| /* |
| * 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 |
