| /* |
| * 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 "HalInterfaces.h" |
| #include "Manager.h" |
| #include "ModelBuilder.h" |
| #include "Tracing.h" |
| #include "Utils.h" |
| |
| #include <mutex> |
| #include <thread> |
| #include <vector> |
| |
| namespace android { |
| namespace nn { |
| |
| 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 { |
| int n = updateDimensionInfo(operand, type); |
| if (n != ANEURALNETWORKS_NO_ERROR) { |
| return n; |
| } |
| uint32_t neededLength = sizeOfData(operand.type, dimensions); |
| if (operand.type != OperandType::OEM && neededLength != length) { |
| 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) { |
| int n = updateDimensionInfo(operand, type); |
| if (n != ANEURALNETWORKS_NO_ERROR) { |
| return n; |
| } |
| uint32_t neededLength = sizeOfData(operand.type, dimensions); |
| if (operand.type != OperandType::OEM && 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::setFromTemporaryMemory(const Operand& operand, |
| uint32_t poolIndex, uint32_t offset) { |
| int n = updateDimensionInfo(operand, nullptr); |
| if (n != ANEURALNETWORKS_NO_ERROR) { |
| return n; |
| } |
| state = ModelArgumentInfo::MEMORY; |
| locationAndLength = |
| {.poolIndex = poolIndex, .offset = offset, .length = sizeOfData(operand)}; |
| buffer = nullptr; |
| return ANEURALNETWORKS_NO_ERROR; |
| } |
| |
| int ModelArgumentInfo::updateDimensionInfo(const Operand& operand, |
| const ANeuralNetworksOperandType* newType) { |
| nnAssert(dimensions.empty()); |
| if (newType == nullptr) { |
| for (auto i : operand.dimensions) { |
| if (i == 0) { |
| LOG(ERROR) << "Setting input/output with unspecified dimensions"; |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| } |
| dimensions = operand.dimensions; |
| } else { |
| uint32_t count = newType->dimensionCount; |
| if (static_cast<OperandType>(newType->type) != operand.type || |
| count != operand.dimensions.size()) { |
| LOG(ERROR) << "Setting input/output with incompatible types"; |
| return ANEURALNETWORKS_BAD_DATA; |
| } |
| |
| dimensions = hidl_vec<uint32_t>(count); |
| for (uint32_t i = 0; i < count; i++) { |
| if (operand.dimensions[i] != 0 && operand.dimensions[i] != newType->dimensions[i]) { |
| LOG(ERROR) << "Overriding a fully specified dimension is disallowed"; |
| return ANEURALNETWORKS_BAD_DATA; |
| } else { |
| dimensions[i] = newType->dimensions[i]; |
| } |
| } |
| } |
| return ANEURALNETWORKS_NO_ERROR; |
| } |
| |
| ExecutionBuilder::ExecutionBuilder(const CompilationBuilder* 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) { |
| 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 (type != nullptr) { |
| int n = validateOperandType(*type, "ANeuralNetworksExecution_setInput", false); |
| if (n != ANEURALNETWORKS_NO_ERROR) { |
| return n; |
| } |
| } |
| 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() |
| |
| 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 (!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) { |
| 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 (type != nullptr) { |
| int n = validateOperandType(*type, "ANeuralNetworksExecution_setOutput", false); |
| if (n != ANEURALNETWORKS_NO_ERROR) { |
| return n; |
| } |
| } |
| 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() |
| |
| 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 (!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); |
| } |
| |
| // Attempt synchronous execution of full model on CPU. |
| // Ensure that executionCallback->notify() is called. |
| static void cpuFallbackFull(const ExecutionBuilder* executionBuilder, |
| const sp<ExecutionCallback>& executionCallback) { |
| NNTRACE_RT(NNTRACE_PHASE_EXECUTION, "cpuFallbackFull"); |
| VLOG(EXECUTION) << "cpuFallbackFull"; |
| StepExecutor executor(executionBuilder, executionBuilder->getModel(), |
| nullptr /* no VersionedIDevice, so CPU */, |
| nullptr /* no IPreparedModel */); |
| executor.mapInputsAndOutputsTrivially(); |
| sp<ExecutionCallback> fallbackCallback; |
| int n = executor.startCompute(&fallbackCallback); |
| if (n != ANEURALNETWORKS_NO_ERROR) { |
| executionCallback->notify(convertResultCodeToErrorStatus(n)); |
| return; |
| } |
| fallbackCallback->wait(); |
| executionCallback->notify(fallbackCallback->getStatus()); |
| } |
| |
| // 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. |
| static bool cpuFallbackPartial(const ExecutionBuilder* executionBuilder, |
| const ExecutionPlan* plan, |
| std::shared_ptr<ExecutionPlan::Controller> controller, |
| const sp<ExecutionCallback>& executionCallback) { |
| 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(); |
| if (fallbackCallback->getStatus() != ErrorStatus::NONE) { |
| cpuFallbackFull(executionBuilder, executionCallback); |
| return false; |
| } |
| return true; |
| } |
| |
| static void asyncStartComputePartitioned(const ExecutionBuilder* executionBuilder, |
| const ExecutionPlan* plan, |
| std::shared_ptr<ExecutionPlan::Controller> controller, |
| bool allowFallback, |
| const sp<ExecutionCallback>& executionCallback) { |
| VLOG(EXECUTION) << "ExecutionBuilder::compute (from plan, iteratively)"; |
| while (true) { |
| std::shared_ptr<StepExecutor> executor; |
| VLOG(EXECUTION) << "looking for next StepExecutor"; |
| int n = plan->next(controller, &executor); |
| if (n != ANEURALNETWORKS_NO_ERROR) { |
| if (allowFallback) { |
| cpuFallbackFull(executionBuilder, executionCallback); |
| } else { |
| executionCallback->notify(convertResultCodeToErrorStatus(n)); |
| } |
| return; |
| } |
| if (executor == nullptr) { |
| executionCallback->notify(ErrorStatus::NONE); |
| return; |
| } |
| |
| sp<ExecutionCallback> stepCallback; |
| n = executor->startCompute(&stepCallback); |
| if (n != ANEURALNETWORKS_NO_ERROR) { |
| if (allowFallback) { |
| if (cpuFallbackPartial(executionBuilder, plan, controller, executionCallback)) { |
| // 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)); |
| return; |
| } |
| } |
| stepCallback->wait(); |
| ErrorStatus status = stepCallback->getStatus(); |
| if (status != ErrorStatus::NONE) { |
| if (allowFallback) { |
| if (cpuFallbackPartial(executionBuilder, plan, controller, executionCallback)) { |
| // 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(status); |
| return; |
| } |
| } |
| } |
| } |
| |
| int ExecutionBuilder::compute(sp<ExecutionCallback>* synchronizationCallback) { |
| 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] { return synchronous ? "compute" : "startCompute"; }; |
| 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; |
| } |
| } |
| |
| // TODO: Remove the non-plan-based path once we've fully integrated ExecutionPlan |
| // with the compilation and execution phases of the NN API? Or retain that path |
| // as a fallback in the case of partitioning failure? |
| // |
| // 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. |
| if (mPartitioning > 0) { |
| const bool allowFallback = DeviceManager::partitioningAllowsFallback(mPartitioning); |
| std::shared_ptr<ExecutionPlan::Controller> controller = mPlan->makeController(this); |
| if (controller == nullptr) { |
| if (!allowFallback) { |
| return ANEURALNETWORKS_OP_FAILED; |
| } |
| } else if (synchronous) { |
| VLOG(EXECUTION) << "ExecutionBuilder::compute (synchronous API)"; |
| sp<ExecutionCallback> localSynchronizationCallback = new ExecutionCallback(); |
| asyncStartComputePartitioned(this, mPlan, controller, allowFallback, |
| localSynchronizationCallback); |
| localSynchronizationCallback->wait(); |
| 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(); |
| 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->bind_thread(std::move(thread)); |
| } |
| *synchronizationCallback = executionCallback; |
| return ANEURALNETWORKS_NO_ERROR; |
| } |
| } |
| |
| // Run on the CPU. |
| VLOG(EXECUTION) << "ExecutionBuilder::startCompute (without plan) on CPU"; |
| StepExecutor executor(this, mModel, |
| nullptr /* no VersionedIDevice, so CPU */, |
| nullptr /* no IPreparedModel */); |
| executor.mapInputsAndOutputsTrivially(); |
| if (synchronous) { |
| sp<ExecutionCallback> localSynchronizationCallback = new ExecutionCallback(); |
| executor.startCompute(&localSynchronizationCallback); |
| localSynchronizationCallback->wait(); |
| return convertErrorStatusToResultCode(localSynchronizationCallback->getStatus()); |
| } else { |
| return executor.startCompute(synchronizationCallback); |
| } |
| } |
| |
| // 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(const ExecutionBuilder* executionBuilder, const ModelBuilder* model, |
| VersionedIDevice* driver, |
| std::shared_ptr<VersionedIPreparedModel> preparedModel) |
| : mExecutionBuilder(executionBuilder), |
| mModel(model), |
| mDriver(driver), |
| mPreparedModel(preparedModel), |
| mInputs(model->inputCount()), |
| mOutputs(model->outputCount()) {} |
| |
| 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::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); |
| return inputOrOutputInfo->setFromTemporaryMemory(inputOrOutputOperand, poolIndex, offset); |
| } |
| |
| 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; |
| } |
| } |
| } |
| |
| int StepExecutor::startCompute(sp<ExecutionCallback>* synchronizationCallback) { |
| if (VLOG_IS_ON(EXECUTION)) { |
| logArguments("input", mInputs); |
| logArguments("output", mOutputs); |
| } |
| if (mDriver == nullptr) { |
| return startComputeOnCpu(synchronizationCallback); |
| } else { |
| return startComputeOnDevice(synchronizationCallback); |
| } |
| } |
| |
| int StepExecutor::startComputeOnDevice(sp<ExecutionCallback>* synchronizationCallback) { |
| nnAssert(mDriver != nullptr); |
| |
| *synchronizationCallback = nullptr; |
| |
| // TODO: Remove the mPreparedModel == nullptr case once we've fully integrated |
| // ExecutionPlan with the compilation and execution phases of the NN API |
| if (mPreparedModel == nullptr) { |
| Model model; |
| mModel->setHidlModel(&model); |
| |
| // TODO Dangerous! In async, the model will outlive it here. Safe for now |
| sp<PreparedModelCallback> preparedModelCallback = new PreparedModelCallback(); |
| // TODO(butlermichael): Propagate user preference to this point instead of |
| // using default value of ANEURALNETWORKS_PREFER_FAST_SINGLE_ANSWER, or |
| // remove this entire block of code since it is a stale path that is only |
| // encountered on an #if-removed code. |
| ExecutionPreference preference = |
| static_cast<ExecutionPreference>(ANEURALNETWORKS_PREFER_FAST_SINGLE_ANSWER); |
| ErrorStatus prepareLaunchStatus = mDriver->prepareModel(model, preference, |
| preparedModelCallback); |
| if (prepareLaunchStatus != ErrorStatus::NONE) { |
| return convertErrorStatusToResultCode(prepareLaunchStatus); |
| } |
| |
| // Immediately synchronize with callback object for now |
| // TODO: change to asynchronous later |
| preparedModelCallback->wait(); |
| ErrorStatus prepareReturnStatus = preparedModelCallback->getStatus(); |
| if (auto preparedModel = preparedModelCallback->getPreparedModel()) { |
| mPreparedModel = std::make_shared<VersionedIPreparedModel>(preparedModel); |
| } |
| if (prepareReturnStatus != ErrorStatus::NONE) { |
| return convertErrorStatusToResultCode(prepareReturnStatus); |
| } |
| if (mPreparedModel == nullptr) { |
| return ANEURALNETWORKS_OP_FAILED; |
| } |
| } |
| |
| 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(); |
| |
| 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, executionCallback); |
| if (!executeStatus.isOk() || executeStatus != ErrorStatus::NONE) { |
| VLOG(EXECUTION) << "**Execute 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) << "**Execute async failed**"; |
| return callbackStatus.isOk() |
| ? convertErrorStatusToResultCode(callbackStatus) |
| : ANEURALNETWORKS_OP_FAILED; |
| } |
| |
| // 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); |
| executionCallback->notify_1_2(convertResultCodeToErrorStatus(err)); |
| } |
| |
| 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. |
| |
| Model model; |
| mModel->setHidlModel(&model); |
| |
| // 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()); |
| bool fail = false; |
| for (const Memory* mem : mMemories) { |
| requestPoolInfos.emplace_back(mem->getHidlMemory(), &fail); |
| } |
| if (fail) { |
| 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(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->bind_thread(std::move(thread)); |
| } |
| |
| *synchronizationCallback = executionCallback; |
| return ANEURALNETWORKS_NO_ERROR; |
| } |
| |
| } // namespace nn |
| } // namespace android |
