/*
* 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 "ExecutionPlan"
#include "ExecutionPlan.h"
#include "Callbacks.h"
#include "CompilationBuilder.h"
#include "ExecutionBuilder.h"
#include "Manager.h"
#include "ModelBuilder.h"
#include "Utils.h"
#include <functional>
#include <map>
#include <queue>
#include <unordered_set>
#include <utility>
#include <vector>
using ::android::hardware::neuralnetworks::V1_0::implementation::ExecutionCallback;
using ::android::hardware::neuralnetworks::V1_0::implementation::PreparedModelCallback;
namespace android {
namespace nn {
static int compile(std::shared_ptr<Device> device, const ModelBuilder* model,
int32_t executionPreference, sp<IPreparedModel>* preparedModel) {
nnAssert(device != nullptr); // nullptr indicates CPU
// Compilation logic copied from ExecutionBuilder::startComputeOnDevice().
Model hidlModel;
model->setHidlModel(&hidlModel);
sp<PreparedModelCallback> preparedModelCallback = new PreparedModelCallback();
Return<ErrorStatus> prepareLaunchStatus = device->getInterface()->prepareModel(
hidlModel, static_cast<ExecutionPreference>(executionPreference), preparedModelCallback);
if (!prepareLaunchStatus.isOk()) {
LOG(ERROR) << "ExecutionStep::finishSubModel compilation failed due to transport error: "
<< prepareLaunchStatus.description();
return ANEURALNETWORKS_OP_FAILED;
}
if (prepareLaunchStatus != ErrorStatus::NONE) {
LOG(ERROR) << "ExecutionStep::finishSubModel compilation failed with error: "
<< toString(static_cast<ErrorStatus>(prepareLaunchStatus));
return ANEURALNETWORKS_OP_FAILED;
}
preparedModelCallback->wait();
ErrorStatus prepareReturnStatus = preparedModelCallback->getStatus();
*preparedModel = preparedModelCallback->getPreparedModel();
if (prepareReturnStatus != ErrorStatus::NONE || *preparedModel == nullptr) {
LOG(ERROR) << "ExecutionPlan compilation on " << device->getName() << " failed:"
<< " prepareReturnStatus=" << toString(prepareReturnStatus)
<< ", preparedModel=" << preparedModel->get();
return ANEURALNETWORKS_OP_FAILED;
}
return ANEURALNETWORKS_NO_ERROR;
}
typedef std::function<void(uint32_t)> OperationReadyCallback;
// This class tracks whether we know the value of an operand as operations
// are processed.
class OperandTracker {
public:
// Creates the tracker for this model. Figure out which operations can be
// executed right away and cb for each one of them.
OperandTracker(const ModelBuilder* model, OperationReadyCallback cb);
// Mark the specified operation as having been processed. The output
// of the operation now being known, this may make new operations to be
// able to run. Call cb for each one of them.
void markProcessed(uint32_t operationIndex, OperationReadyCallback cb);
private:
const ModelBuilder* mModel;
std::multimap<uint32_t, uint32_t> mOperandToOperations;
std::vector<uint32_t> mUnknownInputCount; // For each operation
};
OperandTracker::OperandTracker(const ModelBuilder* model, OperationReadyCallback cb) :
mModel(model) {
const auto& operations = mModel->getOperations();
mUnknownInputCount.resize(operations.size());
for (uint32_t operationIndex = 0; operationIndex < operations.size(); operationIndex++) {
const Operation& operation = operations[operationIndex];
uint32_t count = 0;
for (uint32_t operandIndex : operation.inputs) {
auto lifetime = mModel->getOperand(operandIndex).lifetime;
if (lifetime == OperandLifeTime::TEMPORARY_VARIABLE ||
lifetime == OperandLifeTime::MODEL_OUTPUT) {
count++;
mOperandToOperations.insert(
std::pair<uint32_t, uint32_t>(operandIndex, operationIndex));
}
}
if (count == 0) {
cb(operationIndex);
}
mUnknownInputCount[operationIndex] = count;
}
}
void OperandTracker::markProcessed(uint32_t operationIndex, OperationReadyCallback cb) {
// Mark all its outputs as known.
const Operation& operation = mModel->getOperations()[operationIndex];
for (uint32_t operandIndex : operation.outputs) {
auto range = mOperandToOperations.equal_range(operandIndex);
for (auto i = range.first; i != range.second; i++) {
uint32_t& count = mUnknownInputCount[i->second];
if (--count == 0) {
cb(i->second);
}
}
}
}
ExecutionStep::ExecutionStep(ExecutionPlan* plan, uint32_t stepIndex,
std::shared_ptr<Device> device)
: mPlan(plan), mIndex(stepIndex), mSubModel(), mDevice(device) {}
// Adds an operand if it has not been added already.
// Sets the index in the submodel for the corresponding operand.
int ExecutionStep::addOperand(uint32_t fromOperandIndex, uint32_t* toOperandIndex,
const ModelBuilder& fromModel, OperandKind kind) {
// Have we added this operand already?
auto i = mOperandMap.find(fromOperandIndex);
if (i != mOperandMap.end()) {
nnAssert(kind == INPUT);
*toOperandIndex = i->second;
return ANEURALNETWORKS_NO_ERROR;
}
// First time we add this operand.
*toOperandIndex = mSubModel.operandCount();
mOperandMap.insert(std::pair<uint32_t, uint32_t>(fromOperandIndex, *toOperandIndex));
// Add the operand to the submodel.
const Operand& operand = fromModel.getOperand(fromOperandIndex);
ANeuralNetworksOperandType type = {
.type = static_cast<int32_t>(operand.type),
.dimensionCount = static_cast<uint32_t>(operand.dimensions.size()),
.dimensions = operand.dimensions.size() > 0 ? operand.dimensions.data() : nullptr,
.scale = operand.scale,
.zeroPoint = operand.zeroPoint
};
int n = mSubModel.addOperand(type);
if (n != ANEURALNETWORKS_NO_ERROR) {
LOG(ERROR) << "Previous error occurred when partitioning the graph";
return n;
}
// Sets its value.
switch (operand.lifetime) {
case OperandLifeTime::CONSTANT_COPY: {
const uint8_t* data = fromModel.getPointerToOperandValue(operand.location.offset);
n = mSubModel.setOperandValue(*toOperandIndex, data, operand.location.length);
if (n != ANEURALNETWORKS_NO_ERROR) {
LOG(ERROR) << "Previous error occurred when partitioning the graph";
return n;
}
} break;
case OperandLifeTime::CONSTANT_REFERENCE: {
const Memory* memory = fromModel.getMemories()[operand.location.poolIndex];
n = mSubModel.setOperandValueFromMemory(*toOperandIndex, memory,
operand.location.offset,
operand.location.length);
if (n != ANEURALNETWORKS_NO_ERROR) {
LOG(ERROR) << "Previous error occurred when partitioning the graph";
return n;
}
} break;
case OperandLifeTime::NO_VALUE: {
n = mSubModel.setOperandValue(*toOperandIndex, nullptr, 0);
if (n != ANEURALNETWORKS_NO_ERROR) {
LOG(ERROR) << "Previous error occurred when partitioning the graph";
return n;
}
} break;
case OperandLifeTime::TEMPORARY_VARIABLE: // handled similarly to MODEL_OUTPUT
if (kind == INPUT) {
// The first time we've seen this operand is as an
// input. That means it must be defined by a
// different partition, and is an input to this one.
mTempsAsSubModelInputs.push_back(std::make_pair(fromOperandIndex, *toOperandIndex));
} else {
// The first time we've seen this operand is as an
// output. It may be an input to a different
// partition, so keep track of it.
mPlan->recordTemporaryDef(fromOperandIndex, mIndex);
}
break;
case OperandLifeTime::MODEL_INPUT:
mModelInputs.push_back(std::make_pair(fromOperandIndex, *toOperandIndex));
break;
case OperandLifeTime::MODEL_OUTPUT: // handled similarly to TEMPORARY_VARIABLE
if (kind == INPUT) {
// The first time we've seen this operand is as an
// input. That means it must be defined by a
// different partition, and is an input to this one.
mOutputsAsSubModelInputs.push_back(std::make_pair(fromOperandIndex, *toOperandIndex));
} else {
// The first time we've seen this operand is as an
// output.
mModelOutputs.push_back(std::make_pair(fromOperandIndex, *toOperandIndex));
}
break;
default:
nnAssert(false);
break;
}
return ANEURALNETWORKS_NO_ERROR;
}
int ExecutionStep::addOperation(int operationIndex, const ModelBuilder& fromModel) {
const Operation& operation = fromModel.getOperation(operationIndex);
// Convert the input and output operand indexes.
//
// We expect operations to be added in topological order. Therefore:
//
// - We may not have seen an input if it is a model input, a
// constant, or an operand written by a different partition.
//
// - We should not have seen any outputs.
const uint32_t inputCount = static_cast<uint32_t>(operation.inputs.size());
const uint32_t outputCount = static_cast<uint32_t>(operation.outputs.size());
std::vector<uint32_t> inputs(inputCount);
std::vector<uint32_t> outputs(outputCount);
auto addOperands = [this, &fromModel](const hidl_vec<uint32_t>& globalOperands,
std::vector<uint32_t>& localOperands,
OperandKind kind) -> int {
const uint32_t operandCount = static_cast<uint32_t>(globalOperands.size());
for (uint32_t i = 0; i < operandCount; i++) {
uint32_t localOperand = ~0U;
int n = addOperand(globalOperands[i], &localOperand, fromModel, kind);
if (n != ANEURALNETWORKS_NO_ERROR)
return n;
localOperands[i] = localOperand;
}
return ANEURALNETWORKS_NO_ERROR;
};
int n;
if ((n = addOperands(operation.inputs, inputs, INPUT)) != ANEURALNETWORKS_NO_ERROR ||
(n = addOperands(operation.outputs, outputs, OUTPUT)) != ANEURALNETWORKS_NO_ERROR) {
return n;
}
return mSubModel.addOperation(static_cast<uint32_t>(operation.type), inputCount, inputs.data(),
outputCount, outputs.data());
}
void ExecutionStep::mapInputsAndOutputs(std::shared_ptr<StepExecutor> stepExecutor) const {
for (uint32_t i = 0, e = mInputIndexSubModelToFromModel.size(); i < e; i++) {
stepExecutor->mapInput(mInputIndexSubModelToFromModel[i], i);
}
for (uint32_t i = 0, e = mOutputIndexSubModelToFromModel.size(); i < e; i++) {
stepExecutor->mapOutput(mOutputIndexSubModelToFromModel[i], i);
}
}
void ExecutionPlan::CompoundBody::findTempsAsSubModelOutputs() {
for (const auto& step : mSteps) {
for (const auto& input : step->getTempsAsSubModelInputs()) {
const uint32_t fromModelIndex = input.first;
const auto it = mTemporaryToDefiningStep.find(fromModelIndex);
nnAssert(it != mTemporaryToDefiningStep.end());
const uint32_t stepIndex = it->second;
nnAssert(stepIndex < mSteps.size());
mSteps[stepIndex]->recordTempAsSubModelOutput(fromModelIndex);
}
}
}
void ExecutionStep::logSubModel() const {
VLOG(COMPILATION) << "ExecutionStep::finishSubModel, step " << mIndex;
auto logRemapEntry = [](std::string &toLog, const std::pair<uint32_t, uint32_t>& e) {
if (!toLog.empty()) {
toLog += ", ";
}
toLog += "(";
toLog += std::to_string(e.first);
toLog += "->";
toLog += std::to_string(e.second);
toLog += ")";
};
auto logRemapVector = [&logRemapEntry](const char* name, const RemapVectorType& map) {
std::string toLog;
for (const auto& e : map) {
logRemapEntry(toLog, e);
}
VLOG(COMPILATION) << name << ": " << toLog;
};
auto logRemapSet = [&logRemapEntry](const char* name, const SubModelOutputSetType& set) {
std::string toLog;
for (const auto& e : set) {
logRemapEntry(toLog, e);
}
VLOG(COMPILATION) << name << ": " << toLog;
};
logRemapVector("model inputs", mModelInputs);
logRemapVector("model outputs", mModelOutputs);
logRemapVector("temps as submodel inputs", mTempsAsSubModelInputs);
logRemapSet("temps as submodel outputs", mTempsAsSubModelOutputs);
logRemapVector("outputs as submodel inputs", mOutputsAsSubModelInputs);
}
static void convertModelInputsOrOutputs(
// IN: mModel{Inputs|Outputs}
const ExecutionStep::RemapVectorType& myModelInputsOrOutputs,
// IN: fromModel->{input|output}Count()
uint32_t fromModelInputOrOutputCount,
// IN: fromModel->get{Input|Output}OperandIndex
std::function<uint32_t(uint32_t)> fromModelGetInputOrOutputOperandIndex,
// OUT: for v : mModel{Inputs|Outputs} : v.second
std::vector<uint32_t>* inputsOrOutputs,
// OUT: submodel input-or-output index to original model input-or-output index
std::vector<uint32_t>* inputOrOutputIndexSubModelToFromModel) {
std::map<uint32_t, uint32_t> fromModelIndexMap; // operand index to input-or-output index
for (uint32_t i = 0; i < fromModelInputOrOutputCount; i++) {
fromModelIndexMap[fromModelGetInputOrOutputOperandIndex(i)] = i;
}
for (const auto& myInputOrOutput : myModelInputsOrOutputs) {
inputsOrOutputs->push_back(myInputOrOutput.second);
const uint32_t fromModelInputOrOutputIndex = fromModelIndexMap[myInputOrOutput.first];
inputOrOutputIndexSubModelToFromModel->push_back(fromModelInputOrOutputIndex);
}
}
int ExecutionStep::finishSubModel(const ModelBuilder* fromModel, bool* hasOutputOfUnknownSize,
int32_t executionPreference) {
if (VLOG_IS_ON(COMPILATION)) {
logSubModel();
}
mSubModel.relaxComputationFloat32toFloat16(fromModel->isComputationFloat32RelaxedToFloat16());
// Input order: mModelInputs, mTempsAsSubModelInputs, mOutputsAsSubModelInputs
// Output order: mModelOutputs, mTempsAsSubModelOutputs
//
// ExecutionPlan::next() depends on these orderings.
std::vector<uint32_t> inputs;
convertModelInputsOrOutputs(mModelInputs,
fromModel->inputCount(),
[=](uint32_t i) { return fromModel->getInputOperandIndex(i); },
&inputs,
&mInputIndexSubModelToFromModel);
for (const auto& subModelInput : mTempsAsSubModelInputs) {
inputs.push_back(subModelInput.second);
}
for (const auto& subModelInput : mOutputsAsSubModelInputs) {
inputs.push_back(subModelInput.second);
}
std::vector<uint32_t> outputs;
convertModelInputsOrOutputs(mModelOutputs,
fromModel->outputCount(),
[=](uint32_t i) { return fromModel->getOutputOperandIndex(i); },
&outputs,
&mOutputIndexSubModelToFromModel);
for (const auto& subModelOutput : mTempsAsSubModelOutputs) {
outputs.push_back(subModelOutput.second);
const Operand& operand = mSubModel.getOperand(subModelOutput.second);
for (uint32_t dimension : operand.dimensions) {
if (dimension == 0) {
*hasOutputOfUnknownSize = true;
VLOG(COMPILATION) << "SubModelOutput (operand#" << subModelOutput.first
<< " of original graph) has unknown size: "
<< toString(operand);
break;
}
}
}
{
int n = mSubModel.identifyInputsAndOutputs(inputs.size(), &inputs[0], outputs.size(), &outputs[0]);
if (n != ANEURALNETWORKS_NO_ERROR) {
return n;
}
n = mSubModel.finish();
if (n != ANEURALNETWORKS_NO_ERROR) {
return n;
}
}
{
// Compute mOutputsAsSubModelInputsIndexToFromModel.
std::map<uint32_t, uint32_t> fromModelOperandIndexToOutputIndex;
for (unsigned i = 0, e = fromModel->outputCount(); i < e; ++i) {
fromModelOperandIndexToOutputIndex[fromModel->getOutputOperandIndex(i)] = i;
}
for (unsigned i = 0, e = mOutputsAsSubModelInputs.size(); i < e; i++) {
const uint32_t fromModelOperandIndex = mOutputsAsSubModelInputs[i].first;
const auto it = fromModelOperandIndexToOutputIndex.find(fromModelOperandIndex);
if (it == fromModelOperandIndexToOutputIndex.end()) {
LOG(ERROR) << "Could not find main model output operand " << fromModelOperandIndex
<< " in main model output operand list";
return ANEURALNETWORKS_BAD_STATE;
}
mOutputsAsSubModelInputsIndexToFromModel.push_back(it->second);
}
}
// TODO: Move compilation elsewhere?
if (mDevice == nullptr) {
return ANEURALNETWORKS_NO_ERROR;
}
VLOG(COMPILATION) << "ExecutionStep::finishSubModel, compilation";
return compile(mDevice, &mSubModel, executionPreference, &mPreparedSubModel);
}
void ExecutionStep::dump() const {
Model model;
mSubModel.setHidlModel(&model);
if (VLOG_IS_ON(COMPILATION)) {
VLOG(COMPILATION) << "ExecutionStep#" << mIndex
<< " for " << (mDevice == nullptr ? "CPU" : mDevice->getName());
logModelToInfo(model);
}
}
int ExecutionPlan::CompoundBody::finish(const ModelBuilder* fromModel,
int32_t executionPreference) {
findTempsAsSubModelOutputs();
for (const auto& step : mSteps) {
int n = step->finishSubModel(fromModel, &mHasSubModelOutputOfUnknownSize,
executionPreference);
if (n != ANEURALNETWORKS_NO_ERROR) {
VLOG(COMPILATION) << "ExecutionPlan::CompoundBody::finish -- finishSubModel failed";
return n;
}
}
if (mHasSubModelOutputOfUnknownSize) {
VLOG(COMPILATION) << "ExecutionPlan::CompoundBody::finish -- mHasSubModelOutputOfUnknownSize";
return ANEURALNETWORKS_OP_FAILED;
}
mSuccessfulFinish = true;
return ANEURALNETWORKS_NO_ERROR;
}
int ExecutionPlan::SimpleBody::finish([[maybe_unused]] const ModelBuilder* fromModel,
int32_t executionPreference) {
if (mDevice == nullptr) {
mSuccessfulFinish = true;
return ANEURALNETWORKS_NO_ERROR;
}
VLOG(COMPILATION) << "ExecutionPlan::SimpleBody::finish, compilation";
const int n = compile(mDevice, mModel, executionPreference, &mPreparedModel);
mSuccessfulFinish = (n == ANEURALNETWORKS_NO_ERROR);
return n;
}
int ExecutionPlan::finish(const ModelBuilder* fromModel, int32_t executionPreference) {
nnAssert(mBody != nullptr);
return mBody->finish(fromModel, executionPreference);
}
ExecutionPlan::Controller::Controller(
const ExecutionPlan* plan,
const ExecutionBuilder* executionBuilder,
std::shared_ptr<const SubModelInputsAndOutputsType> subModelInputsAndOutputs,
uint32_t totalSizeOfTemporaries) :
mPlan(plan), mExecutionBuilder(executionBuilder),
mSubModelInputsAndOutputs(subModelInputsAndOutputs), mNextStepIndex(0) {
if (totalSizeOfTemporaries) {
if (mTemporaries.create(totalSizeOfTemporaries) != ANEURALNETWORKS_NO_ERROR) {
LOG(ERROR) << "ExecutionPlan::Controller failed to allocate temporaries";
mNextStepIndex = kBadStepIndex;
}
}
}
std::shared_ptr<ExecutionPlan::Controller> ExecutionPlan::makeController(
const ExecutionBuilder* executionBuilder) const {
nnAssert((mState == EMPTY) == (mBody == nullptr));
if (mBody && !mBody->mSuccessfulFinish) {
VLOG(EXECUTION) << "ExecutionPlan::makeController -- unsuccessful finish";
return std::shared_ptr<Controller>(nullptr);
}
// Create the layout for a Memory object big enough for to hold
// every TEMPORARY in the original model that is live across
// partition boundaries.
//
// TODO: Rethink this approach for managing temporaries. Some
// alternatives:
//
// 1) Adopt a memory layout scheme analogous to stack allocation,
// where objects of non-overlapping lifetime can occupy the same
// storage. We would still have a single Memory object in this
// case.
//
// 2) Do something like what CpuExecutor does, and do allocations
// and deallocations on the fly (during execution) before first
// reference and after last reference, respectively. This would
// mean having one Memory object per TEMPORARY; or, in a more
// complicated implementation, one Memory object per set of
// temporaries that have the same lifetime. Note that the Android
// system limits the number of shared memory objects, which are
// what our Memory objects represent.
//
uint32_t totalSizeOfTemporaries = 0;
std::shared_ptr<Controller::SubModelInputsAndOutputsType> subModelInputsAndOutputs;
if (mState == COMPOUND) {
const ModelBuilder* fromModel = executionBuilder->getModel();
for (const auto& step : compound()->mSteps) {
for (const auto& output: step->getTempsAsSubModelOutputs()) {
const uint32_t fromModelOperandIndex = output.first;
const Operand& fromModelOperand = fromModel->getOperand(fromModelOperandIndex);
if (subModelInputsAndOutputs == nullptr) {
subModelInputsAndOutputs =
std::make_shared<Controller::SubModelInputsAndOutputsType>();
}
const uint32_t size = sizeOfData(fromModelOperand);
totalSizeOfTemporaries += alignBytesNeeded(totalSizeOfTemporaries, size);
subModelInputsAndOutputs->insert(std::make_pair(fromModelOperandIndex, totalSizeOfTemporaries));
totalSizeOfTemporaries += size;
}
}
if (VLOG_IS_ON(EXECUTION) && (subModelInputsAndOutputs != nullptr)) {
for (const auto& io : *subModelInputsAndOutputs) {
VLOG(EXECUTION) << "temp: origOpndIdx = " << io.first
<< ", offset = " << io.second;
}
}
}
return std::shared_ptr<Controller>(new Controller(this, executionBuilder,
subModelInputsAndOutputs,
totalSizeOfTemporaries));
}
// TODO: Find a better way to provide this functionality.
int ExecutionPlan::fallback(std::shared_ptr<Controller> controller,
std::shared_ptr<StepExecutor>* executor) const {
*executor = nullptr;
VLOG(EXECUTION) << "ExecutionPlan::fallback(" << controller << ", " << executor
<< "): mNextStepIndex = " << controller->mNextStepIndex;
if (controller->mNextStepIndex == 0) {
// We haven't called next().
return ANEURALNETWORKS_OP_FAILED;
}
if (controller->mNextStepIndex == Controller::kBadStepIndex) {
// The last call to next() did not produce an executor.
return ANEURALNETWORKS_OP_FAILED;
}
--controller->mNextStepIndex;
return next(controller, executor);
}
int ExecutionPlan::next(std::shared_ptr<Controller> controller,
std::shared_ptr<StepExecutor>* executor) const {
*executor = nullptr;
VLOG(EXECUTION) << "ExecutionPlan::next("
<< SHOW_IF_DEBUG(controller << ", " << executor)
<< "): mNextStepIndex = " << controller->mNextStepIndex;
if (controller->mNextStepIndex == Controller::kBadStepIndex) {
return ANEURALNETWORKS_OP_FAILED;
}
if (mState == EMPTY) {
nnAssert(controller->mNextStepIndex == 0); // end
controller->mNextStepIndex = Controller::kBadStepIndex;
return ANEURALNETWORKS_NO_ERROR;
}
if (mState == SIMPLE) {
if (controller->mNextStepIndex == 0) {
// First (and only) step.
auto simpleBody = static_cast<const SimpleBody*>(mBody);
*executor = std::make_shared<StepExecutor>(
controller->mExecutionBuilder,
simpleBody->mModel,
(simpleBody->mDevice == nullptr ? nullptr : simpleBody->mDevice->getInterface()),
simpleBody->mPreparedModel);
(*executor)->mapInputsAndOutputsTrivially();
controller->mNextStepIndex = 1;
return ANEURALNETWORKS_NO_ERROR;
}
nnAssert(controller->mNextStepIndex == 1); // end
controller->mNextStepIndex = Controller::kBadStepIndex;
return ANEURALNETWORKS_NO_ERROR;
}
auto compoundBody = compound();
if (controller->mNextStepIndex == compoundBody->mSteps.size()) {
// end
controller->mNextStepIndex = Controller::kBadStepIndex;
return ANEURALNETWORKS_NO_ERROR;
}
// Input order: model inputs, temps as submodel inputs, outputs as submodel inputs
// Output order: model outputs, temps as submodel outputs
//
// ExecutionStep::finishSubModel() establishes these orderings.
const auto step = compoundBody->mSteps[controller->mNextStepIndex];
*executor = std::make_shared<StepExecutor>(
controller->mExecutionBuilder,
step->getSubModel(),
(step->getDevice() == nullptr ? nullptr : step->getDevice()->getInterface()),
step->getPreparedSubModel());
step->mapInputsAndOutputs(*executor);
if (controller->mSubModelInputsAndOutputs != nullptr) {
{
// Tell executor about temps as submodel outputs.
const size_t firstSubModelOutputIndex = step->getModelOutputs().size();
const auto& subModelOutputs = step->getTempsAsSubModelOutputs();
uint32_t idx = 0;
for (auto I = subModelOutputs.begin(), E = subModelOutputs.end(); I != E; I++, idx++) {
const uint32_t fromModelOperandIndex = I->first;
const uint32_t offsetOfTemporary =
controller->mSubModelInputsAndOutputs->at(fromModelOperandIndex);
int n = (*executor)->setOutputFromTemporaryMemory(
firstSubModelOutputIndex + idx,
&controller->mTemporaries,
offsetOfTemporary);
if (n != ANEURALNETWORKS_NO_ERROR) {
controller->mNextStepIndex = Controller::kBadStepIndex;
return n;
}
}
}
{
// Tell executor about temps as submodel inputs.
const size_t firstSubModelInputIndex = step->getModelInputs().size();
const auto& subModelInputs = step->getTempsAsSubModelInputs();
uint32_t idx = 0;
for (auto I = subModelInputs.begin(), E = subModelInputs.end(); I != E; I++, idx++) {
const uint32_t fromModelOperandIndex = I->first;
const uint32_t offsetOfTemporary =
controller->mSubModelInputsAndOutputs->at(fromModelOperandIndex);
int n = (*executor)->setInputFromTemporaryMemory(
firstSubModelInputIndex + idx,
&controller->mTemporaries,
offsetOfTemporary);
if (n != ANEURALNETWORKS_NO_ERROR) {
controller->mNextStepIndex = Controller::kBadStepIndex;
return n;
}
}
}
}
{
// Tell executor about outputs as submodel inputs.
const size_t firstOutputsAsSubModelInputIndex =
step->getModelInputs().size() + step->getTempsAsSubModelInputs().size();
const auto& outputsAsSubModelInputsIndexToFromModel =
step->getOutputsAsSubModelInputsIndexToFromModel();
for (uint32_t i = 0, e = outputsAsSubModelInputsIndexToFromModel.size(); i < e; i++) {
uint32_t o = outputsAsSubModelInputsIndexToFromModel[i];
(*executor)->mapOutputToInput(o, firstOutputsAsSubModelInputIndex + i);
}
}
controller->mNextStepIndex++;
return ANEURALNETWORKS_NO_ERROR;
}
std::shared_ptr<ExecutionStep> ExecutionPlan::createNewStep(const std::shared_ptr<Device> device) {
nnAssert(mState != SIMPLE);
if (mState == EMPTY) {
mBody = new CompoundBody();
mState = COMPOUND;
}
auto& steps = compound()->mSteps;
auto step = std::make_shared<ExecutionStep>(this, steps.size(), device);
steps.push_back(step);
return step;
}
void ExecutionPlan::becomeSingleStep(const std::shared_ptr<Device> device,
const ModelBuilder* model) {
nnAssert(mState == EMPTY);
mBody = new SimpleBody(device, model);
mState = SIMPLE;
}
void ExecutionPlan::dump() const {
if (mBody) {
mBody->dump();
} else {
VLOG(COMPILATION) << "EMPTY";
}
}
ExecutionPlan::Kind ExecutionPlan::forTest_getKind() const {
switch (mState) {
case EMPTY:
return Kind::EMPTY;
case SIMPLE:
nnAssert(mBody);
return mBody->mSuccessfulFinish ? Kind::SIMPLE : Kind::ERROR;
case COMPOUND:
nnAssert(mBody);
return mBody->mSuccessfulFinish ? Kind::COMPOUND : Kind::ERROR;
default:
nnAssert(!"unexpected state");
return Kind::ERROR;
}
}
std::shared_ptr<const Device> ExecutionPlan::forTest_simpleGetDevice() const {
nnAssert(mState == SIMPLE);
return static_cast<const SimpleBody*>(mBody)->mDevice;
}
const std::vector<std::shared_ptr<ExecutionStep>>& ExecutionPlan::forTest_compoundGetSteps() const {
return compound()->mSteps;
}
bool ExecutionPlan::forTest_hasSubModelOutputsOfUnknownSize() const {
return mBody->hasSubModelOutputsOfUnknownSize();
}
void ExecutionPlan::SimpleBody::dump() const {
VLOG(COMPILATION) << "SIMPLE for " << (mDevice == nullptr ? "CPU" : mDevice->getName());
}
void ExecutionPlan::CompoundBody::dump() const {
for (const auto& step : mSteps) {
step->dump();
}
}
int ModelBuilder::partitionTheWork(const std::vector<std::shared_ptr<Device>>& devices,
uint32_t preference, ExecutionPlan* plan) const {
// This function uses a heuristic approach to partitioning the graph.
// It should be good enough for the first release.
const size_t nonCpuDeviceCount = devices.size();
// The device count is the number of HAL devices + 1. The +1 is for the CPU.
// Note that deviceCount includes CPU, which has no entry in devices[].
const size_t deviceCount = nonCpuDeviceCount + 1;
const size_t operationCount = mOperations.size();
VLOG(COMPILATION) << "ModelBuilder::partitionTheWork: deviceCount = " << deviceCount
<< ", operationCount = " << operationCount;
// If we only have the CPU, or if the graph has no operations, no need to try to partition.
if (nonCpuDeviceCount == 0 || operationCount == 0) {
// Make sure no op is an OEM operation.
for (auto& op: mOperations) {
if (op.type == OperationType::OEM_OPERATION) {
LOG(ERROR) << "No driver can do the OEM op";
return ANEURALNETWORKS_BAD_DATA;
}
}
plan->becomeSingleStep(nullptr /* CPU */, this);
return plan->finish(this, preference);
}
// Figure out where each operation will best execute.
// The value of the vector is the index in the devices vector, with devices.size()
// representing the CPU.
std::vector<int> bestDeviceForOperation(operationCount);
int status = findBestDeviceForEachOperation(preference, devices, deviceCount,
&bestDeviceForOperation);
if (status != ANEURALNETWORKS_NO_ERROR) {
return status;
}
// If one device will run all the operations, we don't need to split the work.
if (std::adjacent_find(bestDeviceForOperation.begin(), bestDeviceForOperation.end(),
std::not_equal_to<int>()) == bestDeviceForOperation.end()) {
const int bestDeviceIndex = bestDeviceForOperation[0];
const bool cpu = (size_t(bestDeviceIndex) == deviceCount - 1);
VLOG(COMPILATION) << "ModelBuilder::partitionTheWork: only one best device: "
<< bestDeviceIndex << " = "
<< (cpu ? "CPU" : devices[bestDeviceIndex]->getName());
plan->becomeSingleStep(cpu ? nullptr : devices[bestDeviceIndex], this);
return plan->finish(this, preference);
}
// No easy solution, we need to split the work.
// We keep track of the operations that are ready to run for each device.
std::vector<std::queue<uint32_t>> perDeviceQueue(deviceCount);
// This helper function enqueues the operation on the appropriate queue.
auto enqueueOnAppropriateDevice = [&](uint32_t operationIndex) {
int deviceIndex = bestDeviceForOperation[operationIndex];
perDeviceQueue[deviceIndex].push(operationIndex);
VLOG(COMPILATION) << "enqueueOnAppropriateDevice " << operationIndex << " onto "
<< deviceIndex;
};
// This helper function finds a device that has operations ready to process.
// We start by looking at the CPU. We do this to try to maximize the
// size of the graph we'll send to non-CPU devices. If the CPU runs first,
// it will have the chance to prepare more of the inputs required by the
// other devices. This function returns -1 if all queues are empty.
auto findNextDeviceToProcess = [&]() -> int {
for (int i = deviceCount - 1; i >= 0; i--) {
if (!perDeviceQueue[i].empty()) {
return i;
}
}
return -1;
};
OperandTracker tracker(this, enqueueOnAppropriateDevice);
// For each iteration of this loop, we'll create an execution step.
while (true) {
// Find the device we'll do this step for.
int deviceIndex = findNextDeviceToProcess();
VLOG(COMPILATION) << "findNextDeviceToProcess: " << deviceIndex;
if (deviceIndex < 0) {
break;
}
// nullptr represents the CPU.
std::shared_ptr<Device> device =
static_cast<size_t>(deviceIndex) < nonCpuDeviceCount
? devices[deviceIndex] : nullptr;
// Assign as much as possible to this device.
std::shared_ptr<ExecutionStep> step = plan->createNewStep(device);
auto& queue = perDeviceQueue[deviceIndex];
while (!queue.empty()) {
uint32_t operationIndex = queue.front();
queue.pop();
int n = step->addOperation(operationIndex, *this);
if (n != ANEURALNETWORKS_NO_ERROR) {
LOG(ERROR) << "failed to add operation " << operationIndex << " to step";
return n;
}
tracker.markProcessed(operationIndex, enqueueOnAppropriateDevice);
}
}
int n = plan->finish(this, preference);
if (VLOG_IS_ON(COMPILATION)) {
Model model;
setHidlModel(&model);
VLOG(COMPILATION) << "ModelBuilder::partitionTheWork: original model: ";
logModelToInfo(model);
plan->dump();
}
return n;
}
PerformanceInfo ModelBuilder::getPerformanceInfo(const std::shared_ptr<Device> device,
uint32_t operationIndex) const {
const Operation& operation = getOperation(operationIndex);
// TODO This assumes that the type is dictated by the first operand. This is
// currently the case but is not a safe assumption to make in the long term.
const uint32_t operandIndex = operation.inputs[0];
const OperandType operandType = mOperands[operandIndex].type;
switch(operandType) {
case OperandType::FLOAT32:
case OperandType::TENSOR_FLOAT32:
if (mRelaxComputationFloat32toFloat16) {
return device->getRelaxedFloat32toFloat16Performance();
} else {
return device->getFloat32Performance();
}
case OperandType::INT32:
case OperandType::UINT32:
case OperandType::TENSOR_INT32:
case OperandType::TENSOR_QUANT8_ASYMM:
// For OEM, the real selection will be made from who can run the operand.
case OperandType::OEM:
case OperandType::TENSOR_OEM_BYTE:
return device->getQuantized8Performance();
default:
nnAssert(false);
return device->getQuantized8Performance();
}
}
namespace {
// This class determines whether a given device can execute a given operation
class CanDo {
public:
CanDo() {}
void initialize(const ModelBuilder* model, std::shared_ptr<Device> device) {
Model hidlModel;
model->setHidlModel(&hidlModel);
device->getSupportedOperations(hidlModel, &mSupportsOperationByIndex);
}
bool check(size_t operationIndex) const { return mSupportsOperationByIndex[operationIndex]; }
private:
hidl_vec<bool> mSupportsOperationByIndex;
};
}; // anonymous namespace
int ModelBuilder::findBestDeviceForEachOperation(
uint32_t preference,
const std::vector<std::shared_ptr<Device>>& devices,
const size_t deviceCount,
std::vector<int>* bestDeviceForOperation) const {
// Note that deviceCount includes CPU, which has no entry in devices[]
const size_t nonCpuDeviceCount = deviceCount - 1;
std::vector<CanDo> canDo(nonCpuDeviceCount);
for (size_t deviceIndex = 0; deviceIndex < nonCpuDeviceCount; deviceIndex++) {
canDo[deviceIndex].initialize(this, devices[deviceIndex]);
}
// Figure out the best driver for each operation.
const size_t operationCount = mOperations.size();
for (size_t operationIndex = 0; operationIndex < operationCount; operationIndex++) {
// Find which non-CPU device gives the best performance for this operation.
int bestChoice = -1;
float bestPerfVal = 0.0; // Do not check bestPerfVal if bestChoice < 0.
for (size_t deviceIndex = 0; deviceIndex < nonCpuDeviceCount; deviceIndex++) {
const auto& device = devices[deviceIndex];
if (canDo[deviceIndex].check(operationIndex)) {
const PerformanceInfo perf = getPerformanceInfo(device, operationIndex);
const float perfVal =
(preference == ANEURALNETWORKS_PREFER_LOW_POWER ? perf.powerUsage
: perf.execTime);
if (bestChoice < 0 || perfVal < bestPerfVal) {
bestChoice = deviceIndex;
bestPerfVal = perfVal;
}
} else {
// Somewhat noisy logging, but only place where the user of
// NNAPI can get feedback on why an operation was not run on a
// specific device.
// Logs O(operationCount * nonCpuDeviceCount) times, but
// typically nonCpuDeviceCount is very small.
VLOG(COMPILATION) << "Device " << device->getName()
<< " can't do operation "
<< toString(getOperation(operationIndex).type);
}
}
// If it's the OEM op, we'd better have a device able to do it.
if (mOperations[operationIndex].type == OperationType::OEM_OPERATION) {
if (bestChoice < 0) {
LOG(ERROR) << "No driver can do the OEM op";
return ANEURALNETWORKS_BAD_DATA;
}
} else {
// If no driver has been found, or if the best driver is not better than the CPU,
// prefer the CPU. Since the performance is a ratio compared to the CPU performance,
// by definition the performance of the CPU is 1.0.
if (bestChoice < 0 || bestPerfVal >= 1.0) {
bestChoice = nonCpuDeviceCount; // The ID of the CPU.
}
}
(*bestDeviceForOperation)[operationIndex] = bestChoice;
VLOG(COMPILATION) << "ModelBuilder::findBestDeviceForEachOperation("
<< toString(getOperation(operationIndex).type)
<< ") = "
<< (*bestDeviceForOperation)[operationIndex];
}
return ANEURALNETWORKS_NO_ERROR;
}
} // namespace nn
} // namespace android