neuralnetworks/1.2/utils/src/Conversions.cpp - android_hardware_interfaces - Gitiles

 /*
  * Copyright (C) 2020 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.
  */

 #include "Conversions.h"

 #include <android-base/logging.h>
 #include <android/hardware/neuralnetworks/1.2/types.h>
 #include <nnapi/OperandTypes.h>
 #include <nnapi/OperationTypes.h>
 #include <nnapi/Result.h>
 #include <nnapi/SharedMemory.h>
 #include <nnapi/TypeUtils.h>
 #include <nnapi/Types.h>
 #include <nnapi/hal/1.0/Conversions.h>
 #include <nnapi/hal/CommonUtils.h>

 #include <algorithm>
 #include <functional>
 #include <iterator>
 #include <memory>
 #include <type_traits>
 #include <utility>

 namespace {

 template <typename Type>
 constexpr std::underlying_type_t<Type> underlyingType(Type value) {
     return static_cast<std::underlying_type_t<Type>>(value);
 }

 }  // namespace

 namespace android::nn {
 namespace {

 constexpr bool validOperandType(OperandType operandType) {
     switch (operandType) {
         case OperandType::FLOAT32:
         case OperandType::INT32:
         case OperandType::UINT32:
         case OperandType::TENSOR_FLOAT32:
         case OperandType::TENSOR_INT32:
         case OperandType::TENSOR_QUANT8_ASYMM:
         case OperandType::BOOL:
         case OperandType::TENSOR_QUANT16_SYMM:
         case OperandType::TENSOR_FLOAT16:
         case OperandType::TENSOR_BOOL8:
         case OperandType::FLOAT16:
         case OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL:
         case OperandType::TENSOR_QUANT16_ASYMM:
         case OperandType::TENSOR_QUANT8_SYMM:
         case OperandType::OEM:
         case OperandType::TENSOR_OEM_BYTE:
             return true;
         default:
             break;
     }
     return isExtension(operandType);
 }

 using hardware::hidl_handle;
 using hardware::hidl_vec;

 template <typename Input>
 using ConvertOutput = std::decay_t<decltype(convert(std::declval<Input>()).value())>;

 template <typename Type>
 Result<std::vector<ConvertOutput<Type>>> convertVec(const hidl_vec<Type>& arguments) {
     std::vector<ConvertOutput<Type>> canonical;
     canonical.reserve(arguments.size());
     for (const auto& argument : arguments) {
         canonical.push_back(NN_TRY(nn::convert(argument)));
     }
     return canonical;
 }

 template <typename Type>
 Result<std::vector<ConvertOutput<Type>>> convert(const hidl_vec<Type>& arguments) {
     return convertVec(arguments);
 }

 }  // anonymous namespace

 Result<OperandType> convert(const hal::V1_2::OperandType& operandType) {
     return static_cast<OperandType>(operandType);
 }

 Result<OperationType> convert(const hal::V1_2::OperationType& operationType) {
     return static_cast<OperationType>(operationType);
 }

 Result<DeviceType> convert(const hal::V1_2::DeviceType& deviceType) {
     return static_cast<DeviceType>(deviceType);
 }

 Result<Capabilities> convert(const hal::V1_2::Capabilities& capabilities) {
     const bool validOperandTypes = std::all_of(
             capabilities.operandPerformance.begin(), capabilities.operandPerformance.end(),
             [](const hal::V1_2::Capabilities::OperandPerformance& operandPerformance) {
                 const auto maybeType = convert(operandPerformance.type);
                 return !maybeType.has_value() ? false : validOperandType(maybeType.value());
             });
     if (!validOperandTypes) {
         return NN_ERROR()
                << "Invalid OperandType when converting OperandPerformance in Capabilities";
     }

     const auto relaxedFloat32toFloat16PerformanceScalar =
             NN_TRY(convert(capabilities.relaxedFloat32toFloat16PerformanceScalar));
     const auto relaxedFloat32toFloat16PerformanceTensor =
             NN_TRY(convert(capabilities.relaxedFloat32toFloat16PerformanceTensor));
     auto operandPerformance = NN_TRY(convert(capabilities.operandPerformance));

     auto table =
             NN_TRY(Capabilities::OperandPerformanceTable::create(std::move(operandPerformance)));

     return Capabilities{
             .relaxedFloat32toFloat16PerformanceScalar = relaxedFloat32toFloat16PerformanceScalar,
             .relaxedFloat32toFloat16PerformanceTensor = relaxedFloat32toFloat16PerformanceTensor,
             .operandPerformance = std::move(table),
     };
 }

 Result<Capabilities::OperandPerformance> convert(
         const hal::V1_2::Capabilities::OperandPerformance& operandPerformance) {
     return Capabilities::OperandPerformance{
             .type = NN_TRY(convert(operandPerformance.type)),
             .info = NN_TRY(convert(operandPerformance.info)),
     };
 }

 Result<Operation> convert(const hal::V1_2::Operation& operation) {
     return Operation{
             .type = NN_TRY(convert(operation.type)),
             .inputs = operation.inputs,
             .outputs = operation.outputs,
     };
 }

 Result<Operand::SymmPerChannelQuantParams> convert(
         const hal::V1_2::SymmPerChannelQuantParams& symmPerChannelQuantParams) {
     return Operand::SymmPerChannelQuantParams{
             .scales = symmPerChannelQuantParams.scales,
             .channelDim = symmPerChannelQuantParams.channelDim,
     };
 }

 Result<Operand> convert(const hal::V1_2::Operand& operand) {
     return Operand{
             .type = NN_TRY(convert(operand.type)),
             .dimensions = operand.dimensions,
             .scale = operand.scale,
             .zeroPoint = operand.zeroPoint,
             .lifetime = NN_TRY(convert(operand.lifetime)),
             .location = NN_TRY(convert(operand.location)),
             .extraParams = NN_TRY(convert(operand.extraParams)),
     };
 }

 Result<Operand::ExtraParams> convert(const hal::V1_2::Operand::ExtraParams& extraParams) {
     using Discriminator = hal::V1_2::Operand::ExtraParams::hidl_discriminator;
     switch (extraParams.getDiscriminator()) {
         case Discriminator::none:
             return Operand::NoParams{};
         case Discriminator::channelQuant:
             return convert(extraParams.channelQuant());
         case Discriminator::extension:
             return extraParams.extension();
     }
     return NN_ERROR() << "Unrecognized Operand::ExtraParams discriminator: "
                       << underlyingType(extraParams.getDiscriminator());
 }

 Result<Model> convert(const hal::V1_2::Model& model) {
     auto operations = NN_TRY(convert(model.operations));

     // Verify number of consumers.
     const auto numberOfConsumers =
             hal::utils::countNumberOfConsumers(model.operands.size(), operations);
     CHECK(model.operands.size() == numberOfConsumers.size());
     for (size_t i = 0; i < model.operands.size(); ++i) {
         if (model.operands[i].numberOfConsumers != numberOfConsumers[i]) {
             return NN_ERROR() << "Invalid numberOfConsumers for operand " << i << ", expected "
                               << numberOfConsumers[i] << " but found "
                               << model.operands[i].numberOfConsumers;
         }
     }

     auto main = Model::Subgraph{
             .operands = NN_TRY(convert(model.operands)),
             .operations = std::move(operations),
             .inputIndexes = model.inputIndexes,
             .outputIndexes = model.outputIndexes,
     };

     return Model{
             .main = std::move(main),
             .operandValues = NN_TRY(convert(model.operandValues)),
             .pools = NN_TRY(convert(model.pools)),
             .relaxComputationFloat32toFloat16 = model.relaxComputationFloat32toFloat16,
             .extensionNameToPrefix = NN_TRY(convert(model.extensionNameToPrefix)),
     };
 }

 Result<Model::ExtensionNameAndPrefix> convert(
         const hal::V1_2::Model::ExtensionNameAndPrefix& extensionNameAndPrefix) {
     return Model::ExtensionNameAndPrefix{
             .name = extensionNameAndPrefix.name,
             .prefix = extensionNameAndPrefix.prefix,
     };
 }

 Result<OutputShape> convert(const hal::V1_2::OutputShape& outputShape) {
     return OutputShape{
             .dimensions = outputShape.dimensions,
             .isSufficient = outputShape.isSufficient,
     };
 }

 Result<MeasureTiming> convert(const hal::V1_2::MeasureTiming& measureTiming) {
     return static_cast<MeasureTiming>(measureTiming);
 }

 Result<Timing> convert(const hal::V1_2::Timing& timing) {
     return Timing{.timeOnDevice = timing.timeOnDevice, .timeInDriver = timing.timeInDriver};
 }

 Result<Extension> convert(const hal::V1_2::Extension& extension) {
     return Extension{
             .name = extension.name,
             .operandTypes = NN_TRY(convert(extension.operandTypes)),
     };
 }

 Result<Extension::OperandTypeInformation> convert(
         const hal::V1_2::Extension::OperandTypeInformation& operandTypeInformation) {
     return Extension::OperandTypeInformation{
             .type = operandTypeInformation.type,
             .isTensor = operandTypeInformation.isTensor,
             .byteSize = operandTypeInformation.byteSize,
     };
 }

 Result<NativeHandle> convert(const hidl_handle& handle) {
     auto* cloned = native_handle_clone(handle.getNativeHandle());
     return ::android::NativeHandle::create(cloned, /*ownsHandle=*/true);
 }

 Result<std::vector<Extension>> convert(const hidl_vec<hal::V1_2::Extension>& extensions) {
     return convertVec(extensions);
 }

 Result<std::vector<NativeHandle>> convert(const hidl_vec<hidl_handle>& handles) {
     return convertVec(handles);
 }

 Result<std::vector<OutputShape>> convert(const hidl_vec<hal::V1_2::OutputShape>& outputShapes) {
     return convertVec(outputShapes);
 }

 }  // namespace android::nn

 namespace android::hardware::neuralnetworks::V1_2::utils {
 namespace {

 using utils::convert;

 nn::Result<V1_0::OperandLifeTime> convert(const nn::Operand::LifeTime& lifetime) {
     return V1_0::utils::convert(lifetime);
 }

 nn::Result<V1_0::PerformanceInfo> convert(
         const nn::Capabilities::PerformanceInfo& performanceInfo) {
     return V1_0::utils::convert(performanceInfo);
 }

 nn::Result<V1_0::DataLocation> convert(const nn::DataLocation& location) {
     return V1_0::utils::convert(location);
 }

 nn::Result<hidl_vec<uint8_t>> convert(const nn::Model::OperandValues& operandValues) {
     return V1_0::utils::convert(operandValues);
 }

 nn::Result<hidl_memory> convert(const nn::Memory& memory) {
     return V1_0::utils::convert(memory);
 }

 template <typename Input>
 using ConvertOutput = std::decay_t<decltype(convert(std::declval<Input>()).value())>;

 template <typename Type>
 nn::Result<hidl_vec<ConvertOutput<Type>>> convertVec(const std::vector<Type>& arguments) {
     hidl_vec<ConvertOutput<Type>> halObject(arguments.size());
     for (size_t i = 0; i < arguments.size(); ++i) {
         halObject[i] = NN_TRY(convert(arguments[i]));
     }
     return halObject;
 }

 template <typename Type>
 nn::Result<hidl_vec<ConvertOutput<Type>>> convert(const std::vector<Type>& arguments) {
     return convertVec(arguments);
 }

 nn::Result<Operand::ExtraParams> makeExtraParams(nn::Operand::NoParams /*noParams*/) {
     return Operand::ExtraParams{};
 }

 nn::Result<Operand::ExtraParams> makeExtraParams(
         const nn::Operand::SymmPerChannelQuantParams& channelQuant) {
     Operand::ExtraParams ret;
     ret.channelQuant(NN_TRY(convert(channelQuant)));
     return ret;
 }

 nn::Result<Operand::ExtraParams> makeExtraParams(const nn::Operand::ExtensionParams& extension) {
     Operand::ExtraParams ret;
     ret.extension(extension);
     return ret;
 }

 }  // anonymous namespace

 nn::Result<OperandType> convert(const nn::OperandType& operandType) {
     return static_cast<OperandType>(operandType);
 }

 nn::Result<OperationType> convert(const nn::OperationType& operationType) {
     return static_cast<OperationType>(operationType);
 }

 nn::Result<DeviceType> convert(const nn::DeviceType& deviceType) {
     switch (deviceType) {
         case nn::DeviceType::UNKNOWN:
             return NN_ERROR() << "Invalid DeviceType UNKNOWN";
         case nn::DeviceType::OTHER:
         case nn::DeviceType::CPU:
         case nn::DeviceType::GPU:
         case nn::DeviceType::ACCELERATOR:
             return static_cast<DeviceType>(deviceType);
     }
     return NN_ERROR() << "Invalid DeviceType " << underlyingType(deviceType);
 }

 nn::Result<Capabilities> convert(const nn::Capabilities& capabilities) {
     std::vector<nn::Capabilities::OperandPerformance> operandPerformance;
     operandPerformance.reserve(capabilities.operandPerformance.asVector().size());
     std::copy_if(capabilities.operandPerformance.asVector().begin(),
                  capabilities.operandPerformance.asVector().end(),
                  std::back_inserter(operandPerformance),
                  [](const nn::Capabilities::OperandPerformance& operandPerformance) {
                      return nn::validOperandType(operandPerformance.type);
                  });

     return Capabilities{
             .relaxedFloat32toFloat16PerformanceScalar =
                     NN_TRY(convert(capabilities.relaxedFloat32toFloat16PerformanceScalar)),
             .relaxedFloat32toFloat16PerformanceTensor =
                     NN_TRY(convert(capabilities.relaxedFloat32toFloat16PerformanceTensor)),
             .operandPerformance = NN_TRY(convert(operandPerformance)),
     };
 }

 nn::Result<Capabilities::OperandPerformance> convert(
         const nn::Capabilities::OperandPerformance& operandPerformance) {
     return Capabilities::OperandPerformance{
             .type = NN_TRY(convert(operandPerformance.type)),
             .info = NN_TRY(convert(operandPerformance.info)),
     };
 }

 nn::Result<Operation> convert(const nn::Operation& operation) {
     return Operation{
             .type = NN_TRY(convert(operation.type)),
             .inputs = operation.inputs,
             .outputs = operation.outputs,
     };
 }

 nn::Result<SymmPerChannelQuantParams> convert(
         const nn::Operand::SymmPerChannelQuantParams& symmPerChannelQuantParams) {
     return SymmPerChannelQuantParams{
             .scales = symmPerChannelQuantParams.scales,
             .channelDim = symmPerChannelQuantParams.channelDim,
     };
 }

 nn::Result<Operand> convert(const nn::Operand& operand) {
     return Operand{
             .type = NN_TRY(convert(operand.type)),
             .dimensions = operand.dimensions,
             .numberOfConsumers = 0,
             .scale = operand.scale,
             .zeroPoint = operand.zeroPoint,
             .lifetime = NN_TRY(convert(operand.lifetime)),
             .location = NN_TRY(convert(operand.location)),
             .extraParams = NN_TRY(convert(operand.extraParams)),
     };
 }

 nn::Result<Operand::ExtraParams> convert(const nn::Operand::ExtraParams& extraParams) {
     return std::visit([](const auto& x) { return makeExtraParams(x); }, extraParams);
 }

 nn::Result<Model> convert(const nn::Model& model) {
     if (!hal::utils::hasNoPointerData(model)) {
         return NN_ERROR() << "Model cannot be converted because it contains pointer-based memory";
     }

     auto operands = NN_TRY(convert(model.main.operands));

     // Update number of consumers.
     const auto numberOfConsumers =
             hal::utils::countNumberOfConsumers(operands.size(), model.main.operations);
     CHECK(operands.size() == numberOfConsumers.size());
     for (size_t i = 0; i < operands.size(); ++i) {
         operands[i].numberOfConsumers = numberOfConsumers[i];
     }

     return Model{
             .operands = std::move(operands),
             .operations = NN_TRY(convert(model.main.operations)),
             .inputIndexes = model.main.inputIndexes,
             .outputIndexes = model.main.outputIndexes,
             .operandValues = NN_TRY(convert(model.operandValues)),
             .pools = NN_TRY(convert(model.pools)),
             .relaxComputationFloat32toFloat16 = model.relaxComputationFloat32toFloat16,
             .extensionNameToPrefix = NN_TRY(convert(model.extensionNameToPrefix)),
     };
 }

 nn::Result<Model::ExtensionNameAndPrefix> convert(
         const nn::Model::ExtensionNameAndPrefix& extensionNameAndPrefix) {
     return Model::ExtensionNameAndPrefix{
             .name = extensionNameAndPrefix.name,
             .prefix = extensionNameAndPrefix.prefix,
     };
 }

 nn::Result<OutputShape> convert(const nn::OutputShape& outputShape) {
     return OutputShape{.dimensions = outputShape.dimensions,
                        .isSufficient = outputShape.isSufficient};
 }

 nn::Result<MeasureTiming> convert(const nn::MeasureTiming& measureTiming) {
     return static_cast<MeasureTiming>(measureTiming);
 }

 nn::Result<Timing> convert(const nn::Timing& timing) {
     return Timing{.timeOnDevice = timing.timeOnDevice, .timeInDriver = timing.timeInDriver};
 }

 nn::Result<Extension> convert(const nn::Extension& extension) {
     return Extension{
             .name = extension.name,
             .operandTypes = NN_TRY(convert(extension.operandTypes)),
     };
 }

 nn::Result<Extension::OperandTypeInformation> convert(
         const nn::Extension::OperandTypeInformation& operandTypeInformation) {
     return Extension::OperandTypeInformation{
             .type = operandTypeInformation.type,
             .isTensor = operandTypeInformation.isTensor,
             .byteSize = operandTypeInformation.byteSize,
     };
 }

 nn::Result<hidl_handle> convert(const nn::NativeHandle& handle) {
     const auto hidlHandle = hidl_handle(handle->handle());
     // Copy memory to force the native_handle_t to be copied.
     auto copiedHandle = hidlHandle;
     return copiedHandle;
 }

 nn::Result<hidl_vec<Extension>> convert(const std::vector<nn::Extension>& extensions) {
     return convertVec(extensions);
 }

 nn::Result<hidl_vec<hidl_handle>> convert(const std::vector<nn::NativeHandle>& handles) {
     return convertVec(handles);
 }

 nn::Result<hidl_vec<OutputShape>> convert(const std::vector<nn::OutputShape>& outputShapes) {
     return convertVec(outputShapes);
 }

 }  // namespace android::hardware::neuralnetworks::V1_2::utils
	/*
	* Copyright (C) 2020 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.
	*/

	#include "Conversions.h"

	#include <android-base/logging.h>
	#include <android/hardware/neuralnetworks/1.2/types.h>
	#include <nnapi/OperandTypes.h>
	#include <nnapi/OperationTypes.h>
	#include <nnapi/Result.h>
	#include <nnapi/SharedMemory.h>
	#include <nnapi/TypeUtils.h>
	#include <nnapi/Types.h>
	#include <nnapi/hal/1.0/Conversions.h>
	#include <nnapi/hal/CommonUtils.h>

	#include <algorithm>
	#include <functional>
	#include <iterator>
	#include <memory>
	#include <type_traits>
	#include <utility>

	namespace {

	template <typename Type>
	constexpr std::underlying_type_t<Type> underlyingType(Type value) {
	return static_cast<std::underlying_type_t<Type>>(value);
	}

	} // namespace

	namespace android::nn {
	namespace {

	constexpr bool validOperandType(OperandType operandType) {
	switch (operandType) {
	case OperandType::FLOAT32:
	case OperandType::INT32:
	case OperandType::UINT32:
	case OperandType::TENSOR_FLOAT32:
	case OperandType::TENSOR_INT32:
	case OperandType::TENSOR_QUANT8_ASYMM:
	case OperandType::BOOL:
	case OperandType::TENSOR_QUANT16_SYMM:
	case OperandType::TENSOR_FLOAT16:
	case OperandType::TENSOR_BOOL8:
	case OperandType::FLOAT16:
	case OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL:
	case OperandType::TENSOR_QUANT16_ASYMM:
	case OperandType::TENSOR_QUANT8_SYMM:
	case OperandType::OEM:
	case OperandType::TENSOR_OEM_BYTE:
	return true;
	default:
	break;
	}
	return isExtension(operandType);
	}

	using hardware::hidl_handle;
	using hardware::hidl_vec;

	template <typename Input>
	using ConvertOutput = std::decay_t<decltype(convert(std::declval<Input>()).value())>;

	template <typename Type>
	Result<std::vector<ConvertOutput<Type>>> convertVec(const hidl_vec<Type>& arguments) {
	std::vector<ConvertOutput<Type>> canonical;
	canonical.reserve(arguments.size());
	for (const auto& argument : arguments) {
	canonical.push_back(NN_TRY(nn::convert(argument)));
	}
	return canonical;
	}

	template <typename Type>
	Result<std::vector<ConvertOutput<Type>>> convert(const hidl_vec<Type>& arguments) {
	return convertVec(arguments);
	}

	} // anonymous namespace

	Result<OperandType> convert(const hal::V1_2::OperandType& operandType) {
	return static_cast<OperandType>(operandType);
	}

	Result<OperationType> convert(const hal::V1_2::OperationType& operationType) {
	return static_cast<OperationType>(operationType);
	}

	Result<DeviceType> convert(const hal::V1_2::DeviceType& deviceType) {
	return static_cast<DeviceType>(deviceType);
	}

	Result<Capabilities> convert(const hal::V1_2::Capabilities& capabilities) {
	const bool validOperandTypes = std::all_of(
	capabilities.operandPerformance.begin(), capabilities.operandPerformance.end(),
	[](const hal::V1_2::Capabilities::OperandPerformance& operandPerformance) {
	const auto maybeType = convert(operandPerformance.type);
	return !maybeType.has_value() ? false : validOperandType(maybeType.value());
	});
	if (!validOperandTypes) {
	return NN_ERROR()
	<< "Invalid OperandType when converting OperandPerformance in Capabilities";
	}

	const auto relaxedFloat32toFloat16PerformanceScalar =
	NN_TRY(convert(capabilities.relaxedFloat32toFloat16PerformanceScalar));
	const auto relaxedFloat32toFloat16PerformanceTensor =
	NN_TRY(convert(capabilities.relaxedFloat32toFloat16PerformanceTensor));
	auto operandPerformance = NN_TRY(convert(capabilities.operandPerformance));

	auto table =
	NN_TRY(Capabilities::OperandPerformanceTable::create(std::move(operandPerformance)));

	return Capabilities{
	.relaxedFloat32toFloat16PerformanceScalar = relaxedFloat32toFloat16PerformanceScalar,
	.relaxedFloat32toFloat16PerformanceTensor = relaxedFloat32toFloat16PerformanceTensor,
	.operandPerformance = std::move(table),
	};
	}

	Result<Capabilities::OperandPerformance> convert(
	const hal::V1_2::Capabilities::OperandPerformance& operandPerformance) {
	return Capabilities::OperandPerformance{
	.type = NN_TRY(convert(operandPerformance.type)),
	.info = NN_TRY(convert(operandPerformance.info)),
	};
	}

	Result<Operation> convert(const hal::V1_2::Operation& operation) {
	return Operation{
	.type = NN_TRY(convert(operation.type)),
	.inputs = operation.inputs,
	.outputs = operation.outputs,
	};
	}

	Result<Operand::SymmPerChannelQuantParams> convert(
	const hal::V1_2::SymmPerChannelQuantParams& symmPerChannelQuantParams) {
	return Operand::SymmPerChannelQuantParams{
	.scales = symmPerChannelQuantParams.scales,
	.channelDim = symmPerChannelQuantParams.channelDim,
	};
	}

	Result<Operand> convert(const hal::V1_2::Operand& operand) {
	return Operand{
	.type = NN_TRY(convert(operand.type)),
	.dimensions = operand.dimensions,
	.scale = operand.scale,
	.zeroPoint = operand.zeroPoint,
	.lifetime = NN_TRY(convert(operand.lifetime)),
	.location = NN_TRY(convert(operand.location)),
	.extraParams = NN_TRY(convert(operand.extraParams)),
	};
	}

	Result<Operand::ExtraParams> convert(const hal::V1_2::Operand::ExtraParams& extraParams) {
	using Discriminator = hal::V1_2::Operand::ExtraParams::hidl_discriminator;
	switch (extraParams.getDiscriminator()) {
	case Discriminator::none:
	return Operand::NoParams{};
	case Discriminator::channelQuant:
	return convert(extraParams.channelQuant());
	case Discriminator::extension:
	return extraParams.extension();
	}
	return NN_ERROR() << "Unrecognized Operand::ExtraParams discriminator: "
	<< underlyingType(extraParams.getDiscriminator());
	}

	Result<Model> convert(const hal::V1_2::Model& model) {
	auto operations = NN_TRY(convert(model.operations));

	// Verify number of consumers.
	const auto numberOfConsumers =
	hal::utils::countNumberOfConsumers(model.operands.size(), operations);
	CHECK(model.operands.size() == numberOfConsumers.size());
	for (size_t i = 0; i < model.operands.size(); ++i) {
	if (model.operands[i].numberOfConsumers != numberOfConsumers[i]) {
	return NN_ERROR() << "Invalid numberOfConsumers for operand " << i << ", expected "
	<< numberOfConsumers[i] << " but found "
	<< model.operands[i].numberOfConsumers;
	}
	}

	auto main = Model::Subgraph{
	.operands = NN_TRY(convert(model.operands)),
	.operations = std::move(operations),
	.inputIndexes = model.inputIndexes,
	.outputIndexes = model.outputIndexes,
	};

	return Model{
	.main = std::move(main),
	.operandValues = NN_TRY(convert(model.operandValues)),
	.pools = NN_TRY(convert(model.pools)),
	.relaxComputationFloat32toFloat16 = model.relaxComputationFloat32toFloat16,
	.extensionNameToPrefix = NN_TRY(convert(model.extensionNameToPrefix)),
	};
	}

	Result<Model::ExtensionNameAndPrefix> convert(
	const hal::V1_2::Model::ExtensionNameAndPrefix& extensionNameAndPrefix) {
	return Model::ExtensionNameAndPrefix{
	.name = extensionNameAndPrefix.name,
	.prefix = extensionNameAndPrefix.prefix,
	};
	}

	Result<OutputShape> convert(const hal::V1_2::OutputShape& outputShape) {
	return OutputShape{
	.dimensions = outputShape.dimensions,
	.isSufficient = outputShape.isSufficient,
	};
	}

	Result<MeasureTiming> convert(const hal::V1_2::MeasureTiming& measureTiming) {
	return static_cast<MeasureTiming>(measureTiming);
	}

	Result<Timing> convert(const hal::V1_2::Timing& timing) {
	return Timing{.timeOnDevice = timing.timeOnDevice, .timeInDriver = timing.timeInDriver};
	}

	Result<Extension> convert(const hal::V1_2::Extension& extension) {
	return Extension{
	.name = extension.name,
	.operandTypes = NN_TRY(convert(extension.operandTypes)),
	};
	}

	Result<Extension::OperandTypeInformation> convert(
	const hal::V1_2::Extension::OperandTypeInformation& operandTypeInformation) {
	return Extension::OperandTypeInformation{
	.type = operandTypeInformation.type,
	.isTensor = operandTypeInformation.isTensor,
	.byteSize = operandTypeInformation.byteSize,
	};
	}

	Result<NativeHandle> convert(const hidl_handle& handle) {
	auto* cloned = native_handle_clone(handle.getNativeHandle());
	return ::android::NativeHandle::create(cloned, /ownsHandle=/true);
	}

	Result<std::vector<Extension>> convert(const hidl_vec<hal::V1_2::Extension>& extensions) {
	return convertVec(extensions);
	}

	Result<std::vector<NativeHandle>> convert(const hidl_vec<hidl_handle>& handles) {
	return convertVec(handles);
	}

	Result<std::vector<OutputShape>> convert(const hidl_vec<hal::V1_2::OutputShape>& outputShapes) {
	return convertVec(outputShapes);
	}

	} // namespace android::nn

	namespace android::hardware::neuralnetworks::V1_2::utils {
	namespace {

	using utils::convert;

	nn::Result<V1_0::OperandLifeTime> convert(const nn::Operand::LifeTime& lifetime) {
	return V1_0::utils::convert(lifetime);
	}

	nn::Result<V1_0::PerformanceInfo> convert(
	const nn::Capabilities::PerformanceInfo& performanceInfo) {
	return V1_0::utils::convert(performanceInfo);
	}

	nn::Result<V1_0::DataLocation> convert(const nn::DataLocation& location) {
	return V1_0::utils::convert(location);
	}

	nn::Result<hidl_vec<uint8_t>> convert(const nn::Model::OperandValues& operandValues) {
	return V1_0::utils::convert(operandValues);
	}

	nn::Result<hidl_memory> convert(const nn::Memory& memory) {
	return V1_0::utils::convert(memory);
	}

	template <typename Input>
	using ConvertOutput = std::decay_t<decltype(convert(std::declval<Input>()).value())>;

	template <typename Type>
	nn::Result<hidl_vec<ConvertOutput<Type>>> convertVec(const std::vector<Type>& arguments) {
	hidl_vec<ConvertOutput<Type>> halObject(arguments.size());
	for (size_t i = 0; i < arguments.size(); ++i) {
	halObject[i] = NN_TRY(convert(arguments[i]));
	}
	return halObject;
	}

	template <typename Type>
	nn::Result<hidl_vec<ConvertOutput<Type>>> convert(const std::vector<Type>& arguments) {
	return convertVec(arguments);
	}

	nn::Result<Operand::ExtraParams> makeExtraParams(nn::Operand::NoParams /noParams/) {
	return Operand::ExtraParams{};
	}

	nn::Result<Operand::ExtraParams> makeExtraParams(
	const nn::Operand::SymmPerChannelQuantParams& channelQuant) {
	Operand::ExtraParams ret;
	ret.channelQuant(NN_TRY(convert(channelQuant)));
	return ret;
	}

	nn::Result<Operand::ExtraParams> makeExtraParams(const nn::Operand::ExtensionParams& extension) {
	Operand::ExtraParams ret;
	ret.extension(extension);
	return ret;
	}

	} // anonymous namespace

	nn::Result<OperandType> convert(const nn::OperandType& operandType) {
	return static_cast<OperandType>(operandType);
	}

	nn::Result<OperationType> convert(const nn::OperationType& operationType) {
	return static_cast<OperationType>(operationType);
	}

	nn::Result<DeviceType> convert(const nn::DeviceType& deviceType) {
	switch (deviceType) {
	case nn::DeviceType::UNKNOWN:
	return NN_ERROR() << "Invalid DeviceType UNKNOWN";
	case nn::DeviceType::OTHER:
	case nn::DeviceType::CPU:
	case nn::DeviceType::GPU:
	case nn::DeviceType::ACCELERATOR:
	return static_cast<DeviceType>(deviceType);
	}
	return NN_ERROR() << "Invalid DeviceType " << underlyingType(deviceType);
	}

	nn::Result<Capabilities> convert(const nn::Capabilities& capabilities) {
	std::vector<nn::Capabilities::OperandPerformance> operandPerformance;
	operandPerformance.reserve(capabilities.operandPerformance.asVector().size());
	std::copy_if(capabilities.operandPerformance.asVector().begin(),
	capabilities.operandPerformance.asVector().end(),
	std::back_inserter(operandPerformance),
	[](const nn::Capabilities::OperandPerformance& operandPerformance) {
	return nn::validOperandType(operandPerformance.type);
	});

	return Capabilities{
	.relaxedFloat32toFloat16PerformanceScalar =
	NN_TRY(convert(capabilities.relaxedFloat32toFloat16PerformanceScalar)),
	.relaxedFloat32toFloat16PerformanceTensor =
	NN_TRY(convert(capabilities.relaxedFloat32toFloat16PerformanceTensor)),
	.operandPerformance = NN_TRY(convert(operandPerformance)),
	};
	}

	nn::Result<Capabilities::OperandPerformance> convert(
	const nn::Capabilities::OperandPerformance& operandPerformance) {
	return Capabilities::OperandPerformance{
	.type = NN_TRY(convert(operandPerformance.type)),
	.info = NN_TRY(convert(operandPerformance.info)),
	};
	}

	nn::Result<Operation> convert(const nn::Operation& operation) {
	return Operation{
	.type = NN_TRY(convert(operation.type)),
	.inputs = operation.inputs,
	.outputs = operation.outputs,
	};
	}

	nn::Result<SymmPerChannelQuantParams> convert(
	const nn::Operand::SymmPerChannelQuantParams& symmPerChannelQuantParams) {
	return SymmPerChannelQuantParams{
	.scales = symmPerChannelQuantParams.scales,
	.channelDim = symmPerChannelQuantParams.channelDim,
	};
	}

	nn::Result<Operand> convert(const nn::Operand& operand) {
	return Operand{
	.type = NN_TRY(convert(operand.type)),
	.dimensions = operand.dimensions,
	.numberOfConsumers = 0,
	.scale = operand.scale,
	.zeroPoint = operand.zeroPoint,
	.lifetime = NN_TRY(convert(operand.lifetime)),
	.location = NN_TRY(convert(operand.location)),
	.extraParams = NN_TRY(convert(operand.extraParams)),
	};
	}

	nn::Result<Operand::ExtraParams> convert(const nn::Operand::ExtraParams& extraParams) {
	return std::visit([](const auto& x) { return makeExtraParams(x); }, extraParams);
	}

	nn::Result<Model> convert(const nn::Model& model) {
	if (!hal::utils::hasNoPointerData(model)) {
	return NN_ERROR() << "Model cannot be converted because it contains pointer-based memory";
	}

	auto operands = NN_TRY(convert(model.main.operands));

	// Update number of consumers.
	const auto numberOfConsumers =
	hal::utils::countNumberOfConsumers(operands.size(), model.main.operations);
	CHECK(operands.size() == numberOfConsumers.size());
	for (size_t i = 0; i < operands.size(); ++i) {
	operands[i].numberOfConsumers = numberOfConsumers[i];
	}

	return Model{
	.operands = std::move(operands),
	.operations = NN_TRY(convert(model.main.operations)),
	.inputIndexes = model.main.inputIndexes,
	.outputIndexes = model.main.outputIndexes,
	.operandValues = NN_TRY(convert(model.operandValues)),
	.pools = NN_TRY(convert(model.pools)),
	.relaxComputationFloat32toFloat16 = model.relaxComputationFloat32toFloat16,
	.extensionNameToPrefix = NN_TRY(convert(model.extensionNameToPrefix)),
	};
	}

	nn::Result<Model::ExtensionNameAndPrefix> convert(
	const nn::Model::ExtensionNameAndPrefix& extensionNameAndPrefix) {
	return Model::ExtensionNameAndPrefix{
	.name = extensionNameAndPrefix.name,
	.prefix = extensionNameAndPrefix.prefix,
	};
	}

	nn::Result<OutputShape> convert(const nn::OutputShape& outputShape) {
	return OutputShape{.dimensions = outputShape.dimensions,
	.isSufficient = outputShape.isSufficient};
	}

	nn::Result<MeasureTiming> convert(const nn::MeasureTiming& measureTiming) {
	return static_cast<MeasureTiming>(measureTiming);
	}

	nn::Result<Timing> convert(const nn::Timing& timing) {
	return Timing{.timeOnDevice = timing.timeOnDevice, .timeInDriver = timing.timeInDriver};
	}

	nn::Result<Extension> convert(const nn::Extension& extension) {
	return Extension{
	.name = extension.name,
	.operandTypes = NN_TRY(convert(extension.operandTypes)),
	};
	}

	nn::Result<Extension::OperandTypeInformation> convert(
	const nn::Extension::OperandTypeInformation& operandTypeInformation) {
	return Extension::OperandTypeInformation{
	.type = operandTypeInformation.type,
	.isTensor = operandTypeInformation.isTensor,
	.byteSize = operandTypeInformation.byteSize,
	};
	}

	nn::Result<hidl_handle> convert(const nn::NativeHandle& handle) {
	const auto hidlHandle = hidl_handle(handle->handle());
	// Copy memory to force the native_handle_t to be copied.
	auto copiedHandle = hidlHandle;
	return copiedHandle;
	}

	nn::Result<hidl_vec<Extension>> convert(const std::vector<nn::Extension>& extensions) {
	return convertVec(extensions);
	}

	nn::Result<hidl_vec<hidl_handle>> convert(const std::vector<nn::NativeHandle>& handles) {
	return convertVec(handles);
	}

	nn::Result<hidl_vec<OutputShape>> convert(const std::vector<nn::OutputShape>& outputShapes) {
	return convertVec(outputShapes);
	}

	} // namespace android::hardware::neuralnetworks::V1_2::utils