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/*
* 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/1.0/types.h>
#include <android/hardware/neuralnetworks/1.1/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/Validation.h>
#include <nnapi/hal/1.0/Conversions.h>
#include <nnapi/hal/CommonUtils.h>
#include <algorithm>
#include <functional>
#include <iterator>
#include <type_traits>
#include <utility>
#include "Utils.h"
namespace android::nn {
namespace {
using hardware::hidl_vec;
template <typename Input>
using UnvalidatedConvertOutput =
std::decay_t<decltype(unvalidatedConvert(std::declval<Input>()).value())>;
template <typename Type>
GeneralResult<std::vector<UnvalidatedConvertOutput<Type>>> unvalidatedConvert(
const hidl_vec<Type>& arguments) {
std::vector<UnvalidatedConvertOutput<Type>> canonical;
canonical.reserve(arguments.size());
for (const auto& argument : arguments) {
canonical.push_back(NN_TRY(nn::unvalidatedConvert(argument)));
}
return canonical;
}
template <typename Type>
GeneralResult<UnvalidatedConvertOutput<Type>> validatedConvert(const Type& halObject) {
auto canonical = NN_TRY(nn::unvalidatedConvert(halObject));
NN_TRY(hal::V1_1::utils::compliantVersion(canonical));
return canonical;
}
} // anonymous namespace
GeneralResult<OperationType> unvalidatedConvert(const hal::V1_1::OperationType& operationType) {
return static_cast<OperationType>(operationType);
}
GeneralResult<Capabilities> unvalidatedConvert(const hal::V1_1::Capabilities& capabilities) {
const auto quantized8Performance =
NN_TRY(unvalidatedConvert(capabilities.quantized8Performance));
const auto float32Performance = NN_TRY(unvalidatedConvert(capabilities.float32Performance));
const auto relaxedFloat32toFloat16Performance =
NN_TRY(unvalidatedConvert(capabilities.relaxedFloat32toFloat16Performance));
auto table = hal::utils::makeQuantized8PerformanceConsistentWithP(float32Performance,
quantized8Performance);
return Capabilities{
.relaxedFloat32toFloat16PerformanceScalar = relaxedFloat32toFloat16Performance,
.relaxedFloat32toFloat16PerformanceTensor = relaxedFloat32toFloat16Performance,
.operandPerformance = std::move(table),
};
}
GeneralResult<Operation> unvalidatedConvert(const hal::V1_1::Operation& operation) {
return Operation{
.type = NN_TRY(unvalidatedConvert(operation.type)),
.inputs = operation.inputs,
.outputs = operation.outputs,
};
}
GeneralResult<Model> unvalidatedConvert(const hal::V1_1::Model& model) {
auto operations = NN_TRY(unvalidatedConvert(model.operations));
// Verify number of consumers.
const auto numberOfConsumers =
NN_TRY(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(nn::ErrorStatus::GENERAL_FAILURE)
<< "Invalid numberOfConsumers for operand " << i << ", expected "
<< numberOfConsumers[i] << " but found " << model.operands[i].numberOfConsumers;
}
}
auto main = Model::Subgraph{
.operands = NN_TRY(unvalidatedConvert(model.operands)),
.operations = std::move(operations),
.inputIndexes = model.inputIndexes,
.outputIndexes = model.outputIndexes,
};
return Model{
.main = std::move(main),
.operandValues = NN_TRY(unvalidatedConvert(model.operandValues)),
.pools = NN_TRY(unvalidatedConvert(model.pools)),
.relaxComputationFloat32toFloat16 = model.relaxComputationFloat32toFloat16,
};
}
GeneralResult<ExecutionPreference> unvalidatedConvert(
const hal::V1_1::ExecutionPreference& executionPreference) {
return static_cast<ExecutionPreference>(executionPreference);
}
GeneralResult<Capabilities> convert(const hal::V1_1::Capabilities& capabilities) {
return validatedConvert(capabilities);
}
GeneralResult<Model> convert(const hal::V1_1::Model& model) {
return validatedConvert(model);
}
GeneralResult<ExecutionPreference> convert(
const hal::V1_1::ExecutionPreference& executionPreference) {
return validatedConvert(executionPreference);
}
} // namespace android::nn
namespace android::hardware::neuralnetworks::V1_1::utils {
namespace {
using utils::unvalidatedConvert;
nn::GeneralResult<V1_0::PerformanceInfo> unvalidatedConvert(
const nn::Capabilities::PerformanceInfo& performanceInfo) {
return V1_0::utils::unvalidatedConvert(performanceInfo);
}
nn::GeneralResult<V1_0::Operand> unvalidatedConvert(const nn::Operand& operand) {
return V1_0::utils::unvalidatedConvert(operand);
}
nn::GeneralResult<hidl_vec<uint8_t>> unvalidatedConvert(
const nn::Model::OperandValues& operandValues) {
return V1_0::utils::unvalidatedConvert(operandValues);
}
nn::GeneralResult<hidl_memory> unvalidatedConvert(const nn::SharedMemory& memory) {
return V1_0::utils::unvalidatedConvert(memory);
}
template <typename Input>
using UnvalidatedConvertOutput =
std::decay_t<decltype(unvalidatedConvert(std::declval<Input>()).value())>;
template <typename Type>
nn::GeneralResult<hidl_vec<UnvalidatedConvertOutput<Type>>> unvalidatedConvert(
const std::vector<Type>& arguments) {
hidl_vec<UnvalidatedConvertOutput<Type>> halObject(arguments.size());
for (size_t i = 0; i < arguments.size(); ++i) {
halObject[i] = NN_TRY(unvalidatedConvert(arguments[i]));
}
return halObject;
}
template <typename Type>
nn::GeneralResult<UnvalidatedConvertOutput<Type>> validatedConvert(const Type& canonical) {
NN_TRY(compliantVersion(canonical));
return unvalidatedConvert(canonical);
}
} // anonymous namespace
nn::GeneralResult<OperationType> unvalidatedConvert(const nn::OperationType& operationType) {
return static_cast<OperationType>(operationType);
}
nn::GeneralResult<Capabilities> unvalidatedConvert(const nn::Capabilities& capabilities) {
return Capabilities{
.float32Performance = NN_TRY(unvalidatedConvert(
capabilities.operandPerformance.lookup(nn::OperandType::TENSOR_FLOAT32))),
.quantized8Performance = NN_TRY(unvalidatedConvert(
capabilities.operandPerformance.lookup(nn::OperandType::TENSOR_QUANT8_ASYMM))),
.relaxedFloat32toFloat16Performance = NN_TRY(
unvalidatedConvert(capabilities.relaxedFloat32toFloat16PerformanceTensor)),
};
}
nn::GeneralResult<Operation> unvalidatedConvert(const nn::Operation& operation) {
return Operation{
.type = NN_TRY(unvalidatedConvert(operation.type)),
.inputs = operation.inputs,
.outputs = operation.outputs,
};
}
nn::GeneralResult<Model> unvalidatedConvert(const nn::Model& model) {
if (!hal::utils::hasNoPointerData(model)) {
return NN_ERROR(nn::ErrorStatus::INVALID_ARGUMENT)
<< "Mdoel cannot be unvalidatedConverted because it contains pointer-based memory";
}
auto operands = NN_TRY(unvalidatedConvert(model.main.operands));
// Update number of consumers.
const auto numberOfConsumers =
NN_TRY(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(unvalidatedConvert(model.main.operations)),
.inputIndexes = model.main.inputIndexes,
.outputIndexes = model.main.outputIndexes,
.operandValues = NN_TRY(unvalidatedConvert(model.operandValues)),
.pools = NN_TRY(unvalidatedConvert(model.pools)),
.relaxComputationFloat32toFloat16 = model.relaxComputationFloat32toFloat16,
};
}
nn::GeneralResult<ExecutionPreference> unvalidatedConvert(
const nn::ExecutionPreference& executionPreference) {
return static_cast<ExecutionPreference>(executionPreference);
}
nn::GeneralResult<Capabilities> convert(const nn::Capabilities& capabilities) {
return validatedConvert(capabilities);
}
nn::GeneralResult<Model> convert(const nn::Model& model) {
return validatedConvert(model);
}
nn::GeneralResult<ExecutionPreference> convert(const nn::ExecutionPreference& executionPreference) {
return validatedConvert(executionPreference);
}
nn::GeneralResult<V1_0::DeviceStatus> convert(const nn::DeviceStatus& deviceStatus) {
return V1_0::utils::convert(deviceStatus);
}
nn::GeneralResult<V1_0::Request> convert(const nn::Request& request) {
return V1_0::utils::convert(request);
}
nn::GeneralResult<V1_0::ErrorStatus> convert(const nn::ErrorStatus& status) {
return V1_0::utils::convert(status);
}
} // namespace android::hardware::neuralnetworks::V1_1::utils
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