/*
* 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 "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) {
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) {
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::startCompute (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::startCompute(sp<ExecutionCallback>* synchronizationCallback) {
*synchronizationCallback = nullptr;
// TODO validate that we have full types for all inputs and outputs,
// that the graph is not cyclic,
for (auto& p : mInputs) {
if (p.state == ModelArgumentInfo::UNSPECIFIED) {
LOG(ERROR) << "ANeuralNetworksExecution_startCompute not all inputs specified";
return ANEURALNETWORKS_BAD_DATA;
}
}
for (auto& p : mOutputs) {
if (p.state == ModelArgumentInfo::UNSPECIFIED) {
LOG(ERROR) << "ANeuralNetworksExecution_startCompute not all outputs specified";
return ANEURALNETWORKS_BAD_DATA;
}
}
#ifndef DISABLE_PARTITIONED_EXECUTION
{
// 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: 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 {
// 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();
std::thread thread(asyncStartComputePartitioned, this, mPlan, controller,
allowFallback,
executionCallback);
executionCallback->bind_thread(std::move(thread));
*synchronizationCallback = executionCallback;
return ANEURALNETWORKS_NO_ERROR;
}
}
}
#else
{
// Find a driver that can handle all the operations.
// TODO: Does not handle CPU fallback (which is tricky because
// StepExecutor::startCompute() is designed as
// asynchronous).
// TODO: Does not actually behave asynchronously (because
// StepExecutor::startCompute() isn't actually asynchronous
// on a device as opposed to a CPU).
Model hidlModel;
mModel->setHidlModel(&hidlModel);
const std::vector<std::shared_ptr<Device>>& devices = DeviceManager::get()->getDrivers();
for (const auto& device : devices) {
hidl_vec<bool> supports;
VLOG(EXECUTION) << "Checking " << device->getName();
device->getSupportedOperations(hidlModel, &supports);
if (std::find(supports.begin(), supports.end(), false) == supports.end()) {
VLOG(EXECUTION) << "ExecutionBuilder::startCompute (without plan) on " << device->getName();
StepExecutor executor(this, mModel, device->getInterface(),
nullptr /* no IPreparedModel, so compile */);
executor.mapInputsAndOutputsTrivially();
return executor.startCompute(synchronizationCallback);
}
}
}
#endif // DISABLE_PARTITIONED_EXECUTION
// 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();
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) << "ANeuralNetworksExecution_startCompute 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, sp<IPreparedModel> 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");
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();
mPreparedModel = preparedModelCallback->getPreparedModel();
if (prepareReturnStatus != ErrorStatus::NONE) {
return convertErrorStatusToResultCode(prepareReturnStatus);
}
if (mPreparedModel == nullptr) {
return ANEURALNETWORKS_OP_FAILED;
}
}
// 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();
}
// 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();
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()
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 asyncStartComputeOnCpu(const Model& model, const Request& request,
const std::vector<RunTimePoolInfo>& modelPoolInfos,
const std::vector<RunTimePoolInfo>& requestPoolInfos,
const sp<IExecutionCallback>& executionCallback) {
CpuExecutor executor;
int err = executor.run(model, request, modelPoolInfos, requestPoolInfos);
executionCallback->notify(convertResultCodeToErrorStatus(err));
}
int StepExecutor::startComputeOnCpu(sp<ExecutionCallback>* synchronizationCallback) {
// TODO: use a thread pool
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);
// TODO: should model be moved with a std::cref?
std::thread thread(asyncStartComputeOnCpu, 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