/*
* Copyright (C) 2017 The Android Open Source Project
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#define LOG_TAG "ExecutionBuilder"
#include "ExecutionBuilder.h"
#include "CompilationBuilder.h"
#include "CpuExecutor.h"
#include "ExecutionBurstController.h"
#include "HalInterfaces.h"
#include "Manager.h"
#include "ModelBuilder.h"
#include "Tracing.h"
#include "TypeManager.h"
#include "Utils.h"
#include <mutex>
#include <optional>
#include <thread>
#include <vector>
namespace android {
namespace nn {
using HidlToken = hidl_array<uint8_t, ANEURALNETWORKS_BYTE_SIZE_OF_CACHE_TOKEN>;
const Timing kNoTiming = {.timeOnDevice = UINT64_MAX, .timeInDriver = UINT64_MAX};
static MeasureTiming measureTiming(const ExecutionBuilder* execution) {
return execution->measureTiming() ? MeasureTiming::YES : MeasureTiming::NO;
}
static bool checkDimensionInfo(const Operand& operand, const ANeuralNetworksOperandType* newType,
const char* tag, bool allowUnspecified) {
if (newType != nullptr) {
const Extension::OperandTypeInformation* info = nullptr;
if (isExtensionOperandType(operand.type)) {
NN_RET_CHECK(TypeManager::get()->getExtensionOperandTypeInfo(operand.type, &info));
}
if (validateOperandType(*newType, info, tag, allowUnspecified) !=
ANEURALNETWORKS_NO_ERROR) {
LOG(ERROR) << tag << ": Invalid newType";
return false;
}
if (operand.dimensions.size() == 0) {
return true;
}
if (operand.dimensions.size() != newType->dimensionCount) {
LOG(ERROR) << tag << ": Setting with incompatible dimension count";
return false;
}
for (uint32_t i = 0; i < newType->dimensionCount; i++) {
if (operand.dimensions[i] != newType->dimensions[i] && operand.dimensions[i] != 0) {
LOG(ERROR) << tag << ": Overriding a fully specified dimension is disallowed";
return false;
}
}
} else {
if (!allowUnspecified && TypeManager::get()->isTensorType(operand.type) &&
tensorHasUnspecifiedDimensions(operand)) {
LOG(ERROR) << tag << ": Setting with operand type that is not fully specified";
return false;
}
}
return true;
}
int ModelArgumentInfo::setFromPointer(const Operand& operand,
const ANeuralNetworksOperandType* type, void* data,
uint32_t length) {
if ((data == nullptr) != (length == 0)) {
const char* dataPtrMsg = data ? "NOT_NULLPTR" : "NULLPTR";
LOG(ERROR) << "Data pointer must be nullptr if and only if length is zero (data = "
<< dataPtrMsg << ", length = " << length << ")";
return ANEURALNETWORKS_BAD_DATA;
}
if (data == nullptr) {
state = ModelArgumentInfo::HAS_NO_VALUE;
} else {
NN_RETURN_IF_ERROR(updateDimensionInfo(operand, type));
if (operand.type != OperandType::OEM) {
uint32_t neededLength = TypeManager::get()->getSizeOfData(operand.type, dimensions);
if (neededLength != length && neededLength != 0) {
LOG(ERROR) << "Setting argument with invalid length: " << length
<< ", expected length: " << neededLength;
return ANEURALNETWORKS_BAD_DATA;
}
}
state = ModelArgumentInfo::POINTER;
}
buffer = data;
locationAndLength = {.poolIndex = 0, .offset = 0, .length = length};
return ANEURALNETWORKS_NO_ERROR;
}
int ModelArgumentInfo::setFromMemory(const Operand& operand, const ANeuralNetworksOperandType* type,
uint32_t poolIndex, uint32_t offset, uint32_t length) {
NN_RETURN_IF_ERROR(updateDimensionInfo(operand, type));
if (operand.type != OperandType::OEM) {
uint32_t neededLength = TypeManager::get()->getSizeOfData(operand.type, dimensions);
if (neededLength != length && neededLength != 0) {
LOG(ERROR) << "Setting argument with invalid length: " << length
<< ", expected length: " << neededLength;
return ANEURALNETWORKS_BAD_DATA;
}
}
state = ModelArgumentInfo::MEMORY;
locationAndLength = {.poolIndex = poolIndex, .offset = offset, .length = length};
buffer = nullptr;
return ANEURALNETWORKS_NO_ERROR;
}
int ModelArgumentInfo::setFromTemporaryMemory(const Operand& operand, uint32_t poolIndex,
uint32_t offset, uint32_t length) {
NN_RETURN_IF_ERROR(updateDimensionInfo(operand, nullptr));
if (operand.type != OperandType::OEM) {
uint32_t neededLength = TypeManager::get()->getSizeOfData(operand.type, dimensions);
if (neededLength != length) {
LOG(ERROR) << "Setting argument with invalid length: " << length
<< ", expected length: " << neededLength;
return ANEURALNETWORKS_BAD_DATA;
}
}
state = ModelArgumentInfo::MEMORY;
locationAndLength = {
.poolIndex = poolIndex,
.offset = offset,
.length = length,
};
buffer = nullptr;
return ANEURALNETWORKS_NO_ERROR;
}
int ModelArgumentInfo::updateDimensionInfo(const Operand& operand,
const ANeuralNetworksOperandType* newType) {
if (newType == nullptr) {
dimensions = operand.dimensions;
} else {
const uint32_t count = newType->dimensionCount;
dimensions = hidl_vec<uint32_t>(count);
std::copy(&newType->dimensions[0], &newType->dimensions[count], dimensions.begin());
}
return ANEURALNETWORKS_NO_ERROR;
}
ExecutionBuilder::ExecutionBuilder(const CompilationBuilder* compilation)
: mCompilation(compilation),
mModel(compilation->mModel),
mPlan(&compilation->mPlan),
mPartitioning(compilation->mPartitioning),
mInputs(mModel->inputCount()),
mOutputs(mModel->outputCount()) {
VLOG(EXECUTION) << "ExecutionBuilder::ExecutionBuilder";
}
int ExecutionBuilder::setInput(uint32_t index, const ANeuralNetworksOperandType* type,
const void* buffer, size_t length) {
if (mStarted) {
LOG(ERROR) << "ANeuralNetworksExecution_setInput called after the "
"execution has started.";
return ANEURALNETWORKS_BAD_STATE;
}
uint32_t count = static_cast<uint32_t>(mInputs.size());
if (index >= count) {
LOG(ERROR) << "ANeuralNetworksExecution_setInput bad index " << index << " " << count;
return ANEURALNETWORKS_BAD_DATA;
}
if (!checkDimensionInfo(mModel->getInputOperand(index), type,
"ANeuralNetworksExecution_setInput", buffer == nullptr)) {
return ANEURALNETWORKS_BAD_DATA;
}
if (length > 0xFFFFFFFF) {
LOG(ERROR) << "ANeuralNetworksExecution_setInput input exceeds max length " << length;
return ANEURALNETWORKS_BAD_DATA;
}
uint32_t l = static_cast<uint32_t>(length);
return mInputs[index].setFromPointer(mModel->getInputOperand(index), type,
const_cast<void*>(buffer), l);
}
int ExecutionBuilder::setInputFromMemory(uint32_t index, const ANeuralNetworksOperandType* type,
const Memory* memory, size_t offset, size_t length) {
// Should be similar to StepExecutor::setInputOrOutputFromTemporaryMemory()
if (mStarted) {
LOG(ERROR) << "ANeuralNetworksExecution_setInputFromMemory called after the "
"execution has started.";
return ANEURALNETWORKS_BAD_STATE;
}
uint32_t count = static_cast<uint32_t>(mInputs.size());
if (index >= count) {
LOG(ERROR) << "ANeuralNetworksExecution_setInputFromMemory bad index " << index << " "
<< count;
return ANEURALNETWORKS_BAD_DATA;
}
if (!checkDimensionInfo(mModel->getInputOperand(index), type,
"ANeuralNetworksExecution_setInputFromMemory", false)) {
return ANEURALNETWORKS_BAD_DATA;
}
// Both offset & length must be zero for Non-BLOB format AHardwareBuffer.
if (memory->getHidlMemory().name() == "hardware_buffer" && (offset != 0 || length != 0)) {
LOG(ERROR) << "ANeuralNetworksExecution_setInputFromMemory has non-zero offset and length"
<< " for Non-BLOB format AHardwareBuffer.";
return ANEURALNETWORKS_BAD_DATA;
} else if (!memory->validateSize(offset, length)) {
return ANEURALNETWORKS_BAD_DATA;
}
// TODO validate the rest
uint32_t poolIndex = mMemories.add(memory);
return mInputs[index].setFromMemory(mModel->getInputOperand(index), type, poolIndex, offset,
length);
}
int ExecutionBuilder::setOutput(uint32_t index, const ANeuralNetworksOperandType* type,
void* buffer, size_t length) {
if (mStarted) {
LOG(ERROR) << "ANeuralNetworksExecution_setOutput called after the "
"execution has started.";
return ANEURALNETWORKS_BAD_STATE;
}
uint32_t count = static_cast<uint32_t>(mOutputs.size());
if (index >= count) {
LOG(ERROR) << "ANeuralNetworksExecution_setOutput bad index " << index << " " << count;
return ANEURALNETWORKS_BAD_DATA;
}
if (!checkDimensionInfo(mModel->getOutputOperand(index), type,
"ANeuralNetworksExecution_setOutput", true)) {
return ANEURALNETWORKS_BAD_DATA;
}
if (length > 0xFFFFFFFF) {
LOG(ERROR) << "ANeuralNetworksExecution_setOutput input exceeds max length " << length;
return ANEURALNETWORKS_BAD_DATA;
}
uint32_t l = static_cast<uint32_t>(length);
return mOutputs[index].setFromPointer(mModel->getOutputOperand(index), type, buffer, l);
}
int ExecutionBuilder::setOutputFromMemory(uint32_t index, const ANeuralNetworksOperandType* type,
const Memory* memory, size_t offset, size_t length) {
// Should be similar to StepExecutor::setInputOrOutputFromTemporaryMemory()
if (mStarted) {
LOG(ERROR) << "ANeuralNetworksExecution_setOutputFromMemory called after the "
"execution has started.";
return ANEURALNETWORKS_BAD_STATE;
}
uint32_t count = static_cast<uint32_t>(mOutputs.size());
if (index >= count) {
LOG(ERROR) << "ANeuralNetworksExecution_setOutputFromMemory bad index " << index << " "
<< count;
return ANEURALNETWORKS_BAD_DATA;
}
if (!checkDimensionInfo(mModel->getOutputOperand(index), type,
"ANeuralNetworksExecution_setOutputFromMemory", true)) {
return ANEURALNETWORKS_BAD_DATA;
}
// Both offset & length must be zero for Non-BLOB format AHardwareBuffer.
if (memory->getHidlMemory().name() == "hardware_buffer" && (offset != 0 || length != 0)) {
LOG(ERROR) << "ANeuralNetworksExecution_setOutputFromMemory has non-zero offset and length"
<< " for Non-BLOB format AHardwareBuffer.";
return ANEURALNETWORKS_BAD_DATA;
} else if (!memory->validateSize(offset, length)) {
return ANEURALNETWORKS_BAD_DATA;
}
// TODO validate the rest
uint32_t poolIndex = mMemories.add(memory);
return mOutputs[index].setFromMemory(mModel->getOutputOperand(index), type, poolIndex, offset,
length);
}
int ExecutionBuilder::setMeasureTiming(bool measure) {
if (!mCompilation->mExplicitDeviceList || (mCompilation->mDevices.size() != 1)) {
LOG(ERROR) << "ANeuralNetworksExecution_setMeasureTiming called on "
<< "an ANeuralNetworksExecution created from an ANeuralNetworksCompilation "
<< "that was not created by ANeuralNetworksCompilation_createForDevices "
<< "with numDevices = 1";
return ANEURALNETWORKS_BAD_DATA;
}
if (mStarted) {
LOG(ERROR) << "ANeuralNetworksExecution_setMeasureTiming called after the "
"execution has started.";
return ANEURALNETWORKS_BAD_STATE;
}
mMeasureTiming = measure;
return ANEURALNETWORKS_NO_ERROR;
}
int ExecutionBuilder::getDuration(int32_t durationCode, uint64_t* duration) const {
if (!mFinished) {
LOG(ERROR) << "ANeuralNetworksExecution_getDuration called before the "
"execution has finished.";
return ANEURALNETWORKS_BAD_STATE;
}
// NOTE: At the HAL level, timing is in microseconds. At the NDK level, nanoseconds.
const uint64_t kNanoPerMicro = 1000;
if (!mMeasureTiming) {
*duration = UINT64_MAX;
return ANEURALNETWORKS_BAD_STATE;
}
uint64_t microDuration = UINT64_MAX;
switch (durationCode) {
case ANEURALNETWORKS_DURATION_ON_HARDWARE:
microDuration = mTiming.timeOnDevice;
break;
case ANEURALNETWORKS_DURATION_IN_DRIVER:
microDuration = mTiming.timeInDriver;
break;
default:
CHECK(!"unexpected");
}
*duration = (microDuration == UINT64_MAX) ? UINT64_MAX : kNanoPerMicro * microDuration;
VLOG(EXECUTION) << "getDuration(" << durationCode << "): " << *duration;
return ANEURALNETWORKS_NO_ERROR;
}
int ExecutionBuilder::getOutputOperandDimensions(uint32_t index, uint32_t* dimensions) {
if (!mFinished) {
LOG(ERROR) << "ANeuralNetworksExecution_getOutputOperandDimensions called before the "
"execution has finished.";
return ANEURALNETWORKS_BAD_STATE;
}
uint32_t count = static_cast<uint32_t>(mOutputs.size());
if (index >= count) {
LOG(ERROR) << "ANeuralNetworksExecution_getOutputOperandDimensions bad index " << index
<< " " << count;
return ANEURALNETWORKS_BAD_DATA;
}
const auto& dims = mOutputs[index].dimensions;
if (dims.empty()) {
LOG(ERROR) << "ANeuralNetworksExecution_getOutputOperandDimensions can not query "
"dimensions of a scalar";
return ANEURALNETWORKS_BAD_DATA;
}
std::copy(dims.begin(), dims.end(), dimensions);
return mOutputs[index].isSufficient ? ANEURALNETWORKS_NO_ERROR
: ANEURALNETWORKS_OUTPUT_INSUFFICIENT_SIZE;
}
int ExecutionBuilder::getOutputOperandRank(uint32_t index, uint32_t* rank) {
if (!mFinished) {
LOG(ERROR) << "ANeuralNetworksExecution_getOutputOperandRank called before the "
"execution has finished.";
return ANEURALNETWORKS_BAD_STATE;
}
uint32_t count = static_cast<uint32_t>(mOutputs.size());
if (index >= count) {
LOG(ERROR) << "ANeuralNetworksExecution_getOutputOperandRank bad index " << index << " "
<< count;
return ANEURALNETWORKS_BAD_DATA;
}
*rank = static_cast<uint32_t>(mOutputs[index].dimensions.size());
return mOutputs[index].isSufficient ? ANEURALNETWORKS_NO_ERROR
: ANEURALNETWORKS_OUTPUT_INSUFFICIENT_SIZE;
}
// Attempt synchronous execution of full model on CPU.
// Ensure that executionCallback->notify() is called.
// TODO: How should we handle timing in this case?
// For Q this is irrelevant: We only support timing in conjunction
// with an explicit device list; and we do not support CPU fallback
// with an explicit device list. See CompilationBuilder::mExplicitDeviceList.
static void cpuFallbackFull(ExecutionBuilder* executionBuilder,
const sp<ExecutionCallback>& executionCallback) {
NNTRACE_RT(NNTRACE_PHASE_EXECUTION, "cpuFallbackFull");
VLOG(EXECUTION) << "cpuFallbackFull";
StepExecutor executor(executionBuilder, executionBuilder->getModel(),
DeviceManager::getCpuDevice(), /*preparedModel=*/nullptr);
executor.mapInputsAndOutputsTrivially();
sp<ExecutionCallback> fallbackCallback;
int n = executor.startCompute(&fallbackCallback);
if (n != ANEURALNETWORKS_NO_ERROR) {
executionCallback->notify(convertResultCodeToErrorStatus(n), {}, kNoTiming);
return;
}
fallbackCallback->wait();
executionCallback->notify(fallbackCallback->getStatus(), fallbackCallback->getOutputShapes(),
fallbackCallback->getTiming());
}
// Attempt synchronous execution on CPU.
// (1) First, attempt to execute this step on CPU. If successful,
// return true. (Do not call executionCallback->notify().)
// (2) If unsuccessful, attempt to execute the full model on CPU,
// ensure that executionCallback->notify() is called, and return
// false.
// TODO: How should we handle timing in this case?
// For Q this is irrelevant: We only support timing in conjunction
// with an explicit device list; and we do not support CPU fallback
// with an explicit device list. See CompilationBuilder::mExplicitDeviceList.
static bool cpuFallbackPartial(ExecutionBuilder* executionBuilder, const ExecutionPlan* plan,
std::shared_ptr<ExecutionPlan::Controller> controller,
const sp<ExecutionCallback>& executionCallback,
std::vector<OutputShape>* outputShapes) {
NNTRACE_RT(NNTRACE_PHASE_EXECUTION, "cpuFallbackPartial");
VLOG(EXECUTION) << "cpuFallbackPartial";
std::shared_ptr<StepExecutor> executor;
int n = plan->fallback(controller, &executor);
if (n != ANEURALNETWORKS_NO_ERROR || executor->isCpu()) {
cpuFallbackFull(executionBuilder, executionCallback);
return false;
}
sp<ExecutionCallback> fallbackCallback;
if (executor->startComputeOnCpu(&fallbackCallback) != ANEURALNETWORKS_NO_ERROR) {
cpuFallbackFull(executionBuilder, executionCallback);
return false;
}
fallbackCallback->wait();
ErrorStatus status = fallbackCallback->getStatus();
const auto& stepOutputShapes = fallbackCallback->getOutputShapes();
if (!executor->updateOutputShapes(stepOutputShapes, outputShapes)) {
status = ErrorStatus::GENERAL_FAILURE;
}
if (status != ErrorStatus::NONE) {
// OUTPUT_INSUFFICIENT_SIZE is not recoverable
if (status == ErrorStatus::OUTPUT_INSUFFICIENT_SIZE) {
executionCallback->notify(status, *outputShapes, kNoTiming);
} else {
cpuFallbackFull(executionBuilder, executionCallback);
}
return false;
}
return true;
}
static void asyncStartComputePartitioned(ExecutionBuilder* executionBuilder,
const ExecutionPlan* plan,
std::shared_ptr<ExecutionPlan::Controller> controller,
bool allowFallback,
const sp<ExecutionCallback>& executionCallback) {
VLOG(EXECUTION) << "ExecutionBuilder::compute (from plan, iteratively)";
std::vector<OutputShape> outputShapes;
Timing timing = kNoTiming;
executionBuilder->initializeOutputShapes(&outputShapes);
while (true) {
std::shared_ptr<StepExecutor> executor;
VLOG(EXECUTION) << "looking for next StepExecutor";
std::shared_ptr<ExecutionBurstController> burstController = nullptr;
int n = plan->next(controller, &executor, &burstController);
if (n != ANEURALNETWORKS_NO_ERROR) {
if (allowFallback) {
cpuFallbackFull(executionBuilder, executionCallback);
} else {
executionCallback->notify(convertResultCodeToErrorStatus(n), {}, kNoTiming);
}
return;
}
if (executor == nullptr) {
executionCallback->notify(ErrorStatus::NONE, outputShapes, timing);
return;
}
sp<ExecutionCallback> stepCallback;
n = executor->startCompute(&stepCallback, burstController);
if (n != ANEURALNETWORKS_NO_ERROR) {
if (allowFallback) {
if (cpuFallbackPartial(executionBuilder, plan, controller, executionCallback,
&outputShapes)) {
// Successfully executed one step on CPU.
continue;
} else {
// Either successfully executed entire plan on
// CPU, or tried and failed to do so.
return;
}
} else {
executionCallback->notify(convertResultCodeToErrorStatus(n), {}, kNoTiming);
return;
}
}
stepCallback->wait();
ErrorStatus status = stepCallback->getStatus();
const auto& stepOutputShapes = stepCallback->getOutputShapes();
if (!executor->updateOutputShapes(stepOutputShapes, &outputShapes)) {
status = ErrorStatus::GENERAL_FAILURE;
}
if (status == ErrorStatus::NONE) {
// We only support collection of timing information in the case of a
// single step, so it's safe to just keep track of the last step's
// timing information.
timing = stepCallback->getTiming();
} else {
// OUTPUT_INSUFFICIENT_SIZE is not recoverable
if (allowFallback && status != ErrorStatus::OUTPUT_INSUFFICIENT_SIZE) {
if (cpuFallbackPartial(executionBuilder, plan, controller, executionCallback,
&outputShapes)) {
// Successfully executed one step on CPU.
continue;
} else {
// Either successfully executed entire plan on
// CPU, or tried and failed to do so.
return;
}
} else if (status == ErrorStatus::OUTPUT_INSUFFICIENT_SIZE) {
executionCallback->notify(status, outputShapes, kNoTiming);
return;
} else {
executionCallback->notify(status, {}, kNoTiming);
return;
}
}
}
}
int ExecutionBuilder::compute(sp<ExecutionCallback>* synchronizationCallback,
BurstBuilder* burstBuilder) {
CHECK(synchronizationCallback == nullptr || burstBuilder == nullptr)
<< "synchronizationCallback and burstBuilder cannot simultaneously be used";
const bool synchronous = (synchronizationCallback == nullptr);
if (!synchronous) {
*synchronizationCallback = nullptr;
}
// TODO validate that we have full types for all inputs and outputs,
// that the graph is not cyclic,
auto name = [synchronous, burstBuilder] {
return burstBuilder ? "burstCompute" : synchronous ? "compute" : "startCompute";
};
if (mStarted) {
LOG(ERROR) << "ANeuralNetworksExecution_" << name()
<< " called on an execution that has already started";
return ANEURALNETWORKS_BAD_STATE;
}
for (auto& p : mInputs) {
if (p.state == ModelArgumentInfo::UNSPECIFIED) {
LOG(ERROR) << "ANeuralNetworksExecution_" << name() << " not all inputs specified";
return ANEURALNETWORKS_BAD_DATA;
}
}
for (auto& p : mOutputs) {
if (p.state == ModelArgumentInfo::UNSPECIFIED) {
LOG(ERROR) << "ANeuralNetworksExecution_" << name() << " not all outputs specified";
return ANEURALNETWORKS_BAD_DATA;
}
}
auto wrappedFinish = [this](ErrorStatus error, const std::vector<OutputShape>& outputShapes) {
return finish(error, outputShapes);
};
// TODO: For asynchronous execution, entire plan-based-path should run in an
// asynchronous thread -- take the asynchronous thread logic out of
// startComputeOnCpu() and use it to wrap the plan-based-path.
mStarted = true;
const bool allowFallback = DeviceManager::partitioningAllowsFallback(mPartitioning);
std::shared_ptr<ExecutionPlan::Controller> controller =
mPlan->makeController(this, burstBuilder);
if (synchronous) {
VLOG(EXECUTION) << "ExecutionBuilder::compute (synchronous API)";
sp<ExecutionCallback> localSynchronizationCallback = new ExecutionCallback();
localSynchronizationCallback->setOnFinish(wrappedFinish);
asyncStartComputePartitioned(this, mPlan, controller, allowFallback,
localSynchronizationCallback);
localSynchronizationCallback->wait();
if (mMeasureTiming) {
mTiming = localSynchronizationCallback->getTiming();
}
return convertErrorStatusToResultCode(localSynchronizationCallback->getStatus());
} else /* asynchronous */ {
// TODO: use a thread pool
// Prepare the callback for asynchronous execution.
// sp<ExecutionCallback> object is returned when the
// execution has been successfully launched, otherwise a
// nullptr is returned. The executionCallback is
// abstracted in the NN API as an "event".
sp<ExecutionCallback> executionCallback = new ExecutionCallback();
executionCallback->setOnFinish(wrappedFinish);
if (DeviceManager::get()->syncExecRuntime()) {
VLOG(EXECUTION) << "ExecutionBuilder::compute (asynchronous API, non-threaded)";
asyncStartComputePartitioned(this, mPlan, controller, allowFallback, executionCallback);
} else {
VLOG(EXECUTION) << "ExecutionBuilder::compute (asynchronous API)";
std::thread thread(asyncStartComputePartitioned, this, mPlan, controller, allowFallback,
executionCallback);
executionCallback->bindThread(std::move(thread));
}
*synchronizationCallback = executionCallback;
return ANEURALNETWORKS_NO_ERROR;
}
}
void ExecutionBuilder::initializeOutputShapes(std::vector<OutputShape>* outputShapes) const {
outputShapes->resize(mOutputs.size());
for (uint32_t i = 0; i < mOutputs.size(); i++) {
(*outputShapes)[i].dimensions = mOutputs[i].dimensions;
(*outputShapes)[i].isSufficient = true;
}
}
// Check if the dimensions "to" is updatable by dimensions "from", where "from" must
// have a higher specification level.
static bool isUpdatable(const std::vector<uint32_t>& to, const std::vector<uint32_t>& from) {
if (to.size() == 0) return true;
NN_RET_CHECK_EQ(to.size(), from.size());
for (uint32_t i = 0; i < to.size(); i++) {
NN_RET_CHECK(to[i] == from[i] || to[i] == 0);
}
return true;
}
bool ExecutionBuilder::updateOutputShapes(const std::vector<OutputShape>& outputShapes) {
if (outputShapes.size() == 0) {
return true;
}
NN_RET_CHECK_EQ(outputShapes.size(), mOutputs.size());
for (uint32_t i = 0; i < outputShapes.size(); i++) {
// Check if only unspecified dimensions or rank are overwritten.
NN_RET_CHECK(isUpdatable(mOutputs[i].dimensions, outputShapes[i].dimensions));
}
for (uint32_t i = 0; i < outputShapes.size(); i++) {
mOutputs[i].dimensions = outputShapes[i].dimensions;
mOutputs[i].isSufficient = outputShapes[i].isSufficient;
}
return true;
}
ErrorStatus ExecutionBuilder::finish(ErrorStatus, const std::vector<OutputShape>& outputShapes) {
CHECK(!mFinished) << "ExecutionBuilder::finish is called twice";
mFinished = true;
if (!updateOutputShapes(outputShapes)) {
return ErrorStatus::GENERAL_FAILURE;
}
return ErrorStatus::NONE;
}
bool StepExecutor::updateOutputShapes(const std::vector<OutputShape>& from,
std::vector<OutputShape>* to) {
if (from.size() == 0) {
return true;
}
if (mExecutionStep != nullptr) {
const auto& indexMapping = mExecutionStep->getOutputIndexSubModelToFromModel();
NN_RET_CHECK_LE(indexMapping.size(), from.size());
for (uint32_t i = 0, e = indexMapping.size(); i < e; i++) {
uint32_t toIndex = indexMapping[i];
NN_RET_CHECK_GT(to->size(), toIndex);
NN_RET_CHECK(isUpdatable(to->at(toIndex).dimensions, from[i].dimensions));
(*to)[toIndex] = from[i];
}
} else {
NN_RET_CHECK_EQ(from.size(), to->size());
for (uint32_t i = 0, e = from.size(); i < e; i++) {
NN_RET_CHECK(isUpdatable(to->at(i).dimensions, from[i].dimensions));
(*to)[i] = from[i];
}
}
return true;
}
// Figures out how to place each of the input or outputs in a buffer. This just does the layout,
// it does not copy data. Aligns each input a bit.
int StepExecutor::allocatePointerArgumentsToPool(std::vector<ModelArgumentInfo>* args,
Memory* memory) {
uint32_t nextPoolIndex = mMemories.size();
int64_t total = 0;
for (auto& info : *args) {
if (info.state == ModelArgumentInfo::POINTER) {
DataLocation& loc = info.locationAndLength;
// TODO Good enough alignment?
total += alignBytesNeeded(static_cast<uint32_t>(total), loc.length);
loc.poolIndex = nextPoolIndex;
loc.offset = static_cast<uint32_t>(total);
total += loc.length;
}
};
if (total > 0xFFFFFFFF) {
LOG(ERROR) << "StepExecutor::allocatePointerArgumentsToPool: ANeuralNetworksExecution: "
"Size of all inputs or outputs exceeds 2^32.";
return ANEURALNETWORKS_BAD_DATA;
}
hidl_memory hidlMemory;
if (total > 0) {
memory->create(total); // TODO check error
mMemories.add(memory);
}
return ANEURALNETWORKS_NO_ERROR;
}
static void setRequestArgumentArray(const std::vector<ModelArgumentInfo>& argumentInfos,
hidl_vec<RequestArgument>* ioInfos) {
size_t count = argumentInfos.size();
ioInfos->resize(count);
for (size_t i = 0; i < count; i++) {
const auto& info = argumentInfos[i];
(*ioInfos)[i] = {
.hasNoValue = info.state == ModelArgumentInfo::HAS_NO_VALUE,
.location = info.locationAndLength,
.dimensions = info.dimensions,
};
}
}
StepExecutor::StepExecutor(ExecutionBuilder* executionBuilder, const ModelBuilder* model,
std::shared_ptr<Device> device,
std::shared_ptr<VersionedIPreparedModel> preparedModel)
: mExecutionBuilder(executionBuilder),
mModel(model),
mDevice(device),
mPreparedModel(preparedModel),
mInputs(model->inputCount()),
mOutputs(model->outputCount()) {
CHECK(mDevice != nullptr);
}
void StepExecutor::mapInputsAndOutputsTrivially() {
mInputs = mExecutionBuilder->mInputs;
mOutputs = mExecutionBuilder->mOutputs;
mMemories = mExecutionBuilder->mMemories;
}
void StepExecutor::mapInputOrOutput(const ModelArgumentInfo& builderInputOrOutput,
ModelArgumentInfo* executorInputOrOutput) {
*executorInputOrOutput = builderInputOrOutput;
switch (executorInputOrOutput->state) {
default:
nnAssert(!"unexpected ModelArgumentInfo::state");
break;
case ModelArgumentInfo::HAS_NO_VALUE:
case ModelArgumentInfo::POINTER:
case ModelArgumentInfo::UNSPECIFIED:
break;
case ModelArgumentInfo::MEMORY: {
const uint32_t builderPoolIndex = builderInputOrOutput.locationAndLength.poolIndex;
const Memory* memory = mExecutionBuilder->mMemories[builderPoolIndex];
const uint32_t executorPoolIndex = mMemories.add(memory);
executorInputOrOutput->locationAndLength.poolIndex = executorPoolIndex;
break;
}
}
}
int StepExecutor::setInputOrOutputFromTemporaryMemory(const Operand& inputOrOutputOperand,
const Memory* memory, uint32_t offset,
ModelArgumentInfo* inputOrOutputInfo) {
// Should be similar to
// ExecutionBuilder::setInputFromMemory()
// ExecutionBuilder::setOutputFromMemory()
uint32_t poolIndex = mMemories.add(memory);
uint32_t length = TypeManager::get()->getSizeOfData(inputOrOutputOperand);
return inputOrOutputInfo->setFromTemporaryMemory(inputOrOutputOperand, poolIndex, offset,
length);
}
static void logArguments(const char* kind, const std::vector<ModelArgumentInfo>& args) {
for (unsigned i = 0; i < args.size(); i++) {
const auto& arg = args[i];
std::string prefix = kind + std::string("[") + std::to_string(i) + "] = ";
switch (arg.state) {
case ModelArgumentInfo::POINTER:
VLOG(EXECUTION) << prefix << "POINTER(" << SHOW_IF_DEBUG(arg.buffer) << ")";
break;
case ModelArgumentInfo::MEMORY:
VLOG(EXECUTION) << prefix << "MEMORY("
<< "pool=" << arg.locationAndLength.poolIndex << ", "
<< "off=" << arg.locationAndLength.offset << ")";
break;
case ModelArgumentInfo::HAS_NO_VALUE:
VLOG(EXECUTION) << prefix << "HAS_NO_VALUE";
break;
case ModelArgumentInfo::UNSPECIFIED:
VLOG(EXECUTION) << prefix << "UNSPECIFIED";
break;
default:
VLOG(EXECUTION) << prefix << "state(" << arg.state << ")";
break;
}
}
}
bool StepExecutor::isCpu() const {
return mDevice->getInterface() == nullptr;
}
int StepExecutor::startCompute(sp<ExecutionCallback>* synchronizationCallback,
const std::shared_ptr<ExecutionBurstController>& burstController) {
if (VLOG_IS_ON(EXECUTION)) {
logArguments("input", mInputs);
logArguments("output", mOutputs);
}
if (isCpu()) {
return startComputeOnCpu(synchronizationCallback);
} else {
return startComputeOnDevice(synchronizationCallback, burstController);
}
}
int StepExecutor::startComputeOnDevice(
sp<ExecutionCallback>* synchronizationCallback,
const std::shared_ptr<ExecutionBurstController>& burstController) {
CHECK(!isCpu());
// Initialize timing information in case we take an error path to exit.
mExecutionBuilder->reportTiming(kNoTiming);
*synchronizationCallback = nullptr;
// 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(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 status = ErrorStatus::GENERAL_FAILURE;
std::tie(status, mPreparedModel) =
mDevice->getInterface()->prepareModel(model, preference, {}, {}, {});
if (status != ErrorStatus::NONE) {
return convertErrorStatusToResultCode(status);
}
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();
// compute using burst if present
const bool burstCompute = (burstController != nullptr);
bool burstFallback = false;
if (burstCompute) {
std::vector<intptr_t> memoryIds;
memoryIds.reserve(mMemories.size());
for (const Memory* memory : mMemories) {
memory->usedBy(burstController);
memoryIds.push_back(memory->getKey());
}
VLOG(EXECUTION) << "Before ExecutionBurstController->tryCompute() "
<< SHOW_IF_DEBUG(toString(request));
auto [status, outputShapes, timing, fallback] =
burstController->tryCompute(request, measureTiming(mExecutionBuilder), memoryIds);
burstFallback = fallback;
if (!fallback) {
executionCallback->notify(status, outputShapes, timing);
}
}
// compute from IPreparedModel if either:
// (1) burst was not supplied, or
// (2) the burst execution failed and requested a fallback execution
if (!burstCompute || burstFallback) {
if (DeviceManager::get()->syncExecHal()) {
VLOG(EXECUTION) << "Before mPreparedModel->executeSynchronously() "
<< SHOW_IF_DEBUG(toString(request));
auto syncExecuteResult =
mPreparedModel->executeSynchronously(request, measureTiming(mExecutionBuilder));
executionCallback->notify(std::get<0>(syncExecuteResult),
std::get<1>(syncExecuteResult),
std::get<2>(syncExecuteResult));
} else {
VLOG(EXECUTION) << "Before mPreparedModel->execute() "
<< SHOW_IF_DEBUG(toString(request));
// Execute.
// TODO: What happens to the Callback if the service dies abnormally
// -- won't that keep the Callback live forever, because the service
// never has the opportunity to bump the reference count down? Or
// maybe the HIDL infrastructure handles this magically? At worst,
// it seems like this is a small memory leak, if the Callback stays
// alive forever.
Return<ErrorStatus> executeStatus = mPreparedModel->execute(
request, measureTiming(mExecutionBuilder), executionCallback);
if (!executeStatus.isOk() || executeStatus != ErrorStatus::NONE) {
VLOG(EXECUTION) << "**Execute launch failed**";
return executeStatus.isOk() ? convertErrorStatusToResultCode(executeStatus)
: ANEURALNETWORKS_OP_FAILED;
}
}
}
// TODO: Remove this synchronization point when the block of code below is
// removed.
executionCallback->wait();
NNTRACE_FULL_SWITCH(NNTRACE_LAYER_RUNTIME, NNTRACE_PHASE_EXECUTION,
"StepExecutor::startComputeOnDevice::waited");
Return<ErrorStatus> callbackStatus = executionCallback->getStatus();
if (!callbackStatus.isOk() || callbackStatus != ErrorStatus::NONE) {
VLOG(EXECUTION) << "**Execution failed**";
if (callbackStatus == ErrorStatus::OUTPUT_INSUFFICIENT_SIZE) {
*synchronizationCallback = executionCallback;
return ANEURALNETWORKS_NO_ERROR;
}
return callbackStatus.isOk() ? convertErrorStatusToResultCode(callbackStatus)
: ANEURALNETWORKS_OP_FAILED;
}
mExecutionBuilder->reportTiming(executionCallback->getTiming());
// Copy the output data from shared memory to the output buffers.
// TODO: Move this block of code somewhere else. It should not be in the
// startCompute function.
// TODO: outputMemory->update(); outputMemory->commit()
NNTRACE_RT_SWITCH(NNTRACE_PHASE_RESULTS, "StepExecutor::startComputeOnDevice");
for (auto& info : mOutputs) {
if (info.state == ModelArgumentInfo::POINTER) {
DataLocation& loc = info.locationAndLength;
uint8_t* data = nullptr;
int n = outputPointerArguments.getPointer(&data);
if (n != ANEURALNETWORKS_NO_ERROR) {
return n;
}
memcpy(info.buffer, data + loc.offset, loc.length);
}
}
VLOG(EXECUTION) << "StepExecutor::startComputeOnDevice completed";
*synchronizationCallback = executionCallback;
return ANEURALNETWORKS_NO_ERROR;
}
static void computeOnCpu(const Model& model, const Request& request,
const std::vector<RunTimePoolInfo>& modelPoolInfos,
const std::vector<RunTimePoolInfo>& requestPoolInfos,
const sp<IExecutionCallback>& executionCallback) {
NNTRACE_RT(NNTRACE_PHASE_EXECUTION, "computeOnCpu");
CpuExecutor executor;
int err = executor.run(model, request, modelPoolInfos, requestPoolInfos);
const auto& outputShapes = executor.getOutputShapes();
executionCallback->notify_1_2(convertResultCodeToErrorStatus(err), outputShapes, kNoTiming);
}
int StepExecutor::startComputeOnCpu(sp<ExecutionCallback>* synchronizationCallback) {
// TODO: use a thread pool
// TODO(mikie): this could have NNTRACE so we could measure the overhead of
// spinning up a new thread.
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());
for (const Memory* mem : mMemories) {
if (std::optional<RunTimePoolInfo> poolInfo =
RunTimePoolInfo::createFromHidlMemory(mem->getHidlMemory())) {
requestPoolInfos.emplace_back(*poolInfo);
} else {
return ANEURALNETWORKS_UNMAPPABLE;
}
}
// Create as many pools as there are input / output.
auto fixPointerArguments = [&requestPoolInfos](std::vector<ModelArgumentInfo>& argumentInfos) {
for (ModelArgumentInfo& argumentInfo : argumentInfos) {
if (argumentInfo.state == ModelArgumentInfo::POINTER) {
argumentInfo.locationAndLength.poolIndex =
static_cast<uint32_t>(requestPoolInfos.size());
argumentInfo.locationAndLength.offset = 0;
requestPoolInfos.emplace_back(RunTimePoolInfo::createFromExistingBuffer(
static_cast<uint8_t*>(argumentInfo.buffer)));
}
}
};
fixPointerArguments(mInputs);
fixPointerArguments(mOutputs);
Request request;
setRequestArgumentArray(mInputs, &request.inputs);
setRequestArgumentArray(mOutputs, &request.outputs);
if (DeviceManager::get()->syncExecCpu()) {
computeOnCpu(model, request, modelPoolInfos, requestPoolInfos, executionCallback);
} else {
// TODO: should model be moved with a std::cref?
std::thread thread(computeOnCpu, model, std::move(request), std::move(modelPoolInfos),
std::move(requestPoolInfos), executionCallback);
executionCallback->bindThread(std::move(thread));
}
*synchronizationCallback = executionCallback;
return ANEURALNETWORKS_NO_ERROR;
}
} // namespace nn
} // namespace android