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v811_spc009/packages/modules/NeuralNetworks/runtime/Memory.cpp

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/*
* 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 "Memory"
#include "Memory.h"
#include <CpuExecutor.h>
#include <LegacyUtils.h>
#include <android-base/scopeguard.h>
#include <android/hardware_buffer.h>
#include <nnapi/IBurst.h>
#include <nnapi/SharedMemory.h>
#include <nnapi/TypeUtils.h>
#include <nnapi/Types.h>
#include <algorithm>
#include <memory>
#include <set>
#include <tuple>
#include <utility>
#include <vector>
#include "CompilationBuilder.h"
#include "Manager.h"
#include "TypeManager.h"
namespace android {
namespace nn {
namespace {
// The validator for a client-managed single-dimensional memory pool with a known size.
// The memory may be used for request inputs, request outputs, or model constants.
class SizedMemoryValidator : public MemoryValidatorBase {
public:
explicit SizedMemoryValidator(uint32_t size) : kSize(size) {}
bool validate(const CompilationBuilder*, IOType, uint32_t, const ANeuralNetworksOperandType*,
uint32_t offset, uint32_t length) const override {
NN_RET_CHECK(offset + length <= kSize) << "request size larger than the memory size.";
NN_RET_CHECK(offset != 0 || length != 0) << "memory size cannot be implied.";
return true;
}
Metadata getMetadata() const override { return {.logicalSize = kSize}; }
bool updateMetadata(const Metadata& metadata) override {
return metadata.logicalSize == 0 || metadata.logicalSize == kSize;
}
private:
const uint32_t kSize;
};
// The validator for an AHardwareBuffer with Non-BLOB format.
// We require the memory only used for request inputs or request outputs,
// with both offset and length set to zero.
class AHardwareBufferNonBlobValidator : public MemoryValidatorBase {
public:
AHardwareBufferNonBlobValidator() = default;
bool validate(const CompilationBuilder* compilation, IOType, uint32_t,
const ANeuralNetworksOperandType*, uint32_t offset,
uint32_t length) const override {
NN_RET_CHECK(compilation != nullptr)
<< "cannot use Non-BLOB AHardwareBuffer as model constant";
NN_RET_CHECK(offset == 0 && length == 0)
<< "non-zero offset (" << offset << ") and/or length (" << length
<< ") for Non-BLOB format AHardwareBuffer.";
return true;
}
Metadata getMetadata() const override { return {}; }
bool updateMetadata(const Metadata&) override { return true; }
};
// The validator for a memory created from ANNMemory_createFromDesc.
// We require the memory only used as one of the pre-specified roles,
// with both offset and length set to zero.
class DeviceMemoryValidator : public MemoryValidatorBase {
public:
DeviceMemoryValidator(std::set<CompilationRole> roles, Operand operand,
std::vector<uint32_t> dimensions)
: kCompilationRoles(std::move(roles)),
kOperand(std::move(operand)),
kInitialDimensions(std::move(dimensions)),
mUpdatedDimensions(kInitialDimensions) {}
bool validate(const CompilationBuilder* compilation, IOType ioType, uint32_t index,
const ANeuralNetworksOperandType* type, uint32_t offset,
uint32_t length) const override {
NN_RET_CHECK(kCompilationRoles.count({compilation, ioType, index}) > 0)
<< "invalid compilation role.";
NN_RET_CHECK(offset == 0 && length == 0)
<< "non-zero offset and/or length for driver-allocated memory.";
if (type) {
const bool isTensor = TypeManager::get()->isTensorType(kOperand.type);
NN_RET_CHECK(isTensor || type->dimensionCount == 0)
<< "invalid dimensions for scalar memory.";
std::vector<uint32_t> dimensions(type->dimensions,
type->dimensions + type->dimensionCount);
// We only check against kInitialDimensions here.
// For input memories, mUpdatedDimensions will be checked in validateInputDimensions
// at the beginning of a computation.
const auto combined = combineDimensions(dimensions, kInitialDimensions);
NN_RET_CHECK(combined.has_value())
<< "incompatible dimensions between request and memory. (request: "
<< toString(dimensions) << ", memory: " << toString(kInitialDimensions) << ")";
}
return true;
}
bool validateInputDimensions(const std::vector<uint32_t>& dimensions) const override {
NN_RET_CHECK(mInitialized) << "using an uninitialized memory as input";
NN_RET_CHECK(dimensions == mUpdatedDimensions)
<< "incompatible input dimensions between request and memory. (request: "
<< toString(dimensions) << ", memory: " << toString(mUpdatedDimensions) << ")";
return true;
}
Metadata getMetadata() const override {
return {.logicalSize = TypeManager::get()->getSizeOfData(kOperand.type, mUpdatedDimensions),
.dimensions = mUpdatedDimensions,
.operand = kOperand};
}
bool updateMetadata(const Metadata& metadata) override {
NN_RET_CHECK(!metadata.operand.has_value() ||
(metadata.operand->type == kOperand.type &&
metadata.operand->scale == kOperand.scale &&
metadata.operand->zeroPoint == kOperand.zeroPoint &&
metadata.operand->extraParams == kOperand.extraParams));
NN_RET_CHECK(metadata.dimensions.empty() ||
TypeManager::get()->isTensorType(kOperand.type));
auto combined = combineDimensions(metadata.dimensions, kInitialDimensions);
NN_RET_CHECK(combined.has_value());
NN_RET_CHECK(metadata.logicalSize == 0 ||
metadata.logicalSize ==
TypeManager::get()->getSizeOfData(kOperand.type, combined.value()));
mUpdatedDimensions = std::move(combined.value());
return true;
}
bool createdWithUnknownShape() const override {
return TypeManager::get()->getSizeOfData(kOperand.type, kInitialDimensions) == 0;
}
void setInitialized(bool initialized) override { mInitialized = initialized; }
bool isInitialized() const override { return mInitialized; }
private:
const std::set<CompilationRole> kCompilationRoles;
// Keep track of the data type, scale, zero point, and extra parameters of the target operand.
// Other fields will be ignored, including dimensions, lifetime, location, etc.
const Operand kOperand;
// The dimensions of the memory when the memory object is created.
// May have unknown dimensions or rank.
const std::vector<uint32_t> kInitialDimensions;
// The updated dimensions after a successful execution or memory copying.
std::vector<uint32_t> mUpdatedDimensions;
bool mInitialized = false;
};
} // namespace
RuntimeMemory::RuntimeMemory(SharedMemory memory) : kMemory(std::move(memory)) {
CHECK(kMemory != nullptr);
mValidator = std::make_unique<SizedMemoryValidator>(nn::getSize(kMemory));
}
RuntimeMemory::RuntimeMemory(SharedMemory memory, std::unique_ptr<MemoryValidatorBase> validator)
: kMemory(std::move(memory)), mValidator(std::move(validator)) {
CHECK(kMemory != nullptr);
}
RuntimeMemory::RuntimeMemory(SharedBuffer buffer) : kBuffer(std::move(buffer)) {}
Request::MemoryPool RuntimeMemory::getMemoryPool() const {
if (kBuffer != nullptr) {
return kBuffer->getToken();
}
return kMemory;
}
std::optional<RunTimePoolInfo> RuntimeMemory::getRunTimePoolInfo() const {
std::lock_guard<std::mutex> guard(mMutex);
if (!mHasCachedRunTimePoolInfo) {
mCachedRunTimePoolInfo = RunTimePoolInfo::createFromMemory(kMemory);
mHasCachedRunTimePoolInfo = true;
}
return mCachedRunTimePoolInfo;
}
void RuntimeMemory::hold(const IBurst::OptionalCacheHold& cacheHold) const {
if (cacheHold != nullptr) {
std::lock_guard<std::mutex> guard(mMutex);
mHold.insert(cacheHold);
}
}
static int copyHidlMemories(const std::optional<RunTimePoolInfo>& src,
const std::optional<RunTimePoolInfo>& dst) {
if (!src.has_value() || !dst.has_value()) {
LOG(ERROR) << "ANeuralNetworksMemory_copy -- unable to map memory";
return ANEURALNETWORKS_UNMAPPABLE;
}
if (src->getSize() != dst->getSize()) {
LOG(ERROR) << "ANeuralNetworksMemory_copy -- incompatible memory size";
return ANEURALNETWORKS_BAD_DATA;
}
CHECK(src->getBuffer() != nullptr);
CHECK(dst->getBuffer() != nullptr);
std::copy(src->getBuffer(), src->getBuffer() + src->getSize(), dst->getBuffer());
dst->flush();
return ANEURALNETWORKS_NO_ERROR;
}
int copyIBufferToMemory(const SharedBuffer& src, const SharedMemory& dst) {
const auto ret = src->copyTo(dst);
if (!ret.has_value()) {
LOG(ERROR) << "ANeuralNetworksMemory_copy failure: " << ret.error().message;
return convertErrorStatusToResultCode(ret.error().code);
}
return ANEURALNETWORKS_NO_ERROR;
}
int copyMemoryToIBuffer(const SharedMemory& src, const SharedBuffer& dst,
const std::vector<uint32_t>& dimensions) {
const auto ret = dst->copyFrom(src, dimensions);
if (!ret.has_value()) {
LOG(ERROR) << "ANeuralNetworksMemory_copy failure: " << ret.error().message;
return convertErrorStatusToResultCode(ret.error().code);
}
return ANEURALNETWORKS_NO_ERROR;
}
static int copyIBuffers(const SharedBuffer& src, const SharedBuffer& dst,
const MemoryValidatorBase::Metadata& srcMetadata) {
const auto [n, memoryAHWB] = MemoryRuntimeAHWB::create(srcMetadata.logicalSize);
NN_RETURN_IF_ERROR(n);
const SharedMemory& memory = memoryAHWB->getMemory();
if (!validate(memory).ok()) return ANEURALNETWORKS_OUT_OF_MEMORY;
NN_RETURN_IF_ERROR(copyIBufferToMemory(src, memory));
NN_RETURN_IF_ERROR(copyMemoryToIBuffer(memory, dst, srcMetadata.dimensions));
return ANEURALNETWORKS_NO_ERROR;
}
static int copyInternal(const RuntimeMemory& src, const RuntimeMemory& dst) {
if (&src == &dst) return ANEURALNETWORKS_NO_ERROR;
if (!src.getValidator().isInitialized()) {
LOG(ERROR) << "ANeuralNetworksMemory_copy -- uninitialized source memory";
return ANEURALNETWORKS_BAD_DATA;
}
const auto srcMetadata = src.getValidator().getMetadata();
if (!dst.getValidator().updateMetadata(srcMetadata)) {
LOG(ERROR) << "ANeuralNetworksMemory_copy -- incompatible memories";
return ANEURALNETWORKS_BAD_DATA;
}
bool srcHasMemory = validate(src.getMemory()).ok();
bool dstHasMemory = validate(dst.getMemory()).ok();
bool srcHasIBuffer = src.getIBuffer() != nullptr;
bool dstHasIBuffer = dst.getIBuffer() != nullptr;
if (srcHasIBuffer && dstHasIBuffer) {
return copyIBuffers(src.getIBuffer(), dst.getIBuffer(), srcMetadata);
} else if (srcHasMemory && dstHasMemory) {
return copyHidlMemories(src.getRunTimePoolInfo(), dst.getRunTimePoolInfo());
} else if (srcHasMemory && dstHasIBuffer) {
return copyMemoryToIBuffer(src.getMemory(), dst.getIBuffer(), srcMetadata.dimensions);
} else if (srcHasIBuffer && dstHasMemory) {
return copyIBufferToMemory(src.getIBuffer(), dst.getMemory());
}
return ANEURALNETWORKS_OP_FAILED;
}
int RuntimeMemory::copy(const RuntimeMemory& src, const RuntimeMemory& dst) {
int n = copyInternal(src, dst);
dst.getValidator().setInitialized(n == ANEURALNETWORKS_NO_ERROR);
return n;
}
bool MemoryBuilder::badState(const char* name) const {
if (mFinished) {
LOG(ERROR) << "ANeuralNetworksMemoryDesc_" << name << " can't modify after finished";
return true;
}
return false;
}
int MemoryBuilder::addRole(const CompilationBuilder& compilation, IOType ioType, uint32_t index,
float prob) {
const char* tag = ioType == IOType::INPUT ? "addInputRole" : "addOutputRole";
if (badState(tag)) {
return ANEURALNETWORKS_BAD_STATE;
}
if (mRoles.count({&compilation, ioType, index}) > 0) {
LOG(ERROR) << "ANeuralNetworksMemoryDesc_" << tag
<< " -- the same operand is specified twice.";
return ANEURALNETWORKS_BAD_DATA;
}
std::vector<std::tuple<const RuntimePreparedModel*, IOType, uint32_t>> roles;
auto callback = [&roles](const auto* preparedModel, IOType type, uint32_t index) {
roles.emplace_back(preparedModel, type, index);
};
if (ioType == IOType::INPUT) {
if (compilation.forEachStepRoleOfInput(index, callback) != ANEURALNETWORKS_NO_ERROR) {
return ANEURALNETWORKS_BAD_DATA;
}
} else {
if (compilation.forEachStepRoleOfOutput(index, callback) != ANEURALNETWORKS_NO_ERROR) {
return ANEURALNETWORKS_BAD_DATA;
}
}
const ModelBuilder* model = compilation.getModel();
CHECK(model != nullptr);
Operand operand;
if (ioType == IOType::INPUT) {
if (index >= model->inputCount()) {
LOG(ERROR) << "ANeuralNetworksMemoryDesc_addInputRole -- input index out of range.";
return ANEURALNETWORKS_BAD_DATA;
}
operand = model->getInputOperand(index);
} else {
if (index >= model->outputCount()) {
LOG(ERROR) << "ANeuralNetworksMemoryDesc_addOutputRole -- output index out of range.";
return ANEURALNETWORKS_BAD_DATA;
}
operand = model->getOutputOperand(index);
}
if (mOperand.has_value()) {
if (operand.type != mOperand->type || operand.scale != mOperand->scale ||
operand.zeroPoint != mOperand->zeroPoint ||
operand.extraParams != mOperand->extraParams) {
LOG(ERROR) << "ANeuralNetworksMemoryDesc_" << tag
<< " -- incompatible operand metadata.";
return ANEURALNETWORKS_BAD_DATA;
}
}
if (!TypeManager::get()->isTensorType(operand.type) && !mDesc.dimensions.empty()) {
LOG(ERROR) << "ANeuralNetworksMemoryDesc_" << tag << " -- incompatible dimensions.";
return ANEURALNETWORKS_BAD_DATA;
}
auto combined = combineDimensions(mDesc.dimensions, operand.dimensions);
if (!combined.has_value()) {
LOG(ERROR) << "ANeuralNetworksMemoryDesc_" << tag << " -- incompatible dimensions.";
return ANEURALNETWORKS_BAD_DATA;
}
if (prob > 1.0f || prob <= 0.0f) {
LOG(ERROR) << "ANeuralNetworksMemoryDesc_" << tag << " -- invalid frequency " << prob;
return ANEURALNETWORKS_BAD_DATA;
}
mRoles.emplace(&compilation, ioType, index);
for (const auto& [preparedModel, type, ind] : roles) {
uint32_t modelIndex = mDesc.preparedModels.add(preparedModel);
BufferRole role = {.modelIndex = modelIndex, .ioIndex = ind, .probability = prob};
if (type == IOType::INPUT) {
mDesc.inputRoles.push_back(role);
} else {
mDesc.outputRoles.push_back(role);
}
}
mOperand = std::move(operand);
mDesc.dimensions = std::move(combined.value());
return ANEURALNETWORKS_NO_ERROR;
}
int MemoryBuilder::setDimensions(const std::vector<uint32_t>& dimensions) {
if (badState("setDimensions")) return ANEURALNETWORKS_BAD_STATE;
if (mOperand.has_value() && !TypeManager::get()->isTensorType(mOperand->type) &&
!dimensions.empty()) {
LOG(ERROR) << "ANeuralNetworksMemoryDesc_setDimensions -- incompatible dimensions for "
"scalars.";
return ANEURALNETWORKS_BAD_DATA;
}
auto combined = combineDimensions(mDesc.dimensions, dimensions);
if (!combined.has_value()) {
LOG(ERROR) << "ANeuralNetworksMemoryDesc_setDimensions -- incompatible dimensions.";
return ANEURALNETWORKS_BAD_DATA;
}
mDesc.dimensions = std::move(combined.value());
return ANEURALNETWORKS_NO_ERROR;
}
static void logMemoryDescriptorToInfo(const MemoryDescriptor& desc, const Operand& operand) {
LOG(INFO) << "MemoryDescriptor start";
LOG(INFO) << " Data type: " << operand.type;
LOG(INFO) << " Scale: " << operand.scale;
LOG(INFO) << " Zero point: " << operand.zeroPoint;
LOG(INFO) << " Extra params: " << operand.extraParams;
LOG(INFO) << " Dimensions: " << toString(desc.dimensions);
LOG(INFO) << " Prepared models [" << desc.preparedModels.size() << "]:";
for (const auto* preparedModel : desc.preparedModels) {
LOG(INFO) << " service = " << preparedModel->getDevice()->getName();
}
LOG(INFO) << " Input roles [" << desc.inputRoles.size() << "]:";
for (const auto& usage : desc.inputRoles) {
LOG(INFO) << " " << usage;
}
LOG(INFO) << " Output roles [" << desc.outputRoles.size() << "]:";
for (const auto& usage : desc.outputRoles) {
LOG(INFO) << " " << usage;
}
LOG(INFO) << "MemoryDescriptor end";
}
static std::set<const Device*> getDevices(const MemoryDescriptor& desc) {
std::set<const Device*> devices;
for (const auto* preparedModel : desc.preparedModels) {
const auto* device = preparedModel->getDevice();
devices.insert(device);
}
return devices;
}
int MemoryBuilder::finish() {
if (badState("finish")) return ANEURALNETWORKS_BAD_STATE;
if (mRoles.empty()) {
LOG(ERROR) << "ANeuralNetworksMemoryDesc_finish -- no role has been specified.";
return ANEURALNETWORKS_BAD_DATA;
}
CHECK(mOperand.has_value());
if (VLOG_IS_ON(MEMORY)) {
logMemoryDescriptorToInfo(mDesc, mOperand.value());
}
std::set<const Device*> devices = getDevices(mDesc);
if (devices.empty()) {
// This can happen with interpreted control flow.
mAllocator = nullptr;
} else if (devices.size() == 1) {
mAllocator = *devices.begin();
VLOG(MEMORY) << "Using " << mAllocator->getName() << " as allocator.";
} else {
LOG(INFO) << "MemoryBuilder::finish -- cannot handle multiple devices.";
mAllocator = nullptr;
}
mSupportsAhwb = std::all_of(devices.begin(), devices.end(), [](const auto* device) {
return device->getFeatureLevel() >= kHalVersionV1_3ToApi.featureLevel;
});
mShouldFallback = std::none_of(mRoles.begin(), mRoles.end(), [](const auto& role) {
const auto* cb = std::get<const CompilationBuilder*>(role);
return cb->createdWithExplicitDeviceList();
});
const uint32_t size = TypeManager::get()->getSizeOfData(mOperand->type, mDesc.dimensions);
mShouldFallback &= (size != 0);
mFinished = true;
return ANEURALNETWORKS_NO_ERROR;
}
std::pair<int, std::unique_ptr<RuntimeMemory>> MemoryBuilder::allocate() const {
if (!mFinished) {
LOG(ERROR) << "ANeuralNetworksMemory_createFromDesc -- passed an unfinished descriptor";
return {ANEURALNETWORKS_BAD_STATE, nullptr};
}
int n = ANEURALNETWORKS_OP_FAILED;
std::unique_ptr<RuntimeMemory> memory;
CHECK(mOperand.has_value());
// Try allocate the memory on device.
if (mAllocator != nullptr) {
std::tie(n, memory) = mAllocator->allocate(mDesc, mOperand->type);
}
// If failed, fallback to ashmem or BLOB mode AHWB.
if (n != ANEURALNETWORKS_NO_ERROR && mShouldFallback) {
const uint32_t size = TypeManager::get()->getSizeOfData(mOperand->type, mDesc.dimensions);
if (mSupportsAhwb) {
VLOG(MEMORY) << "MemoryBuilder::allocate -- fallback to BLOB mode AHWB.";
std::tie(n, memory) = MemoryRuntimeAHWB::create(size);
} else {
VLOG(MEMORY) << "MemoryBuilder::allocate -- fallback to ashmem.";
std::tie(n, memory) = MemoryAshmem::create(size);
}
}
if (n == ANEURALNETWORKS_NO_ERROR) {
CHECK(memory != nullptr);
auto validator =
std::make_unique<DeviceMemoryValidator>(mRoles, mOperand.value(), mDesc.dimensions);
memory->setValidator(std::move(validator));
}
return {n, std::move(memory)};
}
std::pair<int, std::unique_ptr<MemoryAshmem>> MemoryAshmem::create(uint32_t size) {
auto memory = createSharedMemory(size);
if (!memory.has_value()) {
LOG(ERROR) << "RuntimeMemory::create() failed: " << memory.error().message;
return {convertErrorStatusToResultCode(memory.error().code), nullptr};
}
auto mapping = map(memory.value());
if (!mapping.has_value()) {
LOG(ERROR) << "RuntimeMemory::create() map failed: " << mapping.error().message;
return {convertErrorStatusToResultCode(mapping.error().code), nullptr};
}
return {ANEURALNETWORKS_NO_ERROR,
std::make_unique<MemoryAshmem>(std::move(memory).value(), std::move(mapping).value())};
}
uint8_t* MemoryAshmem::getPointer() const {
return static_cast<uint8_t*>(std::get<void*>(kMapping.pointer));
}
MemoryAshmem::MemoryAshmem(SharedMemory memory, Mapping mapping)
: RuntimeMemory(std::move(memory)), kMapping(std::move(mapping)) {}
std::pair<int, std::unique_ptr<MemoryFd>> MemoryFd::create(size_t size, int prot, int fd,
size_t offset) {
auto memory = createSharedMemoryFromFd(size, prot, fd, offset);
if (!memory.has_value()) {
LOG(ERROR) << "Failed to create memory from fd: " << memory.error().message;
return {convertErrorStatusToResultCode(memory.error().code), nullptr};
}
return {ANEURALNETWORKS_NO_ERROR, std::make_unique<MemoryFd>(std::move(memory).value())};
}
MemoryFd::MemoryFd(SharedMemory memory) : RuntimeMemory(std::move(memory)) {}
std::pair<int, std::unique_ptr<MemoryAHWB>> MemoryAHWB::create(const AHardwareBuffer& ahwb) {
auto memory = createSharedMemoryFromAHWB(const_cast<AHardwareBuffer*>(&ahwb),
/*takeOwnership=*/false);
if (!memory.has_value()) {
LOG(ERROR) << "Failed to create memory from AHWB: " << memory.error().message;
return {convertErrorStatusToResultCode(memory.error().code), nullptr};
}
std::unique_ptr<MemoryValidatorBase> validator;
if (isAhwbBlob(memory.value())) {
validator = std::make_unique<SizedMemoryValidator>(nn::getSize(memory.value()));
} else {
validator = std::make_unique<AHardwareBufferNonBlobValidator>();
}
auto memoryAHWB = std::make_unique<MemoryAHWB>(std::move(memory).value(), std::move(validator));
return {ANEURALNETWORKS_NO_ERROR, std::move(memoryAHWB)};
}
std::pair<int, std::unique_ptr<MemoryRuntimeAHWB>> MemoryRuntimeAHWB::create(uint32_t size) {
AHardwareBuffer* ahwb = nullptr;
const auto usage = AHARDWAREBUFFER_USAGE_CPU_READ_OFTEN | AHARDWAREBUFFER_USAGE_CPU_WRITE_OFTEN;
const AHardwareBuffer_Desc desc = {
.width = size,
.height = 1,
.layers = 1,
.format = AHARDWAREBUFFER_FORMAT_BLOB,
.usage = usage,
.stride = size,
};
int err = AHardwareBuffer_allocate(&desc, &ahwb);
if (err != 0 || ahwb == nullptr) {
LOG(ERROR) << "Failed to allocate BLOB mode AHWB.";
return {ANEURALNETWORKS_OP_FAILED, nullptr};
}
auto memory = createSharedMemoryFromAHWB(ahwb, /*takeOWnership=*/true);
if (!memory.has_value()) {
LOG(ERROR) << "Failed to allocate BLOB mode AHWB: " << memory.error().message;
return {convertErrorStatusToResultCode(memory.error().code), nullptr};
}
auto mapping = map(memory.value());
if (!mapping.has_value()) {
LOG(ERROR) << "Failed to map BLOB mode AHWB: " << mapping.error().message;
return {convertErrorStatusToResultCode(mapping.error().code), nullptr};
}
auto memoryAHWB = std::make_unique<MemoryRuntimeAHWB>(std::move(memory).value(),
std::move(mapping).value());
return {ANEURALNETWORKS_NO_ERROR, std::move(memoryAHWB)};
}
uint8_t* MemoryRuntimeAHWB::getPointer() const {
return static_cast<uint8_t*>(std::get<void*>(kMapping.pointer));
}
MemoryRuntimeAHWB::MemoryRuntimeAHWB(SharedMemory memory, Mapping mapping)
: RuntimeMemory(std::move(memory)), kMapping(std::move(mapping)) {}
std::pair<int, std::unique_ptr<MemoryFromDevice>> MemoryFromDevice::create(SharedBuffer buffer) {
if (buffer == nullptr) {
LOG(ERROR) << "nullptr IBuffer for device memory.";
return {ANEURALNETWORKS_OP_FAILED, nullptr};
}
return {ANEURALNETWORKS_NO_ERROR, std::make_unique<MemoryFromDevice>(std::move(buffer))};
}
MemoryFromDevice::MemoryFromDevice(SharedBuffer buffer) : RuntimeMemory(std::move(buffer)) {}
} // namespace nn
} // namespace android
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