32_t count = static_cast<uint32_t>(mOutputs.size()); |
| if (index >= count) { |
| LOG(ERROR) << "ANeuralNetworksExecution_setOutput bad index " << index << " " << count; |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| if (!checkDimensionInfo(mModel->getOutputOperand(index), type, |
| "ANeuralNetworksExecution_setOutput", true)) { |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| if (length > 0xFFFFFFFF) { |
| LOG(ERROR) << "ANeuralNetworksExecution_setOutput input exceeds max length " << length; |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| uint32_t l = static_cast<uint32_t>(length); |
| return mOutputs[index].setFromPointer(mModel->getOutputOperand(index), type, buffer, l); |
| } |
| |
| int ExecutionBuilder::setOutputFromMemory(uint32_t index, const ANeuralNetworksOperandType* type, |
| const Memory* memory, size_t offset, size_t length) { |
| // Should be similar to StepExecutor::setInputOrOutputFromTemporaryMemory() |
| |
| if (mStarted) { |
| LOG(ERROR) << "ANeuralNetworksExecution_setOutputFromMemory called after the " |
| "execution has started."; |
| return ANEURALNETWORKS_BAD_STATE; |
| } |
| uint32_t count = static_cast<uint32_t>(mOutputs.size()); |
| if (index >= count) { |
| LOG(ERROR) << "ANeuralNetworksExecution_setOutputFromMemory bad index " << index << " " |
| << count; |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| if (!checkDimensionInfo(mModel->getOutputOperand(index), type, |
| "ANeuralNetworksExecution_setOutputFromMemory", true)) { |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| // Both offset & length must be zero for Non-BLOB format AHardwareBuffer. |
| if (memory->getHidlMemory().name() == "hardware_buffer" && (offset != 0 || length != 0)) { |
| LOG(ERROR) << "ANeuralNetworksExecution_setOutputFromMemory has non-zero offset and length" |
| << " for Non-BLOB format AHardwareBuffer."; |
| return ANEURALNETWORKS_BAD_DATA; |
| } else if (!memory->validateSize(offset, length)) { |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| // TODO validate the rest |
| uint32_t poolIndex = mMemories.add(memory); |
| return mOutputs[index].setFromMemory(mModel->getOutputOperand(index), type, poolIndex, offset, |
| length); |
| } |
| |
| int ExecutionBuilder::setMeasureTiming(bool measure) { |
| if (!mCompilation->mExplicitDeviceList || (mCompilation->mDevices.size() != 1)) { |
| LOG(ERROR) << "ANeuralNetworksExecution_setMeasureTiming called on " |
| << "an ANeuralNetworksExecution created from an ANeuralNetworksCompilation " |
| << "that was not created by ANeuralNetworksCompilation_createForDevices " |
| << "with numDevices = 1"; |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| if (mStarted) { |
| LOG(ERROR) << "ANeuralNetworksExecution_setMeasureTiming called after the " |
| "execution has started."; |
| return ANEURALNETWORKS_BAD_STATE; |
| } |
| mMeasureTiming = measure; |
| return ANEURALNETWORKS_NO_ERROR; |
| } |
| |
| int ExecutionBuilder::getDuration(int32_t durationCode, uint64_t* duration) const { |
| if (!mFinished) { |
| LOG(ERROR) << "ANeuralNetworksExecution_getDuration called before the " |
| "execution has finished."; |
| return ANEURALNETWORKS_BAD_STATE; |
| } |
| |
| // NOTE: At the HAL level, timing is in microseconds. At the NDK level, nanoseconds. |
| const uint64_t kNanoPerMicro = 1000; |
| |
| if (!mMeasureTiming) { |
| *duration = UINT64_MAX; |
| return ANEURALNETWORKS_BAD_STATE; |
| } |
| |
| uint64_t microDuration = UINT64_MAX; |
| switch (durationCode) { |
| case ANEURALNETWORKS_DURATION_ON_HARDWARE: |
| microDuration = mTiming.timeOnDevice; |
| break; |
| case ANEURALNETWORKS_DURATION_IN_DRIVER: |
| microDuration = mTiming.timeInDriver; |
| break; |
| default: |
| CHECK(!"unexpected"); |
| } |
| *duration = (microDuration == UINT64_MAX) ? UINT64_MAX : kNanoPerMicro * microDuration; |
| |
| VLOG(EXECUTION) << "getDuration(" << durationCode << "): " << *duration; |
| return ANEURALNETWORKS_NO_ERROR; |
| } |
| |
| int ExecutionBuilder::getOutputOperandDimensions(uint32_t index, uint32_t* dimensions) { |
| if (!mFinished) { |
| LOG(ERROR) << "ANeuralNetworksExecution_getOutputOperandDimensions called before the " |
| "execution has finished."; |
| return ANEURALNETWORKS_BAD_STATE; |
| } |
| uint32_t count = static_cast<uint32_t>(mOutputs.size()); |
| if (index >= count) { |
| LOG(ERROR) << "ANeuralNetworksExecution_getOutputOperandDimensions bad index " << index |
| << " " << count; |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| const auto& dims = mOutputs[index].dimensions; |
| if (dims.empty()) { |
| LOG(ERROR) << "ANeuralNetworksExecution_getOutputOperandDimensions can not query " |
| "dimensions of a scalar"; |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| std::copy(dims.begin(), dims.end(), dimensions); |
| return mOutputs[index].isSufficient ? ANEURALNETWORKS_NO_ERROR |
| : ANEURALNETWORKS_OUTPUT_INSUFFICIENT_SIZE; |
| } |
| |
| int ExecutionBuilder::getOutputOperandRank(uint32_t index, uint32_t* rank) { |
| if (!mFinished) { |
| LOG(ERROR) << "ANeuralNetworksExecution_getOutputOperandRank called before the " |
| "execution has finished."; |
| return ANEURALNETWORKS_BAD_STATE; |
| } |
| uint32_t count = static_cast<uint32_t>(mOutputs.size()); |
| if (index >= count) { |
| LOG(ERROR) << "ANeuralNetworksExecution_getOutputOperandRank bad index " << index << " " |
| << count; |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| *rank = static_cast<uint32_t>(mOutputs[index].dimensions.size()); |
| return mOutputs[index].isSufficient ? ANEURALNETWORKS_NO_ERROR |
| : ANEURALNETWORKS_OUTPUT_INSUFFICIENT_SIZE; |
| } |
| |
| // Attempt synchronous execution of full model on CPU. |
| // Ensure that executionCallback->notify() is called. |
| // TODO: How should we handle timing in this case? |
| // For Q this is irrelevant: We only support timing in conjunction |
| // with an explicit device list; and we do not support CPU fallback |
| // with an explicit device list. See CompilationBuilder::mExplicitDeviceList. |
| static void cpuFallbackFull(ExecutionBuilder* executionBuilder, |
| const sp<ExecutionCallback>& executionCallback) { |
| NNTRACE_RT(NNTRACE_PHASE_EXECUTION, "cpuFallbackFull"); |
| VLOG(EXECUTION) << "cpuFallbackFull"; |
| StepExecutor executor(executionBuilder, executionBuilder->getModel(), |
| DeviceManager::getCpuDevice(), /*preparedModel=*/nullptr); |
| executor.mapInputsAndOutputsTrivially(); |
| sp<ExecutionCallback> fallbackCallback; |
| int n = executor.startCompute(&fallbackCallback); |
| if (n != ANEURALNETWORKS_NO_ERROR) { |
| executionCallback->notify(convertResultCodeToErrorStatus(n), {}, kNoTiming); |
| return; |
| } |
| fallbackCallback->wait(); |
| executionCallback->notify(fallbackCallback->getStatus(), fallbackCallback->getOutputShapes(), |
| fallbackCallback->getTiming()); |
| } |
| |
| // Attempt synchronous execution on CPU. |
| // (1) First, attempt to execute this step on CPU. If successful, |
| // return true. (Do not call executionCallback->notify().) |
| // (2) If unsuccessful, attempt to execute the full model on CPU, |
| // ensure that executionCallback->notify() is called, and return |
| // false. |
| // TODO: How should we handle timing in this case? |
| // For Q this is irrelevant: We only support timing in conjunction |
| // with an explicit device list; and we do not support CPU fallback |
| // with an explicit device list. See CompilationBuilder::mExplicitDeviceList. |
| static bool cpuFallbackPartial(ExecutionBuilder* executionBuilder, const ExecutionPlan* plan, |
| std::shared_ptr<ExecutionPlan::Controller> controller, |
| const sp<ExecutionCallback>& executionCallback, |
| std::vector<OutputShape>* outputShapes) { |
| NNTRACE_RT(NNTRACE_PHASE_EXECUTION, "cpuFallbackPartial"); |
| VLOG(EXECUTION) << "cpuFallbackPartial"; |
| std::shared_ptr<StepExecutor> executor; |
| int n = plan->fallback(controller, &executor); |
| if (n != ANEURALNETWORKS_NO_ERROR || executor->isCpu()) { |
| cpuFallbackFull(executionBuilder, executionCallback); |
| return false; |
| } |
| sp<ExecutionCallback> fallbackCallback; |
| if (executor->startComputeOnCpu(&fallbackCallback) != ANEURALNETWORKS_NO_ERROR) { |
| cpuFallbackFull(executionBuilder, executionCallback); |
| return false; |
| } |
| fallbackCallback->wait(); |
| ErrorStatus status = fallbackCallback->getStatus(); |
| const auto& stepOutputShapes = fallbackCallback->getOutputShapes(); |
| if (!executor->updateOutputShapes(stepOutputShapes, outputShapes)) { |
| status = ErrorStatus::GENERAL_FAILURE; |
| } |
| if (status != ErrorStatus::NONE) { |
| // OUTPUT_INSUFFICIENT_SIZE is not recoverable |
| if (status == ErrorStatus::OUTPUT_INSUFFICIENT_SIZE) { |
| executionCallback->notify(status, *outputShapes, kNoTiming); |
| } else { |
| cpuFallbackFull(executionBuilder, executionCallback); |
| } |
| return false; |
| } |
| return true; |
| } |
| |
| static void asyncStartComputePartitioned(ExecutionBuilder* executionBuilder, |
| const ExecutionPlan* plan, |
| std::shared_ptr<ExecutionPlan::Controller> controller, |
| bool allowFallback, |
| const sp<ExecutionCallback>& executionCallback) { |
| VLOG(EXECUTION) << "ExecutionBuilder::compute (from plan, iteratively)"; |
| std::vector<OutputShape> outputShapes; |
| Timing timing = kNoTiming; |
| executionBuilder->initializeOutputShapes(&outputShapes); |
| while (true) { |
| std::shared_ptr<StepExecutor> executor; |
| VLOG(EXECUTION) << "looking for next StepExecutor"; |
| std::shared_ptr<ExecutionBurstController> burstController = nullptr; |
| int n = plan->next(controller, &executor, &burstController); |
| if (n != ANEURALNETWORKS_NO_ERROR) { |
| if (allowFallback) { |
| cpuFallbackFull(executionBuilder, executionCallback); |
| } else { |
| executionCallback->notify(convertResultCodeToErrorStatus(n), {}, kNoTiming); |
| } |
| return; |
| } |
| if (executor == nullptr) { |
| executionCallback->notify(ErrorStatus::NONE, outputShapes, timing); |
| return; |
| } |
| |
| sp<ExecutionCallback> stepCallback; |
| n = executor->startCompute(&stepCallback, burstController); |
| if (n != ANEURALNETWORKS_NO_ERROR) { |
| if (allowFallback) { |
| if (cpuFallbackPartial(executionBuilder, plan, controller, executionCallback, |
| &outputShapes)) { |
| // Successfully executed one step on CPU. |
| continue; |
| } else { |
| // Either successfully executed entire plan on |
| // CPU, or tried and failed to do so. |
| return; |
| } |
| } else { |
| executionCallback->notify(convertResultCodeToErrorStatus(n), {}, kNoTiming); |
| return; |
| } |
| } |
| stepCallback->wait(); |
| ErrorStatus status = stepCallback->getStatus(); |
| const auto& stepOutputShapes = stepCallback->getOutputShapes(); |
| if (!executor->updateOutputShapes(stepOutputShapes, &outputShapes)) { |
| status = ErrorStatus::GENERAL_FAILURE; |
| } |
| if (status == ErrorStatus::NONE) { |
| // We only support collection of timing information in the case of a |
| // single step, so it's safe to just keep track of the last step's |
| // timing information. |
| timing = stepCallback->getTiming(); |
| } else { |
| // OUTPUT_INSUFFICIENT_SIZE is not recoverable |
| if (allowFallback && status != ErrorStatus::OUTPUT_INSUFFICIENT_SIZE) { |
| if (cpuFallbackPartial(executionBuilder, plan, controller, executionCallback, |
| &outputShapes)) { |
| // Successfully executed one step on CPU. |
| continue; |
| } else { |
| // Either successfully executed entire plan on |
| // CPU, or tried and failed to do so. |
| return; |
| } |
| } else if (status == ErrorStatus::OUTPUT_INSUFFICIENT_SIZE) { |
| executionCallback->notify(status, outputShapes, kNoTiming); |
| return; |
| } else { |
| executionCallback->notify(status, {}, kNoTiming); |
| return; |
| } |
| } |
| } |
| } |
| |
| int ExecutionBuilder::compute(sp<ExecutionCallback>* synchronizationCallback, |
| BurstBuilder* burstBuilder) { |
| CHECK(synchronizationCallback == nullptr || burstBuilder == nullptr) |
| << "synchronizationCallback and burstBuilder cannot simultaneously be used"; |
| |
| const bool synchronous = (synchronizationCallback == nullptr); |
| |
| if (!synchronous) { |
| *synchronizationCallback = nullptr; |
| } |
| |
| // TODO validate that we have full types for all inputs and outputs, |
| // that the graph is not cyclic, |
| |
| auto name = [synchronous, burstBuilder] { |
| return burstBuilder ? "burstCompute" : synchronous ? "compute" : "startCompute"; |
| }; |
| if (mStarted) { |
| LOG(ERROR) << "ANeuralNetworksExecution_" << name() |
| << " called on an execution that has already started"; |
| return ANEURALNETWORKS_BAD_STATE; |
| } |
| for (auto& p : mInputs) { |
| if (p.state == ModelArgumentInfo::UNSPECIFIED) { |
| LOG(ERROR) << "ANeuralNetworksExecution_" << name() << " not all inputs specified"; |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| } |
| for (auto& p : mOutputs) { |
| if (p.state == ModelArgumentInfo::UNSPECIFIED) { |
| LOG(ERROR) << "ANeuralNetworksExecution_" << name() << " not all outputs specified"; |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| } |
| |
| auto wrappedFinish = [this](ErrorStatus error, const std::vector<OutputShape>& outputShapes) { |
| return finish(error, outputShapes); |
| }; |
| |
| // TODO: For asynchronous execution, entire plan-based-path should run in an |
| // asynchronous thread -- take the asynchronous thread logic out of |
| // startComputeOnCpu() and use it to wrap the plan-based-path. |
| mStarted = true; |
| const bool allowFallback = DeviceManager::partitioningAllowsFallback(mPartitioning); |
| std::shared_ptr<ExecutionPlan::Controller> controller = |
| mPlan->makeController(this, burstBuilder); |
| if (synchronous) { |
| VLOG(EXECUTION) << "ExecutionBuilder::compute (synchronous API)"; |
| sp<ExecutionCallback> localSynchronizationCallback = new ExecutionCallback(); |
| localSynchronizationCallback->setOnFinish(wrappedFinish); |
| asyncStartComputePartitioned(this, mPlan, controller, allowFallback, |
| localSynchronizationCallback); |
| localSynchronizationCallback->wait(); |
| if (mMeasureTiming) { |
| mTiming = localSynchronizationCallback->getTiming(); |
| } |
| return convertErrorStatusToResultCode(localSynchronizationCallback->getStatus()); |
| } else /* asynchronous */ { |
| // TODO: use a thread pool |
| |
| // Prepare the callback for asynchronous execution. |
| // sp<ExecutionCallback> object is returned when the |
| // execution has been successfully launched, otherwise a |
| // nullptr is returned. The executionCallback is |
| // abstracted in the NN API as an "event". |
| sp<ExecutionCallback> executionCallback = new ExecutionCallback(); |
| executionCallback->setOnFinish(wrappedFinish); |
| if (DeviceManager::get()->syncExecRuntime()) { |
| VLOG(EXECUTION) << "ExecutionBuilder::compute (asynchronous API, non-threaded)"; |
| asyncStartComputePartitioned(this, mPlan, controller, allowFallback, executionCallback); |
| } else { |
| VLOG(EXECUTION) << "ExecutionBuilder::compute (asynchronous API)"; |
| std::thread thread(asyncStartComputePartitioned, this, mPlan, controller, allowFallback, |
| executionCallback); |
| executionCallback->bindThread(std::move(thread)); |
| } |
| *synchronizationCallback = executionCallback; |
| return ANEURALNETWORKS_NO_ERROR; |
| } |
| } |
| |
| void ExecutionBuilder::initializeOutputShapes(std::vector<OutputShape>* outputShapes) const { |
| outputShapes->resize(mOutputs.size()); |
| for (uint32_t i = 0; i < mOutputs.size(); i++) { |
| (*outputShapes)[i].dimensions = mOutputs[i].dimensions; |
| (*outputShapes)[i].isSufficient = true; |
| } |
| } |
| |
| // Check if the dimensions "to" is updatable by dimensions "from", where "from" must |
| // have a higher specification level. |
| static bool isUpdatable(const std::vector<uint32_t>& to, const std::vector<uint32_t>& from) { |
| if (to.size() == 0) return true; |
| NN_RET_CHECK_EQ(to.size(), from.size()); |
| for (uint32_t i = 0; i < to.size(); i++) { |
| NN_RET_CHECK(to[i] == from[i] || to[i] == 0); |
| } |
| return true; |
| } |
| |
| bool ExecutionBuilder::updateOutputShapes(const std::vector<OutputShape>& outputShapes) { |
| if (outputShapes.size() == 0) { |
| return true; |
| } |
| NN_RET_CHECK_EQ(outputShapes.size(), mOutputs.size()); |
| for (uint32_t i = 0; i < outputShapes.size(); i++) { |
| // Check if only unspecified dimensions or rank are overwritten. |
| NN_RET_CHECK(isUpdatable(mOutputs[i].dimensions, outputShapes[i].dimensions)); |
| } |
| for (uint32_t i = 0; i < outputShapes.size(); i++) { |
| mOutputs[i].dimensions = outputShapes[i].dimensions; |
| mOutputs[i].isSufficient = outputShapes[i].isSufficient; |
| } |
| return true; |
| } |
| |
| ErrorStatus ExecutionBuilder::finish(ErrorStatus, const std::vector<OutputShape>& outputShapes) { |
| CHECK(!mFinished) << "ExecutionBuilder::finish is called twice"; |
| mFinished = true; |
| if (!updateOutputShapes(outputShapes)) { |
| return ErrorStatus::GENERAL_FAILURE; |
| } |
| return ErrorStatus::NONE; |
| } |
| |
| bool StepExecutor::updateOutputShapes(const std::vector<OutputShape>& from, |
| std::vector<OutputShape>* to) { |
| if (from.size() == 0) { |
| return true; |
| } |
| if (mExecutionStep != nullptr) { |
| const auto& indexMapping = mExecutionStep->getOutputIndexSubModelToFromModel(); |
| NN_RET_CHECK_LE(indexMapping.size(), from.size()); |
| for (uint32_t i = 0, e = indexMapping.size(); i < e; i++) { |
| uint32_t toIndex = indexMapping[i]; |
| NN_RET_CHECK_GT(to->size(), toIndex); |
| NN_RET_CHECK(isUpdatable(to->at(toIndex).dimensions, from[i].dimensions)); |
| (*to)[toIndex] = from[i]; |
| } |
| } else { |
| NN_RET_CHECK_EQ(from.size(), to->size()); |
| for (uint32_t i = 0, e = from.size(); i < e; i++) { |
| NN_RET_CHECK(isUpdatable(to->at(i).dimensions, from[i].dimensions)); |
| (*to)[i] = from[i]; |
| } |
| } |
| return true; |
| } |
| |
| // Figures out how to place each of the input or outputs in a buffer. This just does the layout, |
| // it does not copy data. Aligns each input a bit. |
| int StepExecutor::allocatePointerArgumentsToPool(std::vector<ModelArgumentInfo>* args, |
| Memory* memory) { |
| uint32_t nextPoolIndex = mMemories.size(); |
| int64_t total = 0; |
| for (auto& info : *args) { |
| if (info.state == ModelArgumentInfo::POINTER) { |
| DataLocation& loc = info.locationAndLength; |
| // TODO Good enough alignment? |
| total += alignBytesNeeded(static_cast<uint32_t>(total), loc.length); |
| loc.poolIndex = nextPoolIndex; |
| loc.offset = static_cast<uint32_t>(total); |
| total += loc.length; |
| } |
| }; |
| if (total > 0xFFFFFFFF) { |
| LOG(ERROR) << "StepExecutor::allocatePointerArgumentsToPool: ANeuralNetworksExecution: " |
| "Size of all inputs or outputs exceeds 2^32."; |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| hidl_memory hidlMemory; |
| if (total > 0) { |
| memory->create(total); // TODO check error |
| mMemories.add(memory); |
| } |
| return ANEURALNETWORKS_NO_ERROR; |
| } |
| |
| static void setRequestArgumentArray(const std::vector<ModelArgumentInfo>& argumentInfos, |
| hidl_vec<RequestArgument>* ioInfos) { |
| size_t count = argumentInfos.size(); |
| ioInfos->resize(count); |
| for (size_t i = 0; i < count; i++) { |
| const auto& info = argumentInfos[i]; |
| (*ioInfos)[i] = { |
| .hasNoValue = info.state == ModelArgumentInfo::HAS_NO_VALUE, |
| .location = info.locationAndLength, |
| .dimensions = info.dimensions, |
| }; |
| } |
| } |
| |
| StepExecutor::StepExecutor(ExecutionBuilder* executionBuilder, const ModelBuilder* model, |
| std::shared_ptr<Device> device, |
| std::shared_ptr<VersionedIPreparedModel> preparedModel) |
| : mExecutionBuilder(executionBuilder), |
| mModel(model), |
| mDevice(device), |
| mPreparedModel(preparedModel), |
| mInputs(model->inputCount()), |
| mOutputs(model->outputCount()) { |
| CHECK(mDevice != nullptr); |
| } |
| |
| void StepExecutor::mapInputsAndOutputsTrivially() { |
| mInputs = mExecutionBuilder->mInputs; |
| mOutputs = mExecutionBuilder->mOutputs; |
| mMemories = mExecutionBuilder->mMemories; |
| } |
| |
| void StepExecutor::mapInputOrOutput(const ModelArgumentInfo& builderInputOrOutput, |
| ModelArgumentInfo* executorInputOrOutput) { |
| *executorInputOrOutput = builderInputOrOutput; |
| switch (executorInputOrOutput->state) { |
| default: |
| nnAssert(!"unexpected ModelArgumentInfo::state"); |
| break; |
| case ModelArgumentInfo::HAS_NO_VALUE: |
| case ModelArgumentInfo::POINTER: |
| case ModelArgumentInfo::UNSPECIFIED: |
| break; |
| case ModelArgumentInfo::MEMORY: { |
| const uint32_t builderPoolIndex = builderInputOrOutput.locationAndLength.poolIndex; |
| const Memory* memory = mExecutionBuilder->mMemories[builderPoolIndex]; |
| const uint32_t executorPoolIndex = mMemories.add(memory); |
| executorInputOrOutput->locationAndLength.poolIndex = executorPoolIndex; |
| break; |
| } |
| } |
| } |
| |
| int StepExecutor::setInputOrOutputFromTemporaryMemory(const Operand& inputOrOutputOperand, |
| const Memory* memory, uint32_t offset, |
| ModelArgumentInfo* inputOrOutputInfo) { |
| // Should be similar to |
| // ExecutionBuilder::setInputFromMemory() |
| // ExecutionBuilder::setOutputFromMemory() |
| |
| uint32_t poolIndex = mMemories.add(memory); |
| uint32_t length = TypeManager::get()->getSizeOfData(inputOrOutputOperand); |
| return inputOrOutputInfo->setFromTemporaryMemory(inputOrOutputOperand, poolIndex, offset, |
| length); |
| } |
| |
| static void logArguments(const char* kind, const std::vector<ModelArgumentInfo>& args) { |
| for (unsigned i = 0; i < args.size(); i++) { |
| const auto& arg = args[i]; |
| std::string prefix = kind + std::string("[") + std::to_string(i) + "] = "; |
| switch (arg.state) { |
| case ModelArgumentInfo::POINTER: |
| VLOG(EXECUTION) << prefix << "POINTER(" << SHOW_IF_DEBUG(arg.buffer) << ")"; |
| break; |
| case ModelArgumentInfo::MEMORY: |
| VLOG(EXECUTION) << prefix << "MEMORY(" |
| << "pool=" << arg.locationAndLength.poolIndex << ", " |
| << "off=" << arg.locationAndLength.offset << ")"; |
| break; |
| case ModelArgumentInfo::HAS_NO_VALUE: |
| VLOG(EXECUTION) << prefix << "HAS_NO_VALUE"; |
| break; |
| case ModelArgumentInfo::UNSPECIFIED: |
| VLOG(EXECUTION) << prefix << "UNSPECIFIED"; |
| break; |
| default: |
| VLOG(EXECUTION) << prefix << "state(" << arg.state << ")"; |
| break; |
| } |
| } |
| } |
| |
| bool StepExecutor::isCpu() const { |
| return mDevice->getInterface() == nullptr; |
| } |
| |
| int StepExecutor::startCompute(sp<ExecutionCallback>* synchronizationCallback, |
| const std::shared_ptr<ExecutionBurstController>& burstController) { |
| if (VLOG_IS_ON(EXECUTION)) { |
| logArguments("input", mInputs); |
| logArguments("output", mOutputs); |
| } |
| if (isCpu()) { |
| return startComputeOnCpu(synchronizationCallback); |
| } else { |
| return startComputeOnDevice(synchronizationCallback, burstController); |
| } |
| } |
| |
| int StepExecutor::startComputeOnDevice( |
| sp<ExecutionCallback>* synchronizationCallback, |
| const std::shared_ptr<ExecutionBurstController>& burstController) { |
| CHECK(!isCpu()); |
| |
| // Initialize timing information in case we take an error path to exit. |
| mExecutionBuilder->reportTiming(kNoTiming); |
| |
| *synchronizationCallback = nullptr; |
| |
| NNTRACE_RT(NNTRACE_PHASE_INPUTS_AND_OUTPUTS, "StepExecutor::startComputeOnDevice"); |
| // We separate the input & output pools so that we reduce the copying done if we |
| // do an eventual remoting (hidl_memory->update()). We could also use it to set |
| // protection on read only memory but that's not currently done. |
| Memory inputPointerArguments; |
| Memory outputPointerArguments; |
| |
| // Layout the input and output data |
| int n = allocatePointerArgumentsToPool(&mInputs, &inputPointerArguments); |
| if (n != ANEURALNETWORKS_NO_ERROR) { |
| return n; |
| } |
| n = allocatePointerArgumentsToPool(&mOutputs, &outputPointerArguments); |
| if (n != ANEURALNETWORKS_NO_ERROR) { |
| return n; |
| } |
| |
| // Copy the input data that was specified via a pointer. |
| // inputPointerArguments.update(); |
| for (auto& info : mInputs) { |
| if (info.state == ModelArgumentInfo::POINTER) { |
| DataLocation& loc = info.locationAndLength; |
| uint8_t* data = nullptr; |
| int n = inputPointerArguments.getPointer(&data); |
| if (n != ANEURALNETWORKS_NO_ERROR) { |
| return n; |
| } |
| memcpy(data + loc.offset, info.buffer, loc.length); |
| } |
| } |
| // TODO: Add inputPointerArguments.commit() and .update() at all the right places |
| |
| Request request; |
| setRequestArgumentArray(mInputs, &request.inputs); |
| setRequestArgumentArray(mOutputs, &request.outputs); |
| uint32_t count = mMemories.size(); |
| request.pools.resize(count); |
| for (uint32_t i = 0; i < count; i++) { |
| request.pools[i] = mMemories[i]->getHidlMemory(); |
| } |
| |
| NNTRACE_FULL_SWITCH(NNTRACE_LAYER_IPC, NNTRACE_PHASE_EXECUTION, |
| "StepExecutor::startComputeOnDevice::execute"); |
| |
| // Prepare the callback for asynchronous execution. sp<ExecutionCallback> |
| // object is returned when the execution has been successfully launched, |
| // otherwise a nullptr is returned. The executionCallback is abstracted in |
| // the NN API as an "event". |
| // |
| // The sp is used for ref-counting purposes. Without it, the HIDL service |
| // could attempt to communicate with a dead callback object. |
| // |
| // TODO: Explain the "dead callback" problem further, either here or |
| // in the design document. |
| sp<ExecutionCallback> executionCallback = new ExecutionCallback(); |
| |
| // compute using burst if present |
| const bool burstCompute = (burstController != nullptr); |
| bool burstFallback = false; |
| if (burstCompute) { |
| std::vector<intptr_t> memoryIds; |
| memoryIds.reserve(mMemories.size()); |
| for (const Memory* memory : mMemories) { |
| memory->usedBy(burstController); |
| memoryIds.push_back(memory->getKey()); |
| } |
| |
| VLOG(EXECUTION) << "Before ExecutionBurstController->tryCompute() " |
| << SHOW_IF_DEBUG(toString(request)); |
| auto [status, outputShapes, timing, fallback] = |
| burstController->tryCompute(request, measureTiming(mExecutionBuilder), memoryIds); |
| |
| burstFallback = fallback; |
| if (!fallback) { |
| executionCallback->notify(status, outputShapes, timing); |
| } |
| } |
| |
| // compute from IPreparedModel if either: |
| // (1) burst was not supplied, or |
| // (2) the burst execution failed and requested a fallback execution |
| if (!burstCompute || burstFallback) { |
| if (DeviceManager::get()->syncExecHal()) { |
| VLOG(EXECUTION) << "Before mPreparedModel->executeSynchronously() " |
| << SHOW_IF_DEBUG(toString(request)); |
| auto syncExecuteResult = |
| mPreparedModel->executeSynchronously(request, measureTiming(mExecutionBuilder)); |
| executionCallback->notify(std::get<0>(syncExecuteResult), |
| std::get<1>(syncExecuteResult), |
| std::get<2>(syncExecuteResult)); |
| } else { |
| VLOG(EXECUTION) << "Before mPreparedModel->execute() " |
| << SHOW_IF_DEBUG(toString(request)); |
| // Execute. |
| // TODO: What happens to the Callback if the service dies abnormally |
| // -- won't that keep the Callback live forever, because the service |
| // never has the opportunity to bump the reference count down? Or |
| // maybe the HIDL infrastructure handles this magically? At worst, |
| // it seems like this is a small memory leak, if the Callback stays |
| // alive forever. |
| Return<ErrorStatus> executeStatus = mPreparedModel->execute( |
| request, measureTiming(mExecutionBuilder), executionCallback); |
| if (!executeStatus.isOk() || executeStatus != ErrorStatus::NONE) { |
| VLOG(EXECUTION) << "**Execute launch failed**"; |
| return executeStatus.isOk() ? convertErrorStatusToResultCode(executeStatus) |
| : ANEURALNETWORKS_OP_FAILED; |
| } |
| } |
| } |
| |
| // TODO: Remove this synchronization point when the block of code below is |
| // removed. |
| executionCallback->wait(); |
| NNTRACE_FULL_SWITCH(NNTRACE_LAYER_RUNTIME, NNTRACE_PHASE_EXECUTION, |
| "StepExecutor::startComputeOnDevice::waited"); |
| Return<ErrorStatus> callbackStatus = executionCallback->getStatus(); |
| if (!callbackStatus.isOk() || callbackStatus != ErrorStatus::NONE) { |
| VLOG(EXECUTION) << "**Execution failed**"; |
| if (callbackStatus == ErrorStatus::OUTPUT_INSUFFICIENT_SIZE) { |
| *synchronizationCallback = executionCallback; |
| return ANEURALNETWORKS_NO_ERROR; |
| } |
| return callbackStatus.isOk() ? convertErrorStatusToResultCode(callbackStatus) |
| : ANEURALNETWORKS_OP_FAILED; |
| } |
| |
| mExecutionBuilder->reportTiming(executionCallback->getTiming()); |
| |
| // Copy the output data from shared memory to the output buffers. |
| // TODO: Move this block of code somewhere else. It should not be in the |
| // startCompute function. |
| // TODO: outputMemory->update(); outputMemory->commit() |
| NNTRACE_RT_SWITCH(NNTRACE_PHASE_RESULTS, "StepExecutor::startComputeOnDevice"); |
| for (auto& info : mOutputs) { |
| if (info.state == ModelArgumentInfo::POINTER) { |
| DataLocation& loc = info.locationAndLength; |
| uint8_t* data = nullptr; |
| int n = outputPointerArguments.getPointer(&data); |
| if (n != ANEURALNETWORKS_NO_ERROR) { |
| return n; |
| } |
| memcpy(info.buffer, data + loc.offset, loc.length); |
| } |
| } |
| VLOG(EXECUTION) << "StepExecutor::startComputeOnDevice completed"; |
| |
| *synchronizationCallback = executionCallback; |
| return ANEURALNETWORKS_NO_ERROR; |
| } |
| |
| static void computeOnCpu(const Model& model, const Request& request, |
| const std::vector<RunTimePoolInfo>& modelPoolInfos, |
| const std::vector<RunTimePoolInfo>& requestPoolInfos, |
| const sp<IExecutionCallback>& executionCallback) { |
| NNTRACE_RT(NNTRACE_PHASE_EXECUTION, "computeOnCpu"); |
| CpuExecutor executor; |
| int err = executor.run(model, request, modelPoolInfos, requestPoolInfos); |
| const auto& outputShapes = executor.getOutputShapes(); |
| executionCallback->notify_1_2(convertResultCodeToErrorStatus(err), outputShapes, kNoTiming); |
| } |
| |
| int StepExecutor::startComputeOnCpu(sp<ExecutionCallback>* synchronizationCallback) { |
| // TODO: use a thread pool |
| // TODO(mikie): this could have NNTRACE so we could measure the overhead of |
| // spinning up a new thread. |
| |
| const Model model = mModel->makeHidlModel(); |
| |
| // Prepare the callback for asynchronous execution. sp<ExecutionCallback> |
| // object is returned when the execution has been successfully launched, |
| // otherwise a nullptr is returned. The executionCallback is abstracted in |
| // the NN API as an "event". |
| sp<ExecutionCallback> executionCallback = new ExecutionCallback(); |
| *synchronizationCallback = nullptr; |
| |
| std::vector<RunTimePoolInfo> modelPoolInfos; |
| if (!setRunTimePoolInfosFromHidlMemories(&modelPoolInfos, model.pools)) { |
| return ANEURALNETWORKS_UNMAPPABLE; |
| } |
| |
| std::vector<RunTimePoolInfo> requestPoolInfos; |
| requestPoolInfos.reserve(mMemories.size()); |
| for (const Memory* mem : mMemories) { |
| if (std::optional<RunTimePoolInfo> poolInfo = |
| RunTimePoolInfo::createFromHidlMemory(mem->getHidlMemory())) { |
| requestPoolInfos.emplace_back(*poolInfo); |
| } else { |
| return ANEURALNETWORKS_UNMAPPABLE; |
| } |
| } |
| // Create as many pools as there are input / output. |
| auto fixPointerArguments = [&requestPoolInfos](std::vector<ModelArgumentInfo>& argumentInfos) { |
| for (ModelArgumentInfo& argumentInfo : argumentInfos) { |
| if (argumentInfo.state == ModelArgumentInfo::POINTER) { |
| argumentInfo.locationAndLength.poolIndex = |
| static_cast<uint32_t>(requestPoolInfos.size()); |
| argumentInfo.locationAndLength.offset = 0; |
| requestPoolInfos.emplace_back(RunTimePoolInfo::createFromExistingBuffer( |
| static_cast<uint8_t*>(argumentInfo.buffer))); |
| } |
| } |
| }; |
| fixPointerArguments(mInputs); |
| fixPointerArguments(mOutputs); |
| |
| Request request; |
| setRequestArgumentArray(mInputs, &request.inputs); |
| setRequestArgumentArray(mOutputs, &request.outputs); |
| |
| if (DeviceManager::get()->syncExecCpu()) { |
| computeOnCpu(model, request, modelPoolInfos, requestPoolInfos, executionCallback); |
| } else { |
| // TODO: should model be moved with a std::cref? |
| std::thread thread(computeOnCpu, model, std::move(request), std::move(modelPoolInfos), |
| std::move(requestPoolInfos), executionCallback); |
| executionCallback->bindThread(std::move(thread)); |
| } |
| |
| *synchronizationCallback = executionCallback; |
| return ANEURALNETWORKS_NO_ERROR; |
| } |
| |
| } // namespace nn |
| } // namespace android |
