| /* |
| * 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 <algorithm> |
| #include <memory> |
| #include <mutex> |
| #include <optional> |
| #include <string> |
| #include <thread> |
| #include <utility> |
| #include <vector> |
| |
| #include "CompilationBuilder.h" |
| #include "CpuExecutor.h" |
| #include "ExecutionBurstController.h" |
| #include "HalInterfaces.h" |
| #include "Manager.h" |
| #include "ModelArgumentInfo.h" |
| #include "ModelBuilder.h" |
| #include "Tracing.h" |
| #include "TypeManager.h" |
| #include "Utils.h" |
| |
| namespace android { |
| namespace nn { |
| |
| using namespace hal; |
| |
| 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; |
| } |
| |
| 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) { |
| CHECK(executionBuilder != nullptr); |
| CHECK(executionCallback != nullptr); |
| 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.startComputeOnCpuFallback(&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, and the ExecutionPlan is compound, 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) { |
| CHECK(executionBuilder != nullptr); |
| CHECK(plan != nullptr); |
| CHECK(executionCallback != nullptr); |
| CHECK(outputShapes != nullptr); |
| 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->startComputeOnCpuFallback(&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) { |
| // Do not fallback twice if the ExecutionPlan is simple. |
| // OUTPUT_INSUFFICIENT_SIZE is not recoverable |
| if (plan->isSimple() || 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) { |
| CHECK(executionBuilder != nullptr); |
| CHECK(plan != nullptr); |
| VLOG(EXECUTION) << "ExecutionBuilder::compute (from plan, iteratively)"; |
| std::vector<OutputShape> outputShapes; |
| Timing timing = kNoTiming; |
| // Disallow fallback when the ExecutionPlan is simple on CPU. |
| allowFallback &= !plan->isSimpleCpu(); |
| 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 |
| // CpuPreparedModel::execute() 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 |
| // TODO(mikie): this could have NNTRACE so we could measure the overhead |
| // of spinning up a new thread. |
| |
| // 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; |
| } |
| |
| StepExecutor::StepExecutor(ExecutionBuilder* executionBuilder, const ModelBuilder* model, |
| std::shared_ptr<Device> device, |
| std::shared_ptr<PreparedModel> 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 == DeviceManager::getCpuDevice(); |
| } |
| |
| int StepExecutor::startCompute(sp<ExecutionCallback>* synchronizationCallback, |
| const std::shared_ptr<ExecutionBurstController>& burstController) { |
| CHECK(mPreparedModel != nullptr); |
| CHECK(synchronizationCallback != nullptr); |
| *synchronizationCallback = nullptr; |
| |
| if (VLOG_IS_ON(EXECUTION)) { |
| logArguments("input", mInputs); |
| logArguments("output", mOutputs); |
| } |
| |
| const MeasureTiming measure = measureTiming(mExecutionBuilder); |
| const auto [n, outputShapes, timing] = |
| mPreparedModel->execute(mInputs, mOutputs, mMemories, burstController, measure); |
| mExecutionBuilder->reportTiming(timing); |
| |
| if (n != ANEURALNETWORKS_NO_ERROR && n != ANEURALNETWORKS_OUTPUT_INSUFFICIENT_SIZE) { |
| return n; |
| } |
| |
| const ErrorStatus status = convertResultCodeToErrorStatus(n); |
| *synchronizationCallback = new ExecutionCallback(); |
| (*synchronizationCallback)->notify_1_2(status, outputShapes, timing); |
| return ANEURALNETWORKS_NO_ERROR; |
| } |
| |
| // For cpuFallback{Partial,Full}, recompile the model on CPU and then start compute. |
| int StepExecutor::startComputeOnCpuFallback(sp<ExecutionCallback>* synchronizationCallback) { |
| NNTRACE_RT(NNTRACE_PHASE_EXECUTION, "StepExecutor::startComputeOnCpuFallback"); |
| VLOG(EXECUTION) << "Re-compile the model on CPU"; |
| const Model model = mModel->makeHidlModel(); |
| mDevice = DeviceManager::getCpuDevice(); |
| mPreparedModel = nullptr; |
| // TODO: Propagate user preference to this point instead of using default value of |
| // ANEURALNETWORKS_PREFER_FAST_SINGLE_ANSWER. |
| const ExecutionPreference preference = |
| static_cast<ExecutionPreference>(ANEURALNETWORKS_PREFER_FAST_SINGLE_ANSWER); |
| const auto [n, preparedModel] = mDevice->prepareModel(model, preference, {}, {}); |
| mPreparedModel = preparedModel; |
| NN_RETURN_IF_ERROR(n); |
| return startCompute(synchronizationCallback, /*burstController=*/nullptr); |
| } |
| |
| } // namespace nn |
| } // namespace android |
